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title: Role of Lipid Profile and Its Relative Ratios (Cholesterol/HDL-C, Triglyceride/HDL-C,
LDL-C/HDL-C, WBC/HDL-C, and FBG/HDL-C) on Admission Predicts In-Hospital Mortality
COVID-19
authors:
- Jafar Mohammadshahi
- Hassan Ghobadi
- Golchin Matinfar
- Mohammad Hossein Boskabady
- Mohammad Reza Aslani
journal: Journal of Lipids
year: 2023
pmcid: PMC10010876
doi: 10.1155/2023/6329873
license: CC BY 4.0
---
# Role of Lipid Profile and Its Relative Ratios (Cholesterol/HDL-C, Triglyceride/HDL-C, LDL-C/HDL-C, WBC/HDL-C, and FBG/HDL-C) on Admission Predicts In-Hospital Mortality COVID-19
## Abstract
### Background
Lipid profile and its related ratios such as total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C), triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), TG/HDL-C ratio, TC/HDL-C ratio, LDL-C/HDL-C ratio, white blood cell (WBC)/HDL-C ratio, and fasting blood glucose (FBG)/HDL-C ratio are valuable indicators that have been studied in various disorders to predict mortality. The present study was conducted with the aim of investigating the role of lipid profile ratios in predicting mortality in COVID-19 patients.
### Methods
At the beginning of hospitalization, laboratory tests were taken from all patients ($$n = 300$$). The ability of lipid profile ratios to determine the COVID-19 severity was evaluated using receiver-operating characteristic (ROC). In addition, survival probability was determined with the average of Kaplan-Meier curves, so that the end point was death.
### Results
In deceased patients, TG, TC, LDL-C, HDL-C, TC/HDL-C, TG/HDL-C, and LDL-C/HDL-C parameters were significantly lower than those of surviving patients, while WBC/HDL-C and FBG/HDL-C were significantly higher. TC (HR = 3.178, $95\%$CI = 1.064 to 9.491, $P \leq 0.05$), TG (HR = 3.276, $95\%$CI = 1.111 to 9.655, $P \leq 0.05$), LDL-C (HR = 3.207, $95\%$CI = 1.104 to 9.316, $P \leq 0.05$), and HDL-C (HR = 3.690, $95\%$CI = 1.290 to 10.554, $P \leq 0.05$), as well as TC/HDL-C (HR = 3.860, $95\%$CI = 1.289 to 11.558, $P \leq 0.05$), TG/HDL-C (HR = 3.860, $95\%$CI = 1.289 to 11.558, $P \leq 0.05$), LDL-C/HDL-C (HR = 3.915, $95\%$CI = 1.305 to 11.739, $P \leq 0.05$), WBC/HDL-C (HR = 3.232, $95\%$CI = 1.176 to 8.885, $P \leq 0.05$), and FBG/HDL-C ratios (HR = 4.474, $95\%$CI = 1.567 to 12.777, $P \leq 0.01$), were detectably related to survival. The multivariate Cox regression models showed that only FBG/HDL-C ratio (HR = 5.477, $95\%$CI = 1.488 to 20.153, $P \leq 0.01$) was significantly related to survival.
### Conclusion
The results suggested that FBG/HDL-C ratio in hospital-admitted COVID-19 patients was a reliable predictor of mortality.
## 1. Introduction
It has been revealed that mortality in patients with coronavirus disease 2019 (COVID-19) is related to increased inflammation and impaired immune response [1]. Many evidences showed that although most hospitalized patients with COVID-19 are mild, critically ill patients have a high mortality rate due to the development of multiorgan failure, acute respiratory disease, and septic shock [2]. Severity and morbidity in COVID-19 patients have been more evident in comorbidities such as cardiovascular disease, lung disease, diabetes, and kidney failure [3]. On the other hand, disturbance in lipid metabolism has been shown in the pathophysiology of many disorders such as cardiovascular, respiratory, and metabolic syndrome [4–8]. Defective lipid metabolism has also been reported in the COVID-19 [2]. Decreased levels of lipid profiles such as triglyceride (TG), total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and low-density lipoprotein-cholesterol (LDL-C) have been reported in COVID-19 patients, especially in severe patients [9, 10]. Dyslipidemia has also been evident in various system injuries such as cardiovascular, immune, and respiratory systems. In addition, high levels of proinflammatory cytokines are also important factors in developing dyslipidemia [11]. Due to the nature of COVID-19 (increased levels of proinflammatory cytokines and damage to various organs), lipid regulation is impaired.
In predicting the outcome of COVID-19, the use of various indicators has been investigated, such as systemic inflammation indexes [3]. On the other hand, lipid profile and related ratios such as TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C, as well as white blood cell count (WBC)/HDL-C and first blood glucose (FBG)/HDL-C ratios in diagnosing severity and mortality of diseases, have been evaluated in various studies [12–14]. Therefore, the present study is aimed at investigating the predictive mortality in COVID-19 patients based on lipid profile ratios.
## 2. Method
This retrospective study was conducted in Ardabil Imam Khomeini Hospital, Ardabil, Iran, from July to September 2021. COVID-19 patients whose diagnosis was based on PCR test were included in the study. The study was conducted after it was approved by the Ethics Committee of Ardabil University of Medical Sciences (IR.ARUMS.MEDICINE.REC.1400.014).
The data collected included age, sex, medical history, laboratory tests, comorbidities, clinical symptoms, length of hospitalization, and disease outcome (recovery or death) of 300 COVID-19 patients by two trained medical students. The laboratory tests that were obtained from the patients in the first 24 hours, such as total white blood cell (WBC), neutrophil (NT), lymphocyte (LY), monocyte (MN), and platelet (Plt) counts, as well as hematocrit (Hct), hemoglobin (Hb), partial thromboplastin time (PTT), prothrombin time (PT), international normalized ratio (INR), fasting blood glucose (FBG), alkaline phosphatase (ALP), alanine transaminase (ALT), AST, erythrocyte sedimentation rate (ESR), D-dimer, lactate dehydrogenase (LDH), ferritin, urea, creatinine (Cr), sodium (Na), potassium (K), triglyceride (TG), low-density lipoprotein-cholesterol (LDL-C), total cholesterol (TC), and high-density lipoprotein-cholesterol (HDL-C), were analyzed.
In addition, lipid profile ratios were also calculated for all subjects, including TC/HDL-C ratio, TG/HDL-C ratio, LDL-c/HDL-C ratio, WBC/HDL-C ratio, NT/HDL-C ratio, LY/HDL-C ratio, MN/HDL-C ratio, and FBG/HDL-C ratio.
## 2.1. Data Analysis
For variables with normal distribution, mean ± standard deviation (SD) and, for variables abnormally distributed, median values and interquartile range (IQR) were used. Independent group t-test or Mann–Whitney test was used to compare variables. According to the Youden index, receiver-operating characteristic (ROC) curve analysis was conducted to estimate optimal values of cutoff, as well as to maximize sensitivity and specificity. Time zero in the current study was defined as the time of hospitalization for survival analysis. In order to avoid linear bias with univariate analysis for Charlson's index, lipid profile ratios were evaluated separately so that in case of $P \leq 0.2$, confounding factors were corrected. The probability of survival was estimated for the lipid profile ratios with the end point of death using the mean of the Kaplan-Meier curves. Finally, the Cox proportional hazards regression was used for both univariate and multivariate analysis. Data analysis was done by MedCalc version 19.4.1 and SPSS software.
## 3. Results
In the current study, 300 patients with COVID-19 were included, with an average age of 54.57 ± 17.02 (Table 1). The percentage of hospitalized men ($58.7\%$) was higher than that of women ($41.3\%$). The average hospital stay was 9.13 ± 4.21 days.
Laboratory findings of total WBC, Hb, Hct, Plt, PT, PTT, INR, Cr, Na, and K were in the normal range. However, ALT, AST, LDH, ferritin, ESR, urea, D-dimer, FBG, and ALP were higher than the normal range, while lymphocytes were less. The levels of lipid profiles measured at the beginning of hospitalization of the patients were TC: 128.37 ± 9.43, TG: 118.59 ± 15.12, LDL-C: 62.64 ± 4.82, and HDL-C: 42.50 ± 0.39 (Table 1).
## 3.1. Clinical Outcomes
The COVID-19 severity in 14 patients ($4.7\%$) was very severe, in 61 patients ($20.3\%$) severe, and in 225 patients ($75\%$) moderate. Interestingly, it was revealed that the lipid profile (TG, TC, HDL-C, and LDL-C) was significantly lower in very severe and severe compared to moderate patients (both, $P \leq 0.001$) (Figure 1). Out of three hundred patients, 274 ($91.3\%$) were discharged, and 26 died ($8.7\%$).
## 3.2. Laboratory Finding Based on Outcome
The results showed that the following parameters were detectably higher in the dead than in the surviving patients: hospitalization stay ($P \leq 0.001$), age ($P \leq 0.001$), total WBC ($P \leq 0.001$), ALT ($P \leq 0.05$), AST ($P \leq 0.05$), ferritin ($P \leq 0.05$), FBG ($P \leq 0.001$), urea ($P \leq 0.001$), Cr ($P \leq 0.05$), and ALP ($P \leq 0.01$). On the other hand, the lymphocyte count in deceased patients was significantly lower than in surviving patients ($P \leq 0.001$).
The results of lipid profile and related ratios revealed that in patients who died compared to those who recovered, decreased levels occurred such as TC, TG, LDL-C, HDL-C, LDL-C/HDL-C ratio, TG/HDL-C ratio, and Lymphocyte/HDL-C ratio (for all $P \leq 0.001$) (Table 2). Interestingly, WBC/HDL-C and FBG/HDL-C ratios were significantly higher in deceased patients (both, $P \leq 0.001$). In addition, the analysis of the results revealed that the FBG/HDL-C ratio was not significantly different between male and female.
## 3.3. Receiver-Operating Characteristics (ROC)
In the ROC-based analysis for survival assessment, the optimal cut-off values identified for lipid profile and its ratios were as follows: TC (≤123.97), TG (≤111.47), LDL-C (≤60.41), HDL-C (≤42.35), TG/HDL-C ratio (≤2.74), TC/HDL-C ratio (≤2.99), LDL-C/HDL-C ratio (≤1.45), WBC/HDL-C ratio (>169.64), lymphocyte/HDL-C ratio (≤27.88), monocyte/HDL-C ratio (≤4.72), and FBG/HDL-C ratio (>3.42) (Figure 2 and Table 3). In addition, AUD level was significant for TC (0.864), TG (0.870), LDL-C (0.867), HDL-C (0.865), TC/HDL-C ratio (0.864), TG/HDL-C ratio (0.868), LDL-C/HDL-C ratio (0.867), WBC/HDL-C ratio (0.860), lymphocyte/HDL-C (0.644), monocyte/HDL-C ratio (0.619), and FBG/HDL-C ratio (0.866) (Figure 2 and Table 3). The results revealed that AUC values were significantly higher for TC/HDL-C, TG/HDL-C, LDL-C/HDL-C, WBC/HDL-C, and FBG/HDL-C ratios than LM/HDL-C and MN/HDL-C ratios.
The results of the Kaplan-Meier survival curves indicated that low survival was evident with low values of TC (HR = 3.178, $95\%$CI = 1.064 to 9.491, $P \leq 0.05$), TG (HR = 3.276, $95\%$CI = 1.111 to 9.655, $P \leq 0.05$), LDL-C (HR = 3.207, $95\%$CI = 1.104 to 9.316, $P \leq 0.05$), HDL-C (HR = 3.690, $95\%$CI = 1.290 to 10.554, $P \leq 0.05$), TC/HDL-C ratio (HR = 3.860, $95\%$CI = 1.289 to 11.558, $P \leq 0.05$), TG/HDL-C ratio (HR = 3.860, $95\%$CI = 1.289 to 11.558, $P \leq 0.05$), LDL-C/HDL-C ratio (HR = 3.915, $95\%$CI = 1.305 to 11.739, $P \leq 0.05$), WBC/HDL-C ratio (HR = 3.232, $95\%$CI = 1.176 to 8.885, $P \leq 0.05$), MN/HDL-C (HR = 2.712, $95\%$CI = 1.143 to 6.434, $P \leq 0.05$), and FBG/HDL-C ratio (HR = 4.474, $95\%$CI = 1.567 to 12.777, $P \leq 0.01$) (Figure 3 and Table 4). The multivariate Cox regression models showed that only FBG/HDL-C ratio (HR = 5.477, $95\%$CI = 1.488 to 20.153, $P \leq 0.01$) was significantly related to survival.
## 4. Discussion
In summary, the findings of the current study in COVID-19 patients were as follows: In deceased patients, the values of TG, TC, LDL-C, and HDL-C levels, as well as TC/HDL-C, LDL-C/HDL-C, TG/HDL-C, LM/HDL-C, and MN/HDL-C ratios, were significantly less, but WBC/HDL-C and FBG/HDL-C ratios were higher than recovered subjectsThe ROC and Kaplan-Meier survival curves identified that lipid profile (TC, TG, LDL-C, and HDL-C) and its related ratios (TC/HDL-C, TG/HDL-C, LDL-C/HDL-C, WBC/HDL-C, LM/HDL-C, MN/HDL-C, and FBG/HDL-C) were detectably related to survivalOnly FBG/HDL-C ratio was significantly related to survival based on the multivariate Cox regression model Various studies have shown that one of the characteristics of COVID-19 and its associated mortality is immune system dysfunction and severe inflammatory response [15, 16]. Systemic inflammation, sepsis, and metabolic disorders have been reported in severe COVID-19 [17]. Lipid metabolism disruption has been shown to affect the severity of COVID-19 [18]. As a result, it is believed that the disruption of the quantity and activity of the lipid profile may affect mortality due to COVID-19. Considerable evidence has shown that altered levels of lipid profiles, although contradictory, have been evident in COVID-19 patients [2, 12]. In most studies, reduced LDL-C, HDL-C, and TC levels have been reported in patients with COVID-19, especially in severe and critically ill patients [19]. The change in lipid profile levels in viral infections (such as dengue (DENV), *Helicobacter pylori* infection, sepsis, human immunodeficiency (HIV), nosocomial infections, and hepatitis B viruses (HBV)) has been considered an indicator of disease prognostics [9]. The prognostic role of various biomarkers in predicting the outcome of the disease has been discussed in COVID-19 patients [3].
In line with other studies, increased levels of leukocytes, AST, ALT, FBG, ALP, and urea were evident in deceased patients compared to recovered patients [3]. Interestingly, reduced TG, TC, LDL-C, and HDL-C levels were also observed in deceased patients. Compared with mild COVID-19, reduced levels of lipid profiles have been shown in the severe and mortality groups [20–22]. In COVID-19 patients hospitalized in ICU, it has also been demonstrated that reduced levels of HDL were associated with high mortality [23].
Cholesterol plays an important role in SARS-CoV-2 infection through interaction with SARS-CoV-19 S protein [24]. In addition, it has been found that HDL-C exerts an important role in host defense against viral, parasitic, and bacterial infections through anti-inflammatory effects [9]. The anti-inflammatory effects of HDL-C have been revealed by reducing the activity of T-cells and the expression level of inflammatory factors in macrophages, inhibiting the activation of monocytes and the expression of adhesion molecules (such as vascular cell adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), E-selectin, and P-selectin) [25–27]. In addition, other effects observed in relation to HDL-C are as follows: antiapoptotic, antioxidative, antiviral, and antithrombotic effects [9]. However, the HDL-C beneficial effects can be impaired in inflammatory conditions [28]. Proinflammatory cytokines such as CRP and IL-6 can reduce HDL-C production by inhibiting apolipoprotein synthesis enzyme activity [29]. It seems that in SARS-CoV-2 infection, lipid profile changes affect the severity of the disease, which requires additional investigations.
The current study also decreased levels of LDL-C, TC, and TG. Low TG, TC, and LDL-C levels are observed which could be considered malnutrition markers [30]. In addition, it has also been shown that inflammation leads to lower LDL-C levels [2]. In the current study, hypocholesterolemia appears to be a result of malnutrition or the cytokine storm caused by SARS-CoV-2 infection.
Recently, lipid profile ratios have been used in various studies as predictors of the outcome of diseases [12, 31]. Decreased TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, LM/HDL-C, and MN/HDL-C ratios and increased WBC/HDL-C and FBG/HDL-C ratios were markedly observed in the mortality group. In addition, it was identified that survival was related to TC, TG, HDL-C, and LDL-C levels, as well as TC/HDL-C, TG/HDL-C, LDL-C/HDL-C, LM/HDL-C, MN/HDL-C, WBC/HDL-C, and FBG/HDL-C ratios. On the other hand, LM/HDL-C and MN/HDL-C ratios were the lowest in predicting disease severity. Multivariate Cox regression analysis identified that only FBG/HDL-C ratio remained significant with survival. Much evidence has reported that comorbidities such as cardiovascular, diabetes, and kidney diseases have affected the severity of COVID-19 [3]. Hyperglycemia has been shown to exacerbate inflammatory conditions through increased inflammatory markers, oxidative stress, clotting factors, and LDL glycation [12, 32, 33]. It can be concluded that the increase in FBG and the decrease in HDL-C levels in patients with COVID-19 were more significant in predicting mortality than other ratios of lipid profile.
The limitations of the study were as follows: [1] selection of patients from one center, [2] the data were collected from the electronic record system, [3] difference in the severity of the disease during hospitalization, [4] the possibility of the effects of different variants of the results, and [5] sample size limitation.
## 5. Conclusions
In conclusion, the results showed that lipid profile ratios were valuable in diagnosing COVID-19 severity. The FBG/HDL-C ratio among lipid profile ratios had a higher power of predicting COVID-19 mortality. It seems that the use of lipid profile ratios and changes in glucose levels in SARS-CoV-2 infection is more important than other changes in predicting the mortality of patients.
## Data Availability
The datasets used and/or analyzed during the current study are available upon reasonable request from the corresponding author.
## Ethical Approval
The study complied with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Ardabil University of Medical Sciences (IR.ARUMS.MEDICINE.REC.1400.014).
## Conflicts of Interest
The authors declare no competing interests.
## Authors' Contributions
MRA, JM, and HG were responsible for the proposal writing, literature search, data collection and analysis, interpretation of data, manuscript preparation, and review of the manuscript. MHB and GM contributed to the proposal writing, data collection and analysis, and manuscript preparation. Jafar Mohammadshahi, Hassan Ghobadi, and Golchin Matinfar contributed equally to this work and share first authorship.
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|
---
title: 'Characteristics, Management, and Outcomes of Community-Acquired Pneumonia
due to Respiratory Syncytial Virus: A Retrospective Study'
authors:
- Ibrahim Bahabri
- Abdulaziz Abdulaal
- Thamer Alanazi
- Sultan Alenazy
- Yasser Alrumih
- Rakan Alqahtani
- Sameera Al Johani
- Mohammad Bosaeed
- Hasan M. Al-Dorzi
journal: Pulmonary Medicine
year: 2023
pmcid: PMC10010887
doi: 10.1155/2023/4310418
license: CC BY 4.0
---
# Characteristics, Management, and Outcomes of Community-Acquired Pneumonia due to Respiratory Syncytial Virus: A Retrospective Study
## Abstract
### Background
Respiratory syncytial virus (RSV), a well-known cause of bronchiolitis in children, can cause community-acquired pneumonia (CAP) in adults, but this condition is not well studied. Hence, we described the characteristics and outcomes of patients hospitalized for CAP due to RSV.
### Methods
This was a retrospective study of patients admitted to a tertiary-care hospital between 2016 and 2019 with CAP due to RSV diagnosed by a respiratory multiplex PCR within 48 hours of admission. We compared patients who required ICU admission to those who did not.
### Results
Eighty adult patients were hospitalized with CAP due to RSV (median age 69.0 years, hypertension $65.0\%$, diabetes $58.8\%$, chronic respiratory disease $52.5\%$, and immunosuppression $17.5\%$); 19 ($23.8\%$) patients required ICU admission. The median pneumonia severity index score was 120.5 (140.0 for ICU and 102.0 for non-ICU patients; $$p \leq 0.09$$). Bacterial coinfection was rare ($10.0\%$). Patients who required ICU admission had more hypotension (systolic blood pressure < 90 mmHg) and a higher prevalence of bilateral infiltrates on chest X-ray (CXR) ($89.5\%$ versus $32.7\%$; $p \leq 0.001$). Systemic corticosteroids were used in $57.3\%$ of patients (median initial dose was 40 mg of prednisone equivalent) with ICU patients receiving a higher dose compared to non-ICU patients ($$p \leq 0.02$$). Most ($68.4\%$) ICU patients received mechanical ventilation (median duration of 4 days). The overall hospital mortality was $8.8\%$ (higher for ICU patients: $31.6\%$ versus $1.6\%$, $p \leq 0.001$).
### Conclusions
Most patients with CAP due to RSV were elderly and had significant comorbidities. ICU admission was required in almost one in four patients and was associated with higher mortality.
## 1. Introduction
Community-acquired pneumonia (CAP) continues to pose a significant burden on healthcare systems in terms of morbidity and mortality [1, 2]. Viruses have been underrecognized as a cause of CAP, with perhaps influenza being a prominent exception [3]. Newer molecular techniques, including the polymerase chain reaction (PCR), have changed this concept. In one study of 198 samples from the respiratory tract of patients with CAP, PCR detected viruses in nearly a quarter of all cases [4]. The most commonly detected viruses were influenza A and adenovirus [4]. A study of pooled cohorts of CAP patients found that viruses were detected in $22\%$ of cases, rising to $29\%$ in studies where PCR was performed [5]. The most common viral pathogens identified in the review were influenza viruses ($9\%$), followed by human rhinovirus ($5\%$) [5]. Respiratory syncytial virus (RSV), an enveloped, nonsegmented, and ssRNA virus that belongs to the Paramyxoviridae family, has not been well established as a cause of CAP in adults with two studies finding RSV in $2\%$ and $2.5\%$ of overall cases when PCR was performed [4, 5].
RSV is a well-known cause of bronchiolitis in children [6], with a well-known seasonal outbreak pattern leading to endemics between November and April in the northern hemisphere (late autumn to early spring) [7–9]. A US-based study of over 5000 children with lower respiratory tract infections found that RSV was detected in $18\%$ of the total cases and accounted for $20\%$ of hospitalizations [7]. RSV bronchiolitis is associated with a low mortality rate of 0-$0.3\%$ as observed in multiple studies [7–9]. Another spectrum of RSV-related disease is seen in adults, where RSV causes an influenza-like illness [10, 11], with two prominent distinctions between RSV and influenza being the higher frequency of rhinorrhea and the higher likelihood of wheezing (including patients with no history of chronic airway disease) and receiving therapy for bronchospasm in RSV cases [12–14]. RSV has also been implicated in chronic obstructive pulmonary disease exacerbations [15, 16] and severe pneumonia [17, 18]. Elderly populations seem to be at particular risk of developing severe RSV pneumonia. A study of 149 nursing home residents with acute lower respiratory tract infections found that RSV was detected in 62 patients and was associated with a significantly more severe disease compared to rhinovirus [19]. Another study of an RSV outbreak in 40 out of 101 nursing care facility residents showed a mortality rate of $20\%$ [20]. CAP due to RSV can also occur in noninstitutionalized adults [13, 14]. Another group at risk of RSV pneumonia is the immunocompromised patients, especially bone marrow transplant recipients [17, 18, 21, 22]. Treatment is primarily supportive, but usage of ribavirin in some of the immunocompromised patients has demonstrated some clinical improvement, especially with early implementation [18]. RSV infection may be associated with higher mortality compared to influenza [13].
Data on pneumonia caused by viruses other than Middle East respiratory syndrome coronavirus and severe acute respiratory syndrome coronavirus 2 in Middle Eastern populations are scarce [23, 24]. A study on patients with viral pneumonia in a tertiary-care hospital in Riyadh found that the most commonly identified virus was influenza A (non-H1N1)/influenza B (216 patients), followed by H1N1 influenza (150 patients), and Middle East respiratory syndrome coronavirus (82 patients) [23]. In this study, we aimed to characterize patients admitted with CAP due to RSV and report their outcomes.
## 2.1. Patients and Settings
This was a retrospective cohort study of adult patients who had CAP due to RSV and were hospitalized in a 1400-bed tertiary-care referral hospital in Riyadh, Saudi Arabia, between January 1, 2016, and December 31, 2019. Patients aged 14 and older with clinically diagnosed CAP and a positive respiratory PCR sample for RSV within 48 hours of admission were included in our study. Our laboratory used BioFire FilmArray pouch (BioFire™ Diagnostics, Inc., Salt Lake City, UT, USA) for the respiratory multiplex PCR assay. It stored all the necessary reagents for sample preparation, reverse transcription PCR, and detection in a freeze-dried format. In this study, accepted respiratory samples were sputum, endotracheal aspirate, and bronchoalveolar lavage. During a test run, the BioFire System extracted and purified all nucleic acids from the unprocessed sample. Next, it performed nested multiplex PCR in two stages. The first stage included a single, large-volume, multiplexed reaction. The second stage included individual, single-plex reactions to detect the products from the first stage. Using endpoint melting curve data, BioFire System software automatically analyzed the results for each target on the panel. When the run was complete, the software reported whether each pathogen was detected in the sample.
## 2.2. Data Collection
The list of patients who tested positive for RSV within the study period was obtained from the microbiology laboratory of the hospital. We collected the following data: demographics, comorbid conditions, month of hospitalization, signs and symptoms documented in the medical records, and pertinent laboratory results and radiographic findings at presentation. We also calculated the pneumonia severity index (PSI) [25] and noted the provided management (use of antimicrobials and antivirals, steroid use and dosage, ICU admission, intubation, mechanical ventilation, and the use of vasopressors). We also noted the rate of bacterial coinfection and superinfection (positive bacterial sputum culture within 48 hours in cases of coinfection and more than 48 hours in cases of superinfection). The primary outcome of our study was in-hospital mortality. Secondary outcomes included ICU mortality, duration of mechanical ventilation, tracheostomy, length of stay in the ICU and hospital, and hospital readmission within 30 days.
## 2.3. Statistical Analysis
The patients were divided into two main groups based on whether they required ICU admission or not. Continuous variables were presented as median with interquartile range (IQR) and compared using either Student's t-test or Mann–Whitney U-test, depending on the normality of distribution. Categorical variables were presented as frequency with percentage and compared using either the chi-square test or Fisher's exact test, as appropriate. The hospital mortality was compared between clinically important subgroups of patients: age ≤ 65 versus >65 years, PSI ≤ 90 (class I-III, which usually indicates low risk of mortality) versus >90 (class IV-V, which usually indicates moderate-high risk of mortality) [25], immunocompromised versus not immunocompromised, corticosteroid versus no corticosteroid use, ICU admission versus no ICU admission, vasopressor therapy versus no vasopressor therapy, and mechanical ventilation versus no mechanical ventilation. All statistical tests were considered significant at a p value less than 0.05. Statistical analysis was performed using SPSS (SPSS Inc., SPSS for Windows, version 16.0, Chicago, IL, SPSS Inc.).
## 3.1. Baseline Characteristics and Presenting Symptoms and Signs
Eighty adult patients were hospitalized with CAP due to RSV during the study period. Figure 1 describes the number of hospital admissions by the month of the year. It shows that most ($\frac{49}{80}$, $61.3\%$) admissions were from December to February. The baseline data of the study patients are presented in Table 1. The median age of patients was 69 years (IQR: 57.5, 79.7), typically with multiple comorbidities. The most common comorbidities were hypertension ($65\%$) and diabetes ($58.8\%$), followed closely by chronic respiratory disease ($52.5\%$) and heart failure ($38.8\%$). Fourteen ($17.5\%$) patients were immunosuppressed: 8 patients ($10\%$ of the overall cohort) received solid organ transplantation (6 had kidney transplant and 2 liver transplant) and were on a combination of tacrolimus ($$n = 5$$), corticosteroids ($$n = 8$$), mycophenolate ($$n = 5$$), or cyclosporin ($$n = 3$$); 3 patients had hematologic disorders; and 3 were on immunosuppressive medications for tuberculous meningoencephalitis ($$n = 1$$), systemic lupus erythematosus with nephritis ($$n = 1$$), and interstitial lung disease ($$n = 1$$). Sixteen patients ($20\%$) were bedbound at baseline prior to presentation. The most common presenting symptom was shortness of breath ($88.8\%$), followed by productive cough ($72.5\%$). Only $47.5\%$ of patients had fever reported from history or documented on presentation.
Nineteen ($23.8\%$) patients required ICU admission. No significant differences were found in the demographics, comorbidities including the presence of immunosuppression, mean PSI, and the baseline laboratory findings of ICU patients compared to non-ICU patients (Table 1). ICU patients were significantly more likely to have bilateral interstitial infiltrates on chest X-ray compared to non-ICU patients ($89.5\%$ versus $32.7\%$, $p \leq 0.001$) and bilateral pleural effusions ($47.4\%$ versus $14.5\%$, $$p \leq 0.009$$).
Only one patient ($1.25\%$) had RSV and influenza virus coinfection. Bacterial coinfection was found in five non-ICU patients (four cases had *Staphylococcus aureus* where two were methicillin-resistant and one had both *Klebsiella pneumoniae* and Pseudomonas aeruginosa) and three ICU patients (two *Streptococcus pneumoniae* and one Klebsiella pneumoniae).
## 3.2. Management
Management of the study patients is presented in Table 2. Sixty-one ($76.3\%$) patients received empiric oseltamivir (no significant difference between ICU and non-ICU patients and between immunosuppressed and immunocompetent patients). Oral ribavirin was provided to one patient who was immunosuppressed after RSV infection was diagnosed. Most patients in our cohort were started on empiric antibiotics ($96.2\%$). A combination of ceftriaxone and a macrolide was the most commonly prescribed initial regimen ($40.1\%$), followed by piperacillin-tazobactam and a macrolide ($37.6\%$). The use of empiric piperacillin-tazobactam or meropenem in combination with a macrolide was similar in patients who were immunosuppressed and those who were not ($\frac{4}{14}$ patients ($28.6\%$) versus $\frac{19}{66}$ ($28.8\%$), respectively; $$p \leq 1.0$$). On the other hand, patients with bacterial coinfection were more likely to receive piperacillin-tazobactam or meropenem in combination with a macrolide compared to those without bacterial coinfection ($\frac{5}{8}$ patients ($62.5\%$) versus $\frac{18}{72}$ patients ($25.0\%$), respectively; $$p \leq 0.04$$). *In* general, there were no significant differences in the empiric antibiotics between ICU and non-ICU patients. More than half ($53.7\%$) of the study patients received corticosteroids as part of initial therapy, and although there was no significant difference in the rate of steroid use in ICU patients compared to non-ICU patients, ICU patients were started on a higher dose (median of 55 mg of prednisone or its equivalent) compared to non-ICU patients (median of 40 mg of prednisone or its equivalent, $$p \leq 0.02$$).
## 3.3. Outcomes of Patients
Table 3 describes the outcomes of the study patients. The overall hospital mortality in the study cohort was $8.8\%$ with a significantly higher mortality rate in ICU patients ($31.6\%$ versus $1.6\%$, $p \leq 0.001$). The 30-day readmission rate was $24.7\%$ with no significant difference between ICU and non-ICU patients.
Figure 2 describes the hospital mortality in clinically important subgroups of patients. There were no significant differences in the mortality rates between older and younger patients and between immunosuppressed and immunocompetent patients. Patients who received corticosteroids and those who were admitted to the ICU and had organ support had higher mortality rates than those who did not.
## 4. Discussion
The main findings of this study were the following: most cases of CAP due to RSV in adults requiring hospitalization occurred during the colder months in Riyadh, Saudi Arabia (December to February); most patients were elderly with multiple comorbidities; almost one-fourth of hospitalized patients were admitted to the ICU; and almost 1 in 10 patients died in the hospital with most deaths in patients requiring ICU admission and organ support.
The median age in our cohort was 69 years, which conforms with the existing data showing that elderly patients aged ≥65 years represent a high proportion of hospitalized adults with RSV infection [13, 14]. Twenty percent of our study population was bedbound at baseline. These patients are probably at increased risk for viral infections and may correspond to people staying at nursing homes in other countries [19, 20]. Hypertension ($65.0\%$), diabetes ($58.8\%$), and chronic respiratory disease ($52.5\%$) were especially prevalent among our study population, emphasizing that these comorbidities might be significant risk factors for RSV pneumonia requiring hospitalization as observed in previous studies on patients with RSV pneumonia [13, 14]. Chronic immunosuppression, although present in $\frac{14}{80}$ patients ($17.5\%$), was not significantly different between ICU and non-ICU patients. Only $\frac{1}{8}$ patients ($12.5\%$) with a history of an organ transplant required admission to the ICU; this is much lower than the rates of ICU admissions of transplant patients in multiple studies [18, 21]. The median PSI was 120.5 overall (102.0 for non-ICU patients vs. 140.0 for ICU patients); the difference in the PSI was not statistically significant ($$p \leq 0.09$$).
The majority of patients presented with shortness of breath ($88.8\%$) and cough ($96.2\%$), which was more commonly productive than dry (58 patients versus 19 patients); these findings are consistent with previous data regarding RSV pneumonia [11–14]. This prevalence of productive cough cannot be fully attributed to a concomitant bacterial organism as only $10\%$ of all patients had a bacterial coinfection detected by respiratory culture, a lower rate of bacterial coinfection compared to other studies [13, 16, 26]. Five ($6.3\%$) patients had hemoptysis, but only 1 patient required ICU admission. The classic signs of systemic viral illness such as myalgias/arthralgias ($20\%$) and headache ($7.5\%$) were not found in the majority of our patients and were reported at a similar frequency in the study by Mathur et al. [ 12]. Less than half of the patients ($47.5\%$) were febrile on admission, with an even lower rate of fever on presentation among ICU patients ($36.8\%$), indicating that a lack of fever was insufficient to exclude CAP due to RSV or to even indicate less disease severity. This is lower than the findings of Dowell et al. where fever was reported in $61\%$ of patients [14]. In our study, bilateral lung infiltrates and pleural effusions were common, similar to the findings of other studies [17, 18, 21].
In the current study, most patients ($76.3\%$) received empiric oseltamivir with a similar rate among ICU and non-ICU patients. The use of empiric oseltamivir is common in clinical practices [27], especially since most of the study patients were admitted in the winter time, which is the peak time for influenza infection in Saudi Arabia [28]. Only one patient who was immunosuppressed received oral ribavirin. Although oral or nebulized ribavirin has been used for the treatment of severe RSV infections in immunosuppressed adult patients with observed benefits, most studies on the effectiveness of ribavirin were uncontrolled and not adequately powered [29]. Most of the study patients received antibiotics within the first 24 hours of admission, with antipseudomonal beta-lactams being commonly used. No significant difference was found in the usage of antibiotics between ICU and non-ICU patients and between immunosuppressed and immunocompetent patients. Broad-spectrum antibiotics are frequently used in patients with viral pneumonia or other respiratory tract infections [26, 30, 31]. These findings indicate the need for reliable methods that distinguish early viral from bacterial infection in patients with pneumonia and for antibiotic and antiviral stewardship where empirical treatment would be adjusted based on the result of the investigations [32]. Systemic corticosteroids were commonly ($57.3\%$ of patients) used with a median initial dose of 40 mg of prednisone or its equivalent. This could be because chronic respiratory diseases were prevalent in the study patients. Some of our patients may have exhibited wheezing leading to the common use of systemic corticosteroids.
Patients with CAP due to RSV had considerable morbidity and mortality in our study. Nearly one in four patients ($23.8\%$) required ICU admission, which is in line with the 21-$25\%$ ICU admission rate in other studies [13, 14]. The overall mortality in our study was $8.8\%$, which is similar to the $10\%$ mortality rate found by Falsey et al. and Yoon et al. [ 13, 22] but is considerably lower than the rates in studies in immunocompromised patients where mortality ranged from 36 to $50\%$ [17, 18]. Naturally, mortality was significantly higher in ICU patients compared to non-ICU patients ($31.6\%$ versus $1.6\%$, $p \leq 0.001$). Significantly different variables between ICU and non-ICU patients included the presence of bilateral chest infiltrates on chest imaging ($89.5\%$ for ICU patients versus $32.7\%$ for non-ICU patients; $p \leq 0.001$). Thirteen out of 80 ($16.3\%$) patients in our cohort required mechanical ventilation. This was higher than the rate observed by Falsey et al. [ 13] but considerably lower than that of the study by Hertz et al. in immunocompromised patients where 4 out of 11 ($36\%$) required mechanical ventilation [18]. We have observed higher mortality in patients who received corticosteroids. This observation may be explained by the fact that patients who received corticosteroids were sicker.
The main limitation of our study is its retrospective nature, making it difficult to establish causation between characteristics, exposures, and outcomes. The sample size, even though being relatively large compared to that of other studies, prevented the performance of reliable multivariable logistic regression analyses to assess the risk factors for ICU admission and mortality. Being a single-center study also limits the generalizability of our findings. The study also focused on patients hospitalized for CAP where outpatient cases were not accounted for.
In conclusion, most patients with CAP due to RSV were elderly with significant comorbidities. ICU admission was required in almost one in four patients and mechanical ventilation in almost one in six patients. The overall hospital mortality was $8.8\%$ with patients who were admitted to the ICU having significantly higher mortality approaching 1 in 3 patients.
## Data Availability
Data are available on reasonable request from the corresponding author.
## Ethical Approval
The study was approved by the Institutional Review Board of the Ministry of National Guard Health Affairs.
## Conflicts of Interest
All authors declare no conflict of interest.
## Authors' Contributions
IB worked on study concept and design, literature search, data acquisition and interpretation, manuscript preparation, editing, and revision for important intellectual content. AA, TA, SA, YA, and RA worked on study concept and design, literature search, data acquisition and interpretation, and manuscript revision for important intellectual content. SJ worked on data acquisition and interpretation and manuscript revision for important intellectual content. MB worked on study concept and design, data interpretation, and manuscript revision for important intellectual content. HD worked on study concept and design, literature search, data interpretation, statistical analysis, manuscript preparation, editing, and revision for important intellectual content. The manuscript has been read and approved by all the authors. All authors met the requirements for authorship. All authors confirm that the manuscript represents honest work.
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---
title: Cross-Sectional Analysis of the Relationship Between Adherence to the Mediterranean
Diet and Mental Wellness
journal: Cureus
year: 2023
pmcid: PMC10010946
doi: 10.7759/cureus.34878
license: CC BY 3.0
---
# Cross-Sectional Analysis of the Relationship Between Adherence to the Mediterranean Diet and Mental Wellness
## Abstract
The purpose of the study was to determine whether there was a relationship between adherence to the Mediterranean diet (MD) and levels of anxiety, depression, and overall mental well-being. The Mediterranean diet is a popular, healthy diet, aimed to promote wellness and reduce chronic illness. In order to determine the relationship between MD and mental well-being, 100 participants consented to complete an online survey to analyze their adherence to MD, along with levels of anxiety and depression. The validated questionnaires of the 14-item Questionnaire of Mediterranean diet Adherence, Generalized Anxiety Disorder-7 (GAD-7), and Beck's Depression Inventory (BDI) assessments were used to analyze each participant. To evaluate the results of the study, Spearman's rank correlation coefficient analysis was used to identify relationships between MD, depression, and anxiety. There was a significant negative correlation, indicating that MD adherence is associated with reduced depression and anxiety.
## Introduction
First described by Ancel Keys in the 1960s, the Mediterranean diet (MD) is a dietary pattern consisting of healthy foods such as fruits, vegetables, whole grains, legumes, fish, and nuts [1]. Rich in fiber, antioxidants, and omega-3 fatty acids, MD limits consumables such as processed foods, red meats, refined grains, and added sugars [1]. Research demonstrates the benefits of MD, such as preventing cardiovascular disease and reducing the risks associated with diabetes, hypertension, and obesity [1-3]. The Mediterranean diet is also useful in improving insulin resistance (IR) [3].
Mental wellness Growing evidence has shown that partaking in the MD can be beneficial to not only physical health but mental health as well [1]. One group of researchers [4] analyzed the emotional well-being of university students using two different assessments. A 14-point questionnaire on adherence to the MD was given to the students along with a questionnaire measuring various positive and negative moods. The authors determined a significant inverse relationship between MD and emotional well-being. The authors also suggested that the high intake of key components of MD, such as olive oil, fish, fruits, vegetables, and legumes, particularly contributed to the reported lower levels of anxiety and depression. Overall, adherence to MD was positively associated with a more positive outlook and quality of life.
Adherence to MD has been shown to reduce the severity of depressive symptoms in adults living in the United States. In 2019, the National Institute of Mental Health Information Resource Center reported that 19.4 million adults, or $7\%$ of U.S. adults, experienced a major depressive episode [5]. Depression has been shown to be more prevalent in women than men, potentially due to hormonal shifts during puberty, menstruation, pregnancy, and menopause [6]. Depression is also a leading cause of disability with pharmaceutical mechanisms often producing ineffective results or related side effects [5]. An anti-inflammatory diet, such as MD, can prevent or reduce the symptoms of depression [5]. Others have studied the effects of MD adherence on feelings of depression, collecting detailed information about participants' diets using a food frequency questionnaire and other variable factors such as body mass index, level of physical activity, and smoking use. Data collected from 49,261 Swedish women found that adherence to MD was negatively correlated with lower levels of depression [7]. Since depression is so prevalent in the United States [5], more evidence is needed to validate the benefits of using MD to enhance overall well-being.
Despite MD being recommended to the general population, there are known discrepancies in adhering to it, especially between different racial/ethnic groups. One study indicated that positive effects, such as cardiovascular disease benefits, were achieved for only individuals at higher socioeconomic levels [8]. This possibly indicates that the MD may not be available to certain racial/ethnic groups with well-documented socioeconomic disparities and poor food security. Despite this affordability concern, it has been shown that such populations may still be able to adhere to Mediterranean-like foods that are both culturally appropriate and cost-effective. Such foods include beans, canned tuna, and frozen or canned fruits and vegetables [9-10]. These concerns should be kept in consideration when assessing adherence to the MD across all racial/ethnic groups. The well-being of the general public regarding nutrition should be of high priority to healthcare professionals, especially to the most vulnerable groups due to their risk of significant health disparities and disproportionally high multimorbidity [10].
Patients and healthcare professionals need to consider diet when evaluating one’s lifestyle and well-being [11]. The use of self-reflective tools in treatment plans can be enlightening and engage patients in their care. Patients can also report foods that trigger symptoms or unpleasant effects, while also reporting which foods produce "feel good" effects. Various nutritional deficiencies, including zinc and magnesium, can also play a role in a low mood [12]. Since MD encourages the consumption of multiple food groups, people who adhere to it often consume a nutrient-dense diet, while also being at a reduced risk of developing a nutritional deficiency, compared to those who consume a Western diet [13]. The aim of this study was to further document the relationship of MD adherence with self-reported levels of anxiety and depression. Combined in a unique way, we hypothesized that higher adherence to MD would be correlated with lower levels of anxiety and depression.
## Materials and methods
Institutional Review Board The survey research study was submitted for approval to the Institutional Review Board (IRB) at Nova Southeastern University through the Dr. Kiran C. Patel College of Osteopathic Medicine. The study was approved on June 20, 2022, with the IRB number 2022-277.
Recruitment Participants over the age of 18 were eligible to take part in the survey. Recruitment of participants was conducted by marketing the survey on bulletins at fitness clubs in South Florida and globally on the social media platforms Facebook and Instagram. The marketing flier contained the survey's website link and a quick response (QR) code that could be paired with a mobile device.
The inclusion criteria required participants to: 1) be at least 18 years old, 2) be proficient in the English language, 3) have access to a computer to participate in the electronic survey or a mobile device to scan a quick response code, and 4) give implied consent.
The exclusion criteria prohibited participants who: 1) were under the age of 18 years, 2) were not proficient in the English language, 3) did not have access to an electronic device, or 4) did not give implied consent.
Instruments Participants were first presented with the online Informed Consent Form. If consent was given, participants selected the ‘I agree’ button which brought them to the beginning of the survey. The online survey consisted of four separate sections: Demographics, the 14-item Questionnaire of Mediterranean Diet Adherence, as shown in Figure 3 [14-17], Generalized Anxiety Disorder-7 (GAD-7) [18], and Beck’s Depression Inventory (BDI) [19]. The entire survey took approximately 10 minutes to complete.
Demographic information was obtained following informed consent. The data collected in this section included age, gender identity, and race/ethnicity.
The 14-item Questionnaire of Mediterranean Diet *Adherence is* a validated questionnaire that consists of 14 items used to measure adherence to MD [14]. Questions involve inquiring about the participants' typical dietary choices. Each item is associated with one point and is obtained if the criteria for each question are met (as shown in Figure 3 in the Appendices). Higher scores indicate higher adherence to MD [14].
GAD-7 is a commonly used self-reported questionnaire that consists of seven items to measure the severity of anxiety. It is utilized as an initial screening tool for generalized anxiety disorder [18]. In each item, GAD-7 asks participants to rate how often they experience a specific feeling related to a symptom of anxiety over the last two weeks, with the frequency options of "not at all", "several days", "more than half the days", and "nearly every day." The items are measured on a 3-point scale, ranging from 0 (not at all) to 3 (nearly every day). The score of each item is then combined to calculate the GAD-7 total score, which can range from 0 to 21. A score of 0-4 indicates minimal anxiety, 5-9 as mild anxiety, 10-14 as moderate anxiety, and 15-21 as severe anxiety (as shown in Figure 4 in the Appendices) [18]. Thus, higher scores indicate higher levels of anxiety.
BDI is one of the most widely used self-reported questionnaires and consists of 21 items to measure the severity of depression. Each item is composed of a symptom relating to depression, and participants must select the statement within a multiple-choice format that best describes themselves currently. The items are measured on a 3-point scale, and the score of each item is then combined to calculate the total score, which can range from 0 to 63. A score of 1-10 is considered normal, 11-16 indicates mild mood disturbance, 17-20 as borderline clinical depression, 21-30 as moderate depression, 31-40 as severe depression, and a score greater than 40 as extreme depression [19]. The exact questions and the scoring scale are shown in the Appendices (Figures 5-7). Thus, higher scores indicate higher levels of depression.
Data collection and analysis *The anonymous* data from the survey was collected through REDCap (REDCap, Vanderbilt University, Nashville, USA). Spearman's rank correlation coefficient was used to determine the strength and direction of association of the ordinal data between Mediterranean Diet Adherence Assessment and GAD-7 anxiety assessment, as well as Mediterranean Diet Adherence Assessment and BDI [20].
## Results
Sample A total of 117 anonymous subjects' responses were initially collected online through RedCap. Seventeen participants were omitted due to the incompletion of the survey. The final sample size consisted of 100 participants between the ages of 19 and 77. The average age was 37 years old with the sample consisting of $61\%$ women and $31\%$ men. Demographics included $79\%$ White, $10\%$ Hispanic, $5\%$ two or more different ethnicities/races, $3\%$ Asian, $2\%$ African American or Black, and $1\%$ Other.
Findings Scores received by the participants on the 14-item Questionnaire of Mediterranean Diet Adherence [14-17], GAD-7 [18], and BDI [19] were calculated by the researchers according to their specific scoring key created by their original authors. Higher scores for each assessment indicated greater adherence to MD, higher levels of anxiety, and higher levels of depression. The relationship of raw MD and GAD-7 scores is displayed within the scatter plot of Figure 1, while the relationship of raw MD and BDI scores is displayed in Figure 2. Each point in the figures represents an individual participant's scores associated with the designated assessments. Both figures display a general negative relationship, hence higher GAD-7 or BDI scores are related to a lower MD score.
**Figure 1:** *Mediterranean Diet Adherence and Reported Levels of Anxiety Mediterranean diet is represented by MD in the figure. MD scores indicate raw scores obtained from the 14-item questionnaire of Mediterranean Diet Adherence Assessment. GAD-7 scores indicate raw scores obtained from the Generalized Anxiety Disorder-7 questionnaire to assess levels of anxiety. Scores from both questionnaires were plotted together on the appropriate axis for each individual participant and are displayed as a single data point. Raw MD and GAD-7 scores demonstrate a negative relationship, where higher GAD-7 scores are related to lower MD scores.* **Figure 2:** *Mediterranean Diet Adherence and Reported Levels of Depression Mediterranean diet is represented by MD in the figure. MD scores indicate raw scores obtained from the 14-item questionnaire of Mediterranean Diet Adherence Assessment. BDI scores indicate raw scores obtained from the Beck's Depression Inventory questionnaire to assess levels of depression. Scores from both questionnaires were plotted together on the appropriate axis for each individual participant and are displayed as a single data point. Raw MD and BDI scores demonstrate a negative relationship, where higher BDI scores are related to lower MD scores.*
Spearman’s rank correlation coefficient analysis was further used to investigate our hypothesis and identify any possible correlations between MD and depression as well as MD and anxiety. The raw data of each assessment was initially ranked individually to prepare for data analysis via Spearman’s correlation coefficient (rs). As reported in Table 1, the results of the analysis show Sig. ( two-tailed) = 0.0001 for depression and Sig. ( two-tailed) = 0.0191 for anxiety. A p-value < 0.05 was selected to assess statistical significance. Therefore, the results indicated that there is a significant correlation between both MD and depression as well as MD and anxiety. Additionally, both depression and anxiety have negative rs values, implying a negative correlation with MD for both. Due to MD and anxiety having an rs value (-0.234) closer to zero compared to MD and depression (rs = -0.369), MD and anxiety demonstrated a weaker association between their ranks contrary to MD and depression having a stronger association. Overall, our results align with our initial hypothesis suggesting that higher adherence to MD is correlated with lower levels of anxiety and depression.
**Table 1**
| Unnamed: 0 | Unnamed: 1 | Depression | Anxiety |
| --- | --- | --- | --- |
| Mediterranean Diet | rs | -0.369 | -0.234 |
| Mediterranean Diet | N | 100.0 | 100.0 |
| Mediterranean Diet | T-value | 3.93 | 2.38 |
| Mediterranean Diet | df | 98.0 | 98.0 |
| Mediterranean Diet | Sig.(two-tailed) | 0.0001 | 0.0191 |
## Discussion
The main purpose of our study was to determine if there was a correlation between adherence to the MD and levels of anxiety and depression. Our findings support a negative correlation, indicating that participants who had higher adherence to MD also had lower levels of both anxiety and depression. *In* general, the results of our study suggest that one’s diet can potentially play a role in one’s overall mental well-being. Prior literature has also supported this by indicating possible bidirectional relationships between diet and potential causative factors such as inflammation and gut-brain axis [21].
Additionally, our sample consisted of mainly women with an average age of 37. When examining the demographics in regard to mental health, women appear to be more likely than men to experience both anxiety and depression [6,22,23]. The leading reason for this gender gap is suggested to be due to sex hormones, but the underlying mechanisms still remain unclear [6,24]. In contrast to the average age of 37 within our sample, the National Center for Health Statistics reports that levels of anxiety and depression are most prevalent among those aged 18-29 ($21.0\%$) and least prevalent among those aged 30-44 ($16.8\%$) [22,23]. Fluctuations in women's hormone levels throughout their lifetime such as during puberty, pregnancy, and menopause, can be possibly correlated to those differences in age group prevalence of anxiety and depression [6].
Overall, our findings suggest that expanding the knowledge on the relationship between adherence to MD and anxiety and depression can further influence the field of nutrition and promote the importance of diet and lifestyle changes that can positively impact one’s entire mental well-being. Based on the growing evidence between diet and mood, it is crucial for healthcare professionals to consider diet when evaluating a patient’s lifestyle and well-being. This patient-provider partnership can aid in patients having a deeper understanding of the relationship between diet and mental well-being. Ensuring a proper nutritional state in individuals is known to have an important role in treating mental illness by it improving their emotional and cognitive functioning [25]. Utilizing a food diary in one’s treatment and care plan can be the initial step in implementing this [26].
However, individuals must understand that overall mental well-being and diet are not a “one size fits all.” Some individuals may significantly benefit from MD, while others may not notice any positive effects on their mental state. As more literature is published, individuals need to determine what diet and lifestyle are the most beneficial for them and to discuss this with their healthcare provider for further guidance. Through self-reflection during our study, participants had the opportunity to identify flaws in their diet, which can then be used to identify goals for improvement, thus potentially improving their mental health as well. Ultimately, the results of our study can provide potential evidence regarding the significance of diet in mental well-being. Determining a relationship between one's dietary patterns and overall mental health can encourage and promote public health efforts to improve eating habits.
Limitations The small sample size may prevent the findings from being extrapolated to the overall general public. A convenience sampling resulted in $61\%$ of the participants being women and $79\%$ being White. Since the sample was not significantly diverse, the results of the study may have been skewed and thus results are less likely applied to other ethnic/racial groups. In addition, a voluntary online survey to collect data makes it difficult to know whether participants reported accurately. For example, respondents may select the assumed socially acceptable answer, rather than selecting what they actually felt or consumed on a daily basis. Despite the anonymity, the sensitivity of the questions about anxiety and depression may have caused the participants to feel judged and avoid answering the questions truthfully. This original study provided worthwhile results that validate the positive relationship between dietary quality and mental health. Further studies that incorporate physical activity patterns from participants should be considered due to their confounding potential. Additional questions about participants’ entire well-being can be evaluated, such as existing mental health, medication use, alcohol consumption, tobacco use, and sleep patterns.
## Conclusions
Our study successfully expanded evidence about the negative correlation between MD and levels of anxiety and depression in mental well-being. This association was strongest for depression. Our findings demonstrate the potential therapeutic effect of healthy dietary patterns on mental well-being. The results affirm that patient education and nutrition counseling for lifestyle modifications are essential strategies for healthcare professionals. Future studies to assess comprehensive well-being should be considered to address any possible confounding variables to confirm and strengthen our findings.
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|
---
title: Body Uneasiness and Dissatisfaction Among Lesbian, Gay, Bisexual, and Heterosexual
Persons
authors:
- Laura Muzi
- Nicola Nardelli
- Gabriele Naticchioni
- Claudia Mazzeschi
- Roberto Baiocco
- Vittorio Lingiardi
journal: Sexuality Research & Social Policy
year: 2023
pmcid: PMC10010970
doi: 10.1007/s13178-023-00805-3
license: CC BY 4.0
---
# Body Uneasiness and Dissatisfaction Among Lesbian, Gay, Bisexual, and Heterosexual Persons
## Body
Perceptions, feelings, and thoughts about one’s body can have a significant impact on psychological and social health. Previous studies have outlined that body image is a multidimensional umbrella construct, in terms of both its assessment and its associations with related concepts (Banfield & McCabe, 2002; Cash et al., 2004). Body image disturbance (also referred to as a negative body image) refers primarily to negative experiences related to one’s body weight and shape (Grogan, 2006). It has cognitive-affective, perceptual, and behavioral components, including an excessive emphasis on body weight and/or shape on self-evaluation, poor body size perception accuracy, and repeated body checking (e.g., Glashouwer et al., 2020). While a positive body image has been found to relate to psychological resources such as high self-esteem, a negative body image has been associated with a variety of adverse health outcomes (e.g., Thompson, 2004), including disordered eating behaviors, depression, psychological distress, and poor quality of life (Alvy, 2013; Calzo et al., 2015; Pistella et al., 2019; Stice & Shaw, 2002).
The present study focused on the constructs of body uneasiness and body dissatisfaction, as two of the potential declinations that may accompany body image disturbance (Levitan et al., 2019). Body dissatisfaction reflects discontent about one’s physical appearance (Tatangelo et al., 2016). Body uneasiness, in turn, includes not only dissatisfaction with particular body parts, shapes, or functions, but also a general feeling of uneasiness relating to one’s body or weight, which can lead to avoidance behaviors, compulsive checking behaviors, and detachment and/or estrangement feelings towards the body (Cuzzolaro et al., 2006). The study also considered self-criticizing and self-hating attacking behavior towards the self, as these factors have been found to be associated with shame related to body image (e.g., Ferreira et al., 2019).
Recently, research has begun to explore these issues in social identity groups that have not previously been a focus of body image researchers, including sexual minority adults (Andersen & Swami, 2021). However, the relationship between body image disturbance, gender, and sexual orientation identity (hereinafter referred to as sexual identity) remains controversial. The population of sexual minority people—which includes, but is not limited to, lesbian women, gay men, and bisexual individuals—has been widely considered at risk for developing body dissatisfaction and concerns (Calzo et al., 2017; Dahlenburg et al., 2020; Goldhammer et al., 2019; Mason et al., 2018). Sexual minority individuals also report a greater frequency of disordered eating symptoms (including body image disturbance), relative to their heterosexual counterparts (French et al., 1996; Kamody et al., 2020; Laska et al., 2015; Shearer et al., 2015; Yean et al., 2013).
Assigned gender at birth may represent an additional variable of interest for body image disturbance, as men and women are likely to face different pressures to achieve an ideal body and different levels of weight stigma (Myers & Crowther, 2009). Men tend to strive for a muscular and lean body (Laghi et al., 2013; Schaefer & Thompson, 2018; Tiggemann et al., 2007), whereas women tend to strive for a thin body (e.g., Gordon et al., 2010). Furthermore, several studies have revealed significant differences within men, which are less pronounced in women (Dahlenburg et al., 2020). Of note, similar levels of body dissatisfaction and its correlates have been found in samples of women, regardless of sexual identity, and some investigations have noted that levels of body dissatisfaction in women tend to exceed those in men (Basabas et al., 2019; McGuinness & Taylor, 2016).
Despite increasing interest in the impact of body dissatisfaction and uneasiness in sexual minority individuals, research has primarily focused on gay men, and comparatively fewer studies have been devoted to lesbian women and bisexual individuals (Morrison et al., 2004). Evidence suggests that, compared to heterosexual men, gay men are significantly more likely to experience body image concerns, body dissatisfaction, and body image–related anxiety; to prioritize bodily appearance in appraisals of self-worth; and to strive for thinness (Calzo et al., 2015; Feldman & Meyer, 2007; Levesque & Vichesky, 2006; McClain & Peebles, 2016; Yean et al., 2013). Additionally, gay men with high body dissatisfaction and disordered eating symptoms have been found to have impaired psychological well-being (Gil, 2007; Levesque & Vichesky, 2006), characterized by high self-criticism, low self-esteem, and depressive symptoms (Chaney, 2008; Reilly & Rudd, 2006; Russell & Keel, 2002; Tiggemann et al., 2007; Yelland & Tiggemann, 2003).
Conversely, studies comparing sexual minority and heterosexual women have produced mixed results. Some research has found that, compared with heterosexual women, lesbian women tend to place less emphasis on physical attractiveness (Gettelman & Thompson, 1993; Siever, 1994) and are less likely to have a negative body image, body image concerns, and high body dissatisfaction (Dahlenburg et al., 2020; French et al., 1996; Polimeni et al., 2009; Strong et al., 2000). Furthermore, lesbian sexual identity has been found to predict a positive body image and fewer negative attitudes towards eating and weight (Owens et al., 2003). However, other studies have found no differences between lesbian and heterosexual women regarding body dissatisfaction (e.g., Beren et al., 1996). Two meta-analytic reviews provided evidence that sexual minority and heterosexual women may experience similar levels of body image concerns and body image disturbance. Furthermore, correlates of body image concerns (e.g., sociocultural pressure, high negative affect, low self-esteem) have been found to be generally similar among lesbian, bisexual, and heterosexual women (Mason et al., 2018; Morrison et al., 2004). Several studies have also found equivalent rates of disordered eating symptoms in lesbian, bisexual, and heterosexual women (Feldman & Meyer, 2007; Strong et al., 2000), though some have found a higher rate of such symptoms in heterosexual women (Lakkis et al., 1999; Siever, 1994). Sampling bias and other methodological limitations, such as the wide range of measures employed to evaluate negative body image, may have contributed to these contradictory findings.
Research has also shown that gay men, lesbian women, and heterosexual women tend to have higher levels of negative body image and body dissatisfaction than heterosexual men (Dahlenburg et al., 2020; Peplau et al., 2009). However, only limited research has compared lesbian women and gay men, with preliminary findings suggesting that lesbian women report greater levels of body image disturbance than gay men (Dahlenburg et al., 2020). Furthermore, there is a gap in the literature concerning body image disturbance in bisexual individuals, as very few studies have treated bisexual men and/or women as a specific group for analysis (Feldman & Meyer, 2007; Ryan et al., 2010). Despite the paucity of findings on this topic, current evidence suggests that bisexual individuals, and especially bisexual men, share some of the same body image concerns as other sexual minority men (Filiault et al., 2014).
Body weight and body mass index (BMI) are other under-researched variables in this field. Conceivably, these “objective” indices might influence the “subjective” internalization of weight bias and, in turn, bodily perception (Alvy, 2013). Sexual minority women have been found to report higher BMIs than heterosexual women, and some authors have suggested that they may be more likely to experience elevated rates of body size discrimination and harassment due to social stigmatization of overweight (Bowen et al., 2008; Conron et al., 2010; Johns et al., 2017). Furthermore, body weight and BMI have been found to predict body dissatisfaction in gay men (Frederick & Essayli, 2016; Peplau et al., 2009); however, the findings on lesbian and bisexual women are inconsistent.
Previous efforts to explore sexual identity–based differences in body image have mainly been grounded in sociocultural and objectification theories (Brewster et al., 2014). Sociocultural theories posit that idealized body images or norms differ between sexual minority and heterosexual men, with sexual minority men experiencing higher appearance pressure from peers, partners, the media, and the sexual minority community (Calzo et al., 2017; Jankowski et al., 2014). On the other hand, objectification theory (Fredrickson & Roberts, 1997), which was originally developed to account for disordered eating behaviors among women, suggests that societal appearance-related pressure and gender expectations may lead both men and women to view their body as a sexual object, provoking body surveillance behaviors and/or feelings of body-related shame (Gonzales & Blashill, 2021; Matsumoto & Rogers, 2020). Indeed, research has shown that, among gay men, increased sexual objectification may heighten body surveillance, body shame, and restricted eating (Martins et al., 2007; Tiggemann et al., 2007; Wiseman & Moradi, 2010). Among lesbian women, however, the rejection of heteronormative ideals and standards may protect against a negative body image and the development of harmful attitudes around eating and weight (Brown, 1987; Dahlenburg et al., 2020; Owens et al., 2003; Polimeni et al., 2009).
Drawing on the literature, the present study tested the following hypotheses:(a) There would be significant differences in levels of body image disturbance, body dissatisfaction, and self-criticism/self-hate between heterosexual and sexual minority men. More specifically, gay and bisexual men would show higher overall body uneasiness and its dimensions, as well as greater body dissatisfaction, feelings of inadequacy, and self-hate, compared to heterosexual men (e.g., Dahlenburg et al., 2020; Smith et al., 2011; Yean et al., 2013).(b) There would be no significant differences in levels of body uneasiness, body dissatisfaction, and self-criticism/self-hate between heterosexual and sexual minority women. Lesbian and bisexual women would show similar levels of body image disturbance and shame compared to heterosexual women (Mason et al., 2018; Morrison et al., 2004).(c) There would be significant differences between LGB (lesbian, gay, and bisexual) groups. In line with the scarce empirical evidence on sexual minority samples, lesbian women would show more body uneasiness and dissatisfaction compared to gay men (Dahlenburg et al., 2020; Markey et al., 2017).(d) BMI and body weight would predict levels of body uneasiness, body dissatisfaction, and self-criticism/self-hate in LGB people, even after controlling for age, assigned gender at birth, and sexual identity. Higher BMI and body weight would predict a negative body image and greater feelings of inadequacy and shame, especially in gay men (e.g., Frederick & Essayli, 2016; Peplau et al., 2009).
## Abstract
### Introduction
While sexual minority people have been widely considered at risk for developing a range of body image concerns, evidence of body dissatisfaction and shame amongst LGB (lesbian, gay, and bisexual) individuals is mixed. This study investigated differences in body uneasiness, body dissatisfaction, and self-blaming/attacking attitudes between LGB and heterosexual individuals, as well as within LGB groups, while also examining the predictive role of body mass index (BMI).
### Methods
A sample of cisgender lesbian women ($$n = 163$$), gay men ($$n = 277$$), bisexual women ($$n = 135$$), bisexual men ($$n = 39$$), heterosexual women ($$n = 398$$), and heterosexual men ($$n = 219$$) completed an online survey assessing different aspects of body image between May and July 2020.
### Results
Gay and bisexual men reported greater body image disturbance and self-blaming attitudes relative to heterosexual men. In contrast, lesbian women reported lower body uneasiness than their bisexual and heterosexual counterparts, but greater self-hate. Moreover, lesbian and bisexual women showed more body dissatisfaction than gay men, and bisexual individuals reported more body uneasiness than individuals in other sexual minority subgroups. Higher BMI emerged as a significant predictor of body image concerns and dissatisfaction.
### Conclusions
Body image dimensions showed sexual identity–based differences. Determining the specific nuances of body image in LGB individuals can provide important information on potential risk factors that may impact mental health outcomes.
### Policy Implications
In-depth knowledge of body dissatisfaction and uneasiness in individuals with LGB identities may have critical implications for the development of personalized prevention and treatment strategies.
## Participants and Procedure
Participants met the following inclusion criteria: (a) aged 18 years or older, (b) cisgender, and (c) of Italian nationality. They were asked to respond to an online survey, which was administered over a period of 2 months (May–July 2020). All questionnaires were delivered cross-sectionally through an online Google Forms survey, which was accessible via a designated link and required approximately 30–40 min for completion. All subjects who fulfilled the inclusion criteria were asked to participate in a study on self-image and body representation in LGB people. Information about the study was disseminated via organizations, online forums, listservs, and newsletters geared towards LGBTQ + individuals (by email and posts on websites and social networks), to reach a large number of subjects. All survey items had a forced response, to prevent missing data. Software was used to prevent a single individual from responding to the survey multiple times.
An initial sample of $$n = 1$$,248 participants completed the survey. Those who reported a different gender identity than that of their assigned gender ($$n = 4$$) and those who identified themselves as transgender woman/man ($$n = 3$$) or “other (please specify)” ($$n = 10$$) were not included, as such individuals may experience body image and body dissatisfaction uniquely (e.g., Pulice-Farrow et al., 2020; Witcomb et al., 2015). Out of the final study sample of $$n = 1$$,231 participants, 163 ($13.2\%$) self-identified as lesbian women, 277 ($22.5\%$) as gay men, 135 ($11\%$) as bisexual women, 39 ($3.2\%$) as bisexual men, 398 ($32.3\%$) as heterosexual women, and 219 ($17.8\%$) as heterosexual men.
Table 1 displays all descriptive characteristics of the study sample. In the overall sample, the mean age was 30.34 years (SD = 10.90). The average BMI was 23.61 kg/m2 and the mean body weight was 68.65 kg (SD = 15.11). All study subjects were White and Italian. The majority of respondents had a high school diploma ($$n = 440$$, $35.7\%$) or bachelor’s degree ($$n = 364$$, $29.6\%$). A slightly lower percentage of participants ($$n = 262$$, $21.5\%$) had a master’s degree, and 127 ($10.3\%$) had a doctoral degree. Only 35 ($2.8\%$) participants had a secondary school degree. Most participants were single ($$n = 583$$, $47.3\%$) or in a stable relationship ($$n = 508$$, $41.3\%$); a minor percentage of respondents were married ($$n = 113$$, $9.2\%$) or separated/divorced ($$n = 25$$, $2.1\%$). Only 2 participants ($0.2\%$) were widowed. In terms of socioeconomic status, 690 ($56.1\%$) declared a medium income level, 267 ($21.7\%$) a low/medium income level, and 219 ($17.8\%$) a medium/high income level; fewer participants declared a low ($$n = 41$$, $3.3\%$) or high ($$n = 14$$, $1.1\%$) income level. Table 1Descriptive Statistics ($$n = 1$$,231)LGB subgroupsHeterosexual subgroupsVariableGay($$n = 277$$)M (SD)Lesbian($$n = 163$$)M (SD)Bisexual M($$n = 39$$)M (SD)Bisexual W($$n = 135$$)M (SD)HMa($$n = 219$$)M (SD)HWb($$n = 398$$)M (SD)Overall sampleM (SD)Age (years)33.84 (12.29)27.02 (7.27)29.91 (8.84)26.56 (7.01)33.47 (11.34)28.85 (10.12)30.34 (10.90)BMI (kg/m2)23.62 (3.53)23.46 (4.50)22.71 (4.04)23.32 (4.87)25.21 (3.74)22.94 (4.37)23.61 (4.22)Body weight74.34 (12.87)64.28 (13.26)72.41 (14.54)62.92 (15.04)79.68 (13.91)62.04 (12.81)68.65 (15.11)N (%)N (%)N (%)N (%)N (%)N (%)N (%)Marital statusSingle (never married)183 (66.1)66 (40.5)22 (56.4)62 (45.9)87 (39.7)163 (40.9)583 (47.3)Partnered83 (29.9)94 (57.6)15 (38.5)67 (49.6)69 (31.5)180 (45.2)508 (41.3)Married10 (3.6)2 (1.2)2 (5.1)3 (2.2)54 (24.6)42 (10.5)113 (9.2)Divorced/separated1 (0.4)1 (0.6)0 [0]3 (2.2)9 (4.1)11 (2.7)25 (2.1)Widowed0 [0]0 [0]0 [0]0 [0]0 [0]2 (0.5)2 (0.2)Highest education levelSecondary school4 (1.4)5 (3.1)2 (5.1)3 (2.2)16 (7.3)5 (1.2)35 (2.8)High school80 (28.9)74 (45.4)15 (38.4)52 (38.5)89 (40.6)130 (32.6)440 (35.7)Bachelor degree76 (27.4)50 (30.7)10 (25.6)48 (35.5)55 (25.1)125 (31.4)364 (29.6)Master degree76 (27.4)26 (15.9)7 (17.9)26 (19.2)40 (18.2)90 (22.6)265 (21.5)Doctoral degree41 (14.8)8 (4.9)5 (12.8)6 (4.4)19 (8.7)48 (12.1)127 (10.3)Socioeconomic statusLow8 (2.8)8 (4.9)2 (5.1)7 (5.2)7 (3.2)9 (2.3)41 (3.3)Low/medium57 (20.6)39 (23.9)14 (35.9)31(22.9)50 (22.8)76 (19.1)267 (21.7)Medium151(54.5)84 (51.5)16 (41.1)84 (62.2)129 (58.9)226 (56.8)690 (56.1)Medium/high53 (19.1)32 (19.6)6 (15.3)13 (9.6)30 (13.7)85 (21.6)219 (17.8)High8 (2.8)0 [0]1 (2.5)0 [0]3 (1.4)2 (0.5)14 (1.1)aHeterosexual menbHeterosexual women Significant differences emerged in terms of age (F[5, 1230] = 18.31, $p \leq .001$]), BMI (F[5, 1230] = 9.05, $p \leq .001$]), and body weight (F[5, 1230] = 68.02, $p \leq .001$]). Specifically, post-hoc analyses showed that gay and heterosexual men were older than lesbian, bisexual, and heterosexual women. Heterosexual men also had a higher BMI and body weight compared to all other groups, and gay men had a higher body weight than heterosexual, bisexual, and lesbian women.
The study protocol was reviewed and approved by the local research ethics committee and conducted in accordance with the 1964 Helsinki Declaration. Participation was voluntary. Prior to engaging in the survey, all participants provided electronic informed consent, indicating their understanding of both the study procedures and their right to cease their participation at any time, without penalty.
## Body Uneasiness Test – Form A
The Body Uneasiness Test – Form A (BUT-A; Cuzzolaro et al., 2006) is a 34-item self-report measure of several dimensions of body image in clinical and non-clinical populations, including intense fear of being or becoming fat (e.g., “I’m terrified of putting on weight”), body shape and/or weight dissatisfaction (e.g., “I feel I am fatter than others tell me”), avoidance (e.g., “The thought of some defects of my body torments me so much that it prevents me from being with others”), compulsive control behaviors (e.g., “If I begin to look at myself, I find it difficult to stop”), and detachment and estrangement feelings towards one’s body (e.g., “I have the sensation that my body does not belong to me”). Items are rated on a 6-point Likert scale ranging from 0 (never) to 5 (always). The Global Severity Index (GSI), which represents the average rating of all items, ranges from 0–5, with higher scores indicating greater body uneasiness. In the present study, Cronbach’s α was 0.86 for the GSI, 0.89 for the Weight Phobia (WP) subscale, 0.83 for the Body Image Concerns (BIC) subscale, 0.88 for the Avoidance (A) subscale, 0.78 for the Compulsive Self-Monitoring (CSM) subscale, and 0.86 for the Depersonalization (D) subscale.
## Eating Disorder Inventory-3 Referral Form
The Eating Disorder Inventory-3 Referral Form (EDI-3-RF) is an abbreviated version of the original EDI-3 (Garner, 2004), which is a widely used self-report questionnaire for assessing the presence and intensity of psychological traits or symptoms that are clinically relevant to eating disorders in clinical and non-clinical populations. The present study considered the EDI-3-RF Body Dissatisfaction (BD) subscale, which consists of 10 items that explore discontentment with overall body shape and the size of regions of the body that are typically of significant concern to those with eating disorders (i.e., stomach, hips, thighs, buttocks; e.g., “I feel bloated after eating a normal meal”). Compared to previous versions of the scale, the EDI-3-RF has a six-choice format, but scores are recalibrated to a 0–4 format to expand the range of summative scores and improve the psychometric properties with non-clinical populations. The measure has been shown to yield adequate convergent and discriminant validity (Clausen et al., 2011), and all scales included in the EDI-3-RF have been found to have good reliability indices (Garner, 2004). In the present study, Cronbach’s α for the EDI-3-RF Body Dissatisfaction subscale was 0.84.
## Forms of Self-Criticizing/Attacking & Self-Reassuring Scale
The Forms of Self-Criticizing/Attacking & Self-Reassuring Scale (FSCRS; Gilbert et al., 2004) is a self-report measure of the tendency to be self-critical and/or self-attacking in response to setbacks or failure. In line with the aims of the present study, which related to problematic attitudes towards one’s body, and in line with studies finding an association between both self-criticizing and self-attacking behaviors and shame related to body image, the present study employed two of the three FSCRS subscales: (a) Inadequate Self, which is comprised of 9 items measuring feelings of personal inadequacy and deficiency (e.g., “I am easily disappointed with myself”); and (b) Hated Self, which is comprised of 5 items that describe self-hating and self-harming behaviors, including those that are directed at the body (e.g., “I have become so angry with myself that I want to hurt or injure myself”). Items are rated on a 5-point Likert scale ranging from 0 (not at all like me) to 4 (extremely like me). The FSCRS has been shown to have good psychometric properties (Baiᾶo et al., 2015), and it has been previously applied to sexual minority populations (e.g., Petrocchi et al., 2020). In the present study, Cronbach’s α was 0.87 for the Inadequate Self subscale and 0.82 for the Hated Self subscale.
## Demographic Information
The survey included a questionnaire that collected demographic data relating to age, assigned gender at birth (i.e., female, male), gender identity (i.e., cisgender woman, cisgender man, transgender woman, transgender man, other), ethnicity, education level, and sexual identity. Respondents indicated their sexual identity by selecting from one of five response options (1 = gay, 2 = lesbian, 3 = bisexual, 4 = heterosexual, 5 = other, please specify). They also provided their height and weight, for the calculation of BMI (measured as kg/m2).
## Power Analyses
A priori power analysis was conducted with G*Power version 3.1.9.7 (Faul et al., 2007), to determine the minimum sample size required for the analyses of principal interest. A multivariate analysis of variance (MANOVA) in the F-test family was run, with three groups and nine response variables. The input criteria were an error probability of.05, a conventional value of.95 as a threshold power to be reached, and a small effect size (f2) of.10 (Cohen, 1988). The findings showed that the projected minimum sample size was $$n = 156$$ participants.
## Data Analytic Plan
All statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS Version 25) for Windows. First, group differences (in terms of assigned gender at birth and sexual identity) on the BUT-A and FSCRS subscales were analyzed using multivariate analyses of variance (MANOVAs). Subsequently, a one-way analysis of variance (ANOVA) was conducted to compare mean body dissatisfaction scores between LGB groups. Furthermore, partial eta squares for effect sizes were calculated. According to Cohen’s [1988] guidelines, partial η2 ≥ 0.01 was treated as a small effect, partial η2 ≥ 0.06 was regarded as a medium effect, and partial η2 ≥ 0.14 was considered a large effect. The Bonferroni test was applied for post-hoc comparisons. Next, hierarchical multiple regressions were conducted to investigate the relevance of BMI and body weight for overall body uneasiness, body dissatisfaction, and self-criticism amongst LGB subgroups, while controlling for participants’ age, assigned gender at birth, and sexual identity. Change in R2 was used to measure the significance of each step (i.e., block). The F test (i.e., F-change) was used to test whether R2 improvement was statistically significant. Partial correlations (i.e., partial r) were also reported to indicate the unique variance in the outcome variable predicted by each independent variable. There was no missing data at the item level, due to the survey’s forced item response.
## Comparisons Between Gay, Bisexual, and Heterosexual Men
Table 2 presents the results of the multivariate and univariate analyses of variance and the related effect sizes in the subgroups of gay, bisexual, and heterosexual men. A significant effect for sexual identity was detected for the BUT-A GSI and subscales (Wilks’ lambda =.87; F[2, 533] = 7.52; $p \leq .001$, ηp2 =.07), as well as for the FSCRS subscales (Wilks’ lambda =.68, F[2, 533] = 19.91, $p \leq .001$, ηp2 =.22). Significant differences also emerged for EDI-3-RF Body Dissatisfaction (F[2, 533] = 8.29, $p \leq .001$, ηp2 =.04). Post-hoc comparisons indicated that, compared to heterosexual participants, gay and bisexual men scored significantly higher on the BUT-A GSI and subscales, and EDI-3-RF Body Dissatisfaction. Table 2Differences in Body Image Disturbances between Gay, Bisexual, and Heterosexual Men ($$n = 535$$)VariableGay men($$n = 277$$)M (SD)Bisexual men($$n = 39$$)M (SD)Heterosexual men($$n = 219$$)M (SD)Fpη2Post-hocBUT-Aa Weight Phobia1.98 (0.96)2.16 (1.09)1.13 (0.91)36.37 <.001.12G = BM > HM Body Image Concerns1.72 (1.02)1.90 (1.28)1.04 (0.99)22.83 <.001.08G = BM > HM Compulsive Self-Monitoring1.17 (0.89)1.32 (0.78)0.71 (0.68)18.49 <.001.07G = BM > HM Avoidance0.68 (0.67)0.95 (0.68)0.32 (0.59)16.45 <.001.06G = BM > HM Depersonalization0.75 (0.90)0.93 (0.99)0.35 (0.53)18.68 <.001.07G = BM > HM Global Symptomatic Index1.34 (0.98)1.54 (1.11)0.76 (0.68)30.49 <.001.10G = BM > HMEDI-3-RFb Body Dissatisfaction11.37 (5.92)10.72 (5.71)8.32 (5.45)8.29 <.001.04G = BM > HMFSCRSc Inadequate Self4.48 (0.50)4.16 (0.65)3.37 (0.50)43.16 <.001.16G > BM > HM Hated Self4.88 (0.70)4.36 (0.87)3.56 (0.47)45.98 <.001.18G > BM > HMG gay men, BM bisexual men, HM heterosexual menaBody Uneasiness Scale – A (Cuzzolaro et al., 2006)bEating Disorder Inventory-3 Referral Form (Garner, 2004)cForms of Self-Criticizing/Attacking & Self-Reassuring Scale (Gilbert et al., 2004) Gay men also showed greater feelings of inadequacy and self-hate based on FSCRS subscales compared to bisexual men. Bisexual men, in turn, had higher levels of self-criticism than heterosexual men. The greatest effect sizes were found for FSCRS Inadequate Self and Hated Self, and BUT-A Weight Phobia, whereas the smallest effect size was found for EDI-3-RF Body Dissatisfaction.
## Comparisons Between Lesbian, Bisexual, and Heterosexual Women
Table 3 displays the results of the multivariate and univariate analyses of variance and the related effect sizes in the subgroups of lesbian, bisexual, and heterosexual women. Similar to the previous results, sexual identity had a significant effect for the BUT-A GSI and subscales (Wilks’ lambda =.94; F[2, 695] = 3.72; $p \leq .001$, ηp2 =.03), as well as for the FSCRS subscales (Wilks’ lambda =.92, F[2, 695] = 4.87, $p \leq .001$, ηp2 =.05); however, with lower effect sizes than those found in the subgroups of men. Conversely, no significant differences for EDI-3 Body Dissatisfaction were found (F[2, 695] = 1.36, p = ns, ηp2 =.004). More specifically, post-hoc tests revealed that lesbian women scored lower on the BUT-A GSI, Weight Phobia, Body Image Concerns, and Compulsive Self-Monitoring, compared to bisexual and heterosexual women. In turn, bisexual women scored higher on BUT-A Avoidance and Depersonalization than lesbian and heterosexual women. Of note, the FSCRS results were mixed: lesbian women scored lower on Inadequate Self than bisexual women, and bisexual women scored lower on Inadequate Self than heterosexual women; however, lesbian women scored higher on Hated Self than bisexual women, and bisexual women scored higher on Hated Self than heterosexual women. All BUT-A effect sizes were small, whereas all FSCRS effect sizes were medium-large. Table 3Differences in Body Image Disturbances between Lesbian, Bisexual, and Heterosexual Women ($$n = 696$$)VariableLesbian women($$n = 163$$)M (SD)Bisexual women($$n = 135$$)M (SD)Heterosexual women($$n = 398$$)M (SD)Fpη2Post-hocBUT-Aa Weight Phobia1.94 (0.99)2.44 (1.28)2.38 (1.27)7.89 <.001.02L < BW = HW Body Image Concerns1.70 (1.33)2.16 (1.29)1.95 (1.26)4.99.007.01L < BW = HW Compulsive Self-Monitoring1.15 (1.03)1.60 (1.10)1.40 (1.08)6.35.002.02L < BW = HW Avoidance0.83 (0.72)1.11 (0.68)0.85 (0.79)3.43.033.01BW > L = HW Depersonalization0.81 (0.98)1.22 (1.08)0.91 (0.83)6.06.002.02BW > L = HW Global Symptomatic Index1.37 (1.03)1.79 (1.04)1.59 (1.01)6.37.002.02L < BW = HMEDI-3-RFb Body Dissatisfaction15.15 (6.79)16.53 (6.24)16.70 (6.10)1.36.257.004FSCRSc Inadequate Self3.31 (0.58)4.12 (0.67)4.51 (0.61)39.22 <.001.14L < BW < HW Hated Self4.98 (0.30)4.77 (0.81)3.48 (0.65)42.32 <.001.16L > BW > HWL lesbian women, BW bisexual women, HW heterosexual womenaBody Uneasiness Scale – A (Cuzzolaro et al., 2006)bEating Disorder Inventory-3 Referral Form (Garner, 2004)cForms of Self-Criticizing/Attacking & Self-Reassuring Scale (Gilbert et al., 2004)
## Differences Between LGB Subgroups
Table 4 displays the group differences between LGB subgroups. A 3 (sexual identity: lesbian, gay, bisexual) × 2 (assigned gender at birth: man, woman) MANOVA was conducted on body uneasiness (BUT-A GSI and subscales) and self-criticism (FSCRS subscales). The choice of a 3 × 2 design was due to the low number of bisexual men, who were then merged with bisexual women. The analysis revealed a significant effect for sexual identity (Wilks’ lambda =.96; F[2, 614] = 1.90; $$p \leq .04$$, ηp2 =.02), but no significant effect for the assigned gender at birth (Wilks’ lambda =.99; F[2, 614] =.5; $$p \leq .75$$, ηp2 =.004), for the BUT-A subscales. Similarly, a significant effect for sexual identity (Wilks’ lambda =.70; F[2, 614] = 18.6; $p \leq .001$, ηp2 =.15), but no significant effect for assigned gender at birth (Wilks’ lambda = 0.98; F[2, 614] =.5; $$p \leq .69$$, ηp2 =.004), was found for the FSCRS subscales. Table 4Differences in Body Image Disturbances among LGB Individuals ($$n = 614$$)VariableGay men($$n = 277$$)M (SD)Lesbian women($$n = 163$$)M (SD)Bisexual individuals (MW)($$n = 174$$)M (SD)Fpη2Post-hocBUT-Aa Weight Phobia1.98 (0.96)1.94 (0.99)2.38 (1.09)6.29.002.02BMW > G = L Body Image Concerns1.72 (1.02)1.70 (1.33)2.11 (1.28)5.81.003.02BMW > G = L Compulsive Self-Monitoring1.17 (0.89)1.15 (1.03)1.54 (0.78)8.61 <.001.03BMW > G = L Avoidance0.68 (0.67)0.83 (0.72)1.07 (0.68)8.07 <.001.03BMW > G = L Depersonalization0.75 (0.90)0.81 (0.98)0.97 (0.99)6.64 <.001.03BMW > G = L Global Symptomatic Index1.34 (0.98)1.37 (1.03)1.74 (1.06)9.31 <.001.03BMW > G = LEDI-3-RFb Body Dissatisfaction11.37 (5.92)15.15 (6.79)15.41 (7.20)11.99 <.001.04L = BMW > GFSCRSc Inadequate Self4.48 (0.50)3.31 (0.58)4.13 (0.65)38.16 <.001.13G > BMW > L Hated Self4.88 (0.70)4.98 (0.30)4.69 (0.87)35.16 <.001.11L > BMW > GG gay men, L lesbian women, BMW merged bisexual men and womenaBody Uneasiness Scale – A (Cuzzolaro et al., 2006)bEating Disorder Inventory-3 Referral Form (Garner, 2004)cForms of Self-Criticizing/Attacking & Self-Reassuring Scale (Gilbert et al., 2004) Significant differences also emerged with respect to EDI-3-RF Body Dissatisfaction (F[2, 614] = 11.99, <.001, ηp2 =.04). Post-hoc comparisons showed that bisexual individuals scored higher on all BUT-A subscales and the GSI compared to lesbian and gay participants, but with small effect sizes. With respect to EDI-3-RF Body Dissatisfaction, lesbian and bisexual participants scored higher than gay men, with small effect sizes. Similar to previous analyses, the FSCRS findings were mixed: gay men scored higher on Inadequate Self than bisexual individuals, and bisexual individuals scored higher on Inadequate Self than lesbian women; however, lesbian women scored higher on Hated Self than bisexual individuals and gay men. The FSCRS effect sizes were medium.
## The Predictive Role of BMI and Body Weight Among Sexual Minority Subgroups
A series of four hierarchical multiple regression analyses were performed to assess the role of BMI and body weight in predicting overall body uneasiness (BUT-A GSI), EDI-3-RF Body Dissatisfaction, and FSCRS Self-Criticism in LGB individuals (see Table 5). Age was entered in the first step; assigned gender at birth (i.e., man, woman) and sexual identity (i.e., lesbian/gay, bisexual) were entered in the second step; and body weight and BMI were entered in the third and final step. Table 5Hierarchical Regression Analyses for BMI and Body Weight Predicting Body Uneasiness, Body Dissatisfaction, and Self-Criticism in LGB Subgroups ($$n = 614$$)RR2βF-change(Model)pPartialrCriterion variable: BUT-A GSIaStep 1.224.05031.988 <.001 Age−0.224−.230***Step 2.266.0716.606.001 Assigned gender (man = 0, woman = 1)0.048.008 Sexual orientation (bisexual = 0, gay/lesbian = 1)−0.142−.158***Step 3.426.18240.792 <.001 Body weight−0.172−.072 BMI (kg/m2)0.473.208***Criterion variable: EDI-3 BDbStep 1.185.03421.500 <.001 Age−0.185−.157***Step 2.258.06710.485 <.001 Assigned gender (man = 0, woman = 1)0.037.052 Sexual orientation (bisexual = 0, gay/lesbian = 1) −0.164−.132***Step 3.491.24168.893 <.001 Body weight−0.083−.036 BMI (kg/m2)0.487.221***Criterion variable: FSCRS IScStep 1.141.02012.357 <.001 Age0.141.002Step 2.614.37777.507 <.001 Assigned gender (man = 0, woman = 1)−0.473−.396*** Sexual orientation (bisexual = 0, gay/lesbian = 1)−0.334−.386***Step 3.620.3793.513.030 Body weight−0.014−.007 BMI (kg/m2)−0.073−.038Criterion variable: FSCRS ISdStep 1.263.06926.782 <.001 Age −0.263−.086*Step 2.625.41679.367 <.001 Assigned gender (man = 0, woman = 1)0.497.428*** Sexual orientation (bisexual = 0, gay/lesbian = 1)0.167.132**Step 3.628.4171.198.257 Body weight−0.005−.002 BMI (kg/m2)−0.043−.018*p ≤.05; **p ≤.01; *** p ≤.001aBody Uneasiness Scale – A, Global Symptomatic Index (Cuzzolaro et al., 2006)bEating Disorder Inventory-3 Referral Form, Body Dissatisfaction subscale (Garner, 2004)cForms of Self-Criticizing/Attacking & Self-Reassuring Scale, Inadequate Self and Hated Self subscales (Gilbert et al., 2004). Partial correlations reported for the third and last step, in which all predictors were included in the regression model In the final step, younger age (partial r = −.23, $p \leq .001$, and partial r = −.16, $p \leq .001$, respectively), sexual identity (partial r = −.16, $p \leq .001$, and partial r = −.13, $$p \leq .001$$, respectively), and higher BMI (partial $r = .21$, $p \leq .001$, and partial $r = .22$, $p \leq 0.001$, respectively) emerged as significant predictors of the BUT-A GSI and EDI-3-RF Body Dissatisfaction, whereas assigned gender and body weight showed no significant effect. The final model explained $18.2\%$ of the variance in overall body uneasiness, as assessed by the BUT-A GSI, and $24.1\%$ of the variance in overall body dissatisfaction, as evaluated by EDI-3-RF Body Dissatisfaction.
Furthermore, in the last step, only assigned gender (partial r = −.39, $p \leq .001$) and sexual identity (partial r = −.38, $p \leq .001$) emerged as negative predictors of FSCRS Inadequate Self. On the other hand, assigned gender (partial $r = .43$, $p \leq .001$) and sexual identity (partial $r = .13$, $$p \leq .001$$) emerged as positive predictors of FSCRS Hated Self. The final models explained $37.9\%$ and $41.7\%$ of the variance in FSCRS Inadequate and Hated Self, respectively.
## Discussion
In-depth knowledge of the impact of body dissatisfaction and uneasiness among LGB individuals may have critical implications for the prevention and treatment of eating disorders, dysfunctional eating behaviors, and psychological distress among these populations (Thompson, 2000). The present results confirmed the first hypothesis, as gay and bisexual men displayed greater body and weight dissatisfaction, avoidance, compulsive control behaviors, detachment and estrangement feelings towards their body, and body uneasiness relative to heterosexual individuals. These findings are aligned with previous research suggesting that LGB persons—especially gay men—are more likely to experience body image concerns and may be less accurate in their body weight estimations, compared to heterosexual men (Dahlenburg et al., 2020; Morrison et al., 2004; Peplau et al., 2009; Russel & Keel, 2002). The literature suggests that men who are dissatisfied with their body are more likely to report disordered eating patterns, poorer health-related quality of life, greater psychological distress, and lower self-esteem (Bergeron & Tylka, 2007; Tylka, 2011; Wilson et al., 2013). These findings seem particularly relevant to gay men, who have been found to report greater interest in body modification strategies, as well as more pronounced appearance-related pressure and social comparison (Frederick & Essayli, 2016).
Similar results emerged with respect to self-attitude measures, with gay men reporting significantly greater feelings of inadequacy and self-hate than their heterosexual counterparts. As Gilbert et al. [ 2012] suggested, self-criticism and self-hate are highly associated with shame (Lingiardi et al., 2017), and their dysfunctional effects in LGB persons may derive from related self-directed negative emotions, including anger, disgust, and contempt (Petrocchi et al., 2020), which may also be focused on physical appearance and body shape. Interestingly, gay men also reported higher levels of self-criticism and self-hate compared to bisexual men—who, in turn, reported higher levels of self-blame and self-attack than heterosexual men. Despite the paucity of studies devoted explicitly to the self-attitudes and bodily representations of bisexual men, these results seem to partially diverge from previous research showing no differences between gay and bisexual men (e.g., Bridge et al., 2019). This might be explained by the small sample of bisexual men in the present study (discussed further below).
Sociocultural and objectification theories (Brewster et al., 2014) may provide a potential interpretation of the differences between men, in that several studies have found associations between sexual objectification experiences, internalization of body ideals, body surveillance, body shame, and self-criticism in sexual minority men (e.g., Wiseman & Moradi, 2010). Although some authors have suggested that heterosexual men also internalize body ideals, and are thus potentially subject to self-objectification (Daniel & Bridges, 2010; Martins et al., 2007), most studies have reported that sexual minority men are significantly more affected by this internalization (Duggan & McCreary, 2004; Hobza et al., 2007; Martins et al., 2007; Matsumoto & Rodgers, 2020). Minority stress theory (Meyer, 2003) may offer an additional framework for understanding how sexual minority men experience their bodies within the social environment (Mason & Lewis, 2016; Siconolfi et al., 2016), as the experience and internalization of negative feelings, attitudes, beliefs, behaviors, and assumptions about one’s sexual identity may have relevant consequences for self-perception, at both the identity and the bodily level (Balsam, 2001; Herek, 2000; Meyer, 1995; Rostosky et al., 2007).
Concerning the second hypothesis, the results were mixed. In line with expectations, no group differences were found in body dissatisfaction, as measured by the EDI-3-RF. This result is aligned with Morrison et al. ’s [2004] meta-analysis, which found a non-significant effect size for the comparison of heterosexual and lesbian women on body satisfaction. However, with respect to the BUT-A subscales, lesbian women scored lower on Weight Phobia, Body Shape and Weight Dissatisfaction, Estrangement Feelings, Compulsive Control Behaviors, and Overall Body Uneasiness than bisexual and heterosexual women. These findings support the meta-analysis of Dahlenburg et al. [ 2020], which found that heterosexual women reported more significant body image disturbances compared to lesbian women across all measures considered, even with small effect sizes. They also align with evidence suggesting that lesbian women tend to have fewer disordered eating symptoms than heterosexual women (Lakkis et al., 1999; Strong et al., 2000). The present findings for lesbian women may be explained by a combination of objectification (Fredrikson & Roberts, 1997) and protective (Brown, 1987) theories: whereas women might feel pressurized to meet unrealistic standards of attractiveness, the lesbian subculture’s characteristic rejection of heteronormative standards and ideals may provide some protection against body image disturbances (Beck, 2017), body dissatisfaction (Alvy, 2013; Polimeni et al., 2009), negative attitudes towards weight (Owens et al., 2003), and negative appearance pressure (Hazzard et al., 2019) amongst lesbian women.
Similar findings emerged for FSCRS Self-Criticism and Self-Blaming Attitudes, with lesbian women reporting lower feelings of inadequacy relative to bisexual women—who, in turn, reported lower feelings of inadequacy relative to heterosexual women. Some authors have suggested that lesbian women’s lower feelings of inadequacy may be explained by their generally higher BMIs and preference for curvier figures (Alvy, 2013). However, in the present study, lesbian women also reported higher levels of self-hate and self-attack than bisexual and heterosexual women. Previous research with lesbian women has found that sexual identity–based discrimination is associated with less social support from the family, which, in turn, is related to increased negative affect, social anxiety, and disordered eating symptoms (Bell et al., 2019; Mason et al., 2018). Furthermore, shame and self-hate—primarily related to internalized homophobia—have been shown to be central psychological characteristics of disordered eating symptoms and binge behaviors in lesbian and bisexual women (Bayer et al., 2017).
The present results partially confirmed the third hypothesis, showing that lesbian and bisexual women tended to have more body dissatisfaction than gay men, as well as higher levels of self-hate and self-attack. Dahlenburg et al. [ 2020] found that, relative to gay men, lesbian women were more prone to experiencing body image disorders and to perceive themselves as physically unattractive (Markey et al., 2017; Swami, 2009). Although there have been very few studies on this topic, the present findings suggest that, while lesbian women may experience fewer body image disturbances than heterosexual women, they may still be influenced by societal pressure to achieve a certain body shape, and subsequently (consciously or subconsciously) experience higher self-objectification and body dissatisfaction relative to their male counterparts.
Nevertheless, contrary to expectations, bisexual individuals, irrespective of their assigned gender at birth, reported more body uneasiness than lesbian women and gay men. Lesbian women and gay men, in turn, reported similar levels of body uneasiness. Studies have shown that bisexual individuals with a same-sex partner tend to report more body image concerns than their heterosexual counterparts (Austin et al., 2004; Hadland et al., 2014). Furthermore, research has found that bisexual people typically report poorer mental health than gay/lesbian people (Conron et al., 2010; Herek, 2007; Kerr et al., 2013; Pistella et al., 2016), probably due to their greater difficulty achieving community and social support, which may hinder the emergence of positive identity dimensions (Baiocco et al., 2018; Petrocchi et al., 2020), even at a bodily level. However, more research on body image in bisexual populations is needed to support these explanations.
Concerning the fourth and final hypothesis, the results showed that younger age, bisexual identity, and higher BMI were significant predictors of the BUT-A GSI and overall body dissatisfaction. Body weight showed no significant effect. These findings are aligned with previous research suggesting that body concerns and dissatisfaction are related to elevated BMI, especially in samples with a diagnosed eating disorder (e.g., Muzi et al., 2020, 2021; Stice & Shaw, 2002). They also support studies confirming BMI as a predictor of weight-related concerns and weight management behaviors, even in non-clinical populations (Markey & Markey, 2005). Despite these observations, the present results and previous findings on the role of BMI in LGB populations are mixed. In fact, contrary to expectations, BMI did not emerge as a significant predictor of self-blame and self-attack. For instance, Frederick and Essayli [2016] found that, although BMI was a strong predictor of body dissatisfaction in sexual minority men, it did not consistently moderate the association between sexual identity and body image. Furthermore, Alvy [2013] suggested that BMI may represent a significant confounding factor in studies of body image in LGB populations, due to differences in self-reported versus directly measured values of height and weight and higher rates of obesity among lesbian women (e.g., Boehmer et al., 2007; Richmond et al., 2012).
Despite these results, the present study suffered from several limitations. First, the design was cross-sectional in nature and restricted to heterosexual and LGB individuals. Although the sample size was adequate, variables related to ethnicity, culture, and nationality were not measured. This limitation is particularly relevant, given the potential effects of ethnic and cultural representations of body ideals, body discrimination, and body image within LGB subgroups (e.g., Deputy & Boehmer, 2014; Gonzales & Blashill, 2021). Moreover, in the present study, the subsample of bisexual men was significantly smaller than the other subsamples. Thus, future research should seek to improve the present investigation by including larger, representative samples—especially with regard to bisexual individuals—and applying more sophisticated measures of sexual orientation.
Third, the data were collected through online sampling techniques, using only self-report measures. This may have generated different results relative to a research design using traditional in-person methodology and a multi-informant approach. However, a recent meta-analysis showed that online-based samples provide comparable results to those generated through in-person methods, suggesting that these methodologies do not significantly differ (Walter et al., 2019). Another limitation pertains to the data collection period, which immediately proceeded the most severe phases of the SARS-CoV-2 pandemic. Several studies have shown the negative consequences of the pandemic and its associated restrictive measures on psychological well-being and mental health, including disordered eating symptoms (Pierce et al., 2020; Prati & Mancini, 2021; Wu et al., 2021). While future studies should consider the potential long-term effect of the SARS-CoV-2 pandemic on body image concerns and disordered eating symptoms in LGB populations, a recent meta-analysis suggested that the pandemic’s adverse effects on eating were highly heterogeneous and challenging to control in empirical research (Sideli et al., 2021). Finally, the present study focused on cisgender LGB populations in comparison with a cisgender heterosexual population. Future studies should also investigate body image, body image disturbances, and internalized stigma among gender minority populations in comparison with a cisgender population, as previous research has shown that transgender and other gender minority individuals may experience significant body image disorders and concerns (e.g., Diemer et al., 2015). Furthermore, the representativeness of other gender and sexual minorities (e.g., queer, pansexual, asexual) should be enhanced, to generate better insight into the issues faced by the broader LGBTQ + community and to limit the dominance of majority voices (often gay men) in empirical studies on this topic (Salvati & Koc, 2022). Future prevention-oriented studies should also include measures of internalized sexual stigma and homonegativity (e.g., Lingiardi et al., 2012), in line with research suggesting a relationship between internalized homophobia and body shame, body image disorders, disordered eating attitudes, and binge eating in sexual minority individuals (e.g., Bayer et al., 2017; Williamson & Spence, 2001).
## Social Policy Implications
In-depth investigations into body image and body image disturbances in LGB individuals may generate significant clinical implications for interventions to treat and prevent a wide range of adverse health consequences (Calzo et al., 2015; Pistella et al., 2019). This is particularly relevant, as sexual minorities are exposed to unique stressors (i.e., minority stress in its various forms) that inevitably impact health outcomes (Hatzenbuehler & Pachankis, 2016; Kelleher, 2009; Lingiardi & Capozzi, 2004). In addition to being significantly associated with the onset of eating disorders (e.g., Bell et al., 2019; Simone et al., 2020), body image concerns and body dissatisfaction have also been found to predict the risk of bullying victimization, social discrimination, and diminished quality of life (Kamody et al., 2020; Lingiardi et al., 2020; Meyer, 2012).
From a wider perspective, prejudices and stereotyping attitudes against LGBTQ + people, with respect to body image and shape, are still widespread, both outside and within the LGBTQ + community, leading to potentially harmful consequences (Salvati et al., 2020). For instance, some studies have found that perceived stigma towards one’s body and physical appearance in same-sex couples is associated with self-doubt, sexual dissatisfaction, disempowerment, and lifestyle changes (Goldsmith & Byers, 2016; Markey & Markey, 2014; Markey et al., 2017). Other potential consequences of body dissatisfaction and uneasiness in sexual minority individuals might include an increased use of dating apps (e.g., Breslow et al., 2020), more romantic rejection (Foster-Gimbel & Engeln, 2016), and less professional success (Carels et al., 2022). Untangling the specific nuances of body image concerns and body-related experiences in individuals with different sexual identities and orientations may lead to the development of tailored, person-centered resources to help sexual minority individuals feel more positive about their bodies (Dahlenburg et al., 2020).
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|
---
title: Basal re-esterification finetunes mitochondrial fatty acid utilization
authors:
- Anand Kumar Sharma
- Tongtong Wang
- Alaa Othman
- Radhika Khandelwal
- Miroslav Balaz
- Salvatore Modica
- Nicola Zamboni
- Christian Wolfrum
journal: Molecular Metabolism
year: 2023
pmcid: PMC10011057
doi: 10.1016/j.molmet.2023.101701
license: CC BY 4.0
---
# Basal re-esterification finetunes mitochondrial fatty acid utilization
## Abstract
### Objective
Emerging evidence suggest the existence of constant basal lipolysis and re-esterification of a substantial fraction of thus liberated fatty acids. In stimulated lipolysis, the re-esterification is proposed to be a protective mechanism against lipotoxicity; however, the role of the lipolysis coupled to re-esterification under basal conditions has not been deciphered.
### Methods
We used adipocytes (in vitro differentiated brown and white adipocytes derived from a cell line or primary SVF culture) to study the effect of inhibition of re-esterification by pharmacological DGAT1 and DGAT2 inhibitors alone or in combination. We then evaluated cellular energetics, lipolysis flux, and lipidomic parameters along with mitochondrial properties and fuel utilization.
### Results
In adipocytes, DGAT1 and 2 mediated re-esterification is a moderator of fatty acid oxidation. Combined inhibition of both DGATs (D1+2i) increases oxygen consumption, which is largely due to enhanced mitochondrial respiration by lipolysis-derived fatty acids (FAs). Acute D1+2i selectively affects mitochondrial respiration without affecting the transcriptional homeostasis of genes relevant to mitochondrial health and lipid metabolism. D1+2i enhances the mitochondrial import of pyruvate and activates AMP Kinase to counteract CPT1 antagonism, thus facilitating the mitochondrial import of fatty acyl-CoA.
### Conclusions
These data implicate the process of re-esterification in the regulation of mitochondrial FA usage and uncover a mechanism of FAO regulation via crosstalk with FA re-esterification.
## Graphical abstract
Inhibition of basal re-esterification diverts FAs to mitochondria causing increase in OCRImage 1
## Highlights
•Inhibition of re-esterification increases basal OCR by diverting lipolysis-derived FAs to mitochondria.•Unlike stimulated lipolysis when DGAT1 is dominant isofrom, at basal level two DGAT isozymes show compensatory activity.•The adipocyte lipidome changes induced by DGATi are specific to some lipid species.•AMPK activation by D1+2i causes a reduction in malonyl-CoA levels that facilitates increased mitochondrial import of FAs.
## Introduction
Lipids stored in adipose tissue (i.e., triglycerides (TAG) and other lipid species stored in lipid droplets, LDs) are important reserves for energy and the synthesis of structural lipids [1]. LDs also act as scavenging organelles to quench excess FAs that might otherwise be detrimental to systemic health [1]. Thus, an intricate interplay between lipolysis and esterification (lipogenesis) balances systemic lipid homeostasis. Emerging findings suggest that in healthy subjects, the activity of this cycle of unstimulated lipolysis and re-esterification seems to correlate with metabolic health [[2], [3], [4], [5]], however, exactly how this cycle impacts overall health remains unknown. Previous studies have suggested that adipocytes re-esterify a considerable fraction of FAs under basal as well as stimulated lipolytic conditions [[6], [7], [8]]. For example, in response to isoproterenol (iso) stimulation, re-esterification is induced as a mechanism to prevent lipotoxicity, rather than to preserve TAG content [6,8].
The last enzymatic reaction which transfers the fatty acyl group to diacylglycerol (DAG) to synthesize TAG is catalyzed by two evolutionarily unrelated enzymes i.e., Diacylglycerol O-acyltransferase 1 or 2 (DGAT1 or DGAT2) [[9], [10], [11]]. The role of DGAT1 has been studied extensively, both genetically and pharmacologically [6,8,9,[12], [13], [14], [15], [16]]. Dgat1 ablation ameliorates insulin resistance and imparts resistance to diet-induced obesity [17]. Consistently, pharmacological inhibition of DGAT1 improves insulin sensitivity and lipid/glycemic balance [[17], [18], [19]]. On the contrary, partly because of the embryonic lethality of Dgat2 knockout mice, the physiological understanding of the role of DGAT2 is limited [20]; recent studies provide a unique insight into the specific role of DGAT2 in adipose tissue and the liver [9,21]. DGAT2 is shown to facilitate the use of glucose for energy generation by esterifying the glucose-derived de novo generated FAs to a pool of triglyceride that is rapidly hydrolyzed to generate FAs for mitochondrial FAO in stimulated brown adipocytes [22]. However, these studies focused on the stimulated state where the lipolytic flux is very high. The effect of re-esterification, specifically that of lipolysis derived endogenous FAs, remains unclear to date.
DGAT1 and DGAT2, besides a partial functional overlap, show their substrate specificities [9]. We thus argue that only a combined inhibition (D1+2i) can reveal the true role of re-esterification in energy homeostasis. To avoid adaptive response to gene knockdown or knockout, we used specific pharmacological inhibitors of DGAT1 (PF-04,620,110, D1i) and DGAT2 (PF-06,424,439, D2i) [6,8,15,16,23] for combined DGAT inhibition (D1+2i) in murine adipocytes. We show that D1+2i results in a substantial increase in oxygen consumption caused by lipolysis derived FAs. Different sensitivity to D1+2i at basal versus iso-stimulated conditions modulates energetics without perturbing the transcriptional homeostasis. Further, an enhanced mitochondrial pyruvate import, and activation of AMP-activated protein kinase (AMPK) signaling possibly sustain the diversion of FAs for mitochondrial oxidation. Excess intracellular/extracellular FAs can cause cellular stress and lipo/mitotoxicity, therefore it is crucial to maintain FA levels within a certain range. Our energetic studies and lipolysis flux analysis suggest that re-esterification may not only serve as means to moderate the FA concentration to prevent lipotoxicity, but at the same time to regulate cellular energetics and fuel utilization. Consistently, recent findings that adipose-specific Dgat1/Dgat2 double knockout mice show substantially higher energy expenditure and reduced RER [24], support the physiological relevance of this pathway.
## Immortalized brown adipocyte culture differentiation
Murine immortalized (brown) pre-adipocytes (iBA) were a kind gift from the laboratory of Prof. Ronald Kahn [25]. iBA cells were cultured in high glucose DMEM (61,965,026, Gibco) supplemented with $10\%$ FBS in the presence of 1x pen-strep antibiotics. The cells were plated on collagenized dishes and the differentiation was induced with an induction cocktail (culture media supplemented with 500 μM IBMX, 1 μM dexamethasone, 20 nM insulin, 1 nM T3, 125 μM at $100\%$ cell confluence. After 48 h, fresh maintenance media (culture media with 20 nM insulin and 1 nM T3) was added. The maintenance media was replaced every other day. Since these cells grow and differentiate as multi-layered cells, the cells were replated on collagen-coated multi-well experiment plates on day five to achieve adipocyte monolayer with optimum cell density.
## Primary preadipocyte isolation and differentiation
Cell isolation and differentiation were done as described previously [26]. Male C57/BL6 N mice (five weeks old) were purchased from the Charles River laboratories. After one week of acclimatization in our facility, mice were euthanized with CO2 overdose and the whole depots of inguinal WAT or interscapular BAT were collected. From 8 mice, we obtained ∼1600 mg of iWAT and ∼750 mg of iBAT tissue. The adipose tissues were finely minced with scissors and resuspended in collagenase buffer (iWAT in 7 ml buffer, iBAT in 3 ml buffer) with 1 mg/ml collagenase (C6885-1G, Sigma–Aldrich)) and digested for 1 h at 37 °C under agitation. Digested tissue was diluted in an equal volume of culture medium and centrifuged at 300 g for 5 min. The SVF fraction (pellet) was re-suspended in media and passed through a cell strainer (40-μm). Flow-through was then plated on collagen-coated plates. At $100\%$ cell confluency, the differentiation was induced as described earlier [26]. After 48 h, fresh maintenance media (culture media supplemented with 20 nM insulin and 1 nM T3 (for brown cells) or 0.5 μg/ml insulin (for white adipocytes)) was added. The maintenance media was replaced every other day. On day 5, the cells were replated on collagen-coated multi-well experiment plates to achieve adipocyte monolayers at the optimum cell density.
## Extracellular flux analysis
On day 5 of differentiation, adipocytes were replated on seahorse XFe96/XF Pro FluxPak cell culture plates at the density of 10,000 cells/well. On the night of day 6, XFe96/XF Pro sensor cartridge was filled with 200 μl Seahorse XF calibrant solution per well and incubated overnight in a CO2-free incubator at 37 °C. On day 7, cells were washed 2x with seahorse medium (XF assay medium supplemented with 4.5 g/L glucose, 2 mM pyruvate, 2 mM glutamax, pH 7.4). The inhibitors (2 μM) were dissolved in the seahorse medium (with/without $1\%$ BSA) and incubated in a CO2-free incubator for 1 h. Meanwhile, the XFe96/XF Pro sensor cartridge ports were filled with various treatment solutions to be injected and the cartridge was kept in the XFe96 analyzer for equilibration followed by the readout. For the experiment in Figure 1A, the basal oxygen consumption rate (OCR) was measured followed by sequential injection of Oligomycin (1 μg/ml), iso (1 μM), FCCP (1 μg/ml), and Ant/Rot combination (2 μg/ml). For the experiment in Figure 1C, no inhibitor pre-treatment was performed. After baseline OCR measurement, 2 μM DGAT inhibitors (or DMSO), iso, FCCP, and Ant/Rot combination were injected sequentially. For the experiment in Figure 1D–E, cells were pre-treated for 1 h with the inhibitors as for Figure 1A and basal OCR was measured followed by mentioned injections. For the experiment in Figure 2A–C, the cells were pre-treated for 1 h with respective inhibitor combinations in a CO2-free incubator. After basal OCR measurement, iso-stimulated OCR was recorded. Figure 1Inhibition of FA re-esterification enhances OCR. A-B. OCR measurement and its quantification in the iBA cells pre-treated for 1 h with 2 μ M DGAT inhibitor alone or in combinations ($$n = 6$$). C. OCR measurement in iBA cells to examine the immediate effects of on-run treatment of 2 μ M DGAT inhibitor alone or in combinations ($$n = 6$$). D-E. Validation of increase in OCR on in vitro differentiated primary iBAT/scWAT cells; bar graph on the right shows AUCs of respective data ($$n = 6$$). F–H. Lipolytic output analysis in SVF derived primary iWAT cells ($$n = 3$$) and in vitro differentiated primary iBAT cells ($$n = 4$$), and iBA adipocytes ($$n = 6$$) respectively. All data are presented as mean ± SEM. one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ns: non-significant; ∗$P \leq 0.05$; ∗∗$P \leq 0.01$; ∗∗∗$P \leq 0.001.$Figure 1Figure 2Lipolysis-derived FAs mediate D1+2i induced OCR. A-C. Basal and iso-stimulated OCR in the presence of (A) DGAT inhibitors alone, (B) DGAT inhibitors in the presence of the ATGLi (inhibitor of ATGL mediated lipolysis), or (C) DGAT inhibitors in the presence of Etomoxir (inhibitor of CPT1 mediated mitochondrial import of Fatty Acyl-CoA). Respective lower panels show the mean (±SEM) OCR values ($$n = 4$$). Please note that for better visual comparison, the data for DMSO and D1+2i (from Fig. 2A) are represented in Figure 2B,C as well. D-E. Lipolytic output in iBA in the presence of DGAT inhibitors in combination with ATGLi or Etomoxir. ( D) The left panel depicts glycerol release and (E) the panel on the right depicts NEFA levels in the parallel samples collected from the same wells ($$n = 6$$). All data are presented as mean ± SEM. one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ns: non-significant; ∗$P \leq 0.05$; ∗∗$P \leq 0.01$; ∗∗∗$P \leq 0.001.$Figure 2 To calculate various respiratory parameters from the seahorse experiment (Figure 1A,B), non-mitochondrial respiration value (OCR after Rot/Ant injection) was subtracted from all readings to get mitoOCR. All parameters are based on this mitoOCR (e.g., ATP-coupled respiration is the drop in mitoOCR after oligomycin injection; iso-stimulated OCR is the change in mitoOCR after iso injection, etc.). Wherever basal OCR is presented as a bar graph (Figure 1, Figure 2), the average basal OCR of all replicates is taken.
## Lipolysis assay
On day 5 of differentiation, iBA adipocytes were replated on collagen-coated 96 well plates. On day 7, the inhibitor/combinations were dissolved in phenol red-free DMEM (Gibco) and added to washed cells in the presence or absence of 1 μM iso. After a 2-hour incubation in a CO2-free incubator, 40 μl media was collected in two transparent 96 well plates, one each for non-esterified FA (NEFA) and glycerol. Extracellular glycerol was measured using the Glycerol reagent (Sigma–Aldrich) and reading absorbance at 540 nm on Synergy MX/Gen5 software (BioTek). NEFA levels were measured by NEFA-HR kit (Wako) using R1/R2 reagents. The concentrations were calculated by using the absorbance from glycerol (G7793, Sigma) or NEFA (276–76491, Wako) standard. For intracellular NEFA/glycerol measurement, cells were lysed in 50 μl of RIPA lysis solution followed by the addition of 50 μl 2x TBS (with $1\%$ BSA), and 40 μl sample was collected in two transparent 96 well plates, one each for NEFA and glycerol.
## qRT-PCR
Differentiated brown adipocytes were plated on 24 well plates. Cells were treated with the inhibitor combinations for 2 h. The RNA isolation was performed using the standard Trizol chloroform separation method and was digested with DNase to remove genomic DNA. A total of 1 μg RNA was converted into cDNA using a high-capacity cDNA synthesis kit (4,368,814, Applied Biosystems). SYBR green reagent-based qRT-PCR was performed on a ViiA 7 Real-Time instrument (Applied Biosystems) and the data were analysed using ΔΔCT method. The primer sequences are provided in Table 1.Table 1List of the primer sequences used in this study. Table 1GeneForward primer (5' -> 3′)Reverse primer (5' -> 3′)Aqp7ATGAGGCATTCGTGACTGGGCCCCAAGGACGGTAACAAGGAtf4CCTGAACAGCGAAGTGTTGGTGGAGAACCCATGAGGTTTCAAAtf5TGGGCTGGCTCGTAGACTATGTCATCCAATCAGAGAAGCCGAtglCTGAGAATCACCATTCCCACATCCACAGCATGTAAGGGGGAGACatalaseGGAGGCGGGAACCCAATAGGTGTGCCATCTCGTCAGTGAADgat1AACCGAGACACCATAGACTACTCTTCAGGGTGACTGCGTTCTTDgat2TAGAAGAGGACGAGGTGCGAGTCTTTGTCCCGGGTATGGGGyKGTCAGCAACCAGAGGGAAACCCCACGGCATTATAGAGAGGCTHsp60CACAGTCCTTCGCCAGATGAGCTACACCTTGAAGCATTAAGGCTHsp70TGGTGCAGTCCGACATGAAGGCTGAGAGTCGTTGAAGTAGGCLc3TTATAGAGCGATACAAGGGGGAGCGCCGTCTGATTATCTTGATGAGNrf1TATGGCGGAAGTAATGAAAGACGCAACGTAAGCTCTGCCTTGTTParkin1TCTTCCAGTGTAACCACCGTCGGCAGGGAGTAGCCAAGTTPgc1αCTCTCAGTAAGGGGCTGGTTGCGAATGACGCCAGTCAAGCPink1TTCTTCCGCCAGTCGGTAGCTGCTTCTCCTCGATCAGCCPpargGTGGGGATAAAGCATCAGGCCCGGCAGTTAAGATCACACCTASod1AACCAGTTGTGTTGTCAGGACCCACCATGTTTCTTAGAGTGAGGSod2CAGACCTGCCTTACGACTATGGCTCGGTGGCGTTGAGATTGTT
## Confocal microscopy and operetta
On day 5 of differentiation, iBA adipocytes were replated on collagen-coated 96 well μClear black-wall, transparent bottom plates. On day 7, the inhibitor/combinations were dissolved in phenol red-free DMEM (Gibco) and added to cells in the presence or absence of 1 μM iso. After a 2-hour incubation at 37 °C, cells were fixed for 15 min at room temperature with $4\%$ paraformaldehyde. For confocal microscopy, fixed cells were permeabilized with $0.1\%$ Triton ×100 dissolved in PBS. Nonspecific epitopes were blocked in $2\%$ BSA containing PBST (PBS + $0.1\%$ tween 20). Next, cells were incubated with an anti-ATP5I antibody (1:300 in $2\%$ BSA-PBST) at 4 °C overnight. The next day, cells were washed 3 times with PBST and incubated with Alexa flour 568 anti-rabbit antibody (1:500 in $2\%$ BSA-PBST) for 1 h. Cells were then washed three times with PBST and then incubated with PBST containing 1:2000 lipidtox red and 1 μM Hoescht. After 30 min, the cells were washed two times with PBST and kept in PBS and were imaged on Olympus FluoView 3000 confocal microscope.
For operetta analysis of LDs, fixed cells were washed three times with PBST and incubated with PBST containing 1:2000 LD540, 1:1000 Syto60, and 1 μM Hoescht. After 30 min, the cells were washed three times with PBST and kept in PBS and were imaged on an operetta high content analysis system (PerkinElmer). Each group included 6 wells; from each well, 12 fields (with 300–400 cells per field) were analyzed and the well average was used as one replicate. The data was analyzed using harmony software.
BODIPY™ FL C16localization: On day 5 of differentiation, iBA adipocytes were replated onto collagen-coated coverslips. On day 7, cells were treated with 100 μM BODIPY™ FL C16 for 12 h in complete DMEM. Next, cells were washed, and fresh media was added. Following a 6-hour chase period to get rid of free BODIPY™ FL C16 (Thermo Fisher, D3821), cells were washed two times and treated with 5 μM D1+2i or equivalent DMSO in serum-free DMEM (containing $0.1\%$ BSA). After 30 min, 100 μM MitoTracker Deep Red FM (Invitrogen, M22426) was added on top. After another 30 min incubation, cells were washed three times with PBS and fixed in $4\%$ paraformaldehyde followed by mounting onto slides with Fluoromount-G mounting medium, with DAPI (Invitrogen, 00-4959-52). Slides were scanned on Olympus FluoView 3000 confocal microscope.
NBD-palmitoyl-CoA incorporation: Cells were replated as described above. On day 7, cells were treated with 5 μM D1+2i or equivalent DMSO in serum-free DMEM (containing $0.1\%$ BSA). After 30 min, 100 μM NBD-palmitoyl-CoA was added along with 100 μM MitoTracker Deep Red FM. After 30 min incubation, cells were washed three times with PBS and mounted onto slides with Fluoromount-G mounting medium, with DAPI (Invitrogen, 00-4959-52). Slides were scanned on Olympus FluoView 3000 confocal microscope.
Microscopy image analysis: Colocalization was measured by using the JaCoP plugin in Image J software to get Pearson coefficient or mander's colocalization coefficient M (fraction of BODIPY™ FL C16 colocalizing with MitoTracker). The intensity distribution profile was generated using the Graphics plugin in Image J. The intensity of NBD-palmitoyl-CoA was quantified by using image J and data were plotted in Graph Pad Prism 9.2.0.
## ROS assay
On day 5 of differentiation, cells were replated onto the collagen-coated clear bottom, black wall μClear plates. On day 7, cells were washed three times and treated with DMSO or D1+2i for 90 min followed by the addition of ROS reagent as recommended by the kit (ab139476 ROS/Superoxide Detection Assay Kit) and were read on a microplate reader at Ex = 488 nm, Em = 520 nm.
## JC1 aggregation assay
On day 5 of differentiation, cells were replated μClear plates as described above. On day 7, cells were washed three times and treated with DMSO or D1+2i for 90 min followed by the addition of 200 nM JC1 dye (ab113850 JC-1). After 20 min, plates were washed three times with PBS and were read on a microplate reader at Ex = 535 nm, Em = 595 nm.
## 13C-palmitate tracing and endogenous lipidomic analysis
13C-Palmitate was conjugated to BSA to a stoichiometry of 1:3 BSA: palmitate. A total of 1.8 mg 13C-palmitate was dissolved in 50 μl ethanol then dropwise added to BSA solution (in phenol red-free DMEM) and stirred at 900 rpm for 1 h at 37 °C. The BSA-palmitate conjugate was added to pre-washed cells to a final concentration of 20 μM 13C-palmitate. Isotope negative control (INC) cells received an equal amount of 12C palmitate and the 13C enrichment value of INC was subtracted from all experimental groups to normalize for the background. After 2 h, the cells were washed twice with cold PBS. Next, 1-ml of ice-cold methanol: isopropanol 1:1 (v/v) was added, and the plates were shaken manually for 30 s. The plates were incubated at −80 °C for 5 min followed by the collection of cells by scaping. The samples were snap-frozen in liquid nitrogen and stored at −80 °C until processed. For analysis, the samples were centrifuged at 16000 g for 10 min and the supernatant was transferred to a new tube, and dried by speed vac under N2. The dried lipid fraction was dissolved in 50 μl methanol: isopropanol (1:1) by vortex and incubation in a thermomixer for 10 min. The solubilized lipid fraction was centrifuged at 16000 g for 10 min and the supernatant was transferred to glass LC/MS vials. The lipids were separated on C18 reverse phase chromatography (Acquity BEH 100 mm column (Waters) with 2.1 mm internal diameter and 1.7 μm particle diameter) attached to a Vanquish LC pump (Thermo Fisher Scientific) with the following mobile phases: (i) acetonitrile: water (6:4) with 10 mM ammonium acetate and $0.1\%$ formic acid, (ii) isopropanol: acetonitrile (9:1) with 10 mM ammonium acetate and $0.1\%$ formic acid [27,28]. The following gradient (0.6 ml/min) was used: 0.0–2.0 min (isocratic $30\%$ B), 2.0–2.5 min (ramp 30–$48\%$ B), 2.5–11 min (ramp 48–$82\%$ B),11–11.5 min (ramp 82–$99\%$), 11.5–12 min (isocratic $100\%$ B), 12.0–12.1 min (ramp 100-$30\%$ B) and 12.1–15 min (isocratic $30\%$ B).
The liquid chromatography was coupled to a hybrid quadrupole-orbitrap mass spectrometer (Q-Exactive HFx, Thermo Fisher Scientific). A Full scan acquisition in negative and positive ESI was used to scan 200–2000 m/z at a resolution of 120,000 and AGC target of 1e6, max injection time of 200 ms. Data-dependent scans (top 10) were acquired using normalized collision energies (NCE) of 20, 30, and 50, at a resolution of 15,000, and an AGC target of 1e5.
Identification of the specific lipids was achieved using four criteria [1]: high accuracy (m/z within 5 ppm shift from the predicted mass) and high resolution (resolving power 70,000 at 200 m/z) [2], isotopic pattern fitting to expected isotopic distribution [3], comparing the retention time to an in-house database, and [4] the fragmentation pattern matching to an in-house experimentally validated lipid fragmentation database. All the isotopologue peaks were quantified, the mass isotopomer distributions (MDVs) were calculated, and the fractional labeling was calculated as described earlier [29] using Compound Discoverer 3.1 (Thermo Fisher Scientific).
For the lipidomic analysis of endogenous lipids, cells were treated with inhibitor combinations in the presence/absence of BSA for 2 h. After 2 washes with ice-cold PBS, 0.5 ml methanol: isopropanol 1:1 (v/v) containing 2 μl/ml SPLASH internal control standard mix (Avanti, 330,707-1 EA) was added. Following a 5-minute incubation at −80 °C, total lipids were carefully extracted and transferred to Eppendorf tubes. Sample processing and data analysis were performed as described above for 13C palmitate tracer experiments.
## Substrate specificity test
On day 5 of differentiation, iBA cells were replated on seahorse cell culture plates. On day 7, the cells were washed 3x with assay medium (XF base media supplanting with 2 mM Glutamax, 1 mM sodium pyruvate, and 10 mM glucose, and pH was brought to 7.4 with NaOH). Cells were incubated with DMSO or D1+2i in 150 μl assay media and incubated in a CO2-free incubator at 37 °C. After basal OCR measurement, BPTES (6 μM) or UK5099 (2 μM) or a combination of either of the compound with Etomoxir (100 μM) was injected followed by OCR measurements. The OCR values of the four time points were averaged for statistical analysis using one-way ANOVA.
## AdipoRed total lipid assay
On day 5 of differentiation, cells were replated μClear plates as described above. On day 7, cells were washed three times and treated with given inhibitor combinations for 2 h. Cells were then washed two times and incubated with 100 μl (40x dilution in PBS) adipoRed assay reagent (Lonza PT 7009) for 15 min and read on a microplate reader using Ex = 485 nm, Em = 535 nm.
## Western blotting
A total of 20 μg of protein was loaded on $12\%$ SDS-PAGE gel. After electrophoresis and transfer of protein onto nitrocellulose membrane, the membrane was blocked in $5\%$ BSA in TBST followed by overnight incubation at 4 °C with primary antibody (anti-AMPKα: Rabbit mAb #5831 from CST; anti-pAMPKα: Rabbit mAb #2535 from CST; anti-HSP90: Rabbit mAb #4877 from CST; anti-ACC1: #4190 from CST; total anti-pACC (Ser79): Rabbit mAb #11818 from CST) in TBST containing $5\%$ BSA. After four washes with PBST, the membrane was incubated with HRP-conjugated goat anti-rabbit secondary antibody (EMD Millipore #401393-2 ml). After 1 h of incubation at room temperature, the membrane was washed 4x with TBST and a chemiluminescent blot was developed on the ImageQuant system (LAS 4000 mini, GE Healthcare). Band intensity was quantified using Image lab 6 (BioRad laboratories).
## Statistical analysis
All data are presented as mean ± SEM. Two group comparisons were tested for significance using a two-tailed unpaired Student's t-test. Multiple group comparisons were performed by one-way ANOVA. All statistical analyses were performed using GraphPad Prism 9. Statistical differences are indicated ∗ for $P \leq 0.05$, ∗∗ $P \leq 0.01$, and ∗∗∗ $P \leq 0.001.$
## Inhibition of FA re-esterification induces OCR
Although the magnitude may vary in a context-dependent manner (influenced by energetic status, experimental setup, and physiological state of the cells), the fate of the majority of lipolytic FAs in adipocytes seem to be re-esterification to TAG [[6], [7], [8]]. Therefore, inhibition of re-esterification should lead to increased FA levels, however, the fate of excess FAs/FA-CoA upon inhibition of re-esterification (D1+2i) is unknown. We, therefore, tested an acute inhibition of DGAT$\frac{1}{2}$ and measured the oxygen consumption rate in iBAs. We first optimized the inhibitor doses and incubation times and found that 2 μM inhibitors treated for 1 h before seahorse assay is optimum (Fig. S1A, B). At the basal level, D1i caused a mild increase in oxygen consumption rate (OCR) while D2i showed no effect. A combined inhibition (D1+2i, 2 μM each) led to a substantial increase in OCR at basal conditions (Figure 1A,B). A similar trend was observed after iso-stimulation, albeit with a more pronounced effect of D1i (Figure 1A,B). Noticeably, a large fraction of increased OCR was attributed to uncoupled respiration, conceivably due to the uncoupling capacity of brown adipocytes. We next tested if OCR increases acutely after DGAT inhibition or follows a lag phase, which would imply that OCR increase is a secondary, adaptive response. In contrast to 1-hour pre-treatment, in-run injection of inhibitors caused only a mild increase in basal OCR while iso-stimulated OCR was higher in D1i or D1+2i (Fig. 1C). Nonetheless, the extent of increase in OCR was substantially lower compared to 1-hour pre-treatment, suggesting either a cumulative effect or an adaptive intermediate response, which amplifies/sustains the increase in OCR, particularly at the basal state.
We further validated the findings in primary adipocytes from in vitro differentiated murine stromal vascular fraction (SVF) from inguinal brown adipose tissue (iBAT) or inguinal subcutaneous white adipose tissue (scWAT). Since we wanted to compare the total basal and total iso-stimulated OCR, we injected iso after basal readings followed by oligomycin. Although tissue-specific difference in sensitivity towards D1i is likely, the D1+2i-induced increase in OCR was consistently higher (Figure 1D,E). We next examined if D1+2i modulates lipolytic flux by measuring the release of NEFA and glycerol in the culture media. In SVF-derived primary scWAT adipocytes D1i but not D2i led to a mild insignificant increase in glycerol release (Fig. 1F). Interestingly, in line with the OCR data, D1+2i led to a ∼4.5-fold increase in the glycerol levels while the FA levels remained comparable across all groups, suggesting that the excess FAs arising from enhanced lipolysis are possibly used in mitochondrial to drive OCR (Fig. 1F). Consistently, in vitro differentiated primary iBAT adipocytes exhibited a ∼6-fold increase in glycerol release upon D1+2i (Fig. 1G). It is noteworthy that compared to changes in the OCR, primary iBAT adipocytes were more responsive to D1i than the immortalized brown adipocytes. Following scWAT cells or primary iBAT cells, iBA adipocytes also showed a similar trend of glycerol/FA release upon D1+2i (Fig. 1H). To confirm that the OCR phenotype is due to inhibition of re-esterification and is not a DGATi-specific effect, we used MGAT2 inhibitor which induced a very slight change in OCR while a combination of D1+2i and MGAT2i further increased the OCR in an additive manner (Fig. SI1C). An MGAT3 inhibitor (MGAT3 is a pseudogene in mice) was used as a control and did not affect OCR (Fig. SI1D). These data suggest a balancing of re-esterification between lipolysis and FAO and that the inhibition of re-esterification possibly channels activated fatty acids to mitochondria leading to an increase in OCR.
## Lipolysis-derived FAs enter mitochondria to induce OCR upon D1+2i
To further examine if mitochondrial FAs mediate the increased OCR we blocked either lipolysis (ATGLi) or mitochondrial import of FAs (Etomoxir) in combination with DGATi. Inhibition of either DGAT isoforms alone or in combination showed the same basal effect as described before (Fig. 2A). The inhibition of lipolysis by ATGLi resulted in a reduction in OCR at the basal level and blunted iso-stimulated increase in OCR (Fig. 2B). Also, the D1+2i effect was dampened by ATGLi (Fig. 2B) suggesting that lipolysis derived FAs are likely mediators of the increased OCR. The slight increase in OCR observed in ATGLi + D1+2i compared to ATGLi alone could arise from residual ATGL activity or ATGL-independent HSL-mediated lipolysis. Similarly, blockade of mitochondrial import of activated FAs by the CPT1-inhibition largely abolished the effect of D1+2i in the basal as well as iso-stimulated state (Fig. 2C). It should be noted that DGAT inhibition in combination with ATGLi or etomoxir did not reduce OCR below basal levels, indicating a remnant housekeeping activity. These results together suggest that the D1+2i-induced OCR is mediated by an increased mitochondrial influx of lipolysis-derived FAs.
Quantification of lipolysis (glycerol/FA release) further supported the lipolytic contribution to the D1+2i induced OCR (Figure 2D,E). D1i led to a ∼2-fold increase in glycerol release, while D1+2i led to a ∼3-fold increase. Given that the D2i by itself showed no effect, a compensatory contribution could be envisaged. One possibility is that the increase in OCR is mediated by a pool of FAs that are redundantly used by DGAT1 as well as DGAT2 and thus only a combined inhibition (i.e., D1+2i) leads to a large increase in OCR. We also examined the role of ATGLi and etomoxir on lipolysis. ATGLi inhibited the basal glycerol release and blunted the D1+2i-induced increase. Etomoxir treatment also suppressed the D1+2i-induced glycerol release. To rule out the cellular retention of FAs (as NEFA or acyl-CoA) upon D1+2i, we analyzed intracellular FA and glycerol content (Fig. SI2). We did not observe intracellular accumulation of FA in D1+2i treated cells, pointing towards active utilization of excess FAs. These results suggest that the FAs generated from lipolysis are utilized to fuel OCR (Figure 1, Figure 2). The data also imply that the rate of re-esterification by DGATs likely modulates the mode and extent of mitochondrial partitioning of FAs.
## Differential utilization of exogenous 13C-palmitate upon D1+2i
To better understand the mechanism of mitochondrial diversion of FAs, we considered two possibilities: (i) the excess FA-CoAs that were otherwise destined for re-esterification are directly diverted to mitochondria for FAO, or (ii) an adaptive change in energetic signaling mediates the increase in OCR. Therefore, we measured the activation of exogenous 13C-palmitate into acylcarnitine and its direct incorporation into prominent glyceride species (Figure 3). Due to the role of DGATs in esterifying FA to TAGs, we first assessed the incorporation of 13C-palmitate into TAG species (Figure 3A,B). At the basal level, incorporation of 13C-palmitate into 48:1 TAG was inhibited by D1i while D2i showed no effect. Consistent with other results, D1+2i further decreased the label incorporation than D1i alone (Fig. 3A). Under iso-stimulated conditions, the incorporation of 13C-palmitate to 48:1 TAG was enhanced compared to basal control. However, the effect of D1i was so pronounced that the total 13C-palmitate incorporation into 48:1 TAG was even lower than the D1i at the basal level. Interestingly, under the stimulated conditions, DGAT1 appears to be the sole esterifying enzyme as the D1+2i was comparable to D1i (Fig. 3A). These observations hold true also for 50:2 TAG (Fig. 3B). The basal incorporation of 13C-palmitate to DAG (30:1 as well as 34:1) was largely unaffected by DGATi (Figure 3C,D). However, upon iso-stimulation, the incorporation of 13C-palmitate to DAG species in the control group was reduced significantly (Figure 3C,D), possibly due to enhanced cycling. D1i (and D1+2i) led to an increased accumulation of 13C-palmitate DAG as the third acylation reaction was inhibited by DGATi. This could also be due to the availability of excess fatty acyl-CoA due to D1i and suggest the existence of a crosstalk between monoacyl glycerol acyl transferases and DGAT action. These findings demonstrate that: (i) there is a significant re-esterification of FA to TAG/DAG at the basal as well as under the stimulated conditions, (ii) although under iso-stimulated conditions DGAT1 is the major re-esterifying enzyme, under basal conditions, DGAT2 can actively esterify FAs and thus only a combined inhibition results in maximal suppression of DGAT action. Figure 3Differential utilization of exogenous13C palmitate upon D1+2i. A-F. Fraction 13C labelling (%) of FA derivatives upon incubation of iBA adipocytes with 13C Palmitate. Cells ($$n = 4$$) were incubated with 13C Palmitate for 2 h followed by extraction of metabolites and measurement of fractional labelling of (A) TAG 48:1 [M + NH4]+; (B) TAG 50:2 [M + NH4]+; (C) DAG 32:1 [M + NH4]+; (D) DAG 34:1 [M + NH4]+; (E) ACar 16:0 [M+H]+; (F) ACar 18:1 [M+H]+. All data are presented as mean ± SEM. one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ns: non-significant; ∗$P \leq 0.05$; ∗∗$P \leq 0.01$; ∗∗∗$P \leq 0.001.$Figure 3 One of the most striking observations was the changes in 13C-palmitoyl-carnitine (Acyl carnitine, 16:0) levels (Fig. 3E). At basal levels, D1i or D2i led to a small decrease in 13C-palmitoylcarnitine when compared to the control group. Surprisingly, the D1+2i did not alter the 13C-palmitoylcarnitine generation. Given the dependence of OCR on mitochondrial uptake of lipolytic FAs, the unchanged incorporation of 13C-palmitate to 13C-palmitoylcarnitine in DGAT1+2 inhibited cells at basal conditions is difficult to explain. Therefore, we analyzed the total acylcarnitine content (M+0, M+16 or the sum ([M+0] and [M+16])) which was substantially higher in the D1+2i than in the control. Fractional labeling represents the fraction of the total palmitoylcarnitine pool with a 13C isotope (not the absolute quantity). The results suggested that upon D1+2i, 13C palmitoylcarnitine increases but a proportional increase in endogenous (unlabelled) palmitoylcarnitine renders the fractional labeling unchanged despite increased absolute values. A similar phenomenon seems to be responsible for the unchanged DAG levels at basal conditions (Fig. SI3A-C). A comparable level of the sum of isotopologues in controls (with 13C-palmitate versus without 13C-palmitate) demonstrates the consistency of the data (Fig. SI3D). In contrast to basal conditions, under iso-stimulated condition, D1i led to a ∼$30\%$ increase in total 13C-palmitoylcarnitine while D2i caused a small but significant increase in 13C-palmitoylcarnitine levels (Fig. 3E). Combined D1+2i inhibition elicited an additive effect on the accumulation of acylcarnitine. The non-labeled acylcarnitine (18:0) was minimally affected (Fig. 3F). These results suggest a role of re-esterification for a differential utilization of FAs based on the abundance in basal and iso-stimulated conditions.
## Acute inhibition of re-esterification causes a selective shift in fuel utilization
Based on our observation of a shift in FA utilization upon acute D1+2i, we wondered if inhibition of re-esterification causes any global/long-lasting alteration in immortalized adipocytes. Therefore, we tested if D1+2i leads to localized changes in lipid droplet (LD) morphology and/or number. ATP5I staining was used to label the mitochondria. Although ATP5I expression (and that of other representative respiratory complex proteins along with ECSH1) was not significantly different between the groups, we observed marginally increased peri-LD mitochondria in D1+2i treated cells (Figure 4A; Figure SI4; Fig SI5A). We observed a negligible reduction in the total number of LDs upon D1+2i at the basal level, likely due to the low LD turnover rate (Figure 4A,B). The iso-stimulation of control cells led to the appearance of multiple smaller droplets (Figure 4A,B). However, the new small LD appearance was abolished in D1i or D1+2i, but not in D2i. Consistently, Triacsin C treatment, which blocks FA activation, also inhibited the appearance of new small LDs during iso-stimulation (Fig. 4B). These results suggest that DGAT1 is the major re-esterifying enzyme under conditions of iso-stimulated lipolysis. Moreover, under basal conditions, an increase in lipolysis enhances the FA availability without affecting overall LD morphology in the timeframe used here. Besides the LD number, the changes in the size of LDs also reflect consistent and complementary features. Upon D1i or D1+2i, there was a slight reduction in the LD area, possibly because LD-derived FAs are used to drive OCR. Consistently, upon iso-stimulation, all groups displayed significantly smaller LDs (Fig. 4C). Interestingly, however, the average size of LDs in the iso-stimulated D1i or D1+2i group appears to be larger than that of stimulated control cells (Figure 4C; Fig. SI4). To test if under iso-stimulated conditions D1i and D1+2i block recruitment of new LDs whereby the remaining large LD would skew the size estimation, we blocked FA activation by triacsin C, which resulted in a similar phenotype (Fig. 4C).Figure 4DGATi induced changes in LD distribution and FA mobilization to mitochondrial. A. Confocal images of iBA cells stained for mitochondria (ATP5I, green), LDs (Lipidtox, red), and nucleus (Hoechst, blue). Cells were treated for 2 h with vehicle or D1+2i in the absence and presence of iso and then were fixed and stained. Scale bar, 10 μm. B–C. Quantification of (B) average number of LDs per cell, and (C) average LD area. In a parallel experiment to confocal imaging (A), cells were processed for operetta analysis, and the data were analyzed using harmony software provided by the vendor. ( $$n = 6$$ wells per group). Each well value represents an average of 12 fields. D-F. Confocal images of iBA showing localization of Bodipy-palmitate to LDs/mitochondria. ( D) iBA adipocytes were treated overnight with Bodipy-palmitate in complete media. Next day, cells were washed, and cells were incubated in Bodipy-palmitate free media for 6-hour. Cells were then washed and treated with DMSO or D1+2i for 1 h followed by cell fixation and visualization. The panel on the right shows the intensity profile of Mitotracker or Bodipy-palmitate in given linear ROI (red line in merge channel). Scale bar, 10 μm. ( E) Pearson's correlation coefficient, and (F) Mander's colocalization coefficient (fraction of bodipy-palmitate overlapping with mitotracker red) ($$n = 7$$). G. Reltive ROS levels in iBA cells measured after 2 h of treatments ($$n = 5$$). H. JC1 staining of the iBA cells showing increased JC1 aggregates as a function of increased mitochondrial potentilal upon respective treatments ($$n = 5$$). All data are presented as mean ± SEM. For the data presented in graph E and F, two tailed t-test was applied while for other data one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ns: non-significant; ∗$P \leq 0.05$; ∗∗$P \leq 0.01$; ∗∗∗$P \leq 0.001.$Figure 4 We next examined if inhibition of re-esterification also alters the gene expression of lipid metabolism-related genes or the genes involved in mitochondrial health. At basal conditions, iBA adipocytes showed minimal transcriptional response to D1i, D2i, or even D1+2i (Fig. SI5B). Under iso-stimulated conditions, D1+2i led to a marginally decreased expression of several lipid storage genes (Pparg, Gyk, Dgat1, Dgat2), whereas a few other genes involved in mitochondrial homeostasis were down-regulated (Sod1, Sod2, Cat, Hsp60). The mitochondrial chaperon, Hsp70, was slightly upregulated upon D1+2i (Fig. SI5C). D1+2i led to the upregulation of Parkin in the iso-stimulated states. We also tested the expression of Ucp1 and other metabolically relevant genes (Fig. SI5D,E). We found that iso-treatment itself led to an upregulation of Ucp1, Pck1, and Fgf21, and slight downregulation of Pdk1. DGATi did not change Ucp1 expression at basal levels, while in the iso-stimulated state, DGATi caused a slight downregulation of Ucp1. In contrast, Pck1 expression seems to be mainly affected by D1i. Overall, these data suggest that at the tested time points there is only a minimal effect of inhibition of re-esterification at the expression of lipid metabolizing genes and mitochondrial homeostatic genes, rather it selectively impacts LD dynamics and mitochondrial respiration.
We next examined if the lipolysis-derived FAs are channeled to mitochondria by monitoring the mobilization of fluorescent FA (Bodipy Palmitate) from LDs to mitochondria. In control cells, the bodipy signal occasionally colocalized with mitotracker, D1+2i increased the colocalization of bodipy and mitotracker (Figure 4D–F, intensity distribution profile on the right). The mitochondria also seem to reorganize with a higher peri-droplet mitochondrial accumulation observed along with mitochondria infiltration of some partly shreded LDs with the rough, irregular surface (Fig. 4D). In contrast to prelabelled LDs containing bodipy-palmitate, a coincubation of NBD-palmitoyl-CoA with D1+2i showed that D1+2i almost completely blocked the esterification of palmitoyl-CoA into LDs yet some mitochondria showed increased peri-droplet accumulation and increased number of aggregates (Fig. SI5F). It is well established that brown adipocytes generate ROS as a by-product of electron flow across the electron transport chain [30,31]. Two hours of D1+2i treatment caused a mild increase in ROS levels (Fig. 4G). It should be noted that the D1+2i-induced ROS levels are substantially lower than pyocyanin, which was used as a positive control. Furthermore, there was a similar increase in the JC1 aggregate formation (Fig. 4H), an indicator of increased mitochondrial membrane potential. Overall, these results demonstrate that D1+2i increases the mitochondrial channeling of FAs to sustain cellular energy homeostasis.
## D1+2i modulates DAG and acylcarnitine levels while other abundant lipids are minimally perturbed
Our results suggested a large increase in FA utilization upon D1+2i, however, the overall change in total lipid content was minimal (Fig. SI6A). To test if only selected lipid classes are affected by DGATi and to gain insight into the mobilization of key lipid classes, we performed untargeted lipidomic analysis. From all 24 sample groups (x3 replicates), we confidently detected/assigned 500+ species of lipids spanning 12 major lipid classes (Figure 5, Figure 6). For ease of presentation, we only show the average of the main lipid classes; the complete dataset can be accessed from the accompanying data file (Dataset1).Figure 5DGAT inhibition causes selective changes in cellular lipids. A-F. Lipidomic analysis of differentiated iBA adipocytes treated with different pharmacological combination for 2 h demonstrate (A) increased basal DAG lipids after D1+2i when compared to D1i. In the iso-stimulated state, DGAT1 is the main DGAT as the D1i is as effective as D1+2i. A similar trend is seen for (B) acylcarnitines suggesting diversion of FAs to mitochondria. Other lipid species, (C) LPC, (D) LPE, (E) PC, and (F) TAG showed less prominent changes. Data are presented as mean ± SEM ($$n = 3$$). A one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ns: non-significant; ∗$P \leq 0.05$; ∗∗$P \leq 0.01$; ∗∗∗$P \leq 0.001.$Figure 5Figure 6Effect of DGAT inhibition in the presence extracellular FA quencher (BSA). A. OCR measurement and its quantification in the iBA cells pre-treated for 1 h with 2 μ M DGAT inhibitor alone or in combinations in the presence of $1\%$ BSA. Data are mean ± SEM of $$n = 6$.$ Bar graph on the right shows the quantification of the OCR data. B–C. Lipolytic output analysis of (B) free glycerol, and (C) NEFA in iBA adipocytes upon incubation with DGATi in the presence of $1\%$ BSA ($$n = 5$$–6). D-I. Lipidomic analysis of differentiated iBA adipocytes treated with different pharmacological combination for 2 h in the presence of $1\%$ BSA demonstrate (D) increased basal DAG lipids after D1+2i when compared to D1i. In the iso-stimulated state, DGAT1 is the main DGAT as the D1i is as effective as D1+2i. A similar trend is seen for (E) acyl carnitines suggesting diversion of FA to mitochondria. Other lipid species, (F) LPC, (G) LPE, (H) PC, and (I) TAG showed less prominent alteration. Lipidomic data are presented as mean ± SEM ($$n = 3$$). A one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ns: non-significant; ∗$P \leq 0.05$; ∗∗$P \leq 0.01$; ∗∗∗$P \leq 0.001.$Figure 6 Inhibition of DGAT1 (D1i) caused a significant increase in DAG levels (DAG 32:1, DAG 32:2, and DAG 34:2) while D2i did not affect the DAG levels under the basal conditions. The effect of D1+2i was more pronounced than D1i. In contrast, in the iso-stimulated cells, the D1+2i and D1i caused a comparable change in DAG levels that can be attributed to DGAT1 under stimulated conditions but not at the basal level (Fig. 5A). ATGLi treatment led to reduced DAG levels while a combined DGAT and ATGL inhibition (A + D1+2i) led to lower DAGs compared to D1+2i. Although etomoxir alone did not affect DAG levels, Eto+D1+2i led to significantly increased DAG levels, possibly due to reduced mitochondrial usage of activated FAs and its diversion for re-esterification into MAGs to form DAG. Acyl carnitine levels showed a similar pattern of changes wherein basal D1+2i led to higher accumulation than D1i while at iso-stimulated condition D1i was as effective as D1+2i (Fig. 5B). ATGLi reduced the D1+2i-induced acylcarnitine pool while etomoxir fully abolished the D1+2i-induced increase in acylcarnitine levels (Fig. 5B). The basal LPC levels were unchanged by DGAT inhibition, however, under iso-stimulated conditions, D1i and D1+2i increased total LPC levels, possibly as a secondary adaptive mechanism, as etomoxir also led to increased LPC levels (Fig. 5C). Total LPE levels showed a trend similar to LPC (Fig. 5D) although with more pronounced effect under D1i and iso-stimulation. Total PE levels (Fig. SI6B), total PC levels (Fig. 5E), or total TAG levels (Fig. 5F) only showed minimal changes.
Since the study conditions were devoid of any extracellular FA quencher, we next tested if the findings are also reproducible in the presence of BSA (Fig. 6). We first examined the effect of DGATi on the OCR in the presence of $1\%$ BSA. D1i or D2i mildly increased the basal OCR (Fig. 6A), while D1+2i caused a larger increase in OCR, similar to BSA-free conditions. The lipolysis assay further confirmed that D1+2i was more potent than either of the inhibitors alone (Figure 6B,C). Iso treatment induced a strong lipolytic response (Figure 6B,C). Total intracellular FA and glycerol accumulation was comparable across groups (Fig. SI6C,D). The AdipoRed lipid quantification suggested that under basal conditions different treatments did not cause significant changes in the total lipid content while iso treatment slightly depleted the intracellular lipid store (Fig. SI6E) as supported by the increased FA and glycerol accumulation in the media. We next performed a lipidomic analysis which led to the conclusion that except for a few differences, overall changes in cellular lipid species were similar to that seen in the BSA-free conditions. For instance, in the presence of BSA, the accumulation of DAG 32:1 (but not of DAG 32:2 or DAG 34:2) was comparable between D1i and D1+2i (Fig. 6D). Also, the levels of acylcarnitines in D1+2i treated cells were significantly lower than D1i, possibly due to increased intracellular FA turnover to compensate for the OCR and the FA quenching by BSA (Fig. 6E). Other lipid species, LPC (Fig. 6F) and LPE (Fig. 6G) followed trends similar to the ones described for BSA-free conditions, while PC (Fig. 6H), TAG (Fig. 6I), and PE (Fig. SI6F) were largely unperturbed. Overall, the lipidomic analysis suggests that inhibition of re-esterification causes a selective change in cellular lipid profile to accommodate excess FA while maintaining the cellular lipidomic balance.
## AMPK activation and mitochondrial pyruvate influx mediate increased OCR
To delineate the mechanism behind the shuttling of palmitoylcarnitine upon D1+2i at basal levels, we considered alternate possibilities. We examined AMP levels that would arise as a by-product of uncoupling and observed that at the basal level, D1+2i led to a significant increase in AMP levels (Figure 7A). AMP is the prime activator of AMPK, and we could also show an increased AMPK phosphorylation upon D1+2i (Figure 7B; Fig. SI6G). ACC is an important downstream target of AMPK that regulates de novo lipogenesis and AMPK-mediated inhibitory phosphorylation has been shown to lead to a reduction in malonyl CoA [32], which in turn would modulate mitochondrial FA import. We thus measured the ACC phosphorylation which was significantly increased while malonyl CoA levels were significantly decreased (Figure 7B,C), which corroborates with the increased mitochondrial utilization of FAs. To further validate the involvement of the AMPK activation in mediating the OCR surge, we performed OCR measurement in the presence of compound C (Dorsomorphin, AMPKi) a specific inhibitor of AMPK. We found that AMPKi blunted the D1+2i-induced OCR by ∼$40\%$ (Figure 7D; Fig. SI6H). The effect of AMPKi was D1+2i specific as we did not observe any change in OCR levels by AMPKi in control cells. Since AMPK was responsible only for ∼$40\%$ OCR, we analyzed the dependence of the OCR increase on glutamine and pyruvate using BPTES (glutaminase inhibitor) or UK5099 (mitochondrial pyruvate carrier inhibitor). While BPTES did not affect the observed changes in OCR, UK5099 diminished the D1+2i-induced OCR by ∼$40\%$ (Figure 7E,F). Together, these results suggest that AMPK-mediated mitochondrial FA import and independent pyruvate utilization might contribute partly to the energetic demands of increased OCR upon inhibition of re-esterification. Figure 7Concurrent AMPK activation and mitochondrial pyruvate influx mediate increased OCR. A. AMP levels in iBA adipocytes treated with vehicle or D1+2i ($$n = 6$$). B. Western blots showing the change in AMPK and ACC phosphorylation D1+2i treatment ($$n = 3$$). Blots on right show a representative chemiluminsence image and bar graphs on the right show densitometric quantification done using Image lab 6.0 software. C. Malonyl CoA levels in iBA adipocytes treated with vehicle or D1+2i ($$n = 4$$). D. OCR from the samples treated with DMSO or D1+2i in the presence/absence of AMPK inhibitor (Dorsomorphin) ($$n = 6$$). E-F. Substrate dependence of D1+2i induced OCR in iBA cells. The iBA adipocytes were pretreated for 1 h with DMSO or D1+2i ($$n = 5$$). The metabolic inhibitors were auto-injected in the given order. All the data are presented as mean ± SEM. For two group comparisons in A, B, and C, a two-tailed unpaired t-test was applied to test the significance of differences. For the data presented in D, one-way ANOVA with Tukey's post hoc test was applied to test the significance of differences. ∗$P \leq 0.05$; ∗∗∗$P \leq 0.001.$Figure 7
## Discussion
Lipid metabolism lies at the heart of whole-body energy homeostasis. Owing to high energy equivalence, TAGs are the preferred storage molecule [33], however, excessive lipid deposition in adipose tissue, ectopic lipid accumulation in non-adipose organs, or an increase in circulating FA/TAG levels are important contributors to systemic insulin resistance [[34], [35], [36], [37], [38]]. Therefore, besides the absorption of ingested meal-derived lipids and their storage as TAGs, perpetual basal lipolysis during the post-absorptive phase and subsequent re-esterification of spare FA seems to be relevant to ensure a fine-tuned lipid balance.
Dgat1 shows a broad tissue distribution, while Dgat2 seems to be more specific to organs with substantial lipid turnovers such as the liver and the adipose tissue [11]. Interestingly, Dgat1 is prominently expressed in the gastrointestinal tract and thus is implicated in meal-derived lipid absorption [15,17,[39], [40], [41]]. Based on the improved metabolic function observed in Dgat1 knockout mice, it was proposed that DGAT1 inhibitors could be an effective therapeutic strategy to counter metabolic disorders [17]. Chronic treatment with DGAT1 inhibitors recapitulated in part the beneficial effects of Dgat1 KO and improved insulin sensitivity & glycaemic balance [[42], [43], [44], [45]]. Given the prominence of Dgat1 (over Dgat2) in the GI tract and considering reduced tissue fat in Dgat1 KO or the inhibitor-treated animals, it is believed that most of the lipid-related effects of Dgat1 KO originate from the inhibition of fat absorption. Despite the clear distinction of DGAT1 in esterifying the majority of lipolysis-derived FAs, there seems to be some specialization of the function of DGAT1 and 2 possibly in a species and tissue-dependent manner [8,[10], [11], [12],22,33]. DGAT1 loss-of-function mutation in humans causes severe diarrhea [46] but a global Dgat1 knockout or pharmacological inhibition of DGAT1 in mice does not exhibit a similar phenotype [14,18,35], while, a combined DGAT inhibition in mice causes diarrhea-like symptoms [15]. Thus, it seems that intestinal absorption in humans depends on DGAT1, while in mice DGAT2 can in part replace intestinal DGAT1 action.
While studying the key enzyme responsible for the re-esterification of lipolytic FA in 3T3 L1 adipocytes, Chitraju et al., demonstrated the prominence of DGAT1 over DGAT2 during iso-stimulated lipolysis [6]. However, at the basal level-only a combined inhibition (but not the individual DGAT inhibition), significantly impacted re-esterification [6], pointing towards overlapping actions of DGAT1 and DGAT2. Consistent with previous findings, we observed a more pronounced inhibitory response upon DGAT1 inhibition than with DGAT2 [6,8]. Combined inhibition of DGAT1 and 2 led to a large increase in OCR which was mainly driven by mitochondrial FA import. Substrate preference of DGAT$\frac{1}{2}$ may explain this observation. DGAT2 was shown to preferentially use ATGL-derived DAG as substrate [47] and thus might only partially compensate for DGAT1 activity [6,9,13,40]. Similarly, the localization might explain the observed phenomenon as unlike DGAT1 (which is an ER-resident enzyme), DGAT2 also localizes to mitochondria [48]. One possible explanation is that DGAT1 re-esterifies FAs on the endoplasmic reticulum near the LD release site and since DGAT1 is the main isoform, DGAT1 inhibition would divert fatty acyl CoA to mitochondria. In contrast, DGAT2 could partly re-esterify excess FAs, which could account for the small increase in OCR observed upon DGAT1 inhibition. Besides, as reported in HepG2 cells, increased stability or activity of DGAT2 upon DGAT1 inhibition is also a possibility [49].
Complete hydrolysis of one TAG molecule generates one glycerol molecule and three FAs. In our analyses, the FA levels were disproportionately low and the stoichiometric ratio of glycerol:FA (1:3) was never achieved. Based on extracellular glycerol, DGAT1 inhibition or a combined DGAT$\frac{1}{2}$ inhibition caused higher lipolysis/glycerol efflux than control or DGAT2 inhibition. Since this study was performed in the absence of a FA quencher, we also measured intracellular FA levels to rule out intracellular uptake/retention. A comparable amount of intracellular FA suggests the terminal utilization of excess FAs upon DGAT1+2 combined inhibition. Another puzzling observation was an increase in glycerol release after ATGLi + DGAT1+2 inhibition compared to ATGLi alone (Fig. 2D). We speculate that this might originate from glucose-derived G-3-P or the residual lipolysis from ATGL/HSL or other mechanisms. Nevertheless, the extent of the increase was much smaller than D1+2i without ATGLi, highlighting the ATGL-mediated lipolytic contribution.
To derive insight into the fate of FAs upon DGAT$\frac{1}{2}$ inhibition, we performed 13C-palmitate tracing. We observed a similar lipidomic profile of key glyceride species (DAG/TAG/Acylcarnitines) as reported previously [8]. Regarding the difference in the magnitude of incorporation of 13C-palmitate to DAG at basal vs iso-stimulated conditions, we speculate that complete hydrolysis of TAGs at basal conditions generates MAGs and FA which are readily converted to DAGs. In contrast, iso-stimulation causes only partial lipolysis of TAGs to DAGs, and thus the 13C-palmitate labeling of DAGs is reduced. Upon DGAT1 inhibition, the DAGs that would have been esterified to TAG by DGAT1 are possibly further hydrolyzed into MAGs, which could account for the observed enhanced labeling. Besides, untargeted lipidomic analysis of endogenous lipids showed that DGATi leads to the accumulation of endogenous DAG and acylcarnitine species. So, a likely explanation is that the 13C containing DAG and acylcarnitines levels increase. However, due to a proportional increase in labeled and unlabelled DAG and acylcarnitines, the fractional labeling remains constant.
A previous study reported transcriptional changes upon D1+2i [6]. However, we did not observe any substantial transcriptional alterations in the majority of the tested genes at basal conditions. One explanation could be the shorter inhibitor incubation time used here. Under iso-stimulation, a trend towards a decreased expression of lipid storage genes was observed when both DGATs were inhibited, possibly due to increased energy demand. Although a clear role of AMPK is evident, we speculate that D1+2i treated adipocytes possibly adapt to release the CPT1 inhibition through a reduction in malonyl CoA levels via AMPK activation.
Mitochondrial pyruvate influx appears to play a crucial role in the energy production of unstimulated brown adipocytes. Mitochondrial pyruvate carrier (MPC) inhibition is shown to increase the lipolysis coupled re-esterification cycling [50]. Pyruvate is a key substrate for the tricarboxylic acid cycle (TCA cycle) as a source of oxaloacetate or acetyl-CoA. It appears that after D1+2i treatment, the increased influx of mitochondrial FAs serves as a powerful uncoupler. The pyruvate-fueled TCA cycle, in conjunction with FAO, likely plays a role in maintaining a healthy proton gradient. Moreover, given the dynamic exchange of acetyl-CoA, the role of pyruvate in de novo FA synthesis is also a possibility.
A recent pre-print study analyzed the effect of adipose-specific Dgat1/Dgat2 double knockout (aDKO). The aDKO mice on HFD show substantially higher energy expenditure and reduced RER corroborating with our data of increased OCR and FAO. These data provide physiological credence to our findings and suggest that re-esterification may be a fundamental mechanism that also fine-tunes fuel utilization and FAO [24].
Besides these interesting observations, this study has several limitations that should be considered when interpreting the results. It is an in vitro study performed under defined conditions. Therefore, although major findings seem to translate to physiological context as discussed above [24], some results may be condition-specific and thus should be interpreted accordingly. In addition, since some other non-adipocyte cell types have considerable lipid stores, it is worth exploring if the inhibition of re-esterification has a similar effect in other cells [51]. In the physiological context, discerning tissue-specific and systemic effects of blockage of re-esterification (using a pharmacological approach and tissue-specific conditional knockouts) therefore is an important next step. Another interesting finding from the study is the accumulation of various DAG species following DGAT inhibition. As different DAG species serve as precursors for many (phospho)lipid species, and as direct signalling molecules and its availability and location can affect the activity of protein kinase C/D (PKC/PKD), it would be worthwhile to investigate the impact of DAG-mediated PKC signalling in cells/tissue after DGAT inhibition.
Our findings demonstrate a continuous cycle of re-esterification of FA to glyceride species at basal as well as stimulated lipolytic conditions. Given a high ATP equivalent cost of FA re-esterification [52], an energetic consequence could be relevant for whole-body energy homeostasis.
## Funding
The work was supported by the $\frac{10.13039}{100000001}$Swiss National Science Foundation (SNSF; to C·W.) and FreeNovation grant (Novartis; to C·W.).
## Author contribution
A.K.S. and C.W. conceived the study. A.K.S. designed and performed all the experiments and analysis with the help from T.W., R.K., S.M. and M.B. T.W. contributed considerably in primary cell isolation. A.O. and N.Z. performed mass-spec analysis. C.W. supervised and acquired all the funding. A.K.S. and C.W. wrote the manuscript. All authors approved the manuscript.
## Conflict of interest
None declared.
## Supplementary data
The following are the *Supplementary data* to this article:Multimedia component 1Multimedia component 1Multimedia component 2Multimedia component 2
## Data availability
Dataset is provided
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|
---
title: 'Sex-specific association of serum uric acid trajectories with risk of incident
retinal arteriosclerosis in Chinese population: A population-based longitudinal
study'
authors:
- Ruirui Geng
- Qinbei Feng
- Mengmeng Ji
- Yongfei Dong
- Shuanshuan Xu
- Chunxing Liu
- Yufeng He
- Zaixiang Tang
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC10011080
doi: 10.3389/fcvm.2023.1116486
license: CC BY 4.0
---
# Sex-specific association of serum uric acid trajectories with risk of incident retinal arteriosclerosis in Chinese population: A population-based longitudinal study
## Abstract
### Background
The impact of serum uric acid (SUA) trajectories on the development of retinal arteriosclerosis is uncertain. The purpose of this study was to identify adult SUA trajectories by sex and determine their association with risk of retinal arteriosclerosis.
### Methods
In this longitudinal study, 4,324 participants who were aged between 18 and 60 years without retinal arteriosclerosis at or before baseline (from January 1, 2010, through December 31, 2010) were included. Group-based trajectory modeling was used to identify SUA trajectories during the exposure period (from January 1, 2006, through December 31, 2010). Cox proportional-hazards models were applied to evaluate the associations between SUA trajectories and the risk of incident retinal arteriosclerosis during the outcome period (from January 1, 2011, through December 31, 2019).
### Results
4 distinct SUA trajectories were identified in both women and men: low, moderate, moderate-high, and high. During a median follow-up of 9.54 years (IQR 9.53–9.56), 97 women and 295 men had developed retinal arteriosclerosis. In the fully adjusted model, a significant association between the moderate-high SUA trajectory group and incidence of retinal arteriosclerosis was observed only in men (HR: 1.76, $95\%$ CI: 1.17–2.65) compared with the low trajectory group, but not in women (HR: 0.77, $95\%$ CI: 0.39–1.52). Also, the high SUA trajectory group had the highest risk with an adjusted HR of 1.81 ($95\%$ CI, 1.04–3.17) in men. However, they did not exhibit a substantially increased risk in women.
### Conclusion
Higher SUA trajectory groups were significantly associated with an increased risk of incident retinal arteriosclerosis in men but not in women.
## Introduction
Retinal arteriosclerosis/atherosclerosis, including senile degenerative sclerosis and retinal small artery sclerosis, is considered as an indicator of systemic atherosclerotic damage [1]. And the assessment of retinal arteriosclerosis is frequently used as a screening for subclinical atherosclerosis in a general population [1]. Arterial stiffness with age and can be accelerated by different factors, such as diabetes and hypertension [2, 3]. In the most recent studies, growing evidence has shown that the retinal arteriolosclerosis is associated with a range of cardiovascular diseases (CVDs), such as stroke, heart failure, myocardial infarction, coronary heart disease, and other CVDs (4–9). Retinal arteriosclerosis is inevitably accompanied by increased many comorbidities. In addition, with the huge increase in health care costs [4], there is a need to study the predictive factors for early intervention. The retina is also the only deep-seated blood vessel in the body that can be directly visualized in a non-invasive manner [5]. Thus, the retinal arterioles offer an opportunity to noninvasively explore the relation of retinal arteriosclerosis to other diseases. This means that early detection of modifiable risk factors for retinal arteriosclerosis is of the utmost importance.
Serum uric acid (SUA), the ultimate metabolite of purines in the body. Accumulating epidemiological and clinical evidence has demonstrated that elevated SUA is associated with a variety of cardiovascular disorders, including hypertension, diabetes, coronary heart disease, metabolic syndrome, etc. ( 10–17). However, limited research has been performed on the associations of SUA with arteriosclerosis, especially retinal microvessels. And the role of SUA as an independent risk factor in arteriosclerosis incidence is still controversial. Several studies have demonstrated that increased SUA is a significant risk factor for arteriosclerosis [18, 19]. Whereas, some studies showed that the association between SUA concentration and atherosclerosis exists only in men and not in women [20, 21], while others suggested that the relationship is much stronger in women than in men [19]. Furthermore, there are also a few studies that hold opposing views, which showed that uric acid is a natural peroxynitrite scavenger [22], and is not associated with arteriosclerosis [23, 24], even observed that the increase of SUA is associated with the increase of retinal arteriolar caliber in women [25]. A previous population-based cross-sectional study has reported that increased SUA concentrations are risk factors for retinal arteriosclerosis in men but not in women [21]. The study only assessed SUA levels at a single time point, and did not consider the potential impact of SUA levels experienced earlier or the changes in SUA levels over time. However, little is known regarding whether long-term SUA trajectory patterns exist and how those patterns relate to retinal arteriosclerosis events. Characteristics of trajectory patterns may help identify populations at high risk for retinal arteriosclerosis events who would benefit from interventions to modify SUA elevation and prevent retinal arteriosclerosis. The role of trajectory in the association between SUA and retinal arteriosclerosis should be examined.
Therefore, the aims of this study were to use a population-based longitudinal cohort database [1] to identify the sex-specific subgroups of individuals with similar trajectories in SUA during a 5-year exposure period and [2] to determine the independent association of these SUA trajectories with the risk of retinal arteriosclerosis during a subsequent long-term follow-up period among adults without hypertension and diabetes.
## Study design and participants
This population-based longitudinal study included 353,848 non-manual workers who began undergoing health examinations between 2006 and 2019 at Hua Dong Sanatorium. Most of the subjects were members of organizations or employees of companies. During those years of follow-up, all participants were invited to complete physical examinations, health-related questionnaires, and laboratory tests, and they repeated these tests annually. For this study, 349,524 subjects were excluded based on the following criteria: (i) those who were aged <18 years and ≥ 60 years (considering the diameter of the retinal arteriolar is narrower in the elderly and the degenerative sclerosis in older persons [26]); (ii) those had no information of eye diagnosis during exposure period (from January 1, 2006, through December 31, 2010) (Figure 1) and had no information of SUA or body mass index (BMI) during baseline period (from January 1, 2010, through December 31, 2010); (iii) those suffered from retinal arteriosclerosis or hypertension or diabetes during exposure period (because the previous study reported that high SUA levels significant increase the incident hypertension (10, 11, 27–30), and SUA has been confirmed to play an essential role in the pathogenesis of diabetes [13, 14], what’s more hypertension and diabetes would accelerate the process of retinal arteriosclerosis [2, 3]; and (iv) those had less than 3 uric acid measurements during exposure period or had no data of eye diagnosis during outcome period (from January 1, 2011, through December 31, 2019). Thus, a total of 4,324 participants were ultimately included in the analysis of SUA trajectories. Data cleaning steps were presented in Figure 2.
**Figure 1:** *Study design and the time line of exposure and outcome assessment.* **Figure 2:** *Flow chart of participant inclusion.*
The study was approved by the Ethics Committee and the Institutional Review Board of Hua Dong Sanatorium, Wuxi. All methods were implemented in accordance with the Declaration of Helsinki and the relevant guidelines. The informed consent was waived because the research was a retrospective study, and the need to waive informed consent was also supported by the Ethics Committee of Hua Dong Sanatorium. The personal information of the study subjects was confidential.
## Assessment of the SUA
SUA and other biochemical indexes (such as fasting plasma glucose, blood lipids, and creatinine) were determined by AU 5400 BECKMAN COULTER with Enq1zymatic methods at the Hua Dong Sanatorium laboratory.
## Assessment of covariates
A demographic characteristics questionnaire, including age, sex, smoking status (current, former, and never), and alcohol drinking status (current, former, and never) was collected by trained staff. The interview also included medical treatment histories, such as hypertension, diabetes, glaucoma, and so on.
Anthropometric measurements were performed by well-trained examiners. The participants were asked to stand up straight, wearing thin, light clothing, and no hats or shoes to measure height and weight. BMI was calculated as weight in kilograms divided by the squared of height in meters. Hypertension was defined as individuals with systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg or self-reported physician diagnosis of hypertension, or using antihypertensive medications. Diabetes was defined as fasting glucose level ≥ 7.0 mmol/l, or self-reported doctor diagnosed diabetes, or taking oral hypoglycemic drugs or injecting insulin. CVD was defined as definite coronary heart disease, stroke, heart failure, transient ischemic attack, or peripheral arterial disease [12]. Blood samples obtained from the anterior cubital vein in the morning after fasting for at least 8 h. The biochemical parameters, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and creatinine (Cr) were measured by enzymatic methods with an autoanalyzer (AU5400, BECKMAN COULTER). Besides, the estimated glomerular filtration rate (eGFR) was estimated by using the Modification of Diet in Renal Disease (MDRD) equation applied for Chinese patients [28, 31]. The equation: eGFR (mL/min/1.73 m2) = 175 × Cr−1.234 (mg/ dL) × age−0.179(years) × 0.79 (in women).
## Assessment of the outcome
The outcome was to assess the sex-specific association of distinct SUA trajectory groups (identified in a general population using SUA measurements between 2006 and 2010) with the subsequent development of retinal arteriosclerosis (Figure 1).
Fundus examinations were performed by trained ophthalmologists. The procedure followed standardized methods [32]. Briefly, after 5 min of darkness adaptation, a 45° static retinal photo was obtained with the non-dilated fundus camera TRC.NW400TOPCON. Retinal arteriosclerosis was divided into four grades according to the Keith-*Wagener fundus* grading method [33]. Grade I: spasm or mild sclerosis of the retinal artery; Grade II: the degree of retinal arteriosclerosis was more obvious than that of Grade I. Pathological changes of different degrees could be seen at the arteriovenous junction, and the arterial light reflection was widened, copper and silver filaments were visible; Grade III: in addition to retinal artery stenosis and sclerosis, there was retinal edema, cotton lint spots, rigid exudates, hemorrhagic spots, etc.; Grade IV: in addition to grade III changes, there was also optic disk edema. Grades I ~ IV were all diagnosed as retinal arteriosclerosis [21].
## Identification of SUA trajectories
Given the substantial differences in SUA levels existing between sexes, we determined SUA trajectories for different sexes. In this study, we identified distinct trajectory groups according to individual SUA trajectories by using group-based trajectory modeling (GBTM). These models were implemented by SAS Proc Traj procedure [34]. The GBTM is a specialized application of finite mixture modeling. The aim is to identify clusters of individuals with similar trajectories and thus to determine several subpopulations with different trajectories [35]. Several statistically oriented criteria for assessing model fit and the number of trajectories. These include: (i) the higher values of Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) (close to zero, indicating a good fit) [36]; (ii) the average of the posterior probabilities of each group exceeds $70\%$; (iii) the number of participants in each trajectory exceeds $5\%$ of the overall population. We intuitively sketched each person’s trajectory, and we could see from the trajectory that each person’s trajectory had undergone various alterations (Figures 3A,B). Then, we constructed models with different numbers and different forms of trajectories (linear, quadratic, or cubic) [37]. We started with a model with one trajectory and then, constructed the trajectory models with two, three, four, and five. BIC and AIC increased as the number of trajectory groups increased (Supplementary Figure S1). When the model with five trajectories, the number of participants in several trajectory groups was less than $5\%$, as the result, four trajectory groups were identified to fit the best. Starting with all these trajectories in cubic and then quadratic and linear, respectively, we then compared the model fit of models with 4 trajectories with different functional forms. In our final model, we had four trajectories with liner order terms, and from this final model, we calculated the greatest posterior predicted probabilities for each individual of being a member of a given trajectory group, and the average probability of final group membership was 0.90 in men (range 0.89–0.92 across the trajectory groups) and 0.87 in women (range 0.84–0.91 across the trajectory groups) (Supplementary Table S1).
**Figure 3:** *Individual trajectories and fitted trajectories of serum uric acid over 5 years (exposure period). (A) Individual trajectories in women. (B) Individual trajectories in men. (C) Fitted trajectories in women. (D) Fitted trajectories in men. Serum uric trajectories in each sex were identified by Group-based Trajectory Modeling (SAS PROC TRAJ), using serum uric acid levels during 2006–2010 exams. Men and women were categorized into 4 trajectories: the low, moderate, moderate-high, and high groups.*
## Statistical analyzes
Continuous variables were expressed in terms of median (interquartile spacing) and categorical variables in terms of number (percentage). Kolmogorov–Smirnov test was used for the distribution of variables. In order to compare the differences between groups, ANOVA or Kruskal–Wallis test was used for continuous variables, and Chi-square test was used for categorical variables. In accordance with the recommendations for inferences from incomplete data from the National Research Council [38], we assumed that the missing data during follow-up were missing at random, and we performed a Little’s MCAR test. The result showed that the chi-square value was 29.404 and the p value was 0.105, which was greater than 0.05. The missing values could be considered to be missing completely at random (MACR). Therefore, the multiple imputation was used to impute the baseline missing values [39]. *We* generated 5 datasets to account for the missing data at baseline (Supplementary Tables S2, S3) and chose the median of these values for final calculations [40]. The follow-up time was from the baseline date to the diagnosis date of the retinal arteriosclerosis or the censoring date (December 31, 2019), whichever came first, and the median follow-up time was calculated by reverse Kaplan–Meier method [41]. The cumulative incidence curves were estimated by the Kaplan–Meier method, and any differences were evaluated with log-rank tests. To compare the risk of retinal arteriosclerosis in different SUA trajectory groups, univariate and multivariable Cox proportional hazard models were constructed to estimate hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for changes in SUA in women, men, and total populations. The variables included in the multivariate model were statistically significant in the baseline analyzes and were shown to influence outcomes according to previous research. Furthermore, to assess robustness of the observed association between SUA trajectory groups and retinal arteriosclerosis, we examined the effect modification of SUA trajectories for retinal arteriosclerosis development in prespecified subgroups by baseline age (<45 or ≥ 45 years), BMI (<24 or ≥ 24 kg/m2) and SBP (<120 or ≥ 120 mmHg).
P trend tests were performed by assessing the statistical significance across categorical SUA trajectory groups as an ordinal variable, and to evaluate incidence density trends of retinal arteriosclerosis during the median 9.54-year follow-up period in study population, the Cochran–Armitage test was used [28, 42]. Person years were calculated from baseline until the date of diagnosis or the end of follow-up [43].
To evaluate the impact of the continuous changes in SUA on the incidence rate, a multivariable Cox model with restricted cubic splines (RCS) was built. The spline was defined using four knots at the 5th, 25th, 75th, and 95th percentiles [44], and the threshold was determined as the point in SUA level with the smallest hazard ratio [45, 46].
We conducted several sensitivity analyzes. Given the tendency for the trajectories to change smoothly, we further created models to assess serum uric acid levels at a single time point and stratify the baseline SUA according to quartiles instead of the trajectories. We divided participants into four groups based on the quartiles of the cumulative average of SUA levels during the exposure period to investigate the effects of cumulative SUA exposure. Furthermore, we reanalyzed the data after including individuals who had a history of hypertension and diabetes or were over 60 years old during the exposure period, which might affect the outcomes.
All statistical analyzes were performed with SAS version 9.4 (SAS Institute, Cary, NC, United States) and R version 4.1.2 (R Foundation). The two-sided $p \leq 0.05$ was considered statistically significant.
## The trajectory groups of SUA
A total of 4,324 participants (1,781 women and 2,543 men) were enrolled in our study (Figure 2; Table 1). We identified SUA trajectories by sex-specific: four trajectories in women and four similarly shaped trajectories in men, and as a group, men had higher serum uric acid concentrations than women. We classified trajectories as “low,” “moderate,” “moderate-high,” and “high” based on morphological characteristics. $17.65\%$ of women and $16.35\%$ of men had the low trajectory; $44.89\%$ of women and $45.01\%$ of men had the moderate trajectory; $30.53\%$ of women and $32.19\%$ of men had the moderate-high trajectory; and $6.93\%$ of women and $6.45\%$ of men had the high trajectory (Figures 3C,D; Supplementary Table S1).
**Table 1**
| Unnamed: 0 | SUA trajectory group* | SUA trajectory group*.1 | SUA trajectory group*.2 | SUA trajectory group*.3 | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| Characteristics | Low | Moderate | Moderate-high | High | p Value # |
| Women | | | | | |
| Total n = 1,781 | | | | | |
| Age (years) | 46.0 (40.0, 50.0) | 45.0 (40.0, 51.0) | 47.0 (40.0, 52.0) | 50.0 (44.0, 54.0) | <0.0001 |
| BMI (kg/m2) | 21.3 (19.9, 22.8) | 21.6 (20.1, 23.2) | 22.3 (20.8, 24.3) | 23.7 (21.8, 25.8) | <0.0001 |
| SBP (mmHg) | 100.0 (100.0, 110.0) | 103.3 (100.0, 110.0) | 105.0 (100.0, 110.0) | 105.0 (100.0, 120.0) | 0.0006 |
| DBP (mmHg) | 70.0 (60.0, 70.0) | 70.0 (60.0, 70.0) | 70.0 (64.0, 70.0) | 70.0 (65.0, 75.0) | 0.0001 |
| Smoking status (%) | | | | | |
| Current | – | 2 (0.2) | 1 (0.2) | – | >0.9999 |
| Former or never | 293 (100.0) | 836 (99.8) | 528 (99.8) | 121 (100.0) | |
| Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) |
| Current | 268 (91.5) | 750 (89.5) | 476 (90.0) | 109 (90.1) | 0.8166 |
| Former or never | 25 (8.5) | 88 (10.5) | 53 (10.0) | 12 (9.9) | |
| CVD (%) | CVD (%) | CVD (%) | CVD (%) | CVD (%) | CVD (%) |
| Positive | – | 5 (0.6) | 5 (0.9) | - | 0.3829 |
| Negative | 293 (100.0) | 833 (99.4) | 524 (99.1) | 121 (100.0) | |
| Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: |
| FBG (mmol/L) | 5.1 (4.9, 5.3) | 5.1 (4.9, 5.4) | 5.1 (4.9, 5.4) | 5.3 (5.0, 5.6) | <0.0001 |
| TG (mmol/L) | 0.7 (0.6, 1.0) | 0.8 (0.6, 1.1) | 1.0 (0.7, 1.3) | 1.3 (0.9, 1.7) | <0.0001 |
| TC (mmol/L) | 4.7 (4.1, 5.3) | 4.7 (4.2, 5.2) | 4.8 (4.2, 5.4) | 5.1 (4.5, 5.6) | <0.0001 |
| LDL-C (mmol/L) | 2.8 (2.3, 3.3) | 2.8 (2.4, 3.3) | 3.0 (2.6, 3.5) | 3.3 (2.8, 3.7) | <0.0001 |
| HDL-C (mmol/L) | 1.7 (1.5, 1.9) | 1.6 (1.4, 1.9) | 1.5 (1.3, 1.7) | 1.5 (1.2, 1.7) | <0.0001 |
| Cr (μmol/L) | 53.2 (49.0, 57.7) | 55.3 (50.5, 60.2) | 56.0 (51.7, 61.2) | 58.5 (54.3, 62.9) | <0.0001 |
| eGFR (mL/min/1.73 m2) | 167.8 (150.4, 183.9) | 158.2 (143.1, 177.8) | 155.5 (138.8, 173.3) | 146.2 (132.2, 160.8) | <0.0001 |
| SUA (μmol/L) | 200.5 (182.2, 215.1) | 248.7 (229.3, 265.9) | 291.7 (273.3, 316.0) | 359.4 (343.1, 384.6) | <0.0001 |
| Men | Men | Men | Men | Men | Men |
| Total n = 2,543 | Total n = 2,543 | Total n = 2,543 | Total n = 2,543 | Total n = 2,543 | Total n = 2,543 |
| Age (years) | 48.0 (42.0, 53.0) | 46.0 (40.0, 51.0) | 46.0 (41.0, 51.0) | 46.0 (40.0, 51.0) | 0.0065 |
| BMI (kg/m2) | 22.9 (21.1, 24.7) | 24.0 (22.2, 25.7) | 25.1 (23.5, 26.6) | 25.4 (23.9, 27.1) | <0.0001 |
| SBP (mmHg) | 110.0 (105.0, 120.0) | 110.0 (110.0, 120.0) | 115.0 (110.0, 120.0) | 115.0 (110.0, 120.0) | <0.0001 |
| DBP (mmHg) | 70.0 (68.0, 76.0) | 70.0 (70.0, 80.0) | 70.0 (70.0, 80.0) | 75.0 (70.0, 80.0) | <0.0001 |
| Smoking status (%) | Smoking status (%) | Smoking status (%) | Smoking status (%) | Smoking status (%) | Smoking status (%) |
| Current | 187 (45.8) | 536 (46.1) | 397 (48.8) | 72 (45.3) | 0.5979 |
| Former or never | 221 (54.2) | 627 (53.9) | 416 (51.2) | 87 (54.7) | |
| Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) | Alcohol drinking status (%) |
| Current | 131 (32.1) | 292 (25.1) | 149 (18.3) | 30 (18.9) | <0.0001 |
| Former or never | 277 (67.9) | 871 (74.9) | 664 (81.7) | 129 (81.1) | |
| CVD (%) | CVD (%) | CVD (%) | CVD (%) | CVD (%) | CVD (%) |
| Positive | 1 (0.2) | 10 (0.9) | 6 (0.7) | 2 (1.3) | 0.4654 |
| Negative | 407 (99.8) | 1,153 (99.1) | 807 (99.3) | 157 (98.7) | |
| Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: | Fasting blood concentrations of: |
| FBG (mmol/L) | 5.2 (5.0, 5.5) | 5.3 (5.0, 5.5) | 5.3 (5.1, 5.6) | 5.4 (5.1, 5.8) | <0.0001 |
| TG (mmol/L) | 1.2 (0.8, 1.6) | 1.3 (1.0, 1.8) | 1.7 (1.2, 2.4) | 2.1 (1.5, 3.0) | <0.0001 |
| TC (mmol/L) | 4.8 (4.2, 5.3) | 4.8 (4.2, 5.3) | 5.0 (4.5, 5.5) | 5.0 (4.5, 5.6) | <0.0001 |
| LDL-C (mmol/L) | 3.1 (2.6, 3.7) | 3.2 (2.7, 3.6) | 3.3 (2.8, 3.8) | 3.4 (2.8, 3.9) | <0.0001 |
| HDL-C (mmol/L) | 1.3 (1.1, 1.6) | 1.3 (1.1, 1.5) | 1.2 (1.0, 1.4) | 1.1 (1.0, 1.3) | <0.0001 |
| Cr (μmol/L) | 74.2 (68.7, 80.8) | 75.6 (69.7, 81.7) | 77.1 (71.1, 83.3) | 81.2 (73.8, 87.8) | <0.0001 |
| eGFR (mL/min/1.73 m2) | 86.4 (77.4, 95.9) | 84.8 (77.2, 94.2) | 82.5 (75.0, 91.6) | 78.2 (70.6, 89.0) | <0.0001 |
| SUA (μmol/L) | 284.9 (260.3, 302.7) | 347.7 (326.1, 369.1) | 415.6 (392.4, 440.4) | 495.9 (475.6, 526.7) | <0.0001 |
## Patient characteristics of various SUA trajectories at baseline
Table 1 shows the baseline characteristics stratified by SUA trajectories. Among both women and men, the high trajectory group had a higher BMI. In addition, there was a statistical difference in age, BMI, SBP, DBP, FBG, TG, TC, LDL-C, HDL-C, Cr, and eGFR among SUA trajectory groups. However, there was negatively related to smoking status and alcohol drinking status in women, and positively related to alcohol drinking status in men.
## Incidence of retinal arteriosclerosis
Table 2 presents the incidence densities of retinal arteriosclerosis development according to SUA trajectory groups in women and men. We observed that 97 women developed retinal arteriosclerosis during a median follow-up of 9.54 years (IQR 9.53–9.56), and 295 men developed retinal arteriosclerosis, with an overall incidence rate of 8.8 per 1,000 person-years in women. There were 13 ($4.4\%$), 38 ($4.5\%$), 33 ($6.2\%$), and 13 ($10.7\%$) retinal arteriosclerosis events in the low, moderate, moderate-high, and high SUA trajectory groups, respectively, with corresponding incidence densities of 6.8 ($95\%$ CI, 3.1–10.5), 7.3 ($95\%$ CI, 5.0–9.6), 10.3 ($95\%$ CI, 6.8–13.8), and 19.7 ($95\%$ CI, 9.1–30.3) per 1,000 person-years, respectively. And the Cochran-Armitage trend test showed that retinal arteriosclerosis incidence was significantly elevated as SUA changing trajectory increased (p for trend = 0.0104). Analogously, in men, the overall incidence was 17.9 per 1,000 person-years, and there were 31 ($7.6\%$), 115 ($9.9\%$), 125 ($15.4\%$), and 24 ($15.1\%$) retinal arteriosclerosis events in the low, moderate, moderate-high, and high SUA trajectory groups, respectively, with corresponding incidence densities of 11.9 ($95\%$ CI, 7.7–16.0), 15.4 ($95\%$ CI, 12.6–18.2), 23.4 ($95\%$ CI, 19.3–27.4), and 23.2 ($95\%$ CI, 14.0–32.3) per 1,000 person-years, respectively. The incidence of retinal arteriosclerosis trend was similar in women (p for trend < 0.001). However, in each corresponding trajectory group, the incidence of retinal arteriosclerosis was generally higher in men than in women.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | SUA trajectory group | SUA trajectory group.1 | SUA trajectory group.2 | SUA trajectory group.3 | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Variables | All | Low | Moderate | Moderate-high | High | p for trend* |
| Women | Women | Women | Women | Women | Women | Women |
| Participants, n | 1781 | 293 | 838 | 529 | 121 | |
| Participants, n (person-years) | 11010.5 | 1901.5 | 5239.3 | 3210.6 | 659.1 | |
| Incidence, n | 97 | 13 | 38 | 33 | 13 | |
| Incidence density per 1,000 person-years (95% CI) | 8.8 (7.1,10.6) | 6.8 (3.1,10.5) | 7.3 (5.0,9.6) | 10.3 (6.8,13.8) | 19.7 (9.1,30.3) | 0.0104 |
| Men | Men | Men | Men | Men | Men | Men |
| Participants, n | 2543 | 408 | 1163 | 813 | 159 | |
| Participants, n (person-years) | 16459.9 | 2608.8 | 7470.5 | 5344.1 | 1036.6 | |
| Incidence, n | 295 | 31 | 115 | 125 | 24 | |
| Incidence density per 1,000 person-years (95% CI) | 17.9 (15.9,19.9) | 11.9 (7.7,16.0) | 15.4 (12.6,18.2) | 23.4 (19.3,27.4) | 23.2 (14.0,32.3) | <0.0001 |
The cumulative incidence curves also showed that the retinal arteriosclerosis event-free survival rate was significantly lower in the high trajectory group and moderate-high trajectory group than in the other two groups (log-rank test: $$p \leq 0.0023$$ in women; $p \leq 0.001$ in men) (Figure 4).
**Figure 4:** *Cumulative incidence curve for retinal arteriosclerosis according to SUA trajectory groups in women (A) and men (B).*
## Association of SUA trajectory groups and retinal arteriosclerosis risk
Cox proportional-hazards models were conducted to evaluate the relationship between SUA trajectory groups and retinal arteriosclerosis risk (Table 3). In the unadjusted Cox proportional-hazards models, higher SUA trajectory groups were associated with new-onset retinal arteriosclerosis compared with those in the low SUA trajectory group in women and men. All associations were attenuated after adjustment for age, BMI, and other covariates. After adjusting for potential confounders, these associations remained significant only in men. We found that, in men, the relationship between moderate-high and high SUA trajectory groups and retinal arteriosclerosis was statistically significant in all models. And the high trajectory group experienced a greater hazard ratio than the moderate-high trajectory group. Besides, the retinal arteriosclerosis risk increases with the SUA changing trajectory increases in those models (p for trend<0.05). The association of SUA trajectory groups with retinal arteriosclerosis differed in men and women. Overall, the HRs with $95\%$ CI for moderate, moderate-high trajectory group, and high trajectory group were 1.14 ($95\%$ CI, 0.81–1.60), 1.42 ($95\%$ CI, 1.00–2.01), and 1.38 ($95\%$ CI, 0.87–2.20) compared with the low trajectory group, respectively. We also constructed uric acid trajectories in the total population separately, and the results of the trajectories are shown in Supplementary Figure S2. The relationship between SUA and retinal arteriosclerosis in the total population was also not statistically significant (Supplementary Table S4).
**Table 3**
| Unnamed: 0 | Model 1 | Model 1.1 | Model 2 | Model 2.1 |
| --- | --- | --- | --- | --- |
| Variables | HR, (95%CI) | p Value | HR, (95%CI) | p Value |
| Women | Women | Women | Women | Women |
| Low | 1.00 (ref) | – | 1.00 (ref) | – |
| Moderate | 1.03 (0.55,1.93) | 0.9351 | 0.86 (0.46,1.64) | 0.6563 |
| Moderate-high | 1.43 (0.75,2.72) | 0.2734 | 0.77 (0.39,1.52) | 0.4587 |
| High | 2.53 (1.17,5.45) | 0.0182 | 0.73 (0.31,1.75) | 0.4832 |
| p for trend* | | 0.0095 | | 0.4263 |
| Men | Men | Men | Men | Men |
| Low | 1.00 (ref) | – | 1.00 (ref) | – |
| Moderate | 1.31 (0.88,1.94) | 0.1853 | 1.29 (0.86,1.93) | 0.2139 |
| Moderate-high | 2.09 (1.41,3.10) | 0.0002 | 1.76 (1.17,2.65) | 0.0068 |
| High | 2.04 (1.20,3.48) | 0.0086 | 1.81 (1.04,3.17) | 0.0367 |
| p for trend* | | <0.0001 | | 0.0021 |
| Pooled | Pooled | Pooled | Pooled | Pooled |
| Low | 1.00 (ref) | – | 1.00 (ref) | – |
| Moderate | 1.22 (0.87,1.71) | 0.2391 | 1.14 (0.81,1.60) | 0.4481 |
| Moderate-high | 1.93 (1.38,2.69) | 0.0001 | 1.42 (1.00,2.01) | 0.0492 |
| High | 2.18 (1.41,3.37) | 0.0005 | 1.38 (0.87,2.20) | 0.1681 |
| p for trend* | | <0.0001 | | 0.0318 |
Spline regression analyzes also showed a graded association of changes in SUA with the risk of incident retinal arteriosclerosis. Similar to the results of the above model, increasing SUA was associated with a higher risk of retinal arteriosclerosis development in men. The plot showed a slow reduction of the risk within the lower range of SUA, which reached the lowest risk around 317.7 μmol/l and then increased thereafter. The concentration of SUA associated with the lowest risk of retinal arteriosclerosis in multivariable adjusted analyzes was 413.5 μmol/l in the men. However, this did not reach statistical significance in women (Figure 5).
**Figure 5:** *Adjusted HRs (95% CIs) for retinal arteriosclerosis incidence from restricted cubic splines in women (A) and (B) men. These models were adjusted for age, body mass index, systolic blood pressure, diastolic blood pressure at baseline (from June 1, 2010, through June 1, 2011), alcohol drinking status (in men), triglyceride level, total cholesterol, low-density lipoprotein cholesterol level, high-density lipoprotein cholesterol level, creatinine and estimated glomerular filtration rate.*
We further analyzed the association of SUA trajectory groups with retinal arteriosclerosis development. We examined the effect modification by an a priori–selected set of baseline characteristics, including baseline age (<45 or ≥45 years), SBP (<120 or ≥120 mmHg), BMI (< 24 or ≥ 24 kg/m2). The number of patients and incidence density in different subgroups are shown in Supplementary Table S5. p values for interaction were not significant for all subgroups except the age subgroup. The significant association of SUA trends with the retinal arteriosclerosis outcome was consistent across subgroups by BMI and SBP. Which was higher SUA trajectory groups were associated with new-onset retinal arteriosclerosis in men but not in women. In men, in the age subgroup with, test for interaction showed that the effect of SUA level on retinal arteriosclerosis was significantly between age subgroup (< 45 or ≥ 45 years) (p value for interaction = 0.0031). The effect of SUA on outcome was greater at ages older than 45 years. In the subgroup with BMI ≥ 24 kg/m2, participants with moderate, moderate-high or high SUA trajectory had a 2.05-fold, 2.58-fold, and 2.52-fold higher risk of retinal arteriosclerosis than those with a low trajectory. In the subgroup with SBP ≥ 120 mmHg, moderate-high trajectory group and high trajectory group were associated with a high risk of retinal arteriosclerosis development. In contrast, in the subgroup with age < 45 years, BMI < 24 kg/m2, and SBP < 120 mmHg, there was no statistically significant relationship between SUA trajectory groups and retinal arteriosclerosis. However, in women, higher SUA trajectory groups were relatively protective compared to the low group, which may be due to chance and needs further exploration (Supplementary Figures S3, S4).
## Sensitivity analysis
To substantiate the robust association of SUA trajectory groups with the subsequent outcome, several sensitivity analyzes were conducted in this study. Results of all sensitivity analyzes were largely consistent. In men, we observed that high or moderate-high SUA trajectory groups were significantly associated with future retinal arteriosclerosis risk. Whereas, there was no association in women. First, in the analyzes using quartiles at baseline level instead of SUA trajectories, we found a similar association between high SUA levels with risk of retinal arteriosclerosis development in men, but not in women. Second, when the cumulative average of SUA levels during the exposure period was divided into four groups, the associations of SUA levels with retinal arteriosclerosis risk remained significant in men. Finally, we additionally performed analyzes using the complete dataset, including people over 60 years of age and people with hypertension or diabetes, and found the similar results, high SUA trajectory group was associated with a 1.43-fold higher risk of retinal arteriosclerosis development in men (Table 4).
**Table 4**
| Unnamed: 0 | Low | Moderate | Moderate-high | High |
| --- | --- | --- | --- | --- |
| Women | Women | Women | Women | Women |
| Sensitivity analysis 1* | 1.00 (ref) | 0.84 (0.44,1.61) | 0.71 (0.37,1.36) | 1.08 (0.59,1.98) |
| Sensitivity analysis 2# | 1.00 (ref) | 1.32 (0.71,2.45) | 0.65 (0.32,1.33) | 1.46 (0.78,2.72) |
| Sensitivity analysis 3 | 1.00 (ref) | 0.97 (0.66,1.43) | 1.00 (0.66,1.51) | 1.48 (0.89,2.45) |
| Men | Men | Men | Men | Men |
| Sensitivity analysis 1* | 1.00 (ref) | 0.87 (0.60,1.25) | 1.09 (0.77,1.54) | 1.44 (1.02,2.03) |
| Sensitivity analysis 2# | 1.00 (ref) | 0.97 (0.66,1.42) | 1.41 (0.99,2.00) | 1.69 (1.19,2.41) |
| Sensitivity analysis 3 | 1.00 (ref) | 1.12 (0.90,1.38) | 1.33 (1.07,1.66) | 1.43 (1.08,1.90) |
## Discussion
In this population-based longitudinal study of Chinese adults without diabetes and hypertension, we identified 4 SUA trajectory groups in women and men during a 5-year exposure period: low, moderate, moderate-high, and high. Besides, in the multivariate model, higher trajectory groups were positively associated with retinal arteriosclerosis risk, but only in men. In addition, spline regression analyzes also suggested that this relationship was more robust among participants with higher SUA levels. In men, these observed associations between SUA trajectory groups and retinal arteriosclerosis were independent of BMI, age, blood pressure, and lipid parameters. However, in women, the association of higher SUA trajectory groups with retinal arteriosclerosis events was attenuated to non-significance after adjustment for covariates.
To our knowledge, this is the first study to date to assess the associations between SUA trajectory groups and the risk of retinal arteriosclerosis. Although there have been many studies on the prevalence of retinal arteriosclerosis [1, 21, 47], there are fewer studies reporting on the incidence. Our study found that the incidence of retinal arteriosclerosis in Chinese men was higher than women. The incidence densities of retinal arteriosclerosis were 17.9 per 1,000 person-year ($95\%$ CI: 15.9–19.9) for men and 8.8 ($95\%$ CI: 7.1–10.6) for women. Retinal arteriosclerosis is a microvascular disease of arteriosclerosis, in order to exclude the influence of traditional factors on arterial stiffness, hypertension and diabetes were excluded in this study. And our study showed that the association between SUA trajectory groups and retinal arteriosclerosis was statistically significant only in men. SUA has been linked to retinal arteriosclerosis in previous cross-sectional analyzes [21]. The cross-sectional nature of prior investigations limits the ability to understand the temporal association of serum uric acid with retinal arteriosclerosis. Our findings demonstrated that SUA in early exposure period, and its patterns of change over time were associated with retinal arteriosclerosis. Besides, the sex discrepancy seen in the cross-sectional research has been borne out in our study and supported that SUA plays a greater role in the pathogenesis of retinal arteriosclerosis in men than in women. The sex difference has also been reported in previous studies. Another cross-sectional population-based study of 779 subjects showed that the high-normal SUA levels were associated with an increased risk of arterial stiffness in healthy Korean men, with an OR of 2.91 ($95\%$ CI: 1.39–6.11) for 4th vs. 1st SUA quartiles [20]. A prospective research including 3,686 subjects found a significant positive correlation between SUA levels and cIMT, a recognized marker of atherosclerosis, in men [48]. Whereas, a cross-sectional study conducted by Chen et al. ( including 12,988 subjects) found that hyperuricemia was related to a higher risk of atherosclerotic cardiovascular disease in both sexes, and the relationship was much stronger in women than in men. This discrepancy may be due to Chen’s study that almost all women with high levels of uric acid were over 45 years old and likely to be in menopause. As the secretion of estrogen decreases with age and its protective effect on the cardiovascular system may gradually diminish, making it more susceptible to elevated uric acid levels [19]. We observed that the HRs for retinal arteriosclerosis in BMI ≥ 24Kg/m2, moderate-high trajectory group and high trajectory group were 2.58,2.52, respectively. However, there was no statistically significant relationship between high SUA levels and retinal arteriosclerosis when BMI < 24Kg/m2. Indeed, the previous study has reported that overweightness or obesity appeared significantly associated with increased arterial stiffness [49]. In addition, subgroup analyzes also showed that positive association between SUA trajectory groups and retinal arteriosclerosis development was observed only in SBP ≥ 120 mmHg. Hypertension is a well-known risk factor for retinal arteriosclerosis [3], and it has been reported that blood pressure indexes had high predictive performance for arteriosclerosis in eastern Chinese adults, and the optimal cut-off point for SBP was 123.5 mmHg [50]. Further studies are needed to confirm the specific SBP cut-off point in other populations and settings.
The mechanisms by which SUA reflects the risk for retinal arteriosclerosis are not fully understood, even though previous studies have been done in this area. High uric acid levels might generate superoxide and oxidative stress via the xanthine oxidase pathway and promote the development of atherosclerosis by stimulating inflammation [20]. In addition, experimental evidence suggested that adverse effects of SUA on the vasculature were associated with increased chemokine and cytokine expressions, induction of the renin-angiotensin system, and increased vascular C-reactive protein (CRP) expression [51]. SUA also reduced nitric oxide (NO) bioavailability, ensuring endothelial dysfunction, inflammation, and vasoconstriction [52, 53]. Vitro and in vivo data also suggested that hyperuricemia disturbs the balance of the asymmetric dimethylarginine / dimethylarginine dimethylaminotransferases-2 axis, results in endothelial cell dysfunction, and, consequently, accelerates atherosclerosis [54]. One animal experiment has shown that high SUA levels promote atherosclerosis by targeting NRF2-mediated autophagy dysfunction and ferroptosis [55]. A pathophysiological hypothesis may explain these sex differences, SUA metabolism is genetically regulated, and sex differences play an important role in regulating SUA concentrations. Distinct patterns of metabolic alteration between sexes, possibly due to different sex hormones exposure, may influence men to be more susceptible the deleterious effects of SUA levels on atherosclerosis risk [48]. However, it has also been shown that SUA was considered an antioxidant and could be expected to benefit the cardiovascular system [56]. Therefore, it is necessary to conduct a large cohort study to investigate the role of SUA levels in the progression of atherosclerosis.
Sensitivity analyzes revealed the association between SUA trajectory groups and retinal arteriosclerosis incidence was not affected by age and the presence of hypertension or diabetes. These were included in the sensitivity analyzes, and the results were consistent with the overall results. In addition, the clinical reference value of SUA is 420 μmol/l in men and 360 μmol/l in women [25], the sensitivity analyzes of the entire study demonstrated that SUA around or greater than 412 μmol/l was a risk factor for retinal arteriosclerosis development in men. Although the trajectories changed smoothly, if a single time point was used to predict, the median of SUA to achieve statistical significance was around 440 μmol/l and not accurate enough (Table 5). Thus, SUA trajectories did predict incident retinal arteriosclerosis better than serum uric acid at a single time point. We also did observe differences in the patterns of risk in men and women. Nevertheless, it must be considered that SUA levels in women in this study were almost below the clinical reference, the diagnostic criteria for hyperuricemia, therefore we cannot rule out an association between SUA levels and retinal arteriosclerosis progression in women, which would require longer follow-up.
**Table 5**
| Unnamed: 0 | Serum uric acid, median (interquartile range), μmol/L | Serum uric acid, median (interquartile range), μmol/L.1 | Serum uric acid, median (interquartile range), μmol/L.2 | Serum uric acid, median (interquartile range), μmol/L.3 |
| --- | --- | --- | --- | --- |
| | Low | Moderate | Moderate-high | High |
| Women | Women | Women | Women | Women |
| Main analysis | 200.5 (182.2, 215.1) | 248.7 (229.3, 265.9) | 291.7 (273.3, 316.0) | 359.4 (343.1, 384.6) |
| Sensitivity analysis 1* | 206.3 (191.2, 218.3) | 243.6 (235.9, 251.5) | 272.7 (265.4, 280.4) | 320.7 (302.7, 348.9) |
| Sensitivity analysis 2 # | 213.0 (199.0, 223.5) | 246.3 (239.9, 253.4) | 274.5 (267.6, 283.2) | 316.2 (302.4, 339.3) |
| Sensitivity analysis 3 | 205.8 (187.9, 223.2) | 257.2 (238.2, 275.5) | 309.2 (285.5, 332.6) | 382.3 (359.9, 411.6) |
| Men | Men | Men | Men | Men |
| Main analysis | 284.9 (260.3, 302.7) | 347.7 (326.1, 369.1) | 415.6 (392.4, 440.4) | 495.9 (475.6, 526.7) |
| Sensitivity analysis 1* | 296.0 (271.7, 310.2) | 343.5 (333.8, 354.6) | 386.3 (375.6, 397.8) | 447.1 (426.6, 476.7) |
| Sensitivity analysis 2# | 302.6 (280.8, 316.8) | 349.4 (338.6, 358.5) | 386.4 (377.5, 396.8) | 439.8 (422.7, 466.7) |
| Sensitivity analysis 3 | 283.5 (261.0, 302.2) | 348.5 (327.4, 371.5) | 412.6 (389.9, 437.9) | 486.1 (463.8, 513.5) |
The study also has clinical implications. As described above, previous studies evaluating the relationship between retinal arteriosclerosis and CVD have demonstrated that retinal arteriosclerosis is associated with a high incidence of CVD, including micro-vascular diseases (2–9). Studies have shown retinal microvasculature may play a role in the pathogenesis of atherosclerosis [57], and retinal arteriosclerosis can also be considered as a marker of systemic vascular aging [1]. Thus, from a public health perspective, it is clinically important to stratify the risk of developing retinal arteriosclerosis and identify the high-risk group. However, considering the inconvenience of retinal examination or the disadvantages of computed tomography in assessing CVD, such as radiation exposure and cost, it was not practical to use computed tomography as a tool for risk assessment in the general population. In this study, we found that assessment of SUA levels might help identify individuals at high risk for future retinal arteriosclerosis and subsequent CVD development. SUA examinations can be performed on the general population because this examination is inexpensive and minimally invasive. Therefore, SUA examinations may hold the most promise as a simple assessment tool for evaluating potential atherosclerosis and subsequent risk stratification for CVD risk, even in a busy clinical setting and in the general population.
The strengths and limitations of this study are worth noting. Major strengths of this study are the large sample size and long follow-up of cohorts among the Chinese. The large sample size also allowed us to perform the sex-stratified analyzes with sufficient statistical power. In addition, we constructed the method of GBTM to evaluate the relations of SUA and incident retinal arteriosclerosis. We also conducted a series of sensitivity analyzes to show the robustness of the findings. Nevertheless, we also acknowledge several limitations. First, information on covariates was only used at baseline, we did not capture the long-term covariates trajectories changes. Future studies with repeated measurements are preferred. Second, SUA levels were affected by diet changes and drugs [58, 59]. For example, diuretic could affect the SUA levels and was protective factors in fatal myocardial infarction [60]. There was also a study showed diuretic-related hyperuricemia was not associated with a higher risk of CVD [59]. However, we did not collect dietary information and specific information on SUA-lowering drugs and could not assess the changes of SUA levels were intentional or unintentional in the current study, which limited our ability to further investigate the associations between SUA and retinal arteriosclerosis. And a variety of potential confounding factors should be accounted in the future study. Third, the examination date was used to define the incident date instead of the exact event date. Lastly, given the irregular physical examination of the participants, most of them were excluded from the study, and this study was based on cohorts from a single hospital. Which limits our ability to assess the association between SUA trajectories and retinal arteriosclerosis. In view of this, the generalization of the association needs to be verified by multi-center and larger population studies. Despite these limitations, our results provide important insights into the incidence of retinal arteriosclerosis and its relationship with SUA trajectories and have clinical importance for retinal arteriosclerosis prevention in reminding men to pay attention to SUA levels and its changing trajectory.
In conclusion, our study identified 4 distinct trajectories of SUA in women and men and the findings indicate that SUA trajectory groups differ in their associations with retinal arteriosclerosis. Higher SUA trajectory groups were found to correlate with incident retinal arteriosclerosis in men, whereas such association did not reach statistical significance in women, suggesting that these high-risk persons should be pour closer attention to SUA management to prevent retinal arteriosclerosis.
## 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 Hua Dong Sanatorium. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
RG, YH, and ZT contributed to the conceptualization and project administration. QF, MJ, and YH collected participant information. RG and ZT analyzed the data. RG and QF wrote the manuscript, with assistance from MJ, YD, SX, CL, YH, and ZT. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Natural Science Foundation of China [81773541], funded from the Priority Academic Program Development of Jiangsu Higher Education Institutions at Soochow University, the State Key Laboratory of Radiation Medicine and Protection (GZK1201919) to ZT. The Clinical Special Program of Shanghai Municipal Health Commission (Grant number: 20194Y0437), China.
## 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.2023.1116486/full#supplementary-material
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|
---
title: Integrated metabolite analysis and health-relevant in vitro functionality of
white, red, and orange maize (Zea mays L.) from the Peruvian Andean race Cabanita
at different maturity stages
authors:
- Lena Gálvez Ranilla
- Gastón Zolla
- Ana Afaray-Carazas
- Miguel Vera-Vega
- Hugo Huanuqueño
- Huber Begazo-Gutiérrez
- Rosana Chirinos
- Romina Pedreschi
- Kalidas Shetty
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10011086
doi: 10.3389/fnut.2023.1132228
license: CC BY 4.0
---
# Integrated metabolite analysis and health-relevant in vitro functionality of white, red, and orange maize (Zea mays L.) from the Peruvian Andean race Cabanita at different maturity stages
## Abstract
The high maize (Zea mays L.) diversity in Peru has been recognized worldwide, but the investigation focused on its integral health-relevant and bioactive characterization is limited. Therefore, this research aimed at studying the variability of the primary and the secondary (free and dietary fiber-bound phenolic, and carotenoid compounds) metabolites of three maize types (white, red, and orange) from the Peruvian Andean race Cabanita at different maturity stages (milk-S1, dough-S2, and mature-S3) using targeted and untargeted methods. In addition, their antioxidant potential, and α-amylase and α-glucosidase inhibitory activities relevant for hyperglycemia management were investigated using in vitro models. Results revealed a high effect of the maize type and the maturity stage. All maize types had hydroxybenzoic and hydroxycinnamic acids in their free phenolic fractions, whereas major bound phenolic compounds were ferulic acid, ferulic acid derivatives, and p-coumaric acid. Flavonoids such as luteolin derivatives and anthocyanins were specific in the orange and red maize, respectively. The orange and red groups showed higher phenolic ranges (free + bound) (223.9–274.4 mg/100 g DW, 193.4– 229.8 mg/100 g DW for the orange and red maize, respectively) than the white maize (162.2–225.0 mg/100 g DW). Xanthophylls (lutein, zeaxanthin, neoxanthin, and a lutein isomer) were detected in all maize types. However, the orange maize showed the highest total carotenoid contents (3.19–5.87 μg/g DW). Most phenolic and carotenoid compounds decreased with kernel maturity in all cases. In relation to the primary metabolites, all maize types had similar fatty acid contents (linoleic acid > oleic acid > palmitic acid > α-linolenic acid > stearic acid) which increased with kernel development. Simple sugars, alcohols, amino acids, free fatty acids, organic acids, amines, and phytosterols declined along with grain maturity and were overall more abundant in white maize at S1. The in vitro functionality was similar among Cabanita maize types, but it decreased with the grain development, and showed a high correlation with the hydrophilic free phenolic fraction. Current results suggest that the nutraceutical characteristics of orange and white Cabanita maize are better at S1 and S2 stages while the red maize would be more beneficial at S3.
## 1. Introduction
Maize (Zea mays L. ssp. mays) originated about 9,000 years ago in Mexico, and Latin *America is* considered the center of its genetic diversity and primary domestication (1–3). This cereal is staple food in Mesoamerican and Latin American countries since it is the base of many traditional preparations. It has been reported that the conservation and sustainable use of Latin American maize landrace diversity is fundamental for worldwide food security [1]. Hence, efforts at multiple levels should be focused on the characterization of the genetically heterogenous landrace material as the base for further breeding improvements relevant for food security and health among indigenous food systems [1, 4].
The diversity of maize landrace populations is represented in races, which are identified according to their common botanical characteristics, geographical distribution, ecological adaptation, and cultural importance (uses and customs) (5–7). Mexico and Peru have concentrated around 30 percent of the Latin American maize diversity including 59 and 52 races, respectively [7, 8]. The Peruvian Andean region with its great variety of ecological features has the highest maize phenotypic diversity worldwide [7, 9, 10]. However, limited scientific information exist about Andean maize diversity which is compromising its adequate conservation and essential health relevant uses.
Whole cereal grains are valuable sources of carbohydrates, proteins, dietary fiber, minerals, and vitamins along with other critical bioactive metabolites with known health-promoting benefits [11, 12]. The regular intake of whole grains has been inversely correlated with lower incidence of several chronic non-communicable diseases including type 2 diabetes [13], cardiovascular disease [14], and some types of cancer [15, 16]. Maize contains nutritionally relevant macro and micronutrients mainly carbohydrates, lipids (with mono and polyunsaturated fatty acids), vitamins, minerals, and resistant starch [17]. In addition, biologically active functional compounds such as phenolic compounds, carotenoids, tocopherols, and phytosterols have been reported in maize [17]. In fact, unique phenolic and carotenoid profiles have been reported in different maize landraces linked to variable nutraceutical properties [18]. Accordingly, more studies are needed to fully characterize maize landraces, targeting those that are the base of needs of food security and economy in many geographical areas such as the Andean region.
The maize race Cabanita has been cultivated since the Pre-Inca period in the southern Andean region of Arequipa in Peru at around 3,000 meters of altitude [19]. Ears of Cabanita race have a conic-cylindrical shape and exhibit variable kernel pigmentations with predominance of white and partially red colored-pericarps [19]. In a previous study, some Peruvian maize races including Arequipeño, Cabanita, Kculli, Granada, and Coruca races were evaluated in relation to their phenolic composition, in vitro anti-hyperglycemia, and anti-obesity potential [20]. Cabanita kernels showed the second highest total oxygen radical absorbance antioxidant capacity (ORAC) and hyperglycemia management-relevant α-amylase inhibition following the purple-colored maize group (Kculli race) [20]. More recently, Cabanita maize from two different provinces in Arequipa (Peru) were evaluated in relation to their physical characteristics, bioactive (phenolic and carotenoid) composition and in vitro antioxidant capacity [19]. Although Cabanita samples from both provinces showed a certain grade of similarity according to the multivariate PCA (Principal Component Analysis), in general maize cultivated under Andean environments with naturally higher ecological stress factors such as higher altitudes and lower temperatures showed higher phenolic and antioxidant capacity ranges [19].
The intake of this traditional Andean maize is mostly in the mature dried form. Andean farmers still maintain the postharvest traditional practices along the Cabanita maize production chain. Once the maize ears have reached their highest length and a certain moisture level which is subjectively measured based on the farmer’s experience, the plants are cut and dried in a piled form in the same land. Thereafter, dried plants are transported to the farmer’s warehouses where maize ears are unshelled and exposed directly to the sun until complete drying [19]. Dried grains are then consumed roasted, or further milled and used as flour in different culinary preparations.
Several studies focused mostly on sweet and waxy maize improved varieties have shown that bioactive compounds such as phenolic antioxidants and carotenoids vary depending on the kernel maturation stage. Phenolic compounds such as anthocyanins from different Asian colored waxy maize genotypes increased along the kernel maturation from 20 to 35 days after pollination (DAP) [21]. Similarly, Zhang et al. [ 22] observed an increase of the total phenolic contents (TPC) in mature kernels from a yellow maize variety (48 DAP). The increase of carotenoids such as lutein, zeaxanthin, α-cryptoxanthin, β-cryptoxanthin has also been reported during the kernel maturation of some sweet maize varieties from China [23]. On the contrary, the total phenolic and total carotenoids contents decreased at the end of grain maturation in yellow maize bred in United States (116 DAS, days after seeding) [24]. These discrepancies reveal that different factors including genetic factors (variety), the time of harvest, and the agroecological conditions of maize cultivation may influence the bioactive composition during the maize kernel maturation. Consequently, the research on this topic should be performed case by case, according to specific ecological environments.
As a second stage follow up studies of previous advances to characterize the Peruvian Andean maize Cabanita [19], the objective of current research was to study the primary (polar compounds and fatty acids) and secondary metabolite composition (free and bound phenolics and carotenoid compounds), and the in vitro health-relevant functional properties of three selected Cabanita types (white, red, and orange pigmented kernels) harvested at different maturity stages, using targeted and untargeted metabolomic platforms. The in vitro model based functionality of Cabanita maize was evaluated in relation to its antioxidant potential and inhibitory activity against key digestive enzymes (α-amylase and α-glucosidase) relevant for hyperglycemia modulation. Results from this study will contribute with important biochemical and metabolomic information for the characterization, and conservation of the maize race Cabanita. In addition, information from this research would be important to diversify the consumption options of this Andean maize beyond the traditional mature form. This would likely lead to potential beneficial effects of food crops at health and economical levels among indigenous communities in the future.
## 2.1. Cultivation of Cabanita maize and sampling
The germplasm of Cabanita maize (Zea mays L.) collected in a previous study was used [19]. Maize with sample codes CAW, CCR, COM representing white, red, and orange kernels were selected for current study considering the pigmentation diversity found in Cabanita maize race [19]. These maize samples were obtained from the province of Caylloma (Cabanaconde district) located in the southern Andean region of Arequipa in Peru and were stored under refrigeration (2–5°C) [19]. The field experiment was performed in the nursery garden Santa Maria at the Universidad Catolica de Santa Maria located in the Sachaca district (S: 16° 41′ 93.9″; W: 071° 56′ 34.0″; 2,240 meters of altitude), province of Arequipa (Arequipa, Peru).
Cabanita maize was cultivated in 20 L pots under open air and sun light exposure from 25 November 2020 to 28 June 2021. Commercial prepared soil (containing humus, field soil, and manure) was used and its physico-chemical characteristics are shown in Supplementary Table S1. The meteorological conditions during the maize plant development until sample harvest are shown in Supplementary Table S2. Groups of 6 pots were sown in three consecutive weeks (total 18 pots per maize type) and 5 Cabanita seeds were sown in each pot (sowing dates: 25 November, 2 December, and 9 December 2020). This procedure was applied to ensure the number of biological replicates (four) at three maturity stages per type of maize (white, red, orange) for the current study. The group of ears harvested from a single pot was considered a biological replicate (from 1 to 4 ears were obtained per pot). Additional supplementation with commercial fertilizers (urea and NPK + micronutrients) was performed during the vegetative period of maize plants (from week 2 to 11 after sowing) and the phytosanitary control was undertaken using conventional practices for the cultivation of maize in combination with the use of ecological insect traps. Well water was used for the irrigation which was carried out under field capacity in a similar way as in field cultivation.
During the plant reproductive stage, the female inflorescences were promptly protected with a plastic bag until the emergence of the styles (silks). The pollination was manually developed using composite pollen collected from mature tassels (male inflorescence) of plants from the same maize type. Once pollinated, each ear was protected with paper bags until physiological maturity. This procedure avoided the cross-pollination among different maize types. Ears were collected at three different grain maturity stages according to the grain physical appearance and moisture contents [25, 26]. The milky stage (S1) is characterized by the starch accumulation and the observation of a milky white fluid upon finger pressure [25]. In the dough stage (S2), the grain is still soft and humid, with intermediate humidity, whereas the physiological mature stage (S3) corresponds to the completion of kernel development, and a black layer is formed at the base of the kernel [25, 26]. In case of the white maize type, S1 corresponded to 28 DAP and $79\%$ moisture, S2 to 39 DAP and $68\%$ moisture, and S3 to 75 DAP and $45\%$ moisture. For the red type maize, S1 was at 33 DAP and $74\%$ moisture, S2 at 36 DAP and $68\%$ moisture, and S3 was at 77 DAP and $45\%$ moisture. In the orange type, S1 corresponded to 32 DAP and $75\%$ moisture, S2 to 43 DAP and $64\%$ moisture, and S3 to 76 DAP and $46\%$ moisture.
After harvest, ear samples were immediately stored under refrigeration (5–8°C) and transported to the laboratory. Husks were eliminated, and samples (ear and kernels) were evaluated in relation to their physical characteristics as will be described in next section. Kernels were separated, pooled per biological replicate, and frozen (−20°C). This process was developed within the 24 h after harvest. Afterwards, samples were freeze-dried in a FreeZone benchtop freeze dryer (Labconco, Kansas, MO, United States) for 60 h, at –40°C, and 0.008 mbar of vacuum pressure. Then, dried kernels were milled in a A11 Basic analytical mill (IKA, Germany) with liquid nitrogen to a powdered flour, packed in 50 ml polypropylene tubes protected from light, and stored at –20°C until analysis.
## 2.2. Enzymes and reagents
Baker yeast α-glucosidase (EC 3.2.1.20), and porcine pancreas α-amylase (EC 3.2.1.1) were from Sigma-Aldrich (St. Louis, MO, United States). Phenolic standards (gallic acid, vanillic acid, caffeic acid, ferulic acid, p-coumaric acid, cyanidin chloride, and quercetin aglycone), carotenoid standards (lutein, zeaxanthin, β-cryptoxanthin), and the Folin–Ciocalteu reagent were from Sigma-Aldrich. The (±)-6-hydroxy-2,5,7,8-tetramethyl-chromane-2-carboxilic acid (Trolox), and the 2,2-diphenyl-1-picrylhydrazyl (DPPH˙), and 2–2′-azino-bis(3ethylbenothiazoline-6-sulfonic acid) (ABTS·+) radicals were purchased from Sigma-Aldrich. Pyridine, phenyl-β-d-glucopyranoside, methoxyamine hydrochloride, N,O-bis(trimethylsilyl)trifluoroacetamide (BSTFA), and methyl undecanoate were from Sigma-Aldrich.
## 2.3. Physical measurements and moisture determination
Relevant physical descriptors were evaluated in fresh harvested ears and kernels (per maturity stage and maize type) per replicate according to the International Board for Plant Genetic Resources [27]. The weight (g), length (cm), and central diameter (cm) were measured in ears whereas the length (mm), width (mm), and thickness (mm) were determined in kernels. The kernel moisture was monitored periodically to characterize the maturity stage for harvest and was determined by a gravimetric method at 105°C [28].
## 2.4.1. Free and bound phenolic fractions
The extraction of phenolic compounds from the lyophilized maize samples were performed according to Ranilla et al. [ 29] with some modifications. An amount of 1 g of maize sample was mixed with 4 ml of $0.1\%$ HCl methanol/acetone/water (45, 45, 10, v/v/v) for the extraction of the free phenolic fraction. The bound phenolic compounds were released from the insoluble free-phenolic residue by alkaline hydrolysis with 3 N NaOH following same procedure as Ranilla et al. [ 29]. Final extracts were reconstituted in milliQ water and stored at-20°C until analysis.
## 2.4.2. Carotenoids
The procedure of Fuentes-Cardenas et al. [ 19] was followed. A saponification process was first applied with $80\%$ KOH (w/v) and methanol:ethyl acetate (6, 4, v/v) solvent was used for the carotenoid extraction until a clear final extract was obtained. Carotenoids were extracted under light and oxygen protection and analyzed by ultra high-performance liquid chromatography (UHPLC) after the extraction process the same day.
## 2.5.1. Analysis of phenolic compounds by ultra high-performance liquid chromatography
Free and bound phenolic extracts were filtered using a polyvinylidene difluoride filters (PVDF, 0.22 μm) and the separation was carried out in a Kinetex C18 reverse-phase analytical column (100 × 2.1 mm i.d., 1.7 μm) with a Kinetex C18 guard column (5 × 2.1 mm i.d., 1.7 μm) (Phenomenex Inc., Torrance, CA, United States). The injection volume was 5 μl and samples were injected at 0.2 ml/min flow rate in an Ultimate 3,000 RS UHPLC system (Thermo Fisher Scientific, Waltham, MA, United States) with a diode array detector, a quaternary pump, an autosampler, and column oven. Acetonitrile and $0.1\%$ formic acid in water were used as mobile phases and the same gradient and chromatographic parameters reported by Ranilla et al. [ 29] and Vargas-Yana et al. [ 30] were applied. Eluates were monitored from 200 to 600 nm. The Chromeleon SR4 software version 7.2 (Thermo Fisher Scientific) was used for chromatograms and data processing. The identification of phenolic compounds was based on their retention times and ultraviolet–visible spectra characteristics compared with those of the library data and external standards. Calibration curves with external standards were used for the quantification of phenolic compounds (r2 ≥ 0.9990). Hydroxybenzoic phenolic acids (HBA) (unidentified 1 and 2) were quantified at 280 nm and expressed as vanillic acid. Vanillic acid derivatives (with similar UV–VIS spectra as that of vanillic acid but with different retention times) were expressed also as vanillic acid. Hydroxycinnamic phenolic acids (HCA) including ferulic, p-coumaric, and caffeic acid derivatives were quantified at 320 nm and expressed as ferulic, p-coumaric, and caffeic acids, respectively. Flavonoids such as luteolin derivatives (with similar UV–VIS spectra as that of luteolin, but with different retention times), and anthocyanins were detected at 360 and 525 nm, and quantified using quercetin aglycone and cyanidin chloride external standards, respectively. All results were expressed as mg per 100 g DW (dried weight).
## 2.5.2. Analysis of carotenoid compounds by UHPLC
The analysis of carotenoid compounds was performed with a YMC carotenoid C30 reverse-phase analytical column (150 × 4.6 mm i.d., 3 μm) coupled to a YMC C30 guard column (10 × 4.0 mm, 3 μm) (YMC CO., LTD, Japan) using the same UHPLC system as previously described for the phenolic compound analyses. Filtered carotenoid extracts (0.22 μl, PVDF filter) were injected at 1.7 ml/min flow rate and monitored at 450 nm. A ternary gradient elution was used (methanol, dichloromethane, acetonitrile) and same reverse-phase chromatographic conditions as those reported by Fuentes-Cardenas et al. [ 19] were applied. The retention time and UV–VIS spectra characteristics of external carotenoid standards and the library data were used for the identification of carotenoid compounds in evaluated samples. In addition, the information of carotenoid analyses in other maize samples from reported literature was also useful for the identification of carotenoid isomers. Calibration curves made with external standards were used for the quantification of carotenoids (r2 ≥ 0.9900) and results were presented as μg per g sample DW. Lutein and zeaxanthin compounds and their isomers were quantified as lutein and zeaxanthin, respectively. Unidentified carotenoid compounds, neoxanthin, and violaxanthin isomers were expressed as lutein. β-cryptoxanthin isomers were expressed as β-cryptoxanthin.
## 2.5.3. Analysis of the total phenolic contents
The TPC in the free and bound phenolic extracts were evaluated according to Singleton and Rossi [31] using the Folin–Ciocalteu method. Results were presented as mg of gallic acid equivalents (GAE) per 100 g DW.
## 2.5.4. Fatty acids profiles by gas-chromatography with flame ionization detector
The analysis was adapted from Uarrota et al. [ 32]. The fatty acid methyl ester synthesis (FAME) was obtained by combining ~55 mg of lyophilized maize sample with 70 μl 10 N KOH (prepared in HPLC water) and 530 μl of HPLC grade methanol in a reaction tube. The mix was incubated in a water bath at 55–60°C for 1.5 h with periodic agitation every 30 min. Tubes were then cooled down to room temperature and drops of fuming H2SO4 (24 N) were carefully added. An incubation step was repeated as previously described. Samples were cooled, then 500 μl of hexane and 10 μl of internal standard (methyl undecanoate, 26.16 mg/ml) were added. The tubes were vortexed for 2 min and centrifuged at 17,000 g for 10 min at 4°C. The upper layer was transferred to a vial with an insert and 1 μl was injected in an Agilent 7890B gas chromatography system coupled to a flame ionization detector (FID) (Agilent Technologies, Santa Clara, CA, United States). A 2560 capillary gas-chromatography (GC) column (100 m × 250 μm × 0.2 μm) (Supelco, Bellefonte, PA, United States). The injector temperature was set at 220°C, the FID detector at 225°C, air flow (400 ml/min), hydrogen flow (35 ml/min), helium flow (1.6 ml/min), using an injection with a split ratio of 50:1. The chromatographic run was set up at 80°C (initial temperature) and increased to 225°C with a heating ramp at a rate of 25°C per min, and held for 25 min. The retention times of detected peaks from samples were compared with those of external standards for fatty acid identification. Calibration curves (r2 ≥ 0.9900) with palmitic, stearic, oleic, linoleic, and α-linolenic acids were used for the quantification of fatty acids in maize samples and results are presented as mg per g sample DW.
## 2.6. Untargeted metabolomic analysis of polar compounds by gas chromatography mass spectrometry
The extraction of polar metabolites from maize samples, the derivatization process, and the instrumental parameters for the gas chromatography mass spectrometry (GC–MS) analysis were the same as reported by Fuentealba et al. [ 33]. An Agilent 7890B gas chromatography system equipped with a 5977A single quadrupole MS, a PAL3 autosampler, an electron impact ionization source was used (Agilent Technologies). A HP-5 ms Ultra Inert column (30 m × 0.25 mm × 0.25 μm) (Agilent) was used for the separation of polar compounds. The Mass Hunter Quantitative software (Agilent Technologies) was used for the deconvolution and data processing. For peak identification, their retention times and mass spectra were compared with data from NIST14 and a home library (obtained with commercial standards). Results are shown as the relative response of each compound calculated considering their respective sample weight, an internal standard (phenyl-β-d-glucopyranoside), and a quality control (QC) composite sample from all maize samples [33].
## 2.7.1. Extraction of hydrophilic and lipophilic fractions
The soluble hydrophilic and lipophilic fractions from lyophilized maize samples were considered for the in vitro assays. The hydrophilic and lipophilic fractions were extracted with $80\%$ methanol and dichloromethane; respectively, following same extraction parameters described by Fuentes-Cardenas et al. [ 19].
## 2.7.2. Antioxidant capacity by the 2,2-diphenyl-1-picrylhydrazyl radical scavenging assay
The method of Duarte-Almeida et al. [ 34] adapted to a microplate reader (Biotek Synergy HTX, Agilent Technologies) with modifications reported by Fuentes-Cardenas et al. [ 19] was applied. Results are shown as μmol Trolox equivalents per 100 g DW using Trolox calibration curves prepared in methanol (20–160 μM), and dichloromethane (10–120 μM) for the hydrophilic and lipophilic fractions, respectively.
## 2.7.3. Antioxidant capacity by the 2.2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) radical cation (ABTS·+) scavenging assay
The hydrophilic and lipophilic extracts were evaluated according to Fuentealba et al. [ 35] using a Biotek Synergy HTX microplate reader (Agilent Technologies). Results were expressed as μmol Trolox equivalents per 100 g DW based on calibration curves built with Trolox standard in methanol and dichloromethane for the evaluation of hydrophilic and lipophilic fractions, respectively. The Trolox concentration ranges used in calibration curves were the same as those shown for the DPPH method.
## 2.7.4. α-Glucosidase and α-amylase inhibitory activity
The hydrophilic and lipophilic fractions used for the determination of the α-amylase inhibitory activity were obtained similarly as Fuentes-Cardenas et al. [ 19] but using a different sample and solvent ratio for the extraction (0.5 g sample in 12.5 ml $80\%$ methanol). Final extracts (hydrophilic and lipophilic) were vacuum-evaporated to dryness at 45°C and reconstituted in 2 ml 0.02 M NaPO4 buffer (pH 6.9) [35]. In case of the α-glucosidase inhibition analysis, same extraction conditions were assayed as Fuentes-Cardenas et al. [ 19], and final hydrophilic and lipophilic extracts were also vacuum-evaporated to dryness but resuspended in 1 ml of 0.1 M KPO4 buffer (pH 6.9) [35]. The inhibitory activity against α-amylase and α-glucosidase enzymes were determined with the same methodology reported by Gonzalez-Muñoz et al. [ 36]. The percentage (%) of inhibition at different sample amounts was reported.
## 2.8. Statistical analysis
Results (from four independent biological replicates) were expressed as means ± standard deviation. A two-way analysis of variance (ANOVA) with the LSD test were carried out to determine significant differences between the means ($p \leq 0.05$) using the software Infostat1 (accessed from October to November, 2022). Pearson correlations among all data were explored using the Statgraphics Centurion XVI software (StatPoint Inc., Rockville, MD, United States). All data (from the targeted and untargeted metabolite analyses, the physical characteristics, and the functionality assays) were evaluated through the multivariate principal component analysis (PCA) using the Metaboanalyst software version 5.02 (accessed on 2 October, 2022). For the PCA, data were first mean-centered and divided by the standard deviation of each variable. Afterward, an ANOVA with the Tukey’s HSD post hoc analysis ($p \leq 0.01$) was carried out to identify significant variables. The heat map or cluster analysis was performed using the Euclidean distance and the Ward algorithm in Metaboanalyst with the top significant metabolites or variables ($p \leq 0.01$).
## 3.1. Physical changes of Cabanita maize types at different maturity stages
The maturity stages of Cabanita maize characterized by the DAP and moisture levels were similar among evaluated maize types. However, their vegetative periods (from the plant emergence stage to the end of the tasseling time) were somewhat different [26]. This period occurred at around 9, 10, and 11 weeks after the sowing stage in case of the orange, white, and red maize types, respectively. Consequently, the start of the reproductive period (emergence of the silk or the female inflorescence) was earlier in the orange maize type (13 weeks after sowing), followed by the white (15 weeks after sowing), and the red maize (16 weeks after sowing). This may be important for the adequate planning of maize cultivation periods since Andean farmers traditionally sow maize mixing different Cabanita types in the same land.
The physical changes of ears and kernels from the three types of Cabanita maize along the maturity stages are shown in Figures 1–3. The development of all maize types was characterized by changes in the pericarp color. The white maize type varied from light-white to white-yellow at S3 which may be related to the accumulation of dry matter with maturity (Figure 1) [37]. In case of the red type, the pigmentation appeared in the S2 stage as a small spot at the stigma-end of the kernel that then extended toward almost the half of the grain at S3 (Figure 2). Hong et al. [ 38] observed a similar trend in a purple-pericarp sweet corn; however, the purple pigment fully spread until the base of the kernel at the highest maturity phase (32 DAP). The orange maize varied from light-yellow at S1 to orange at S3 and this pigmentation only reached the middle of the kernels similarly as in the red case.
**Figure 1:** *Changes of ear (A) and kernel (B) physical characteristics of white Cabanita maize at different maturity stages (S1, S2, S3, from left to right).* **Figure 2:** *Changes of ear (A) and kernel (B) physical characteristics of red Cabanita maize at different maturity stages (S1, S2, S3, from left to right).* **Figure 3:** *Changes of ear (A) and kernel (B) physical characteristics of orange Cabanita maize at different maturity stages (S1, S2, S3, from left to right).*
Table 1 shows the physical characteristics evaluated in kernels sampled at different developmental stages. No significant interaction between the maize type (M) and the maturity stage (S) factors was found in any of the measured physical parameters. The variation of the ear weight and diameter, and the kernel width along the maturation period was similar in all maize types. Nevertheless, the ear length, kernel length, and thickness were influenced by the maize type. The ear length was higher in the white and red maize than in the orange type, but this latter showed higher kernel length ranges than the former. The maturity stage factor (S) was significant in all the physical characteristics except the ear length which remained almost similar during the maturation. The ear weight, and diameter along with the kernel length, width, and thickness increased with maturation. Overall, the yield-relevant physical parameter (ear weight) was similar among all maize classes; however, some morphological differences have been observed among the kernel types (Figures 1–3). Fuentes-Cardenas et al. [ 19] also studied the Peruvian maize race Cabanita and reported no differences in the quantitative physical characteristics between the CAW (white), CCR (red), and COM (orange) maize samples (which are the parental seeds of the white, red, and orange maize types evaluated in the current study). However, mature, and dried ears and kernels were evaluated in such study.
**Table 1**
| Maize type | Stage | Ear (cm) | Ear (cm).1 | Ear (cm).2 | Kernel (mm) | Kernel (mm).1 | Kernel (mm).2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Maize type | Stage | Weight | Length | Diameter | Length | Width | Thickness |
| White | S1 | 70.4 ± 19.9c | 9.1 ± 0.7ab | 4.2 ± 0.4f | 0.80 ± 0.04e | 0.79 ± 0.07d | 0.59 ± 0.04e |
| White | S2 | 150.0 ± 72.9a | 9.9 ± 1.2a | 5.3 ± 0.2abcd | 1.11 ± 0.06 cd | 0.80 ± 0.05 cd | 0.61 ± 0.02de |
| White | S3 | 138.4 ± 51.7ab | 9.6 ± 0.7ab | 5.6 ± 0.7abc | 1.44 ± 0.10b | 0.97 ± 0.10ab | 0.71 ± 0.10abcd |
| Red | S1 | 88.8 ± 21.5bc | 8.5 ± 0.9abc | 4.6 ± 0.5def | 0.95 ± 0.15de | 0.83 ± 0.17bcd | 0.63 ± 0.08cde |
| Red | S2 | 106.9 ± 36.6abc | 9.9 ± 1.8a | 5.1 ± 0.3 cde | 0.93 ± 0.12e | 0.89 ± 0.11abcd | 0.74 ± 0.08ab |
| Red | S3 | 144.1 ± 64.6ab | 9.3 ± 0.4ab | 5.9 ± 0.8a | 1.43 ± 0.15b | 0.95 ± 0.14abc | 0.73 ± 0.07ab |
| Orange | S1 | 77.0 ± 22.8c | 8.1 ± 1.2bc | 4.5 ± 0.6ef | 1.19 ± 0.06c | 0.82 ± 0.06bcd | 0.66 ± 0.04bcde |
| Orange | S2 | 76.0 ± 16.8c | 7.4 ± 0.9c | 5.1 ± 0.3bcde | 1.21 ± 0.10c | 0.82 ± 0.07 cd | 0.72 ± 0.01abc |
| Orange | S3 | 121.1 ± 18.3abc | 8.6 ± 0.7abc | 5.8 ± 0.5ab | 1.65 ± 0.19a | 1.03 ± 0.11a | 0.77 ± 0.10a |
| F value | Maize (M) | 1.49ns | 7.23** | 0.16ns | 17.32**** | 0.46ns | 4.40* |
| F value | Stage (S) | 5.75** | 0.99ns | 19.68**** | 66.24**** | 9.29*** | 7.75** |
| F value | M × S | 1.09ns | 1.42ns | 0.50ns | 2.32ns | 0.60ns | 0.66ns |
## 3.2. Phenolic contents and profiles of Cabanita maize types at different maturity stages
The phenolic profiles and contents determined in the free and bound phenolic fractions of Cabanita maize samples are shown in Table 2. In case of the free phenolic fraction, all maize samples contained phenolic acids such as hydroxybenzoic (HBA) and hydroxycinnamic acids (HCA), but specific flavonoid types such as anthocyanin and luteolin derivatives were detected only in red and orange maize types, respectively.
**Table 2**
| Fraction | Compound | White | White.1 | White.2 | Red | Red.1 | Red.2 | Orange | Orange.1 | Orange.2 | F-value | F-value.1 | F-value.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Fraction | Compound | S1 | S2 | S3 | S1 | S2 | S3 | S1 | S2 | S3 | Maize (M) | Stage (S) | M × S |
| Free | HBA-1 | 30.6 ± 11.2a | 16.6 ± 2.6c | 2.8 ± 1.0d | 23.2 ± 4.7abc | 22.2 ± 1.6bc | 2.5 ± 1.8d | 27.6 ± 9.5ab | 17.4 ± 1.3c | 5.9 ± 1.8d | 0.10ns | 58.83**** | 1.78ns |
| Free | HBA-2 | 0.06 ± 0.05a | ND1 | ND | ND | ND | ND | 0.11 ± 0.08a | ND | ND | | | |
| Free | Vanillic acid derivatives2 | 13.6 ± 1.4a | 5.6 ± 1.2c | 2.0 ± 1.1d | 10.8 ± 3.5ab | 9.6 ± 1.2b | 2.5 ± 0.5 cd | 11.4 ± 4.8ab | 5.2 ± 1.2c | 2.5 ± 0.5 cd | 0.98ns | 58.05**** | 2.88* |
| Free | Total HBA | 44.2 ± 11.6a | 22.2 ± 3.1c | 4.8 ± 2.0d | 34.1 ± 7.8b | 31.8 ± 1.9bc | 5.1 ± 2.2d | 39.2 ± 14.2ab | 22.6 ± 2.1c | 8.4 ± 1.8d | 0.01ns | 69.29**** | 2.47* |
| Free | p-Coumaric acid derivatives3 | 0.8 ± 0.3 cd | 0.6 ± 0.2 cd | 2.2 ± 0.9a | 0.4 ± 0.1 cd | 0.3 ± 0.1d | 1.1 ± 0.4bc | 0.6 ± 0.2 cd | 0.53 ± 0.04 cd | 1.6 ± 0.8ab | 5.39* | 22.38**** | 1.02ns |
| Free | Ferulic acid derivatives4 | 3.3 ± 0.5a | 1.4 ± 0.2c | 1.7 ± 0.5c | 2.5 ± 0.5b | 2.8 ± 0.5ab | 1.7 ± 0.8c | 2.5 ± 0.5b | 1.2 ± 0.3c | 1.7 ± 0.4c | 3.96* | 18.79**** | 6.44*** |
| Free | Caffeic acid derivatives5 | ND | 0.2 ± 0.1bc | 1.2 ± 0.6a | ND | ND | 1.5 ± 0.6a | 0.03 ± 0.00bc | 0.02 ± 0.00c | 0.6 ± 0.6Bb | | | |
| Free | Total HCA | 4.1 ± 0.8abc | 2.1 ± 0.2de | 5.1 ± 1.3a | 3.0 ± 0.6 cde | 3.1 ± 0.5bcd | 4.3 ± 1.0ab | 3.1 ± 0.6bcd | 1.7 ± 0.3e | 3.8 ± 1.7bc | 3.07ns | 17.15**** | 1.78ns |
| Free | Luteolin derivatives6 | ND | ND | ND | ND | ND | ND | 22.7 ± 12.5a | 11.3 ± 4.9ab | 5.1 ± 4.4b | | | |
| Free | Total anthocyanins7 | ND | ND | ND | 0.6 ± 0.2a | 1.4 ± 1.3a | 14.5 ± 18.7a | ND | ND | ND | | | |
| Free | Total flavonoids | ND | ND | ND | 0.6 ± 0.2b | 1.4 ± 1.3b | 14.5 ± 18.7ab | 22.7 ± 12.5a | 11.3 ± 4.9ab | 5.1 ± 4.4b | | | |
| Free | Total UHPLC free | 48.3 ± 11.6b | 24.3 ± 3.2 cde | 10.0 ± 3.3f | 37.6 ± 8.2bc | 36.2 ± 3.5bcd | 23.8 ± 17.7de | 65.0 ± 14.1a | 35.6 ± 4.8bcd | 17.3 ± 3.9ef | 4.80* | 38.22**** | 4.11* |
| Free | Free – TPC8 | 57.9 ± 17.3ab | 27.0 ± 3.2c | 32.3 ± 3.3c | 38.0 ± 6.5bc | 44.6 ± 6.5abc | 53.6 ± 26.1ab | 53.0 ± 11.4ab | 37.6 ± 6.2bc | 58.7 ± 23.5a | 1.72ns | 3.13ns | 2.93* |
| Bound | p-Coumaric acid | 4.9 ± 1.2bc | 5.2 ± 1.1bc | 7.5 ± 1.9ab | 6.4 ± 4.3abc | 4.1 ± 1.1c | 9.4 ± 2.6a | 5.9 ± 0.5bc | 7.8 ± 2.4ab | 7.2 ± 2.2abc | 0.77ns | 4.28* | 1.98ns |
| Bound | Ferulic acid | 163.3 ± 7.4abc | 150.1 ± 14.9bc | 133.2 ± 21.2c | 161.9 ± 42.8abc | 142.5 ± 17.7c | 177.2 ± 12.0ab | 190.3 ± 11.8a | 179.3 ± 23.5ab | 177.5 ± 29.2ab | 6.84** | 1.29ns | 1.64ns |
| Bound | Ferulic acid derivatives4 | 8.5 ± 0.7d | 10.8 ± 0.5d | 11.6 ± 6.0d | 12.0 ± 3.0 cd | 10.6 ± 4.1d | 19.4 ± 2.4ab | 13.1 ± 1.2 cd | 16.7 ± 6.2bc | 22.0 ± 1.9a | 11.92*** | 11.08*** | 1.75ns |
| Bound | Total UHPLC bound | 176.7 ± 7.9abc | 166.1 ± 16.4bc | 152.3 ± 28.4c | 180.3 ± 49.8abc | 157.1 ± 21.9c | 206.0 ± 12.4a | 209.4 ± 13.2a | 203.8 ± 29.4ab | 206.6 ± 29.7a | 7.68** | 0.97ns | 1.72ns |
| Bound | Bound – TPC8 | 155.8 ± 4.5bc | 144.3 ± 10.1c | 142.2 ± 38.0c | 146.0 ± 23.4c | 122.6 ± 35.1c | 187.0 ± 7.0ab | 181.3 ± 14.2ab | 188.1 ± 29.9ab | 197.1 ± 21.8a | 11.23*** | 3.12ns | 2.72ns |
| Total (free + bound) | UHPLC TPC | 225.0 ± 9.2bcd | 190.4 ± 14.2de | 162.2 ± 29.2e | 217.9 ± 44.4bcd | 193.4 ± 21.3cde | 229.8 ± 22.4bc | 274.4 ± 26.8a | 239.4 ± 27.4ab | 223.9 ± 26.8bcd | 12.45*** | 6.11** | 2.70ns |
| Total (free + bound) | TPC8 | 213.7 ± 18.8bcd | 171.4 ± 8.7e | 174.5 ± 35.8de | 184.0 ± 19.5cde | 167.2 ± 37.6e | 240.7 ± 29.1ab | 234.3 ± 23.2ab | 225.7 ± 32.1abc | 255.8 ± 39.4a | 10.90*** | 4.67* | 3.14* |
For the HBA group, the contents were more influenced by the maturity stage (S) than by the maize type (M). The interaction of both factors (M × S) was significant on the vanillic acid derivatives and the total HBA contents. The highest total HBA contents were observed at stage S1, and white and orange maize had higher levels than red maize (44.2, 39.2, and 34.1 mg/100 g DW, for the white, orange, and red maize, respectively). With the kernel maturation, the total HBA concentrations decreased around 80–$90\%$ in all cases (from S1 to S3). At least 3 classes of HBA have been detected in all Cabanita samples (Supplementary Figures S1–S3). Major HBA at S1 was HBA-1 (λmax = 279 nm), followed by vanillic acid derivatives (λmax = 249, 289 nm). Contents of both HBA then decreased with maturity to reach similar concentrations at S3. A minor HBA compound (HBA-2) was only found at S1 in white and orange maize types. Giordano et al. [ 39] reported that the free vanillic acid levels found in open-pollinated maize varieties from Italy with variable kernel pigmentations decreased from 1.8–15 to 0–0.08 mg/100 g DW when maturation stages varied from 5 to 76 DAS, respectively. Similarly, the contents of vanillic and protocatechuic acids significantly decreased or disappeared with the grain development of waxy maize from 86–109 to 110–138 DAS [37]. Besides vanillic acid, syringic and p-hydroxybenzoic acids have been also reported in maize [40, 41]. Total free HBA ranges from current study (4.8–44.2 mg/100 g DW) were comparable to levels found in US yellow and Indian specialty maize kernels (~33.7 and 2.7–38 mg/100 g DW, respectively) [40, 41]. Other cereals such as barley, wheat, and oat have shown lower free HBA concentrations (~15.5, 12.5, and 4.6 mg/100 g, respectively) [40].
A variable trend was observed in case of the HCA group (Table 2 and Supplementary Figures S4–S6). All Cabanita maize types contained p-coumaric, and ferulic acid derivatives whereas caffeic acid derivatives were detected at some maturity stages. These HCA derivatives may be soluble conjugated phenolic acids such as hydroxycinnamic acid amides (HCAAs) as was previously reported in different cereals [42, 43]. Several HCAAs derived mostly from p-coumaric, ferulic and caffeic acids (N,N-di-p-coumaroylspermine, N-p-coumaroyl-N-feruloylputrescine, caffeoylputrescine) have been previously reported in the free phenolic fraction of maize from different origins [44, 45]. Hence, further studies are necessary to better identify the HCA derivatives found in current research. The maturity (S) and maize type (M) showed an important effect on p-coumaric and ferulic acid derivatives, but the interaction of both factors was significant only on the ferulic acid derivatives contents. p-Coumaric acid and caffeic acid derivatives increased with kernel development. The increase of p-coumaric acid derivatives levels from S1 to S3 was on average 2.6-fold, and white and orange maize types exhibited higher ranges than the red maize (0.8–2.2, 0.6–1.6, and 0.4–1.1 mg/100 g DW for white, orange, and red maize, respectively). Conversely, the concentrations of ferulic acid derivatives declined by 32–$48\%$ from S1 to S3 in all maize groups. Different studies have shown variable tendencies of the HCA compounds with kernel maturity. The free ferulic and chlorogenic acid contents reduced with kernel maturation in several Italian maize varieties, and the same trend was observed in Chinese waxy pigmented maize samples with ferulic and p-coumaric acids [37, 39]. Recently, Hu et al. [ 46] observed an overall increment of ferulic and p-coumaric acids during kernel maturation of sweet maize from China, whereas chlorogenic acid declined (from 15 to 30 DAP) after an initial increase (from 10 to 15 DAP). In the current study, the total HCA contents first decreased from S1 to S2 in white and orange maize types, then increased at S3 in all cases. The origin, maize type (genetic factors), and the harvesting time may explain differences found in this study.
Anthocyanins were present only in red maize, showing an increase from 0.6 mg/100 g DW at S1 to 14.5 mg/100 g DW by the end of kernel maturity. Other flavonoids such as luteolin derivatives were specific for orange maize samples and significantly decreased by $80\%$ from S1 to S3 (from 22.7 to 5.1 mg/100 g DW). No flavonoids were detected in white Cabanita. Hong et al. [ 38] observed a continuous anthocyanin accumulation from 105 mg/100 g DW at 20 DAP to 179 mg/100 g DW at 36 DAP in purple-pericarp “supersweet” sweet maize. The increase of the total monomeric anthocyanin contents with kernel ripening has been also confirmed by different studies [21, 37, 47]. The flavone luteolin has been reported in Indian Himalayan pigmented maize accessions and some Chinese maize hybrids [48, 49]. In addition, a C-glycosylflavone known as maysin (a luteolin derivative) has also been found in mature maize seeds [49]. The luteolin derivatives found in the current study (λmax = 256, 270, 349 nm) may be maysin or similar compounds that should be confirmed in future studies. However, higher concentrations of these flavones were found in the orange Cabanita maize at all maturity stages compared to levels obtained by Zhang et al. [ 49] (1.13 ng/g DW of maysin in mature seeds). C-glycosylflavones have shown potential neuroprotective properties relevant for Alzheimer’s disease prevention [50, 51].
The total free phenolic fraction decreased from S1 to S3, and its composition was variable depending on the kernel stage and maize type. HBA were the most important compounds in white and red maize at S1 and S2. In case of the orange group, HBA and luteolin derivatives highly contributed to the total free phenolic fraction at S1 and S2. This maize showed the highest total free phenolic contents at S1 among all samples (65 mg/100 g DW). The red maize was rich in HBA at S1, and S2 whereas anthocyanins were the major contributors to the free phenolic fraction at S3.
Major compound in the bound phenolic fraction was ferulic acid, followed by ferulic acid derivatives, and p-coumaric acid (Supplementary Figures S7–S9). The M × S interaction was not significant for any of the bound phenolic compounds; however, the maize type showed an important effect on the ferulic acid, ferulic acid derivatives, and the total bound UHPLC phenolic contents (Table 2). Orange and red maize types had higher ranges of ferulic acid (177.5–190.3 mg/100 g DW, 142.5–177.0 mg/100 g DW, and 133.2–163.3 mg/100 g DW, for the orange, red, and white maize, respectively), and ferulic acid derivatives than the white group (13.1–22.0 mg/100 g DW, 10.6–19.4 mg/100 g DW, and 8.5–11.6 mg/100 g DW, for the orange, red, and white maize, respectively). Consequently, higher total bound phenolic levels determined by UHPLC were found in orange and red maize, specially at S3 than in the white type. Kernel maturity highly influenced the p-coumaric acid and ferulic acid derivatives contents. Both compounds showed an increase of around 1.2–1.7-fold from S1 to S3. Ferulic acid remained almost stable from S1 to S3 in white and orange maize samples, and a similar trend was observed in their total UHPLC bound phenolic contents. In case of the red maize, ferulic acid and the total bound phenolic compounds first decreased from S1 to S2, to further increase at S3. Similar results as those obtained for white and orange Cabanita maize have been reported by Zhang et al. [ 22] in yellow maize. In that study, the total bound phenolic contents were stable with kernel maturation from 15 to 48 DAP [22]. Nonetheless, the levels of bound ferulic and p-coumaric acids significantly reduced with kernel development (from 5 to 76 DAS) in several Italian maize samples whereas in other research same bound HCA showed a variable tendency depending on the genotype [39, 46].
On the whole, these results suggest differences in the metabolism of phenolic compounds during kernel development among the three types of Cabanita maize. A possible metabolic flux of precursors of hydroxybenzoic acids such as some intermediates of the shikimate or the phenylpropanoid pathways toward the biosynthesis of HCA derivatives may occur in case of the white and orange grains [52]. HCA may be used as precursors for the biosynthesis of anthocyanins in case of the red maize. Enzymes involved in the biosynthesis of cell wall-relevant phenolic compounds may have been upregulated toward the flavone pathway in the orange maize explaining its overall higher ranges of bound phenolic compounds through the kernel growing process [53, 54].
Ultra high-performance liquid chromatography total phenolic contents (free+bound) declined with kernel maturity in white and orange grains whereas in red maize the contents first decreased from S1 to S2, and then increased at S3. Concentrations at the physiological maturity stage were higher in the case of the orange and red maize type (223.9 and 229.8 mg/100 g DW, for the orange and red maize, respectively) than results obtained by Fuentes-Cardenas et al. [ 19] in Cabanita race (134.3 and 190.9 mg/100 g DW, for the orange and red maize, respectively). However, above authors reported higher total phenolic contents in the white maize type (206 mg/100 g DW) than in the current research (162.2 mg/100 g DW). Differences in the postharvest treatments and the agroecological conditions for the growth of Cabanita maize may explain such variations. Generally, phenolic contents measured with the Folin–Ciocalteu method showed the same trend as those analyzed with the UHPLC method. However, the lack of specificity of the Folin–Ciocalteu method may be associated with differences observed specially in results from the free phenolic fraction [55].
## 3.3. Carotenoid contents and profiles of Cabanita maize types at different maturity stages
Cabanita maize types at different maturity stages were also evaluated in terms of their carotenoid composition (Table 3, Supplementary Figures S10–S12). In contrast to the variable effect of studied factors (M and S) on phenolic compounds, carotenoid contents were highly influenced by the maize type. Only xanthophylls were found in all Cabanita samples while no carotenes were detected. Moreover, different profiles were observed among studied Cabanita maize groups. White and red maize had similar profiles and all-trans-neoxanthin, neoxanthin isomer (~13-cis-neoxanthin), all-trans-zeaxanthin, and a lutein isomer (~13-cis-lutein) were the major carotenoids. All-trans-lutein and all-trans-zeaxanthin were the main compounds in the orange maize, followed by ~13-cis-lutein, and neoxanthin compounds. β-cryptoxanthin isomers along with some unidentified carotenoids [2, 3] were only detected in this maize type. The concentrations of all mentioned carotenoids in orange maize were higher than values found in white and red types. A violaxanthin isomer (~9-cis-violaxanthin) was detected in white and orange maize at all maturity stages, and only at S3 in the red grain. These xanthophylls diversity may be based on the fact that β-cryptoxanthin is the metabolic precursor of zeaxanthin which is further metabolized to violaxanthin and then to neoxanthin [56]. Several studies have confirmed that predominant carotenoid compounds in maize are generally lutein, zeaxanthin, β-cryptoxanthin along with other minor xanthophylls such as zeinoxanthin, antheraxanthin, violaxanthin, neoxanthin, and their isomers (23, 57–59). However, carotene compounds such as α-carotene and β-carotene have been also reported in comparable concentrations in yellow maize varieties [44, 60]. Liu et al. [ 23] reported that two genotypes of Chinese sweet maize showed variable carotenoid profiles and contents during the grain maturation from 10 to 30 DAP. This indicates an important influence of genetic factors and the kernel maturity stage [61].
**Table 3**
| Compound | White | White.1 | White.2 | Red | Red.1 | Red.2 | Orange | Orange.1 | Orange.2 | F-value | F-value.1 | F-value.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Compound | S1 | S2 | S3 | S1 | S2 | S3 | S1 | S2 | S3 | Maize (M) | Stage (S) | M × S |
| Neoxanthin isomer2 (~13-cis-neoxantin) | 0.18 ± 0.02c | 0.14 ± 0.04c | 0.11 ± 0.02c | 0.11 ± 0.04d | 0.15 ± 0.05c | 0.08 ± 0.01c | 0.39 ± 0.07a | 0.29 ± 0.13b | 0.31 ± 0.08ab | 38.69**** | 2.42ns | 1.24ns |
| All-trans-neoxanthin2 | 0.19 ± 0.03b | 0.15 ± 0.11b | 0.22 ± 0.01b | 0.16 ± 0.16b | 0.17 ± 0.07b | 0.18 ± 0.02b | 0.17 ± 0.11b | 0.22 ± 0.11b | 0.37 ± 0.12a | 2.79ns | 2.76ns | 1.21ns |
| Unidentified carotenoid-12 | 0.05 ± 0.02 | ND1 | ND | ND | ND | ND | ND | ND | ND | | | |
| Violaxanthin isomer2 (~9-cis-violaxanthin) | 0.06 ± 0.02b | 0.04 ± 0.02b | 0.05 ± 0.01b | ND | ND | 0.04 ± 0.02b | 0.13 ± 0.04a | 0.14 ± 0.06a | 0.13 ± 0.05ab | | | |
| Unidentified carotenoid-22 | ND | ND | ND | ND | ND | ND | 0.14 ± 0.05b | 0.26 ± 0.04a | 0.17 ± 0.06b | | | |
| Lutein isomer2 (~13-cis-lutein) | 0.10 ± 0.03c | 0.14 ± 0.08c | 0.14 ± 0.05c | 0.12 ± 0.06c | 0.18 ± 0.11c | 0.14 ± 0.03c | 0.64 ± 0.27a | 0.75 ± 0.24a | 0.41 ± 0.04b | 48.72**** | 2.69ns | 2.40ns |
| Unidentified carotenoid-32 | ND | ND | ND | ND | ND | ND | 0.12 ± 0.03a | 0.11 ± 0.03a | ND | | | |
| Zeaxanthin isomer3 (~13-cis-zeaxanthin) | ND | ND | ND | ND | 0.03 ± 0.01c | ND | 0.18 ± 0.05ab | 0.21 ± 0.08a | 0.12 ± 0.03b | | | |
| All-trans-lutein | 0.14 ± 0.04c | 0.07 ± 0.06c | ND | 0.06 ± 0.03c | 0.03 ± 0.02c | ND | 1.48 ± 0.62b | 2.00 ± 0.40a | 1.02 ± 0.34b | | | |
| All-trans-zeaxanthin | 0.07 ± 0.04c | 0.16 ± 0.04c | 0.08 ± 0.03c | 0.15 ± 0.07c | 0.23 ± 0.06bc | 0.09 ± 0.03c | 1.44 ± 0.35a | 1.35 ± 0.20a | 0.54 ± 0.22b | 155.08**** | 17.18**** | 11.56**** |
| Lutein isomer2 (~9 or 9′-cis-lutein) | ND | ND | ND | ND | ND | ND | ND | ND | 0.07 ± 0.03 | | | |
| β-cryptoxanthin isomer4 (~13 or 13′-cis-β-cryptoxanthin) | ND | ND | ND | ND | ND | ND | 0.17 ± 0.06a | 0.20 ± 0.09a | 0.06 ± 0.01b | | | |
| β-cryptoxanthin isomer4 (~9 or 9′-cis-β-cryptoxanthin) | ND | ND | ND | ND | ND | ND | 0.10 ± 0.04a | 0.19 ± 0.12a | ND | | | |
| Total carotenoids | 0.77 ± 0.10d | 0.69 ± 0.22d | 0.62 ± 0.10d | 0.66 ± 0.28d | 0.84 ± 0.27d | 0.56 ± 0.11d | 4.97 ± 1.26b | 5.87 ± 0.73a | 3.19 ± 0.61c | 209.22 **** | 10.53*** | 7.21*** |
The maturity stage and the interaction of both factors (M × S) significantly influenced the all-trans-zeaxanthin, and the total carotenoid contents. Neoxanthin isomer, all-trans-neoxanthin, violaxanthin isomer, and ~13-cis-lutein did not show significant changes with kernel development in white and red maize types, but all-trans-lutein was not detected at S3. Overall, both maize types showed similar total carotenoid concentrations which were somewhat stable along the kernel growth (ranges of 0.77–0.62 μg/g DW and 0.84–0.56 μg/g DW, for the white and red maize, respectively). In the orange Cabanita, all-trans-lutein increased by ~$35\%$ from S1 to S2 (1.48 and 2.0 μg/g DW at S1 and S2, respectively), but then decreased by $50\%$ at S3 (1.02 μg/g DW). All-trans-zeaxanthin remained almost constant from S1 to S2 (1.44 and 1.35 μg/g DW, respectively). However, it declined by $60\%$ at S3 (0.54 μg/g DW). Similar carotenoid reductions at S3 were observed in case of the 13-cis-lutein, 13-cis-zeaxanthin, β-cryptoxanthin isomers, and the other unidentified compounds. All-trans-neoxanthin and its isomers showed a certain increase at S3 which indicates the downstream metabolic conversion of β-cryptoxanthin, and zeaxanthin [56]. The contents of lutein, zeaxanthin, α-cryptoxanthin, and β-cryptoxanthin have shown to steadily increase with kernel development from 10 to 30 DAP in sweet corn [23]. The increase of zeaxanthin and lutein with kernel maturation from 16 to 24 DAP has been also reported in other sweet maize hybrids [62]. Variable zeaxanthin and lutein patterns were observed in some zeaxanthin-biofortified sweet maize depending on the genotype and the kernel position on the cob [61]. However, an overall lutein and zeaxanthin accumulation was reported in same study [61]. In the current research, higher maturity stages were evaluated (from 28–32 to 75–77 DAP) which likely explains contrasting results compared with previous studies. Xu et al. [ 24] evaluated a yellow maize variety during maturation from 74 to 116 DAS and found that the contents of zeaxanthin decreased at the end of kernel maturity, whereas lutein increased from 74 to 98 DAS to finally decrease at 116 DAS.
The orange maize exhibited higher total carotenoid contents (3.19–5.87 μg/g DW) than white and red maize (0.77–0.62 μg/g DW and 0.84–0.56 μg/g DW, for the white and red maize, respectively). Nevertheless, carotenoids significantly decreased by ~$50\%$ at S3 (from 5.87 to 3.19, at S2 and S3, respectively). Fuentes-Cardenas et al. [ 19] found lower total carotenoid values in the orange Cabanita maize type at physiological maturity (1.95 μg/g DW, COM code) than in the present study. In addition, no carotenoids were detected in the corresponding red and white parental seeds (CCR, CAW) [19]. Carotenoids are highly sensitive to light, heat, and oxygen, therefore postharvest practices applied by Andean farmers such as the sun-drying of Cabanita ears first in the plant and later on the field for undetermined time may lead to the degradation of carotenoid compounds.
Higher total carotenoid amounts than those from current research have been reported mostly in yellow and sweet maize varieties. Xu et al. [ 24] found concentrations of 22.78–28.76 μg/g DW at different maturity stages in yellow maize. Ranges of 0.55–43.23 μg/g DW and 11.4–24.0 μg/g DW have been shown in sweet maize harvested at maturity stages from 10 to 32 DAP [23, 63]. Floury maize types generally show lower carotenoid contents than hard maize classes such as pop, dent, or flint [64]. Therefore, it is expected to find lower carotenoid levels in the amylaceous floury Cabanita maize [19].
## 3.4. Fatty acid composition of Cabanita maize types at different maturity stages
The fatty acid composition of Cabanita maize is shown in Table 4. No significant effect was found by the maize type indicating similar fatty acid profiles and contents among all samples. Polyunsaturated fatty acids (PUFA) including linoleic and α-linolenic acids represented the major fatty acid fraction in all Cabanita maize types (55–$59\%$ of the total fatty acid content). The monounsaturated oleic acid contributed with 21–$28\%$ of the total fatty acids, followed by saturated acids (palmitic and stearic acids, 18–$21\%$). Among all detected fatty acids, linoleic and oleic acids were the most abundant compounds in maize kernels (50–$54\%$, and 21–$28\%$ for linoleic and oleic acids, respectively).
**Table 4**
| Maize type | Stage | Saturated fatty acids | Saturated fatty acids.1 | Unsaturated fatty acids | Unsaturated fatty acids.1 | Unsaturated fatty acids.2 | Total fatty acids |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Maize type | Stage | Palmitic acid | Estearic acid | Oleic acid | Linoleic acid | α-Linolenic acid | Total fatty acids |
| White | S1 | 5.1 ± 0.9ab | 0.9 ± 0.1bcd | 5.9 ± 1.3d | 14.7 ± 3.2c | 1.6 ± 0.1a | 28.0 ± 5.3c |
| White | S2 | 5.1 ± 0.6ab | 0.8 ± 0.1 cd | 6.6 ± 1.4 cd | 15.6 ± 2.7bc | 1.3 ± 0.1de | 29.3 ± 4.7bc |
| White | S3 | 5.9 ± 0.9ab | 1.1 ± 0.2a | 11.0 ± 2.2a | 20.5 ± 4.1a | 1.3 ± 0.1e | 39.8 ± 7.2a |
| Red | S1 | 5.3 ± 0.8ab | 0.8 ± 0.1 cd | 6.6 ± 1.9 cd | 15.3 ± 3.3c | 1.5 ± 0.1b | 29.5 ± 6.1bc |
| Red | S2 | 5.2 ± 0.6ab | 0.8 ± 0.1 cd | 7.5 ± 0.9bcd | 15.2 ± 2.3c | 1.4 ± 0.1 cde | 30.1 ± 3.8bc |
| Red | S3 | 6.3 ± 1.3a | 1.0 ± 0.2ab | 9.8 ± 2.5ab | 21.1 ± 4.4a | 1.30 ± 0.03e | 39.5 ± 8.2a |
| Orange | S1 | 4.9 ± 0.6b | 0.7 ± 0.1d | 5.7 ± 0.9d | 14.5 ± 1.5c | 1.5 ± 0.1b | 27.2 ± 2.8c |
| Orange | S2 | 6.0 ± 1.0ab | 0.9 ± 0.2bc | 9.0 ± 1.9abc | 20.4 ± 4.5ab | 1.4 ± 0.1bcd | 37.6 ± 7.6ab |
| Orange | S3 | 5.9 ± 0.6ab | 0.9 ± 0.1abc | 9.6 ± 1.6ab | 21.2 ± 2.9a | 1.4 ± 0.1bc | 39.0 ± 4.8a |
| F-value | Maize (M) | 0.35ns | 0.62ns | 0.06ns | 0.97ns | 2.84ns | 0.48ns |
| F-value | Stage (S) | 3.91* | 11.72*** | 17.63**** | 10.29**** | 20.45**** | 11.19*** |
| F-value | M × S | 0.81ns | 2.01ns | 1.55ns | 1.08ns | 4.24** | 1.08ns |
Comparable percentages of linoleic acid have been also reported in Mexican subtropical maize populations (41–$51\%$), sweet maize from the United States (50–$63\%$), and maize varieties from Turkey (50–$53\%$) (65–67). In the case of other Peruvian germplasm, similar fatty acids profiles and concentrations were observed in mature native varieties such as Chullpi, Piscorunto, Giant Cuzco, Sacsa, and purple, with ranges of 18.3–25.2 mg/g DW and 9.8–14.6 mg/g DW for linoleic and oleic acids, respectively [68]. However, α-linolenic acid concentrations were almost 2 to 3.5-fold higher in Cabanita samples (1.3–1.4 mg/g DW, at S3 maturity stage) than in the other Peruvian varieties (0.4–0.7 mg/g DW) [68]. Lower α-linolenic acid percentages have been also reported in sweet maize (1.7–$2.1\%$) compared with Cabanita maize (3.2–$3.7\%$) [66]. Furthermore, this fatty acid was not even detected in several Korean maize hybrids [69]. The increase of α-linolenic acid from 0.61 to $4.93\%$ along with the oil contents have been obtained after a long-term breeding process of Mexican maize [65]. The contribution of this fatty acid in relation to the total fatty acid content was higher at early maturity stages in Cabanita samples (5.1–$5.8\%$).
Linoleic (ω-6) and α-linolenic acids (ω-3) are essential fatty acids that cannot be synthesized by humans [70]. After the ingestion, α-linolenic acid is transformed into long-chain ω-3 PUFAs such as docosahexaenoic and eicosapentaenoic acids which play important roles within the organism [71]. Dietary α-linolenic acid, and its ω-3 metabolic derivatives have been reported to show antioxidant and anti-inflammatory properties with potential for the prevention of brain malfunction, and cardiovascular disease (72–74). Moreover, diets with ω-6:ω-3 ratios close to 1:1 have been associated with less incidence of chronic diseases including diabetes and cardiovascular diseases [70]. Higher ω-6:ω-3 ratios have been found in unbred maize cultivars from Mexico (59–80:1), sweet maize from US (29–33:1), and Peruvian germplasm from other races (36–50:1) in comparison with ratios found in evaluated Cabanita maize at physiological maturity stage (15–16:1) [65, 66, 68]. Genetic factors may play a role on observed differences. In addition, differences in the agroecological and post-harvest management conditions may also be involved. Based on the current results, Cabanita maize shows potential as a dietary source of health relevant PUFAs.
The maturity stage had a strong influence on fatty acid variability in all Cabanita types (Table 4). Saturated acids slightly increased at S3, but their proportions with respect to the total fatty acid contents decreased in all cases from 18 to $15\%$ on average. Contents and percentages of stearic acid almost remained constant with maturation. Oleic acid increased from 21 to $22\%$ at S1 to 25–$28\%$ at S3. Linoleic acid concentrations also increased and were high at S3, but their percentages in relation to the total fatty acid contents showed almost no variation with kernel growth (from 52 to $51\%$, from 52 to $53\%$, and from 53 to $54\%$ for white, red and orange maize, respectively). The α-linolenic acid concentrations and percentages decreased from S1 to S3 (5.1–$5.8\%$ to 3.2–$3.7\%$). Palmitoyl-CoA is a metabolic precursor of palmitic acid, and of stearoyl-CoA which in turn serves as a precursor of oleic, linoleic, and linolenic acids biosynthesis via several desaturase enzymes [56]. A possible metabolic change toward the biosynthesis of oleic and linoleic acids instead of palmitic acid may explain its percentage decrease with maturity. It is noteworthy that ω-6:ω-3 ratios are lower at S1 stage (9.2–10.4:1) than at S3 (15–16:1) indicating better PUFAs balance when Cabanita maize is at milk stage.
## 3.5.1. DPPH and ABTS antioxidant capacity
The antioxidant capacity was evaluated with two different in vitro methods and only in the bioavailable-relevant soluble hydrophilic and lipophilic fractions of Cabanita maize samples (Table 5). The interaction of maize type (M) and the maturity stage (S) was not significant in all cases; however, all variables were influenced by S. The DPPH hydrophilic antioxidant capacity (DPPH-HF) declined with grain growth and there were differences depending on the maize type (M significant). Orange Cabanita showed higher values (422.3–821.7 μmol TE/100 g DW) than white and red types (310.7–490.6 and 308.9–520.2 μmol TE/100 g DW, for the white and red maize, respectively). The DPPH-HF decreased from S1 to S3 by 37, 41 and $49\%$ in the white, red, and orange maize, respectively. The opposite trend was observed in case of the DPPH lipophilic antioxidant capacity (DPPH-LF). Values increased from S1 to S3 around 3.4 and 4.8-fold in the orange and red maize, respectively. In the white maize, the DPPH-LF was not detected at S1, but then it increased to 7.8 and 20.8 μmol TE/100 g DW with kernel development at S2 and S3, respectively.
**Table 5**
| Maize type | Stage | Inhibition of DPPH | Inhibition of DPPH.1 | Inhibition of ABTS | Inhibition of ABTS.1 |
| --- | --- | --- | --- | --- | --- |
| Maize type | Stage | HF | LF | HF | LF |
| White | S1 | 490.6 ± 57.3bcd | ND | 2065.2 ± 98.7ab | 36.4 ± 5.0f |
| White | S2 | 313.5 ± 79.2 cd | 7.8 ± 4.6 cd | 1959.7 ± 278.1ab | 45.1 ± 7.0ef |
| White | S3 | 310.7 ± 20.7d | 20.8 ± 3.5ab | 1009.0 ± 86.0d | 70.0 ± 3.7bc |
| Red | S1 | 520.2 ± 329.6bc | 4.1 ± 3.6d | 2012.3 ± 121.8ab | 40.6 ± 3.0ef |
| Red | S2 | 507.5 ± 106.0bcd | 14.3 ± 7.7bc | 1819.8 ± 138.6b | 48.4 ± 12.9de |
| Red | S3 | 308.9 ± 150.8d | 19.9 ± 12.5ab | 1297.5 ± 353.1c | 81.0 ± 15.0b |
| Orange | S1 | 821.7 ± 114.5a | 8.0 ± 3.4 cd | 2194.4 ± 121.5a | 58.9 ± 5.5 cd |
| Orange | S2 | 582.2 ± 135.8b | 12.7 ± 4.5bcd | 1949.6 ± 185.2ab | 71.7 ± 1.6b |
| Orange | S3 | 422.3 ± 53.1bcd | 27.5 ± 4.1a | 1085.2 ± 45.9 cd | 94.2 ± 6.4a |
| F-value | Maize (M) | 8.51** | | 0.38ns | 30.91**** |
| F-value | Stage (S) | 10.05*** | | 92.36**** | 68.17**** |
| F-value | M × S | 1.24ns | | 2.01ns | 0.50ns |
The ABTS hydrophilic antioxidant capacity (ABTS-HF) also decreased with kernel maturity in a range of 36–$51\%$, similarly as in the case of the DPPH-HF. However, the maize type did not show an important effect. Ranges were almost comparable among all Cabanita types at all maturity stages (1009.0–2065.2 μmol TE/100 g DW, 1297.5–2012.3 μmol TE/100 g DW, and 1085.2–2194.4 μmol TE/100 g DW, for the white, red, and orange maize, respectively). In addition, the ABTS lipophilic antioxidant capacity (ABTS-LF) increased 1.6–2.0-fold from S1 to S3 as also was noticed in case of the DPPH-LF. The orange maize exhibited the highest value at S3 (94.2 μmol TE/100 g DW) among samples (M significant).
The hydrophilic extracts strongly inhibited both free radicals more than lipophilic fractions indicating higher contents of hydrophilic antioxidants such as soluble polyphenols. In fact, higher concentrations of total free phenolic compounds have been determined in this study in comparison with the total carotenoid contents (ranges of 10–65 mg/100 g DW and 0.56–5.87 μg/g DW, for the total free phenolic and total carotenoid contents, respectively). The UHPLC total free phenolic contents highly correlated with the antioxidant capacity ($r = 0.7709$ and $r = 0.7863$, $p \leq 0.05$ for the DPPH-HF and ABTS-HF, respectively). Hydroxybenzoic acid compounds such as vanillic acid derivatives showed a positive correlation with this property ($r = 0.5740$ and $r = 0.7502$, $p \leq 0.05$ for the DPPH-HF and ABTS-HF, respectively). Likewise, the HBA-1 compound, and the total HBA contents were correlated with the antioxidant capacity measured with both methods ($r = 0.5790$ and 0.7962, $p \leq 0.05$ for the DPPH-HF and ABTS-HF, respectively in case of the HBA-1; and $r = 0.5900$ and $r = 0.7987$, $p \leq 0.05$ for the DPPH-HF and ABTS-HF, respectively in case of the total HBA contents). Moreover, free luteolin derivatives also contributed to the antioxidant capacity in the orange maize group ($r = 0.6246$, $p \leq 0.05$ for the DPPH-HF). Consistent with the current study, soluble phenolic compounds were correlated with high antioxidant capacity evaluated with the ferric reducing antioxidant power (FRAP) and DPPH methods in several Italian maize landraces [75]. Flavonoids including anthocyanins have shown to highly contribute to the free radical antioxidant capacity in Mexican red and purple-pigmented maize [76, 77]. In the current study, no correlation was found between the total anthocyanin contents and the antioxidant capacity in Cabanita red maize which may be due to its lower anthocyanin ranges compared to HBA concentrations specially at S1 and S2 stages.
In case of the lipophilic antioxidant capacity, a moderate correlation was found between this functional quality and the total carotenoid contents ($r = 0.5354$, $p \leq 0.05$ with the DPPH method). Other lipophilic compounds such as tocopherols and tocotrienols common in the germ of maize grains but not analyzed in the current study may also play a role [17]. Some tocopherol compounds from spelt grain (Triticum spelta) have shown significant correlation with the antioxidant capacity measured with the DPPH method [78].
A continuous decrease of the DPPH and FRAP antioxidant capacity was observed in the soluble phenolic fractions from yellow maize at different developmental stages (from 74 to 116 DAS) [24]. Hu and Xu [37] and Giordano et al. [ 39] reported that the free radical inhibitory activity along the kernel growth stages was highly variable depending on the maize variety. An increase of the antioxidant response with kernel maturity were reported in sweet and yellow maize in maturity periods shorter (17–25 DAP and 15–48 DAP, respectively) than in the current research (28–77 DAP) [22, 79]. Furthermore, Liu et al. [ 23] observed that the lipophilic oxygen radical absorbance antioxidant capacity (ORAC) increased with grain maturity (10–30 DAP) in sweet maize showing correlation with the total and individual carotenoids such as lutein and zeaxanthin. Differences in the current results from those of above studies indicates an important influence of genetic factors, the maturity stage, and the origin of maize.
In a previous study with Peruvian white, red, and orange Cabanita maize, Fuentes-Cardenas et al. [ 19] pointed out that hydrophilic compounds strongly contributed to the in vitro antioxidant capacity (DPPH and ABTS methods) than lipophilic fractions like this study. Nevertheless, lower ABTS-HF was reported by above authors (566.3–685.4 μmol TE/100 g DW) than in this research (1009.0–1297.5 μmol TE/100 g DW) at physiological maturity stage. This may suggest that postharvest management also plays a role in the observed bioactive variability and associated functional quality.
## 3.5.2. Inhibitory activity against α-amylase and α-glucosidase enzymes
The intake of natural inhibitors of key intestinal carbohydrate-hydrolyzing enzymes such as α-amylase and α-glucosidase may represent an important dietary strategy for hyperglycemia management relevant for the type-2 diabetes prevention (80–82). Table 6 shows the potential in vitro inhibitory activity of the soluble hydrophilic and lipophilic fractions from Cabanita maize samples against α-amylase and α-glucosidase enzymes.
**Table 6**
| Maize type | Stage | α-Glucosidase inhibitory activity (%) | α-Glucosidase inhibitory activity (%).1 | α-Glucosidase inhibitory activity (%).2 | α-Glucosidase inhibitory activity (%).3 | α-Glucosidase inhibitory activity (%).4 | α-Glucosidase inhibitory activity (%).5 | α-Amylase inhibitory activity (%) | α-Amylase inhibitory activity (%).1 | α-Amylase inhibitory activity (%).2 | α-Amylase inhibitory activity (%).3 | α-Amylase inhibitory activity (%).4 | α-Amylase inhibitory activity (%).5 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Maize type | Stage | 3 mg1 | 3 mg1 | 6 mg | 6 mg | 10 mg | 10 mg | 25 mg | 25 mg | 62 mg | 62 mg | 125 mg | 125 mg |
| Maize type | Stage | HF | LF | HF | LF | HF | LF | HF | LF | HF | LF | HF | LF |
| White | S1 | 17.2 ± 3.5a | 6.1 ± 4.3a | 31.3 ± 4.0a | 7.5 ± 2.6ab | 40.0 ± 6.3a | 8.9 ± 2.0b | 7.0 ± 3.0a | ND2 | 18.6 ± 8.3a | ND | 55.0 ± 25.4a | 4.3 ± 5.9 |
| White | S2 | 10.7 ± 4.2 cd | 4.7 ± 4.9a | 16.6 ± 4.8de | 7.1 ± 1.7ab | 20.2 ± 4.9 cd | 11.9 ± 3.3ab | 1.4 ± 1.6b | ND | 4.1 ± 3.0c | ND | 13.5 ± 4.4b | ND |
| White | S3 | 6.5 ± 1.4d | 5.6 ± 1.6a | 10.9 ± 2.9e | 7.3 ± 0.3ab | 14.8 ± 2.8d | 8.6 ± 0.7b | ND | ND | 1.8 ± 2.7c | ND | 8.1 ± 1.7b | ND |
| Red | S1 | 18.4 ± 3.3a | 3.4 ± 2.7a | 28.0 ± 5.4ab | 8.1 ± 4.2ab | 32.3 ± 6.9b | 12.2 ± 4.0ab | 2.4 ± 3.4b | ND | 6.6 ± 5.9bc | ND | 18.2 ± 8.9b | ND |
| Red | S2 | 16.1 ± 1.7ab | 3.8 ± 2.9a | 23.0 ± 4.3bc | 8.7 ± 2.5ab | 31.8 ± 2.8b | 12.3 ± 2.8ab | 2.0 ± 2.8b | ND | 8.0 ± 3.0bc | ND | 16.3 ± 3.2b | ND |
| Red | S3 | 7.5 ± 3.8 cd | 5.5 ± 4.6a | 15.2 ± 6.8de | 9.7 ± 3.0a | 20.2 ± 7.6 cd | 12.2 ± 3.5ab | 2.1 ± 2.6b | ND | 4.2 ± 4.5c | ND | 8.3 ± 2.9b | ND |
| Orange | S1 | 20.5 ± 2.3a | 4.9 ± 5.8a | 30.0 ± 3.4a | 5.7 ± 6.2ab | 41.0 ± 2.1a | 8.6 ± 5.8b | 6.8 ± 1.8a | ND | 12.8 ± 4.4ab | ND | 19.0 ± 3.2b | ND |
| Orange | S2 | 10.8 ± 3.0 cd | 1.9 ± 2.9a | 17.4 ± 3.5 cd | 4.6 ± 3.2b | 22.7 ± 4.1c | 10.2 ± 2.2ab | ND | ND | 3.9 ± 1.6c | ND | 9.3 ± 4.3b | ND |
| Orange | S3 | 12.1 ± 4.2bc | 3.4 ± 2.8a | 18.3 ± 1.9 cd | 8.7 ± 1.3ab | 22.6 ± 4.1c | 15.1 ± 5.3a | ND | ND | 3.0 ± 2.0c | ND | 7.9 ± 0.5b | ND |
| F-value | Maize (M) | 3.02ns | 0.88ns | 1.22ns | 1.86ns | 1.99ns | 1.39ns | | | 0.66ns | | 7.11** | |
| F-value | Stage (S) | 30.13**** | 0.48ns | 38.26**** | 1.08ns | 43.89**** | 1.09ns | | | 15.76**** | | 19.26**** | |
| F-value | M × S | 2.60ns | 0.29ns | 2.48ns | 0.50ns | 5.12** | 1.71ns | | | 4.10* | | 6.78*** | |
All hydrophilic (HF) and lipophilic (LF) maize extracts inhibited the α-glucosidase enzyme in a sample dose dependent manner (3–10 mg). However, HF extracts showed higher inhibition than LF fractions. The type of maize (M) was not significant on the α-glucosidase inhibitory activity of both HF and LF fractions at all evaluated sample doses. This indicates similar inhibitory potential among all Cabanita maize types. No effect of the M x S interaction was found on results from HF and LF fractions, except at 10 mg (HF). In this case, HF fractions from white and orange maize exhibited greater inhibition than red maize at S1 (40.0, 41.0, and $32.3\%$, for the white, orange, and red maize, respectively). Nonetheless, the inhibitory activity of red maize was higher than white and orange at S2 (31.8, 20.2, $22.7\%$, for the red, white, and orange samples, respectively), whereas results of all HF fractions were almost similar at S3.
The maturity stage (S) had a significant influence on the HF inhibitory activity at all sample doses. At 10 mg, this property decreased with kernel development (from 28–33 to 75–76 DAP) by 63, 37 and $45\%$ in the white, red, and orange group, respectively. This reduction occurred at all sample doses, indicating that hydrophilic inhibitors may be related to the soluble phenolic fraction which also declined with kernel maturity as previously stated. This in vitro functional quality had high correlation with the free UHPLC soluble phenolic compounds at all sample doses ($r = 0.7386$, $r = 0.8064$, and $r = 0.8545$, $p \leq 0.05$ at 3, 6, and 10 mg sample dose, respectively). Specific phenolic compounds such as the vanillic acid derivatives, HBA-1, and the total HBA contents positively correlated with the inhibitory potential of Cabanita maize samples ($r = 0.7962$, $r = 0.7728$, and $r = 0.7961$, $p \leq 0.05$, respectively, 10 mg sample dose). Among free HCA compounds, ferulic acid derivatives also showed a significant correlation ($r = 0.6597$, $p \leq 0.05$, 10 mg sample dose). In case of LF fractions, results were not influenced by S, showing comparable α-glucosidase inhibition during the grain growth. However, a certain increase (from 5.7 to $8.7\%$ and from 8.6 to $15.1\%$ at 6, and 10 mg of sample dose, respectively) was observed in the orange maize. No significant correlations were found between the LF α-glucosidase inhibitory activity, and any metabolites measured in the current study.
The α-amylase enzyme, relevant for the hydrolysis of α-1,4-glucan polysaccharides into maltose and maltooligosaccharides [83], was inhibited only by HF maize fractions in a dose-dependent manner (Table 6). All values decreased with kernel maturity (S significant) similarly as in the case of the α-glucosidase inhibitory activity. When the maturity stage changed from S1 to S3, the white maize (125 mg dose) showed the highest loss of the inhibitory potential (around $85\%$), followed by the red, and orange maize (~54 and $58\%$ in red and orange maize, respectively). Both M × S interaction and M factors greatly influenced the α-amylase inhibition. White maize samples had higher α-amylase inhibition at S1 among Cabanita maize types. The α-amylase inhibitory potential positively correlated with the free UHPLC total phenolic contents ($r = 0.6358$ and $r = 0.6574$, $p \leq 0.05$, at 62 and 125 mg of sample dose, respectively). Furthermore, all HBA compounds and free ferulic acid derivatives were correlated with this in vitro functional property ($r = 0.5278$–0.6471 at all sample doses, and $r = 0.5340$–0.5599 at 62–125 mg, respectively).
Different studies have highlighted the role of phenolic compounds for hyperglycemia prevention and countering associated oxidative complications through several mechanisms including the modulation of gastric enzymes at intestinal level (84–87). Cereal-derived phenolic acids including several HBA, and HCA compounds have shown inhibitory potential against the intestinal α-glucosidase enzyme which was highly dependent on the number of hydroxyl and methoxy groups in their structure [88]. HBA derivatives such as methyl vanillate (a vanillic acid derivative), syringic acid, and vanillic acid from Thai colored rice showed higher inhibition of α-glucosidase than on α-amylase with a mixed-type inhibition mode against α-glucosidase [89]. In same study, in silico analysis revealed that the inhibition involved the molecular interaction between HBA and the binding sites of digestive enzymes through 3–4 hydrogen bonds depending on the phenolic compound and the enzyme [89]. Conjugated hydroxycinnamic acids amides (HCCA) identified in maize and in other grains from the Poaceae family grains such as N,N-di-feruloylputrescine, N-p-coumaroyl-N-feruloylputrescine have also been targeted as α-glucosidase inhibitors [90, 91]. Recently, another ferulic acid derivative named 6’-O-feruloylsucrose isolated from black rice bran showed high α-glucosidase inhibition when in vitro and in silico studies were performed [92].
Based on above information, the free phenolic fraction including HBA, and some HCA derivatives likely involving ferulic acid derivatives detected in the current research may explain the in vitro anti-hyperglycemia potential of Cabanita maize HF extracts. However, other polar compounds may also be involved. Some compounds detected with the untargeted GC–MS analysis as will be discussed in next section, have shown direct correlation with results from both enzymatic assays. Alcohol sugars including d-sorbitol, meso-erythritol and phytosterols such as campesterol, and sitosterol positively correlated with the α-amylase ($r = 0.7588$, $r = 0.6556$, $r = 0.6235$, and $r = 0.7850$, $p \leq 0.05$, at 125 mg dose respectively) and α-glucosidase inhibitory activities ($r = 0.7275$, $r = 0.7062$, $r = 0.6965$, and $r = 0.7218$, $p \leq 0.05$, at 10 mg, respectively). Some studies have pointed out the role of erythritol and triterpenoid compounds for the management of postprandial blood glucose through the inhibition of α-glucosidase enzyme [93, 94]. Stigmasterol among other phytochemicals identified in maize silk have been indicated as potential inhibitors of both α-glucosidase and α-amylase enzymes [95].
The observed LF α-glucosidase inhibitory activity may be ascribed to lipophilic compounds that increase in maize kernel with maturity time specially in case of the orange maize. The contents of α-tocopherol, β-tocotrienol, γ-tocotrienol, and δ-tocotrienol had increased with maturity in grains of *Amaranthus cruentus* [96]. Lipophilic extracts from *Vicia fava* L. seeds containing α-tocopherol and γ-tocopherol compounds exhibited high α-glucosidase inhibition [97]. It is possible that the inhibitory activity of Cabanita maize extracts against digestive enzymes may be due to the synergistic action of HL and LF compounds as also was reported by Parizad et al. [ 98] in several pigmented cereals. Future studies are necessary to reveal the identity of HF and LF compounds from Cabanita maize and their molecular mechanisms of inhibition against hyperglycemia-relevant enzymes.
α-Amylase inhibitory activity results from this study are comparable with those reported by Ranilla et al. [ 20] in mature Cabanita maize kernels from Peru (8.9–$10.2\%$, at 125 mg sample dose). However, higher α-glucosidase inhibitory activities were obtained by above authors (34.9–$40.8\%$, at 12.5 mg of sample dose) which may be linked to the higher sample doses evaluated. On the other hand, lower inhibitory activities against α-amylase and α-glucosidase were found in the free phenolic fraction from Chinese fresh waxy maize harvested at milk stage (~28–$72\%$ and ~32–$48\%$ for the α-amylase and α-glucosidase inhibitory activities, respectively, at 1,000 mg sample dose) than in current study at similar maturity stage (2.4–$55.0\%$ at 25–125 mg sample dose, and 17.2–$41.0\%$ at 3–10 mg sample dose for the α-amylase and α-glucosidase inhibitory activities, respectively) [99]. Several studies have shown the anti-hyperglycemic potential of maize from different origins [36, 98, 100]. Nevertheless, the impact of the maturity stage on this functional property in selected samples from the Peruvian Cabanita maize diversity is shown for the first time in this study.
## 3.6. Primary polar metabolites analysis by GC–MS of Cabanita maize types at different maturity stages and principal component analysis
The GC–MS analysis allowed to detect 63 polar metabolites including sugars, amino acids, other nitrogen-containing compounds, free fatty acids, organic acids, sugar alcohols, and phytosterols. A PCA analysis was performed considering all data from the targeted, untargeted metabolomic analyses, and the in vitro functional quality to reveal underlying relationships among all variables (Figure 4). The first two principal components from the PCA model explained $58.1\%$ of the total dataset variability. Different groups were observed based on the maize type and maturity stage which were explained by 98 significant variables. The heat map considering the top 60 significant variables is shown in Figure 5. PC 1 ($45.2\%$ of explained variance) separated (from top to the bottom) all maize types at S3 (OIII, RIII, and WIII, for the orange, red, and white maize at S3, respectively) from samples at earlier maturity stages (I, II, for S1 and S2, respectively).
**Figure 4:** *Principal component analysis (PCA) score plot of all data from white (W), red (R), and orange (O) Cabanita race at different maturity stages (I: S1, II: S2, III: S3).* **Figure 5:** *Heat map considering 60 significant variables from white (W), red (R), and orange (O) Cabanita race at different maturity stages (I: S1, II: S2, III: S3).*
Maize samples at S3 were characterized by overall lower concentrations of secondary metabolites (phenolic and carotenoid compounds), along with reduced in vitro antioxidant and anti-hyperglycemia potential than maize at S1 and S2. In relation to the primary polar metabolites detected by GC–MS, free monosaccharides (glucose, fructose, galactose), disaccharides (sucrose) among other unidentified sugar molecules decreased with kernel maturity in all maize types (Figure 5). Furthermore, several amino acids (glutamic acid, glycine, alanine, phenylalanine, proline, isoleucine, leucine, valine, and serine) also declined with grain maturity. Simple sugars may have been used as carbon sources for cellular energy metabolism, and the biosynthesis of starch which is known to accumulate in mature cereal grains [101]. The decrease of sugar contents with the concurrent increase of starch during grain development was observed by Xu et al. [ 24] and Saikaew et al. [ 101] in yellow and purple waxy maize, respectively. Amino acids may have been transformed into proteins or used as metabolic precursors for the synthesis of secondary metabolites [102]. The increase of protein with kernel maturation has been reported in maize and rice kernels [101, 103]. Among data from all maize types at S3, the orange maize clearly separated and stood out from the red and white groups, whereas some replicates from these last maize types overlapped. This difference was mainly correlated with the highest carotenoid contents in the orange group than in red and white maize.
PC 2 ($12.9\%$ of explained data variability) separated orange maize (at all maturity stages) from white and red types (from left to right). The orange maize showed unique flavonoids such as luteolin derivatives and had the highest carotenoid concentrations (especially at S1 and S2) as previously stated. Interestingly, the white maize was different from the other maize types specifically at the S1 stage (Figure 5). Primary metabolites including simple sugars (monosaccharides and sucrose), amino acids, free fatty acids (linoleic and palmitic acids), organic acids (4-aminobutanoic acid or GABA, fumaric acid), amines (putrescine, ethanolamine), myo-inositol and xylitol were more abundant in the white maize at milk stage than in the other maize groups at same maturity period.
Phytosterols such as campesterol and β-sitosterol were detected in all maize groups. The orange and red maize showed comparable campesterol contents, but the white maize exhibited the highest sitosterol abundance at S1. Phytosterols are bioactive compounds with potential for the prevention of cardiovascular diseases because of their cholesterol-lowering properties [104]. Both detected phytosterols decreased with grain maturity in all Cabanita types. The reduction of β-sitosterol has been also observed during *Camellia chekiangoleosa* Hu. seeds development [105]. β-sitosterol is the key precursor of sitosterol-β-glucoside which play a role on the biosynthesis of cellulose [106]. The increase of dietary fibre, which is composed of cellulose, hemicellulose, lignin, among other polymers has been reported in *Amaranthus cruentus* with grain maturity [96]. Furthermore, the cell wall feruloylation along with the lignin-cross links of the wheat grain outer layers increased during kernel development [107]. This likely explains the decrease of free ferulic acid derivatives and the increase of some cell wall phenolics (bound ferulic acid derivatives) specially in the red and orange maize at S3 maturity stage.
Other detected primary metabolites such as free fatty acids (palmitic acid, linoleic acid), organic acids (glyceric acid, fumaric acid, ribonic acid, GABA), amines (ethanolamine, putrescine), myo-inositol, and alcohol sugars (erythritol, xylitol) were also reduced with grain maturity in all maize types. Free fatty acids may have been metabolized for the synthesis of triacylglycerols (TAG) since the total fatty acid contents (derived from the triacylglycerol saponification) increased with grain maturity in all Cabanita groups (Table 4). The increase of the total lipid contents during the grain development of yellow maize has been related to the late embryo formation [24]. The reduction of ethanolamine and glyceric acid may have been targeted as precursors of glycerophospholids (components of the cell membranes) during grain growth. Some types of phosphatidilethanolamines esterified with variable fatty acids have increased during wheat kernel filling [108]. The reduction of intermediate metabolites involved in the tricarboxylic acid cycle (TCA) such as fumaric acid and GABA-derived succinic acid might reflect a high mitochondrial activity for energy generation during the maize grain development [109]. Polyamines including putrescine have essential roles in many biochemical and physiological processes, in particular stress and senescence responses during plant growth and development [110]. Putrescine content also decreased with kernel maturity in Cabanita maize as observed in other polar metabolites. This polyamine may have been used for the biosynthesis of some HCAAs such as N-p-coumaroyl-N-feruloylputrescine and caffeoylputrescine which have been detected in mature maize [44]. In the current study, p-coumaric and caffeic acid derivatives (possibly HCAAs) have increased with Cabanita kernel maturation.
## 4. Conclusion
Maize with different kernel pigmentations (white, red, and orange) and representing the diversity of the Peruvian Andean maize race Cabanita showed variable primary (polar metabolites), and secondary metabolite composition (phenolic and carotenoid compounds) and were greatly influenced by the grain maturity stage. All maize types showed free HBA and HCA phenolic compounds, but luteolin derivatives and anthocyanins were only detected in orange and red maize, respectively. Major bound phenolic compounds were ferulic acid, followed by ferulic acid derivatives, and p-coumaric acid in all Cabanita groups. However, orange, and red types had higher bound ferulic acid, and total phenolic contents (free + bound) (223.9–274.4 mg/100 g DW, 193.4–229.8 mg/100 g DW for the orange and red maize, respectively) than the white maize (162.2–225.0 mg/100 g DW). Xanthophylls including lutein, zeaxanthin, neoxanthin, lutein isomer (~13-cis-lutein) were detected in all maize types. The orange maize had the highest total carotenoid contents (3.19–5.87 μg/g DW) and contained specific carotenoids such as β-cryptoxanthin and zeaxanthin isomers. Most phenolic and carotenoid compounds decreased with kernel maturity in all Cabanita maize types. With respect to the primary metabolites, all maize types showed similar fatty acid contents (linoleic acid > oleic acid > palmitic acid > α-linolenic acid > stearic acid) which increased with kernel development. Other primary metabolites such as simple sugars, alcohols, amino acids, free fatty acids, organic acids, amines, and phytosterols declined with grain maturity and were overall more abundant in white maize at S1. The in vitro antioxidant potential and the inhibitory activity against digestive enzymes (α-amylase and α-glucosidase) were high in the hydrophilic fractions and correlated with the free phenolic fraction. *In* general, all Cabanita maize types had similar in vitro health-relevant functionality which significantly decreased with grain development. Based on above results, recommended harvesting periods for the consumption of the orange and white Cabanita would be at S1 and S2 stages due to their higher phenolic, carotenoid contents (in case of the orange type), in vitro functional qualities, phytosterol concentrations, and better ω-6:ω-3 PUFAs balance. The red Cabanita maize would be more valuable at S3 because of its higher total anthocyanin, and phenolic contents. Nevertheless, the potential changes on Cabanita technological and processing characteristics at lower maturity stages should be further evaluated. Current study provides relevant metabolomic and biochemical information that contribute to the characterization of the Andean Cabanita maize diversity. Insights from this research would be important for promoting its consumption beyond its mature form as it is currently applied, and for future improvements at postharvest level. Studies at transcriptomic level would help to reveal the mechanisms involved in the metabolic changes related to the secondary and primary metabolites during Cabanita maize maturation. Next level studies should focus on improving the nutraceutical and nutritional properties of Cabanita maize with its sustainability and consumer-relevant yield characteristics within the context of Andean food systems.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
LR and GZ conceived and designed the study. LR directed the research and wrote the manuscript. AA-C performed the experiments. RC helped with the experimental work. HH contributed with the direction of crop management and pollination control. MV-V helped with the statistical analysis. HB-G coordinated and helped with the original sample collection. RP developed the untargeted metabolomic and free fatty acid analysis. GZ, RC, RP, HH, and KS critically reviewed the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This research was supported by PROCIENCIA-CONCYTEC (Peru) under the Basic Research Program E041-2018-01, Contract N°114-2018-FONDECYT.
## 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.2023.1132228/full#supplementary-material
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|
---
title: 'Intermittent fasting and immunomodulatory effects: A systematic review'
authors:
- Zhangyuting He
- Haifeng Xu
- Changcan Li
- Huayu Yang
- Yilei Mao
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10011094
doi: 10.3389/fnut.2023.1048230
license: CC BY 4.0
---
# Intermittent fasting and immunomodulatory effects: A systematic review
## Abstract
### Introduction
strategy of periodic food restriction and fixed eating windows, could beneficially modify individuals by losing body weight, regulating glucose or lipid metabolism, reducing blood pressure, and modulating the immune system. Specific effects of IF and its mechanisms have not yet been assessed collectively. Thus, this systematic review aims to summarize and compare clinical trials that explored the immunomodulatory effects of IF.
### Methods
After screening, 28 studies were included in this systematic review.
### Results
In addition to weight loss, IF could benefit health subjects by strengthening their circadian rhythms, migrating immune cells, lower inflammatory factors, and enriching microbials. In addition of the anti-inflammatory effect by regulating macrophages, protection against oxidative stress with hormone secretion and oxidative-related gene expression plays a key beneficial role for the influence of IF on obese subjects.
### Discussion
Physiological stress by surgery and pathophysiological disorders by endocrine diseases may be partly eased with IF. Moreover, IF might be used to treat anxiety and cognitive disorders with its cellular, metabolic and circadian mechanisms. Finally, the specific effects of IF and the mechanisms pertaining to immune system in these conditions require additional studies.
## 1. Introduction
Fasting has recently received increasing attention for its advantages on body health [1]. Dietary habits that involve fat-rich foods and snacks may lead to chronic diseases [2]. Intermittent fasting (IF), as a dieting strategy, combines periodic energy restriction and fixed-duration eating windows [3]. Different types of IF that incorporate varied combinations of fasting and eating windows have been proposed; examples include alternate-day fasting (36 h of fasting and 12 h of ad libitum eating) [4] and time-restricted fasting (16 h of fasting and 8 h of ad libitum eating) [5] (Figure 1).
**Figure 1:** *Content and presumed influences on body of intermittent fasting.*
It has been shown that IF is effective for decreasing body weight [6], and it can help to regulate glucose or lipid metabolism and reduce blood pressure [7] (Figure 1). In one study, numerous subjects with metabolic syndrome experienced improvements in lipid and glucose metabolism after IF [8]. Another study had also noted that healthy and lean people may experience metabolic improvements by resetting their dietary intake with a schedule of fasting and eating [9]. As studies of additional parameters including pre-inflammatory markers have been conducted, other effects of fasting have been observed.
One area of great interest is the influence of fasting on the immune system, which responds to stressful and harmful events in the body [10]. The immune system can be regulated by weight reduction; changes in lipid and glucose metabolism; and other processes, such as circadian rhythm changes (10–14). Whether the influence of fasting on the immune system would benefit different populations—including healthy people, people with metabolic syndromes, and those with other physiological or pathophysiological conditions—is subject to discussion.
In this systematic review, we summarize clinical trials that studied the immunomodulatory effects of IF. All types of subjects were included and divided into different groups including healthy subjects, obese subjects and others, to clarify the cross-effect between IF and subjects under different physiological and pathophysiological situations, including pregnancy, perioperative period，endocrine disease, cancer and autoimmune diseases. The purpose of this systematic review is to analyze and compare current trials on this topic and to provide insight into the possible influence of IF on the immune system.
## 2. Methods
This systematic review was conducted and presented according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines (Supplementary Table S1) and Assessment of Multiple Systematic Reviews 2(AMSTAR 2) tools (Supplementary Table S2). Various databases were searched, including Cochrane, PubMed, and Embase, from January 2005 to August 2022. The terms used for the literature research were “time-restricted feeding,” “time-restricted eating,” “intermittent fasting,” “feeding schedule,” “food timing,” “meal frequency,” “compressed feeding,” and “restricted food intake.” These terms were then united with “OR.” In addition, the terms “normal human,” “adult,” “patient,” and “human” were linked with “OR.” The terms “immune,” “immunity,” “immunologic,” “lymphocyte,” “chemokine,” “interleukin,” “C-reactive protein,” “CRP,” “neutrophils,” “oxidative stress,” “oxidative burst,” “inflammatory,” “inflammation,” “immunoglobulin,” “autoimmune,” “lipid peroxidation,” “homocysteine,” “malondialdehyde,” “MDA,” “glutathione,” and “GSH” were united with “OR” and then added together with the aforementioned terms.
The inclusion criteria were as follows: randomized control trials and cohort studies; age > 18 years; one type of IF conducted; and at least one immunomodulatory marker analyzed. Exclusion criteria were as follows: intervention not strictly followed; no fasting procedure included in the intervention; IF combined with other eating interventions, such as liquid diet, protocol; and review articles.
A total of 3,558 potentially eligible articles were collected from the databases. After screening, 89 articles were selected for full-text review, of which 61 were excluded for unexpected interventions (Figure 2). Twenty-eight studies were later grouped into effects on healthy people, effects on obese subjects, and effects on other subjects according to the trial set. These grouping procedures were performed by two independent researchers before August 2022. The following parameters were extracted from the original articles for comparison: participants, trial length, intervention, control group, immunomodulatory effect, metabolic information, and body weight.
**Figure 2:** *Search and study selection for systematic reviews (PRISMA) flow chart.*
The Cochrane Collaboration tool (Supplementary Table S3) was applied to assess risk of bias in all included studies. The levels of evidence were as follows: randomized trials, nonrandomized controlled trials, historically controlled cohort studies, and single-arm noncontrolled trials. Because different trials had different levels of bias, a meta-analysis was not performed.
## 3.1. Effects on non-obese healthy people
Eleven studies measured the immunomodulatory effect of IF on healthy people, and some included assessment of body weight changes or metabolic differences (Table 1).
**Table 1**
| Reference | Intervention | Control | Participants | Trial length | Immune immunomodulatory effect | Glucose metabolism | Lipid metabolism | Others | Body weight |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Varady et al. (5) | ADF | RCT | BMI26 | 12w | CRP: ↓(p < 0.01) Leptin: ↓(p < 0.03) Adiponectin: ↑(p < 0.01) | | TC: ⌀ LDL: ⌀ HDL: ⌀ TC*: ↓(p < 0.01) | DBP: ↓ (p < 0.05) SBP: ⌀ | ↓, −6.5 ± 1.0% (p < 0.001), on average 5.2 kg. |
| Wegman et al. (15) | ADF | Crossover | BMI23 | 3w | Gene-upregulated*: SIRT1, SIRT3, SOD2, TFAM | Insulin: ↓(p = 0.0023) | | | |
| Paoli et al. (16) | TRF | Before-after study | resistance-trained male | 8w | Adiponectin: ↑(p = 0.0000) Leptin: ↓(p = 0.0001) IL-1b: ↓(p = 0.0235) TT: ↓(p = 0.0476) IGF-1: ↓(p = 0.0397) IL-6*: ↓(p = 0.0035) TNF-α*: ↓(p = 0.0001) | Insulin: ↓(p = 0.0303) Glucose: ↓(p = 0.0011) | TG: ↓(p = 0.0201) HDL: ↑(p = 0.0142) LDL: ⌀ | | ↓(p = 0.0448) |
| Lauridsen et al. (3) | IF | Before-after study | lean | 4w | TNF-α: ⌀ IL-6: ⌀ IL-10: ⌀ Adiponectin: ⌀ Leptin: ⌀ Cortisol: ⌀ | Glucose: ⌀ Insulin: ⌀ HOMAIR: ⌀ HbA1c: ⌀ | HDL: ⌀ LDL: ⌀ TG: ⌀ TC: ⌀ | ALT: ⌀ SBP: ⌀ DBP: ⌀ | ↓(p = 0.05), on average 1.0 kg. |
| Gasmi et al. (17) | TRF | RCT | Young and aged | 12w | Red cells: ⌀ Monocytes: ⌀ Neutrophils: ↓ White blood cells: ↓ Lymphocytes: ↓ Natural killer cell: ↓ | | | | ↓ young, (p < 0.05) |
| Madeo et al. (18) | ADF | Cross-sectional | healthy middle-aged | 4w | Monocytes: ⌀ Lymphocyte: ⌀ B cell: ⌀ CD4 T cell: ⌀ β-hydroxybutyrate*: ↓, (p = 0.003) | | TC: ↓(p = 0.004) HDL: ⌀ LDL: ↓(p = 0.011) VLDL: ↓(p = 0.009) TG: ↓(p = 0.010) | SBP: ↓(p = 0.006) DBP: ↓(p = 0.0302) | ↓, (p < 0.0001), on average 3.5 kg. |
| McAllister et al. (19) | TRF | RCT | BMI28 | 4w | Cortisol*: ↓ Adiponectin*: ↑ CRP: ↑ | Glucose: ⌀ Insulin*: ↑ | LDL: ↑ HDL: ⌀ TG: ↓ TC: ↓ | SBP: ↑ (P = 0.04) DBP: ⌀ | |
| Li et al. (20) | TRF | RCT | healthy man | 25d | IL-1β: ⌀ TNF-α: ⌀ Gene-upregulated: Bmal1(p = 0.0020), Clock(p = 0.0302), SIRT1(p = 00068) Microbial richness: ↑ (p < 0.005) | | TC: ↓ (p < 0.0001) TG: ↓(p = 0.0052) LDL: ⌀ HDL: ↑(p < 0.0001) | AKP: ↓(p < 0.009) AST: ↓(p = 0.0268) ALT: ↓(p = 0.0174) Albumin: ↓, (p < 0.0001) | |
| Moro et al. (9) | TRF | RCT | cyclist | 4w | TT: ↓(p = 0.0497) CRP: ⌀ ESR: ⌀ IL-6: ⌀ Adiponectin: ⌀ TNF: ⌀ TSH: ⌀ T3: ⌀ Cortisol*: ↓(p = 0.0005) IGF-1: ⌀ | Glucose: ⌀ Insulin: ⌀ | TC: ⌀ TG: ⌀ | Cr: ⌀ | ↓,2%(P = 0.04) |
| Paoli et al. (1) | TRF | RCT | healthy | 2 m/12 m | TT: ↓(p < 0.001) IGF-1: ↓(p = 0.039) Adiponectin: ↑(p = 0.001) Leptin: ↓(p < 0.001) Il-6: ↓(p = 0.038) IL-1β: ↓(p < 0.001) TNF-α: ↓(p = 0.042) | Glucose: ↓(p < 0.0001) Insulin: ↓(p < 0.0001) HOMA-IR: ↓(p < 0.0001) | TC: ⌀(p = 0.289) HDL: ↑(p < 0.001) LDL: ⌀(p = 0.129) TG: ↓(p < 0.0001) | | ↓(p = 0.001), on average 2.89 kg. |
| Mao et al. (21) | TRF | RCT | healthy | 5w | TNF-α: ↓(p = 0.024) IL-8: ↓(p = 0.045) CRP: ⌀ WBC: ⌀ Microbial-diversity: ↑ (p = 0.049) Resistin: ⌀ Leptin: ⌀ Ghrelin: ⌀ gene-upregulated: SIRT1, BMAL1, PER2, SIER1 | HOMA-IR: ↓, (p < 0.001, p = 0.002) Glucose: ↓(p = 0.005) | HDL: ⌀ LDL: ⌀ TC: ⌀ TG: ⌀ | SBP: ⌀ DBP: ⌀ AST: ↓(p = 0.046) ALT: ⌀ ALP: ⌀ GGT: ⌀ | ↓(P = 0.009), on average 1.6 kg. |
Various parameters were selected to investigate the immunomodulatory effects of IF in the eleven studies. Two studies measured the effects on immune cells but had different results. Madeo et al. found that almost all cell subsets remained the same [18], whereas Gasmi et al. observed that neutrophils, lymphocytes, and natural killer cells changed after a twelve-week trial of IF [17]. Several studies have focused on classic inflammatory biomarkers. Lower levels of C-reactive protein (CRP), leptin, and adiponectin were observed in a study by Varady et al. [ 5]. Similar results were reported by Paoli, both in an one-year (long-term) and an 8-week (short-term) trial [1]). However, Lauridsen et al. found that measurements of parameters such as tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), and interleukin 10 (IL-10) were not significantly changed after a course of IF [3]. Mao et al. reported that lower levels of TNF-α and IL-8 could be observed after 5 weeks of IF [21]. A study by Mcallister et al. [ 19] measured cortisol levels and found no significant change and these results were replicated in a study by Moro et al. [ 9]. Moro et al. also reported a significant decrease in testosterone levels [9]. Two studies measured microbial diversity after IF and concluded that IF generated great richness [20]. Li et al. attempted to explain this change and found that sirtuin1 (SIRT1) expression was higher after IF compared with baseline levels [20], which was regarded as a stimulator for circadian genes and correlated with microbial diversity. A study by Wegman et al. also measured Sirt-1–related genes and reported similar results [15].
In eight trials, the decrease of body weight was observed after several weeks; three additional studies did not assess this factor. With regard to glucose metabolism, seven studies measured levels of fasting insulin and fasting glucose and conducted the test of homeostatic model assessment of insulin resistance (HOMA-IR) [1]. Two studies found no significant changes in these parameters [3, 9], whereas improvements in these parameters were observed in five other studies [1, 15, 16, 19, 21].
Nine studies measured parameters related to lipid metabolism, including total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL). Six of them found improvements in multiple parameters; IF was associated with higher HDL, lower TC, lower TG, and lower LDL [1, 5, 18, 20]. The remaining three studies found no significant changes in these parameters [3, 9, 21]. Effects on different parameters, such as systolic blood pressure, diastolic blood pressure, and alanine transaminase, have been reported in other studies [18, 20].
Sleep quality and appetite were evaluated in some studies [5, 19, 21], and there was no significance after IF [21]. Another study showed that during fasting, satiety and fullness of subjects were lower than controlled group, but no differences were found in nausea scores between two groups [3]. Alertness, focus perceiving and mood perceiving were measured insignificantly in one study [19].
## 3.2. Effects on obese subjects
The effects of IF on obese subjects have received much attention. Twelve studies that assessed this topic were identified (Table 2).
**Table 2**
| Reference | Intervention | Control | Participants | Trial length | Immune immunomodulatory effect | Glucose metabolism | Lipid metabolism | Others | Body weight |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Varady et al. (22) | ADF | Before-after study | Obese | 8w | CRP: ⌀ Homocysteine: ⌀ Adiponectin: ↓, −30% (p < 0.05) Leptin: ↓, −21 ± 6% (p < 0.05) Resistin: ↓, −23 ± 6% | | TC 4w: ↓, −20%(p < 0.05) LDL 4w: ↓, −31%(p < 0.05) HDL 4w: ⌀ TG: 4w ↓, −19% | | ↓, −3.83%, on average 5.7 kg. |
| Varady et al. (2) | ADF | RCT | Obese | 12 m | CRP: ⌀ Homocysteine: ⌀ | Glucose: ⌀ Insulin: ⌀ | HDL: ↑ | BP: ⌀ | *↓, −6% |
| Peterson et al. (23) | TRF | RCT | Prediabetes | 5w | 8-isoprostane: ↓, −11 pg./ml (p = 0.05) TNF-α: ⌀ cortisol: ⌀ | Glucose: ⌀ Insulin: ↓(p = 0.13) | HDL: ⌀ LDL: ⌀ TC: ↑(p = 0.0007) | SBP: ↓, -11 mmHg (p = 0.03) DBP: ↓, -10 mmHg (p = 0.03) | *↓(p = 0.12) |
| Bowen et al. (24) | ADF | RCT | Obese | 24w (16w + 82) | CRP: ↓ | Insulin: ↓ Glucose: ↓ | HDL*: ↑ LDL*: ↓ TC*: ↓ TG*: ↓ | SBP*: ↓ DBP*: ↓ | *↓, on average 11.2 kg. |
| Haus et al. (5) | ADF | RCT | Obese | 24w | Adiponectin: ↓ Leptin: ↓ IL-6: ↑ TNF-α: ⌀ | Glucose: ↓, (p = 0.031) Insulin: ↓, (p = 0.115) HOMA-IR: ↓, (p = 0.031) | | | ↓, (p < 0.001) |
| Heilbronn et al. (25) | IF | RCT | Obese | 8w | TNF-α: ⌀ IL-6: ⌀ IL-10: ⌀ Macrophage: ↓ | HOMA-IR: ↓ | | | ↓ |
| Varady et al. (6) | TRF | RCT | Obese | 10w | 8-isoprostane: ↓(p = 0.02) TNF-α: ⌀ IL-6: ⌀ | Glucose: ⌀ Insulin: ↓, (p = 0.02, p = 0.04) Insulin resistance: ↓, (p = 0.03, p = 0.04) | LDL: ⌀ HDL: ⌀ TG: ⌀ | SBP: ⌀ DBP: ⌀ | ↓,3.2%(4 h) ↓,3.2%(6 h) |
| Zouhal et al. (26) | IF | RCT | Obese | 30d | IL-6*: ↓, (p = 0.02) TNF-α*: ↓, (p = 0.019) | | | AST: ⌀ ALT: ⌀ LDH: ⌀ Urea: ⌀ | ↓,2.7% (P = 0.002) |
| Mindikoglu et al. (10) | IF | Before-after study | Metabolic syndrome | 4w | leptin: ⌀ Adiponectin: ⌀ CRP: ⌀ Homocysteine: ↑ (p = 0.0004) IL-1: ⌀ IL-6: ⌀ IL-8: ⌀ TNF-α: ⌀ Gene-upregulated: AP5Z1, YPS8, INTS6, IGFBP5, POLRMT, KIT, CROCC, PIGR, CALU Gene-downregulated: POLK, CD109, SRGN, CAMP | HOMA-IR: ⌀ Glucose: ⌀ Insulin: ⌀ | TG: ⌀ HDL: ⌀ TC: ⌀ LDL: ⌀ | SBP: ↓(P = 0.023) DBP: ↓(p = 0.002) ALT: ⌀ AST: ⌀ GGT: ⌀ ALP: ⌀ Albumin: ⌀ | ↓(p < 0.0001), on average 2,5 kg. |
| Horne et al. (27) | IF | RCT | Metabolic syndrome | 4w/13w/26w | Galectin-3: ↑(p = 0.021) | | | | |
| Heilbronn et al. (28) | IF | RCT | obese women | 8w | Gene-nonregulated: LIPE, ACACA, FASN, DGAT1 Gene-upregulated: PLIN5 Gene-downregulated: SOD1, SOD2 β-hydroxybutyrate:↑(p < 0.05) | | | | ↓(p < 0.05) |
| Safavi et al. (8) | ADF | RCT | Metabolic syndrome | 4 m | CRP: ↓(p = 0.03) TNF-α: ↓(p = 0.60) IL-6: ↓(p = 0.49) PT: ↑(p < 0.001) APTT: ↑(p = 0.04) | Glucose: ↓(p = 0.03) | | | ↓(p = 0.02), on average 6.43 kg. |
Heilbronn et al. found that levels of TNF-α, IL-6, and IL-10 changed insignificantly during 8 weeks of IF, whereas macrophage counts increased significantly [25]. Changes in CRP levels have been measured in several trials; however, almost no significant differences were observed [2, 10, 23, 24, 29]. Conversely, Varady et al. found that 8-isoprostane decreased after 10 weeks of IF [6]; these results were similar to those of a trial by Peterson et al. [ 23]. Haus et al. reported that adiponectin and leptin levels decreased after a course of 24 weeks [29], and these results were confirmed by Varady et al. in a before–after study [22]. Significant changes in IL-6 and TNF-α levels were observed in a study by Zouhal et al. [ 26]. After a four-month trial conducted by Safavi et al., subjects had lower CRP levels [8]. Mindikoglu et al. attempted to determine the immunomodulatory effects of gene expression like AP5Z1 after finding almost no significant change on inflammatory parameters [10]. Heilbronn et al. also found that gene expression like PLIN5 may result in immune system changes [28].
Significant body weight reductions were observed in all studies except that of Horne et al., which only identified significant changes in galectin-3 levels [27]. Because metabolic syndrome is often related to obesity, glucose and lipid metabolism have been extensively researched in obese subjects. Mindikoglu et al. compared fasting glucose and insulin levels before and after 4 weeks of IF and found no significant changes [10]. Varady et al. also found no improvements in glucose metabolism in obese subjects who completed IF, but that study did identify higher level HDL [2]. Six studies found that fasting insulin, fasting glucose, and HOMA-IR levels were improved after IF than before [6, 8, 23, 24, 26, 29]. Augmentation of lipid metabolism was observed in a study by Varady et al. in obese subjects [22]. However, other studies on lipid metabolism did not show such significant results. In addition to the collected metabolic findings, four studies found that IF could reduce blood pressure levels [10, 23, 24, 26].
## 3.3. Effects in other conditions
Five studies focused on the effects of IF on special populations, including individuals in special physiological states, such as during pregnancy or before or after an operation, and individuals with conditions such as polycystic ovary syndrome (PCOS), multiple sclerosis (MS), or chronic myelogenous leukemia (CML) (Table 3).
**Table 3**
| References | Intervention | Control | Participants | Trial length | Immune immunomodulatory effect | Glucose metabolism | Lipid metabolism | Others | Body weight |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Ozturk et al. (30) | IF | RCT | Pregnant | 4w | Oxidative stress index (OSI): ⌀ Total oxidant status (TOS): ⌀ Total anti-oxidant status (TAS): ⌀ | | | | |
| Nashwan et al. (31) | IF | Retrospective study | CML | | WBC, NEUT, PLT, HGB*: ⌀ BCR/ABL*:⌀ | | | | |
| Bing he et al. (32) | Eating on 8:00–16:00 | Before-after study | PCOS | 5w | TT: ↓(p = 0.048) CRP: ↓(p = 0.040) IGF-1: ↑(p = 0.006) | Glucose: ⌀ Insulin: ↓ (p = 0.017) HOMA-IR: ↓(p = 0.025) | TG: ⌀(p = 0.715) TC: ⌀(p = 0.328) LDL: ⌀(p = 0.984) | AST: ↓(p = 0.113) ALT: ↓(p = 0.027) | ↓(p < 0.001), on average 1.3 kg. |
| Fitzgerald et al. (33) | IF | RCT | Obese, multiple sclerosis | 8w | Leptin: ⌀ Adiponectin: ⌀ Memory T cell subsets: ↓ Naïve subset: ↑ Th1 cell: ↓ | | | | ↓ |
| Ginhoven et al. (34) | IF | RCT | Kidney donation，BMI25 | | CRP: ⌀ WBC, B cell, T cell: ⌀ NK cell: ↓after surgery (P < 0.001) IL-10, IL-6: ⌀ TNF-α: ⌀ before surgery, ↓after surgery Cytokine: ⌀ IL-8: ↑(p = 0.018) | | | | |
Ozturk et al. conducted a study of Ramadan IF in pregnant women. Total antioxidant status, total oxidant status, and related indices were measured; however, none showed significant changes after 4 weeks of the intervention. Pregnancy complications and birth weights were measured but showed no significant results between the IF-treated group and the controlled group [30]. A study by Ginhoven et al. focused on IF during the perioperative period; 30 subjects who underwent kidney donation surgery were randomly assigned into a 1-day fasting group and a four-day restriction group. Many indicators were examined including CRP, white blood cells (WBCs), B cells, T cells, natural killer cells, IL-10, IL-6, TNF-α, and lipopolysaccharide. No statistically significant preoperative differences between groups were observed, with the exception of IL-8, which peaked at 6 hours after surgery in both groups but was significantly higher in the restriction group ($$p \leq 0.018$$). After surgery, the restriction group showed lower natural killer cell counts, lower WBC counts, and lower TNF-α levels [34].
Yassin et al. conducted a retrospective study of the effects of IF in subjects with CML. Forty-nine subjects were enrolled and tested before, during, and after fasting. BCR-ABL expression levels were measured and showed no significant difference among the three time points. Various hematological parameters, including WBC, hemoglobin, and platelet levels, showed no significant changes [31].
An eight-hour IF was conducted in 15 women with PCOS for 5 weeks; participants reported significant decreases in body weight [32]. Metabolic parameters were also assessed, and lipid metabolism had insignificant changes, whereas fasting insulin levels and HOMA-IR decreased significantly after IF compared with their baseline levels [32]. Total testosterone decreased by approximately $10\%$, but changes in luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels were not significant [32]. A reduction in high-sensitivity CRP (hsCRP) and alanine transaminase (ALT) levels was observed, and insulin-like growth factor 1 (IGF-1) was upregulated [32]. Fitzgerald et al. found that IF could alter T-cell subsets and metabolic markers in subjects with multiple sclerosis. The subjects in that study lost an average of 3.0 kg after the eight-week trial and had no significant changes in leptin and adiponectin levels. Individuals in the IF group showed significant reductions in memory T cells and increased naïve cell subsets [33].
## 4. Discussion
For non-obese healthy people, it is believed that the body could maintain a steady state in which lipid and glucose metabolism are effective and the immune system works well [18]. From some perspectives, IF could still benefit healthy people. After IF, WBC subsets in two trials changed in different ways [17, 18]. The reduction in neutrophils may have resulted from the migration to extravascular lymphoid tissues [17]. This process requires a long intervention, so another shorter trial conducted by Madeo et al. did not show such results [18]. More studies showed that the elimination of old damaged cells would process during fasting, and more active immune cells would be generated when fasting ended [35]. In this way, IF could protect various tissues against diseases with more active immune cells by hormesis mechanisms that increase cellular stress resistance [36]. A decrease in natural killer cells is mainly linked to a decrease in IL-2 or IGF-1. Neither of which were measured in the trial by Madeo et al., but it could be observed in two studies by Paoli et al. [ 1, 16]. Besides IGF-1, other measurements also show significant changes. Adiponectin may interact with adenosine 5′-monophosphate-activated protein kinase (AMPK) [19], which then helps to regulate insulin resistance [9]. High level of adiponectin would stimulate fatty acid oxidation in skeletal muscle and inhibit glucose production in the liver, which benefit to energy homeostasis [37]. Meanwhile, adiponectin is an anti-inflammatory agent, a reduction of inflammatory markers including CRP and TNF-α could be observed in some studies [1, 5]. Changes in gene expression provide more information on immunomodulatory effects: Wegman et al. concluded that an increase in SIRT1 and sirtuin3 (SIRT3) expression could be detected after a 3-week trial [15]. For SIRT1, other studies have also shown an increase level [21]. SIRT1 is linked to circadian rhythms and cellular mechanisms, such as cell repair, division, metabolism, and growth [20]. It could be concluded that IF could protect bodies from cardiovascular diseases. SIRT3 is a member of the sirtuin family of histone deacetylases, which are primary mitochondrial protein deacetylases. Moreover, it could regulate cell metabolism, thus maintaining myocardial energy steady. SIRT3 is also believed as a protection for cardiomyocytes from oxidative stress-mediated cell damage [38]. Besides that, some animal studies showed more exciting results through SIRT3 regulation of IF. High expressions of SIRT3 in cerebral cortical and hippocampal cells could benefit for treating anxiety and cognitive disorders, which was found as considerable overlap mechanisms by which IF and exercise enhance brain function of Alzheimer’s Disease patients [39, 40]. A study by Mao et al. investigated clock genes and showed that levels of genes such as BMAL1 and PER2 were elevated in a five-week trial [21], indicating that IF could partly modulate the immune system by improving the circadian rhythm. The reinforcement of circadian rhythm could benefit body immune through promoting system recovery and the clearance of harmful cellular element [41]. Another potential immunomodulatory effect involves microbial diversity in two studies [20, 21]: Low gut microbial diversity is associated with metabolic disease [42], and high diversity may be due to the high expression of SIRT1 and high levels of HDL [20] and improve body immune system, such as liver function mentioned in the study by Li et al. Emerging evidence showed that SIRT1 could promote gut microbial population shifts by influence inflammation and circadian rhythm [43]. It has also been suggested that IF could benefit healthy people lose weight [9], even cyclists and men who practice resistance training [9, 16]. After the trial, it was concluded that IF could lose almost fat and maintain muscle mass with the measurement of muscle area of the thigh and arm. Healthy individuals might already have high insulin sensitivity at baseline; thus, IF seems to have less influence on glucose metabolism in these non-obese and healthy individuals [18]. Things were similar when focusing on lipid metabolism. A decrease in leptin was found in many studies [1, 16], which might suggest that IF could partly strengthen lipid metabolism in healthy individuals. To sum up, IF could benefit immune system of healthy people through migration of immune cells, regulation of oxidative-related and circadian-related genes, increasing gut microbial diversity and improvement of muscle-fat ratio. Trials with longer durations and more factors including anxiety degree, cognition state, microbial diversity (44–46), key gene expression, and inflammatory markers are needed to better clarify the immunomodulatory effects of IF on healthy people.
Most obese subjects would harbor inflamed adipose tissue, which could cause a persistent, low-grade, inflammatory response. Obesity is often associated with the metabolic syndrome, because fat accumulation would cause insulin resistance [47]. And evidence accumulated that persistent inflammation of adipose tissue is a central mechanism through which obesity promotes cancer risk [48]. From the perspective of immune cells, A decrease in macrophages was observed in a study by Heilbronn et al. [ 25]. Most cytokines that are produced by adipose tissue originate from nonfat cells and macrophages [29], thus the result confirmed that IF could be beneficial for inflammation associated with obesity. Recent studies have suggested that IF inhibits the nuclear factor kappa-B signaling pathway, which is an important regulator of downstream parameters including TNF-α and IL-6 [25], which is consistent with the results that IF could partly eliminate the inflammation caused by adipose tissue, with lower CRP and TNF-α [26]. There were insignificant changes of some inflammatory markers in some studies, which might be related with short trial duration and inadequate weight loss [6]. The concentration of galectin-3, which plays various roles in humans, was measured increasingly by Horne et al. in 2021 [27]. It has been shown that galectin-3 could stimulate the expression of some antiviral genes and protect against inflammation, which may result in improvements in glucose metabolism. Although changes in inflammatory factors were less significant in obese people compared with healthy subjects, the immunomodulatory effect of IF observed in obese people might reflect a suppression of oxidative stress [26]. Heilbronn et al. found that the ketone bodies, especially β-hydroxybutyrate, which protects against lipotoxicity and stimulates lipid oxidation, was significantly elevated in obese subjects [28]. As it was regarded as an epigenetic regulator in terms of histone methylation, acetylation, IF could help to delay various age-related diseases. A decrease in 8-isoprostane, a marker of oxidative stress in lipids, was observed in two studies [6, 23]. Oxidative stress is a definition of the imbalance between the production and elimination of reactive oxygen species [49]. Some other studies have suggested that, though IF might have little effect on inflammation, it may greatly influence oxidative stress, which is linked to insulin resistance [26]. Interestingly, improvements in glucose metabolism were observed in two studies that reported decreased oxidative stress markers [6, 23]. Significant changes in leptin, which is regarded as a special body weight regulating hormone, were also noted [29], meaning that the resistance to leptin is partly improved in obese subjects. Besides the ability to regulate metabolic syndrome, including lowering glucose and lipid synthesis [50], leptin is one of the mediators responsible for the inflammatory state [51]. In addition to the findings about immune cells and inflammatory markers, other study conduct tests of gene expression. Heilbronn et al. found that genes related to oxidative stress were down-regulated such as SOD1 and SOD2 [28], and Mindikoglu et al. found that the expression of other genes including the tumor activators POLK, NIFK, SRGN, CAMP, and D109, were downregulated [10], which are consistent with remitting oxidative effect and lowering cancer risk by IF. To sum up, besides the advantages of IF on obese subjects including losing body weight, regulate lipid metabolism and improve insulin resistance, which was almost suggested in all studies, IF could reduce oxidative stress and remit inflammatory state through macrophage adjustment and hormone secretion. Moreover, although evidence is accumulating that gut microbial is involved in the etiology of obesity [52] and altered by modified IF [4], relevant researches were still rare. Another issue waiting for more studies was the influence between IF and nervous system on obese subjects. Neuroinflammation, which has emerged as a crucial cause of cognitive dysfunction, such as Alzheimer’s Disease, could be caused through inflamed adipose tissue of obesity [53]. A study in obese rat showed that IF could prevent memory loss in comparison to ad libitum by regulating body metabolism [54], which offering a new sight for the advantages of IF to remit neuroinflammation.
Pregnancy is a state of high oxidative stress, which contributes to preeclampsia and restriction of fetal growth [30]. Maternal IF resulted in detrimental influence on fetal development and maternal stress stage by changing the metabolite profiles in animal studies [55]. However, IF has no significant influence on the high oxidative stress and fetal development in the human study [30]. The reason could be the different circadian rhythms between rats and humans. A case related with gestational diabetes mellitus was reported that IF is a useful intervention to reduce maternal body weight, plasma glucose, and psychological distress without any adverse effects [56]. Surgery is regarded as a shock or an acute stress, and IF is able to improve resistance to this stress. In a study by Ginhoyen et al. [ 34], a higher preoperative IL-8 level may counter the proinflammatory influence of subsequent surgery, thus TNF-α was lower in the food-restriction group after surgery. Compared with subjects in the non-fasting group, subjects in the restriction group showed a more moderate postoperative inflammatory response. For healthy people in special physiological states, such as those observed during the perioperative period, IF could reduce acute stress. More trials are needed to identify the influence on pregnant subjects, including the fetal and maternal safety, anti-stress effect and body metabolism regulation. It is worth nothing that study include in this review on pregnant subjects was a Ramadan IF trial, which might be less convincing as subjects in this study had experienced such interventions before.
PCOS is an endocrine condition closely linked to metabolic disorders. Because obesity is closely related to PCOS, it is not surprising that IF could provide benefits by reducing insulin resistance and easing hyperandrogenemia [32]. Whether IF could be applied in subjects with cancer remains unclear [57], because it may also affect chemotherapy. In a study of subjects with CML, Yassin et al. reported that fasting did not result in significant immunological effects with measurements including BCR-ACL levels and hematological parameters [31]. It was suggested that IF in some patients who have cancer could be capable of decreasing chemotherapy-related toxicity and tumor growth, however [58], more clinical trials were needed to clarify. MS is an autoimmune disease characterized by degeneration of the central nervous system [59]. The epidemiology of this condition includes a history of childhood obesity. Although no significant changes in leptin or adiponectin levels have been observed in studies of IF in MS, an observed difference in T-cell subsets in intestines might explain the immunological effects of IF that have been reported in studies [33, 60], which was also a kind of possible therapy for MS [59]. The components of the intestinal microbiome could also raise the propensity to develop MS strongly [59]. Researches about gut microbial of the influence of IF on subjects who have MS were expected as a result of migration of intestine immune cell subsets. As mentioned before, IF is beneficial for nervous system by cellular, metabolic and circadian mechanisms and a promising therapy for brain disorders, future research should disentangle whether positive effects of IF could be applied in clinical situations [61]. Besides IF, other types of diet, including energy-restricted fasting and ketogenic diet [62], were also evaluated as nutrition therapy for MS [63]. Some advantages were concluded that ketone bodies produced in these diets could serve as an alternative energy source for the brain [62], and during 3-day cycles of a fasting mimicking diet, it was found that the clinical symptoms of experimental autoimmune encephalomyelitis mice. More results was put forward that the improvement of this diet was related with immune system, including reducing inflammatory cytokines and immune cell migration. However, these diets might cause deficiency of various nutrients in long term [63]. To sum up, a special diet could serve as a unique nutrition therapy for MS with disadvantages of nutrition deficiency, which was nowadays a popular and promising topic.
The different evidence levels should be taken into consideration when analyzing the results of these studies. Of the 28 selected trials, 19 were randomized, controlled, parallel, or crossover studies. Some trials were cohort studies, and the trial focusing on CML was a retrospective study; the lack of a control group in that trial may lead to inaccurate conclusions. Trials differed in terms of baseline characteristics, study durations, meal types, and IF types. These differences may interfere with the final results. For example, Gasmi et al. studied whether young people and old people would act differently while undertaking IF [17], Paoli et al. compared all factors in a 2-month trial and in a 1-year trial [1], and Varady et al. focused on whether the influence of IF would vary with different durations of eating windows [6]. In the future, more studies on this topic should be conducted to provide new data.
This systematic review finds substantial evidence that IF can modulate the immune system in non-obese healthy people, obese people, and subjects in other physiological or pathophysiological states and these effects were clinically relevant with cognitive improvement, lipid and metabolism regulation, and inflammatory state remission. The mechanisms influenced and regulated to drive changes in each population differ. For example, non-obese healthy people can metabolize lipids and glucose efficiently, so the immunomodulatory effect is reflected in immune cell subset migration, lower inflammatory factors, upregulation of circadian rhythm–related gene expression, and greater microbial diversity. Although weight reduction has also been observed in healthy people, changes in parameters of lipid and glucose metabolism remained insignificant in most cases. In obese people, IF contributes to body health by regulating macrophages, which is related to the inflammatory stage of adipose tissue. Although many inflammatory factors did not show significant changes in obese subjects, other important factors, including 9-isoprastane, leptin, and galectin-3, had significant changes. *The* gene expression of cancer activators and lipid oxidative activators provides insights into the mechanisms behind these immunomodulatory effects. In pregnant women, IF seems safe to be conducted and possibly useful to treat endocrine disorders during pregnancy. Moreover, IF is able to improve resistance to the stress of surgery. IF can be beneficial for the immune system of individuals with PCOS by improving endocrine function. Limited trials studying the effects of IF on cancer have been conducted. For nervous system, IF is believed to be applicable to treat anxiety and cognitive disorders by cellular, metabolic and circadian mechanisms. However, more trials are needed to better understand the effects and mechanisms by which IF modulates the immune system.
## 5. Conclusion
Our systematic review, analyzing data from IF studies in different populations, suggests that IF could have immunomodulatory effects in healthy people, obese people, and people with special physiological and pathophysiological conditions. Different mechanisms may contribute to these effects. IF can benefit non-obese healthy individuals by strengthening circadian rhythms, migrating immune cells, lower inflammatory factors, and enriching microbial diversity. In addition of the anti-inflammatory effect by regulating macrophages, protection against oxidative stress with hormone secretion and oxidative-related gene expression plays a key beneficial role for the influence of IF on obese subjects. Physiological stress by surgery and pathophysiological disorders by endocrine diseases may be partly eased with IF. Moreover, IF might be used to treat anxiety and cognitive disorders with its cellular, metabolic and circadian mechanisms. Finally, the specific effects of IF and the mechanisms pertaining to immune system in these conditions require additional studies.
## 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
ZH, HX, HY, and YM contributed to conception and design of the study. ZH and HX organized the methodology, investigation, and data collection. ZH and CL performed the statistical analysis. ZH wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.
## Funding
This work was supported by grants from CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2021-I2M-1-058) and National High Level Hospital Clinical Research Funding (2022-PUMCH-B-034).
## 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.2023.1048230/full#supplementary-material
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|
---
title: 'Decomposing Disability Inequality in Unmet Healthcare Needs and Preventable
Hospitalizations: An Analysis of the Korea Health Panel'
authors:
- Sujin Kim
- Boyoung Jeon
journal: International Journal of Public Health
year: 2023
pmcid: PMC10011105
doi: 10.3389/ijph.2023.1605312
license: CC BY 4.0
---
# Decomposing Disability Inequality in Unmet Healthcare Needs and Preventable Hospitalizations: An Analysis of the Korea Health Panel
## Abstract
Objectives: This study examines the inequality between people with and without disabilities regarding unmet healthcare needs and preventable hospitalization.
Methods: We used the Korea Health Panel of 2016–2018; the final analytical observations were 43,512, including $6.95\%$ of persons with disabilities. We examined the differences in contributors to the two dependent variables and decomposed the observed differences into explained and unexplained components using the Oaxaca-Blinder approach.
Results: Unmet healthcare needs and preventable hospitalizations were $5.6\%$ p ($15.36\%$ vs. $9.76\%$) and $0.68\%$ p ($1.82\%$ vs. $0.61\%$), respectively, higher in people with disabilities than in those without, of which $48\%$ and $35\%$ were due to characteristics that the individual variables cannot explain. Decomposition of the distributional effect showed that sex, age, and chronic disease significantly increased disparities for unmet healthcare needs and preventable hospitalization. Socioeconomic factors such as income level and Medical aid significantly increased the disabled–non-disabled disparities for unmet healthcare needs.
Conclusion: Socioeconomic conditions increased the disparities, but around $35\%$–$48\%$ of the disparities in unmet healthcare needs and preventable hospitalization were due to unexplained factors, such as environmental barriers.
## Introduction
One billion people, $15\%$ of the world’s population, experience some form of disability [1]. Disability-inclusive development is increasing globally; for example, the United Nations Convention on the Rights of Persons with Disabilities promotes the full integration of persons with disabilities in societies. Additionally, the 2030 Agenda for Sustainable Development clearly states that disability cannot be a reason for the lack of access to development programming or human rights [1]. In this regard, there have been many efforts to achieve “better health for all people with disabilities” worldwide; the WHO global disability action plan seeks to remove barriers and improve access to health services and programs as one of their objectives between 2014 and 2021 [2]. The health targets for people with disabilities were included in Healthy People 2000 and later expanded in Healthy People 2010 and Healthy People 2020 [3]. However, little research has investigated disability disparities within the broader health disparities field; there are calls to reduce the disparities and include people with disabilities in the research area [3, 4].
People with disabilities have a higher prevalence of chronic diseases and are less likely to receive preventive care than persons without disabilities [5]. Mainly, people with multiple types of limitations are more likely to have problems receiving clinical preventive services, such as dental checkups and cancer screenings [6], and have poor health outcomes, such as chronic conditions and health status [7].
In Korea, the government established a national registration system for the disabled population according to the “Welfare of People with Disability Act” in 1988. Based on the system, the government provides welfare benefits for people with disabilities according to the level of legal disability, including 15 types of disabilities-physical disabilities, brain lesion disorders, visual impairment, hearing impairment, language disabilities, intellectual disabilities, autistic disorders, mental disabilities, renal impairment, cardiac impairment, respiratory impairment, hepatic impairment, facial disfigurement, intestinal or urinary fistula, and epilepsy disorder [8]. As of 2020, the system had 2.63 million, accounting for $5.1\%$ of the entire population [9]. According to previous literature, people with disabilities are more likely to have chronic diseases. For example, $84.3\%$ of people with disabilities have chronic conditions, 1.8 times higher than those without disabilities ($46.5\%$) in 2017 [10]. In addition, people with disabilities are physically inactive, have a higher proportion of osteoporosis, and have an impaired quality of life compared to those without disabilities [11]. While they are less likely to receive preventive screening services, e.g., cervical cancer screenings [11, 12] or gastric cancer screenings [13].
Meanwhile, they are more likely to use healthcare services; for example, they have a higher length of stay for inpatient care [14], and their healthcare expenditure is four times higher than persons without disabilities (5,375 thousand KRW vs. 1,298 thousand KRW) in 2017 [10].
Although people with disabilities spend more resources on their healthcare, they meet the problems of access to healthcare. According to the concept of “Patient Centered Access to Healthcare,” there are key three outcomes: reduction of unmet health care needs, avoidable hospitalization, and emergency department admission [15, 16]. Increasing access to primary care services is known to be associated with an improvement of these three outcomes, complementarily [15]. Among them, there are considerable problems in unmet health care needs and avoidable hospitalization among persons with disabilities. For example, persons with disabilities, such as those with brain and physical impairments, experience more unmet healthcare needs [17, 18], and those with intellectual and developmental disabilities are more likely to be hospitalized due to diabetes-related ambulatory care-sensitive conditions [19]. As a result, they experience poorer health outcomes, e.g., a higher incidence of cardiovascular disease and higher mortality rates than those without disabilities [20].
Previous literature proposed a conceptual framework for understanding healthcare disparities experienced by individuals with disabilities, conceptualizing how a discrepancy between personal and environmental factors may cause limited access to healthcare and quality [21]. For example, people with disabilities are socio-economically disadvantaged, have lower income and education levels, and have challenges participating in the workplace [22, 23]. Their lower socioeconomic status intensifies the barriers to access to healthcare services, as they are vulnerable to cost-related difficulties, for example, a lack of health insurance or living near the poverty level without medical aid (18, 24–26).
In addition, they experience overlapped barriers because of disabilities, which are not usually observed or measured in surveys. Such barriers may include difficulties in public transportation [18, 27], lack of accommodations specific to their particular needs, and difficulties finding adequate medical professionals who welcome people with disabilities [28]. There are also unmeasurable factors in personal characteristics, e.g., psychological distance to physician meetings [29] and health literacy problems [30], which determine the attitudes toward healthcare utilization or preventive behaviors. Therefore, we need to pay attention to the role of unobserved factors, which may cause healthcare disparities and can be further worsened by the presence of disability [31].
Despite the well-known disparities in access to healthcare by persons with disabilities, there is little evidence about the relative contribution of observed and unobserved characteristics that explain the gaps between persons with and without disabilities. The extent to which socioeconomic differences can explain the disparities between disabled and non-disabled individuals is unclear. Among the outcomes of access to healthcare, this study focused on reduction of avoidable hospitalization and unmet health care needs [15]. To our knowledge, none has distinguished between explained disparities (using covariates) and unexplained disparities (as discrimination) among the total inequalities on “unmet healthcare needs” and “preventable hospitalization” respectively. Thus, the purpose of this study is to examine the inequality between people with and without disabilities on unmet healthcare needs and preventable hospitalization, and measuring the explained and unexplained disparities in the two dependent variables using the Oaxaca-Blinder approach.
## Data Source and Participants
We obtained data from the Korea Health Panel (KHP), a nationally representative longitudinal study operated by the Korea Institute for Health and Social Affairs and the National Health Insurance Service of South Korea since 2008. Sample households were selected using a two-stage cluster method from the population census data of Statistics Korea. Surveys were conducted annually on all eligible household members using the computer-assisted personal interviewing technique. The KHP provides information on health conditions, unmet needs, healthcare utilization, socioeconomic characteristics, and demographic characteristics and has been used to analyze unmet needs and healthcare utilization [32, 33].
The KHP survey questions for defining the disability are as following: “Has (name of household member) been assessed for disability?”, and if a respondent answered “Yes, assessed as having a disability + registration,” the respondent has been defined as “persons with disability.” The disability registration system is operated by Ministry of Health and Welfare and Ministry of Patriots and Veterans Affairs, and the definition of persons with disabilities refer to persons who have been severely restricted in daily life or social life for a long time due to physical or mental disabilities: Among them, physical disability refers to major external body function disorders and internal organ disorders, and mental disability refers to a disability caused by a developmental disability or mental illness [34].
The baseline sample included 7,866 households and 24,616 household members in 2008, and about 2,500 households were added in 2013 to compensate for panel attrition [35]. The sample included 6,821 households and 18,870 household members in 2016, 6,497 households and 17,453 household members in 2017, and 6,493 households and 17,160 household members in 2018.
This study used data from individuals aged 18 years or older from the 2016–2018 KHP. Our sample was 43,517, including 3,027 ($7\%$) observations with disability and 40,490 ($93\%$) observations without disabilities. The analytical observations for unmet needs were 43,512 because the dependent variable of unmet healthcare needs has a missing value for five observations - three observations without disability and two observations with disability. We received institutional review board exemptions from the Public Institutional Bioethics Committee designated by the Ministry of Health and Welfare (IRB No. P01-202107-22-021).
## Dependent Variables
Our analyses used unmet healthcare needs and preventable hospitalization as dependent variables. Unmet healthcare needs were measured as “yes” replies to the question, “Have you ever missed seeing a doctor or getting a medical checkup that was necessary during the last year?” referring to previous literature [32, 33].
We measured preventable hospitalization as hospitalization due to ambulatory care sensitive conditions (ACSC)-related diseases. ACSC has been used to assess the quality of primary and community care, that is, access to appropriate primary care that could prevent the need for admission to hospitals. In Korea, Jeong et al. proposed Korean ACSCs [36]. They consulted a panel of Korean clinicians with the original US version of 22 ACSCs to identify Korean ACSCs and proposed a total of 13 conditions for the Korean ACSCs (KACSCs) [36]. It includes grand mal status epilepticus, convulsions, severe ear, nose, and throat infections, chronic obstructive pulmonary diseases, asthma, congestive heart failure, hypertension, angina, cellulitis, diabetes, hypoglycemia, gastroenteritis, and kidney/urinary tract infections [36]. While KHP provided three diagnoses related to hospitalization, we classified the cases where the primary disease was consistent with ACSC-related diseases as hospitalizations due to ACSC, referring to the previous study [36].
## Explanatory Variables
We included demographic and socioeconomic factors and health conditions in the analysis. Demographic factors included age, age squared, and sex. Socioeconomic factors included the existence of a spouse (yes or no), household income (low, middle, or high), an education level (middle school, high school, or university or above), employment status (employed or unemployed), residence (metropolitan or rural areas), and healthcare coverage (National Health Insurance (NHI) or Medical aid). Income groups were categorized into three groups, lower than $50\%$, $50\%$–$150\%$, and higher than $150\%$ of the median of equivalized household income. The medical aid program is a public aid scheme to secure access to health services for the low-income population. Health conditions included having chronic diseases (0, 1, 2, or 3+) and year dummy variables [2016, 2017, 2018].
## Statistical Analysis
We performed a descriptive analysis of dependent and explanatory variables for people with and without disabilities. We examined differences in contributors to the incidence of unmet healthcare needs and preventable hospitalization for adults with or without disabilities, respectively, using Ordinary Least Square (OLS) methods, that is, the linear probability model (LPM), referring to previous studies [37, 38]. When using the logit or probit model, the estimation in the Oaxaca-Blinder decomposition depends on reference groups. For binary outcomes, a convenient alternative might be to use the Oaxaca-Blinder approach with the linear probability model [39]. Thus, we interpreted the results from the linear probability model while showing both results from LPM and the logit model. With LPM, we interpreted βj as the expected change in the probability of an event occurring due to a unit change in Xj, holding all other variables constant. We tried to reduce the potential sources of bias by adjusting demographic, socioeconomic variables, and health conditions. Next, we used an Oaxaca-Blinder approach to decompose the observed differences in dependent variables by disability status into explained and unexplained components [40, 41]. The explained component reflects part of the gap attributable to the group differences in the explanatory variables, such as demographic and socioeconomic factors. The unexplained component reflects the residual difference that cannot be accounted for by the explanatory variables. We examined the detailed decomposition of the explained component using the Oaxaca command [42] and conducted all analyses using STATA software, version 16. Y¯W−Y¯WO=∑jβjWX¯jW−∑jβjWOX¯jWO=∑jβjWX¯jW−X¯jWO−∑jβjW−βjWOX¯jWO Note: w: with disability, wo: without disability.
## General Description
Table 1 shows the descriptive characteristics of disability status. The proportion of unmet needs was $15.36\%$ and $9.76\%$ for persons with and without disabilities, respectively. The proportion of preventable hospitalizations was $1.82\%$ and $0.61\%$ for persons with and without disabilities, respectively. In addition, persons with disabilities had more disadvantaged characteristics, such as a higher proportion of older individuals, no spouses, low education levels, unemployment, low income, chronic illness, being Medical aid recipients, and living in small cities compared to those without disabilities. These differences in characteristics of persons without disabilities may explain the gaps in unmet healthcare needs and preventable hospitalization, which were $5.6\%$ points and $1.21\%$ points, respectively (Table 1).
**TABLE 1**
| Unnamed: 0 | Unnamed: 1 | Without disability | With disability | p-value a |
| --- | --- | --- | --- | --- |
| Dependent variables | Dependent variables | Dependent variables | Dependent variables | Dependent variables |
| Unmet healthcare needs* | | 9.76% | 15.36% | <0.001 |
| Preventable hospitalization | | 0.61% | 1.82% | <0.001 |
| Explanatory variables | Explanatory variables | Explanatory variables | Explanatory variables | Explanatory variables |
| Sex | Female | 50.51% | 44.11% | <0.001 |
| Sex | Male | 49.49% | 55.89% | |
| Age | Age (mean) | 46.24 | 61.28 | <0.001 |
| Age | Age square (mean) | 2424.20 | 4020.55 | |
| Spouse | No | 39.61% | 44.13% | <0.001 |
| Spouse | Yes | 60.39% | 55.87% | |
| Education level | Less than high school | 30.22% | 56.57% | <0.001 |
| Education level | High school and over | 69.78% | 43.43% | |
| Working status | Unemployed | 36.12% | 64.34% | <0.001 |
| Working status | Employed | 63.88% | 35.66% | |
| Income level | Low income | 13.21% | 41.62% | <0.001 |
| Income level | Middle income | 64.36% | 49.39% | |
| Income level | High income | 22.42% | 8.99% | |
| Residence | Metropolitan | 45.85% | 37.27% | <0.001 |
| Residence | Rural areas | 54.15% | 62.73% | |
| Chronic disease | No chronic disease | 47.02% | 13.42% | <0.001 |
| Chronic disease | One chronic disease | 20.12% | 16.23% | |
| Chronic disease | Two chronic diseases | 11.61% | 15.67% | |
| Chronic disease | Three or more chronic diseases | 21.26% | 54.69% | |
| Health care coverage | National Health Insurance | 97.41% | 79.36% | <0.001 |
| Health care coverage | Medical aid | 2.59% | 20.64% | |
| year | 2016 | 33.02% | 32.15% | 0.850 |
| year | 2017 | 33.35% | 33.79% | |
| year | 2018 | 33.63% | 34.06% | |
| No. of observations | | 40490 | 3027 | |
## Unmet Healthcare Needs for People With and Without Disability
The first and second columns of Table 2 show the regression results on unmet healthcare needs. For persons without disabilities, the rate of unmet healthcare needs of male persons without disabilities was approximately $2.1\%$ lower than that of female persons without disabilities. Compared to the no-spouse group, those with spouses had a lower rate of unmet healthcare needs, about $1.7\%$, and those employed had unmet healthcare needs at a rate of approximately $4.1\%$ lower. When the income level is high, the rate of unmet healthcare needs also decreases. When the income level is in the middle or high class, the unmet healthcare experience rate is reduced compared to those with low income. The unmet healthcare experience rate of Medical aid beneficiaries was about $6.7\%$ higher than that of NHI enrollees. For persons with disabilities, the unmet healthcare needs of male persons with disabilities were approximately $4.0\%$ lower than that of female persons with disabilities. When the income level is middle or high, the rate of unmet healthcare needs also decreases. The coefficients of sex and income level were greater for persons with disabilities than those without disabilities (Table 2).
**TABLE 2**
| Unnamed: 0 | Linear probability model | Linear probability model.1 | Linear probability model.2 | Linear probability model.3 | Logit model | Logit model.1 | Logit model.2 | Logit model.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Without disability | Without disability | With disability | With disability | Without disability | Without disability | With disability | With disability |
| | Coeff. (s.e.) | p-value | Coeff. (s.e.) | p-value | Coeff. (s.e.) | p-value | Coeff. (s.e.) | p-value |
| Male | −0.0211 | *** | −0.0404 | * | −0.2447 | *** | −0.3290 | * |
| (ref. = female) | (0.004) | | (0.019) | | (0.043) | | (0.142) | |
| Age | 0.0026 | ** | 0.0075 | * | 0.0335 | *** | 0.0656 | * |
| Age | (0.001) | | (0.003) | | (0.010) | | (0.029) | |
| Age square | −0.0000 | * | −0.0001 | * | −0.0002 | * | −0.0005 | * |
| Age square | (0.000) | | (0.000) | | (0.000) | | (0.000) | |
| With spouse | −0.0165 | ** | −0.0201 | | −0.1614 | * | −0.1352 | |
| (ref. = no) | (0.006) | | (0.021) | | (0.064) | | (0.160) | |
| High school and over | 0.0032 | | 0.0246 | | 0.0947 | | 0.1997 | |
| (ref. = less than high school) | (0.005) | | (0.022) | | (0.063) | | (0.172) | |
| Employed | 0.0412 | *** | −0.0321 | | 0.4977 | *** | −0.2746 | |
| (ref. = unemployed) | (0.004) | | (0.020) | | (0.052) | | (0.170) | |
| Middle income | −0.0330 | *** | −0.0748 | *** | −0.3337 | *** | −0.5640 | *** |
| (ref. = low income) | (0.007) | | (0.019) | | (0.069) | | (0.147) | |
| High income | −0.0509 | *** | −0.1082 | *** | −0.5667 | *** | −0.9356 | ** |
| (ref. = low income) | (0.008) | | (0.028) | | (0.091) | | (0.310) | |
| Rural areas | −0.0025 | | 0.0207 | | −0.0331 | | 0.1730 | |
| (ref. = metropolitan) | (0.005) | | (0.020) | | (0.055) | | (0.162) | |
| One chronic disease | 0.0226 | *** | −0.0262 | | 0.2722 | *** | −0.2539 | |
| (ref. = no) | (0.006) | | (0.031) | | (0.066) | | (0.300) | |
| Two chronic diseases | 0.0304 | *** | 0.0182 | | 0.3387 | *** | 0.1178 | |
| (ref. = no) | (0.008) | | (0.036) | | (0.083) | | (0.312) | |
| Three or more chronic diseases | 0.0271 | *** | −0.0120 | | 0.2993 | *** | −0.1252 | |
| (ref. = no) | (0.007) | | (0.033) | | (0.078) | | (0.299) | |
| Medical aid | 0.0673 | *** | 0.0431 | | 0.5729 | *** | 0.2946 | |
| (ref. = National Health Insurance) | (0.016) | | (0.027) | | (0.113) | | (0.173) | |
| 2017 | 0.0050 | | −0.0262 | | 0.0606 | | −0.2125 | |
| (ref. = 2016) | (0.004) | | (0.020) | | (0.053) | | (0.156) | |
| 2018 | 0.0128 | ** | −0.0175 | | 0.1470 | ** | −0.1354 | |
| (ref. = 2016) | (0.004) | | (0.020) | | (0.052) | | (0.154) | |
| Constant | 0.0274 | | 0.0166 | | −3.3234 | *** | −3.0789 | *** |
| Constant | (0.018) | | (0.084) | | (0.213) | | (0.843) | |
| F | 31.48 | *** | 3.993 | *** | | | | |
| No. of observations | 40487 | | 3025 | | | | | |
Table 3 shows the results of decomposing the disabled–non-disabled gap using descriptive statistics and regression analysis results. The disabled–non-disabled disparities in unmet healthcare needs due to characteristic effects, that is, distributional effect, was $2.92\%$ point, accounting for $52\%$ of the total gap. If people with and without disabilities had the same characteristics, the unmet need for medical care for those without disabilities would have increased by about $2.92\%$ from $9.76\%$. Detailed decomposition of the distributional effect showed that sex, age, working status, income level, Medical aid, and chronic disease significantly increased the disabled–non-disabled disparities. For example, low-income persons were more concentrated in the disabled group (Table 1) and were more likely to experience unmet needs (Table 2), which led to an increase in the disabled–non-disabled disparities (Table 3). However, sex and working status appeared to decrease the disabled–non-disabled disparities. That is, sex led to a decrease in disabled–non-disabled disparities (Table 3) since disabled groups had a higher rate of male participants (Table 1) who were less likely to experience unmet healthcare needs than female participants (Table 2). Results from logit models were similar to those from LPM.
**TABLE 3**
| Unnamed: 0 | Unnamed: 1 | Linear probability model | Linear probability model.1 | Linear probability model.2 | Logit model | Logit model.1 | Logit model.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Contribution | (s.e.) | p-value | Contribution | (s.e.) | p-value |
| Overall contribution to the gap | Total gap | 0.0560 | (0.010) | *** | 0.0560 | (0.010) | *** |
| Overall contribution to the gap | Distributional effect | 0.0292 | (0.004) | *** | 0.0265 | (0.004) | *** |
| Overall contribution to the gap | Coefficient effect | 0.0267 | (0.010) | ** | 0.0295 | (0.010) | ** |
| Detailed decomposition on distributional effect | Sex | −0.0014 | (0.000) | ** | −0.0015 | (0.000) | ** |
| Detailed decomposition on distributional effect | Age | 0.0091 | (0.003) | ** | 0.0118 | (0.003) | *** |
| Detailed decomposition on distributional effect | Spouse | 0.0007 | (0.000) | | 0.0007 | (0.000) | |
| Detailed decomposition on distributional effect | Education level | −0.0008 | (0.001) | | −0.0023 | (0.002) | |
| Detailed decomposition on distributional effect | Working status | −0.0116 | (0.001) | *** | −0.0132 | (0.002) | *** |
| Detailed decomposition on distributional effect | Income level | 0.0118 | (0.002) | *** | 0.0118 | (0.002) | *** |
| Detailed decomposition on distributional effect | Residency | −0.0002 | (0.000) | | −0.0003 | (0.000) | |
| Detailed decomposition on distributional effect | Chronic disease | 0.0094 | (0.003) | *** | 0.0097 | (0.003) | *** |
| Detailed decomposition on distributional effect | Medical aid | 0.0121 | (0.003) | *** | 0.0097 | (0.002) | *** |
| Detailed decomposition on distributional effect | Year | 0.0001 | (0.000) | | 0.0001 | (0.000) | |
## Preventable Hospitalization for People With and Without Disability
Table 4 shows the regression analysis results on preventable hospitalization for people with and without disabilities. As for persons without disabilities, the preventable hospitalization rate was higher in males than females by approximately $0.3\%$. Those with three or more chronic diseases were more likely to experience preventable hospitalization by $1.1\%$. For persons with disabilities, the preventable hospitalization rate of male persons with disabilities was higher than that of female persons with disabilities by approximately $1.5\%$. People not living in metropolitan and with three or more chronic diseases were more likely to experience preventable hospitalization by approximately $1.2\%$ and $2.3\%$, respectively. The coefficients of sex and chronic disease were higher for persons with disabilities than those without disabilities (Table 4).
**TABLE 4**
| Unnamed: 0 | Linear probability model | Linear probability model.1 | Linear probability model.2 | Linear probability model.3 | Logit model | Logit model.1 | Logit model.2 | Logit model.3 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Without disability | Without disability | With disability | With disability | Without disability | Without disability | With disability | With disability |
| | Coeff. (s.e.) | p-value | Coeff. (s.e.) | p-value | Coeff. (s.e.) | p-value | Coeff. (s.e.) | p-value |
| Male | 0.0030 | ** | 0.0154 | * | 0.5743 | *** | 0.8214 | ** |
| (ref. = female) | (0.001) | | (0.007) | | (0.160) | | (0.296) | |
| Age | −0.0004 | | 0.0008 | | 0.0006 | | 0.0559 | |
| | (0.000) | | (0.001) | | (0.028) | | (0.067) | |
| Age square | 0.0000 | * | −0.0000 | | 0.0002 | | −0.0005 | |
| | (0.000) | | (0.000) | | (0.000) | | (0.001) | |
| With spouse | −0.0009 | | −0.0024 | | −0.2369 | | −0.1741 | |
| (ref. = no) | (0.001) | | (0.008) | | (0.197) | | (0.377) | |
| High school and over | 0.0015 | | −0.0044 | | 0.0570 | | −0.3614 | |
| (ref. = less than high school) | (0.001) | | (0.009) | | (0.157) | | (0.503) | |
| Employed | −0.0006 | | −0.0095 | | −0.1350 | | −0.7272 | |
| (ref. = unemployed) | (0.001) | | (0.005) | | (0.165) | | (0.406) | |
| Middle income | 0.0019 | | −0.0066 | | 0.2088 | | −0.3264 | |
| (ref. = low income) | (0.002) | | (0.005) | | (0.180) | | (0.298) | |
| High income | 0.0001 | | −0.0079 | | −0.2124 | | −0.6490 | |
| (ref. = low income) | (0.002) | | (0.007) | | (0.268) | | (0.821) | |
| Rural areas | −0.0001 | | 0.0117 | * | −0.0156 | | 0.7442 | * |
| (ref. = metropolitan) | (0.001) | | (0.006) | | (0.143) | | (0.356) | |
| One chronic disease | 0.0019 | * | −0.0005 | | 0.6587 | ** | 0.2384 | |
| (ref. = no) | (0.001) | | (0.005) | | (0.237) | | (0.958) | |
| Two chronic diseases | 0.0006 | | −0.0033 | | 0.4817 | | −0.3834 | |
| (ref. = no) | (0.001) | | (0.005) | | (0.329) | | (1.038) | |
| Three or more chronic diseases | 0.0106 | *** | 0.0229 | * | 1.5977 | *** | 1.7688 | * |
| (ref. = no) | (0.002) | | (0.009) | | (0.252) | | (0.856) | |
| Medical aid | 0.0082 | | 0.0172 | | 0.5696 | * | 0.6033 | * |
| (ref. = National Health Insurance) | (0.004) | | (0.010) | | (0.269) | | (0.275) | |
| 2017 | −0.0006 | | −0.0066 | | −0.0975 | | −0.3816 | |
| (ref. = 2016) | (0.001) | | (0.006) | | (0.152) | | (0.297) | |
| 2018 | 0.0002 | | −0.0072 | | 0.0232 | | −0.3902 | |
| (ref. = 2016) | (0.001) | | (0.005) | | (0.150) | | (0.270) | |
| Constant | 0.0060 | | −0.0184 | | −6.6906 | *** | −7.1880 | ** |
| | (0.004) | | (0.039) | | (0.718) | | (2.259) | |
| F | 8.22 | *** | 2.34 | ** | | | | |
| No. of observations | 40490 | | 3027 | | 40490 | | 3027 | |
Table 5 shows the results of decomposing the disabled–non-disabled gap using descriptive statistics and regression analysis results. The disabled–non-disabled disparities in preventable hospitalization due to characteristic effects, distributional effect, was $0.79\%$ point, accounting for $65\%$ of the total gap. In other words, if people with and without disabilities had the same characteristics, the preventable hospitalization of people without disabilities would have increased by about $0.79\%$ from $0.61\%$. Detailed decomposition of the distributional effect showed that sex, age, and chronic disease significantly increased the disabled–non-disabled disparities (Table 5). For example, persons with chronic disease were more concentrated in the disabled group (Table 1), while they were more likely to experience preventable hospitalization (Table 4).
**TABLE 5**
| Unnamed: 0 | Unnamed: 1 | Linear probability model | Linear probability model.1 | Linear probability model.2 | Logit model | Logit model.1 | Logit model.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | Contribution | (s.e.) | p-value | Contribution | (s.e.) | p-value |
| Overall contribution to the gap | Total gap | 0.0121 | (0.003) | *** | 0.0121 | (0.003) | *** |
| Overall contribution to the gap | Distributional effect | 0.0079 | (0.001) | *** | 0.0087 | (0.001) | *** |
| Overall contribution to the gap | Coefficient effect | 0.0042 | (0.003) | | 0.0034 | (0.003) | |
| Detailed decomposition on distributional effect | Sex | 0.0002 | (0.000) | * | 0.0003 | (0.000) | * |
| Detailed decomposition on distributional effect | Age | 0.0032 | (0.001) | *** | 0.0027 | (0.001) | *** |
| Detailed decomposition on distributional effect | Spouse | 0.0000 | (0.000) | | 0.0001 | (0.000) | |
| Detailed decomposition on distributional effect | Education level | −0.0004 | (0.000) | | −0.0001 | (0.000) | |
| Detailed decomposition on distributional effect | Working status | 0.0002 | (0.000) | | 0.0003 | (0.000) | |
| Detailed decomposition on distributional effect | Income level | −0.0003 | (0.000) | | −0.0000 | (0.000) | |
| Detailed decomposition on distributional effect | Residency | −0.0000 | (0.000) | | −0.0000 | (0.000) | |
| Detailed decomposition on distributional effect | Chronic disease | 0.0035 | (0.001) | *** | 0.0046 | (0.001) | *** |
| Detailed decomposition on distributional effect | Medical aid | 0.0015 | (0.001) | | 0.0009 | (0.000) | |
| Detailed decomposition on distributional effect | Year | −0.0000 | (0.000) | | −0.0000 | (0.000) | |
## Discussion
To our knowledge, this is the first study to evaluate the explained disparities (as covariates) and unexplained disparities (as discrimination) about “unmet healthcare needs” and “preventable hospitalization” between persons with and without disabilities in South Korea. Overall, persons with disabilities experienced a higher rate of unmet healthcare needs ($15.36\%$ vs. $9.76\%$) and preventable hospitalization ($1.82\%$ vs. $0.61\%$). The decomposition results showed that different characteristics between persons with and without disabilities accounted for $48\%$ and $35\%$ of the total gap for unmet healthcare needs and preventable hospitalization, respectively. It means that more than half of the difference in unmet healthcare needs and preventable hospitalization between persons with and without disabilities were unexplained components, which are not explained by the observed differences using the explanatory variables. To ensure the reliability of the analysis results, we showed the both of LPM and logit models, and we interpreted the results from the LPM because the results were not different regardless of using LPM and the logit model.
The current study showed that the gap was $5.6\%$ for unmet healthcare needs due to disability, and the explanatory variables explained $52\%$ of the total gap. The detailed decomposition showed that the gap increased with income level and Medical aid. The proportion of low-income and Medical aid was much higher among persons with disabilities than those without disabilities. Low income and Medical aid may intensify the higher probability of experiencing unmet healthcare needs among persons with disabilities because even relatively small expenses can be catastrophic to poor households with members with disabilities [43]. People with low family income and high healthcare needs due to a disability may experience high medical expenditure burdens [43, 44], which may reduce their visits to adequate healthcare services and increase the experience of unmet healthcare needs. The results were similar to those of a previous study that found socioeconomic status to be one of the main factors of healthcare disparities between persons with and without disabilities [21].
The detailed decomposition in the present study showed that the gap was decreased by sex and working status, while a previous study found that currently employed groups are less likely to receive necessary healthcare services due to “lack of time” [45], regardless of disability status. Our results showed that a higher proportion of working people among those without disabilities than those with disabilities reduced the disability-related gap.
Our finding showed that the unexplained component (coefficient effect) accounted for $48\%$ of the gap. Diverse factors can cause the unexplained component of the gap in unmet healthcare needs, e.g., environmental barriers, such as health delivery system factors (such as the geographic location of services, transportation), support and relationships (such as caregivers and immediate family members), provider access factors (such as accessibility of buildings and equipment, availability of specialists) [21], and communication skills of healthcare providers for persons with disabilities [46]. In addition, there are unobserved factors in psychological needs [29] and personal health literacy [30], which affect the attitudes to hospital visits or preventive treatment, that are not measurable using survey questionnaires.
When the dependent variable was “preventable hospitalizations,” the result showed that the gap was $1.21\%$ between persons with and without disabilities, and the explanatory variables, such as sex, age, chronic disease, and Medical aid, explained $65\%$ of the total gap. When considering the effect of age and chronic conditions, the average age of persons with disabilities was 61.3, which is about 15 years higher than those without disabilities, and chronic conditions were more prevalent among persons with disabilities than those without disabilities. That is, persons with disabilities are composed of an older population, and many have disability-related secondary or age-related chronic conditions [47]. According to previous literature, the prevalence of hypertension is 2.7 times higher, diabetes is 2.8 times higher, and cerebrovascular disease is about five times higher than that of people without disabilities [10]. Since these diseases are ACSC-related, the probability of preventable hospitalization could be higher among persons with disabilities.
When persons with disabilities live in rural areas, compared to those living in metropolitans, they are more likely to experience preventable hospitalization (Table 4). Our result reflects the accessibility problems in rural areas, as persons with disabilities may experience inadequate transportation and lower personal aid while at a higher risk of social exclusion [48, 49]. Thus, the accumulated problems would have increased the probability of experiencing preventable hospitalization in rural areas.
The unexplained component (coefficient effect) accounted for $35\%$ of the total gap between persons with and without disabilities for preventable hospitalization. The unobserved factors may be individual factors, such as secondary conditions or functional limitations, environmental factors, such as difficulties in finding a good quality of primary care, or the limited timeliness of care [12, 13, 21]. Persons with disabilities may also be at increased risk of preventable hospitalization because they experience difficulties in the daily management of ACSC. In addition, the related information, including symptoms and prevention, is not available in accessible formats such as print materials in Braille, sign language interpretation, audio provision, or graphics [1]. Meanwhile, there was no significant effect of unmet healthcare needs on preventable hospitalization, for people with and without disabilities (Supplementary Material).
In South Korea, the government introduced several policies to reduce the gap between persons with and without disabilities. First, the “Act on Guarantee of Right to Health and Access to Medical Services for Persons with Disabilities” was enacted in 2015 and implemented in December 2017 [50]. The *Act is* to improve the health of persons with disabilities by providing for matters concerning support to guarantee the right to health, establish a healthcare system, and guarantee access to healthcare for persons with disabilities. Based on this Act, a “pilot program of primary care physician system for persons with disabilities” has been implemented to improve continuous and comprehensive care [51]. Additionally, to improve general health checkups more safely and conveniently, “disability-friendly medical examination institutions” have been implemented since 2018 [51]. The above programs may reduce the unexplainable components of healthcare access disparities. Thus, further studies are needed to evaluate the effect of these new policy efforts.
There were several limitations of this study. First, the original sample retention rate was $53.8\%$ since the KHP started in 2008 ($72.3\%$ for the additional sampling of 2013) for the 2016–2018 data [35]. It may be susceptible to response bias, reducing the representativeness of the results. Second, the proportion of persons with disability ($6.95\%$) was higher than that of national statistics ($4.9\%$ of the adults aged 20 years or older and $5.1\%$ of the whole population, which includes only the disability registration system of Ministry of Health and Welfare) [9] because our study included all persons with disability in disability registration system both of Ministry of Health and Welfare and Ministry of Patriots and Veterans Affairs. Additionally, we did not distinguish the types and severity of the disability, as the sample size was insufficient to analyze the differences by disability type. Further studies need to consider the disability characteristics and the difference among persons with disability.
## Conclusion
This study compared unmet healthcare needs and preventable hospitalizations between persons with and without disabilities and divided the gap into explained and unexplainable components. The unmet healthcare needs and preventable hospitalizations were $5.6\%$ and $1.21\%$ higher in people with than without disabilities, respectively, of which $48\%$ and $35\%$ were due to characteristics that observed variables could not explain. Individual and environmental characteristics such as physical accessibility, having a caregiver to accompany them to a hospital, lack of appropriate primary care services, and invisible discrimination can be the possible components of the gap. This study is meaningful in showing the impact of invisible factors and the explainable personal characteristics in our society that causes the gaps in unmet healthcare needs and preventable hospitalization between persons with and without disabilities.
## Author Contributions
SK: study conceptualization and design, methodology, formal analysis, writing—original draft, reviewing and editing. BJ: study conceptualization and design, writing—original draft, reviewing and editing.
## Conflict of Interest
The authors declare that they do not have any conflicts of interest.
## Supplementary Material
The Supplementary Material for this article can be found online at: https://www.ssph-journal.org/articles/10.3389/ijph.2023.1605312/full#supplementary-material
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|
---
title: 'Association between serum 25-hydroxyvitamin D and osteoarthritis: A national
population-based analysis of NHANES 2001–2018'
authors:
- Guoyu Yu
- Yuan Lin
- Hanhao Dai
- Jie Xu
- Jun Liu
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC10011108
doi: 10.3389/fnut.2023.1016809
license: CC BY 4.0
---
# Association between serum 25-hydroxyvitamin D and osteoarthritis: A national population-based analysis of NHANES 2001–2018
## Abstract
### Background
Previous studies have not provided a consensus on the effect of serum 25-hydroxyvitamin D [25(OH)D] on osteoarthritis (OA). We aimed to evaluate the association using a large, nationally representative sample.
### Methods
The cross-sectional data were obtained from the 2001 to 2018 National Health and Nutrition Examination Survey (NHANES). Individuals aged ≥40 years who had information of serum 25(OH)D, self-report OA, and related covariates were included. Multivariable logistic regression analysis was employed to assess the association between serum 25(OH)D and osteoarthritis.
### Results
Among the 21,334 participants included (weighted mean age, 56.9 years; $48.5\%$ men), the proportion of participants with high serum 25(OH)D concentrations (≥100 nmol/L) increased significantly from $4.2\%$ in 2001–2006 to $18.8\%$ in 2013–2018. Higher serum 25(OH)D levels were associated with more osteoarthritis prevalence in fully adjusted model (odd ratio [OR] 1.25 [$95\%$ CI: 1.10, 1.43] for the 50–75 nmol/L group; OR 1.62 [$95\%$ CI: 1.42, 1.85] for the 75–100 nmol/L group; OR 1.91 [$95\%$ CI: 1.59, 2.30] for the ≥100 nmol/L group; with <50 nmol/L group as the reference) ($p \leq 0.001$ for trend). The association was consistent across several sensitivity analyses, including propensity score methods and excluding participants who had received vitamin D supplement. In subgroup analysis, the OR for the association increased significantly with body mass index (BMI) (BMI < 25 kg/m2, 1.01 [$95\%$ CI: 1.04, 1.08]; BMI 25–30 kg/m2, 1.05 [$95\%$ CI: 1.01, 1.08]; BMI ≥ 30 kg/m2, 1.10 [$95\%$ CI: 1.06, 1.13]; $$p \leq 0.004$$ for interaction).
### Conclusion
There was a positive correlation between serum 25(OH)D and osteoarthritis with a possible modification by BMI. Our finding raises concerns about the potential adverse effects of high serum 25(OH)D on osteoarthritis, particularly among obese individuals. More well-designed studies are still needed to validate our findings in future.
## 1. Introduction
More than 32.5 million US adults suffer from osteoarthritis (OA), which places a heavy burden on society and the economy [1]. OA risk factors can be divided into person-level factors (age, gender, genetics, and obesity) and joint-level factors (injury, malalignment, and abnormal loading), which interact with each other in a complex way [2]. Although the underlying causes of OA are still unclear, the disease is regarded as “wear and tear” arthritis characterized by a gradual loss of cartilage, inflammation of the synovium, osteophyte formation, and subchondral bone changes [3]. There are currently no disease-modifying medications available in clinical practice, emphasizing the necessity of identifying risk factors associated with OA for disease prevention and treatment.
As a steroidal hormone, vitamin D has diverse biological effects on a variety of tissues [4]. Its primary function is thought to regulate bone metabolism and calcium homeostasis. Thus, vitamin D abnormalities probably impede subchondral bone growth and remodeling, which play a critical role in the pathogenesis of OA [3]. Vitamin D is also believed to affect inflammation and muscle strength [4], which are involved in OA progression. Vitamin D has received extensive attention for its role in osteoarthritis pathogenesis, since it has such a potential impact on bone, inflammation, and muscle [5].
Previous researches have yet to achieve consensus on the effect of serum 25-hydroxyvitamin D [25(OH)D] on the incidence or development of OA. Some cross-sectional and longitudinal studies have shown that vitamin D deficiency is associated with osteoarthritis (6–8), yet others did not [9, 10]. Additionally, three large, long-term cohort studies have shown that individuals with higher serum 25(OH)D have an increased risk of OA or joint replacement (11–13). These discrepancies may be caused by a number of variables, including differences in baseline vitamin D status, geographic and ethnic differences, population characteristics, sample size, and so on [14].
Given these inconsistent findings, a better understanding of serum 25(OH)D’s effect on OA is imperative to guide public health recommendations. The purpose of this study is to assess the associations between serum 25(OH)D and osteoarthritis in a large-scale cross-sectional data set from the National Health and Nutrition Examination Survey (NHANES), which represents a nationally representative sample of the US population.
## 2.1. Study population
National Health and Nutrition Examination *Survey is* a continuous national survey conducted by the National Center for Health Statistics, a unit of the Centers for Disease Control and Prevention. In each 2 years cycle, NHANES chose a nationally representative sample of non-institutionalized civilians from the US population using a complicated, multistage probability design. All participants underwent a thorough in-home interview, followed by a detailed physical examination and blood collection in specially equipped mobile examination centers (MECs). The National Center for Health Statistics Research Ethics Review Board approved the NHANES research protocol, which included written, informed consent from all participants following the principles of the Helsinki Declaration. NHANES data have been extensively used to reliably assess the prevalence and relevant risk factors in various chronic illnesses due to the thoroughness of its research methodology. All the NHANES data used in this study were publicly accessible at https://www.cdc.gov/nchs/nhanes/ (accessed date: 15 July 2022).
For this analysis, a total of 91,351 participants were enrolled in the NHANES survey over nine cycles (2001–2018). Participants being under 40 years old ($$n = 58$$,284) were excluded. We further excluded participants with missing data on serum 25(OH)D concentrations ($$n = 3$$,702), osteoarthritis information ($$n = 3$$,641), and other covariates ($$n = 4$$,390). Finally, this study included a large national representative sample ($$n = 21$$,334) of the general adult US population. The flowchart of the study is shown in Figure 1.
**FIGURE 1:** *Flowchart of participant selection.*
## 2.2. Measurement of serum 25(OH)D
Participants’ blood samples for measuring serum 25(OH)D concentrations were collected at MECs. The samples were promptly frozen at −30°C and shipped to the National Center for Environmental Health (CDC, Atlanta, GA, USA) for analysis.
Total serum 25(OH)D, calculated as the sum of 25(OH)D3 and 25(OH)D2, is the best indicator of vitamin D levels. Serum 25(OH)D concentrations were tested using a radioimmunoassay kit (DiaSorin, Stillwater, MN, USA) in NHANES 2001–2006. The CDC applied a more analytically accurate assay involving ultra-high performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method in NHANES 2007–2018. To compare 25(OH)D levels across cycles, the CDC standardized 25(OH)D concentrations measured from radioimmunoassays to predicted LC-MS/MS equivalents using regression equations [15]. A detailed description of detection methods and standardization can be found on the official website.1
## 2.3. Assessment of osteoarthritis
Self-report OA is commonly used for case definition in epidemiological studies [16, 17]. Research by March et al. demonstrated $85\%$ consistency between self-reported OA and clinically well-defined OA [17], indicating that OA could typically be reliably self-reported. Each participant was defined as having OA if he/she responded “yes” to the question “*Has a* doctor or other health professional ever told you that you had arthritis?” and selected “OA” (2001–2010) or “OA or degenerative arthritis” (2011–2018) to the subsequent question “Which type of arthritis was it?”
## 2.4. Covariates
Previous researchers have identified age, gender, race, education level, poverty income ratio (PIR), body mass index (BMI), season of examination, alcohol consumption, smoking status, recreational physical activity, and self-reported health as potential confounders. In this study, confounders were classified in accordance with previous NHANES publications.
Age (years) was used as a continuous variable. Gender was classified as male or female. Race in NHANES was classified as Mexican Americans, Other Hispanics, Non-Hispanic Whites, and Non-Hispanic Blacks, as well as Other Races. In this study, it was collapsed into four categories: Non-Hispanic White, Non-Hispanic Black, Mexican Americans, and Other Race [18]. Education was divided into three categories: under high school, high school or equivalent, and college or above [18]. Alcohol consumption was defined as the response to the question: “In any one year, had at least 12 drinks of any type of alcoholic beverage?,” and divided into two groups (yes or no). According to the answer to the question: “Have you smoked at least 100 cigarettes in your entire life.” and “Do you now smoke cigarettes?,” smoking status was classified as current smoking, former smoking, and never smoking. Depending on BMI, individuals were classified as low to normal weight (<25 kg/m2), overweight (25–30 kg/m2), and obesity (≥30 kg/m2). The family income was categorized into three groups by the PIR: low income (PIR < 1.3), middle income (1.3 ≤ PIR < 3.5), and high income (PIR ≥ 3.5) [19]. There were two examination seasons: November–April (winter) and May–October (summer). The level of recreational physical activity was divided into two categories: active and inactive. Individuals were categorized as active if they reported moderate or vigorous recreational physical activity over the past 30 days (2001–2006) or in a typical week (2007–2018). Those who reported no moderate or vigorous recreational physical activity were categorized as inactive [20]. Dietary supplement information was obtained from the questionnaire, which was used to collect detailed information on dietary supplement use. In the current study, dietary vitamin D-containing supplement use was defined as consuming more than one dietary supplement containing vitamin D in the past 30 days.
## 2.5. Statistical analysis
Data management was conducted with R version 4.2.0.2 To ensure nationally representative estimates, analyses were adjusted for sampling weights and survey design with the R “survey” package version 4.1-1. The new sample weight was created under the NHANES analytical guidelines. Baseline characteristics were conducted according to different serum 25(OH)D levels (<50 nmol/L; 50–75 nmol/L; 50–75 nmol/L; ≥100 nmol/L) corresponding to the common clinical cut-offs. Continuous variables were summarized using means (standardized error, SE) or medians (interquartile range, 25–$75\%$), and categorical variables were described using proportions (%). Variables were compared using one-way ANOVA (normal distribution), Kruskal–Wallis (skewed distribution) test, and the Chi-square tests (categorical variables), respectively. Multivariate logistic regression analysis was adopted to assess the independent association between serum 25(OH)D and osteoarthritis with different covariates adjusted models. Odds ratio (OR) with a corresponding $95\%$ confidence interval (CI) was calculated using 25(OH)D concentrations both as continuous (per 10 nmol/L increase) and as categorical variables. Tests for linear trend were performed by using the median value of each category as a continuous variable. Subgroup and interaction analysis were performed by multivariate regression analysis adjusting for relevant effect covariates. In addition, sensitivity analyses were performed to assess the robustness of the results. First, considering the potential reverse causation by vitamin D supplement use, we conducted a sensitivity analysis that excluded participants with vitamin D supplement use. Second, as osteoarthritis is more common in older people, participants over 60 years old were re-collected and analyzed. Third, we performed inverse probability of treatment weighting (IPTW) and covariate adjustment using the propensity score (propensity score adjusted regression) to address potential confounders. The following variables were used to generate the models: age, gender, race, education level, PIR, BMI, season of examination, alcohol consumption, smoking status, recreational physical activity, vitamin D supplements, and self-reported health. In all tests, a two-sided p-value < 0.05 was considered statistically significant.
## 3.1. Baseline characteristics of participants
The weighted baseline characteristics stratified by the level of serum 25(OH)D are shown in Table 1. A total of 21,334 participants were included in our study, of whom $48.5\%$ were male, and the mean age was 56.9 years old. The overall prevalence of osteoarthritis was $18.2\%$. Participants with a higher level of serum 25(OH)D concentrations have a higher prevalence of osteoarthritis than those with a lower level of serum 25(OH)D (<50 nmol: $13.5\%$, 50–75 nmol: $15.7\%$, 75–100 nmol: $20.9\%$, ≥100 nmol/L: $28.6\%$, $p \leq 0.001$).
**TABLE 1**
| Unnamed: 0 | Serum 25(OH)D concentrations (nmol/L) | Serum 25(OH)D concentrations (nmol/L).1 | Serum 25(OH)D concentrations (nmol/L).2 | Serum 25(OH)D concentrations (nmol/L).3 | Serum 25(OH)D concentrations (nmol/L).4 |
| --- | --- | --- | --- | --- | --- |
| Characteristics | Total (n = 21,334) | <50 (n = 6,453) | 50–75 (n = 8,094) | 75–100 (n = 4,759) | ≥100 (n = 2,028) |
| Serum 25(OH)D (nmol/L), mean ± SE | 70.7 ± 0.5 | 37.6 ± 0.2 | 62.8 ± 0.1 | 85.6 ± 0.1 | 122.1 ± 0.8 |
| Age (years), mean ± SE | 56.9 ± 0.1 | 55.4 ± 0.2 | 55.8 ± 0.2 | 57.5 ± 0.2 | 61.2 ± 0.4 |
| Gender, % (95% CI) | Gender, % (95% CI) | Gender, % (95% CI) | Gender, % (95% CI) | Gender, % (95% CI) | Gender, % (95% CI) |
| Female | 51.5 (48.9, 54.1) | 53.5 (51.8, 55.2) | 46.0 (44.7, 47.3) | 50.4 (48.4, 52.4) | 67.3 (64.7, 69.9) |
| Male | 48.5 (46.1, 50.9) | 46.5 (44.8, 48.2) | 54.0 (52.7, 55.3) | 49.6 (47.6, 51.6) | 32.7 (30.1, 35.3) |
| Race/Ethnicity, % (95% CI) | Race/Ethnicity, % (95% CI) | Race/Ethnicity, % (95% CI) | Race/Ethnicity, % (95% CI) | Race/Ethnicity, % (95% CI) | Race/Ethnicity, % (95% CI) |
| Non-Hispanic White | 75.4 (70.0, 80.7) | 53.7 (50.5, 56.8) | 75.9 (73.8, 77.9) | 86.3 (84.6, 88.0) | 88.8 (87.1, 90.5) |
| Non-Hispanic Black | 9.1 (8.3, 9.9) | 23.2 (20.7, 25.7) | 6.5 (5.7, 7.3) | 3.7 (3.0, 4.4) | 3.7 (2.9, 4.5) |
| Mexican-American | 5.8 (4.9, 6.7) | 10.2 (8.3, 12.0) | 6.6 (5.5, 7.7) | 3.0 (2.4, 3.7) | 1.4 (0.9, 1.9) |
| Other | 9.8 (8.9, 10.6) | 13.0 (11.4, 14.6) | 11.0(9.8, 12.2) | 7.0(5.9, 8.0) | 6.1(4.8, 7.3) |
| Season of examination, % (95% CI) | Season of examination, % (95% CI) | Season of examination, % (95% CI) | Season of examination, % (95% CI) | Season of examination, % (95% CI) | Season of examination, % (95% CI) |
| Winter | 41.2 (37.1, 45.2) | 53.6 (48.7, 58.5) | 40.1 (35.9, 44.3) | 35.2 (31.0, 39.4) | 35.1 (29.0, 41.2) |
| Summer | 58.8 (53.1, 64.6) | 46.4 (41.5, 51.3) | 59.9 (55.7, 64.1) | 64.8 (60.6, 69.0) | 64.9 (58.8, 71.0) |
| Education level, % (95% CI) | Education level, % (95% CI) | Education level, % (95% CI) | Education level, % (95% CI) | Education level, % (95% CI) | Education level, % (95% CI) |
| <High school | 14.9 (13.9, 16.0) | 20.7 (19.3, 22.1) | 16.0 (14.8, 17.3) | 11.3 (10.0, 12.7) | 9.3(7.5, 11.0) |
| High school | 23.9 (22.3, 25.5) | 25.2 (23.5, 27.0) | 23.3 (21.8, 24.8) | 24.5 (22.8, 26.3) | 21.8 (19.2, 24.3) |
| >High school | 61.2 (57.8, 64.5) | 54.1 (52.1, 56.0) | 60.7 (58.8, 62.6) | 64.1 (61.8, 66.4) | 69.0 (66.1, 71.9) |
| Family income to poverty ratio, % (95% CI) | Family income to poverty ratio, % (95% CI) | Family income to poverty ratio, % (95% CI) | Family income to poverty ratio, % (95% CI) | Family income to poverty ratio, % (95% CI) | Family income to poverty ratio, % (95% CI) |
| <1.3 | 16.3 (15.1, 17.5) | 24.4 (22.7, 26.1) | 16.3 (14.9, 17.7) | 12.4 (11.1, 13.7) | 10.1(8.5, 11.6) |
| 1.3–3.5 | 34.3 (32.3, 36.3) | 37.4 (35.5, 39.4) | 34.6 (33.0, 36.1) | 32.6 (30.3, 34.8) | 31.8 (28.8, 34.7) |
| ≥3.5 | 49.4 (46.2, 52.7) | 38.2 (35.7, 40.7) | 49.2 (46.9, 51.4) | 55.1 (52.3, 57.8) | 58.2 (54.6, 61.8) |
| BMI (kg/m2), % (95% CI) | BMI (kg/m2), % (95% CI) | BMI (kg/m2), % (95% CI) | BMI (kg/m2), % (95% CI) | BMI (kg/m2), % (95% CI) | BMI (kg/m2), % (95% CI) |
| <25.0 | 26.5 (24.9, 28.0) | 20.6 (19.2, 21.9) | 23.2 (21.9, 24.5) | 31.1 (29.2, 33.0) | 36.8 (34.2, 39.5) |
| 25.0–30 | 35.7 (33.7, 37.7) | 30.6 (29.0, 32.2) | 37.4 (35.9, 38.8) | 37.8 (36.2, 39.4) | 35.3 (32.8, 37.8) |
| ≥30.0 | 37.8 (35.9, 39.8) | 48.8 (46.9, 50.7) | 39.4 (37.7, 41.2) | 31.1 (29.1, 33.1) | 27.8 (25.6, 30.1) |
| Smoking status, % (95% CI) | Smoking status, % (95% CI) | Smoking status, % (95% CI) | Smoking status, % (95% CI) | Smoking status, % (95% CI) | Smoking status, % (95% CI) |
| Never smoker | 50.9 (48.5, 53.4) | 48.1 (46.3, 50.0) | 50.9 (49.3, 52.5) | 51.4 (49.3, 53.5) | 54.9 (52.2, 57.6) |
| Ever smoker | 31.0 (28.9, 33.0) | 25.7 (24.2, 27.3) | 31.6 (30.1, 33.1) | 33.7 (31.9, 35.6) | 32.5 (29.9, 35.0) |
| Current smoker | 18.1 (16.9, 19.3) | 26.1 (24.6, 27.7) | 17.5 (16.3, 18.7) | 14.8 (13.3, 16.4) | 12.6 (10.6, 14.7) |
| Recreational physical activity, % (95% CI) | Recreational physical activity, % (95% CI) | Recreational physical activity, % (95% CI) | Recreational physical activity, % (95% CI) | Recreational physical activity, % (95% CI) | Recreational physical activity, % (95% CI) |
| Active | 54.8 (51.7, 57.9) | 42.2 (40.3, 44.1) | 56.1 (54.2, 57.9) | 60.6 (58.2, 63.0) | 61.0 (58.1, 63.8) |
| Inactive | 45.2 (42.6, 47.8) | 57.8 (55.9, 59.7) | 43.9 (42.1, 45.8) | 39.4 (37.0, 41.8) | 39.0 (36.2, 41.9) |
| Alcohol consumption (drink/year), % (95% CI) | Alcohol consumption (drink/year), % (95% CI) | Alcohol consumption (drink/year), % (95% CI) | Alcohol consumption (drink/year), % (95% CI) | Alcohol consumption (drink/year), % (95% CI) | Alcohol consumption (drink/year), % (95% CI) |
| <12 | 27.9 (26.2, 29.7) | 33.9 (32.0, 35.9) | 26.1 (24.3, 27.9) | 25.3 (23.4, 27.2) | 28.6 (25.2, 32.1) |
| ≥12 | 72.1 (68.0, 76.1) | 66.1 (64.1, 68.0) | 73.9 (72.1, 75.7) | 74.7 (72.8, 76.6) | 71.4 (67.9, 74.8) |
| Vitamin D supplements, % (95% CI) | Vitamin D supplements, % (95% CI) | Vitamin D supplements, % (95% CI) | Vitamin D supplements, % (95% CI) | Vitamin D supplements, % (95% CI) | Vitamin D supplements, % (95% CI) |
| Yes | 46.6 (44.0, 49.2) | 20.6 (19.1, 22.1) | 42.1 (40.3, 43.8) | 59.2 (56.8, 61.6) | 79.9 (77.2, 82.6) |
| No | 53.4 (50.7, 56.1) | 79.4 (77.9, 80.9) | 57.9 (56.2, 59.7) | 40.8 (38.4, 43.2) | 20.1 (17.4, 22.8) |
| Self-reported health, % (95% CI) | Self-reported health, % (95% CI) | Self-reported health, % (95% CI) | Self-reported health, % (95% CI) | Self-reported health, % (95% CI) | Self-reported health, % (95% CI) |
| Fair/poor | 18.4 (17.2, 19.5) | 26.6 (24.9, 28.3) | 17.7 (16.5, 19.0) | 14.9 (13.6, 16.2) | 13.4 (11.6, 15.2) |
| Moderate | 33.9 (32.2, 35.7) | 38.1 (36.6, 39.7) | 34.8 (33.4, 36.1) | 30.7 (29.0, 32.4) | 31.0 (28.0, 34.0) |
| Excellent/very good | 47.7 (44.8, 50.5) | 35.2 (33.5, 36.9) | 47.5 (45.8, 49.2) | 54.4 (52.3, 56.5) | 55.7 (52.1, 59.2) |
| Osteoarthritis, % (95% CI) | Osteoarthritis, % (95% CI) | Osteoarthritis, % (95% CI) | Osteoarthritis, % (95% CI) | Osteoarthritis, % (95% CI) | Osteoarthritis, % (95% CI) |
| Yes | 18.2 (16.9, 19.5) | 13.5 (12.4, 14.6) | 15.7 (14.5, 16.9) | 20.9 (19.4, 22.4) | 28.6 (25.9, 31.4) |
| No | 81.8 (77.9, 85.6) | 86.5 (85.4, 87.6) | 84.3 (83.1, 85.5) | 79.1 (77.6, 80.6) | 71.4 (68.6, 74.1) |
The unweighted baseline characteristics of participants with complete data vs. missing data are shown in Supplementary Table 1. Those missing data for serum vitamin D and osteoarthritis were found to be more likely to be older (63.0 vs. 59.3 of those with complete data), non-Hispanic Black (25.7 vs. $19.7\%$ of those with complete data), and female (54.4 vs. $49.5\%$ of those with complete data).
## 3.2. Temporal trends in serum 25(OH)D and osteoarthritis
In this study (Table 2), the mean serum 25(OH)D concentrations significantly increased from 62.1 ± 0.7 nmol/L in 2001–2006 to 76.9 ± 1.0 nmol/L in 2013–2018 ($p \leq 0.001$). And the prevalence of vitamin D deficiency (<30 nmol/L) reduced from $5.5\%$ ($95\%$ CI: 4.5, $6.5\%$) in 2001–2006 to $3.8\%$ ($95\%$ CI: 3.1, $4.4\%$) in 2013–2018 ($p \leq 0.001$).
**TABLE 2**
| Variables | 2001–2006 | 2007–2012 | 2013–2018 | p-Value |
| --- | --- | --- | --- | --- |
| Serum 25(OH)D (nmol/L), mean ± SE | 62.1 ± 0.7 | 71.9 ± 0.9 | 76.9 ± 1.0 | <0.001 |
| 25(OH)D cut-off group, % (95% CI) | 25(OH)D cut-off group, % (95% CI) | 25(OH)D cut-off group, % (95% CI) | 25(OH)D cut-off group, % (95% CI) | 25(OH)D cut-off group, % (95% CI) |
| <30 nmol/L | 5.5 (4.5, 6.5) | 4.8 (3.9, 5.7) | 3.8 (3.1, 4.4) | <0.001 |
| <50 nmol/L | 28.8 (26.2, 31.3) | 21.2 (18.8, 23.5) | 17.8 (15.7, 19.9) | <0.001 |
| ≥75 nmol/L | 25.5 (23.0, 28.1) | 42.5 (39.8, 45.2) | 48.4 (45.7, 51.1) | <0.001 |
| ≥100 nmol/L | 4.2 (3.2, 5.2) | 13.1 (11.4, 14.9) | 18.8 (17.1, 20.5) | <0.001 |
| Vitamin D supplements use, % (95% CI) | 45.0 (43.1, 47.0) | 45.2 (42.9, 47.5) | 49.2 (47.2, 51.2) | 0.01 |
| Osteoarthritis, % (95% CI) | 14.3 (13.4, 15.2) | 16.3 (15.0, 17.7) | 23.2 (21.5, 24.8) | <0.001 |
There was a significant rise in the percentage of persons with high serum 25(OH)D concentrations (≥100 nmol/L) (Table 2) from $4.2\%$ ($95\%$ CI: 3.2, $5.2\%$) in 2001–2006 to $18.8\%$ ($95\%$ CI: 17.1, $20.5\%$) in 2013–2018 ($p \leq 0.001$), with an obviously increased tendency toward the prevalence of osteoarthritis from $14.3\%$ ($95\%$ CI: 13.4, $15.2\%$) to $23.2\%$ ($95\%$ CI: 21.5, $24.8\%$) ($p \leq 0.001$).
## 3.3. The association between serum 25(OH)D and osteoarthritis
In this study, multiple regression analysis was employed to assess the association between serum 25(OH)D and osteoarthritis in four models. In model 1, no covariates were adjusted. Model 2 was adjusted for demographics (age, gender, race, education level, PIR, BMI, and season of examination). Model 3 was further adjusted (from model 2) for lifestyle factors (alcohol consumption, smoking status, recreational physical activity, and vitamin D supplement). As there is a close association between vitamin D status and ill health [21], model 4 was further adjusted (from model 3) for self-reported health (Table 3).
**TABLE 3**
| Exposure | OR (95% CI) | OR (95% CI).1 | OR (95% CI).2 | OR (95% CI).3 |
| --- | --- | --- | --- | --- |
| | Model 1 | Model 2 | Model 3 | Model 4 |
| Per 10 nmol/L increase | 1.11 (1.09, 1.13)1 | 1.08 (1.06, 1.10)1 | 1.07 (1.05, 1.09)1 | 1.07 (1.05, 1.09)1 |
| Clinical cut-offs | Clinical cut-offs | Clinical cut-offs | Clinical cut-offs | Clinical cut-offs |
| <50 nmol/L | 1 (reference) | 1 (reference) | 1 (reference) | 1 (reference) |
| 50–75 nmol/L | 1.19 (1.06, 1.35)1 | 1.23 (1.08, 1.40)1 | 1.22 (1.07, 1.40)1 | 1.25 (1.10, 1.43)1 |
| 75–100 nmol/L | 1.69 (1.48, 1.92)1 | 1.62 (1.41, 1.85)1 | 1.58 (1.38, 1.81)1 | 1.62 (1.42, 1.85)1 |
| ≥100 nmol/L | 2.56 (2.16, 3.04)1 | 1.93 (1.61, 2.32)1 | 1.86 (1.55, 2.25)1 | 1.91 (1.59, 2.30)1 |
| p for trend | <0.001 | <0.001 | <0.001 | <0.001 |
We found that a higher level of serum 25(OH)D was associated with a higher risk of osteoarthritis [OR for a 10 nmol/L increase of serum 25(OH)D, model 1, 1.11 ($95\%$ CI: 1.09, 1.13); model 2, 1.08 ($95\%$ CI: 1.06, 1.10); model 3, 1.07 ($95\%$ CI: 1.05, 1.09); model 4, 1.07 ($95\%$ CI: 1.05, 1.09); all $p \leq 0.001$].
Compared with the lowest 25(OH)D group (<50 nmol/L), the fully adjusted OR (model 4) was 1.25 ($95\%$ CI: 1.10, 1.43, $p \leq 0.001$) for the 50–75 nmol/L group, 1.62 ($95\%$ CI: 1.42, 1.85, $p \leq 0.001$) for the 75–100 nmol/L group, and 1.91 ($95\%$ CI: 1.59, 2.30, $p \leq 0.001$) for the ≥100 nmol/L group, indicating a positive relationship between serum 25(OH)D and osteoarthritis with a statistical significance (all $p \leq 0.001$ for trend).
## 3.4. Sensitivity analyses
After excluding participants with vitamin D supplement use (unweighted $$n = 12$$,282), serum 25(OH)D was still positively associated with osteoarthritis, either entered as a continuous variable (OR = 1.10; $95\%$ CI: 1.06, 1.13) or as a categorical variable (Table 4).
**TABLE 4**
| Analysis | Adjusted OR (95% CI) | p-Value | p for trend |
| --- | --- | --- | --- |
| Excluding participants with vitamin D supplements use1 | Excluding participants with vitamin D supplements use1 | Excluding participants with vitamin D supplements use1 | Excluding participants with vitamin D supplements use1 |
| Serum 25(OH)D per 10 nmol/L increase | 1.10 (1.06, 1.13) | <0.001 | |
| Clinical cut-offs | Clinical cut-offs | Clinical cut-offs | Clinical cut-offs |
| <50 nmol/L | 1 (reference) | | <0.001 |
| 50–75 nmol/L | 1.22 (1.03, 1.45) | 0.022 | |
| 75–100 nmol/L | 1.58 (1.28, 1.95) | <0.001 | |
| ≥100 nmol/L | 2.07 (1.45, 2.94) | <0.001 | |
| Participants aged ≥ 60 years2 | Participants aged ≥ 60 years2 | Participants aged ≥ 60 years2 | Participants aged ≥ 60 years2 |
| Serum 25(OH)D per 10 nmol/L increase | 1.07 (1.05, 1.10) | <0.001 | |
| Clinical cut-offs | | | |
| <50 nmol/L | 1 (reference) | | <0.001 |
| 50–75 nmol/L | 1.23 (1.04, 1.47) | 0.020 | |
| 75–100 nmol/L | 1.59 (1.33, 1.89) | <0.001 | |
| ≥100 nmol/L | 1.88 (1.50, 2.35) | <0.001 | |
| Inverse probability treatment weighted analyses3 | Inverse probability treatment weighted analyses3 | Inverse probability treatment weighted analyses3 | Inverse probability treatment weighted analyses3 |
| <50 nmol/L | 1 (reference) | | <0.001 |
| 50–75 nmol/L | 1.24 (1.04, 1.46) | 0.014 | |
| 75–100 nmol/L | 1.66 (1.41, 1.95) | <0.001 | |
| ≥100 nmol/L | 1.95 (1.57, 2.40) | <0.001 | |
| Covariate adjustment using the propensity score3 | Covariate adjustment using the propensity score3 | Covariate adjustment using the propensity score3 | Covariate adjustment using the propensity score3 |
| <50 nmol/L | 1 (reference) | | <0.001 |
| 50–75 nmol/L | 1.15 (1.01, 1.32) | 0.037 | |
| 75–100 nmol/L | 1.45 (1.26, 1.68) | <0.001 | |
| ≥100 nmol/L | 1.68 (1.39, 2.04) | <0.001 | |
Participants over 60 years old were re-collected and analyzed (unweighted $$n = 10$$,422). Serum 25(OH)D was still positively associated with osteoarthritis, either entered as a continuous variable (OR = 1.07; $95\%$ CI: 1.05, 1.10) or as a categorical variable (Table 4).
We further used two propensity score methods to address potential confounders. After IPTW and covariate adjustment using the propensity score, the positive association between serum 25(OH)D and osteoarthritis remained (Table 4).
## 3.5. Subgroup analysis
Subgroup analyses were further performed to explore the association in a different population setting, including age, gender, BMI, self-reported health, and vitamin D supplement use. After adjusting for the confounders, the effect size for each subgroup remains relatively stable, as shown in Figure 2. The interaction test was significant for BMI but not for age, gender, self-reported health, and vitamin D supplement use. The OR for association between serum 25(OH)D and osteoarthritis increased significantly with an increase of BMI (BMI < 25 kg/m2, 1.04 [$95\%$ CI: 1.00, 1.08]; BMI 25–30 kg/m2, 1.05 [$95\%$ CI: 1.01, 1.08]; BMI ≥ 30 kg/m2, 1.10 [$95\%$ CI: 1.06, 1.13]) ($$p \leq 0.004$$ for interaction).
**FIGURE 2:** *Subgroup analysis. Adjusted for age, gender, race, education level, PIR, BMI, season of examination, alcohol consumption, smoking status, recreational physical activity, vitamin D supplements, and self-reported health.*
## 4. Discussion
In the present cross-sectional survey, we evaluated the association between serum 25(OH)D and the risk of osteoarthritis in a large, nationally representative dataset comprising 21,334 older (>40 years) adults in the United States. Our results showed the higher level of serum 25(OH)D was independently associated with a higher risk of osteoarthritis, which was further demonstrated by subgroup and sensitivity analyses. Meanwhile, we found that BMI might be a modification for the association between serum 25(OH)D and osteoarthritis.
Previous studies on the association between serum 25(OH)D and osteoarthritis did not provide consistent results. These inconsistencies may be partly explained by confounding factors associated with low serum 25(OH)D. For instance, a cross-sectional study of 2,756 participants (1,654 females) found that the presence of osteoarthritis was higher in individuals of the lowest 25(OH)D quartiles group. Yet, individuals in the study were old (mean age 79.04 ± 7.75 years old) and had a much lower level of serum 25(OH)D (23.09 ± 9.15 nmol/L) in females [6]. Recent research indicates that low serum 25(OH)D may only be a marker but not a maker of ill-health across many conditions [21]. Thus, in older people, vitamin D deficiency may explain as a consequence of the inflammatory process and less sunlight exposure due to low activity levels with osteoarthritis, rather than a cause of disease. In another longitudinal cohort study, the authors found that vitamin D deficiency (<25 nmol/L) predicts incident or worsening of joint pain [7]. Regarding the association between vitamin D deficiency and generalized pain [22], the results could not demonstrate vitamin D deficiency independently indicates the incident or worsening of osteoarthritis. Several randomized controlled trials (RCTs) assessing the effect of vitamin D supplement on OA progression did not demonstrate positive outcomes [23]. These RCTs are limited by small sample size (<500), short length of supplement (<3 years), patients with severe osteoarthritis and relatively high vitamin D levels (≥50 nmol/L).
Conversely, we found that adults aged 40 years and older with higher serum 25(OH)D status had a higher prevalence of osteoarthritis, which was consistent with several previous researches. A Finland cohort study of 5,274 participants followed up for 10 years (50,134 person-years) showed a significant positive association between the serum 25(OH)D and the risk of knee and hip osteoarthritis [13]. In an Australian study of 9,135 individuals followed up for 9.1 (SD 2.7) years, they found that higher serum 25(OH)D was associated with an increment of hip replacement due to osteoarthritis [12]. In another independent cohort followed up for 15.7 (SD 4.6) years, they found similar results [11].
Because vitamin D is believed to have beneficial effects on bone, muscle, and inflammation, it may be easier to illustrate the association between serum 25(OH)D deficiency and the risk of osteoarthritis. But we should consider the problem from different angles or in combination. The bone-cartilage unit is a functional complex formed by subchondral bone and cartilage that play a vital role in the pathogenesis of osteoarthritis at both mechanical and biochemical levels [24, 25]. In the course of the disease, the subchondral bone is progressively remodeled and becomes thicker and stiffer, resulting in a less deformable subchondral bone with impaired shock absorbing capacity [24, 25]. As a result, excessive load is directly transferred to the articular cartilage, resulting in cartilage loss and radiographic osteoarthritis [26]. Vitamin D plays a vital role in calcium absorption and bone metabolism, and it is correlated with increased bone mineral density (BMD) [4]. Furthermore, osteoblasts from OA patients exhibit a higher vitamin D-induced bone-forming capacity [5]. As a consequence, vitamin D’s effect on bone formation might result in subchondral bone sclerosis and osteophyte, and there is growing evidence that vitamin D is related to tibial subchondral BMD [27]. Consistent with these findings, some studies have indicated that higher BMD could contribute to osteoarthritis onset and progression [28]. We found that obesity acts as a modifier of the association between serum 25(OH)D and osteoarthritis, as the odds ratio in the obesity group is much higher than in the non-obesity group. The mechanism for this interaction may be explicable. In weight-bearing joints, such as the knee, subchondral bone remodels quickly in response to mechanical loading [25]. Therefore, higher BMI combined with vitamin D may drastically accelerate the subchondral sclerosis progress. Further studies to elucidate this association at biochemical and mechanical levels may help develop new ways to treat and prevent OA.
Over the past few decades, a wealth of studies have connected vitamin D deficiency with variety non-skeletal diseases, which has generated strong interest and even excessive enthusiasm about the promising benefits of vitamin D supplement. The serum levels of 25(OH)D to be considered sufficiency varied from 25–30 to 50, 75, and even >100 nmol/L [29]. The average serum 25(OH)D in this study was as high as 70.72 ± 0.54 nmol/L, with an increasing tendency. Hence, recognizing the threshold effect of serum 25(OH)D on osteoarthritis is critical. However, in recent years, many RCTs revealed that vitamin D supplement for vitamin D-replete individuals (>50 nmol/L) do not provide demonstrable health benefits [30]. There is increasing evidence that high doses of vitamin D supplement, or high serum 25(OH)D poses risks besides just hypercalcemia or hypercalciuria. Some high-quality studies found that high-dose vitamin D supplement increased the risk of fractures and falls [31, 32], and these regimens are related with serum 25(OH)D higher than 100–112 nmol/L (40–45 ng/ml) [33]. In this study, there was a dramatic rise in the percentage of participants with high serum 25(OH)D, and as high as $18.77\%$ of participants achieved ≥100 nmol/L in NHANES 2013–2018. We also found that the risk of osteoarthritis corresponded in time with an increase in serum 25(OH)D. Meanwhile, there were only 3.75–$5.49\%$ of participants with severe vitamin D deficiency (<30 nmol/L). So, we should be more concerned about vitamin D overdose in the future, and more high-quality clinical trials with various baseline serum 25(OH)D are required to further distinguish the threshold effect of serum 25(OH)D on osteoarthritis. The availability of new data, however, vitamin D supplement for vitamin D-replete (≥50 nmol/L) individuals with OA risk factors (especially for obesity) may be unwise.
The strengths of the current study include a large sample size, the availability of comprehensive information for adjusting a multitude of potential confounders and the use of a nationally representative survey of U.S. civilian, which facilitates the generalization of our findings. Our study also has several limitations. First, serum 25(OH)D levels are known to be influenced by many different conditions. Although, we considered and adjusted many potential confounders in the study, residual confounders may still exist. And the assessment of serum 25(OH)D with a single measurement has the potential to misclassify the individuals’ long-term 25(OH)D status. However, several sensitivity analyses were conducted to demonstrate the reliability of the findings, and serum 25(OH)D is relatively stable over time [34]. Second, there are some differences between the baseline characteristics of respondents and non-respondents, which raise the possibility of non-response bias. Third, we have no information on OA sites and related radiographic imaging. Therefore, we cannot analyze data stratified by OA sites and severity. Although some individuals with early-stage asymptomatic osteoarthritis may have been missed, it is unlikely to significantly change the findings. Finally, the most concern is that osteoarthritis is prevalent among the elderly who are more aware of vitamin D supplement, the higher serum vitamin D in OA subjects may explain as a consequence. However, indications for vitamin D supplement might mainly be older or bone health rather than osteoarthritis. In addition, the NHANES data have been extensively utilized to reliably evaluate the risk factors or prevalence of many chronic diseases like OA. For instance, poor serum 25(OH)D status associated with risk of T2DM was documented in some NHANES studies, in line with other large long-term prospective studies [4]. To reduce the possibility of reverse causation, we excluded people with vitamin D supplement use in the sensitivity analysis. Nevertheless, given its cross-sectional design, reverse causation cannot be entirely removed, although this seems less likely.
## 5. Conclusion
In summary, this represents the largest study to demonstrate an association between serum 25(OH)D and the risk of osteoarthritis. We found a significantly positive association and, for the first time, reported that the association was modified by BMI. This study raises concerns about the potential adverse effects of high serum 25(OH)D on osteoarthritis, particularly among obese individuals. More well-designed studies are still needed to validate our findings in future.
## 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 in the article/Supplementary material.
## Ethics statement
The studies involving human participants were reviewed and approved by the National Center for Health Statistics Research Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JX and JL designed and conceived the research, supervised the study, and revised drafts of the manuscript. GY directed the analytic strategy. GY, YL, and HD analyzed the data and interpreted the results. GY and YL drafted the manuscript. All authors 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/fnut.2023.1016809/full#supplementary-material
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|
---
title: 'Medical student perceptions of autism education: A qualitative study'
authors:
- Laura Gallaher
- Ceri Butler
- Sube Banerjee
- Juliet Wright
- Ann White
- Stephanie Daley
journal: Frontiers in Rehabilitation Sciences
year: 2023
pmcid: PMC10011116
doi: 10.3389/fresc.2023.1096117
license: CC BY 4.0
---
# Medical student perceptions of autism education: A qualitative study
## Abstract
### Background
The global prevalence of autism is reported to be at least $1\%$ and is rising. Autistic people have a range of comorbidities resulting in a high use of health services. Doctors of nearly all specialties are likely to encounter autistic people in their practice. Autistic people report dissatisfactory care and encounter disproportionately worse health-related outcomes than non-autistic people, which in part has been attributed to a lack of skill and awareness in the medical workforce. At present, autism education is not always included in undergraduate medical curricula. In England, the Department of Health and Social Care has mandated that autism education should be included in all undergraduate medical curricula but current evidence relating to the delivery and receipt of autism education is poor. A greater understanding of medical student perceptions of autism education is required to inform curriculum development. This qualitative study sought to explore the perceptions of autism education in final year medical students at a medical school in South-East England by 1) assessing their perceived preparedness to care for autistic people once they have graduated from medical school and 2) determining their perceived acceptability of a new undergraduate education programme, Time for Autism (TfA).
### Materials and methods
A purposeful sample of ten final-year medical students were recruited. Students completed in-depth, individual interviews. Data was analysed using thematic analysis.
### Results
Four key themes were identified: Learning environment, Exposure, Relevance and Curricular priority. The findings of this study indicate that medical students perceived greatest value in autism education when it was directly relevant to developing preparedness for practice. Value was influenced by the perceived curricular priority attached to autism education. The new autism programme, Time for Autism was perceived to add relevance and priority to autism education in the existing curriculum in this medical school setting.
### Discussion
The study findings shed new light on medical education literature, emphasising the importance of congruence between the provision of autism education and the prioritisation of autism education within the curriculum. Consideration of relevance and curricular priority can be used to support the development of autism education in future medical curricula.
## Introduction
The rate of autism diagnosis is increasing globally [1]. In the United Kingdom, prevalence is currently estimated to be $1\%$–$2\%$, with approximately 1 in 100 children and 2 in 100 adults diagnosed with autism [2]. Autistic people are more likely to experience comorbidities, leading to high service use across a wide range of areas of medicine [3]. Collectively, this implies that doctors of all specialties are increasingly likely to encounter autistic people in their practice.
Autism is characterised by impairments of social interaction, language and communication skills and atypical, repetitive behaviours and interests [1]. Doctors need to be equipped with the knowledge and skills required to: recognise the signs and symptoms of autism; initiate appropriate diagnostic pathways and; provide good quality care to autistic people.
In the United Kingdom, the average time between the initial presentation and autism diagnosis is 3.6 years in children and approximately 5 years for adults [4]. In children, this delay reduces the demonstrable cognitive, linguistic and behavioural benefits of early intervention, compounds parental stress and delays the adoption of effective coping strategies within families [3, 5]. Furthermore, early intervention has been shown to ameliorate core symptoms of autism, which are associated with premature mortality [6]. Timely diagnosis relies upon doctors having the knowledge and skills to recognise the diverse signs and symptoms and varying presentations of the autistic spectrum.
The core features of autism often manifest as being unable to tolerate change and feeling threatened by unfamiliar people and environments. The associated anxiety, sensory overload and distress significantly impact the patient experience [7]. In the United Kingdom, the Equality Act requires doctors to make reasonable adjustments for autistic people in order to support their needs [8]. Autistic people and their caregivers value this flexible responsiveness as a key determinant in patient satisfaction [9].
Despite this need, doctors report a lack of understanding about autism [10]. This lack of understanding could negatively impact the quality of life of autistic people and lead to serious consequences in terms of health-related outcomes. Reports of dissatisfactory care (11–13), disproportionately worse health-related outcomes [14] and premature mortality [6] have all been linked to concerns relating to a lack of skill and knowledge in the medical workforce.
In response to these concerns, a new legislative requirement in England has been introduced to ensure that the health and social care workforce undertake autism (and learning disability) training appropriate to their specific role [15], with a supporting skills framework for caring for autistic people [16]. Within this new legislative requirement, this includes the future workforce, namely medical students, however, it is not clear as to how medical students are best taught about autism.
In the United Kingdom, medical student knowledge about autism [17] and perceived preparedness to care for autistic people [18] are both reported to be low. Autism education for medical students is described to be sparse and, in many cases, absent entirely [19]. The paucity of autism education in the training of doctors represents a missed opportunity for developing competency in this area.
A medical school in the South of England recognised the need for enhancing autism education and a new educational programme called Time for Autism (TfA) has been developed. Within Time for Autism, pairs of medical students will be partnered with a family with a school-aged autistic child. Medical students will visit the family three times over a period of 1 year, during the fourth year of their medical training. Students will be encouraged to engage with the family to learn from their experiences, to understand the challenges autistic people (and their families) encounter and to explore how doctors can best support them. Based on clinical experience, it is assumed that between $15\%$–$20\%$ of parents taking part in TfA will also be autistic, meaning the programme is not specifically focused on autistic children. TfA was based on the Time for Dementia model of education, which demonstrated that longitudinal contact between medical students and those living with dementia had a positive impact on student knowledge and attitude [20, 21]. To support the development of TfA, this qualitative study was undertaken. The aim of the study was to gain an understanding of medical student's perceptions of autism education and the acceptability of the proposed autism education programme, TfA.
## Design
A qualitative study was undertaken to understand the reality of participants' perceptions through an exploration of their experiences and the interpretations they attached to them, within a broader social construct. This contextualist epistemological approach (a bridge between realism and constructivism) [22] loaned itself to the phenomenological methodology of thematic analysis and is steered by Braun and Clarke's guided approach to thematic analysis [23].
## Sample and setting
A target of a purposive sample of 20 final year medical students from a medical school in the south of England was sought. All final year medical students ($$n = 132$$) were eligible to participate, not just those students planning to become paediatricians. All students had received one hour of autism teaching in the classroom and a further two hours of teaching during their paediatric rotation, as well as ad-hoc opportunities to meet with autistic children and adults during rotations.
## Procedure
First, all final year medical students were emailed with a brief introduction of the study, and a study information sheet. Second, a researcher (LG) attended a teaching session, which 92 students attended, and all students were invited to give consent to be contacted about taking part in the study. No incentives for participation were offered. 57 students provided written consent to be contacted about the study. Third, all 57 students were invited to take part in a qualitative interview by email, and 16 responded positively. However only 10 students responded to possible interview times. Fourth, at the beginning of each interview, written informed consent was obtained.
A topic guide was used to support the interviews. The topic guide was developed from a review of the literature on autism teaching, and is provided as a Supplementary File. Four topic areas were covered: student's previous experiences of autism; perceptions of autism teaching during their medical training; perceived preparedness to care for autistic people at the point of graduation from medical school and; perceived acceptability of TfA. A series of prompts were included to explore each area further. Students were asked, in order, to critique how autism was included in their undergraduate curriculum; to discuss their perceived preparedness to treat autistic patients and; to discuss their perceived acceptability of the TfA model. Any comparisons drawn or preferences stated were provided by the participants independently.
Interviews were conducted by LG, who was unknown to the participants. LG had undertaken post-graduate training on qualitative research methods and was supervised by two qualitative researchers (SD and CB). Interviews took place at times and venues convenient to the participant. Nine interviews were conducted in medical school buildings and one interview was conducted by telephone, all during normal working hours. All interviews were audio-recorded and transcribed verbatim and checked for accuracy by the researcher.
The target of 20 students was not met, and only ten students participated in in-depth individual interviews. On average, interviews lasted 27 min and ranged between 18 and 47 min in duration. The interviews took place between February and April 2019.
## Analysis
Interview data were analysed inductively using thematic analysis practices described by Braun and Clarke [23]. The first two interview transcripts were reviewed independently by two researchers (LG and SD), who assigned descriptive codes to meaningful sections of text and then met to discuss and agree the list of potential codes. This was refined by coding one further transcript and a coding framework was developed, which was subsequently applied to the remaining transcripts. The researchers met regularly to discuss, refine, separate and discard themes, and to identify relationships between the themes. The thematic analysis was supported by NVivo 12 (QSR International) software, which was used to highlight meaningful portions of text and to categorise data into themes. Thematic saturation was determined through the consistency of repeated themes within transcripts and the paucity of newly emerging themes in the last four transcripts analysed.
## Ethical considerations
This study was approved by the Health Research Authority, Health and Care Research (Wales) (Ref: 19/SC/0041). Students were provided with full details of the study and advised that participation was voluntary. Confidentiality was maintained through processes of data anonymisation and redaction of identifiable information from transcripts. Original recordings were destroyed following transcription. There was a small risk that students could become upset during interviews. If students became upset, it was agreed that they would be asked if they wanted to terminate the interview and be signposted to relevant university support services.
## Results
Ten medical students completed in-depth individual research interviews. The characteristics of participants are summarised in Table 1.
**Table 1**
| Total Number of students | Study sample | Entire student year group |
| --- | --- | --- |
| | 10 | 156 |
| Characteristic | Median | Median |
| Age (years) | 23.5 (range = 22–34) | 24 (range 22–50) |
| | Number (%) | No (%) |
| Gender | Gender | Gender |
| Male | 4 (40) | 64 (41) |
| Female | 6 (60) | 92 (59) |
| Ethnicity | Ethnicity | Ethnicity |
| White British/European | 7 (70) | 103 (66) |
| Asian/Asian British | 1 (10) | 26 (16) |
| Mixed/multiple ethnic groups | 1 (10) | 7 (5) |
| Black/African/Caribbean/Black British | 0 (0) | 6 (4) |
| Other | 1 (10) | 5 (3) |
| Prefer not to state | – | 9 (6) |
| Personal experience of autism | Personal experience of autism | Personal experience of autism |
| Yes | 4 (40) | –* |
| No | 6 (60) | – |
| Details of personal experience | Details of personal experience | Details of personal experience |
| Had been assessed for autism | 1 (10) | – |
| Autistic family member | 1 (10) | – |
| Vocational experience working with autistic people | 1 (10) | – |
| Autistic family member and vocational experience with autistic people | 1 (10) | – |
Sociodemographic characteristics of age, gender and ethnicity were broadly representative of the final year cohort, but participants varied in their experiences of autism outside of their curriculum. One student had been assessed for autism. Two students had autistic siblings, one of whom also had vocational experience working with autistic people. One further student also had vocational experience working with autism.
Four overarching themes were derived from the analysis. These were Learning environment, Exposure, Relevance and Curricular priority.
## Theme 1: Learning environment
Students provided a detailed critique of the perceived value of three distinct learning environments: classroom-based didactic teaching, learning in a clinical setting, and meeting autistic people in the community. Students reflected on didactic and clinical experiences within their medical education. They drew upon their experience of undertaking a longitudinal dementia programme: Time for Dementia, to consider the perceived learning value of meeting autistic people and their families in the community.
## Didactic teaching
Students had limited recall of a child development lecture which provided teaching about autism in the pre-clinical phase of their course. Students reflected that more focussed and dedicated didactic teaching during the clinical phase of their course would have been useful in providing a theoretical foundation to supplement encounters with autistic people in that phase of their training.
## Clinical setting
All of the students reflected on ad-hoc opportunities through their rotations to attend neurodevelopmental, GP specialist and adult autism clinics. Students who attended neurodevelopmental clinics appreciated the opportunity to observe an autism assessment, including observing a child's behaviour, the different components of the assessment procedure and multidisciplinary teamwork. Collectively, this helped students understand the complexity of autism assessments and which traits were important for diagnosis.
The value students placed on ad-hoc learning opportunities in clinic related to their level of involvement during the experience. For example, several students were asked to play with the child during the neurodevelopmental clinic. Some students felt that this precluded the opportunity to learn from the interaction between the parent and the clinician, whereas those students who were provided with an opportunity to discuss their observations with clinicians, perceived practical learning benefit from the experience.
Whilst students described the aforementioned experience as an opportunity to observe the clinician's communication skills and adjustments to practice with autistic people, it was also recognised that it wasn’t a standardised learning opportunity for all students.
For students who lacked personal experience of autism, placement exposure wasn't sufficient to enable them to suggest any adjustments which might support the care of autistic people beyond the use of simple and literal language. Students were unclear how they might translate these adjustments into their own professional practice and questioned their preparedness.
Students identified the broader challenges of learning in all clinical settings when a clinician might need to prioritise patient care over teaching students. This was particularly the case for some students who observed an autism assessment, but where the clinic appointment concluded before a diagnosis had been made. This left the students unclear of whether their own observations were consistent with, or sufficient for, a diagnosis of autism, with no opportunity to discuss their observations.
## Community setting
Students questioned the value of the clinic setting for observation-based learning, reflecting that the duration and nature of the clinic environment might not provide opportunity to truly understand autistic behaviours, and applauded the proposal, that TfA would include opportunities to meet autistic people in the community.
Students considered the perceived learning benefits of meeting families in various community settings, including the family home, the child's school, a play/support group and a playground, alongside expressing concern for the autistic child.. Most students supported visiting families in multiple locations, as a way of providing opportunity to observe different behaviours.
Whilst students positively appraised the perceived added value of meeting autistic people in the community, concerns were expressed about the additional time burden, travelling, additional academic demands and the repetition of visits.
## Theme 2: exposure
All students agreed that exposure to autistic individuals provided a more valuable learning experience than theory-based learning.
The value placed on exposure to autistic people over theory-based learning was associated with learning from lived experience, longitudinal contact and breadth of exposure.
## Learning from lived experience
Students drew from their experience of the Time for Dementia programme and perceived that learning from lived experience might help to understand what it is like to live with autism and the challenges that it poses.
Furthermore, most students perceived that increased exposure to families with an autistic child could aid the development of communication skills and help them feel more confident in interacting with autistic people, post-graduation from medical school.
## Breadth of exposure
Most students positively rated the opportunity for relational learning and saw benefit in learning through longitudinal contact, as proposed in the TfA programme.
However, some students were concerned about how much could be learned from repeated exposure to a single family and perceived that it limited the opportunity to learn about the heterogeneity of the autistic spectrum. Students offered various mitigations to overcome this perceived limitation of TfA, including visiting more families but less frequently or ensuring additional opportunities for exposure to autism elsewhere in the curriculum. Others were more open to sharing reflective experiences and learning from peers.
## Theme 3: relevance
All students acknowledged that they were likely to encounter autistic people in their practice, regardless of their chosen specialty. Students felt that education needed to be directly relevant to preparing for practice, highlighting the perceived importance of learning in a context that corresponds to training in the first two years after medical school graduation, emphasising the need to develop autism-specific skills required for practice in the immediate period post-graduation.
Students recognised the importance of both hard and soft skills to prepare for practice, and an awareness that these skills are learned in different ways and require different modes of delivery in the curriculum. Students perceived that development of hard skills, such as clinical knowledge, was associated with didactic teaching in the context of parallel exposure to autism. The development of soft skills, such as empathy and communication skills, was associated with interaction with autistic people. Observation was not deemed sufficient for the development of soft skills.
Students deliberated the position of autism education in the medical undergraduate curriculum and agreed that a programme that includes exposure to autistic children, such as TfA, would feel most relevant alongside their paediatric rotation, to complement learning about child development. However, students were concerned about the additional burden it might pose in an already busy clinical phase of their training.
Most students emphasised the perceived importance of autism education having clearly defined learning outcomes that link directly to caring for autistic people. Despite concerns relating to additional burden in the curriculum, students assigned value to reflection. Students considered that reflection would serve multiple purposes including being able to contemplate, as well as demonstrate, learning in the context of perceived preparedness.
## Theme 4: curricular priority
Students perceived that a curriculum which does not guarantee autism learning opportunities or which only includes non-mandatory, non-standardised learning opportunities gives the impression that autism is not an important condition to learn about, leading to poor student engagement.
## Standardised learning
Despite reservations about non-standardised learning, students recognised the value of authentic learning experiences which are, in most instances, non-standardised and cannot guarantee the opportunity for all students. Students suggested the inclusion of supplementary standardised learning experience as a means of ensuring learning and enhancing the perceived necessity for competency in understanding autism.
## Learning outcomes
Without sufficient perceived emphasis in the current curriculum, some students felt unclear about what competencies were required to care for autistic people and what the learning outcomes of autism education were.
Most students described themselves as having good, and transferable, communication skills and added that they would be able to defer to a caregiver if they had any difficulty communicating with an autistic person. These students felt sufficiently prepared to care for autistic people. A small number of students were less certain about their competency and described a lack of preparedness.
## Assessment
Students expressed a significant exam-focus, sharing that they prioritised components of the curriculum that were assessed. They promoted the use of formal assessment as a way of encouraging students to engage in autism education.
## Discussion
This study sought to gain an understanding of medical students' perceptions of autism education. Students reflected on autism learning experiences within their undergraduate curriculum, recognising limitations in their knowledge and understanding. Despite describing incompetence, students presented mixed views about their perceived preparedness to care for autistic people. Students perceived that the TfA programme would be broadly acceptable to students and would provide additional valuable opportunities for learning from lived experience but expressed concerns relating to burden and breadth of exposure.
The findings of this study provide valuable insights into how students perceive autism education. Students placed most value to learning experiences that prepare them for practice and perceived that student engagement depends upon the perceived relevance of autism education to future practice and the perceived curricular priority with which autism education is included in the curriculum.
Students expressed the need for experiential learning in a context relevant to training in the first two years after medical school graduation, to facilitate preparedness. This included the development of harder skills within a clinical setting and softer skills when provided the opportunity to learning from lived experience. This adds to existing literature about the relationship between availability of contextualised experiential learning opportunities and preparedness [18, 24] and mirrors well-established learning theories, such as Kolb's experiential learning cycle, which imparts that learning facts and ‘seeing’ is not sufficient and that learning requires ‘doing’ and active reflection [25].
This presented a problem in the current curriculum: students understood their learning needs in relation to preparedness but did not perceive that the autism education within their curriculum was appropriate to meet them. This awareness contributed to the judgements that students made about the priority with which autism education was included in their curriculum.
Perceived curricular priority was associated with a mandatory autism education that includes both didactic teaching and guaranteed exposure to autistic people, as well as formalised assessment of clearly defined learning objectives. Perceived curricular priority was negated if the opportunity to develop hard skills was non-standardised and if exposure to autistic people did not guarantee an opportunity to develop soft skills (and hard skills to a lesser extent) through experiential learning. Perceived curricular priority was further negated if the development of such skills was not scrutinised by student assessment.
Including autism education without demonstrable curricular priority presented a paradox for students. Students sensed that they were being offered elective opportunities for autism education, without any clear understanding of why, leading to poor student engagement. This conundrum exemplifies the power of the ‘hidden curriculum’ [26]. A hidden curriculum that contradicts the formal curriculum risks students misinterpreting the priority of autism education and discourages student engagement, which interferes with development of preparedness.
Despite students asserting that they had not received sufficient relevant autism education, most students perceived that transferable communication skills would be sufficient for preparedness. This incongruence suggests that lack of curricular priority prevented students acquiring an awareness of, or an understanding of the importance of, the competencies required to care for autistic people. Students were unconsciously incompetent, unaware of their lack of preparedness.
The interplay between the provision of relevant autism education and perceived curricular priority, and the subsequent development of preparedness can be mapped onto Maslow's Conscious Competence model [27], as shown in Figure 1.
**Figure 1:** *The contribution of curricular priority (conscious-provoking) and relevant (competency-driven) autism education of the development of preparedness.*
In this revised construct, preparedness is described as conscious competence and is the sum of: 1) a formal curriculum that includes autism education that is competency-driven (relevant) and; 2) hidden and formal curricula that provoke conscious awareness of autism-specific competencies (curricular priority). This model illustrates the importance of congruence between the formal and hidden curriculum in the development of preparedness.
Students described that they engage most with education they perceive to be relevant and prioritised within the curriculum. A curriculum that provides autism education in this way has the greatest potential to engage students, develop their preparedness and improve health-related outcomes for autistic people.
Students perceived TfA to be a broadly acceptable education programme that provides opportunity to improve preparedness through delivery of relevant autism education. The relevance attributed to TfA was associated with experiential opportunities perceived to extend knowledge acquired through didactic teaching; develop and apply skills through interacting with autistic people and develop a deeper understanding of the impact of autism on an individual and their family. Some students perceived that in-depth longitudinal learning with the same family might compromise the breadth of autism exposure. To address this concern, in TfA, medical students will undertake three visits to a family with an autistic child, as well as attending a wider stakeholder conference where they will meet other families with an autistic child. Students also perceived that TfA added priority to autism education through provision of mandatory autism education. However, TfA adds to an already busy curriculum, and students encouraged the use of assessment to enhance student engagement, which has been incorporated into the programme.
This study rationalises the following recommendations for TfA and for undergraduate medical curriculum design more broadly. First that the curricula should provide autism education that is directly relevant to practice, immediately post-graduation from medical school. Second, that curriculum design should emphasise autism education in such a way that the priority identified at a national level is perceived by students, encouraging motivation to engage. Within England, it is possible that the statutory requirements for autism training will bring pressure to medical schools to prioritise autism education. Future research which seeks to survey medical schools to ascertain curriculum priority (through teaching delivery and assessment) to review progress should be undertaken, as has been carried out in other neglected areas of teaching such as dementia [28] and learning disability [29].
## Strengths and limitations
The intention of this study was to understand medical student experiences and views of routine autism education to support the development of a new autism programme, TfA. We are not aware of any other studies which have sought to understand medical student experiences and views about routine autism education. The findings from this study, along with a concurrent study on the views of parents of autistic children about medical care and TfA [30], allow for lived experience to be fully incorporated within the TfA programme. The findings also offer value to other educators.
The study was designed to optimise rigor in accordance with four elements of the trustworthiness criteria for qualitative research [31]; namely credibility, transferability, dependability and confirmability, This was achieved as follows: Credibility: Choice of research methods, independent researcher, confidentialityTransferability: Focus on the general experience of autism teaching for medical students. Level/(traditional) approach to teaching likely to be similar to other medical schools, and therefore of value to other educators. Dependability: *Joint data* analysis between two of research team, the use of reflexivity, Audio recording of interviews. Confirmability: Audio recording of interviews, use of topic guide and Nvivo software. This study has four limitations. The first and main limitation of this study relates to the potential for sampling bias. The original research proposal sought to complete 20 individual in-depth interviews, with a view to being sufficient to attain thematic saturation. Despite 57 students consenting to being contacted, only 16 responded and 6 of them were unable to commit to a time to meet before leaving the area to go on their final medical elective. Thematic saturation was achieved but the sample might not have been representative of the study population by including students with a special interest in autism/strong feelings about the curriculum, as it is very unlikely that $40\%$ of the entire student year group had experience of autism. Despite these limitations, we believe that our findings are of value, given the need for improved autism education and the lack of evidence in this area. The findings shine a light on the student experience of routine autism teaching (didactic lectures and opportunistic placements) which are very likely to be a common teaching experience in many medical schools.
Second, students were only asked to consider the perceived value of autism education in their current education and in the proposed TfA model. This limits any conclusions that can be drawn from different models of delivery for autism education. Whilst the evidence of the effectiveness of autism teaching for medical students is limited, using a panel of professionals and parents along with lectures has been found to improve levels of self-reported knowledge, skills, confidence and comfort in working with autistic people [32], as well as the use of video materials of lived experience to support autism teaching [33, 34]. For the existing workforce, the ECHO model of remote ‘clinics’, which provides an interdisciplinary expert team including brief didactic teaching, case studies and guided practice, has demonstrated changes in practice following participation [35]. Presenting any of these evidence-based models may also have been acceptable to participants. It is also possible that more focussed use of ‘real life’ learning in rotation settings could have been acceptable for students, although this raises the significant challenge of standardisation and difficulty in ensuring guaranteed clinical contact.
Third, interviews were brief as they only focussed on autism education, with the mean average of interviews being 24 min for those without experience of autism, and 29 min with those with lived experience. Therefore, the broader context of the experiences of students and their perceptions of their medical education, which might have added to the interviews, was not explored.
Fourth, the final limitation is that the research only took place in one medical school. Further research is indicated to explore medical student perceptions in different medical schools in order to explore the transferability of findings.
In conclusion, this study sought to explore final year medical student perceptions of autism education. Final year medical student perceptions of autism education are intrinsically linked to the perceived relevance of autism education to future practice and the perceived priority with which it is included in the curriculum. This study advocates student-centred learning and highlights the value of involving students in pedagogic consultation.
## 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 Health Research Authority (HRA) South Central Hampshire B. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Concept: SB and JW. Design of the study: SD and SB. Participant interviews: LG. Analysis: LG and SD. Draft manuscript: LG, SD, CB. Review of manuscript: LG, SD, CB, SB, AW, JW. Completion of a MRes project: LG. 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/fresc.2023.1096117/full#supplementary-material.
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---
title: Multi-ancestry genome-wide association analyses improve resolution of genes
and pathways influencing lung function and chronic obstructive pulmonary disease
risk
authors:
- Nick Shrine
- Abril G. Izquierdo
- Jing Chen
- Richard Packer
- Robert J. Hall
- Anna L. Guyatt
- Chiara Batini
- Rebecca J. Thompson
- Chandan Pavuluri
- Vidhi Malik
- Brian D. Hobbs
- Matthew Moll
- Wonji Kim
- Ruth Tal-Singer
- Per Bakke
- Katherine A. Fawcett
- Catherine John
- Kayesha Coley
- Noemi Nicole Piga
- Alfred Pozarickij
- Kuang Lin
- Iona Y. Millwood
- Zhengming Chen
- Liming Li
- Sara R. A. Wijnant
- Lies Lahousse
- Guy Brusselle
- Andre G. Uitterlinden
- Ani Manichaikul
- Elizabeth C. Oelsner
- Stephen S. Rich
- R. Graham Barr
- Shona M. Kerr
- Veronique Vitart
- Michael R. Brown
- Matthias Wielscher
- Medea Imboden
- Ayoung Jeong
- Traci M. Bartz
- Sina A. Gharib
- Claudia Flexeder
- Stefan Karrasch
- Christian Gieger
- Annette Peters
- Beate Stubbe
- Xiaowei Hu
- Victor E. Ortega
- Deborah A. Meyers
- Eugene R. Bleecker
- Stacey B. Gabriel
- Namrata Gupta
- Albert Vernon Smith
- Jian’an Luan
- Jing-Hua Zhao
- Ailin F. Hansen
- Arnulf Langhammer
- Cristen Willer
- Laxmi Bhatta
- David Porteous
- Blair H. Smith
- Archie Campbell
- Tamar Sofer
- Jiwon Lee
- Martha L. Daviglus
- Bing Yu
- Elise Lim
- Hanfei Xu
- George T. O’Connor
- Gaurav Thareja
- Omar M. E. Albagha
- Said I. Ismail
- Said I. Ismail
- Wadha Al-Muftah
- Radja Badji
- Hamdi Mbarek
- Dima Darwish
- Tasnim Fadl
- Heba Yasin
- Maryem Ennaifar
- Rania Abdellatif
- Fatima Alkuwari
- Muhammad Alvi
- Yasser Al-Sarraj
- Chadi Saad
- Asmaa Althani
- Karsten Suhre
- Raquel Granell
- Tariq O. Faquih
- Pieter S. Hiemstra
- Annelies M. Slats
- Benjamin H. Mullin
- Jennie Hui
- Alan James
- John Beilby
- Karina Patasova
- Pirro Hysi
- Jukka T. Koskela
- Annah B. Wyss
- Jianping Jin
- Sinjini Sikdar
- Mikyeong Lee
- Sebastian May-Wilson
- Nicola Pirastu
- Katherine A. Kentistou
- Peter K. Joshi
- Paul R. H. J. Timmers
- Alexander T. Williams
- Robert C. Free
- Xueyang Wang
- John L. Morrison
- Frank D. Gilliland
- Zhanghua Chen
- Carol A. Wang
- Rachel E. Foong
- Sarah E. Harris
- Adele Taylor
- Paul Redmond
- James P. Cook
- Anubha Mahajan
- Lars Lind
- Teemu Palviainen
- Terho Lehtimäki
- Olli T. Raitakari
- Jaakko Kaprio
- Taina Rantanen
- Kirsi H. Pietiläinen
- Simon R. Cox
- Craig E. Pennell
- Graham L. Hall
- W. James Gauderman
- Chris Brightling
- James F. Wilson
- Tuula Vasankari
- Tarja Laitinen
- Veikko Salomaa
- Dennis O. Mook-Kanamori
- Nicholas J. Timpson
- Eleftheria Zeggini
- Josée Dupuis
- Caroline Hayward
- Ben Brumpton
- Claudia Langenberg
- Stefan Weiss
- Georg Homuth
- Carsten Oliver Schmidt
- Nicole Probst-Hensch
- Marjo-Riitta Jarvelin
- Alanna C. Morrison
- Ozren Polasek
- Igor Rudan
- Joo-Hyeon Lee
- Ian Sayers
- Emma L. Rawlins
- Frank Dudbridge
- Edwin K. Silverman
- David P. Strachan
- Robin G. Walters
- Andrew P. Morris
- Stephanie J. London
- Michael H. Cho
- Louise V. Wain
- Ian P. Hall
- Martin D. Tobin
journal: Nature Genetics
year: 2023
pmcid: PMC10011137
doi: 10.1038/s41588-023-01314-0
license: CC BY 4.0
---
# Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk
## Abstract
Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 588,452 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. *These* genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.
Multi-ancestry genome-wide association analyses and systematic variant-to-gene mapping strategies implicate new genes and pathways influencing lung function and chronic obstructive pulmonary disease risk.
## Main
Lung-function abnormality predicts mortality and is a diagnostic criterion for chronic obstructive pulmonary disease (COPD)1, which is the most prevalent respiratory disease globally2 and lacks disease-modifying treatments. Although smoking and other environmental risk factors for COPD are well known and genetic susceptibility is recognized, the molecular pathways underlying COPD are incompletely understood. As with other complex traits there has been a lack of ancestral diversity in genome-wide association studies (GWAS)3 of lung function4–6. Multi-ancestry studies improve the power and fine-mapping resolution of GWAS and increase the prospects for prediction, prevention, diagnosis and treatment in diverse populations3,4,7.
Understanding of the genes, proteins and pathways involved in disease-related traits underpins modern drug development. A high yield of genetic-association signals, improved signal resolution and integration with functional evidence assist confident identification of causal genes as well as the variants and pathways that impact gene function and regulation. Although datasets and in silico tools to connect GWAS signals to causal genes are improving, the findings from different datasets and tools have lacked consensus8,9, highlighting a need for frameworks to integrate functional evidence types and compare findings10.
Aggregation of lung-function-associated genetic variants into a genetic risk score (GRS) provides a tool for COPD prediction5. When a GRS comprises many variants, partitioning the GRS according to the biological pathways the variants influence could provide a tool to explore their aggregated consequences across different traits through phenome-wide association studies (PheWAS). Just as PheWAS of individual genetic variants predicts the consequences of perturbations of specific protein targets, informing assessment of drug efficacy, drug safety and drug repurposing11, PheWAS of pathway-partitioned GRS could inform the understanding of the consequences of perturbations of specific pathways.
Through the largest global assembly of lung-function genomics studies to date we: [1] undertook a multi-ancestry GWAS meta-analysis of lung-function traits in 588,452 individuals to detect novel signals, improve fine mapping and estimate heterogeneity in allelic effects attributable to ancestry; [2] tested whether lung-function signals are age- or smoking-dependent, and assessed their relationship to height; [3] investigated cell-type and functional specificity of lung-function association signals; [4] fine-mapped signals through annotation-informed credible sets, integrating functional data such as respiratory cell-specific chromatin accessibility signatures; [5] applied a consensus-based framework to systematically investigate and identify putative causal genes, integrating eight locus-based or similarity-based criteria; [6] developed and applied a GRS for the ratio of forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC) in different ancestries in the UK Biobank and COPD case–control studies; and [7] applied PheWAS to individual variants, GRS for each lung-function trait and GRS partitioned by pathway. Through these approaches, we aimed to detect novel lung-function signals and putative causal genes as well as provide new insights into the mechanistic pathways underlying lung function, some of which may be amenable to drug therapy.
## Results
We undertook genome-wide association analyses of FEV1, FVC, FEV1/FVC and peak expiratory flow rate (PEF) from 49 cohorts (Methods and Supplementary Tables 1,2). Our study of up to 588,452 participants comprised individuals of African (AFR; $$n = 8$$,590), American/Hispanic (AMR; $$n = 14$$,668), East Asian (EAS; $$n = 85$$,279), South Asian (SAS; $$n = 4$$,270) and European ancestry (EUR; $$n = 475$$,645; Supplementary Fig. 1a,b). In cohort-specific analyses we adjusted for age, age squared, sex and height, accounting for population structure and relatedness (Methods and Supplementary Tables 2–4), and then applied genomic control using the linkage disequilibrium (LD) score regression intercept12. After filtering and meta-analysis across multi-ancestry cohorts, 66.8 million variants were available in each of four lung-function traits, with genomic inflation factors λ of 1.025, 1.022, 0.984 and 0.996 for FEV1, FVC, FEV1/FVC and PEF, respectively (Supplementary Figs. 2,3 and Supplementary Table 5).
## 1,020 signals for lung function
After excluding eight signals associated with smoking behavior (Supplementary Table 26) and combining signals that co-localized across traits, we identified 1,020 distinct signals for lung function using a stringent threshold of $P \leq 5$ × 10−9 (ref.13; Fig. 1a). Of these, 713 are novel with respect to the signals and studies described in the Supplementary Note (Supplementary Table 6). These 1,020 signals explain $33.0\%$ of FEV1/FVC heritability ($21.3\%$ for FEV1, $17.3\%$ for FVC and $21.4\%$ for PEF; Methods).Fig. 1Study overview.a, Discovery meta-analysis. * For signals present in more than one trait, the signal is only counted once (for the most significant trait). b, Pathway analyses, GRS analyses and PheWAS studies.
To facilitate fine mapping, we included larger, more diverse populations than previous lung-function GWAS. We performed multi-ancestry meta-regression with MR-MEGA7, which incorporates axes of genetic ancestry as covariates to model heterogeneity (Methods). We then incorporated functional annotation for chromatin accessibility and transcription-factor binding sites in respiratory-relevant cells and tissues, and enriched genomic annotations14 to weight prior causal probabilities of association for putative causal variants (Methods). Overall reductions in credible set size and higher maximum posterior probabilities of association for the most likely causal variants were evident after multi-ancestry meta-regression and after functional annotations were incorporated (Supplementary Fig. 4). Following fine mapping, 438 ($43\%$) signals had a single putative causal variant (posterior probability > $50\%$) and the median credible set size was nine variants (Supplementary Note).
Of the 960 sentinels represented in ≥7 cohorts, 109 signals showed heterogeneity attributable to ancestry (PHet < 0.05; Supplementary Fig. 5 and Supplementary Table 7), which was more than expected (binominal test, $$P \leq 3.93$$ × 10−15). Among these, five signals (rs9393688, rs28574670 (LTBP4), rs7183859 (THSD4), rs59985551 (EFEMP1) and rs78101726 (MECOM)) showed significant ancestry-correlated heterogeneity (Bonferroni correction for 960 signals tested, PHet < 5.21 × 10−5; Supplementary Fig. 6a–e). The intronic variant rs7183859 in THSD4, which we previously implicated in lung function15, showed larger effect-size estimates in non-EUR ancestries and in particular AFR ancestries (PHet = 3.33 × 10−5; Supplementary Fig. 6c).
We examined associations of lung-function-associated SNPs in children’s cohorts (Supplementary Table 8) and tested for differences in the estimated effect sizes of lung-function-associated SNPs between children and adults as well as between ever-smokers and never-smokers in EUR individuals (Methods). The effect-size estimates between children and adults were correlated (r from 0.51 for FEV1/FVC to 0.79 for FEV1; Supplementary Fig. 7), although 113 signals showed nominal evidence ($P \leq 0.05$) of age-dependent effects (more than expected, binomial $$P \leq 2.56$$ × 10−13). Three signals (rs7977418 (CCDC91), rs34712979 (NPNT) and rs931794 (HYKK) showed age-dependent effects (Bonferroni-corrected $P \leq 4.64$ × 10−5; Supplementary Table 9). We observed nominal evidence ($P \leq 0.05$) of smoking-dependent effects for 69 of 1,020 signals (Supplementary Fig. 8), more than expected (binomial $$P \leq 0.0079$$). The intronic SNP rs7733410 in HTR4, a signal we previously reported for lung function15, showed a $76.2\%$ larger effect on FEV1 in ever-smokers compared with never-smokers ($$P \leq 4.09$$ × 10−5; Supplementary Table 10). As height is a determinant of lung growth, we compared height and lung-function associations, and tested the impact of additional height adjustments for sentinel SNPs. We found no correlation between estimated effect sizes for height and lung function (Supplementary Fig. 9), and the addition of height squared and height cubed covariates had little impact on effect-size estimates (Supplementary Fig. 10).
## Cell-type and functional specificity
We assessed whether our association signals were enriched for regulatory or functional features in specific cell types. Using stratified LD-score regression16, we found enrichment of all histone marks tested (H3K27ac, H3K9ac, H3K4me3 and H3K4me1) in lung- and smooth-muscle-containing cell lines (Supplementary Table 11). Using GARFIELD17 we assessed for enrichment of our signals for DNase l hypersensitivity sites and chromatin accessibility peaks, showing enrichment in a wide variety of cell types, including higher enrichment in both fetal and adult lung and blood for FEV1, FEV1/FVC and PEF as well as fibroblast enrichment for FVC (Supplementary Fig. 11a). Our signals were enriched for transcription-factor footprints in fetal lung for FEV1, FEV1/FVC and PEF, for footprints in skin for FVC and also in blood for PEF (Supplementary Fig. 11b). Genic annotation enrichment patterns were similar across all traits, with enrichment mainly in exonic, 3′ UTR and 5′ UTR regions (Supplementary Fig. 11c). For all traits, we saw enrichment for transcription start sites, weak enhancers, enhancers and promoter flanks, with cell types for weak enhancer enrichment including endothelial cells for FEV1, FEV1/FVC and PEF (Supplementary Fig. 11d). For transcription-factor binding sites, we observed a similar enrichment pattern across all of the lung-function traits, with the largest fold enrichment observed for endothelial cells (Supplementary Fig. 11e). Our signals were enriched for assay for transposase-accessible chromatin using sequencing (ATAC–seq) peaks (Supplementary Note) in matrix fibroblast 1 for FVC, matrix fibroblast 2 for FEV1, myofibroblast for FEV1, FEV1/FVC and PEF, and alveolar type 1 cells in FEV1/FVC; furthermore, genic annotations showed enrichment of exon variants for FEV1, FEV1/FVC and 3′ UTR variants for FEV1 and FVC. We also found enrichment of transcription-factor binding sites in lung across all phenotypes and in bronchus for FEV1/FVC (Supplementary Table 12).
## Identification of putative causal genes and variants
To identify putative causal genes, we systematically integrated orthogonal evidence using eight locus- or similarity-based criteria (Supplementary Note): [1] the nearest gene to the sentinel SNP, [2] co-localization of the GWAS signal and expression quantitative trait loci (eQTL) or [3] protein quantitative trait loci (pQTL) signals in relevant tissues (Methods), [4] rare variant association in whole-exome sequencing in the UK Biobank, [5] proximity to a gene for a Mendelian disease with a respiratory phenotype (±500 kb), [6] proximity to a human ortholog of a mouse-knockout gene with a respiratory phenotype (±500 kb), [7] an annotation-informed credible set14 containing a missense/deleterious/damaging variant with a posterior probability of association >$50\%$ and [8] the gene with the highest polygenic priority score (PoPS)9. We identified 559 putative causal genes satisfying at least two criteria, of which 135 were supported by at least three criteria (Figs. 1b, 2 and Supplementary Fig. 12). Among the 20 genes supported by ≥4 criteria (Supplementary Table 13), six previously implicated genes (TGFB2, NPNT, LTBP4, TNS1, SMAD3 and AP3B1)5,15,18–20 were supported by additional criteria compared with the original reports. Fourteen of the 20 genes supported by ≥4 criteria have not been previously confidently implicated in lung function (CYTL1, HMCN1, GATA5, ADAMTS10, IGHMBP2, SCMH1, GLI3, ABCA3, TIM1, CFH, FGFR1, LRBA, CLDN18 and IGF2BP2). These are involved in smooth-muscle function (FGFR1, GATA5 and STIM1), tissue organization (ADAMTS10), alveolar and epithelial function (ABCA3 and CLDN18), and inflammation and immune response to infection (CFH, CYTL1, HMCN1, LRBA and STIM1).Fig. 2135 genes prioritized with ≥3 variant-to-gene criteria. The number of variant-to-gene criteria implicating the gene is in brackets after the gene name. The gray in the first eight columns indicates that at least one variant implicates the gene as causal via the evidence for that column. The last four columns indicate the level of association of the most significant variant implicating the gene as causal with respect to the FEV1/FVC decreasing allele; red indicates that this association is in the same direction of effect as the FEV1/FVC decreasing allele and blue indicates the opposite direction with the shade indicating P < the corresponding value in the legend.
To supplement our understanding of the biological pathways and clinical phenotypes influenced by lung-function-associated variants, we undertook PheWAS of selected individual variants. We selected 27 putative causal genes implicated by ≥4 criteria (20 genes) or by a single putative causal missense variant that was deleterious (five genes: ACAN, ADGRG6, SCARF2, CACNA1S and HIST1H2BE) or rare (two genes: SOS2 and ADRB2; Supplementary Table 14). We interpreted the PheWAS findings (shown in full in Supplementary Fig. 13 and Supplementary Table 15) alongside literature reviews (Supplementary Table 16) and highlight examples below.
The putative causal deleterious missense ABCA3 rs149989682 (A allele; frequency of $0.6\%$) variant associated with reduced FEV1/FVC was reported to cause pediatric interstitial lung disease21. ABCA3, which is expressed in alveolar type II cells and localized to lamellar bodies, is involved in surfactant-phospholipid metabolism, and ABCA3 mutations cause severe neonatal surfactant deficiency22. The putative causal deleterious missense GATA5 rs200383755 (C allele, frequency of $0.6\%$) variant associated with lower FEV1 was associated with increased asthma risk, higher blood pressure and reduced risk of benign prostatic hyperplasia (Supplementary Fig. 13i). GATA5 associations have not been previously noted in asthma GWAS, although Gata5-deficient mice show airway hyperresponsiveness23. GATA5 encodes a transcription factor expressed in bronchial smooth muscle, bladder and prostate; a previous benign prostatic hyperplasia GWAS reported a GATA5 signal23,24. CLDN18 was implicated by four criteria, including a mouse knockout with abnormal pulmonary alveolar epithelium morphology25. Through calcium-independent cell adhesion, CLDN18 influences epithelial-barrier function through tight-junction-specific obliteration of the intercellular space26. Its splice variant, CLDN18.1, is predominantly expressed in the lung27. Reduced CLDN18 expression was reported in asthma26. However, our PheWAS showed no association with asthma susceptibility or other traits (CLDN18 rs182770 in Supplementary Table 15). LRBA was also implicated by four criteria. Mutations resulting in LRBA deficiency cause common variable immunodeficiency-8 with autoimmunity, which can include coughing, respiratory infections, bronchiectasis and interstitial lung disease28,29. The putative causal LRBA tolerated missense variant rs2290846 (posterior probability of $56.3\%$) was associated with 31 traits (false discovery rate (FDR) < $1\%$; Supplementary Fig. 13n and Supplementary Table 15); the G allele, associated with lower FVC and lower FEV1, was associated with lower neutrophils as well as lower risk of cholelithiasis, cholecystitis30 and diverticular disease.
FGFR1, encoding Fibroblast growth factor receptor 1, has roles in lung development and regeneration31. Loss-of-function FGFR1 mutations cause hypogonadotropic hypogonadism32. The T allele of rs881299, associated with lower FEV1/FVC and higher FVC, was strongly associated with higher testosterone (particularly in males) and higher sex-hormone-binding globulin (SHBG), lower body-mass index (BMI) as well as lower levels of alanine transaminase and urate (Supplementary Fig. 13w–y and Supplementary Table 15). The missense SOS2 variant rs72681869 also showed association with SHBG; in both sexes, the G allele, associated with lower FVC and lower FEV1, was associated with lower SHBG, higher alanine aminotransferase (ALT) and aspartate aminotransferase (AST), higher fat mass, HbA1c and higher systolic and diastolic blood pressure, higher urate and creatinine, and in males lower testosterone and reduced inguinal hernia risk (Supplementary Fig. 13z–bb). Mutations in SOS2 have been reported in individuals with Noonan syndrome. The A allele of rs7514261 implicating CFH, associated with lower FVC, was strongly associated with reduced risk of macular degeneration33 as well as raised albumin (Supplementary Fig. 13g).
CACNA1S is one of several putative causal genes encoding calcium voltage-gated channel subunits in skeletal muscle (CACNA1S, CACNA1D and CACNA2D3 supported by ≥2 criteria; CACNA1C was supported by PoPS). CACNA1S mutations have been reported in hypokalemic periodic paralysis34 and malignant hyperthermia35. CACNA1S is strongly expressed in skeletal muscle but at much lower levels in airway smooth muscle. The common CACNA1S missense variant rs3850625 (A allele, frequency of $11.8\%$ in EUR and $21.4\%$ in SAS) was associated with lower FVC, lower FEV1, lower whole body fat-free mass, reduced hand grip strength as well as lower AST and creatinine levels (Supplementary Fig. 13f). CACNA1S and CACNA1D are targeted by dihydropyridine calcium channel blockers, which previously produced small improvements in lung function in asthma36. For the low-frequency missense ADRB2 variant rs1800888 (T; $1.49\%$ in EUR), associated with lower FEV1 and lower FEV1/FVC, the strongest PheWAS association was with increased eosinophil count (Supplementary Fig. 13d).
## Druggable targets
Using the Drug Gene Interaction Database, we surveyed 559 genes supported by ≥2 criteria. CheMBL interactions identified 292 drugs mapping to 55 genes (Supplementary Table 17), including ITGA2, which encodes integrin subunit alpha 2. The reduced expression of ITGA2 in lung tissue associated with the C allele of rs12522114 mimics vatelizumab-induced ITGA2 inhibition; this allele is associated with higher FEV1 and FEV1/FVC, indicating the potential to repurpose vatelizumab, which increases T regulatory cell populations37, for COPD treatment.
## Pathway analysis
Using ConsensusPathDB38, we tested biological pathway enrichment for 559 causal genes supported by ≥2 criteria, highlighting pathways relevant for development, tissue integrity and remodeling (Supplementary Table 18). These include pathways not previously implicated in pathway enrichment analyses for lung function—such as PI3K–Akt signaling, integrin pathways, endochondral ossification, calcium signaling, hypertrophic cardiomyopathy and dilated cardiomyopathy—as well as those previously implicated via individual genes5 such as TNF signaling, actin cytoskeleton, AGE–RAGE signaling, Hedgehog signaling and cancers. We found strengthened enrichment through newly identified genes in previously described pathways, such as extracellular matrix organization (34 new genes), elastic fiber formation (eight new genes) and TGF–Core (four new genes). Consistent with our ConsensusPathDB findings, Ingenuity Pathway Analysis (https://digitalinsights.qiagen.com/IPA)39 highlighted enrichment of cardiac hypertrophy signaling and osteoarthritis pathways and also implicated pulmonary and hepatic fibrosis signaling pathways, axonal guidance and PTEN signaling as well as the upstream regulators TGFB1 and IGF-1 (Supplementary Table 19).
## Multi-ancestry GRS for FEV1/FVC and COPD
We built multi-ancestry and ancestry-specific GRSs weighted by FEV1/FVC effect sizes and tested association with FEV1/FVC and COPD (GOLD stage 2–4) within groups of individuals of different ancestries in the UK Biobank (Methods). Our new GRS improved lung-function and COPD prediction compared with a previous GRS based only on individuals of EUR ancestry5 (Fig. 3a,b and Supplementary Table 20), and the multi-ancestry GRS outperformed the ancestry-specific GRS in all UK Biobank ancestries. We then tested the multi-ancestry GRS in five independent COPD case–control studies (Supplementary Table 21 and Methods). Stronger COPD susceptibility associations were observed across five EUR-ancestry studies compared with a previous GRS5 (Fig. 3c and Supplementary Table 22). In the meta-analysis of these EUR studies, the odds ratio for COPD per s.d. of GRS increase was 1.63 ($95\%$ confidence interval (CI), 1.56–1.71; $$P \leq 7.1$$ × 10−93); members of the highest GRS decile had a 5.16-fold higher COPD risk than the lowest decile ($95\%$ CI, 4.14–6.42; $$P \leq 1.0$$ × 10−48; Fig. 3d and Supplementary Table 23). The results for individuals in the SPIROMICS study of AFR ancestry were comparable to individuals from the UK Biobank with AFR ancestry but lower in magnitude compared with the COPDGene AFR population (Fig. 3c).Fig. 3GRS performance.a, Prediction performance of three GRSs across ancestry groups for FEV1/FVC shown as the s.d. change in FEV1/FVC per s.d. increase in GRS for individuals in the UK Biobank grouped according to ancestry. Sample sizes: AFR, $$n = 4$$,227; AMR, $$n = 2$$,798; EAS, $$n = 1$$,564; and EUR, $$n = 320$$,656. b, Prediction performance of three GRSs for COPD shown as COPD odds ratio per s.d. increase in GRS. Sample sizes: AFR, 250 cases and 3,977 controls; AMR, $$n = 151$$ cases and 2,647 controls; EUR, 24,062 cases and 296,594 controls. UKB, UK Biobank. c, Odds ratio for COPD per s.d. change in GRS in COPD case–control studies. P values were calculated from a logistic regression adjusted for age, age squared, sex, height and principal components, followed by fixed-effect meta-analysis. d, *Decile analysis* meta-analyzed across five EUR studies shown as the COPD odds ratio compared between members of each decile and the reference decile. $$n = 11$$,074 (4,328 cases and 6,746 controls). Statistical tests were two-sided, the height of the bars show the point estimate of the effect and whiskers show the $95\%$ CI. OR, odds ratio.
## PheWAS of trait-specific GRSs
To study the aggregate effects of lung-function-associated genetic variants on a wide range of diseases and disease-relevant traits, we created GRSs for FEV1, FVC, FEV1/FVC and PEF, each comprising sentinel variants ($P \leq 5$ × 10−9) with weights estimated from the multi-ancestry meta-regression (Methods), and tested these in PheWAS. These GRS values showed distinct patterns of associations with respiratory and non-respiratory phenotypes (Supplementary Fig. 14 and Supplementary Table 24). A GRS for lower FEV1 was most strongly associated with increased risk of asthma and COPD, family history of chronic bronchitis/emphysema, lower hand grip strength, increased fat mass, increased HbA1c and type 2 diabetes risk, and elevated C-reactive protein. In addition, associations were observed with increased asthma exacerbations and lower age of onset for COPD (Supplementary Fig. 14a). The GRS for lower FEV1/FVC was associated with key respiratory phenotypes: increased risk of COPD and asthma, family history of chronic bronchitis/emphysema, increased emphysema risk, increased risk of respiratory insufficiency or respiratory failure and younger age of onset for COPD but a slightly lower risk of COPD exacerbations (Supplementary Fig. 14b). In contrast, the GRS for lower FVC was strongly associated with many traits—among the strongest associations were high C-reactive protein, increased fat mass, raised HbA1c and type 2 diabetes, raised systolic blood pressure, lower hand grip strength and raised ALT as well as increased risk of clinical codes for asthma and COPD (Supplementary Fig. 14c). Although the GRS for lower FEV1/FVC was associated with increased standing and sitting height, the GRSs for lower FEV1 and FVC were associated with increased standing height but reduced sitting height. Broadly similar phenome-wide associations were seen for the PEF and the FEV1 GRS (Supplementary Fig. 14d).
## PheWAS of GRSs partitioned by pathway
Finally, we hypothesized that partitioning our lung-function GRS into pathway-specific GRSs according to the biological pathways the variants influence could inform understanding of mechanisms underlying impaired lung function, and the probable consequences of perturbing specific pathways. Informed by the above prioritization of putative causal genes and classification of these genes by pathway (‘Pathway analysis’ section), we undertook PheWAS for FEV1/FVC-weighted GRSs partitioned by each of the 29 pathways enriched (FDR < 10−5) for the 559 genes implicated by ≥2 criteria (Methods). Partitioning of GRSs in this way highlighted markedly different patterns of phenome-wide associations (Supplementary Fig. 15 and Supplementary Table 25). Figures 4–7 highlight four pathway-specific GRS examples; all demonstrated association with COPD clinical codes and family history of chronic bronchitis/emphysema, although the associations with other traits varied. The GRS for lower FEV1/FVC specific to elastic fiber formation was associated with increased risk of inguinal, abdominal, diaphragmatic and femoral hernia; diverticulosis; arthropathies; hallux valgus as well as genital prolapse; reduced carpal tunnel syndrome risk and BMI; and increased asthma risk (Fig. 4). In contrast, the GRS for lower FEV1/FVC specific to PI3K–Akt signaling was associated with increased asthma risk, lower IGF-1, lower liver enzymes (ALT, AST and gamma glutamyltransferase (GGT)), lower lymphocyte counts, raised eosinophils, lower fat mass and BMI, and reduced diabetes risk (Fig. 5). The GRS for lower FEV1/FVC specific to the hypertrophic cardiomyopathy pathway was associated with reduced liver enzymes (ALT and GGT) as well as lower apolipoprotein B, LDL, IGF-1 and mean platelet volume (Fig. 6). The GRS associations for lower FEV1/FVC partitioned to signal transduction were specific to respiratory traits, including asthma and emphysema (Fig. 7). Variable height associations were evident: the GRS for lower FEV1/FVC showed association with increased height when partitioned to elastic fiber formation or hypertrophic cardiomyopathy (Figs. 4 and 6), reduced height when partitioned to ESC pluripotency (Supplementary Fig. 15g) and no height association when partitioned to PI3K–Akt signaling or signal transduction (Figs. 5 and 7).Fig. 4PheWAS for FEV1/FVC-weighted GRS partitioned according to elastic fiber formation. Reactome pathway database. CP, composite phenotype and DFP, Data-Field ID phenotype (Methods). The peach-colored line means FDR $1\%$.Fig. 5PheWAS for FEV1/FVC-weighted GRS partitioned according to the PI3K–Akt signaling pathway in Homo sapiens. Kyoto Encyclopedia of Genes and Genomes. CP, composite phenotype; DFP, Data-Field ID phenotype (Methods). The peach-colored line means FDR $1\%$.Fig. 6PheWAS for FEV1/FVC-weighted GRS partitioned according to hypertrophic cardiomyopathy in H. sapiens. Kyoto Encyclopedia of Genes and Genomes. CP, composite phenotype; DFP, Data-Field ID phenotype (Methods). The peach-colored line means FDR $1\%$.Fig. 7PheWAS for FEV1/FVC-weighted GRS partitioned according to signal transduction. Reactome pathway database. CP, composite phenotype (Methods). The peach-colored line means FDR $1\%$.
We hypothesized that individuals may have high GRS for ≥1 pathways and low GRS for other pathways. Comparisons of the GRSs of individuals across pairs of pathways for each of the 29 pathways (Supplementary Fig. 16a) and in detail for the elastic fiber, PI3K–Akt signaling, hypertrophic cardiomyopathy and signal transduction pathways (Supplementary Fig. 16b) demonstrated how GRS profiles may be concordant or discordant across pathways, which could have implications for the choice of therapy.
## Discussion
We present a large ancestrally diverse lung-function GWAS and a comprehensive initiative to relate lung-function- and COPD-associated variants to functional annotations, cell types, genes and pathways. It is the first to investigate possible consequences of intervening in relevant pathways through PheWAS studies, utilizing pathway-partitioned GRS.
The 1,020 signals identified were enriched in functionally active regions in alveolar type 1 cells, fibroblasts, myofibroblasts, bronchial epithelial cells, and adult and fetal lung. We showed effect heterogeneity attributable to ancestry for 109 signals (including LTBP4, THSD4, EFEMP1 and MECOM), between ever-smokers and never-smokers (HTR4), and differences in effects between adults and children (including CCDC91 and NPNT). We mapped lung-function signals to 559 putatively causal genes meeting ≥2 independent criteria. *Exemplar* genes supported by ≥4 criteria or by deleterious or rare putative causal missense variants implicated surfactant-phospholipid metabolism, smooth-muscle function, epithelial morphology and barrier function, innate immunity, calcium signaling, adrenoceptor signaling, and lung development and regeneration. Among the pathways enriched for putative causal genes were PI3K–Akt signaling, integrin pathways, endochondral ossification, calcium signaling, hypertrophic cardiomyopathy and dilated cardiomyopathy. These pathways have not been previously implicated in lung function using GWAS.
Combined as a GRS weighted by FEV1/FVC effect size, the 1,020 variants strongly predicted COPD in the UK Biobank and in COPD case–control studies, with a more than fivefold change in risk between the highest and lowest GRS deciles. This GRS more strongly predicted FEV1/FVC and COPD across all ancestries than a previous GRS5. Partitioning the FEV1/FVC GRS by the pathways defined by specific variants, informed by detailed, systematic variant-to-gene mapping and pathway analyses, and using our new Deep-PheWAS platform40, illustrated unique patterns of phenotype associations for each pathway GRS. These patterns of PheWAS findings are relevant to the potential efficacy and side effects of intervention in these pathways. As a proof-of-concept, the GRS associated with lower FEV1/FVC specific to PI3K–Akt signaling was associated with increased risk of COPD but a lower risk of diabetes; PI3K inhibition impairs glucose uptake in muscle and increases hepatic gluconeogenesis, contributing to glucose intolerance and diabetes41. The PheWAS and druggability analyses we conducted have the potential to identify drug repurposing opportunities for COPD.
The patterns of pleiotropy we show through PheWAS for individual variants, trait-specific GRS and pathway-partitioned GRS may help explain variants and pathways that increase susceptibility to more than one disease and thereby predispose to particular patterns of multimorbidity. For example, the elastic fiber pathway GRS was associated with increased risk of muscular (for example, hernias) and musculoskeletal conditions related to connective-tissue laxity. Our findings also further inform the complex relationship between height, BMI and obesity, and lung function and their genetic determinants5,42. Lung-function and height associations were uncorrelated, and height relationships differed between GRS for different lung-function traits, and even between sitting and standing height for the same trait. The pathway-partitioned GRS analyses indicate that the relationship between genetic variants, height and lung-function traits depends on the pathways through which the variants act.
The last comprehensive attempt to map lung-function-associated variants to genes identified 107 putative causal genes, mostly through eQTLs only, and only eight genes were then implicated by ≥2 criteria5. In contrast, we implicated 559 causal genes meeting ≥2 criteria by drawing on new data and methodologies, such as single-cell epigenome data, rare variant associations identified in sequencing data in the UK Biobank and similarity-based approach PoPS9. Nevertheless, our study has limitations. We focused on multi-ancestry rather than ancestry-specific signals, as the sample sizes for lung-function genomics studies in all non-EUR ancestry groups were limited, particularly for the AFR ancestries4. Non-EUR ancestries are under-represented in genomic studies3, constraining GWAS and PheWAS studies in these populations. Correcting this will require substantial global investment in suitably phenotyped and genotyped studies, with appropriate community participation and workforce development. Improved sample sizes across all ancestries would improve power in ancestry-specific studies42 and fine mapping of multi-ancestry meta-analysis signals.
Strategies for in silico mapping of association signals to causal genes are evolving and difficult to evaluate without a reference set of fully functionally characterized lung-function-associated variants and causal genes. Our variant-to-gene mapping framework parallels one that was recently adopted10 and could help prioritization of genes for functional experiments such as gene editing in relevant organoids with appropriate readouts to confirm mechanism. An additional limitation is that classifications of pathways may be imperfect; we used multiple pathway classifications as it is unclear which is superior across all component pathways and we present the pathway-partitioned PheWAS results as a resource to others.
In summary, our multi-ancestry study highlights new putative causal variants, genes and pathways, some of which are targeted by existing drug compounds. These findings bring us closer to understanding mechanisms underlying lung function and COPD and will inform functional genomics experiments to confirm mechanisms and consequently guide the development of therapies for impaired lung function and COPD.
## GWAS in each cohort
Following cohort-level quality control of the lung-function phenotypes (Supplementary Note), all phenotypes were rank inverse-normal transformed after adjustment for age, sex, height, smoking, ancestry principal components and relatedness (mixed models in BOLT-LMM or SAIGE). Quality control of the imputation and association summary statistics in each cohort was performed by the central analysis team (Supplementary Note). We assigned each cohort to one of the five 1000 Genomes super-populations—EUR, AFR, AMR, EAS or SAS—based on self-reported ancestry, apart from the UK Biobank ($57.4\%$ of the total sample size), where we used ADMIXTURE v1.3.0 (ref. 43) to determine ancestry (Supplementary Note and Supplementary Table 4). We also acquired lung-function-association results from each cohort using untransformed phenotypes for analysis using MR-MEGA.
## Meta-analysis
Before meta-analysis, association statistics in each cohort were adjusted by the LD-score regression intercept calculated in each cohort to adjust for any residual confounding (Supplementary Table 5); the appropriate ancestry-specific LD reference was used for each cohort (10,000 UK Biobank samples for EUR and 1000 Genomes Project samples for AFR, AMR, SAS and EAS). Before meta-analysis, variants with imputation INFO < 0.5 or minor-allele counts (MAC) < 3 were excluded. As transformed effects were not on comparable scales, we meta-analyzed across cohorts using sample-size weighted Z-score meta-analysis with METAL (released version 28 August 2018)44. No genomic control was applied post meta-analysis. Following meta-analysis, variants with MAC < 20 were excluded.
## Signal selection and conditional analysis
We chose a genome-wide significance threshold of $P \leq 5$ × 10−9, as recommended from sequencing studies13. We selected 2-Mb regions centered on the most significant variant for all regions containing a variant with $P \leq 5$ × 10−9. Regions within 500 kb of each other were merged for conditional analysis. Stepwise conditional analysis was run in each region in each cohort using GCTA v1.93.2beta45 with an ancestry-specific LD reference for each cohort (Supplementary Note), and then the conditional results were meta-analyzed across cohorts and any new conditionally independent signals with $P \leq 5$ × 10−9 were added to our list of signals. We used moloc v0.1.0 (ref. 46) to co-localize signals across the four lung-function traits to obtain a set of distinct signals, which were then co-localized with previously reported signals to obtain a set of novel lung-function signals (Supplementary Note).
## Exclusion of smoking signals from follow-up
We checked our sentinels for association with the smoking quantitative traits ‘age of initiation’ ($$n = 262$$,990) and ‘cigarettes per day’ ($$n = 263$$,954), and the binary traits ‘smoking cessation’ ($$n = 139$$,453 cases and $$n = 407$$,766 controls) and ‘smoking initiation’ ($$n = 557$$,337 cases and $$n = 674$$,754 controls) in the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN) consortium47 (proxies with a squared correlation coefficient (r2) > 0.8 were checked for sentinels not present in GSCAN). We excluded eight lung-function signals from further analysis, which we determined to be primarily driven by smoking behavior (Supplementary Table 26), according to the following criteria: [1] $P \leq 4.86$ × 10−5 (Bonferroni-corrected $5\%$ threshold for 1,028 signals) for association with any smoking trait and [2] the same ‘risk’ allele that increases smoking exposure behavior and decreases lung function.
## Heritability estimate
We calculated the proportion of variance explained by the sentinels reported for each trait using the formula\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{{\mathop {\sum }\nolimits_{$i = 1$}^n 2f_{\mathrm{i}}(1 - f_{\mathrm{i}})\beta _{\mathrm{i}}^2}}{V}$$\end{document}∑$i = 1$n2fi(1−fi)βi2Vwhere n is the number of variants, fi and βi are the frequency and effect estimates of the ith variant from the UK Biobank European ancestry untransformed results, respectively, and V is the phenotypic variance (always one as our phenotypes were inverse-normal transformed). We assumed a heritability of $40\%$ (refs. 48,49) to estimate the proportion of additive polygenic variance.
## Ancestry-adjusted trans-ethnic meta-analysis using MR-MEGA
To improve the fine-mapping resolution using LD differences between ancestries and to estimate the heterogeneity of variant associations attributable to ancestry, we undertook multi-ancestry meta-regression using MR-MEGA v0.2 (ref. 7), which incorporates axes of genetic ancestry as covariates. MR-MEGA uses multidimensional scaling of allele frequencies across cohorts to derive principal axes of genetic variation to use for ancestry adjustment (Supplementary Note). The location of the cohorts on the first two multidimensional scaling-derived principal components, plotted in Supplementary Fig. 17, shows clustering in accordance with the assigned ancestry groups. We used four principal components for ancestry adjustment, as this captured most of the variance. MR-MEGA implements genomic control at study level; therefore, no further genomic control was applied. We ran MR-MEGA at each locus containing ≥1 signals; in the loci with multiple signals, we ran MR-MEGA multiple times, each time conditioning on all except one signal at the locus. For each sentinel, we obtained an estimated ancestry-associated (P-value_ancestry_het) and residual (P-value_residual_het) heterogeneity. In addition, MR-MEGA reports the log-transformed Bayes factor, which can be used for the construction of credible sets.
## Effects in children
To obtain unbiased effect estimates for comparison between adults and children, we first redefined 1,077 lead SNPs for lung function in the UK Biobank EUR population ($$n = 320$$,656) by selecting 1-Mb regions centered on the most significant variant for regions containing a variant with $P \leq 5$ × 10−8. For these SNPs, we then took the untransformed effect estimates from the meta-analysis of the non-UK Biobank EUR cohorts (34 cohorts for FEV1 and FVC, $$n = 128$$,071; 33 cohorts for FEV1/FVC, $$n = 123$$,429; 15 cohorts for PEF, $$n = 60$$,122). Next, we meta-analyzed two EUR-ancestry children’s cohorts—ALSPAC and Raine Study (age, 13–15 yr, $$n = 6$$,070)—to obtain effect estimates in children at the new lead SNPs. To investigate the age-dependent effects of genetic variants on lung function, we compared the effect sizes estimated in adults and children using a Welch’s t-test; a Bonferroni significance threshold for 1,077 tests was applied ($P \leq 4.64$ × 10−5).
## Stratified LD-score regression
We tested for enrichment of regulatory features at variants overlapping four histone marks (H3K27ac, H3K9ac, H3K4me3 and H3K4me1) that are specific to adult lung, fetal lung, and peripheral blood mononuclear primary and smooth-muscle-containing cell lines (colon and stomach) using stratified LD-score regression12. We only considered EUR-specific meta-analysis with 39 cohorts for FVC, FEV1 and FEV1/FVC (17 cohorts for PEF). For the analysis of cell-type-specific annotations, we assessed statistical significance at the 0.05 level after Bonferroni correction for 60 hypotheses tested. Given that these annotations are not independent, a Bonferroni correction is conservative. We also report results with FDR < 0.05 using the Benjamini–Hochberg method.
## Regulatory and functional enrichment using GARFIELD
We tested enrichment of SNPs at functionally annotated regions (DNase I hypersensitivity hotspots, open chromatin peaks, transcription-factor footprints and formaldehyde-assisted isolation of regulatory elements, histone modifications, chromatin segmentation states, genic annotations and transcription-factor binding sites) using GARFIELD17. We used the EUR meta-analysis with 17 cohorts for PEF and 39 cohorts for FVC, FEV1 and FEV1/FVC. We applied GARFIELD to DNase I hypersensitivity hotspot annotation in 424 cell lines and primary cell types from ENCODE and Roadmap Epigenomics and derived enrichment estimates at trait-genotype association P-value thresholds of $P \leq 5$ × 10−5 and $P \leq 5$ × 10−9.
## Enrichment of annotations in respiratory-relevant cell types and tissues
We curated annotations from assays of respiratory-relevant cells and tissues—that is, [1] single-cell genome ATAC–seq data50 from 19 cell types (myofibroblast, pericyte, ciliated, T cell, club, capillary endothelial 1 and 2, basal, matrix fibroblast 1 and 2, arterial endothelial, pulmonary neuroendocrine, natural killer cell, macrophage, B cell, erythrocyte, lymphatic endothelial, alveolar type 1 and 2 (downloaded from https://www.lungepigenome.org/)), [2] ATAC–seq data for five human primary lung-cell types implicated in COPD pathobiology51 (large and small airway epithelial cells, alveolar type 2, pneumocytes and lung fibroblasts (downloaded from http://www.copdconsortium.org/)) and [3] tissue-specific transcription-factor binding sites from DNase-seq footprinting of 589 human transcription factors in lung and bronchus52. We tested for cell- and tissue-specific enrichment of these annotations at our lung-function signals using functional GWAS (fGWAS)14 (Supplementary Note).
## eQTL and pQTL co-localization
Three eQTL resources were used for co-localization of lung-function signals with gene expression signals: [1] GTEx V8 (downloaded from https://www.gtexportal.org/, July 2020; tissues: stomach, small-intestine terminal ileum, lung, esophagus muscularis, esophagus gastroesophageal junction, colon transverse, colon sigmoid, artery tibial, artery coronary and artery aorta), [2] eQTLgen53 blood eQTLs and [3] UBC lung eQTL54. Two blood pQTL resources were used to co-localize with associations with protein levels, that is, INTERVAL pQTL55 and SCALLOP pQTL. The coloc_susie method56 was used to test eQTL and pQTL co-localization (Supplementary Note).
## Rare variants from exome sequencing
We checked for rare (MAF < $1\%$) exonic associations near (±500 kb) our lung-function sentinels using both single-variant and gene-based collapsing tests from [1] 281,104 UK Biobank exomes from the AstraZeneca PheWAS Portal57 (https://azphewas.com/), [2] loss-of-function and missense variants in 454,787 UK Biobank participants58 and [3] gene-based tests on whole-exome imputation in 500,000 UK Biobank participants59. We used a threshold of $P \leq 5$ × 10−6 for both single-variant and gene-based tests (Supplementary Note).
## Nearby Mendelian respiratory-disease genes
We selected rare Mendelian-disease genes from ORPHANET (https://www.orpha.net/) within ±500 kb of a lung-function sentinel that were associated with respiratory terms matching regular expression—that is, respir, lung, pulm, asthma, COPD, pneum, eosin, immunodef, cili, autoimm, leukopenia, neutropenia and Alagille syndrome. We implicated the gene if it had a corresponding respiratory term match in the disease name or if it occurs frequently in human phenotype ontology terms for that disease (Supplementary Note).
## Nearby mouse-knockout orthologs with a respiratory phenotype
We selected human orthologs of mouse-knockout genes with phenotypes in the ‘respiratory’ category, as listed in the International Mouse *Phenotyping consortium* (https://www.mousephenotype.org/), within ±500 kb of a lung-function sentinel (Supplementary Note).
## PoPS
We calculated a gene-level PoPS9 based on the assumption that if the associations enriched in genes share functional characteristics with a gene near to a lung-function signal, then that gene is more likely to be causal. The full set of gene features used in the analysis included 57,543 total features—40,546 derived from gene expression data, 8,718 extracted from a protein–protein interaction network and 8,479 based on pathway membership. In this study we prioritized genes for all autosomal lung-function signals within a 500-kb (±250 kb) window of the sentinel and reported the top prioritized genes in the region. For the signals that did not have prioritized genes within the 500-kb window, we looked for prioritized genes using a 1-Mb (±500 kb) window (Supplementary Note).
## Annotation-informed credible sets
We used the enriched annotations in respiratory-relevant cell types and tissues and enriched genic annotations (Supplementary Table 12) to create annotation-informed $95\%$ credible sets using fGWAS based on the MR-MEGA ancestry-adjusted meta-regression results (Supplementary Note). We implicated a putative causal missense variant if it accounted for >$50\%$ of the posterior probability in the credible set and annotated these using Ensembl Variant Effect Predictor60 to check for a deleterious effect by the SIFT, PolyPhen or CADD metrics.
## Allocation of genes prioritized with ≥3 variant-to-gene to lung-function biology categories
We allocated prioritized genes with ≥3 criteria to different lung-function roles (epithelial, inflammatory, peripheral lung (including alveolus and endothelial), lung remodeling (including connective tissue), chest-wall movement and lung development) based on literature reviews, including GeneCards (https://www.genecards.org) and PubMed (https://pubmed.ncbi.nlm.nih.gov). Eighteen of the genes were difficult to assign to a specific category on this basis, mainly because they were involved in generic processes such as transcriptional control in a wide variety of cell types; these are not shown in Supplementary Fig. 12 but are included in Supplementary Table 13.
## Interaction with smoking
Association testing for lung-function traits (FEV1, FVC, FEV1/FVC and PEF) was calculated separately in ever- and never-smoker subgroups and meta-analyzed across EUR-ancestry cohorts. We included untransformed phenotypes with ever- and never-smoking summary statistics ($$n = 28$$ cohorts) comprising 206,162 ever-smokers and 229,046 never-smokers. A z-test was used to compare genetic effect between the untransformed association results for the ever- and never-smokers:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$z = \frac{{\beta _1 - \beta _2}}{{\sqrt {\mathrm{se}_1^2 + \mathrm{se}_1^2} }}$$\end{document}z=β1−β2se12+se12where se is the standard error of the effect β. We considered a significant interaction any signal with a $P \leq 4.9$ × 10−5 ($5\%$ Bonferroni-corrected for 1,020 signals tested).
## GRS
We selected four ancestry groups in the UK Biobank (UKB) as test datasets (SAS was excluded from GRS analyses because UKB SAS was the only cohort in the multi-ancestry analysis for SAS): UKB EUR, UKB AMR, UKB EAS and UKB AFR. All of the other cohorts except UKB SAS and Qatar Biobank were used as discovery datasets.
We repeated the multi-ancestry meta-regression (MR-MEGA), after excluding the four test GWAS, incorporating the same four axes of genetic variation as covariates to account for ancestry. Autosomal signals for each lung-function trait that were reported in the target ancestry population were included in downstream analysis for each ancestry. For ancestry j (j = EUR, AMR, EAS or AFR), we estimated ancestry-specific predicted allelic effects for the ith SNP to be used as weights in the multi-ancestry GRS by\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat b_{\mathrm{ij}} = \alpha _{0{\mathrm{i}}} + \mathop {\sum }\limits_{$k = 1$}^4 \alpha _{\mathrm{ki}}\bar x_{\mathrm{kj}}$$\end{document}b^ij=α0i+∑$k = 14$αkix¯kjwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\bar x_{\mathrm{kj}}$$\end{document}x¯kj is the averaged position of discovery studies with ancestry j on the kth axis of genetic variation from multi-ancestry meta-regression, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha _{0{\mathrm{i}}}$$\end{document}α0i and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha _{\mathrm{ki}}$$\end{document}αki denote the intercept and effect of the kth axis of genetic variation for the ith SNP from the multi-ancestry meta-regression.
We ran each of the ancestry-specific fixed-effect meta-analyses after excluding the test GWAS from the ancestry group using METAL using the inverse-variance weighting method. For comparison, SNPs used as weights in multi-ancestry GRS were selected to build ancestry-specific GRS for each ancestry.
## Testing GRS in independent COPD case–control cohorts
We tested the association of multi-ancestry GRS with COPD susceptibility in five EUR-ancestry COPD case–control studies: COPDGene (non-Hispanic white), ECLIPSE, GenKOLS, NETT/NAS and SPIROMICS (non-Hispanic EUR) (Supplementary Table 21). We also tested the association in two AFR ancestry COPD case–control studies: COPDGene (African American) and SPIROMICS (African American) (Supplementary Table 21). Associations were tested using logistic regression models, adjusted for age, age squared, sex, height and principal components. In each COPD case–control study, we divided individuals into deciles according to their weighted GRS. For each decile, logistic models were fitted to compare the risk of COPD for members of the test decile with those with the lowest decile (that is, those with the lowest genetic risk). The results were meta-analyzed by ancestry-specific study groups using the fixed-effect model.
## PheWAS
We used Deep-PheWAS40, which addresses both phenotype matrix generation and efficient association testing while incorporating the following developments that are not yet available in current platforms and online resources: [1] clinically curated composite phenotypes for selected health conditions that integrate different data types (including primary and secondary care data) to study phenotypes that are not well captured by current classification trees; [2] integration of quantitative phenotypes from primary care data, such as pathology records and clinical measures; [3] clinically curated phenotype selection for traits that are extremely highly correlated and [4] GRSs. The platform includes 2,421 phenotypes in the UK Biobank, with a subset of 2,243 recommended for association testing—some phenotypes that are generated are used solely in the definition of other phenotypes. We removed the four measures of lung function and added seven phenotypes defined in-house (P4002-6) to give 2,246 as our final maximum number of phenotypes for association. Deep-PheWAS then filters these, requiring a minimum case number; we chose to keep the default settings of a 50-case minimum for binary phenotypes and a 100-case minimum for quantitative phenotypes. After limiting to EUR ancestry and filtering for case numbers, 1,909 phenotypes were left for association analysis (Supplementary Table 27). No additional phenotypes were removed when removing pairs related up to second degree (KING kinship coefficient ≥ 0.0884).
There are five types of phenotypes within Deep-PheWAS categorized according to the data and methods used to create them. Composite phenotypes are made using linked hospital and primary care data, including in some cases primary care prescription data, alongside any of the UK Biobank field-IDs (DFP), including self-reported non-cancer diagnosis and self-reported operations. Phecodes are defined using only linked hospital data (https://phewascatalog.org/phecodes_icd10). Formula phenotypes combine available data using bespoke R code per phenotype rather than the in-built functions of phenotype development available in Deep-PheWAS. Added phenotypes are lists of cases and controls that have been added to the PheWAS and not developed by the Deep-PheWAS phenotype matrix generation pipeline. More complete definitions for all none-added phenotypes can be found in the Deep-PheWAS description40. All phenotypes were adjusted for age, sex and the first ten principal components.
## Single-variant PheWAS
We ran 28 single-variant PheWAS across 1,909 traits (Supplementary Table 27) in up to 430,402 unrelated EUR individuals in the UK Biobank. We selected the variant with the most significant P value for each of the 20 genes with ≥4 lines of evidence for being causal (Supplementary Table 13). A further seven variants were included in single-variant PheWAS that were putatively causal (accounted for >$50\%$ posterior probability in the credible set and had a deleterious annotation; Supplementary Table 14) but in a gene that was implicated by fewer than four lines of evidence. The single-variant PheWAS was aligned to the lung-function-trait decreasing allele. Where we noted associations with testosterone and SHBG, we also undertook sex-stratified PheWAS.
## Association with trait-specific GRS
We created four GRSs for the UK Biobank EUR samples, one for each trait FEV1, FVC, FEV1/FVC and PEF, including all conditionally independent sentinel variants for the trait that were associated with $P \leq 5$ × 10−9, yielding 425, 372, 442 and 194 variants in each trait-specific GRS, respectively. Each of the four GRS were weighted by the effect sizes from the multi-ancestry meta-regression for the relevant trait and then checked for association with 1,909 traits in the PheWAS.
## Association with pathway-specific GRS
We selected 29 pathways that were enriched at FDR < 10−5 for our 559 genes implicated by ≥2 lines of evidence (Supplementary Table 18). We created a weighted GRS (weights estimated from multi-ancestry meta-regression for FEV1/FVC) for each of the 29 pathways by including for each gene in the pathway (as for ‘Single-variant PheWAS’) the variant with the most significant P value for the trait that implicates the gene in our variant-to-gene mapping (Supplementary Table 13). Each of the 29 GRSs were then checked for association with 1,909 traits in the PheWAS.
## Reporting summary
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## Online content
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## Supplementary information
Supplementary InformationSupplementary Note, Figs. 1–17 and Tables 2–4, 9, 11, 12, 16 and 20–23. Reporting Summary Peer Review File Supplementary TableSupplementary Tables 1, 5–8, 10, 13–15, 17–19 and 24–33. The online version contains supplementary material available at 10.1038/s41588-023-01314-0.
## Peer review information
Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: 'Predictors of cognitive impairment in newly diagnosed Parkinson’s disease
with normal cognition at baseline: A 5-year cohort study'
authors:
- Jing Chen
- Danhua Zhao
- Qi Wang
- Junyi Chen
- Chaobo Bai
- Yuan Li
- Xintong Guo
- Baoyu Chen
- Lin Zhang
- Junliang Yuan
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC10011149
doi: 10.3389/fnagi.2023.1142558
license: CC BY 4.0
---
# Predictors of cognitive impairment in newly diagnosed Parkinson’s disease with normal cognition at baseline: A 5-year cohort study
## Abstract
### Background and objective
Cognitive impairment (CI) is a substantial contributor to the disability associated with Parkinson’s disease (PD). We aimed to assess the clinical features and explore the underlying biomarkers as predictors of CI in patients with newly diagnosed PD (NDPD; less than 2 years).
### Methods
We evaluated the cognitive function status using the Montreal Cognitive Assessment (MoCA) and a battery of neuropsychological tests at baseline and subsequent annual follow-up for 5 years from the Parkinson’s Progression Markers Initiative (PPMI) database. We assessed the baseline clinical features, apolipoprotein (APO) E status, β-glucocerebrosidase (GBA) mutation status, cerebrospinal fluid findings, and dopamine transporter imaging results. Using a diagnosis of CI (combined mild cognitive impairment and dementia) developed during the 5-year follow-up as outcome measures, we assessed the predictive values of baseline clinical variables and biomarkers. We also constructed a predictive model for the diagnosis of CI using logistic regression analysis.
### Results
A total of 409 patients with NDPD with 5-year follow-up were enrolled, 232 with normal cognitive function at baseline, and 94 patients developed CI during the 5-year follow-up. In multivariate analyses, age, current diagnosis of hypertension, baseline MoCA scores, Movement disorder society Unified PD Rating Scale part III (MDS-UPDRS III) scores, and APOE status were associated with the development of CI. Predictive accuracy of CI using age alone improved by the addition of clinical variables and biomarkers (current diagnosis of hypertension, baseline MoCA scores, and MDS-UPDRS III scores, APOE status; AUC 0.80 [$95\%$ CI 0.74–0.86] vs. 0.71 [0.64–0.77], $$p \leq 0.008$$). Cognitive domains that had higher frequencies of impairment were found in verbal memory (12.6 vs. $16.8\%$) and attention/processing speed (12.7 vs. $16.9\%$), however, no significant difference in the prevalence of CI at annual follow-up was found during the 5-year follow-up in NDPD patients.
### Conclusion
In NDPD, the development of CI during the 5-year follow-up can be predicted with good accuracy using a model combining age, current diagnosis of hypertension, baseline MoCA scores, MDS-UPDRS III scores, and APOE status. Our study underscores the need for the earlier identification of CI in NDPD patients in our clinical practice.
## Introduction
Parkinson’s disease (PD) is the second-most common neurodegenerative disorder, that affects 2–$3\%$ of the population ≥ 65 years, with a prevalence set to double by 2030 (Poewe et al., 2017; Aarsland et al., 2021). Dementia occurs in at least $75\%$ of patients who have had PD for more than 10 years (Aarsland and Kurz, 2010). Cognitive impairment (CI) can potentially occur at different stages (Aarsland et al., 2001), severely affect the quality of life and function, and increase caregiver burden and health-related costs (Aarsland et al., 2021). As the focus has been on early cognitive changes among PD patients, the course of mild cognitive impairment (MCI) can be quite variable. Given that PD patients who revert from MCI to normal cognition have an increased long-term risk for dementia (Pedersen et al., 2017; Jones et al., 2018), earlier risk factor stratification for CI could help to prognosticate the disease course and appropriate interventions in the early PD population.
The prior studies reported that some risk factors were related to CI in patients with PD. A recent meta-analysis suggested that the following variables were independently associated with the future development of MCI or dementia: the presence of hallucinations, older age, the overall severity of motor symptoms, presence of speech impairment, older age at onset, bradykinesia severity, higher Hoehn and Yahr stage, axial impairment, a low level of education, presence of depression, and male sex (Marinus et al., 2018). Studies have shown that current diagnoses of diabetes mellitus and hypertension were two important modifiable predictors of cognitive decline in PD (Mollenhauer et al., 2019; Nicoletti et al., 2021; Athauda et al., 2022). Baseline global cognitive function, hyposmia, rapid eye movement (REM) sleep behavior disorder (RBD), dysautonomia, apolipoprotein (APO) E status, β-glucocerebrosidase gene (GBA) status, and dopamine deficit on dopamine transporter (DAT)-imaging have all been suggested as predictors of MCI or dementia in patients with PD (Hu et al., 2014; Mata et al., 2014; Liu et al., 2017; Schrag et al., 2017; Leta et al., 2021; Barrio et al., 2022; Dijkstra et al., 2022). Varieties of studies have explored the association of serum uric acid and cerebral spinal fluid (CSF) findings, however, the results were still controversial (Liu et al., 2015; Pellecchia et al., 2016; Terrelonge et al., 2016; Johar et al., 2017; Seifar et al., 2022). The stage and duration of disease varied among participants in those studies, most of the studies included PD patients with CI at baseline, and some studies used healthy people as control. As a result, the above findings may not apply to newly diagnosed PD (NDPD) patients with normal cognition at baseline.
In our present study, we will select NDPD patients (diagnosed with PD for 2 years or less at screening visit) with normal cognition at baseline and evaluate the contribution of those risk factors to predict the development of CI (combining MCI and dementia) during the 5-year follow-up. Our study provides a correlation between specific risk factors and the onset of CI leading to improvement in the management of dementia in NDPD.
## Study design and participants
In this cohort study, we investigated the clinical and biomarker predictors and clinical characteristics of CI in NDPD (followed up for 5 years) using data from the Parkinson’s Progression Markers Initiative (PPMI).1 The PPMI is an international, multi-center, longitudinal, observational study aiming to identify biomarkers of PD progression in de novo PD patients (diagnosed within 2 years). Details of the PPMI eligibility criteria are given on the PPMI website. Written informed consent was provided by each PPMI participant, and the PPMI study was approved by the institutional board at each study site.
The data were downloaded from the PPMI website on August 1, 2022. Firstly, we included PD patients meeting the following criteria to investigate the predictors of CI: [1] with annual follow-ups for 5 years; [2] MoCA scores at baseline and 5-year follow-up were available; and [3] cognitive function at baseline was normal. PD patients who followed for less than 5 years were excluded. Then the included PD patients were divided into those with CI and those without CI based on whether they had CI at the annual follow-up for 5 years. Secondly, we included all the PD patients with annual follow-ups for 5 years to assess their cognitive performance at each visit. And the prevalence of CI at baseline and 5-year cumulative incidence of CI were calculated. Thirdly, PD patients with MoCA scores at baseline and 5-year follow-up available were included. The global cognitive function fluctuation in the early stage of PD was evaluated by changes in MoCA scores at baseline and 5-year follow-up.
## Outcomes
The outcome was defined as CI (combining MCI and dementia). Global cognitive function was assessed by MoCA, with suggested cutoffs of < $\frac{26}{30}$ for MCI and < $\frac{21}{30}$ for dementia by Movement Disorder Society MCI task force level guidelines (Emre et al., 2007; Dalrymple-Alford et al., 2010; Litvan et al., 2012). A diagnosis of MCI required that cognitive deficits are not sufficient to interfere significantly with functional independence, although subtle difficulties in complex functional tasks may be present (Litvan et al., 2012). A diagnosis of PD dementia (PDD) required evidence that the functional impairment caused by CI is sufficient to interfere with activities of daily living (Emre et al., 2007). The cognitive domains were assessed by a battery of neuropsychological tests, which included the Hopkins Verbal Learning Test (HVLT) total recall and HVLT recognition discrimination for verbal memory, Benton Judgment of Line Orientation (JOLO) for visuospatial function, Letter-Number Sequencing (LNS) and the Semantic (animal) fluency Test (SFT) for executive function/working memory, and the Symbol-Digit Modalities Test (SDMT) for attention/processing speed. Based on the impairment of cognitive domains, MCI was defined according to the PPMI protocol and the Movement Disorder Society MCI task force level I guidelines (Litvan et al., 2012). PD patients with MCI (PD-MCI) were defined as scores on two or more of the HVLT total recall, HVLT recognition discrimination, JOLO, LNS, SFT, and SDMT were more than 1.5 standard deviations below normal, with no functional impairment due to CI. These criteria have been applied and validated in several studies (Schrag et al., 2017; Chen et al., 2021).
## Candidate predictors
Clinical variables included in our study were the age of onset, sex, years of education, disease duration, current diagnosis of diabetes mellitus, and hypertension. PD motor severity was measured by the MDS-UPDRS part III (MDS-UPDRS III), the Hoehn and Yahr stage. Axial impairment was assessed by tremor dominant, postural instability/gait difficulty, or indeterminate phenotypes of PD calculated with the use of published methods (Stebbins et al., 2013). The presence of speech impairment was considered dichotomous variable depending on the sum of item 2.1 and 3.1 of the MDS-UPDRS scored 0 vs. ≥ 1. PD non motor symptoms (NMS) were assessed by the University of Pennsylvania Smell Identification Test (UPSIT) for sense of smell, the 15-item Geriatric Depression Scale (GDS) for depression, the Scale for Outcomes in Parkinson’s disease for Autonomic symptoms (SCOPA-AUT) for dysautonomia, and the question 6 of RBD Screening Questionnaire (RBDSQ-q6) for probable RBD (pRBD; Schrag et al., 2017). Neurological orthostatic hypotension (nOH; Norcliffe-Kaufmann et al., 2018) was also used to assess autonomic function. The presence of psychosis was considered dichotomous variable depending on item 1.2 of the MDS-UPDRS scored 0 vs. ≥ 1. The presence of apathy was considered dichotomous variables depending on item 1.5 of the MDS-UPDRS scored 0 vs. ≥ 1. For biomarker studies, we included serum uric acid, APOE ε4 status (ε4 homozygous, heterozygous, or negative), GBA mutation status, and DAT imaging data for mean caudate and putaminal uptake relative to uptake in the occipital area, and asymmetry of caudate and putaminal uptake (side with the highest divided by side with the lowest uptake; Schrag et al., 2017). We evaluated CSF for α-synuclein, Aβ42, total tau (t-tau), phosphorylated tau181 (p-tau), and the calculated ratio of Aβ42 to t-tau.
## Statistical analysis
We compared groups using χ2 tests for categorical data, student t tests for normally distributed variables, and Mann–Whitney tests for non-parametric data. Univariate logistic regression analysis was used to identify possible risk factors for CI developed during the 5-year follow-up. Variables with values of $p \leq 0.05$ in univariate logistic regression analysis and no high correlation (r > 0.5) with each other were included in a multivariate model. If there was a correlation between variables, the one with a smaller p value was selected. Variables were removed from the multivariate model with the backward selection method until all variables were significant at $p \leq 0.05.$ Receiver operating characteristic curves were drawn and areas under the curve were calculated to estimate the prediction accuracy. Statistical analysis was carried out with SPSS version 26, and $p \leq 0.05$ was considered significant.
## Results
There were 409 NDPD patients with a minimum 5-year follow-up in the PPMI database. 51 cases did not have recorded MoCA scores at baseline or 5-year follow-up and 126 cases diagnosed with MCI at baseline. These cases were excluded. A total of 232 subjects were included to investigate predictors of CI, of whom 94 met the CI criteria during the 5-year follow-up (Figure 1). Baseline clinical characteristics and biomarkers of the patients with or without CI were shown in Table 1. At baseline, NDPD patients with CI were older and they had a higher proportion of male sex, current diagnosis of hypertension, Hoehn and Yahr stage 2, and APOE ε4 homozygotes than those without CI during the 5-year follow-up. Lower baseline MoCA and UPSIT scores and higher SCOPA-AUT gastrointestinal domain, RBDSQ question 6, and MDS-UPDRS III scores were observed in NDPD patients with CI (Table 1). No significant difference was found in GBA mutation status, CSF findings, and DAT biomarkers between the two groups.
**Figure 1:** *Flowchart of patient selection and classification.* TABLE_PLACEHOLDER:Table 1 *No data* were missing for the age of onset, sex, years of education, medical history, disease duration, baseline MoCA scores, baseline results of the neuropsychologic tests, UPSIT scores, GDS scores, SCOPA-AUT scores, RBDSQ scores, MDS-UPDRS scores, GBA mutation status data, and the APOE status data. The results of the neuropsychological tests were missing for 14 patients at 1-year follow-up, for 20 patients at 2-year follow-up, for seven patients at 3-year follow-up, for 20 patients at 4-year follow-up, and for 21 patients at 5-year follow-up. The data was missing for two patients for Hoehn and Yahr stage information and 16 patients for serum uric acid. Baseline CSF findings were missing for Aβ42 and α-synuclein in 53 patients, for p-tau in 55 patients, and t-tau in 56 patients. The analyses were repeated by imputing missing predictor variable data with means (data not shown). These missing data did not change the overall results of any analysis.
In univariate analysis, the age of onset, sex, current diagnosis of hypertension, baseline MoCA scores, UPSIT scores, SCOPA-AUT gastrointestinal domain scores, RBDSQ-q6 scores, MDS-UPDRS III scores, the Hoehn and Yahr stage, and APOE status were associated with CI (Table 2). In multivariate analyses, CI was associated with the age of onset, current diagnosis of hypertension, baseline MoCA scores, MDS-UPDRS III scores, and APOE status (Table 2). In a logistic regression analysis with CI as the dependent variable, using the age of onset, current diagnosis of hypertension, baseline MoCA scores, MDS-UPDRS III scores, and APOE ε4 status as independent variables (Table 2), predictive accuracy was higher than for age alone (AUC 0.80 [$95\%$ CI 0.74–0.86] vs. 0.71 [0.64–0.77], $$p \leq 0.003$$; Figure 2).
The prevalence of CI at baseline was $30.8\%$, and the 5-year cumulative incidence of CI was $40.5\%$ in NDPD patients. The higher frequencies of impairment of cognitive domains were seen in verbal memory (12.6 vs. $16.8\%$) and attention/processing speed (12.7 vs. $16.9\%$; Table 3), the lower levels of impairment were seen in executive function/working memory and visuospatial function in the early stage of PD (Table 3). The cognitive fluctuation of 336 NDPD patients were shown in Table 4. Of the 71 subjects who scored 21–25 at baseline MoCA, seven ($9.9\%$) subjects scored less than 21 and 37 ($52.1\%$) subjects scored 26–30 at 5-year follow-up (Table 4).
## Discussion
In this study, we identified the predictors of CI developed during the 5-year follow-up in NDPD with normal cognition at baseline. Apart from older age, the strongest clinical predictors were current diagnosis of hypertension, lower baseline MoCA scores, APOE ε4 status, and to a lesser extent, higher baseline MDS-UPDRS III scores. We also find that the natural course of CI is variable during the 5-year follow-up in NDPD.
Previous studies have identified clinically relevant risk factors for CI and dementia in PD patients. In line with previous studies, we found that age of onset, current diagnosis of hypertension, baseline MoCA scores, and MDS-UPDRS III scores were independent predictors of CI. PD patients who developed CI during the 5-year follow-up had higher proportions of the male sex, hyposmia, dysautonomia, pRBD, and higher Hoehn and Yahr stage. Older age and hypertension are reliable predictors of CI in the general population, which are not unique to PD. Higher scores at baseline MoCA and MDS-UPDRS III, and higher Hoehn and Yahr stage usually indicate more serious pathology underlying PD, which is associated with an increased risk of CI. Hyposmia, constipation, and sleep disorders have also been reported to be associated with cognitive decline in the early stage of PD (Hu et al., 2014; Schrag et al., 2017; Leta et al., 2021). They are the earlier risk factors and prodromal features for non-motor symptoms (NMS) of PD due to the propagation of α-synuclein following caudo-rostral from the periphery to the central nervous system (Blesa et al., 2022). Besides, we also found some inconsistent data such as no difference between the two groups in education years, current diagnosis of diabetes mellitus, depression, psychosis, apathy, orthostatic hypotension, presence of speech impairment, and the motor subtype. In terms of education years, the reason may be that subjects in PPMI studies generally have long years of schooling. The inconsistency of the rest clinical variables is attributed to the disease duration and cognitive status of participants in different studies.
The occurrence and progression of CI in PD are associated with the APOE ε4 allele in the absence of other genetic variants at the genome-wide level (Iwaki et al., 2019; D'souza and Rajkumar, 2020; Tan et al., 2021; Real et al., 2022). In accordance with prior studies (Mata et al., 2014; Schrag et al., 2017), we found that homozygous of the APOE ε4 allele is a predictor of CI in NDPD with normal cognition at baseline. GBA mutations reduce glucocerebrosidase and lysosomal activities as independent risk factors for both PD and dementia with Lewy body and are associated with accelerated cognitive decline in PD (Cilia et al., 2016; Liu et al., 2016; Chia et al., 2021). Studies have shown that GBA mutations are reliable predictors of dementia in PD patients (Liu et al., 2017; Phongpreecha et al., 2020), but no difference was found between PD patients with normal cognition and MCI (Phongpreecha et al., 2020), similar to this early-stage study, which may be partially related to the fact that the GBA penetrance in PD patients increases with age (Anheim et al., 2012; Gan-Or et al., 2019).
Low levels of CSF Aβ42 are an independent predictor of cognitive decline in PD in previous studies (Johar et al., 2017). However, the associations of p-tau and t-tau concentrations with cognitive decline in PD patients were still controversial (Johar et al., 2017). Indeed, in one study of PPMI, low Aβ42 levels and mean caudate uptake in DAT imaging were associated with the occurrence of MCI or dementia at a 2-year follow-up (Schrag et al., 2017). However, no differences were found between PD patients with and without CI on the CSF Aβ42, t-tau, p-tau concentrations, Aβ42: t-tau ratio, and DAT biomarkers in our study. The inconsistencies in the findings of different studies could be attributed to differences in the participants and the intervals of follow-up. Our study only included PD patients with normal cognitive function at baseline, however, most of the previous studies included PD patients with CI at baseline to explore the risk factors of CI (Hall et al., 2015; Schrag et al., 2017). These different findings might reflect the different pathology of CI underlying in PD patients with normal cognitive function and PD patients with cognitive impairment at baseline.
Our study has some strengths. Firstly, participants included in this study are NDPD patients with normal cognition at baseline and followed up for 5 years. Secondly, the risk factors and neuropsychological tests were quite comprehensive, and such predictors in this model are obtained relatively easily in clinical practice. Thirdly, the different findings on biomarkers of CSF suggest that the underlying pathology of CI may be distinct in PD patients with normal cognitive function and PD patients with cognitive impairment at baseline.
There are also several limitations in our study. First, in the PPMI study, the MoCA scores and five neuropsychological tests covered four cognitive domains except for language was used to assess cognitive function. According to the MDS Task Force level II criteria (Litvan et al., 2012), some patients who met MCI criteria might be missed, which would affect the accuracy of results, although studies have supported the prognostic validity of the abbreviated MCI in PD criteria (Hoogland et al., 2019). Second, limited by the sample size and follow-up time, we did not subdivide the CI groups into MCI and PDD, nor did we analyze the conversion from MCI to dementia. Third, we did not analyze the effect of PD medication on cognitive function since the subjects in our study were not treated with medication at baseline, and such influencing factors should not be ignored. Fourth, in spite that we built such a prediction model, however, we did not validate the efficacy and feasibility of the model in different populations of PD patients such as from China. Therefore, a larger sample and much more comprehensive assessment, and prolonged follow-up will be required in a future study.
In summary, we explored the predictor model of the development of CI in NDPD during the 5-year follow-up. Our study may contribute to the early identification of CI in PD patients. In a future study, our study should be validated and a larger sample, much more comprehensive assessment, and longer follow-up time will be needed.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: *The data* were sourced from the Parkinson’s Progression Markers Initiative (PPMI) database. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Requests to access these datasets should be directed to [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the PPMI study was approved by the institutional board at each study site. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JY and JiC contributed to the conception and design of the research. XG and JuC collected the data. JiC and CB contributed to the analysis and interpretation of the data. JiC: wrote the first draft of the manuscript. DZ, QW, YL, BC, LZ, and JY helped with the critical revision of the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Natural Science Foundation of China [82071552] and the Chinese Academy of Sciences Grant (JCTD-2021-06).
## 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: Study on the mechanism of Wumei San in treating piglet diarrhea using network
pharmacology and molecular docking
authors:
- Huihui Yin
- Wei Liu
- Xiaoyu Ji
- Guoqing Yan
- Xueyan Zeng
- Wu Zhao
- Yanhua Wang
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10011153
doi: 10.3389/fvets.2023.1138684
license: CC BY 4.0
---
# Study on the mechanism of Wumei San in treating piglet diarrhea using network pharmacology and molecular docking
## Abstract
Wumei San (WMS) is a traditional Chinese medicine that has been widely applied in the treatment of piglet diarrhea (PD). However, the mechanism of WMS in PD has not been investigated. In this study, the main active compounds of WMS and the target proteins were obtained from the Traditional Chinese Medicine Systematic Pharmacology, PubChem, and SwissTargetPrediction databases. The molecular targets of PD were identified using GeneCards, OMIM, and NCBI databases. The common targets of WMS and PD were screened out and converted into UniProt gene symbols. PD-related target genes were constructed into a protein-protein interaction network, which was further analyzed by the STRING online database. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to construct the component-target gene-disease network. Molecular docking was then used to examine the relationship between the core compounds and proteins. As a result, a total of 32 active compounds and 638 target genes of WMS were identified, and a WMS-compound-target network was successfully constructed. Through network pharmacology analysis, 14 core compounds in WMS that showed an effect on PD were identified. The targets revealed by GO and KEGG enrichment analysis were associated with the AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, IL-17 signaling pathway, and other pathways and physiological processes. Molecular docking analysis revealed that the active compounds in WMS spontaneously bind to their targets. The results indicated that WMS may regulate the local immune response and inflammatory factors mainly through the TNF signaling pathway, IL-17 signaling pathway, and other pathways. WMS is a promising treatment strategy for PD. This study provides new insights into the potential mechanism of WMS in PD.
## Introduction
Piglet diarrhea (PD) is a common disease, especially in 1- to 3-month-old piglets, with a high incidence in farming production [1]. The incidence of diarrhea in weaned piglets is more than $30\%$ and the mortality is $15\%$. PD may lead a decrease in the growth of piglets and a decrease in the feed reward. PD is also accompanied by physical decline, depressed immunity, weakened resistance to disease, and susceptibility to secondary and mixed infection of other infectious diseases. Without treatment, PD leads to a high death rate of piglets and severe economic injury to pig production [2, 3]. PD is caused by general internal medicine diseases, sewage consumption, improper feed change, sudden weather changes, and cold stimulation. Other cases are caused by infectious diseases. Piglet yellow dysentery and piglet white dysentery are common bacterial diseases, and transmissible gastroenteritis, porcine epidemic diarrhea and rotavirus infection are common viral diseases in PD. The main strategies for treating PD in pig farms include antibiotics and medical levels of zinc oxide [4, 5]. However, long-term medication and high zinc diet causes adverse effects on human health and the environment by contributing to the development of antimicrobial resistance among bacteria and to high soil concentrations of zinc, a heavy metal.
Because of their natural, eco-friendly, and safe nature, Chinese herbal feed additives and formulations have been used in increasing numbers in recent years for the prevention and treatment of PD [6, 7]. According to the theory of traditional Chinese medicine, spleen and stomach weakness, cold dampness, dampness heat, and food injury are the important pathological factors of PD. The principle of treating PD is clearing heat and removing toxin, and astringing intestines and checking diarrhea. Wumei San (WMS) is a classic prescription for treating diarrhea in young animals. Its application history can be traced back to Hezi San from the earliest existing veterinary pharmacy Anji Prescription in Tang-Song period. WMS including Mume Fructus (MF), *Coptis chinensis* Franch. ( CC), *Curcuma longa* L. (CL), *Terminalia chebula* Retz. ( TC) and Diospyros kaki L.f. ( DK) is now the latest recorded formula in Chinese Veterinary Pharmacopeia (2020 edition) II. Over thousands of years of evolution, only one or two herbs have changed in the prescription. MF, composed of organic acids, flavonoids and fatty acids, used to treat chronic cough, prolonged diarrhea, and other inflammation-related diseases [8]. Previous studies have shown that weaned piglets fed with antibiotic-free diets supplemented with MF gained more weight and were healthier by modifying the gut microbial composition [9]. CC is a Chinese herbal medicine with strong anti-inflammatory activity, and has obvious clinical medicinal value. Berberine, an isoquinoline alkaloid, mainly found in CC, with antibacterial effects on Shigella and Escherichia coli, is believed to exert gut health-promoting effects through modulation of the gut microbiota [10]. CL has been used as a traditional Chinese medicinal material to treat gastrointestinal diseases for many years [11]. TC is a widely used herbal drug in traditional medicine prescriptions. Chebulinic acid, a phenolic compound found in TC, is reported to exhibit both anti-inflammatory, anti-oxidant activity and anti-tumor property [12]. DK is a popular cultivated and consumed fruits in China. Clinical studies showed that DK can help the gastrointestinal tract to digest and promote the recovery of appetite after diarrhea in sick pigs [13]. The prescription of the compound WMS includes 15 g of MF, 24 g of DK, 6 g of CC, 6 g of CL, and 9 g of TC. The above five herbs are evenly combined, crushed, sieved, and combined. In this prescription, MF plays the role of promoting fluid production to quench thirst and astringing intestines to treat diarrhea as the main drug. TC and DK, as the auxiliary drugs, work through astringing intestines and consolidating. CL and CC are the assistant medicinal. CC takes effect of clearing heat and removing the toxin, and drying dampness to treat diarrhea. CL moves qi and activate blood to relieve pain. However, the mechanism of WMS is still unclear.
The multi-component, multi-target, and multi-channel characteristics of Chinese traditional veterinary medicine have made it difficult to elucidate the complex mechanisms, leading to a lack of data in pharmacological research. Network pharmacology has created a new framework for investigating how medications and disorders interact [14, 15].
In this study, network pharmacology and molecular docking were applied to examine the potential mechanism of WMS in the treatment of PD. The study overview is shown in Figure 1.
**Figure 1:** *The flowchart of this study.*
## Active components and target gene analysis of WMS
The formula of WMS containing MF, CC, CL, TC, and DK was obtained from Chinese Veterinary Pharmacopeia (2020 edition) II. The Traditional Chinese Medicine Systematic Pharmacology (TCMSP) (https://www.tcmspw.com/tcmsp.php) database, which is a systems pharmacology resource for traditional Chinese medicines or related compounds and offers details on the absorption, distribution, metabolism, and excretion properties of a drug with potential biological effects at a systematic level, was used to identify the compounds in the herbs of MF, CC, CL, TC, and DK. Oral bioavailability (OB) and drug likeness (DL) are two essential characteristics of medications taken orally. OB and DL can assess the effectiveness of a drug's systemic circulation, and the resemblance between a molecule and a known medication, respectively. The putative active components were screened using OB and DL in this research. Active substances were identified from prior investigations and data from key databases of Chinese herbal medicine. Active compounds were then screened and considered as putative main constituents and retained using OB ≥30 % and DL ≥0.18 based on data from prior studies and pertinent Chinese herbal medicine databases [16, 17]. For example, an herb name was entered into the search box, and the compounds in the herb were examined. The potential active compounds were obtained then by setting OB ≥30 % and DL ≥0.18. The primary functional components for the treatment of PD that are not found in TCMSP were sorted out by examining previous studies [18, 19]. Active compounds were input into the TCMSP database to obtain known targets. The structures of the active compounds were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and imported into the SwissTargetPrediction database with relevant parameters for prediction of target genes (probability > 0). The potential targets were then collected. The target genes corresponding to the compounds were uniformly standardized in UniProt (http://www.uniprot.org/).
## Candidate targets of PD
“Piglet diarrhea” was used as the keyword to explore disease-related genes at GeneCards (https://www.genecards.org/), OMIM (https://www.omim.org/), and NCBI (https://www.ncbi.nlm.nih.gov/gene/). All potentially relevant genes were obtained. All the disease gene targets were then normalized and converted into gene symbols through UniProt database after removing the redundancy.
## Venn Diagram analysis
The predicted target genes of WMS and the projected target genes of PD were analyzed by Venn *Diagram analysis* (https://www.bioinformatics.com.cn/?keywords=%E$6\%$$96\%$$87\%$E$6\%$$81\%$A$9\%$E$5\%$9B%BE).
## Construction of a network of herbs, natural compounds, and targets
From the above data, a network of herbs, active compounds, and common targets was constructed using Cytoscape (v 3.9.1), and the relationships among them were analyzed. Herbs, compounds, and targets were represented by nodes in the network, and their interactions were represented by edges connecting nodes.
From the above data, a network of the complex interactions among components of WMS, effective compounds, and target genes was visualized using Cytoscape; the network comprised 133 nodes and 393 edges (Figure 3). The top 10 compounds by degree value are (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl) chroman-4-one, moupinamide, epiberberine, sennoside E_qt, cheilanthifoline, quercetin, berlambine, obacunone, kaempferol, and 7-dehydrosigmasterol (Table 2). These compounds may be the core compounds of WMS responsible for anti-PD effects. This network allowed for easy observation of relationships among herbs, ingredients, and targets. These findings suggested that the pharmacological effects of WMS in the treatment of PD are the result of multi-component and multi-target effects.
**Figure 3:** *The herb-compound-disease target network. The ellipse nodes, the triangle nodes, and the hexagon nodes represent the herbs, compounds, and targets, respectively. The connections represent the interactions among the three. The node size and color are related to the degree value. The larger the node area and the darker the color in the figure, the more important it is in the network.* TABLE_PLACEHOLDER:Table 2
## Construction of the protein—Protein interaction network
The potential target genes of WMS in PD were uploaded to STRING database (https://string-db.org/) (v 11.5) to draw a protein—Protein interaction (PPI) network. The organism was limited to “Sus scrofa.” The results showed the confidence of the interaction between the proteins by scores. The minimum required interaction score was selected at medium confidence data >0.4 to ensure the reliability of the analysis. The data from STRING database were then analyzed by the “Analysis Network” tool in Cytoscape 3.9.1 software to obtain a PPI network. The relevant parameters of degree (DC), betweenness centrality (BC), closeness centrality (CC), and stress were calculated for topology analysis on the PPI network to obtain the key targets.
## GO and KEGG pathway enrichment analyses
The potential targets from the intersection were imported into DAVID database [2021] (https://david.ncifcrf.gov/tools.jsp) for Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis with “Sus scrofa” as the species. P ≤ 0.01 was considered to be significantly enriched. The top 10 most significantly enriched GO biological processes (BP), cell component (CC), and molecular function (MF) and top 20 items of KEGG pathway results were then mapped (https://www.bioinformatics.com.cn/?keywords=pathway) to draw the enrichment bubble diagram.
After inputting common targets into the DAVID 2021 database, 145 KEGG pathways, 184 GO BP, 31 GO cell components and 57 GO molecular functions that met the enrichment criteria of $P \leq 0.01$ were identified.
The top 10 most significantly enriched GO BP were selected for analysis (Figure 5A). The major biological processes enriched were response to hypoxia, positive regulation of interleukin-8 production, inflammatory response, positive regulation of angiogenesis, and vascular endothelial growth factor receptor signaling pathway. The major molecular functions were MAP kinase activity, steroid binding, protein homodimerization activity, protein kinase binding, and protein homodimerization activity. The cellular components were mainly enriched in the perinuclear region of cytoplasm, caveola, extracellular space, receptor complex, and membrane.
**Figure 5:** *GO (A) and KEGG (B) pathway enrichment analysis.*
The top 20 most significantly enriched KEGG pathways were selected for analysis (Figure 5B). The potential target genes of WMS in PD were involved in the AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, and IL-17 signaling pathway.
Using the herb, compound, target, and pathway analyses, an entire herb, chemical, target and pathway network was then constructed by Cytoscape using the top 20 signaling pathways. As shown in Figure 6, the interaction network has 114 nodes and 593 edges. The results showed that 55 of the 94 potential targets were involved in the top 20 pathways. These findings indicate that WMS has multi-component, multi-target, and multi-channel effects in the treatment of PD.
**Figure 6:** *The herb-compound-disease target-pathway network. The orange ellipse, blue triangle, purple diamond, and green hexagon correspond to herbs, compounds, pathways, and target genes, respectively.*
## Construction of a network of herbs, compounds, pathways, and targets
The interaction information between intersection targets and the top 20 most significantly enriched KEGG pathways were combined with the screened drug components and intersection targets. Data were then uploaded into Cytoscape 3.9.1 to construct a network of herbs, active compounds, target, and pathway.
## Molecular docking
The 10 important compounds in accordance with the degree values in WMS were selected to dock to the top 10 core targets from the PPI analysis. The three-dimensional crystal structure of the target protein was downloaded from the RCSB database (https://www.rcsb.org/) and saved in pdb format after removing solvent and organic through the PyMol software. The 2D chemical structure of the compounds was obtained from PubChem and converted to mol2 format after minimizing energy through Chem3D 18.0. The ligand and receptor were then converted to pdbqt file format through AutoDockTools-1.5.7. Molecular docking was performed by AutoDock Vina 1.1.2. A binding energy of <-4.25 kcal/mol indicates that the ligand and receptor molecules bind spontaneously. The binding energy of <-5.0 kcal/mol indicates that ligand and receptor molecules are stably bound. The binding energy of <-7.0 kcal/mol indicates that the two have strong binding activity. Results with strong binding force were selected and visualized by Pymol software.
From the degree analysis in the network, the 10 main active compounds (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one (MF3), epiberberine (CC8), moupinamide (CC11), berlambine (CC10), sennoside E_qt (TC2), quercetin (A1), obacunone (CC12), kaempferol (MF2), cheilanthifoline (TC7), and 7-dehydrosigmasterol (TC3) were chosen for molecular docking verification with the 10 core targets MAPK14, CASP3, ESR1, MAPK3, VEGFA, TNF, CCND1, HSP90AA, CAV1, and NOS3. The statistical results of binding energy are shown in Figure 7.
**Figure 7:** *Molecular docking heat map. A darker color indicates a stronger binding force between the ligand molecule and the receptor protein.*
Among the 100 receptor-ligand docking combinations, the binding energy of all molecules to proteins was <−5.0 kcal/mol, and 62 groups ($62\%$ of all combinations) had binding energy <−7.0 kcal/mol, which indicated that the main active ingredients in WMS have a strong binding activity with the core targets. The affinity of the compound obacunone to target NOS3 had the lowest binding energy of −11.1 kcal/mol. The binding modes of some key targets to the core active compounds are shown in Figure 8.
**Figure 8:** *Molecular and key target docking verifications. (A) Sennoside E_q-ESR1, (B) Quercetin-TNF, (C) Moupinamide-HSP90AA1, (D) Kaempferol-CAV1, (E) Obacunone-NOS3, (F) Moupinamide-MAPK3.*
## The natural active ingredients in WMS
Using the databases, previous reports, and the criteria (OB ≥$30\%$; DL ≥0.18) described above, the compounds of WMS were screened out. After the removal of non-target compounds, 8, 14, 3, 8, and 4 natural compounds of TC, CC, CL, MF, and DK were obtained, respectively. After removing any duplicate compounds, a total of 34 active compounds in the WMS formula were obtained (Table 1).
**Table 1**
| ID | Mol ID | Molecule name | OB (%) | DL | Source |
| --- | --- | --- | --- | --- | --- |
| TC1 | MOL001002 | ellagic acid | 43.06 | 0.43 | TC |
| TC2 | MOL002276 | Sennoside E_qt | 50.69 | 0.61 | TC |
| TC3 | MOL006376 | 7-dehydrosigmasterol | 37.42 | 0.75 | TC |
| TC4 | MOL009135 | ellipticine | 30.82 | 0.28 | TC |
| TC5 | MOL009136 | Peraksine | 82.58 | 0.78 | TC |
| TC6 | MOL009137 | (R)-(6-methoxy-4-quinolyl)-[(2R,4R,5S)-5-vinylquinuclidin-2-yl]methanol | 55.88 | 0.4 | TC |
| TC7 | MOL009149 | cheilanthifoline | 46.51 | 0.72 | TC |
| TC8 | MOL006826 | chebulic acid | 72.0 | 0.32 | TC |
| CC1 | MOL000622 | magnograndiolide | 63.71 | 0.19 | CC |
| CC2 | MOL000762 | palmidin A | 35.36 | 0.65 | CC |
| CC3 | MOL000785 | palmatine | 64.6 | 0.65 | CC |
| CC4 | MOL001454 | berberine | 36.86 | 0.78 | CC |
| CC5 | MOL001458 | coptisine | 30.67 | 0.86 | CC |
| CC6 | MOL002668 | worenine | 45.83 | 0.87 | CC |
| CC7 | MOL002894 | berberrubine | 35.74 | 0.73 | CC |
| CC8 | MOL002897 | epiberberine | 43.09 | 0.78 | CC |
| CC9 | MOL002903 | (R)-canadine | 55.37 | 0.77 | CC |
| CC10 | MOL002904 | berlambine | 36.68 | 0.82 | CC |
| CC11 | MOL008647 | moupinamide | 86.71 | 0.26 | CC |
| CC12 | MOL013352 | obacunone | 43.29 | 0.77 | CC |
| CC13 | MOL002907 | corchoroside A_qt | 104.95 | 0.78 | CC |
| A1 | MOL000098 | quercetin | 46.43 | 0.28 | CC, MF |
| CL1 | MOL000493 | campesterol | 37.58 | 0.71 | CL |
| B1 | MOL000449 | stigmasterol | 43.83 | 0.76 | CL, MF |
| B2 | MOL000953 | cholesterol | 37.87 | 0.68 | CL, MF |
| DK1 | MOL000004 | procyanidin B1 | 67.87 | 0.66 | DK |
| DK2 | MOL002773 | β-carotene | 37.18 | 0.58 | DK |
| DK3 | MOL000096 | catechin | 49.68 | 0.24 | DK |
| DK4 | MOL000073 | epicatechin | 48.96 | 0.24 | DK |
| MF1 | MOL000358 | beta-sitosterol | 36.91 | 0.75 | MF |
| MF2 | MOL000422 | kaempferol | 41.88 | 0.24 | MF |
| MF3 | MOL001040 | (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl) chroman-4-one | 42.36 | 0.21 | MF |
| MF4 | MOL005043 | campest-5-en-3beta-ol | 37.58 | 0.71 | MF |
| MF5 | MOL008601 | methyl arachidonate | 46.9 | 0.23 | MF |
## Targets of the effective compounds of WMS
TCMSP, PubChem, and SwissTargetPrediction databases were used to predict the target genes of compounds in WMS. The results identified 638 targets, including 325, 447, 58, 75 and 271 targets for TC, CC, CL, DK and MF, respectively.
From the GeneCards website and OMIM, 352 genes were determined as highly likely to be associated with PD. The 352 candidate PD-associated genes were compared with the 638 target genes from WMS using Venn diagram analysis (Figure 2A). A total of 94 ($11\%$) overlapping genes were extracted. The intersection between PD, CC, TC, CL, DK and MF which contains 3 common genes (Figure 2B).
**Figure 2:** *Venn diagram. (A) Common target genes of WMS and PD. (B) Common target genes of CC, TC, CL, MF, DK, and PD.*
## Construction and analysis of the target PPI network
The 94 intersection genes were uploaded to the STRING database, and a PPI network was obtained depicting the BP of WMS treatment of PD in vivo. The network comprised 90 nodes and 607 edges. The results were then imported into Cytoscape to construct a network diagram.
The PPI network and relevant parameters were obtained with the Analysis Network tool in Cytoscape software. Using the four parameters DC, BC, CC and Stress, the indicators above the median value were selected as the key indicators, and two screenings were performed. The critical values of the first screening were DC >11, CC >0.4623, BC >0.0032, and Stress >255. Through topological analysis, 30 key targets were obtained and screened again. The screening criteria were DC >15, CC >0.6744, BC >0.0093, and Stress >60. Finally, a total of 14 key targets of WMS acting on PD were obtained. The specific screening strategy is shown in Figure 4.
**Figure 4:** *The topology screening process of the protein interaction network. The node size and color were related to the degree value. The color and size of the node are adjusted in accordance with the degree value. The darker colors and larger nodes indicate a larger degree value.*
## Discussions
PD is a common disease in pig farms with a high morbidity and mortality that leads to productivity loss. Antibiotics and medical zinc oxide are the main treatment strategies for PD in pig production [20]. However, long-term medication and a high zinc diet leads to adverse effects on human health and the environment by contributing to the development of antimicrobial resistance among bacteria and high zinc levels in soil. Therefore, the identification of a natural drug for PD prevention and treatment is critical. Some traditional Chinese medicines and formulations have been found to be effective in the treatment of PD. The WMS formula, which is composed of MF, CC, CL, DK, and TC, has the functions of regulating metabolism and enhancing immune effects. WMS has been clinically used in the treatment of PD in China and has a significant effect. However, the mechanism has been unclear.
In this study, network pharmacology was applied to analyze the effective components and mechanisms of WMS in the treatment of PD. A total of 32 active compounds in the WMS formula were obtained and 10 core compounds were identified. The compounds included terpenoids, flavonoids, alkaloids, and tannins. Flavonoids have the effects of reducing ulcerative colitis and anti-gastric ulcers, improving functional dyspepsia, inhibiting ileal motility, protecting gastric mucosa, and exerting antibacterial and antiviral effects [21]. Quercetin is a flavonoid polyphenol molecule commonly found in vegetables, fruits, and Chinese herbal medicines; it promotes the reconstruction of epithelial tight junctions, enhances barrier integrity, and inhibits the production of proinflammatory cytokines such as IL17, TNF-α, and IL6 [22]. Some of its metabolites reduce inflammation and prevent colitis [23]. Previous studies showed that adding quercetin to the diet of weaned piglets increases the antioxidant capacity of piglets, regulates the structure and metabolism of intestinal microorganisms, and alleviates diarrhea and intestinal injury [24]. Kaempferol is a polyphenol that is widely distributed in many vegetables, fruits, and beans. It has various pharmacological activities such as antiviral, apoptosis, anti-inflammation, and anti-oxidation effects [25]. These effects may be related to kaempferol's inhibition of oxidative stress and attenuation of inflammatory factors, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), cyclooxygenase-2 (COX-2), and nuclear factor κB (NF-κB) and the modulation of apoptosis and mitogen-activated protein kinase (MAPK) signaling pathways [26]. Berberine is an alkaloid that reduces the expression of TNF-α, IL1β, and IL8 genes and the occurrence of intestinal inflammation through inhibiting the expression of TLR4 and NOD1 genes in intestinal mucosa [27, 28]. Epiberberine is an isomer of berlambine; it exhibits anti-adipogenesis effects by modulating the Akt and ERK pathways, anti-dyslipidemia effects by inhibition on cholesterol synthesis, anti-cancer effects by impacting the p53/*Bax apoptosis* pathway, and antibacterial activities [29]. 7-dehydrosigmasterol is a sterol compound. Phytosterols exhibit anti-inflammatory activities, improve the immunity of weaned piglets, and reduce the rate of PD [30]. Epicatechin is a natural plant tannin compound that exerts anti-inflammatory effects by reducing the secretion of inflammatory cytokines and inhibiting the phosphorylation of P38 MAPK, extracellular signal-regulated kinase (ERK), and c-jun N-terminal kinase protein (JNK) in the MAPK signaling pathway [31]. Supplementation of some tannins may help prevent PD in 21-day-old weaned piglets [32]. These compounds form the material basis of the mechanism of action of WMS on PD, and further research is necessary to elucidate the detailed mechanisms.
The mapped PPI network was analyzed and 14 core targets were identified: MAPK14, HSP90AA1, TNF, NOS3, RELA, CCND1, TLR4, VEGFA, MAPK3, CAV1, MAPK1, ESR1, CASP3, and ALB. Intestinal inflammation is one of the main internal causes of piglet diarrhea, and the expression of the intestinal cytokines is one of the main characteristics of intestinal inflammatory response. Previous studies showed that, the gene expression of IL-6, TNF-α and IL-β in jejunum of piglets were increased [33, 34]. The weaning stress may activate MAPK signaling pathways, NF-κB pathway and other pathways in the intestine [35, 36]. Bacteria and lipopolysaccharide invade the intestinal mucosa of weaned piglets, activate the intestinal inflammatory signaling pathway, promote the transcription of downstream inflammatory factors, and cause intestinal inflammation in piglets. As can be seen from the Figure 3, the 14 core targets are the targets of the compounds in WMS that can directly act on PD. TLR4 corresponds to peraksine; MAPK14 corresponds to magnograndiolide, berberine, obacunone, and methyl arachidonate; VEGFA corresponds to (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one, procyanidin B1 and β-carotene; TNF corresponds to obacunone; RELA corresponds to palmidin A; CAV1 corresponds to β-carotene. The results showed the characteristics of multi-component and multi-target effects of WMS in the treatment of PD. TNF is a critical cytokine with a wide range of sources. TNF-α is an indispensable immunomodulatory factor that maintains internal stability and functions in the resistance against various pathogenic factors. It has dual biological activities. At low concentrations, TNF-α is involved in resisting pathogenic microbial infection, promoting tissue repair, and regulating the inflammatory response [37]. The initial line of defense against diarrhea is comprised of intestinal epithelial cells. TNF-α can induce epithelial cell apoptosis and destroys intestinal barrier function by rearranging the adhesion proteins in intestinal epithelial cells [38]. The expression of NHE3 and DRA, the main transporter proteins regulating colonic Na+ inward transport and the main transporter protein regulating Cl−/HCO3- exchange in the apical membrane of mammalian intestinal epithelial cells, respectively, can be inhibited by the rising TNF-α expression in ulcerative colitis [39, 40]. RELA is a member of the NF-κB family. Abnormal inflammation associated with inflammatory bowel disease is caused by excessive activation of RELA/NF-κB [41]. MAPK14, also named P38α, plays an important role in the normal immune and inflammatory responses. The MAPK14 pathway plays a role in inflammatory bowel disease [42]. TLR4 maintains immune tolerance and intestinal homeostasis, and inflammation is alleviated by TLR4 regulating the TLR4/NF-κB pathway [43, 44]. CAV1 is a multifunctional protein, with roles in cellular defense by its inhibition of nitrosative stress and mucosal barrier injury [45]. VEGFR is a receptor that binds to VEGF and initiates a signaling cascade that stimulates angiogenesis. A lack of VEGFR in newborn animals leads to intestinal microvascular dysplasia and colitis [46]. Epidermal growth factor can promote the development of intestinal mucosal morphology in weaned piglets, activate gastric and intestinal digestive enzymes and disaccharidase, and improve weaning stress [47]. We speculate that WMS exerts its effects on PD by acting on MAPK14, TNF, RELA, TLR4, VEGFA, CAV1, and other factors to regulate the inflammatory response, maintain the intestinal mucosal barrier, and improve stress.
GO enrichment analysis showed that WMS may play a therapeutic role by regulating the immune response of piglets, modulating cell growth, and regulating apoptosis. PD is caused by impaired intestinal barrier function, disruption of intestinal flora homeostasis, and disturbances in intestinal chemical, mechanical, and immune barriers [48, 49]. The impaired intestinal barriers may promote bacterial translocation and the entry of allergic compounds from the intestine into the body, leading to increased immune response and susceptibility. GO enrichment analysis showed that the active ingredients of WMS exert activities on cell components and structures such as the cytoplasm, caveola, cell surface, extracellular space, receptor complex, and membrane, which are related to the maintenance of intestinal epithelial tissue integrity. Thus, WMS may prevent intestinal barrier damage and the entry of sensitive substances such as bacteria from the intestine into the body.
PD progresses through a variety of pathophysiological processes, including stimulation of intestinal structural damage, digestive dysfunction, oxidative stress, and inflammation response. KEGG pathway enrichment analysis showed that the pharmacological effects of WMS in treating PD included effects on the AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, and IL-17 signaling pathway. As can be seen from the Figure 6, the compounds berberine, moupinamide, palmidin A, obacunone, β-carotene, catechin, and methyl arachidonate in WMS modulate the AGE-RAGE signaling pathway by regulating MAPK14, TNF, VEGFA, RELA and other targets to treat piglets. Peraksine, β-carotene, catechin, and (2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one modulate the PI3K-Akt signaling pathway by regulating the TLR4, VEGFA, and other targets. Berberine, ellagic acid, sennoside E_qt, quercetin, chebulic acid, β-carotene, catechin, and epicatechin modulate the TNF signaling pathway, IL-17 signaling pathway and NOD-like receptor signaling pathway by regulating TNF, MAPK14, RELA, MMP3, CASP8, PTGS2, and other targets to treat PD. The AGE-RAGE signaling pathway elicits activation of multiple intracellular signal pathways involving NADPH oxidase, protein kinase C, and MAPKs, then resulting in NF-κB activity. NF-κB promotes the expression of pro-inflammatory cytokines such as IL-1, IL-6, and TNF-α [50]. TNF induces a wide range of intracellular signal pathways including apoptosis, cell survival, inflammation, and immunity pathways. TNF-α is an indispensable immunomodulatory factor and an important downstream factor of the NF-κB pathway. When the intestinal epithelial cells was attacked by transmissible gastroenteritis virus, adding leucine can inhibit the expression of the pro-inflammatory factor TNF-α and anti-inflammatory factor IL10 by inhibiting the phosphorylation level of NF-κB, thus reducing the inflammatory response in the small intestine [51]. The PI3K-Akt signaling pathway is activated by many types of cellular stimuli or toxic insults and regulates fundamental cellular functions such as transcription, translation, proliferation, growth, and survival. Previous studies showed that flavonoids ameliorate dysregulated inflammatory responses, the intestinal barrier, and gut microbiome in ulcerative colitis via the PI3K-AKT pathway [52]. IL17 is associated with bacterial infection, and γδT cells secreting IL17 are the key component of the mucosal defense against infection [53]. NOD-like receptors are representatives of mediating inflammatory responses. After activation, they can induce the release of inflammatory factors by mediating the NF-κB pathway and MAPK pathway [54]. Together these results indicate that the AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, TNF signaling pathway, IL-17 signaling pathway, and other pathways may play key roles in the effects of WMS on PD.
## Conclusion
WMS is a traditional Chinese medicine formula that is effective in treating PD. In this study, network pharmacology combined with molecular docking was applied to explore the mechanism underlying the effect of WMS on PD. The results indicated that WMS may exert its anti-PD activities through the TNF signaling pathway and IL-17 signaling pathway. The AGE-RAGE signaling pathway, PI3K-Akt signaling pathway, and NOD-like receptor signaling pathway may also be involved. Further experiments are required to elucidate the precise mechanism by which WMS functions in PD.
## 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
HHY wrote the manuscript. HHY, WL, XYJ, and GQY designed the figures and edited the manuscript. HHY supervised data analysis and manuscript editing, in cooperation with XYZ, WZ, and YHW. 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: The self-medication behaviors of residents and the factors related to the consideration
of drug efficacy and safety—A cross-sectional study in China
authors:
- Pu Ge
- Zi-Wei Zhang
- Jin-Zi Zhang
- Ke Lyu
- Yu-Yao Niu
- Yu-Ting Tong
- Ping Xiong
- Rong Ling
- Qi-Yu Li
- Wen-Li Yu
- He-Wei Min
- Yu-Qian Deng
- Yu-Jia Wang
- Xiao-Nan Sun
- Xin-Ying Sun
- Lian Yu
- Yi-Bo Wu
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC10011170
doi: 10.3389/fphar.2023.1072917
license: CC BY 4.0
---
# The self-medication behaviors of residents and the factors related to the consideration of drug efficacy and safety—A cross-sectional study in China
## Abstract
Background: Over-the-counter (OTC) drugs facilitates residents self-medication. However, inappropriate self-medications have become a serious problem in China and even all over the world.
Objectives: To make an investigation on the current status of Chinese residents’ self-medication behaviors and important considerations, and to explore the factors related to the considerations of drug efficacy and safety.
Design: A quantitative, cross-sectional study.
Methods: Multi-stage sampling was used to conduct a cross-sectional investigation in China 22 provinces, 5 autonomous regions and 4 municipalities directly under the Central Government. State that an interviewer-administrated questionnaire, was used for data collection. The questionnaire that was used in the investigation included demographic sociological characteristics, health literacy scale-short form (HLS-SF), the 10-item Big Five Inventory (BFI-10), the EuroQol-5D visual analogue scale (EQ-5D VAS), self-medication status and important considerations when self-medicating. Descriptive statistics were performed, and the Chi-square test was used for univariate analysis. Log-binomial regression was used for multivariate analysis on whether residents regard drug efficacy or safety as an important consideration.
Results: 9256 respondents were included in the data analysis. The self-medication rate of Chinese adults was as high as $99.1\%$. Paracetamol and other analgesics were the most common types of OTC medication that respondents purchased, followed by vitamins/minerals. Medical staff recommendations, drug safety and efficacy were the top three important considerations. The residents in the east, central and western regions who consider safety is $63.5\%$, $61.5\%$, and $66.8\%$ respectively. The proportion of curative effect was $60.2\%$, $55.7\%$, and $61.4\%$ respectively. Log-binomial regression showed that western respondents, retired people, those who mainly used ways including basic medical insurance for employees, commercial medical insurance, free medical treatment to cover their medical cost, respondents with high neuroticism, high health literacy were more likely to consider drug safety as an important factor ($p \leq 0.05$). Eastern respondents, employed, main way of medical expenses borne was Out-of-pocket Payment, those with chronic disease were more likely to consider drug efficacy as an important factor ($p \leq 0.05$). Female, respondents with high levels of agreeableness, conscientiousness, openness, and self-rated health status were more likely to regard both drug safety and efficacy as important considerations ($p \leq 0.05$).
Conclusion: Self-medication is practiced by most Chinese adults. Whether Chinese adults take drug efficacy or safety as an important consideration is related to their demographic and sociological characteristics, Big Five personality characteristics, health literacy and self-assessed health status. There is a need to strengthen the management of OTC drugs and public education about self-medication.
## Highlights
What does this paper contribute to the wider global clinical community?1. Providing a reference for decision-makers to formulate policies.2. Actively strengthening the management of OTC drugs and public health education on self-medication.3. The health department needs to establish a system that promotes the partnership between physicians, patients and pharmacists.4. Mobilizing relevant media to publicize drug-related knowledge and common sense through various forms of media.5. Encouraging medical staff to provide more guidance on the safety and efficacy of drugs to help patients choose drugs.
## 1 Introduction
The World Health Organization defined self-medication as the use of drugs to treat self-diagnosed disorders or symptoms, or the intermittent or continuous use of prescribed medication to treat a chronic or recurrent disease or symptoms (World Health Organization, 2000). A large portion of the public uses self-medication as an important way to treat minor illnesses or relieve symptoms A study in Iran found that the self-medication rate was as high as $90\%$ (Fereidouni et al., 2019). In a study in the United States, the majority of the population ($87.0\%$) took at least one OTC drug (Stoehr et al., 1997). Self-medication has become more prevalent as the COVID-19 outbreak has led to fewer people going out and going to the hospital to avoid infection.
The most widely self-medicated substances are OTC drugs, which are used to treat common health issues at home. These drugs do not require a doctor’s prescription and are available in some countries at supermarkets and convenience stores (WORLD SELF-MEDICATION INDUSTRY, 2022). The U.S. Food and Drug Administration (FDA) defines OTC drugs as those that can be purchased without a medical prescription (U.S. Food and drug administration, 2022). In China, there are also categories governing prescription and OTC drugs (Lei et al., 2018).
The growing variety of OTC drugs facilitates residents’ self-medication behavior. The appropriateness of residents’ self-medication behavior has a significant impact on the efficacy and safety of the drugs. Appropriate use of OTC drugs can timely relieve minor symptoms of chronic diseases, such as upper respiratory tract infection, headache, atopic dermatitis and stomach pain, etc., thereby reducing the economic and time cost of patient care and reducing the pressure on medical institutions and the burden on medical insurance (Lei et al., 2018). However, inappropriate self-medication behaviors can delay or obscure the diagnosis of patients’ serious diseases and increase the risk of potential adverse effects in patients. Inappropriate self-medication has caused harm to some residents and is increasingly becoming a serious public problem globally (Tesfamariam et al., 2019). In China, about 2.5 million people are hospitalized each year due to inappropriate self-medication, and about 100,000 people die from adverse drug reactions (Zhao et al., 2017).
Residents purchase OTC drugs based on a variety of factors, such as drug price, efficacy, and safety, as well as advice from medical staff, family, friends, and so on (Shrestha et al., 2022; Tavares et al., 2022). Personality represents a series of personality characteristics, thinking patterns and habitual behaviors within an individual, and influences the individual’s response to external stimuli and interaction with others in society (Getzmann et al., 2021). Health literacy is an individual’s ability to acquire health information and understand disease-related knowledge. Good health literacy is conducive to residents’ accurate judgment and rational use of health information, so as to maintain their own health (DE Wit et al., 2017). It has been found that personality characteristics and health literacy will affect an individual’s health behavior (Zhang et al., 2021).
At present, the research on residents and self-medication in China mostly investigates residents’ knowledge, attitude and behavior of self-medication, and the research on influencing factors mostly involves some surveys on demographic sociology. This study not only investigates the current situation of self-medication among residents in China, but also explores the correlation between factors such as demographic sociology, personality, health literacy and self-assessed health status, etc., and the importance of residents’ perception of drug efficacy and safety. In addition, the previous studies in China are mostly surveys from a small area (such as provinces and cities) and a small sample, and the research results can only represent the residents in this area. The purpose of this study is to carry out a cross-sectional study of large samples covering all provinces of China in China, and the results can represent the overall situation of China. The research results can better support the improvement of rational self-medication behavior of residents in China, make healthy decisions, and provide reference for residents’ health management.
The objective of this study is to investigate the current status of Chinese residents’ self-medication behaviors (including residents’ self-medication rate and the types of drugs they purchase and use), important factors to consider when purchasing OTC drugs (including drug efficacy, drug safety, recommendations from medical staff, family members, friends, etc.) and to explore the relationship between the residents’ demographic and sociological characteristics and their view of drug efficacy and safety as important considerations when purchasing OTC drugs.
## 2.1 Study design
The study was conducted in mainland China from 10 July 2021 to 15 September 2021, using a multi-stage sampling method. The specific research design is described in our group’s previously published paper (Zhang Z et al., 2022).
## 2.2.1 Calculation of minimum sample size
We used the following formula to calculate the minimum sample size (Samo et al., 2022). n=Zα/22pq/δ In the above formula, n represents the sample size, p represents the estimated self-medication rate, $q = 1$-p, α = 0.05, Z α/2 = 1.96 ˜ 2, δ is the allowable error, δ = 0.1*p. According to literature reports, the self-medication rate of people around the world is about $32.5\%$–$81.5\%$ (Kassie et al., 2018), the smaller value is used for sample size calculation, and the minimum sample size calculated by substituting the formula is 831. Considering $20\%$ of invalid questionnaires, the minimum number of questionnaires that should be distributed is 1039.
## 2.2.2 Inclusion criteria
[1] Age>18; [2] Had the nationality of the People’s Republic of China; [3] China’s permanent resident population with the annual travel time≤1 month; [4] Participate in the study and fill in the informed consent form voluntarily; [5] Participants can complete the network questionnaire survey by themselves or with the help of investigators; [6] Participants can understand the meaning of each item in the questionnaire; [7] Participants who have self-medicated behavior, in other words, the participant must have purchased and used OTC drugs on their own.
## 2.2.3 Exclusion criteria
[1] Persons with unconsciousness, or mental disorders; [2] Those who are participating in other similar research projects. [ 3] Medical staff. ( Medical staff has relatively specialized knowledge of medicines, and the objective of this study was to study the self-medication behavior of residents, so medical staff was excluded from this study.) Initially, 11,031 participants from 120 cities in mainland China finished the questionnaire. After excluding questionnaires that do not meet the requirements of the study, 9256 residents were enrolled in this study. Figure 1 shows a detailed flowchart of the enrollment. The effective rate of the investigation reached $83.91\%$.
**FIGURE 1:** *Flowchart of participant enrollment.*
## 2.3 Instruments
The questionnaire consists of three parts focusing on the current status of residents’ self-medication behaviors and related influencing factors. The first part investigated the social-demographic characteristics of the residents, such as gender, age, province, place of permanent residence (urban, rural), education level, per capita monthly income of the family, marital status, the current main way of bearing medical expenses, current occupational status (student, on-the-job, no fixed occupation or retired), currently diagnosed chronic diseases, etc. The second part investigates the current status of residents’ self-medication behaviors and important considerations, including 3 questions (1 single-choice question, and 2 multiple-choice questions). The third part is a series of standard scales, including the 10-item short version of the Big Five Inventory (BFI-10), the Short-Form Health Literacy Instrument (HLS-SF12), and the EQ-5D visual analogue scale (EQ-VAS). Permission was obtained from the developers of HLS-SF12, while the other two scales are available for free for non-commercial use (Duong, T. V et al., 2019; EQ-5D-5L Scale, 2021; Rammstedt and John, 2007; Carciofo et al., 2016).
## 2.3.1 Items for resident self-medication status and important considerations
This section includes three entries (1 single-choice question, and 2 multiple-choice questions). All entries in this section were designed based on the current sales of OTC drugs in the Chinese market, relevant literature and personal practical experience, combined with expert consultation (Brabers et al., 2013; Barrenberg and Garbe, 2015; Hedenrud et al., 2019; YAN et al., 2019; Watanabe, 2020; Sánchez-Sánchez et al., 2021; El-Gamal et al., 2022). The expert group consists of eight pharmacists with bachelor’s degrees or above working in secondary or tertiary hospitals.
The single-choice question is “Have you ever purchased and used OTC medicines on your own?”. Respondents who answered “No” to this question were excluded from the study.
The first multiple-choice question is “What kinds of OTC drugs have you ever purchased and used? “, this question has 10 options, namely, [1] Antipyretic analgesics (e.g.,: paracetamol); [2] Digestive system drugs (e.g.,: ranitidine hydrochloride capsules); [3] Respiratory system drugs (e.g.,: aminocaffeine tablets); [4] Vitamins/minerals (e.g.,: vitamin C tablets); [5] Antibacterial drugs (e.g.,: metronidazole buccal tablets); [6] Drugs for external use (e.g.,: compound beclomethasone camphor cream); [7] Chinese patent drugs (e.g.,: Xiao Chai Hu granules, Xiao Jianzhong granules, Sijunzi pills); [8]Gynecological drugs (e.g.,: miconazole nitrate suppositories); [9] Anti-allergic drugs (e.g.,: loratadine capsules); [10] Others.
The second multiple-choice question is “Which of the following factors are important considerations when purchasing OTC drugs?”. The 16 options for this question are: [1] The price of the drug; [2] The efficacy of the drug; [3] The safety of the drug; [4] The taste of the drug; [5] Brand awareness; [6] Whether the drug can be reimbursed by medical insurance; [7] Advice from medical staff (including doctors, pharmacists, etc.); [ 8] Advice from family members; [9] Advice from friends; [10] Personal experience; [11] Advertising; [12] After-sales service; [13] Corporate reputation; [14] Ease of taking the drug; [15] Packaging of the drug; [16] Dosage form of the drug. The order in which the options appear in the two multiple-choice questions is random for each respondent.
## 2.3.2 The 10-item short version of the Big Five Inventory (BFI-10)
The 10-item short version of the Big Five Inventory (BFI-10) was applied to measure the personality characteristics of residents, including Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness, on a 5-point Likert-type scale ranging from 1 (totally disagree) to 5 (totally agree) (Rammstedt and John, 2007; Carciofo et al., 2016). The scores of Extraversion were summed of the scores of item 1R and item 6, the scores of Agreeableness were combined with the scores of item 2 and 7R, Conscientiousness as 3R and 8, Neuroticism as 4R + 9, and Openness as 5R + 10 (R = item is reversed-scored). Several studies have shown that BFI-10 has good reliability and validity (Johann et al., 2020; Eichenberg et al., 2021; Nikčević et al., 2021). In a previous study, the reliability levels of the BFI-10 proved satisfactory using Cronbach’s α analysis: Extraversion (α = 0.723), Agreeableness (α = 0.759), Conscientiousness (α = 0.786), Neuroticism (α = 0.753) and Openness to experience (α = 0.714) (Wang et al., 2014). The higher the score of a personality trait of the respondent, the more significant the personality trait of the respondent is. In this study, referring to relevant literature, the five personality characteristics of the respondents were divided into a high group (7–10 points) and a low group (6 points and below) (Heilmann et al., 2021).
## 2.3.3 The short-form health literacy instrument (HLS-SF12)
The health literacy of the respondents was measured by HLS-SF12 (Duong, T. V et al., 2019). The scale includes 3 dimensions of healthcare, disease prevention, and health promotion, with a total of 12 items, and each item is scored on a 4-point scale (1 = very difficult, 2 = difficult, 3 = easy, 4 = very easy). In the study, the Cronbach’s coefficient of the scale was 0.940, and the Cronbach’s coefficients of the three subscales of healthcare, disease prevention and health promotion were 0.856, 0.860, and 0.868, respectively, with good reliability. The higher the respondent’s score on this scale, the higher the health literacy of the respondent. In this study, referring to relevant literature, the health literacy of the surveyed subjects was divided into a high group (over 33 points) and a low group (33 points and below) (The European Health Literacy Project 2009–2012; Cheong et al., 2021).
## 2.3.4 The EQ-5D visual analogue scale
The European Five-dimensional Health Scale (EQ-5D-5L) is one of the most widely used health-related quality of life measurement tools to measure the health status of the population (Jiang et al., 2021). EQ-VAS is a part of EQ-5D-5L. Respondents filled in integers between 0 and 100 to indicate their health status. 100 represents the respondent’s best-imagined health status, while 0 represents the respondent’s worst-imagined health status (Ping et al., 2020). In this study, referring to relevant literature, the EQ-VAS scores of the respondents were divided into a high group (81–100 points) and a low group (80 points and below) (Wang et al., 2017).
## 2.4 Statistical methods
Data entry and analysis were performed using SPSS™ for Windows (version 27.0) (SPSS Inc, Chicago, IL, United States of America). First, the common method bias was tested. Then the quantity and percentage of categorical variables were calculated using descriptive statistics. Scale scores were tested for normality. For normally distributed data, the mean and standard deviation were used for statistical description, and non-normally distributed data, the median and interquartile range were used for statistical description. Regarding the relevant literature, all scale scores were converted into dichotomous variables (high grouping and low grouping). The independent variables in the study include the demographic and sociological characteristics, health literacy, personality, and self-assessed health status of the respondents. There are two dependent variables, which are whether the respondents consider drug efficacy or safety as an important factor when purchasing OTC drugs. The Chi-square test was used for univariate analysis. In the study, the percentage of respondents who cited drug efficacy or safety as an important consideration in purchasing OTC drugs both exceeded fifty percent. In this case, using traditional logistic regression for multifactor analysis may overestimate the association between the independent and dependent variables; therefore, multilevel log-bionmial regression was used for multifactor analysis to overcome the level effects (Barros and Hirakata, 2003; Deddens and Petersen, 2008; Knol et al., 2012; Marschner and Gillett, 2012), and conduct stratified analysis according to different locations (Eastern part of China, Central part of China and Western part of China). Unless otherwise stated, the test level of statistical tests was α = 0.05. A supplementary subgroup analysis of demographic characteristics was performed, and subgroups were divided according to gender (male, female), age (35 years old and below, above 35 years old), permanent residence (urban, rural) in the proportion of residents considering efficacy or safety in different regions of China are highlighted in the paper.
## 2.5 Quality control
The study conducted two rounds of pre-investigation and two rounds of expert consultation before the formal survey. Trained investigators distributed questionnaires to respondents and registered their codes one-on-one and face-to-face. Every Sunday evening during the investigation process, members of the research group communicated with the investigators to summarize, evaluate, and give feedback on the questionnaires they collected. After the questionnaires were collected, two people conducted back-to-back logic checks and data screening. If singular values are found during data analysis, the original questionnaire must be found and checked with the investigator before proceeding to the next step of the analysis.
## 3.1 Common method bias test
Harman’s single-factor method showed five factors with eigenvalues greater than 1, and the variance contribution rate of the first main factor was $34.98\%$, which did not exceed $40\%$, indicating that there was no common method bias (Podsakoff et al., 2003).
## 3.2 Demographic and sociological characteristics of the respondents
The investigation showed that $99.06\%$ (9256 of 9344) of Chinese people aged 18 years or older had self-medication behaviors.
Among respondents 9256 who had self-medication behaviors, there were 4289 males ($46.3\%$) and 4967 females ($53.7\%$) among the respondents; 4722 ($51.0\%$) were located in eastern China, 2391 ($25.8\%$) were located in central China, and 2143 ($23.2\%$) were located in western China; 6674 ($72.1\%$) were urban residents, 2582 ($27.9\%$) were rural; 4246 ($45.9\%$) were 19–35 years old, and 3935 ($42.5\%$) were 36–59 years old, 1075 ($11.6\%$) were aged 60 and above. The demographic and sociological characteristics of the survey respondents are shown in Table 1.
**TABLE 1**
| Variables | Number | Percentage (%) |
| --- | --- | --- |
| Gender | | |
| Male | 4289.0 | 46.3 |
| Female | 4967.0 | 53.67 |
| Age(years) | | |
| 19–35 | 4246.0 | 45.9 |
| 36–59 | 3935.0 | 42.5 |
| ≥60 | 1075.0 | 11.6 |
| Education level | | |
| High/Secondary School and lower | 3685.0 | 39.8 |
| Junior college | 1300.0 | 14.0 |
| Undergraduate | 3654.0 | 39.5 |
| Postgraduate degree (including Masters and Ph.D. students) | 617.0 | 6.7 |
| Location | | |
| Eastern part of China | 4722.0 | 51.0 |
| Central part of China | 2391.0 | 25.8 |
| Western part of China | 2143.0 | 23.2 |
| The main way of medical expenses borne | | |
| Out-of-pocket Payment | 1840.0 | 19.9 |
| Resident Basic Medical Insurance (RBMI) | 4472.0 | 48.3 |
| Others (Basic medical insurance for employees, Commercial medical insurance, Free medical treatment) | 2944.0 | 31.8 |
| Place of residence | | |
| Urban | 6674.0 | 72.1 |
| Rural | 2582.0 | 27.9 |
| Monthly income (RMB) | | |
| 0–4500 (0$-666$) | 4735.0 | 51.2 |
| 4501–9000 (666.148$-1332$) | 3146.0 | 34.0 |
| >9000 (1332$) | 1375.0 | 14.9 |
| Marital Status | | |
| Unmarried | 5765.0 | 62.3 |
| Married | 3072.0 | 33.2 |
| Divorced | 193.0 | 2.1 |
| Widowed | 226.0 | 2.4 |
| Employment status | | |
| Employed | 4129.0 | 44.6 |
| Student | 2144.0 | 23.2 |
| Unemployed | 2174.0 | 23.5 |
| Retired | 809.0 | 8.7 |
| Chronic diseases condition | | |
| No chronic diseases | 7357.0 | 79.5 |
| Hypertension | 1051.0 | 11.4 |
| Diabetes Mellitus | 243.0 | 2.6 |
| Dyslipidemia | 285.0 | 3.1 |
| Coronary atherosclerotic heart disease | 211.0 | 2.3 |
| Chronic gastritis | 314.0 | 3.4 |
| Fatty liver disease | 106.0 | 1.2 |
| Chronic enteritis | 89.0 | 1.0 |
| Asthma | 60.0 | 0.7 |
| Chronic nephritis | 42.0 | 0.5 |
| Cerebral apoplexy (Cerebral infarction, cerebral hemorrhage, etc.) | 71.0 | 0.8 |
| Malignant tumor | 40.0 | 0.4 |
| Viral hepatitis | 22.0 | 0.2 |
| Chronic obstructive pulmonary diseases | 15.0 | 0.2 |
| Parkinson’s disease | 18.0 | 0.2 |
## 3.3 Status of self-medication of the respondents
Among respondents 9256 who had self-medication behaviors, the investigation showed that $99.1\%$ (9256 of 9344) of Chinese people aged 18 years or older had self-medication behaviors. The types of drugs used by the respondents during self-medication are shown in Figure 2. Antipyretic analgesics (5421, $58.6\%$) and vitamins/minerals (4851, $52.4\%$) ranked the top two among all types of drugs. In addition to the “other” option, the number of users of gynecological drugs (1057, $11.4\%$) and anti-allergy drugs (1322, $14.3\%$) ranked the bottom two.
**FIGURE 2:** *Types of OTC drugs purchased by respondents.*
## 3.4 Important considerations for respondents when purchasing OTC drugs
Among the 16 factors to be considered, the top three selected are medical staff advice (7979, $86.2\%$), drug safety (5901, $63.7\%$) and drug efficacy (5492, $59.3\%$), the last three choices were exquisiteness of medicine packaging (445 people, $4.8\%$), the taste of medicines (871 people, $9.4\%$) and advertising (878 people, $9.5\%$) (Figure 3).
**FIGURE 3:** *Important considerations when respondents purchase OTC drugs.*
## 3.5 The scores of each scale of the respondents
The scores of the respondents on each scale are shown in Table 2. Since the scores on each scale do not satisfy the normal distribution, the median and upper and lower quartiles were used to describe the central tendency and dispersion of the scores of each scale. 5942 respondents ($64.2\%$) had high health literacy; There are 3349 people ($36.2\%$) with high extroversion, 5182 people ($56.0\%$) with high agreeableness, 4757 people ($51.4\%$) with high conscientiousness, 2257 people ($24.4\%$)with high neuroticism, 3565 people ($38.5\%$)with high openness; and 5460 people ($59.0\%$) with high self-rated health status.
**TABLE 2**
| Unnamed: 0 | No. of items | Score range | Kolmogorow-smironov Z | p-Value from K-S test | Median | Q1-Q3 | No. and percentage of high score group | No. and percentage of low score group |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| HLS—SF12 | 12.0 | 0–50 | 0.208 | <0.001 | 33.33 | 30.56–37.50 | 5942 (64.2%) | 3314 (35.8%) |
| BFI-10 | | | | | | | | |
| Extraversion | 2.0 | 2–10 | 0.203 | <0.001 | 6.0 | 5–7 | 3349 (36.2%) | 5907 (63.8%) |
| Agreeableness | 2.0 | 2–10 | 0.187 | <0.001 | 7.0 | 6–8 | 5182 (56.0%) | 4074 (44.0%) |
| Conscientiousness | 2.0 | 2–10 | 0.2 | <0.001 | 7.0 | 6–8 | 4757 (51.4%) | 4499 (48.6%) |
| Neuroticism | 2.0 | 2–10 | 0.217 | <0.001 | 6.0 | 5–6 | 2257 (24.4%) | 6999 (75.6%) |
| Openness | 2.0 | 2–10 | 0.221 | <0.001 | 6.0 | 5–7 | 3565 (38.5%) | 5691 (61.5%) |
| EQ-VAS | 1.0 | 0–100 | 0.148 | <0.001 | 84.0 | 73–96 | 5460 (59.0%) | 3796 (41.0%) |
## 3.6 Chi-square test results of two factors of drug efficacy and safety
Univariate analysis was performed using the chi-square test for the likelihood that respondents considered drug efficacy or safety as an important consideration. The possibility of respondents taking drug safety as an important consideration varies in gender, age, region, mode of paying medical expenses, chronic disease, extraversion, agreeableness, conscientiousness, neuroticism, openness, health literacy and self-assessed health status ($p \leq 0.05$). The possibility of respondents taking drug efficacy as an important consideration varies in gender, age, region, marital status, occupational status, chronic disease, extraversion, agreeableness, conscientiousness, openness, health literacy, and self-assessed health status ($p \leq 0.05$). The results of the chi-square test are shown in Supplementary Tables S1, S2.
## 3.7 Differences in the percentage of residents who focus on safety or efficacy of self-medication in different regions of China
A chi-square test was used to conduct a single factor analysis of differences in the percentage of residents who focused on safety or efficacy when self-medicating. The results of the chi-square test are shown in Supplementary Tables S1, S2. The results showed that the distribution of residents who focused on safety o refficacy differed across different regions of China (east, central, and west) (Safety:$$p \leq 0.001$$; Efficacy: $p \leq 0.001$). Multiple comparisons were further performed by the Bonferroni method, and it is shown that a significantly higher percentage of residents in the west ($66.8\%$) focus on safety than those in the east ($63.5\%$) and central ($61.5\%$) regions. As for efficacy, the proportion of residents in the central region ($55.7\%$) who focused on efficacy was significantly lower than those in the eastern ($60.2\%$) and western regions ($61.4\%$) (See Figure 4 for details).
**FIGURE 4:** *Distribution of residents focusing on safety or efficacy in different regions of China (a:safety; b:efficacy).*
## 3.8.1 Drug safety
Multilevel log-binomial regression analysis was carried out with the possibility of the respondents considering drug safety as an important factor as the dependent variable, the demographic and sociological characteristics of the respondents and the grading of each scale score as the independent variables. Three regression models were developed, the first regression model with respondents’ demographic and sociological characteristics as independent variables, the second regression model with respondents’ scale score grading as independent variables, and the third regression model with respondents’ demographic and sociological characteristics and scale score grading as independent variables. Using the third model as the main result of this study, the multilevel regression results showed that model 3 was robust.
The Omnibus test result of the model 3 is $p \leq 0.001$, the log-likelihood value is −5515.351, indicating that the model is of good quality. Log-binomial regression analysis showed that gender, location, employment status, the main way of medical expenses borne, agreeableness, conscientiousness, neuroticism, openness, health literacy, and self-rated health status were related to whether respondents considered drug safety as an important consideration when purchasing OTC drugs. Compared with men, women were more likely to take drug safety as an important consideration (PRR < Percentage rate ratio ≥1.052, $95\%$CI 1.021–1.085, $$p \leq 0.001$$).
Compared with the respondents in the east, the respondents in western parts were more likely to consider drug safety as an important factor (PRR = 1.044, $95\%$CI 1.007–1.082, $$p \leq 0.020$$); Compared with the respondents who were employed, retired people were more likely to consider drug safety as an important factor (PRR = 1.090, $95\%$CI 1.020–1.166, $$p \leq 0.012$$); Compared with the respondents whose main way of medical expenses borne was Out-of-pocket Payment, those who mainly used ways including basic medical insurance for employees, commercial medical insurance, free medical treatment to cover their medical cost were more likely to consider drug safety as an important factor (PRR = 1.053, $95\%$CI 1.002–1.106, $$p \leq 0.040$$). Compared with high agreeableness respondents, low agreeableness respondents were less likely to consider drug safety as an important consideration (PRR = 0.896, $95\%$CI 0.865–0.927, $p \leq 0.001$); Compared with high conscientiousness respondents, low conscientiousness respondents were less likely to consider drug safety as an important consideration (PRR = 0.931, $95\%$CI 0.900–0.963, $p \leq 0.001$); Compared with those with high neuroticism, respondents with low neuroticism were less likely to consider drug safety as an important consideration (PRR = 0.959, $95\%$CI 0.928–0.992, $$p \leq 0.014$$); Compared with those with high openness, those with low openness were less likely to consider drug safety as an important consideration (PRR = 0.941, $95\%$CI 0.911–0.972, $p \leq 0.001$); Compared with respondents with high health literacy, respondents with low health literacy were less likely to consider drug safety as an important factor (PRR = 0.960, $95\%$CI 0.927–0.993, $$p \leq 0.018$$); Compared with respondents with better self-rated health status, respondents with poorer self-rated health status were less likely to consider drug safety as an important consideration (PRR = 0.915, $95\%$CI 0.885–0.945, $p \leq 0.001$) (See Table 3 for details). Subgroup analysis was carried out according to gender, age, and place of permanent residence, and a total of six models were established. The independent variables and model parameters of the six subgroup analysis models were similar to the model built by all respondents. ( See Supplementary Tables S3–S8 for details.
**TABLE 3**
| Models | Variables | β | SE | Wald χ 2 | p | PRR | The lower limit of 95%CI | The upper limit of 95%CI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Model 1 | Gender (control group = Male) | | | | | | | |
| Model 1 | Female | 0.073 | 0.0159 | 21.135 | <0.001 | 1.076 | 1.043 | 1.11 |
| Model 1 | Age (control group = 19–35) | | | | | | | |
| Model 1 | 36–59 | 0.046 | 0.0234 | 3.916 | 0.048 | 1.047 | 1.0 | 1.097 |
| Model 1 | 60 or above | −0.071 | 0.0409 | 3.038 | 0.081 | 0.931 | 0.86 | 1.009 |
| Model 1 | Educational level (control group = High/Secondary School and lower) | | | | | | | |
| Model 1 | Junior college | 0.009 | 0.0255 | 0.132 | 0.716 | 1.009 | 0.96 | 1.061 |
| Model 1 | Undergraduate | 0.007 | 0.0228 | 0.101 | 0.750 | 1.007 | 0.963 | 1.053 |
| Model 1 | Postgraduate | −0.048 | 0.038 | 1.586 | 0.208 | 0.953 | 0.885 | 1.027 |
| Model 1 | Location (control group = Eastern part of China) | | | | | | | |
| Model 1 | Central part of China | −0.024 | 0.0197 | 1.454 | 0.228 | 0.977 | 0.94 | 1.015 |
| Model 1 | Western part of China | 0.057 | 0.0189 | 9.106 | 0.003 | 1.059 | 1.02 | 1.099 |
| Model 1 | Place of residence (control group = Rural) | | | | | | | |
| Model 1 | Urban | −0.026 | 0.0189 | 1.851 | 0.174 | 0.975 | 0.939 | 1.011 |
| Model 1 | Marital Status (control group = Unmarried) | | | | | | | |
| Model 1 | Married | 0.010 | 0.0285 | 0.129 | 0.720 | 1.01 | 0.955 | 1.068 |
| Model 1 | Divorced | −0.107 | 0.0608 | 3.07 | 0.080 | 0.899 | 0.798 | 1.013 |
| Model 1 | Widowed | −0.064 | 0.0615 | 1.07 | 0.301 | 0.938 | 0.832 | 1.059 |
| Model 1 | Employment status (control group = Employed) | | | | | | | |
| Model 1 | Student | 0.036 | 0.0307 | 1.378 | 0.240 | 1.037 | 0.976 | 1.101 |
| Model 1 | Unemployed | 0.006 | 0.0241 | 0.054 | 0.816 | 1.006 | 0.959 | 1.054 |
| Model 1 | Retired | 0.077 | 0.0347 | 4.988 | 0.026 | 1.08 | 1.01 | 1.156 |
| Model 1 | The main way of medical expenses borne (control group = Out-of-pocket Payment) | | | | | | | |
| Model 1 | Resident Basic Medical Insurance (RBMI) | 0.008 | 0.0218 | 0.141 | 0.707 | 1.008 | 0.966 | 1.052 |
| Model 1 | Others (Basic medical insurance for employees, Commercial medical insurance, Free medical treatment) | 0.080 | 0.0259 | 9.595 | 0.002 | 1.083 | 1.03 | 1.14 |
| Model 1 | Chronic diseases condition (control group = No chronic diseases) | | | | | | | |
| Model 1 | Suffer from chronic diseases | −0.037 | 0.0221 | 2.751 | 0.097 | 0.964 | 0.923 | 1.007 |
| Model 1 | Monthly income (RMB) (control group = 0–4500 (0$-666$)) | | | | | | | |
| Model 1 | 4501–9000 (666.148$-1332$) | −0.010 | 0.0182 | 0.323 | 0.570 | 0.99 | 0.955 | 1.026 |
| Model 1 | >9000 (1332$) | 0.008 | 0.0246 | 0.105 | 0.746 | 1.008 | 0.961 | 1.058 |
| Model 2 | Extraversion (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.024 | 0.0162 | 2.169 | 0.141 | 0.976 | 0.946 | 1.008 |
| Model 2 | Agreeableness (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.119 | 0.0175 | 46.069 | <0.001 | 0.888 | 0.858 | 0.919 |
| Model 2 | Conscientiousness (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.075 | 0.0167 | 20.055 | <0.001 | 0.928 | 0.898 | 0.959 |
| Model 2 | Neuroticism (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.049 | 0.0172 | 8.144 | 0.004 | 0.952 | 0.921 | 0.985 |
| Model 2 | Openness (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.059 | 0.0162 | 13.401 | <0.001 | 0.942 | 0.913 | 0.973 |
| Model 2 | Health Literacy (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.041 | 0.017 | 5.911 | 0.015 | 0.959 | 0.928 | 0.992 |
| Model 2 | EQ-VAS (control group = High score group) | | | | | | | |
| Model 2 | Low score group | −0.095 | 0.0168 | 32.46 | <0.001 | 0.909 | 0.88 | 0.939 |
| Model 3 | Gender (control group = Male) | | | | | | | |
| Model 3 | Female | 0.051 | 0.0156 | 10.704 | 0.001 | 1.052 | 1.021 | 1.085 |
| Model 3 | Age (control group = 19–35) | | | | | | | |
| Model 3 | 36–59 | 0.036 | 0.0231 | 2.36 | 0.124 | 1.036 | 0.99 | 1.084 |
| Model 3 | 60 or above | −0.072 | 0.0409 | 3.128 | 0.077 | 0.93 | 0.859 | 1.008 |
| Model 3 | Educational level (control group = High/Secondary School and lower) | | | | | | | |
| Model 3 | Junior college | 0.018 | 0.0245 | 0.512 | 0.474 | 1.018 | 0.97 | 1.068 |
| Model 3 | Undergraduate | −0.012 | 0.0222 | 0.27 | 0.603 | 0.989 | 0.947 | 1.032 |
| Model 3 | Postgraduate | −0.060 | 0.0373 | 2.584 | 0.108 | 0.942 | 0.875 | 1.013 |
| Model 3 | Location (control group = Eastern part of China) | | | | | | | |
| Model 3 | Central part of China | −0.029 | 0.0193 | 2.252 | 0.133 | 0.971 | 0.935 | 1.009 |
| Model 3 | Western part of China | 0.043 | 0.0184 | 5.424 | 0.020 | 1.044 | 1.007 | 1.082 |
| Model 3 | Place of residence (control group = Rural) | | | | | | | |
| Model 3 | Urban | −0.030 | 0.0184 | 2.59 | 0.108 | 0.971 | 0.936 | 1.006 |
| Model 3 | Marital Status (control group = Unmarried) | | | | | | | |
| Model 3 | Married | 0.019 | 0.0279 | 0.477 | 0.49 | 1.019 | 0.965 | 1.077 |
| Model 3 | Divorced | −0.100 | 0.0592 | 2.834 | 0.092 | 0.905 | 0.806 | 1.017 |
| Model 3 | Widowed | −0.05 | 0.0615 | 0.664 | 0.415 | 0.951 | 0.843 | 1.073 |
| Model 3 | Employment status (control group = Employed) | | | | | | | |
| Model 3 | Student | 0.027 | 0.0297 | 0.807 | 0.369 | 1.027 | 0.969 | 1.088 |
| Model 3 | Unemployed | 0.03 | 0.0236 | 1.62 | 0.203 | 1.031 | 0.984 | 1.079 |
| Model 3 | Retired | 0.086 | 0.0342 | 6.381 | 0.012 | 1.09 | 1.02 | 1.166 |
| Model 3 | The main way of medical expenses borne (control group = Out-of-pocket Payment) | | | | | | | |
| Model 3 | Resident Basic Medical Insurance (RBMI) | −0.007 | 0.0212 | 0.112 | 0.738 | 0.993 | 0.953 | 1.035 |
| Model 3 | Others (Basic medical insurance for employees, Commercial medical insurance, Free medical treatment) | 0.052 | 0.0252 | 4.226 | 0.04 | 1.053 | 1.002 | 1.106 |
| Model 3 | Chronic diseases condition (control group = No chronic diseases) | | | | | | | |
| Model 3 | Suffer from chronic diseases | −0.014 | 0.0218 | 0.414 | 0.52 | 0.986 | 0.945 | 1.029 |
| Model 3 | Monthly income (RMB) (control group = 0–4500(0$-666$)) | | | | | | | |
| Model 3 | 4501–9000(666.148$-1332$) | −0.017 | 0.0177 | 0.916 | 0.339 | 0.983 | 0.95 | 1.018 |
| Model 3 | >9000(1332$) | 0.005 | 0.024 | 0.035 | 0.851 | 1.005 | 0.958 | 1.053 |
| Model 3 | Extraversion (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.027 | 0.0161 | 2.891 | 0.089 | 0.973 | 0.943 | 1.004 |
| Model 3 | Agreeableness (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.11 | 0.0175 | 39.581 | <0.001 | 0.896 | 0.865 | 0.927 |
| Model 3 | Conscientiousness (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.071 | 0.0172 | 17.071 | <0.001 | 0.931 | 0.9 | 0.963 |
| Model 3 | Neuroticism (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.042 | 0.0171 | 6.002 | 0.014 | 0.959 | 0.928 | 0.992 |
| Model 3 | Openness (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.061 | 0.0165 | 13.555 | <0.001 | 0.941 | 0.911 | 0.972 |
| Model 3 | Health Literacy (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.041 | 0.0175 | 5.57 | 0.018 | 0.96 | 0.927 | 0.993 |
| Model 3 | EQ-VAS (control group = High score group) | | | | | | | |
| Model 3 | Low score group | −0.089 | 0.0169 | 27.766 | <0.001 | 0.915 | 0.885 | 0.945 |
## 3.8.2 Drug efficacy
Multilevel log-binomial regression analysis was carried out with the possibility of the respondents taking drug efficacy as an important consideration factor as the dependent variable, and the demographic and sociological characteristics of the respondents and the grading of each scale score as the independent variables. Three regression models were developed, the model 4 with respondents’ demographic and sociological characteristics as independent variables, the model 5 with respondents’ scale score grading as independent variables, and the model 6 with respondents’ demographic and sociological characteristics and scale score grading as independent variables. Using the third model as the main result of this study, the multilevel regression results showed that model six was robust.
The Ominbus test result of the model six is $p \leq 0.001$, the log-likelihood value is −5689.403, indicating that the model is of good quality. Log-binomial regression analysis showed that gender, location, employment status, the main way of medical expenses borne, chronic disease, agreeableness, conscientiousness, openness, and self-rated health status were related to whether respondents considered drug efficacy as an important consideration when purchasing OTC drugs. Compared with men, women were more likely to consider drug efficacy as an important factor (PRR = 1.056, $95\%$CI 1.021–1.092, $$p \leq 0.001$$); Compared with eastern respondents, midlands respondents were less likely to consider drug efficacy as an important factor (PRR = 0.916, $95\%$CI 0.878–0.956, $p \leq 0.001$); Compared with the respondents who were employed, unemployed people were less likely to consider drug efficacy as an important factor (PRR = 0.939, $95\%$CI 0.890–0.990, $$p \leq 0.019$$); Compared with the respondents whose main way of medical expenses borne was Out-of-pocket Payment, those who mainly used Resident Basic Medical Insurance to cover their medical cost were less likely to consider drug efficacy as an important factor (PRR = 0.946, $95\%$CI 0.904–0.989, $$p \leq 0.015$$). Compared with those without chronic disease, those with chronic disease were more likely to consider drug efficacy as an important factor (PRR = 1.069, $95\%$CI 1.022–1.118, $$p \leq 0.004$$); Compared with high agreeableness respondents, low agreeableness respondents were less likely to consider drug efficacy as an important factor (PRR = 0.900, $95\%$CI 0.867–0.935, $p \leq 0.001$); Compared with high conscientiousness respondents, low conscientiousness respondents were less likely to consider drug efficacy as an important factor (PRR = 0.881, $95\%$CI 0.848–0.915, $p \leq 0.001$); Compared with high openness respondents, low openness respondents were less likely to consider drug efficacy as an important factor (PRR = 0.953, $95\%$CI 0.920–0.988, $$p \leq 0.009$$); Compared with respondents with better self-rated health status, respondents with poor self-rated health status were less likely to consider drug efficacy as an important consideration (PRR = 0.916 $95\%$CI 0.883–0.950, $p \leq 0.001$) (See Table 4 for details). Subgroup analysis was carried out according to gender, age and place of permanent residence, and a total of six models were established. The independent variables and model parameters of the six subgroup analysis models were similar to the model built by all respondents. ( See Supplementary Tables S9–S14 for details).
**TABLE 4**
| Models | Variables | β | SE | Wald χ 2 | p | PRR | The lower limit of 95%CI | The upper limit of 95%CI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Model 4 | Gender (control group =Male) | | | | | | | |
| Model 4 | Female | 0.072 | 0.0175 | 16.784 | <0.001 | 1.074 | 1.038 | 1.112 |
| Model 4 | Age (control group =19–35) | | | | | | | |
| Model 4 | 36–59 | 0.034 | 0.0256 | 1.808 | 0.179 | 1.035 | 0.984 | 1.088 |
| Model 4 | 60 or above | 0.022 | 0.044 | 0.259 | 0.611 | 1.023 | 0.938 | 1.115 |
| Model 4 | Educational level (control group =High/Secondary School and lower) | | | | | | | |
| Model 4 | Junior college | 0.035 | 0.0285 | 1.479 | 0.224 | 1.035 | 0.979 | 1.095 |
| Model 4 | Undergraduate | 0.036 | 0.0254 | 1.972 | 0.160 | 1.036 | 0.986 | 1.089 |
| Model 4 | Postgraduate | 0.002 | 0.0411 | 0.003 | 0.959 | 1.002 | 0.925 | 1.086 |
| Model 4 | Location (control group =Eastern part of China) | | | | | | | |
| Model 4 | Central part of China | −0.078 | 0.0219 | 12.74 | <0.001 | 0.925 | 0.886 | 0.965 |
| Model 4 | Western part of China | 0.023 | 0.021 | 1.235 | 0.266 | 1.024 | 0.982 | 1.066 |
| Model 4 | Place of residence (control group =Rural) | | | | | | | |
| Model 4 | Urban | −0.026 | 0.0209 | 1.552 | 0.213 | 0.974 | 0.935 | 1.015 |
| Model 4 | Marital Status (control group =Unmarried) | | | | | | | |
| Model 4 | Married | −0.042 | 0.0318 | 1.726 | 0.189 | 0.959 | 0.901 | 1.021 |
| Model 4 | Divorced | −0.097 | 0.0653 | 2.214 | 0.137 | 0.907 | 0.799 | 1.031 |
| Model 4 | Widowed | 0.002 | 0.0576 | 0.001 | 0.978 | 1.002 | 0.895 | 1.121 |
| Model 4 | Employment status (control group =Employed) | | | | | | | |
| Model 4 | Student | −0.005 | 0.0348 | 0.024 | 0.877 | 0.995 | 0.929 | 1.065 |
| Model 4 | Unemployed | −0.080 | 0.0275 | 8.469 | 0.004 | 0.923 | 0.875 | 0.974 |
| Model 4 | Retired | 0.002 | 0.0576 | 0.001 | 0.978 | 1.002 | 0.895 | 1.121 |
| Model 4 | The main way of medical expenses borne (control group =Out-of-pocket Payment) | | | | | | | |
| Model 4 | Resident Basic Medical Insurance (RBMI) | −0.030 | 0.0234 | 1.651 | 0.199 | 0.97 | 0.927 | 1.016 |
| Model 4 | Others (Basic medical insurance for employees, Commercial medical insurance, Free medical treatment) | −0.011 | 0.0282 | 0.15 | 0.699 | 0.989 | 0.936 | 1.045 |
| Model 4 | Chronic diseases condition (control group =No chronic diseases) | | | | | | | |
| Model 4 | Suffer from chronic diseases | 0.047 | 0.0234 | 3.981 | 0.046 | 1.048 | 1.001 | 1.097 |
| Model 4 | Monthly income (RMB) (control group =0–4500 (0$-666$)) | | | | | | | |
| Model 4 | 4501–9000 (666.148$-1332$) | −0.022 | 0.0199 | 1.223 | 0.269 | 0.978 | 0.941 | 1.017 |
| Model 4 | >9000 (1332$) | −0.044 | 0.0276 | 2.567 | 0.109 | 0.957 | 0.906 | 1.01 |
| Model 5 | Extraversion (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.004 | 0.0179 | 0.05 | 0.824 | 0.996 | 0.962 | 1.032 |
| Model 5 | Agreeableness (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.110 | 0.0192 | 32.892 | <0.001 | 0.896 | 0.863 | 0.93 |
| Model 5 | Conscientiousness (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.136 | 0.0188 | 52.354 | <0.001 | 0.873 | 0.841 | 0.906 |
| Model 5 | Neuroticism (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.014 | 0.0195 | 0.509 | 0.476 | 0.986 | 0.949 | 1.025 |
| Model 5 | Openness (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.046 | 0.0179 | 6.665 | 0.010 | 0.955 | 0.922 | 0.989 |
| Model 5 | Health Literacy (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.009 | 0.0185 | 0.232 | 0.630 | 0.991 | 0.956 | 1.028 |
| Model 5 | EQ-VAS (control group =High score group) | | | | | | | |
| Model 5 | Low score group | −0.075 | 0.0185 | 16.56 | <0.001 | 0.928 | 0.895 | 0.962 |
| Model 6 | Gender (control group =Male) | | | | | | | |
| Model 6 | Female | 0.055 | 0.0172 | 10.183 | 0.001 | 1.056 | 1.021 | 1.092 |
| Model 6 | Age (control group = 19–35) | | | | | | | |
| Model 6 | 36–59 | 0.017 | 0.0254 | 0.475 | 0.491 | 1.018 | 0.968 | 1.07 |
| Model 6 | 60 or above | 0.029 | 0.0434 | 0.457 | 0.499 | 1.03 | 0.946 | 1.121 |
| Model 6 | Educational level (control group =High/Secondary School and lower) | | | | | | | |
| Model 6 | Junior college | 0.036 | 0.0279 | 1.665 | 0.197 | 1.037 | 0.982 | 1.095 |
| Model 6 | Undergraduate | 0.02 | 0.025 | 0.626 | 0.429 | 1.02 | 0.971 | 1.071 |
| Model 6 | Postgraduate | −0.004 | 0.0407 | 0.008 | 0.928 | 0.996 | 0.92 | 1.079 |
| Model 6 | Location (control group =Eastern part of China) | | | | | | | |
| Model 6 | Central part of China | −0.088 | 0.0215 | 16.618 | <0.001 | 0.916 | 0.878 | 0.956 |
| Model 6 | Western part of China | 0.009 | 0.0204 | 0.213 | 0.645 | 1.009 | 0.97 | 1.051 |
| Model 6 | Place of residence (control group =Rural) | | | | | | | |
| Model 6 | Urban | −0.026 | 0.0203 | 1.681 | 0.195 | 0.974 | 0.936 | 1.014 |
| Model 6 | Marital Status (control group =Unmarried) | | | | | | | |
| Model 6 | Married | −0.02 | 0.0314 | 0.401 | 0.526 | 0.98 | 0.922 | 1.043 |
| Model 6 | Divorced | −0.089 | 0.0638 | 1.935 | 0.164 | 0.915 | 0.808 | 1.037 |
| Model 6 | Widowed | 0.012 | 0.0571 | 0.041 | 0.84 | 1.012 | 0.905 | 1.131 |
| Model 6 | Employment status (control group =Employed) | | | | | | | |
| Model 6 | Student | −0.018 | 0.0343 | 0.289 | 0.591 | 0.982 | 0.918 | 1.05 |
| Model 6 | Unemployed | −0.063 | 0.0271 | 5.461 | 0.019 | 0.939 | 0.89 | 0.99 |
| Model 6 | Retired | 0.018 | 0.0378 | 0.229 | 0.633 | 1.018 | 0.946 | 1.096 |
| Model 6 | The main way of medical expenses borne (control group =Out-of-pocket Payment) | | | | | | | |
| Model 6 | Resident Basic Medical Insurance (RBMI) | −0.056 | 0.0229 | 5.891 | 0.015 | 0.946 | 0.904 | 0.989 |
| Model 6 | Others (Basic medical insurance for employees, Commercial medical insurance, Free medical treatment) | −0.054 | 0.0278 | 3.718 | 0.054 | 0.948 | 0.897 | 1.001 |
| Model 6 | Chronic diseases condition (control group =No chronic diseases) | | | | | | | |
| Model 6 | Suffer from chronic diseases | 0.067 | 0.023 | 8.446 | 0.004 | 1.069 | 1.022 | 1.118 |
| Model 6 | Monthly income (RMB) (control group =0–4500(0$-666$)) | | | | | | | |
| Model 6 | 4501–9000 (666.148$-1332$) | −0.025 | 0.0194 | 1.706 | 0.191 | 0.975 | 0.939 | 1.013 |
| Model 6 | >9000 (1332$) | −0.049 | 0.0271 | 3.269 | 0.071 | 0.952 | 0.903 | 1.004 |
| Model 6 | Extraversion (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.008 | 0.0178 | 0.193 | 0.661 | 0.992 | 0.958 | 1.027 |
| Model 6 | Agreeableness (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.105 | 0.0192 | 29.899 | <0.001 | 0.9 | 0.867 | 0.935 |
| Model 6 | Conscientiousness (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.127 | 0.0194 | 42.549 | <0.001 | 0.881 | 0.848 | 0.915 |
| Model 6 | Neuroticism (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.011 | 0.0195 | 0.331 | 0.565 | 0.989 | 0.952 | 1.027 |
| Model 6 | Openness (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.048 | 0.0181 | 6.92 | 0.009 | 0.953 | 0.92 | 0.988 |
| Model 6 | Health Literacy (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.022 | 0.0191 | 1.307 | 0.253 | 0.978 | 0.942 | 1.016 |
| Model 6 | EQ-VAS (control group =High score group) | | | | | | | |
| Model 6 | Low score group | −0.088 | 0.0188 | 21.896 | <0.001 | 0.916 | 0.883 | 0.95 |
## 3.8.3 Results of subgroup analysis in different regions
In addition to the above subgroup analysis, we also conducted subgroup analysis by region (eastern China, central China, and western China), adjust factors gender, age, education level, the main way of medical expenses borne, place of residence, monthly income, marital Status, employment status, chronic diseases condition, mainly analyze the influence of the Big Five Inventory (BFI-10) Health Life (HLS-SF12) health-related quality of life (EQ-5D-5L) on the safety and effectiveness of self-medication, and the results are shown in Tables 5, 6. The correlates of the dependent variable vary among residents of different regions.
In terms of whether to focus on safety, the influencing factors of personal character, health literacy and health status for eastern residents were agreeableness, conscientiousness, health literacy, and self-rated quality of life; for the central region, the relevant factors were agreeableness, conscientiousness, neuroticism, and self-rated quality of life; for the western region, the relevant factors were agreeableness, and openness.
In terms of whether to focus on efficacy, the influencing factors of personal character, health literacy and health status for eastern residents were agreeableness, conscientiousness, self-rated quality of life; for the central region, the relevant factors were agreeableness, conscientiousness and self-rated quality of life; and for the western region, the relevant factors were agreeableness, and conscientiousness.
## 4.1 Current situation of self-medication in mainland China residents
Self-medication is becoming an increasingly common behavior, and people are becoming more independent in making decisions about their health management. Self-medication can help reduce the cost of producing, selling, and administering prescription drugs. However, people may suffer from misdiagnosis, prolonged or insufficient medication, neglect of drug interactions, and overdose due to their improper self-medication behaviors, which can delay their recovery process and even have serious, life-threatening consequences (Hughes et al., 2001).
Self-medication is a common practice around the world. The prevalence of self medication in Thailand reached $88.2\%$ (Chautrakarn et al., 2021). The prevalence of self medication in the Indian population ranges from $8.3\%$ to $93\%$ (Rashid et al., 2020). $67.3\%$ of people in Syria take drugs by themselves (Abdelwahed et al., 2022). In a study involving several European countries, self-medication rates among residents ranged from $33\%$ in Turkey to $92\%$ in the Czech Republic and $97\%$ in Cyprus (Kamekis et al., 2018). This indicates that in many countries and regions, a large number of residents purchase and use OTC medicines by themselves as an important means of treating their diseases. Self-medication is also prevalent in China. The self-medication rate among the respondents in this study was $99.1\%$. The two most common types of OTC drugs purchased and used by the respondents were antipyretic and analgesics (5421 people, $58.6\%$) and vitamins/minerals (4851 people, $52.4\%$). Long et al. found that the self-medication rate of urban residents in China was $73.5\%$. A 2017 study by Chang J et al. showed that in China, $32.7\%$ of people aged 45 and older used OTC for self-treatment within 4 weeks before the survey (Chang et al., 2017). Many studies have shown that self medication is related to sociodemographic characteristics and economic conditions (Subashini and Udayanga, 2020; Zeid et al., 2020).
## 4.2 Analysis of influencing factors of residents taking drug safety or efficacy as important considerations
The safety and efficacy of drugs are important considerations for most residents purchasing OTC drugs (Hanna and Hughes, 2011). A global study covering 51 countries reported similar findings that consumers value safety (“I know it is safe”) and effectiveness (“I know it works”) as the most important considerations while buying OTC drugs 42. This study showed that the number of respondents who purchased OTC medicines mainly considered drug safety as an important factor reached 5901, accounting for $63.7\%$, and the number of people who took drug efficacy as an important consideration reached 5492, accounting for $59.3\%$. An investigation of Australian consumers’ considerations for purchasing OTC drugs also showed that most consumers cite efficacy ($\frac{1420}{1627}$, $87\%$) and safety ($\frac{1348}{1625}$, $83\%$) as important considerations for purchasing OTC drugs (Bevan et al., 2019). In Japan, a developed region in Asia, information about self medication can be obtained from the Internet and government publicity. People with high quality of life can manage their symptoms according to their knowledge (Ohta et al., 2022). German research also shows that providing more OTC information for patients may improve patient safety (Eickhoff C,. et al., 2012). Self medication is associated with more severe disease grade and lower economic level of quality of life (Parvinroo et al., 2022).
We used log-binomial regression to analyze the associated factors of the dependent variables. In the log-binomial regression analysis of safety, gender, location, employment status, the main way of medical expenses borne, agreeableness, conscientiousness, neuroticism, openness, health literacy, and self-rated health status were significantly associated with the likelihood of drug safety being a consideration when purchasing OTC drugs.
Self-medication behaviors carry potential risks, so safety should be an important consideration when engaging in self-medication, and safety awareness should be an important part of how patients can manage their health. In this study, females were more likely to consider drug safety as an important consideration. This finding is in line with a previous study on drug safety awareness, in which males were more willing to accept risks, while females were more likely to seek information about drugs and place greater importance on safety (See et al., 2020).
Respondents who were retired were more likely to pay great attention to drug safety. Because retired people were elder and pays attention to safety, and has a certain level of knowledge and social experience, so that can make decisions on drug safety. Respondents those who mainly used ways including basic medical insurance for employees, commercial medical insurance, free medical treatment to cover their medical cost were more likely to consider drug safety as an important factor. People who participate in these types of insurance have a stronger risk awareness, and will pay more attention to safety when reimbursement funds are available.
Personality largely determines patients’ health behaviors, and when purchasing OTC drugs, personality may influence how much importance patients place on them. In this study, we found that people with high agreeableness, high conscientiousness, high neuroticism, and high openness were more likely to regard drug safety as an important consideration. High conscientiousness is a state of caution or vigilance that shows a desire to do something well (Vlieland et al., 2019). Therefore, people with high conscientiousness will pay more attention to the potential risks of OTC drugs to their health and are sensitive to safety. A higher level of neuroticism predicts anxiety due to worry-related problems (Beyer et al., 2015). This group is more worried about the adverse consequences when buying OTC drugs and thinks that drug safety is extremely important. People with high agreeableness and high openness will consider the advice of professionals more and make choices about their safety. Health beliefs are closely related to self-medication (Ting et al., 2018). People with high health literacy and self-assessed health levels have stronger health beliefs and safety awareness and are more likely to pay attention to drug safety when purchasing OTC drugs.
Another important factor in purchasing OTC drugs is the efficacy of the drug. In the log-binomial regression analysis of efficacy, we found that gender, location, employment status, the main way of medical expenses borne, chronic disease, agreeableness, conscientiousness, openness, and self-rated health status were all related to drug efficacy when respondents purchased OTC drugs. Many studies have shown that efficacy (usually determined by personal experience or advice from health professionals) is one of the factors most valued by consumers when considering safety (Hanna and Hughes, 2011). Compared with males, females cited drug efficacy as an important consideration when purchasing OTC medications ($p \leq 0.05$). Females visit pharmacies more frequently and may receive more information about drugs. A previous study in Sweden showed that females paid more attention to drug efficacy (Håkonsen et al., 2020). Compared to people without chronic diseases, people with chronic diseases pay more attention to drug efficacy due to the need for frequent medication, and they need cost-effective and effective medicines to treat diseases and relieve symptoms. Respondents who were unemployed were less likely to consider drug efficacy as an important factor. The knowledge level and social experience of unemployed are low, and their understanding of the practicality of drugs is insufficient (Chiappini et al., 2022). Respondents whose main way of medical expenses borne was Out-of-pocket Payment, were more likely to consider drug efficacy as an important factor, and this was because this group pays more attention to the cost-effectiveness (Lai et al., 2021).
## 4.3 Consideration of safety and efficacy in self-medication in eastern, central, and western regions
When residents self-medicate, the proportion of residents in the east, central and western regions who consider safety is $63.5\%$, $61.5\%$, and $66.8\%$ respectively. The proportion of curative effect was $60.2\%$, $55.7\%$, and $61.4\%$ respectively. The results of multifactor analysis showed that the residents in the west considered the safety of medication more than those in the east, while the residents in the middle considered the efficacy less than those in the east.
Stratified analysis of individual personality, health literacy, and the impact of self-assessment of health status on safety effectiveness in the eastern, central and western regions shows that in the eastern region, residents who are more agreeableness, more conscientiousness, and have higher health literacy, and who have higher self-rated quality of life pay more attention to safety. In the central region, residents with agreeableness, more conscientiousness, more neurotic and higher self-rated quality of life pay more attention to safety. In the western region, more agreeableness, and openness residents pay more attention to safety. In terms of curative effect, in the east, residents with more agreeableness, conscientious and self-rated quality of life pay more attention to drug effect. In the central region, residents with more agreeableness, conscientious and higher self-rated quality pay more attention to drug effect. For the western region, residents with more agreeableness and conscientious pay more attention to effect.
With the expansion of China’s regional economic development gap, the supply of medical and health services is unbalanced in the east, central and western regions, and the supply level of medical and health services in the eastern region is the highest (Cavaco et al., 2018). There is inequality in the distribution of economic and medical resources among different regions in China, and there are significant differences in the practice and guarantee of drug safety among regions. The study found that the drug safety practice of pharmacists in regions with high per capita GDP and rich medical resources is better than that in regions with low per capita GDP and relatively scarce medical resources (Tang L, 2012). Because the safety practice of drug use in the western region is lower than that in the central region, they are more likely to be unable to receive timely treatment due to the fear of adverse drug use events and when they experience adverse drug events such as anaphylactic shock. This is why western respondents tend to take drug safety as an important consideration compared with eastern respondents.
The research shows that in China, the score of openness in the western region is higher than that in other regions and provinces, and the openness of the central and western residents of the Big Five personality is more obvious (Xu L et al., 2022). The results of this study show that when self-medication, the openness characteristics of the Big Five personality of Chinese residents also appear in the western region, and the western residents with high openness are more concerned about the safety of self-medication. The agreeableness and conscientious personality traits significantly predict drug compliance (Abu EK L et al., 2022), suggesting that agreeableness and conscientious traits can significantly affect residents’ ability to make healthy choices. A sense of responsibility can enhance healthy behaviors, and a high sense of responsibility is related to beneficial self-care behaviors (Skinner et al., 2014). Therefore, both eastern and western regions show that the more pleasant and responsible they are, the more attention they pay to safety and effectiveness in self-medication.
People in the eastern region are changing their living habits and improving their health management ability. The overall health literacy in the central and western regions is not high. We should pay attention to strengthening health education and popularizing health knowledge. Therefore, residents with different levels of health literacy only show differences in the eastern region when considering the safety of self-medication behavior (Wang F et al., 2022). Both the multifactor analysis of safety and effectiveness and the subgroup analysis of the region showed that the patient compliance EQ-VAS was positively correlated with the health compliance medication behavior. Social determinants are closely related to health and play an important role in health-related quality of life. The eastern provinces of China are better than the central and western provinces in terms of population density, culture, economic status, and social infrastructure, affecting the significant differences in health-related quality of life between the eastern provinces and the central and western provinces. The EQ-VAS of residents in the east is higher, which significantly affects the consideration of the safety and effectiveness of self-medication.
High agreeableness, high conscientiousness, high openness, and high self-rated health status group were more likely to consider drug efficacy as an important factor. People with high conscientiousness pay more attention to whether OTC drugs would achieve a beneficial effect on the body. When purchasing OTC medicines, people with high agreeableness and high openness will listen more to the explanations of professionals and pay more attention to drugs when communicating with medical staff. People with high-level of self-assessed health status are in better health and therefore will pay more attention to their health and consider whether the drug can restore and maintain their health quickly and effectively when taking medication.
## 4.4 Advantages and limitations of the study
This study has several advantages. First, we obtained extensive and representative data using cross-sectional survey data across mainland China in 2021. In addition, this study combined the Big Five personality, health literacy, and health-related quality of life theories to analyze residents’ purchase behavior of OTC drugs. Theories such as the Big Five have made empirical contributions and expanded the value of new theoretical applications of self-medication health behaviors. At the same time, it makes targeted suggestions for the future practice of self-medication supervision and health education. It makes further practical contributions.
The study also has several limitations. First, the data were based exclusively on self-report questionnaires, which may be influenced by social expectations, self-report errors, and poor memory. Second, this study used a cross-sectional design, and the results were only used to explore the correlates of the dependent variables. Third, the participants in this study were all located in China, and the sample was homogeneous and cannot be generalized to other countries. We used the most recent data available to us [2021], however, the behavioral characteristics of residents and the important factors considered in OTC drug purchases are likely to change further in the ensuing years. Also, Due to the limited length of the paper, only two important properties of the drugs themself, namely, drug efficacy and safety, are discussed in this paper. Other considerations for purchasing OTC drugs will be further investigated in follow-up studies.
## 5 Relevance to clinical practice
Self-medication plays an important role in disease prevention, health promotion, and the treatment of mild illnesses. Consumers can obtain OTC drugs without a prescription, which allows them to access treatment more quickly and can reduce the burden on the healthcare system. However, there are certain potential risks associated with self-medication. For example, when residents take drugs by themselves, they are prone to overdose, underdose, and wrong medication.
For China, with a large population and great differences in health literacy, it is necessary to strengthen the management of OTC drugs and health education on public self-medication. We make the following recommendations for the health sector, pharmaceutical manufacturers and distributors, the media, medical personnel and the general public. The health sector needs to build a regulatory system, a partnership between doctors, patients and pharmacists, strengthen behavioral supervision and education medical staff. Drug manufacturers and distributors should recognize that drugs are a special commodity, and ensure that the drugs produced and sold are safe, effective, stable, and controllable in quality. Relevant media should publicize drug knowledge and common sense, and increase public opinion supervision on violations. Medical staff should improve their professional ability, strengthen training and professional experience and always play an important role in imparting knowledge to consumers. Pharmacy staff should provide more guidance on drug safety and efficacy to assist patients in choosing drugs. Residents should improve their health literacy, reserve drug-related knowledge and common sense, read the instruction of the drugs before using them, understand the indications and adverse reactions of commonly used OTC medications and take self-medication behaviors cautiously.
## 6 Conclusions
Self-medication is widespread among Chinese adults. The two most common OTC drugs that people buy and use on their own are antipyretic analgesics and vitamins/minerals. When Chinese adults buy OTC drugs, the safety and efficacy of the drug and the doctor’s recommendations are important considerations. The likelihood that people consider drug efficacy and safety as important considerations when purchasing OTC drugs is influenced by their demographic sociological characteristics, health literacy, self-assessed health status, and personality characteristics. The health sector should take measures to strengthen the management of OTC drugs, Pharmacy staff should provide more guidance on drug safety and efficacy to assist patients in choosing drugs. Improve residents’ drug-related health literacy, and enhance their attention to drug efficacy and safety, thereby reducing inappropriate self-medication behaviors and injuries and deaths caused by these inappropriate behaviors.
## 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 Committee of Ji’nan University, Guangzhou, China. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conception and design:PG, X-YS, and Y-BW; Acquisition of data: W-LY; Analysis and interpretaion of data: PG and KL; Drafting of the manuscript: PG, Z-WZ, J-ZZ, and RL; Critical revision of the manuscript for important intellectual content: PX, Q-YL, H-WM, Y-QD, Y-JW, X-YS, Y-TT, LY, and Y-BW; Statistical analysis: PG and KL; Obtaining funding: LY and Y-BW; Administrative, technical, or material support: Y-BW; Supervision: LY and Y-BW; Others: language polish: YN.
## 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.2023.1072917/full#supplementary-material
## Abbreviations
OTC, Over-the-counter; HLS-SF, The health literacy scale-short form; BFI-10, The 10-item Big Five Inventory; FDA, The U.S. Food and Drug Administration; EQ-VAS, The EQ-5D visual analogue scale; RBMI, Resident Basic Medical Insurance
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|
---
title: Retinal blood vessel diameters in children and adults exposed to a simulated
altitude of 3,000 m
authors:
- Tinkara Mlinar
- Tadej Debevec
- Jernej Kapus
- Peter Najdenov
- Adam C. McDonnell
- Anton Ušaj
- Igor B. Mekjavic
- Polona Jaki Mekjavic
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10011172
doi: 10.3389/fphys.2023.1026987
license: CC BY 4.0
---
# Retinal blood vessel diameters in children and adults exposed to a simulated altitude of 3,000 m
## Abstract
Introduction: Technological advances have made high-altitude ski slopes easily accessible to skiers of all ages. However, research on the effects of hypoxia experienced during excursions to such altitudes on physiological systems, including the ocular system, in children is scarce. Retinal vessels are embryologically of the same origin as vessels in the brain, and have similar anatomical and physiological characteristics. Thus, any hypoxia-related changes in the morphology of the former may reflect the status of the latter.
Objective: To compare the effect of one-day hypoxic exposure, equivalent to the elevation of high-altitude ski resorts in North America and Europe (∼3,000 m), on retinal vessel diameter between adults and children.
Methods: 11 adults (age: 40.1 ± 4.1 years) and 8 children (age: 9.3 ± 1.3 years) took part in the study. They spent 3 days at the Olympic Sports Centre Planica (Slovenia; altitude: 940 m). During days 1 and 2 they were exposed to normoxia (FiO2 = 0.209), and day 3 to normobaric hypoxia (FiO2 = 0.162 ± 0.03). Digital high-resolution retinal fundus photographs were obtained in normoxia (Day 2) and hypoxia (Day 3). Central retinal arteriolar equivalent (CRAE) and venular equivalents (CRVE) were determined using an Automated Retinal Image Analyser.
Results: Central retinal arteriolar and venular equivalents increased with hypoxia in children (central retinal arteriolar equivalent: 105.32 ± 7.72 µm, hypoxia: 110.13 ± 7.16 µm, central retinal venular equivalent: normoxia: 123.39 ± 8.34 µm, hypoxia: 130.11 ± 8.54 µm) and adults (central retinal arteriolar equivalent: normoxia: 105.35 ± 10.67 µm, hypoxia: 110.77 ± 8.36 µm; central retinal venular equivalent: normoxia: 126.89 ± 7.24 µm, hypoxia: 132.03 ± 9.72 µm), with no main effect of group or group*condition interaction. A main effect of condition on central retinal arteriolar and venular equivalents was observed (central retinal arteriolar equivalent:normoxia: 105.34 ± 9.30 µm, hypoxia: 110.50 ± 7.67 µm, $p \leq 0.001$; central retinal venular equivalent: normoxia: 125.41 ± 7.70 µm, hypoxia: 131.22 ± 9.05 µm, $p \leq 0.001$).
Conclusion: A 20-hour hypoxic exposure significantly increased central retinal arteriolar and venular equivalents in adults and children. These hypoxia-induced increases were not significantly different between the age groups, confirming that vasomotor sensitivity of the retinal vessels to acute hypoxia is comparable between adults and prepubertal children.
## 1 Introduction
Skiing is a popular family winter activity, to which children are introduced at a young (prepubertal) age. Technological developments (chairlifts and cable cars) have made many high-altitude (≥3,000 m; please see Table 1) ski slopes easily accessible to all levels of skiers, including children (Meijer and Jean, 2008; Giesenhagen et al., 2019). At these altitudes, several environmental factors, such as cold, ultraviolet radiation, and hypoxia, may significantly impact not only physical performance (Cymerman et al., 1996; Jaki Mekjavic et al., 2021), but other physiological and psychological systems as well. Indeed, impairment of the aerobic performance, resulting from the lower partial pressure of oxygen (PO2) at altitude (Wehrlin and Hallén, 2006), has been observed in both adults and children, with the magnitude of such being similar (Kriemler et al., 2016; Kapus et al., 2017). The present study investigated the effect of hypoxia on retinal vessels, which share similar anatomical, physiological, and embryological characteristics with cerebral vessels (Baker et al., 2008). Thus, any changes observed in the retinal vasculature may reflect similar processes occurring within the brain vasculature.
**TABLE 1**
| Unnamed: 0 | Europe | North America |
| --- | --- | --- |
| Number of ski resorts (peak altitude: ≥3,000 m) | 45 | 49 |
| Minimum base altitude (m) | 768 | 1924 |
| Maximum base altitude (m) | 2760 | 3290 |
| Maximum peak altitude (m) | 3899 | 3914 |
| Maximum Δ altitude in one resort (m) | 2807 | 1340 |
The ocular apparatus is a commonly affected system at altitude (Jaki Mekjavic et al., 2021). As a consequence of the retina’s high oxygen demand as highly metabolically active tissue (Eshaq et al., 2014), adequate retinal oxygenation is essential for the maintenance of normal visual function. To match the increased retinal oxygen demand during hypoxic exposure, retinal vessels dilate and become more tortuous to allow for the required increases in blood flow (Neumann et al., 2016). Supplementary to vasodilation of the vessels, other retinal changes such as cotton wool spots, haemorrhages, and papilledema, commonly known under the umbrella term high altitude retinopathy (HAR), may be observed upon ascent to altitude (Morris et al., 2006). The formation of these symptoms may be further exacerbated by strenuous exercise, such as skiing (Honigman et al., 2001) or mountaineering (Shults and Swan, 1975), and the examination of these features in prepubertal children is lacking.
To ensure safety at altitude, particularly during activities such as skiing, it is essential to determine the effect of hypoxia on the visual system in different age groups. Therefore, within the framework of the KidSki project (Kapus et al., 2017; Ušaj et al., 2019), we set out to investigate the effect of a one-day exposure to hypoxia, equivalent to the elevation of high-altitude ski resorts in North America and Europe (∼3,000 m), on the retinal blood vessels, specifically the arterioles and venules, between adults and children.
## 2.1 Study design
Details of the study protocol have been outlined previously (Kapus et al., 2017). Briefly, each participating family (parents and children) visited the Olympic Sports Centre Planica (Rateče, Slovenia) on one occasion for 3 days. On Day 1, participants arrived at the facility in the afternoon and were familiarised with the researchers, laboratory, equipment, and experimental procedures. They also underwent a medical examination and spent the first (Day 1) and second day (Day 2) in normoxia [altitude of the facility: 940 m; FiO2 = $20.9\%$; PiO2 = 134.0 ± 0.4 mmHg]. The participants entered the normobaric hypoxic environment at 20:00 on the second night where they remained throughout the night and Day 3 until the termination of the experimental procedure. By reducing the oxygen content of the air, the normobaric hypoxic environment simulated an equivalent altitude of 3,000 m [FiO2 = 0.162 ± 0.03; PiO2 = 105.0 ± 0.6 mmHg]. Throughout Day 2 and Day 3 the participants took part in a series of physiological tests (for further details cf. Kapus et al., 2017). The experimental procedures on Day 2 (normoxia) and Day 3 (hypoxia) were conducted in the same order and at the same time of the day to avoid any diurnal fluctuations in the measured variables. The testing schedule (Figure 1) was designed in a way that the constraints for each test were met, and limited interaction was present between the tests. A paediatrician was present throughout the study.
**FIGURE 1:** *Testing schedule for Day 1 (normoxia) and Day 2 (hypoxia) (Note: LLS, Lake Louise score; SpO2, peripheral oxygen saturation; HR, heart rate).*
The normobaric hypoxic conditions were established with a vacuum-pressure swing adsorption system (VPSA), which delivered hypoxic gas to all the rooms and laboratory. The system sampled the gas in all rooms at 15-min intervals to ensure that the pre-set level of oxygen fraction was being maintained. In the event that the oxygen fraction decreased below the pre-set in any of the rooms, the system would cease the delivery of hypoxic gas to that room. Should the oxygen fraction not return to the desired level within two sampling cycles, the system would automatically initiate a fan that would deliver external air to the room, and also trigger an alarm. All rooms were equipped with portable clip-on type oxygen sensors programmed to initiate an alarm, should the oxygen fraction decrease below a pre-set threshold. There were no such untoward events during the course of the study.
## 2.2 Participants
In total, 13 adults (7 males, 6 females) and 13 children (Tanner stage 1; 7 males, 6 females) took part in the KidSki project. Children’s participation in the study was subject to the consent of the parents, who also participated in the study, and upon the approval of the paediatrician, who conducted the medical examination. Due to the specifications of the Automated Retinal Image Analyser (ARIA; Peter Bankhead, Queen’s University Belfast) software, only retinal scans of 11 adults (7 males, 4 females) and 8 children (4 males, 4 females) were used in the final analysis. All participants were lowland residents with no hypoxic exposure in the 2 months prior to taking part in the study as specified in the inclusion criteria. Participants’ physical characteristics are presented in Table 2.
**TABLE 2**
| Unnamed: 0 | Children | Adults |
| --- | --- | --- |
| Number (M/F) | 8 (4/4) | 11 (7/4) |
| Age (years) | 9.3 ± 1.3 | 40.1 ± 4.1 |
| Height (cm) | 141.2 ± 11.2 | 176.0 ± 8.5 |
| Weight (kg) | 31.2 ± 7.5 | 72.9 ± 12.1 |
| BMI (kg·m-2) | 15.4 ± 1.6 | 23.4 ± 2.2 |
| BF% (%) | 10.8 ± 5.9 | 20.2 ± 9.2 |
Exclusion criteria included smoking (adults only), asthma, hypertension, haematological or kidney disorders, exposure to altitude (>2,500 m) in the preceding 2 months, and any eye condition that could influence retinal vessels. Throughout the study, adult participants were requested not to consume any caffeine or alcohol.
The study conformed to the standards set by the Declaration of Helsinki, except for registration in a database. Prior to taking part in the study, the participants were thoroughly informed about the aims, methodology, and potential risks of the study, after which signed consent forms were obtained from the adults, and the children gave their vocal consent. The children’s consent forms were subsequently signed by the children and final consent was obtained from their parents/guardians, who were also participants in the study. The study was approved (approval no. $\frac{164}{05}$/13) by the National Medical Ethics Committee at the Ministry of Health (Republic of Slovenia).
## 2.3 General acclimation to hypoxia
The participants’ resting morning heart rate (HR) and oxygen saturation (SpO2) were measured in normoxia (Day 2) and hypoxia (Day 3) upon waking using a finger pulse oximeter (Nellcor, BCI 3301, Boulder, United States). Additionally, to assess for the potential presence of acute mountain sickness (AMS), participants completed the self-assessment section of the Lake Louise mountain sickness questionnaire (Roach et al., 1993) to obtain the Lake Louise score (LLS; 0–15). The collection of children’s LLS was adapted following the recommendations of the International Federation for Climbing and Mountaineering Medical Commission guidelines (Meijer and Jean, 2012).
## 2.4 Retinal fundus examination
Digital high-resolution retinal colour fundus photographs of the left and right eyes centred on the optic disc were taken according to procedures described elsewhere (De Boever et al., 2014). Measurements were performed by an ophthalmologist using a 45° 6.3 megapixel digital non-mydriatic camera (Canon, Hospithera, Brussels, Belgium). During the fundus photography, patients were seated on a chair in a darkened room with their chin resting on a chin support. The presence of HAR was determined with direct fundus ophthalmoscopy.
## 2.5 Data processing and analysis
Retinal vessels were analysed so that left and right eye central retinal arteriolar (CRAE) and venular equivalent (CRVE) in hypoxia and normoxia were determined using the ARIA. The calibration was set at 8.077 µm per pixel. Only retinal scans where the same three venules and three arterioles within the region between 0.5- and 1.0-disc diameters away from the disc margin were able to be identified in both normoxia and hypoxia, were used in the statistical analysis. In the event that an obtained retinal scan was of poor quality, the scan was repeated. Upon completion of the study, all scans were analysed with ARIA. The image analysis software deemed some of the scans of insufficient quality to be used in the analysis. Since the aim of the study was a comparison of images obtained before and after the normoxic and hypoxic confinements, a poor-quality scan obtained either pre- or post-exposure for a given participant, would render the results of this participant unusable by the software, requiring exclusion of the participant from the analysis. As a consequence, only retinal scans of 8 children (4 females) and 11 adults (4 females) were of quality that could be properly analysed by ARIA. Due to the high inter-eye correlation reported previously (Leung et al., 2003a), the average value of the left and right eyes was used in the statistical analysis. The eye examinations were performed by an ophthalmologist, thus any changes of clinical concern would have been identified immediately.
All of the statistical analyses were performed using SPSS (v.25, IBM, NY, United States) software. The data are presented as mean ± SD unless indicated otherwise. The significance level for all statistical tests in this study was set at $p \leq 0.05$, a priori. Unbiased effect sizes were estimated using Hedges’ g test and defined as small when g ≤ 0.2, moderate when g ≤ 0.5, and large when g ≤ 0.8 (Hedges, 1981). All data were assessed for normality using the Shapiro-Wilk test of normality. A paired-samples t-test was used to assess whether the effect of hypoxia (hypoxia vs. normoxia) was present in the morning HR and SpO2 measurements, and Wilcoxon signed-rank test to assess the effect of hypoxia in LLS. Potential hypoxia-induced CRAE and CRVE differences between both age groups were investigated using a mixed model ANOVA [group (children, adults)*condition (hypoxia, normoxia)] and potential changes between CRAE and CRVE within each age group were assessed using a two-way repeated measures ANOVA [vessel (CRAE, CRVE)*condition (hypoxia, normoxia)]. Where appropriate, a Bonferroni post hoc test was applied to investigate interaction effects in greater detail.
## 3 Results
No negative events related to the hypoxic environment were reported by the participants, nor were they observed by the attending paediatrician.
## 3.1 General acclimation to hypoxia
Morning resting HR was significantly higher in hypoxia compared to normoxia in children [t[7] = −3.108, $g = 0.74$, $$p \leq 0.017$$] but not in adults [t[10] = −0.359, $$p \leq 0.727$$], as seen in Table 3.
**TABLE 3**
| Unnamed: 0 | Children | Children.1 | Adults | Adults.1 |
| --- | --- | --- | --- | --- |
| Condition | Normoxia | Hypoxia | Normoxia | Hypoxia |
| Heart rate (min-1) | 80.1 ± 15.4 | 92.5 ± 16.1* | 64.2 ± 14.8 | 65.5 ± 13.0 |
| SpO2 (%) | 98.0 ± 1.4 | 92.4 ± 1.7* | 96.5 ± 0.8 | 90.1 ± 2.7* |
| LLS (median (range)) | 0 (0–2) | 1.5 (0–4) | 0 (0–2) | 1 (0–4) |
Morning resting SpO2 decreased significantly in hypoxia in both age groups (children: t[7] = 6.840, $g = 3.42$, $p \leq 0.001$; adults: t[10] = 8.061, $g = 3.07$, $p \leq 0.001$). There was no statistical difference in SpO2 between the two groups.
Hypoxia had no significant effect on children’s (Z = −1.897, $$p \leq 0.058$$) or adults’ (Z = −1.549, $$p \leq 0.121$$) LLS. LLS values ≥ 3 were observed in two adults and two children following a 12-hour night-time exposure to hypoxia. The median (range) LLS values in hypoxia were comparable between the two age groups. No other signs of AMS were observed by the attending paediatrician.
## 3.2.1 High altitude retinopathy
Based on direct fundus ophthalmoscopy, the ophthalmologist confirmed that no signs of HAR were present.
## 3.2.2 Central retinal arteriolar equivalent
CRAE increased with hypoxia in both children (normoxia: 105.32 ± 7.72 µm, hypoxia: 110.13 ± 7.16 µm) and in adults (normoxia: 105.35 ± 10.67 µm, hypoxia 110.77 ± 8.36 µm), as seen in Figure 2. No main effect of group ($$p \leq 0.933$$) or group*condition interaction ($$p \leq 0.785$$) was present. In contrast, a main effect of condition on CRAE was observed (normoxia: 105.34 ± 9.30 µm, hypoxia: 110.50 ± 7.67 µm, $p \leq 0.001$, $g = 0.59$).
**FIGURE 2:** *Adults’ and children’s central retinal arteriolar equivalents (CRAE) in normoxia (NOR) and hypoxia (HYP).*
## 3.2.3 Central retinal venular equivalent
Similarly to CRAE, CRVE increased with hypoxia in both children (normoxia: 123.39 ± 8.34 µm, hypoxia: 130.11 ± 8.54 µm) and adults (normoxia: 126.89 ± 7.24 µm, hypoxia: 132.03 ± 9.72 µm), as seen in Figure 3. Again, no main effect of group ($$p \leq 0.488$$) or group*condition interaction ($$p \leq 0.446$$) was present. A main effect of condition on CRVE was observed (normoxia: 125.41 ± 7.70 µm, hypoxia: 131.22 ± 9.05 µm, $p \leq 0.001$, $g = 0.68$).
**FIGURE 3:** *Adults’ and children’s central retinal venular equivalents (CRVE) in normoxia (NOR) and hypoxia (HYP).*
## 3.2.4 Central retinal arteriolar vs. venular equivalent
A main effect for vessels was observed both in children (CRAE: 107.73 ± 7.61 µm, CRVE: 126.75 ± 8.86 µm, $$p \leq 0.001$$, $g = 2.25$) and adults (CRAE: 108.06 ± 9.76 µm, CRVE: 129.46 ± 8.77 µm, $p \leq 0.001$, $g = 2.27$). However, no main effect of vessel*condition interaction was observed in either age group (children: $$p \leq 0.218$$; adults: $$p \leq 0.878$$).
## 4 Discussion
The main finding of the present study is that a 20-hour hypoxic exposure equivalent to an altitude of ∼3,000 m caused significant increases in the diameter of retinal venules and arterioles in adults and children. Furthermore, hypoxia-induced increases in CRAE and CRVE were not significantly different between the two age groups. This confirms that the vasomotor sensitivity of the retinal vessels to acute hypoxia is comparable between adults and prepubertal children.
## 4.1 The effect of hypoxia on retinal vessel diameters
Despite the high blood flow, the total blood volume of the retinal vessels is relatively low due to their small diameter and sparse distribution (Wolf et al., 1991). As mentioned previously, the retina is one of the most metabolically active tissues in the body, with a high arteriovenous PO2 difference and poor capacity to tolerate periods of low perfusion (Funk, 1997). Both the central retinal artery, which supplies the majority of blood to the retina (Kur et al., 2012), and the vein lack neural innervation which could provide regulation of the vascular tone. Therefore, blood flow in the retina is maintained by autoregulation, that is mainly dependent on local myogenic and metabolic factors, including arterial blood gases, pH, and lactate (Venkataraman et al., 2006; Garhöfer et al., 2003). In case of reduced oxygen availability, as reflected by the reduced SpO2, adequate retinal blood flow is achieved through vasodilation to match the augmented oxygen demand (Morris et al., 2006; Neumann et al., 2016). In the present study, morning SpO2 following a 12-hour overnight hypoxic exposure decreased from $97.2\%$ ± $1.2\%$ in normoxia (Day 2) to $91.3\%$ ± $2.4\%$ (both age groups combined), initiating vasodilation in both retinal arterioles and venules. The reactivity of adult retinal vessels to various levels of PO2 was first reported by Cusick et al. [ 1942]. Their findings have since been replicated and confirmed during exposure to both acute and chronic hypoxia (Louwies et al., 2016; Neumann et al., 2016), including in the present study. Additionally, retinal changes, including changes in retinal vessel diameter, have previously been observed in both normobaric and hypobaric hypoxia. The effect on the retinal vessel diameter was not dependent on the ambient pressure (i.e., normobaric versus hypobaric hypoxia), but solely on PO2 (Neumann et al., 2016). No such research has previously been conducted in children.
Arteriolar walls consist of smooth muscle cells, whereas the venular walls are thinner and consist of a single layer of endothelial cells and a few smooth muscle cells (Kur et al., 2012). It has been proposed that venular walls are therefore more compliant and potentially exhibit larger autoregulatory responses (Louwies et al., 2016). The results of several studies investigating the diameters of retinal vessels while breathing hyperoxic or hypoxic gas mixtures are equivocal. While some authors reported greater reactivity of venules than arterioles (Cusick et al., 1942; Louwies et al., 2016), other studies observed no differences in reactivity (Fallon et al., 1985; Neumann et al., 2016), including the present study where the diameter of retinal arterioles increased by 5.16 ± 4.67 µm in hypoxia, and similarly, the diameter of venules increased by 5.81 ± 4.28 µm (both age groups combined).
## 4.2 Adults vs. children
Retinal vessel diameters decrease with age, independent of blood pressure and other factors (Leung et al., 2003b; Wong et al., 2003). On the contrary, neither CRAE nor CRVE of the children and adults participating in the present study were significantly different. Most likely, the adults in the present study were not old enough (40.1 ± 4.1 years) for the age-related retinal changes to be manifest. For example, participants in studies conducted by Leung et al. ( 2003b); Wong et al. [ 2003] were aged >49 and >43 years, respectively, whereas the age range of adult participants in our study varied from 32 to 47 years (median: 40 years). Due to the low sample size, correlation analysis of age and CRAE and CRVE was not conducted.
Normoxic retinal vessel diameters measured in children and adults participating in the present study are smaller than the values reported in the literature (Mitchell et al., 2007; Li et al., 2011; Neumann et al., 2016). A similar phenomenon was observed in adults’ CRAE and CRVE measurements during exposure to hypoxia (Neumann et al., 2016). This can primarily be attributed to the differences in the analysis technique. In the present study, only three venules and three arterioles per retinal scan were included in the analysis. Due to the limited region of interest (between 0.5- and 1.0-disc diameters away from the optic disc margin), overlapping of vessels, and their branching, it was not always possible to select the six widest vessels.
Hypoxia-induced increases in retinal vessel diameters observed in both adults and children were not significantly different between the two age groups. To our knowledge, this is the first study to investigate children’s retinal vessel diameter changes during exposure to hypoxia. As discussed previously, hypoxia-induced vasodilation in retinal vessels in adults observed in the present study is in line with the previous research (Cusick et al., 1942; Louwies et al., 2016; Neumann et al., 2016). When looking at adults’ responses, a considerable individual variability can be noted, especially in the adult’s CRVE, compared to the children’s. This is also reflected in the standard deviation of the responses (±1.70 µm in children and ±5.46 µm in adults). Perhaps, such a range in individual responses of CRVE to hypoxia could be partially attributed to a considerable age range of adult participants.
## 4.3 High altitude headache, high altitude retinopathy and acute mountain sickness
HAR and AMS encompass a spectrum of physiological and pathological changes commonly occurring in unacclimatised individuals exposed to hypoxia. High-altitude headache (HAH), a type of headache occurring during ascents to altitudes above 2,500 m and resolving spontaneously within 24 h after descent, can appear along with other signs and symptoms which constitute AMS (Burtscher et al., 2011; Bian et al., 2013) or as an isolated symptom.
HAR has been noted to occur more frequently in individuals undergoing strenuous activity during hypoxic exposure, especially when Valsalva manoeuvres are involved (Arora et al., 2011). Meanwhile, symptoms of AMS are more commonly observed in adults than in children (Rexhaj et al., 2011; Kriemler et al., 2014). Other risk factors for the development of AMS, HAR and/or HAH include the level of hypoxic exposure, the rate and length of ascent, low arterial oxygen saturation, and an individual’s susceptibility (McFadden et al., 1981; Schneider et al., 2002; Bian et al., 2013).
Arteriolar and venular retinal vessel diameter has previously been correlated with the development of AMS (Bosch et al., 2009). Additionally, HAH burden was found to correlate strongly with retinal venous vasodilatation (Wilson et al., 2013).
In the present study, the presence of HAR, AMS, and/or HAH was not observed or reported by any of the participants. This is likely the result of the following two factors: 1) the level of hypoxia was low, and 2) the duration of the exposure was short.
## 4.4 Clinical implications
The retinal and cerebral macro- and microvasculature share many morphological and physiological properties, including similar vascular regulatory processes (Delaey and Van de Voorde, 2000). Due to these similarities, changes in cerebral microvasculature, resulting from diseases such as vascular dementia (Varma et al., 2002) and stroke (Powers and Zazulia, 2003) are often reflected in changes in retinal microvasculature. Furthermore, hypoxia-induced cerebral venous vasodilation has been observed to correlate strongly with the venous vasodilation observed in the retina (Wilson et al., 2013). It has been proposed that the small retinal vessel leakage that occurs in individuals exposed to high altitudes could also be mimicked in the vessels in their brain, contributing to the brain volume increase of a few millilitres observed following hypoxic exposures (Kallenberg et al., 2007; Willmann et al., 2013).
The role of high altitude-induced changes to the retina in predicting the manifestation of a life-threatening high altitude cerebral oedema is not yet fully understood (Barthelmes et al., 2011; Willmann et al., 2014).
Exposure to hypoxia can have a significant effect on human vision, manifesting as changes in colour discrimination (Connolly et al., 2008), reduction in dark adaptation (Kobrick and Appleton, 1971), and loss of contrast sensitivity (Pescosolido et al., 2015), with the changes being more evident in low light environments (Connolly et al., 2008). In contrast, high altitude has no effect on visual acuity (Bosch et al., 2009). In the event of blood leakage into the vitreous humour or when HAR-related haemorrhages manifest on the macula, the result can be an acute severe visual impairment. Most commonly, these hypoxia-induced vision changes are not clinically important and are reversed within weeks upon return to normoxia. Similarly, vasodilation of retinal vessels is reversible when adequate oxygen availability is restored (Bosch et al., 2009; Jaki Mekjavic et al., 2021). Visual function tests were not performed in the present study, however, no apparent hypoxia-related effects on vision were observed or reported by the participants, most likely due to low levels of hypoxia.
## 4.5 Limitations
A major limitation of the present study is a small sample size, especially in the younger group, mainly due to measurement errors when obtaining fundus photographs. Altogether retinal scans from 7 participants were discarded and not used in the final analysis because of blurriness, inappropriate lighting and/or composition (e.g., images not centred on the optic disc, mainly due to the inability of younger children to fix their gaze during the measurement). In future studies, this problem can be minimised or eliminated with more thorough familiarisation of the participants with the experimental equipment and procedures, improved measuring techniques, and taking duplicates or triplicates of each retinal scan.
Another limitation of the present study is that mean arterial pressure (MAP) was not measured during the retinal scans. It has previously been reported that MAP is inversely related to retinal vessel diameter (Wong et al., 2003; Kaushik et al., 2007), thus any hypoxia-induced increment in MAP would have caused a decrease in the retinal vessel diameter. In the present study, exposure to hypoxia resulted in retinal vessel vasodilation in both children and adults. Therefore, it can be speculated that retinal vascular regulation is more strongly affected by hypoxia than by the changes in MAP.
## 5 Conclusion
The present study mimicked a one-day family skiing trip to an altitude equivalent to the elevation of ski resorts in North America and Europe (∼3,000 m). No presence of AMS, HAR or HAH was observed in any of the participants. Significant increases in retinal vessel diameters with hypoxia were observed in both children and adults. The level of hypoxia-induced vasodilation did not differ between the two age groups. Adults and children appear to be similarly sensitive to changes in ambient PO2, therefore when travelling to altitude, children should do so with the same precautions as adults. No acute hypoxia-related effects on the ocular system were observed in any of the participants. Since retinal vessel vasodilation on its own does not have any clinical consequences, we conclude that skiing at altitudes up to 3,000 m is, from an ophthalmological perspective, safe for both adults and children.
## 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 study involving human participants was reviewed and approved by National Medical Ethics Committee at the Ministry of Health (Republic of Slovenia; approval no. $\frac{164}{05}$/13). Prior to participating in this study, all the participants provided a written informed consent. Children's written informed consent was provided by the participant's legal guardian/next of kin.
## Author contributions
The experiments were performed at the Olympic Sports Centre Planica (Rateče, Slovenia). IBM conceived and designed the study. All authors contributed to the data acquisition and analysis. TM prepared the figures and tables, and drafted the manuscript. All authors edited and revised the manuscript. All authors read 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.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2023.1026987/full#supplementary-material
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|
---
title: A whole cell fluorescence quenching-based approach for the investigation of
polyethyleneimine functionalized silver nanoparticles interaction with Candida albicans
authors:
- Atul Kumar Tiwari
- Munesh Kumar Gupta
- Roger J. Narayan
- Prem C. Pandey
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC10011178
doi: 10.3389/fmicb.2023.1131122
license: CC BY 4.0
---
# A whole cell fluorescence quenching-based approach for the investigation of polyethyleneimine functionalized silver nanoparticles interaction with Candida albicans
## Abstract
The antimicrobial activity of metal nanoparticles can be considered a two-step process. In the first step, nanoparticles interact with the cell surface; the second step involves the implementation of the microbicidal processes. Silver nanoparticles have been widely explored for their antimicrobial activity against many pathogens. The interaction dynamics of functionalized silver nanoparticles at the biological interface must be better understood to develop surface-tuned biocompatible nanomaterial-containing formulations with selective antimicrobial activity. Herein, this study used the intrinsic fluorescence of whole C. albicans cells as a molecular probe to understand the cell surface interaction dynamics of polyethyleneimine-functionalized silver nanoparticles and antifungal mechanism of the same. The results demonstrated that synthesized PEI-f-Ag-NPs were ~ 5.6 ± 1.2 nm in size and exhibited a crystalline structure. Furthermore, the recorded zeta potential (+18.2 mV) was associated with the stability of NPS and shown a strong electrostatic interaction tendency between the negatively charged cell surface. Thus, rapid killing kinetics was observed, with a remarkably low MIC value of 5 μg/mL. PEI-f-Ag-NPs quenched the intrinsic fluorescence of C. albicans cells with increasing incubation time and concentration and have shown saturation effect within 120 min. The calculated binding constant (Kb = 1 × 105 M−1, $$n = 1$.01$) indicated strong binding tendency of PEI-f-Ag-NPs with C. albicans surface. It should also be noted that the silver nanoparticles interacted more selectively with the tyrosine-rich proteins in the fungal cell. However, calcofluor white fluorescence quenching showed non-specific binding on the cell surface. Thus, the antifungal mechanisms of PEI-f-Ag-NPs were observed as reactive oxygen species (ROS) overproduction and cell wall pit formation. This study demonstrated the utility of fluorescence spectroscopy for qualitative analysis of polyethyleneimine-functionalized silver nanoparticle interaction/binding with C. albicans cell surface biomolecules. Although, a quantitative approach is needed to better understand the interaction dynamics in order to formulate selective surface tuned nanoparticle for selective antifungal activity.
## Introduction
Nanoparticles (NPs) are a focus of materials research since they often exhibit different properties from those of their bulk material counterparts (Baun et al., 2008). Ecological and health considerations have been raised in relation to the interaction of NPs with the environment (Gupta and Gupta, 2005; Brunner et al., 2006). For example, the NPs may build up on cell surfaces (Zhu et al., 2009; Schwegmann et al., 2010), cause the loss of cellular mobility (Baun et al., 2008), produce membrane translocation (Wong-Ekkabut et al., 2008), and facilitate DNA damage (Zhang et al., 2010). Many of these biological effects have been noted with microorganisms (Li et al., 2010) in wastewater treatment plants (Kiser et al., 2010), the natural environment, and the human digestive system (Zhang et al., 2009).
Silver nanoparticles (Ag-NPs) have been considered for use in sensors, cosmetics, paint/varnishes, and biomedical products due to their unusual optical, electronic, and catalytic properties (Al-Rajhi et al., 2022; Salem and Fouda, 2021; Salem et al., 2022a,b; Elakraa et al., 2022). Their unique physicochemical properties have made them one of the most commercialized nanomaterials (Chen and Schluesener, 2008). Polyethyleneimine is a polymer with polycationic and hydrophilic properties that is used for DNA transfection of mammalian cells, as a membrane perforating agent, and as a coating material in the biomedical and industrial fields. In our previous studies, we have documented the ultrafast, size-controlled, and water-dispersible synthesis of polyethyleneimine-functionalized silver and gold nanoparticles based on the polyethyleneimine molecular weight (Pandey et al., 2017; Tiwari et al., 2020). Other than polyethyleneimine, silica-containing organic polymers such as 3-aminopropyltrimethoxysilane and 3-glycidoxytrimethoxysilane and organic reducing agents such as formaldehyde and cyclohexanone are used to synthesize mono-, bi-, and trimetallic noble metal nanoparticles (Pandey et al., 2020). Thus, imine groups in PEI facilitate the nucleation and act as stabilizers (Tiwari et al., 2020).
There are few studies involving the adsorption of NPs on cell surfaces; quantitative studies involving particle adsorption kinetics have also not been previously considered. Wilhelm et al. considered NP binding on cell surfaces via a pseudo first-order kinetic model (Wilhelm et al., 2002). Other research teams used this kinetic model to understand NP interactions with human cells (Cho et al., 2009; Zhang et al., 2010). It should be noted that a pseudo-first-order model does not indicate the mechanism of action. Efforts are needed to develop mechanistic models for NP adsorption to microbial cells. The surface charge and size of Ag-NPs as well as the type of bacterial species determine the potency of the antimicrobial effect; for example, smaller-diameter Ag-NPs often demonstrated more potent antibacterial activity. Since the surfaces of bacteria are negatively charged, positively charged Ag-NPs exhibited greater antimicrobial activity than negatively charged Ag-NPs. Despite significant research activity involving Ag-NPs, the relationship between antimicrobial activity and Ag-NP/bacterial surface interactions is not fully elucidated.
Cells contain various biomolecules (e.g., NADH, flavins, and proteins) that fluoresce under UV irradiation and show intrinsic autofluorescence at a specific excitation wavelength. The three aromatic amino acids tyrosine, tryptophan, and phenylalanine exhibit excitation maxima of 280, 275, and 260 nm, respectively; the emission maxima are 350, 300, and 280 nm, respectively. In addition, several proteins become expressed on the cell surface, act as receptors for exogenous ligands, and play a potential role in pathogenesis. Since these proteins are excited and auto-fluoresced on UV irradiation at 280 nm, they can be used as a fluorescent molecular probe to monitor the interaction between nanoparticles and cells. It is well established that physical characteristics such as shape, surface area, particle size, and surface charge are parameters that are associated with interactions between nanoparticles and microorganisms (Karakoti et al., 2006). Fluorescence spectroscopy has been extensively utilized for molecular structure and function studies in chemistry and biochemistry. However, its use for monitoring the molecular-level interaction phenomena between nanoparticles and living cells at the nano-bio interface has not been considered as extensively. Herein, we exploited fluorescence spectrophotometry to understand the interaction dynamics of polyethyleneimine-functionalized silver nanoparticles at the C. albicans cell bio-interface. Furthermore, the agar well diffusion, micro broth dilution, flow cytometry, and compound light microscopy were used for the elucidation of the antifungal mechanism of the synthesized silver nanoparticles.
## Fungal strain, culture media, and growth conditions
Candida albicans (ATCC 90028, American Type Culture Collection, Manassas, VA, United States) was received from the Department of Microbiology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India. After collection, the culture was revived in YPD (Yeast extract peptone) broth (supplemented with $1\%$ glucose) at neutral pH (7.2) and preserved in $25\%$ glycerol at-80oC for further investigation. Polyethyleneimine, silver nitrate (AgNO3), calcofluor white, bovine serum albumin (BSA), tryptophan, tyrosine, amphotericin B, and other routine chemicals were obtained from Sigma Aldrich Chemicals Private Limited (Bangalore, Karnataka, India). RPMI (Rosewell Park Memorial Institute media) and other media constituents were obtained from Hi-Media Laboratories Ltd. (Mumbai, Maharashtra, India). Plasticware was obtained from Tarsons Products Limited (Kolkata, West Bengal, India). The solvents, including ultra-purified water, were obtained from Merck Life Science Private Limited (Bangalore, Karnataka, India). All of the reagents were of analytical grade. *The* generated experimental data were plotted and analyzed using Origin 8.5 software (Origin Lab Corporation, Northampton, MA, United States).
## Synthesis and physical characterization of PEI-functionalized silver nanoparticles
The polyethyleneimine (PEI)-functionalized Ag-NPs were synthesized as reported previously with slight modifications (Tiwari et al., 2020). In a 2 mL glass vial, ethylene glycol diacetate (8–$10\%$) and a methanolic solution of 1-vinyl 2-pyrrolidone (VPP, 50 μL from a 50 mM stock solution) were combined; a methanolic solution of AgNO3 (10 mM solution), cyclohexanone (20 μL), and PEI (16.4 mg/mL; 40 μL) was added to the mixture. The mixture was thoroughly agitated on a vortex mixer for 30 s and subsequently placed in a microwave oven; for complete reduction of the Ag + cations, the 5 s cycle was repeated four to six times. The resulting mixture exhibited a deep brown color, which denoted the creation of PEI-f-Ag-NP. The nanoparticles were reconstituted in ultra-pure water to conduct additional characterization studies.
The synthesized Ag-NPs were characterized using a U-2900 UV–Vis spectrometer (Hitachi, Tokyo, Japan); 200–800 nm wavelengths were used for data collection. Transmission electron microscopy of the Ag-NPs was performed using a Tecnai G2 20 Twin (FEI, Hillsboro, OR, United States); the sample was diluted in methanol and then drop-cast over 300 mesh carbon-coated copper grids. Zeta potential analysis of the samples was accomplished with a Zetasizer instrument (Malvern Panalytical, Malvern, UK). X-ray diffraction analysis of synthesized nanoparticles was performed by forming a film of the sample on a glass coverslip and drying the sample at 70°C for 2 h. Spectra were recorded using a MiniFlex 600 X-ray diffractometer (Rigaku, Tokyo, Japan).
## Assessment of antifungal activity and MIC determination of PEI-functionalized silver nanoparticles
The agar well diffusion method was utilized to evaluate the antifungal activity of functionalized silver nanoparticles. In short, log-phase cells of C. albicans in RPMI media were adjusted to 106 cfu/mL, swabbed on MHA (Muller Hinton ager) plates with a sterile cotton swab, followed by surface drying for 10 min. Agar wells were formed through the wide mouth of the sterile pipette tip. A 20 μL of constituted silver nanoparticles was poured into an agar well and incubated at 28°C for 48 h.
The broth microdilution method was used to obtain the MIC (Minimum Inhibitory Concentration) value of PEI functionalized Ag-NPs against chosen fungus; as described previously, a sterile flat-bottom 96-well microtiter plate was used for the study (Andrews, 2001; Cavaleri et al., 2005; Li et al., 2005). In short, log-phase cells of C. albicans grown in RPMI broth were centrifuged, resuspended in a fresh RPMI medium, and adjusted 0.5 MacFarland standard fungal suspension for MIC determination measurements (McFarland, 1907). An active suspension of 50 μg/mL of functionalized Ag-NPs was prepared in ultra-pure water and diluted serially; the final concentration was obtained as 0.43–50 μg/mL. Hundred microliter of the fungal suspension was then dispensed into each well. Amphotericin B was used as a positive control in this study. The microtiter plate was incubated at 28°C for 24 h; a visual demonstration of complete fungal inhibition (i.e., a visually clear well) was assigned the MIC value. Subsequently, a 5 μL suspension from a visually clear well was sub-cultured on the YPD plates for 24 h and fungal growth was noted. To ensure the accuracy and minimizing the handling errors, all measurements were performed in triplicate.
## Fluorescence spectroscopy
The log phase culture of C. albicans in RPMI media was obtained; it was centrifuged at 3500 rpm for 4 min, washed twice with PBS (Phosphate Buffer Saline) to remove media traces, and resuspended in phosphate buffer. The least cell count (5×102 cells/mL) was adjusted in a 1 cm quartz cuvette to avoid an inner filter effect. A F-7000 fluorescence spectrophotometer (Hitachi, Tokyo, Japan) was used to obtain fluorescence emission (FL emission) spectra at room temperature. The routine excitation wavelength for proteins (λ = 280 nm) was used to obtain the emission spectra of C. albicans cells and bovine serum albumin V (BSA) (10 μL of 1 μg/mL). In comparison, λ = 270 nm was utilized for tryptophan and tyrosine (10 μL of 1 μg/mL) in phosphate buffer (pH 7.2). The slit width values for excitation and emission were set to $\frac{5.0}{10}$ nm, respectively. The 3D FL spectra were recorded at λex/em = $\frac{280}{300}$ nm matrix. The number of scans was 20 at 10 nm per scan increment. Further, FL spectra were recorded as a control to observe the effect of UV exposure on the C. albicans cells. The measurements were made in triplicate in phosphate buffer (pH 7.2).
## Intracellular ROS generation assay
To investigate the antifungal mechanism, the intracellular ROS level in treated cells was evaluated using an oxidation-sensitive fluorescent dye called 2′,7′-dichlorodihydrofluorescein diacetate (H2DCFDA, Molecular Probes, Eugene, OR, United States). The cells were washed with PBS buffer and incubated with PEI-f-Ag-NPs for 2 h at 28°C. After centrifugation, the cells were incubated with H2DCFDA for 30 min in the dark and washed twice. Ten thousand counts were collected; the FL intensity was measured using a cytoFLEX LX flow cytometry instrument (Beckman Coulter Inc., Pasadena, CA, United States) (Seong and Lee, 2018).
## Results and discussion
The antifungal action of silver nanoparticles is a two-step process; the first step involves the nonspecific binding (or adsorption) of cationic nanoparticles on the cell surface. The second step involves the execution of antimicrobial action. Herein, we reported the binding dynamics of PEI-functionalized nanoparticles on the C. albicans cell surface via FL quenching phenomena by using cell surface proteins as fluorescent molecular probe. The second part of the study involves the elucidation of antifungal mechanism of functionalized silver nanoparticles.
## Physical characterization of synthesized polyethyleneimine functionalized silver nanoparticles
The silver nanoparticle was stabilized and functionalized by branched polyethyleneimine with a molecular weight of 60,000 Da. The synthesized silver nanoparticles were characterized using UV–Vis spectroscopy, TEM, zeta potential, and X-ray diffraction methods. Figure 1A shows the UV–Vis absorption spectra at 419 nm; these results are consistent with the characteristic absorption band of Ag-NPs from surface plasmon resonance (SPR). The size of the PEI-functionalized silver nanoparticles in a water solution was recorded using TEM (Figures 1B,D). The average size of the PEI-f-Ag-NPs was 5.6 ± 1.2 nm (mean ± SD) with a spherical shape; the corresponding histograms revealed that the particle size distributions were between 3–9 nm and revealed near monodispersed distribution (Figure 1D). In addition, the ζ potential of silver nanoparticles was recorded as 18.2 ± 2 mV, indicating high stability (Figure 1E). The colloidal stability of synthesized silver nanoparticles was ensured by interparticle electrostatic repulsion. The XRD pattern was recorded for the 2θ angle from 5 to 80 degrees. *The* generated diffractogram was compared with the standard powder diffraction card of JCPDS, silver file No. 04–0783. The observed Peaks at 2θ degrees of 38.116, 44.583, 64.418, and 77.438 degrees in the diffractogram were noted to correspond to (hkl) values of the [111], [200], [220], and [311] planes of silver as indicated in Figure 1C. Thus, the XRD study confirmed that the functionalized silver nanoparticles possess a face-centered cubic crystal structure.
**Figure 1:** *Physical characterization of PEI-f-Ag-NPs. (A) UV–VIS spectra: inset dispersed in water, (B) respective TEM image, (C) XRD diffractogram, (D) size distribution histogram, and (E) Zeta potential distribution.*
## Antifungal activity and minimum inhibitory concentration of PEI-f-Ag-NPs
The synthesized PEI-f-Ag-NPs were evaluated for their antifungal activity by the agar well diffusion method. The 16 ± 2 mm zone of inhibition indicated potent antifungal activity against C. albicans (Figure 2A). Further, the observed MIC value of PEI-f-Ag-NPs against C. albicans was 5 ± 0.5 μg/mL. Furthermore, the cells were treated with PEI-f-Ag-NPs at their MIC value (5 μg/mL) in a time-dependent manner (5, 15, 30, 60, 120, and 240 min); the cell morphology was observed using a compound light microscope (Figures 2B–H). The cell wall/membrane was damaged (a visual pit hole formation) over time and showed a strong antifungal activity within 30 min of treatment (a reduction of ~$70\%$ cell viability). Complete damage to cellular architecture was observed at 240 min of treatment.
**Figure 2:** *(A) Image shows the zone of inhibition of C. albicans by PEI-f-Ag-NPs. Compound light microscopy photographs of PEI-f-Ag-NPs treated C. albicans cells for various times: (B) untreated control; (C) treated for 5 min; (D) 15 min; (E) 30 min; (F) 60 min; (G) 120 min; and (H) 240 min.*
## Fluorescence spectroscopy investigation of cell-nanoparticle binding
All of the FL spectroscopic experiments were performed at room temperature and in phosphate buffer having neutral pH. The number of C. albicans cells and the concentration of functionalized silver nanoparticles per ml were kept low to avoid any inner filter effect. A control experiment was performed by exposing cells for a short time to evaluate the effect of UV light on proteins, which shows a slight effect (Figure 3). The FL result showed that PEI-f-Ag-NPs quenched the autofluorescence of C. albicans surface protein with an increasing time of incubation (incubated for 5, 15, 30, 60, 120, and 240 min) (Figure 4A); a similar result was noted with an increasing concentration (1, 2, 3 and 5 μg/mL) (Figure 4C). The characteristic FL quenching by PEI-f-Ag-NPs indicates strong binding with C. albicans cells at the nano-bio interface. The autofluorescence of the C. albicans cell surface protein quenching ratio by PEI-f-Ag-NPs was assessed with an increasing incubation time and concentration of silver nanoparticles, which showed rapid binding (Figures 4B,D). The plot of the quenching ratio against incubation time indicated multiple binding sites on the C.albicans surface for PEI-f-Ag-NPs and binding saturation over time. FL quenching achieves a steady state at 60 min of incubation because of binding site saturation (Figure 4B). Similarly, with an increasing concentration of PEI-f-Ag-NPs, the FL quenching ratio was reached at a steady state at 5 μg/mL. This result can be interpreted as saturation of binding sites on the cell surface (Figure 4D). A similar saturation effect was previously described by Wilhelm et al. [ 2002]; however, they studied HeLa cells to understand the interaction dynamics of anionic super magnetic iron oxide nanoparticles. Further, Zheng et al. reported that smaller hematite nanoparticles had faster adsorption than larger ones on the E. coli surface (Zhang et al., 2010). These studies strongly support the rationality and underlying binding mechanism and the adsorption dynamics of functionalized silver nanoparticles on C. albicans cells surface.
**Figure 3:** *UV exposure effect at various times on bare C. albicans cells intrinsic protein fluorescence.* **Figure 4:** *FL emission spectra of C. albicans cell surface proteins at various incubation times with and without PEI-f-Ag-NPs. (A) 2D fluorescence emission spectrum, (B) FL quenching ratio by PEI-f-Ag-NPs at various incubation times, (C) FL emission spectra of C. albicans cell surface proteins with increasing concentration of PEI-f-Ag-NPs, and (D) FL quenching ratio with an increasing PEI-f-Ag-NPs concentration.*
## Mechanism of FL quenching
Fluorescence involves short-term emission of light (of no greater than 10−8 s after excitation), which is associated with the absorption of electromagnetic radiation (e.g., infrared visible, or ultraviolet light) (Monici, 2005). The term “autofluorescence” is used to differentiate the intrinsic fluorescence associated with cells and tissues from the fluorescence associated with exposing samples to exogenous fluorescent markers that attach to cell and tissue components. The autofluorescence properties of biomolecules have been previously utilized to identify pathogenic microorganisms and cellular biomolecules (Richards-Kortum and Sevick-Muraca, 1996; Fusi et al., 2002).
FL quenching describes phenomena that are associated with a reduction in the FL intensity (Monici, 2005). These phenomena can be associated with intermolecular interactions (e.g., energy transfer, molecular rearrangements, and excited-state reactions); moreover, the quenching phenomena can exhibit dynamic (e.g., related to diffusive encounters between the quencher and the fluorophore within the excited state lifetime) or static (e.g., related to complex formation between the quencher and the fluorophore in the ground state (Monici, 2005). C. albicans exhibits an outer rigid cell wall structure covered by the cell membrane, which is composed of complex sugar moieties such as β-glucans and several anchored proteins that act as receptors for exogenous ligands. When excited with UV irradiation, these surface-anchored proteins fluoresce due to the aromatic amino acid tyrosine, tryptophan, and phenylalanine and act as a binding site for PEI-f-Ag-NPs. The fluorescence quenching data were correlated with the time-dependent kill kinetics of the cell; the results indicate that cellular architecture was destabilized as treatment time increased from 5–240 min (Figures 2, 4).
To better understand the interaction of PEI-f-Ag-NPs with molecules other than proteins, the cells were stained with Evans blue and calcofluor white for 15 min and then washed with water; 2D and 3D fluorescence spectra were obtained in the presence of PEI-f-Ag-NPs at various times. Calcofluor white (CFW) is a fluorescent blue-colored dye that is used for diagnosing fungal onychomycosis, which binds to the 1–3 and 1–4, β-linkage of chitin and cellulose in fungal, plant, and algal cell walls. The excitation wavelength for CFW is 380 nm; the emission maxima are between 440 and 475 nm (Shetty et al., 2019). The results showed that PEI-f-Ag-NPs quenched the fluorescence of CFW within 5 min; however, complete quenching was recorded at 120 min of incubation (Figure 5).
**Figure 5:** *2D and 3D FL emission spectra of calcofluor white-stained C. albicans cells at various incubation times treated with PEI-f-Ag-NPs and without treatment: (A) 2D FL emission spectrum, (B) 3D FL spectrum without PEI-f-Ag-NPs (cell control), (C) 3D FL spectrum involving incubation with PEI-f-Ag-NPs for 5 min, (D) 3D FL spectrum involving incubation with PEI-f-Ag-NPs for 15 min, (E) 3D FL spectrum involving incubation with PEI-f-Ag-NPs for 30 min, (F) 3D FL spectrum involving incubation with PEI-f-Ag-NPs for 60 min, (G) 3D FL spectrum involving incubation with PEI-f-Ag-NPs for 120 min, and (H) 3D FL spectrum involving incubation with PEI-f-Ag-NPs for 240 min.*
The function of electrostatic interactions between the cell surface and charged nanoparticles has previously been described for cellular uptake (Farquhar, 1978; Ghinea and Simionescu, 1985; Mutsaers and Papadimitriou, 1988). Cationic liposomes were noted to bind more efficiently than anionic and neutral ones (Lee et al., 1993; Miller et al., 1998; Chenevier et al., 2000). Cationic ferritin particles were noted to uniformly adsorb to the plasma membrane of fixed mammalian cells; this phenomenon was attributed to the many large anionic domains on the cell surface (Farquhar, 1978; Mutsaers and Papadimitriou, 1988). These previous findings, strongly supporting our approach to study the inter-molecular interactions at nano bio interface. Since, our present study was dedicated to qualitative investigation of functionalized silver nanoparticle binding on living system surface biomolecules.
Additional studies were carried out to investigate the specific binding of PEI-f-Ag-NPs with the protein BSA and selected aromatic amino acids, tryptophan and tyrosine residues. The FL emission of the BSA-PEI-f-Ag-NP system was measured in phosphate buffer at neutral pH and room temperature. The BSA was incubated with functionalized silver nanoparticles at various times and concentrations similar to C. albicans cells. BSA (*Bovine serum* albumin) is comprised of 607 amino acids (66 kDa), with 24 tyrosine residues and two tryptophan residues (Ray et al., 2009). Tyrosine and tryptophan residues exhibit intrinsic fluorescence; the tryptophan emission dominates the UV fluorescence spectra for BSA (Ray et al., 2009). The results indicated that the silver nanoparticles bound and quenched FL of BSA with increasing contact time and PEI-f-Ag-NP concentration similar to C. albicans cells with a comparable dynamic (Figure 6). The characteristic emission band at 340 nm decreased as increment in concentration of PEI-f-Ag-NPs; this correlation suggested a strong interaction between nanoparticles and BSA. Mariam et al. performed a similar study in which 10 mg/mL BSA and 90–812 mL Ag-NPs were used (Mariam et al., 2011). In another study, Huang et al. described the interaction between gold nanoparticles and BSA (Huang et al., 2014). The quenching ratio was studied; with an increase in incubation time and concentration, the binding of BSA on nanoparticles achieved a steady state (Figure 6); this result indicated the saturation of vacant binding sites on silver nanoparticles. Additional studies are underway to estimate the number of bindings of BSA molecules on the surface of a single PEI functionalized silver nanoparticle.
**Figure 6:** *FL emission spectra of BSA protein at various times with and without PEI-f-Ag-NPs: (A) fluorescence emission spectrum at various incubation times, (B) FL quenching ratio at various incubation times, (C) FL emission spectra with increasing concentration of PEI-f-Ag-NPs, and (D) FL quenching ratio with increasing PEI-f-Ag-NPs concentration.*
The interaction of PEI-f-Ag-NPs with tryptophan and tyrosine residues was investigated in a more detailed study as a standard fluorophore. Tryptophan and tyrosine exhibit intrinsic emission upon excitation on 280 and 270 nm, respectively. Figure 7 shows the FL emission spectra and quenching ratio of tryptophan and tyrosine recorded with various PEI-f-Ag-NPs concentrations. The fluorescence intensity of tryptophan and tyrosine was noted to decrease as the silver nanoparticle concentration was increased. Silver nanoparticles were able to interact with tryptophan and tyrosine, thus quenching the fluorescence intensity. PEI-f-Ag-NPs exhibit strong interactions with tyrosine compared to tryptophan residues, indicating a strong probability for interactions with tyrosine-rich proteins. The presented work conclusively, demonstrating a complex formation between cell surface anchored proteins and functionalized silver nanoparticle and quenched the fluorescence of protein with a saturation kinetic model. At this point, it is obvious to study, whether, functionalized nanoparticles are binding on specialized cell surface domains or binding uniformly. Depending on the type of cells, the distribution and nature of surface biomolecules varies. Thus it is interesting to note the cell and functionalizing agent specific binding kinetics of nanoparticle interaction with cells.
**Figure 7:** *FL emission spectra of tyrosine and tryptophan amino acid standards at various concentrations with and without PEI-f-Ag-NPs: (A) fluorescence emission spectrum of tyrosine, (B) FL quenching ratio by PEI-f-Ag-NPs of tyrosine at various concentrations, (C) FL emission spectra of tryptophan at various concentrations of PEI-f-Ag-NPs, and (D) FL quenching ratio at various PEI-f-Ag-NPs concentration.*
## Measurement of binding constant and number of binding sites
The modified Stern-volmer equation (double logarithm regression curve) can be used to obtain the binding constant as well as the number of binding sites/macromolecules, in which it is presumed that PEI-f-Ag-NPs bind independently to cell surface macromolecules (equivalent sites) (Hu et al., 2004; Xu et al., 2011). logF0−F/F=logKb+nlogQ.
In this equation, Kb represents the binding constant, F0 represents fluorescence intensity without a quencher, F represents fluorescence intensity with a quencher, and n represents the number of binding sites (Xu et al., 2011). The number of binding sites and the binding constant were obtained for C. albicans cells. Plots of log [(F0–F)/F] versus log [PEI-f-Ag-NP] are shown in Figure 8A. The values of Kb and n were determined from the intercept on the y-axis and the slope of this plot, respectively. The Kb and n values of PEI-f-Ag-NPs for C. albicans cells were 1× 105 M−1 and 1.01, respectively (Figure 8A). The high binding constant values shows strong binding between PEI-f-Ag-NPs and C. albicans cell surface. Also, it is indicated that each interacting cell anchored protein can bind to 1.0 PEI-f-Ag-NP at a time. However, this phenomenon may be different for various types of cells because different types of cells have variable biomolecules composition with distinct physico-chemical properties. The apparent binding constant is dependent on several physical parameters, including the concentration of Ag-NPs and temperature; further, it is also dependent on the size-shape and surface tuning parameters of the Ag-NPs, which in turn are dependent on the synthesis protocol (Dasgupta et al., 2016). It should be noted that the apparent association constant is also dependent on the temperature; the role of temperature was not considered in this study as the focus of the study was to determine the nature and dynamics of the interaction. The binding constants (Kb) indicate that the affinity of PEI-f-Ag-NPs for BSA is very low in comparison to the reported binding constants, which range from 104 to 108; this finding supporting previous reports that serum albumin contains few binding sites for endogenous and exogenous ligands, which are commonly bound in a reversible manner (Rahman and Sharker, 2009). Hence, the above result concluded that, PEI-f-Ag-NPs have a strong binding tendency with complex cell wall polysaccharides and surface anchored proteins. The microscopic demonstration of surface binding of PEI-f-Ag-NPs on the C. albicans cell can be seen in Figure 8B.
**Figure 8:** *(A) Binding constant (Kb) and number of binding sites (n) for PEI-f-Ag-NP with C. albicans; (B) Schematic depicts adsorbed PEI-f-Ag-NPs on the surface of C. albicans cells.*
## Antifungal mechanism of PEI functionalized silver nanoparticles
Evidence of the antimicrobial use of silver ions appears in the historical record (Alexander, 2009). The antimicrobial mechanism of silver, either in a free ionic form or in a nanoparticle form, is not clearly defined. The known mechanism of silver indicates a multidimensional action to disrupt many fundamental functions of the microbial cell (Rai et al., 2012; Mikhailova, 2020). The antimicrobial activity of Ag-NPs is dependent on their shape and size; some of the observed effects can be associated with the release of Ag+ ions from the nanoparticle surface (Kędziora et al., 2018; Tang and Zheng, 2018). The cationic Ag-NPs exhibit a high affinity for the anionic surface of microorganisms (Mikhailova, 2020). Once nanoparticles adhere to the microbial surface, they initiate a “pitting” effect in the cell wall, which is associated with architectural collapse; this process results in leakage of intracellular contents, dissipation of the transmembrane potential, as well as denaturation to membrane-associated proteins (Tang and Zheng, 2018; McNeilly et al., 2021). Silver nanoparticles also initiate the production of toxic intracellular ROS that result in cell damage (McNeilly et al., 2021). The antifungal mechanism in PEI-f-Ag-NPs treated C.albicans cells in this work is associated with a huge generation of endogenous ROS accumulation and pit hole formation in cell wall (Figure 9). As seen in Figure 8B, it appears that intracellular ROS was the main cause of cell damage. Our previous study found that PEI-f-AgNP-1 interacts with *Rhizopus arrhizus* sporangiospores by generating stress, followed by the generation of ROS species, which ultimately damages the spore wall (Tiwari et al., 2022). A schematic representation of antifungal mechanism associated with the PEI-f-Ag-NPs against C. albicans cells can be seen in Figure 10.
**Figure 9:** *Intracellular ROS level in PEI-f-Ag-NPs treated C. albicans cells. (A) All events (B) Selected events (10000) (C) Unstained Control (D) Stained Control (E) PEI-f-Ag-NPs treated.* **Figure 10:** *Schematic showing the possible antifungal mechanism of polyethyleneimine-functionalized silver nanoparticles. The silver nanoparticle interacts with C. albicans cells via two different pathways, which can be direct or indirect. In the direct pathway, the cationic Ag-NPs interact electrostatically with surface structural proteins and phosphor-mannolipids; these processes induce electrostatic stress followed by ROS generation and inactivation of cytoplasmic proteins. In addition, nanoparticle-generated stress induces mitochondrial ROS inside the cell, which damages cytoplasmic biomolecules such as proteins and DNA through oxidation. The ROS can also damage the cell membrane through lipid peroxidation, resulting in membrane perforation and eventual cell death (steps 1, 2, 3, 5, and 6) as indicated in the diagram. The indirect action involves silver nanoparticles passing through the cell wall/membrane via water channels or other channels and becoming internalized inside the cell. These silver nanoparticles disintegrate the cytoplasmic biomolecules (e.g., structural proteins and enzymes) and nuclear DNA, collapsing cell cytoarchitecture and resulting in cell death (step 4) as indicated in the diagram.*
## Conclusion
The study sought to understand how the physicochemical properties of silver nanoparticles affect their antimicrobial activity. When the nanoparticles are added to the microbe-containing medium, they first encounter the surface of the microbe. To understand the nanoparticle interactions with the C.albicans cell surface, we used polyethyleneimine-functionalized silver nanoparticles with a size of 5.6 nm having a + 18 mV zeta potential. The PEI-functionalized silver nanoparticles have shown potent biocidal activity with a MIC value of 5 μg/mL. The study showed that PEI-f-Ag-NPs interacted strongly with proteins and cell wall polysaccharide components within 60 min of treatment. The binding constant (Kb = 1.0 × 105 M−1) as well as the number of binding sites ($$n = 1$.01$) were high for the cell surface proteins. Furthermore, the intrinsic fluorescence was quenched by PEI-f-Ag-NPs, indicating a strong interaction with tyrosine residues rather than tryptophan residues. This study provides an approach to qualitatively understand the molecular-level interactions of metal nanoparticles and biomolecules by exploiting cell proteins as a molecular probe. Understanding the interaction of NPs and surface proteins will facilitate the development of smart antimicrobial nanomaterials with enhanced biocidal properties against many types of microorganisms.
## Future directions
The presented work investigated only qualitative static interactions via fluorescence spectroscopy of silver nanoparticles with C. albicans surface proteins. A qualitative and quantitative investigation involving binding kinetics, binding isotherm studies, and cell type-dependent interaction studies is underway; these studies are needed to understand molecular-level interactions of functionalized metal nanoparticles at the nano-bio interface to avoid cytotoxicity concerns, formulation of nano antimicrobials, and harness the antimicrobial potential of metal nanoparticles.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
AT conceived and designed the experiments. AT and MG conducted the sample preparation, conducted the experiment, and analyses. AT and PP wrote the manuscripts. RN and PP oversaw the completion of this study and finally edited the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was partially supported by IoE development fund, which was released for MG at the Institute of Medical Sciences, Banaras Hindu University. This submissions will utilize the pilot partnership between UNC Library and Frontiers (https://library.unc.edu/$\frac{2022}{10}$/frontiers-partnership/).
## 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: Alternative splicing is not a key source of chemerin isoforms diversity
authors:
- Kamila Kwiecien
- Pawel Majewski
- Maciej Bak
- Piotr Brzoza
- Urszula Godlewska
- Izabella Skulimowska
- Joanna Cichy
- Mateusz Kwitniewski
journal: Molecular Biology Reports
year: 2023
pmcid: PMC10011272
doi: 10.1007/s11033-022-08174-7
license: CC BY 4.0
---
# Alternative splicing is not a key source of chemerin isoforms diversity
## Abstract
### Background
Chemerin is a chemoattractant protein with adipokine and antimicrobial properties encoded by the retinoic acid receptor responder 2 (RARRES2) gene. Chemerin bioactivity largely depends on carboxyl-terminal proteolytic processing that generates chemerin isoforms with different chemotactic, regulatory, and antimicrobial potentials. While these mechanisms are relatively well known, the role of alternative splicing in generating isoform diversity remains obscure.
### Methods and results
Using rapid amplification of cDNA ends (RACE) PCR, we determined RARRES2 transcript variants present in mouse and human tissues and identified novel transcript variant 4 of mouse Rarres2 encoding mChem153K. Moreover, analyses of real-time quantitative PCR (RT-qPCR) and publicly-available next-generation RNA sequencing (RNA-seq) datasets showed that different alternatively spliced variants of mouse Rarres2 are present in mouse tissues and their expression patterns were unaffected by inflammatory and infectious stimuli except brown adipose tissue. However, only one transcript variant of human RARRES2 was present in liver and adipose tissue.
### Conclusion
Our findings indicate a limited role for alternative splicing in generating chemerin isoform diversity under all tested conditions.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11033-022-08174-7.
## Introduction
Protein isoforms can play important roles in various biological processes, such as growth, differentiation, and signal transduction. They can originate from separate genes, or a single gene can code for multiple proteins through alternative mRNA splicing. Alternative polyadenylation, RNA editing, and posttranslational modification can also create functionally distinct proteins. However, the alternative splicing of transcripts is one of the main sources of proteomic diversity in eukaryotes. Despite sharing a high degree of amino acid sequence homology, each isoform can have various, even opposite, biological roles [1–3]. Therefore, discovering novel mRNA transcripts and protein isoforms can uncover new biological roles and functions of genes [4].
Chemerin is a multifunctional chemoattractant, adipokine, and antimicrobial agent that regulates different biological processes, including immune cell migration, adipogenesis, osteoblastogenesis, angiogenesis, glucose homeostasis, and microbial growth [5, 6]. *The* gene encoding chemerin is called retinoic acid receptor responder 2 (RARRES2) or tazarotene-induced gene 2 (TIG2). Liver and adipose tissue are reportedly the major sites of chemerin production. Nevertheless, RARRES2 mRNA is present in other tissues, including the adrenal glands, ovaries, pancreas, lungs, kidneys, and skin [7, 8] Chemerin-induced signaling is mediated predominantly through chemokine-like receptor 1 (CMKLR1), which is expressed by many cells, including hepatocytes, adipocytes, keratinocytes, plasmacytoid dendritic cells (pDCs), and macrophages [7, 9–13].
Chemerin is secreted as pro-chemerin, a functionally inert precursor protein called hChem163S (human) and mChem162K (mouse), where the number and capital letter indicate the terminal amino acid position and code, respectively [14]. Pro-chemerin is converted to chemotactically active isoforms through posttranslational carboxyl-terminal processing by proteases belonging to the coagulation, fibrinolytic, and inflammatory cascades. The most active form of human chemerin, hChem157S, is produced by direct cleavage of six C-terminal amino acids by neutrophil elastase or cathepsin G. [15]. Different proteolytic activities can generate isoforms with low or no activity, including 152G, 153Q, 154 F, 155 A, 156 F, and 158 K [15–18]. Several murine chemerin isoforms have been characterized in a mouse model of obesity, with mChem156S and mChem155F exhibiting the highest biological activity. Mouse chemerin undergoes tissue-specific proteolytic cleavage similar to human chemerin [19].
While mechanisms of proteolytic processing in generating chemerin isoforms are relatively well described, the role of alternative splicing remains obscure. Both mouse and human RARRES2 genes are comprised of six exons and five introns [20, 21]. While in humans, only a single transcript (NM_002889.4) encoding a 163 amino acid (aa) protein has been described, [20] in the mouse, three alternatively spliced transcripts (NM_001347168.1, NM_027852.3, and NM_001347167.1) encoding 162 or 163 aa proteins have been reported [21]. The mChem162K and hChem163S proteins are the major chemerin forms present in mouse and human plasma, respectively [19].
Generating multiple chemerin isoforms is critical for controlling its local and context-specific bioactivity. Therefore, understanding the mechanisms underlying the diversity of chemerin isoforms is particularly important. Here, we show that alternatively spliced variants of mouse Rarres2 are present across different tissues and organs. Moreover, in addition to the variants encoding mChem163K and mChem162K, we have identified a novel transcript variant encoding mChem153K. We demonstrate that inflammatory and infectious conditions do not affect the expression pattern of Rarres2 splice variants. In contrast to murine chemerin, only one transcript variant was found in human liver and adipose tissue. We provide novel insights into the mechanisms that may contribute to chemerin isoform diversity and activity.
## Materials
All chemicals were obtained from Sigma-Aldrich (St. Louis, MO, USA) unless otherwise stated. Phosphate-buffered saline (PBS) buffer was obtained from PAN Biotech (Aidenbach, Germany). Mouse recombinant IL-1β and OSM were obtained from R&D Systems (Minneapolis, MN, USA).
## Clinical material
Visceral WAT and liver samples were collected during bariatric surgery. All human studies were approved by the Jagiellonian University Institutional Bioethics Committee (protocol number KBET/87/B/2014) and adhered to the Declaration of Helsinki. All participants provided their written informed consent to participate in these studies.
## Animal studies
This study used male 8- to 12-week-old C57BL/6 mice. The mice were maintained under specific pathogen-free conditions in the animal care facility in the Faculty of Biochemistry, Biophysics, and Biotechnology at Jagiellonian University. IL-1β and OSM were injected intraperitoneally at doses of 10 µg/kg body weight (BW) and 160 µg/kg BW, respectively, as previously described [22]. After 48 h (h), different tissues were isolated and subjected to RT-qPCR analysis. All animal procedures were approved by the First Local Ethical Committee on Animal Testing at the Jagiellonian University (Krakow, Poland; permit number $\frac{41}{2014}$) in accordance with the ARRIVE guidelines and the Guidelines for Animal Care and Treatment of the European Community. The mice were sacrificed by an overdose of anesthesia (a mixture of ketamine and xylazine), followed by cervical dislocation.
## RACE PCR
Total RNA was extracted for all tissues as described by Chomczynski and Sacchi [23], and 3’ and 5’ RACE PCR was performed with the 3’ and 5’ RACE System Kits (Invitrogen; Carlsbad, CA, USA) according to the manufacturer’s recommended protocol. The following Rarres2 specific primers were used: 5’-GTGTGGACAGAGCTGAAGAAGTGCTCTTC-3’ (3’ RACE) and 5’-CTGGAGAAGGCAAACTGTCCAGGTAGGAAGTAG-3’ (5’ RACE). RACE PCR products were separated by agarose gel electrophoresis, with bands of interest excised from the gel and purified using the Gel-Out Concentrator kit (A&A Biotechnology; Gdynia, Poland), and ligated into the pTZ57/RT vector using InsTAclone PCR Cloning Kit (Thermo Scientific; Waltham, MA, USA), followed by heat shock transformation of the plasmid into chemically competent Top10 E.coli (Invitrogen; Carlsbad, CA, USA). Selected bacterial colonies were subjected to colony PCR using standard M13 primers. Plasmid DNA was recovered from positive clones using GeneJET Plasmid Miniprep Kit (Thermo Scientific; Waltham, MA, USA) and sequenced at Genomed (Warsaw, Poland). All results were analyzed using SnapGene Viewer (GSL Biotech LLC; San Diego, CA, USA).
## RT-QPCR and quantification of RARRES2 transcript variants
Total RNA was extracted with the Total RNA Zol-Out Kit (A&A Biotechnology; Gdynia, Poland) and converted to complementary DNA (cDNA) using NxGen M-MulV reverse transcriptase (Lucigen Corporation; Middleton, WI, USA) with random primers (Promega Corporation; Madison, WI, USA) and oligo dT (Genomed; Warsaw, Poland). RT-PCR was performed with a CFX96 thermocycler (Bio-Rad Laboratories; Hercules, CA, USA) using SYBR Green I and a universal PCR master mix (A&A Biotechnology; Gdynia, Poland) with the following mouse-specific primers: chemerin_all_variants (5’-CTTCTCCCGTTTGGTTTGATTG-3’, 5’-TACAGGTGGCTCTGGAGGAGTTC-3’), mChem162K (5’-CCTCAGGAGTTGCAATGCATTAAGAT-3’, 5’-GTACAGGGAGTAAGGTGAAGTCCTGT-3’), mChem153K (5’-CAATCAAACCAAACGGGAGAAGGC-3’, 5’-CGCCAGCCTGTGCTATCTGAG-3’), cyclophilin A (5’-AGCATACAGGTCCTGGCATCTTGT-3’, 5’-CAAAGACCACATGCTTGCCATCCA-3’), β-actin (5’-CCTTCTTGGGTATGGAATCCTG-3’, 5’-TGGCATAGAGGTCTTTACGGA-3’), GAPDH (5’-TGTGTCCGTCGTGGATCTGA-3’, 5’-TTGCTGTTGAAGTCGCAGGAG-3’). The expression stabilities of commonly used reference genes were assessed as previously described [22]. *Relative* gene expression normalized to the geometric mean of these housekeeping genes was calculated using the 2−ΔΔCT method [24]. RIV were obtained using the method of Londoño et al. [ 25]. The PCR efficiency of each primer set was calculated using CFX Maestro Software (Bio-Rad; Hercules, CA, USA) using pcDNA3.1 plasmids encoding mChem162K and mChem153K as a template.
## Alternative splicing analyses of RNA-seq datasets
Rarres2 expression levels in different tissues and cell lines and isoform quantities were obtained from VastDB [26]. To assess isoform ratios in publicly available RNA-seq datasets, we calculated PSI scores with vast-tools [26]. We also analyzed datasets from the gene expression omnibus (GEO) database maintained by The National Center for Biotechnology Information (NCBI; Bethesda, MA, USA) that investigated the molecular effects of a high-fat diet (accessions GSE76133, GSE75984, and GSE117249) and transcriptional changes after infection with *Staphylococcus aureus* (GSE108718), *Toxoplasma gondii* (GSE119855), and influenza virus (GSE114232). Differential splicing analyses were performed with the diff module of vast-tool.
## Statistical analysis
Differential splicing quantification in RNA-seq datasets was performed using vast-tools with flags -r 0.95 and -m 0.1. All other data were analyzed using STATISTICA 13 (StatSoft; Tulsa, OK, USA). Results were visualized using Prism (GraphPad Software; San Diego, CA, USA) and presented as mean ± standard deviation (SD). Comparison between groups used the Student’s t-test. For multiple group comparisons, analysis of variance (ANOVA) with Tukey’s posthoc test was used. Differences were considered statistically significant if they had a p-value < 0.05.
## Characterization of alternatively spliced RARRES2 transcript variants
To identify transcript variants of mouse and human chemerin present in tissues, including liver and white adipose tissue (WAT), 3’ and 5’ rapid amplification of cDNA ends (RACE) PCR was performed. We detected one RARRES2 transcript variant in human tissues and three variants in mouse tissues (Fig. 1 A-B and Fig. S1 A-B). Mouse Rarres2 variant 1 is the longest transcript and encodes the longer isoform 1 (mChem163K). Rarres2 variant 2 uses an alternate in-frame splice site in the 3’ coding region and encodes the shorter protein isoform 2 (mChem162K). Rarres2 variant 3 differs in the 5’ UTR and uses the same alternate in-frame splice site in the 3’ coding region as variant 2, and therefore also encodes the shorter protein isoform 2 (mChem162K). In addition to the previously reported variants 1, 2, and 3, we have identified a novel variant 4, created by an alternate in-frame splice site in the 3’ coding region (Fig. 1 A-B). While it contains exons 1 to 6, a 30 bp fragment is missing from exon 5 (Fig. 1 A). This novel transcript variant 4 of mouse Rarres2 was not predicted nor annotated by Ensembl [27] and RefSeq [28].
Fig. 1Schematic representation ofRARRES2alternatively spliced transcript variants. Schematic representation of RARRES2 transcript variants detected in human and mouse tissues using 3’ and 5’ RACE PCR.
We performed a multiple sequence alignment of the four murine chemerin protein isoforms (Fig. 2). Isoform mChem162K, encoded by Rarres2 transcript variants 2 and 3, is the major form of chemerin in plasma [19]. Isoform mChem163K, encoded by Rarres2 transcript variant 1, has one extra glutamine at position 128. Interestingly, the newly discovered isoform mChem153K, encoded by Rarres2 transcript variant 4, is missing 10 amino acids (128–137) relative to mChem163K, consistent with its 27 bp deletion in exon 5. Notably, all amino acid changes found in murine pro-chemerin isoforms are due to exon 5.
Fig. 2Multiple sequence alignment of nucleotide and protein sequences of mouse chemerin alternatively spliced isoforms. Alignment of nucleotide sequences of Rarres2 transcript variants (A). Alignment of predicted chemerin protein isoforms (B)
## Expression pattern of mouse Rarres2 splice variants across different tissues and experimental conditions
Because only one RARRES2 transcript variant was found in human liver and adipose tissue, we focused on the role of Rarres2 alternative splicing in mouse tissues. We first determined the tissue expression patterns of the four alternatively-spliced transcripts using publicly-available next-generation RNA sequencing (RNA-seq) data and standard real-time quantitative PCR (RT-qPCR).
Using VastDB, [26] an atlas of alternative splicing profiles and functional associations in vertebrate cell and tissue types, we quantified transcript variants encoding mChem162K and mChem163K, but not mChem153K because transcript variant 4 is not present in the VastDB. We found transcript variants 2 and 3 (mChem162K) to be the dominant forms expressed in all investigated tissues, with an average percent spliced in (PSI) score of ~ 68.5 (Fig. 3 A). However, transcript variant 1 (mChem163K) accounted for up to $42\%$ of transcripts in the cerebellum and pancreas. Our findings with the RNA-seq data were consistent with these patterns (Fig. 3B). However, Rarres2 transcript variant 4 (mChem153K) was rare, with a PSI score of < 1.5 (Fig. 3 A). Notably, the expression patterns of the Rarres2 transcript variants were unaffected by a high-fat diet or viral, bacterial, and parasite infections. In addition, there were no statistically significant differences between the control and treatment groups. However, levels of the newly discovered Rarres2 transcript variant 4 tended to increase in the kidney and skin after a high-fat diet and S. aureus infection. Further studies are needed to investigate this finding.
Fig. 3Analyses of RNA-seq datasets and VastDB database reveals tissue-wide expression ofRarres2splice variants. Alternative splicing events of Rarres2 in distinct mouse tissues were acquired from the VastDB database (A). The effect of a high-fat diet and S. aureus, T. gondii, influenza, and lymphocytic choriomeningitis viral infection on Rarres2 splicing patterns determined from publicly available RNA-seq datasets (B). Percent spliced-in (PSI) values Consistent with our analysis of publicly available RNA-seq data, Rarres2 transcript variant 4 was found to be rare in our RT-qPCR data, with its highest expression level found in the heart (Fig. 4 A). The median of the relative incidence values (RIV)[25] of transcript variant 4 varied from ~ $0.14\%$ in the liver to ~ $2.13\%$ in the large intestine (Fig. 4B).
We have previously shown that acute-phase cytokines, interleukin 1β (IL-1β), and oncostatin M (OSM) regulate chemerin expression in mouse adipocytes and human 3D skin cultures [7, 22]. Therefore, we explored whether these cytokines affected the balance between newly discovered Rarres2 transcript variant 4 and the other transcript variants in mouse tissues. We found diminished levels of transcript variant 4 in brown adipose tissue (BAT) of IL-1β and OSM treated animals (Fig. 4 C). This was associated with up-regulation of total Rarres2 mRNA levels (Fig. S2). The transcript ratio remained stable in other tissues, and there were no statistically significant differences between control and cytokine-treated mice.
Fig. 4Acute-phase cytokines do not affect the relative incidence ofRarres2transcript variant 4 levels across distinct mouse tissues. Rarres2 transcript variant 4 expression in different organs and tissues of control animals presented as relative expression values (A) or RIV (B). The relative incidence of Rarres2 transcript variant 4 in selected tissues of acute-phase cytokine or PBS treated animals (C). Data are presented as the mean ± standard deviation (A and C) or as the median (B) of at least three independent replicates. Statistical significance between the control (PBS) and the cytokine-treated animals is indicated by an asterisk; *$p \leq 0.05$ by the Student’s t-test. Key: V. WAT – visceral white adipose tissue, S. WAT – subcutaneous white adipose tissue, INTEST. – intestine
## Discussion
Our understanding of the posttranslational modifications of chemerin that generate a variety of protein isoforms has increased significantly over the last two decades. However, these studies focused mainly on the proteolytic processing of human (hChem163S) or mouse pro-chemerin (mChem162K) by extracellular proteases [15, 16, 19, 29, 30]. Alternative splicing is a key factor in increasing cellular and functional complexity. However, how the alternative splicing of RARRES2 contributes to isoform diversity remains to be determined.
In this study, we have described for the first time a novel transcript variant 4 of mouse Rarres2 that encodes a 153 aa chemerin isoform 3 (mChem153K). Compared to isoform 1 (mChem163K), mChem153K is missing 10 aa at positions 128–137. This deletion may significantly affect protein structure because it removes a cysteine residue involved in forming one of three intrachain disulfide bonds [14]. Indeed, conformational changes due to this deletion may underlie our inability to purify mChem153K expressed in E.coli to determine the physiological role of this isoform (data not shown). Our in silico and in vivo studies have revealed that transcript variant 4 accounts for only a small fraction of Rarres2 transcripts under physiological conditions. The average percentage across all mouse tissues investigated using RNA-seq and RT-qPCR was $0.55\%$ and $1.31\%$, respectively. Therefore, transcript variant 4 might reflect inaccurate or inappropriate splicing creating abnormal transcripts of no functional significance. This phenomenon has been observed with almost all genes, and its frequency has been estimated to be at least $0.1\%$ for each intron [31].
A frequent outcome of alternative splicing is decreased gene function due to the production of non-functional instead of functional isoforms which can be caused by alterations in protein functional domains [32]. Nonetheless, all Rarres2 transcript variants are generated by an alternate in-frame splice site in the 3’ coding region of exon 5 or differences in the 5’ UTR (variant 3 only). These modifications do not affect the C-terminal region of chemerin, which is crucial for its bioactivity [5].
Alternative transcripts are often differentially expressed between cells and tissues and possess different functions [33–35]. Moreover, changes in alternative splicing events can be associated with exposure to different stimuli [36]. Indeed, altered chemerin expression may be relevant in pathological conditions such as obesity, cancer, and inflammation [10, 29, 37–39]. Various inflammatory and metabolic mediators regulate chemerin expression in a cell-type-dependent manner [22, 40]. We have previously shown that IL-1β and OSM upregulate chemerin expression in human skin cultures [7] and mouse adipocytes [22]. Moreover, bacteria such as S. aureus upregulate chemerin levels in models of the human epidermis and mouse skin [7]. Indeed, skin transcriptome analyses of antimicrobial peptides differentially regulated after skin infection with C. acnes or *Leishmania braziliensis* revealed elevated RARRES2 transcript levels [41]. In this study, we have shown that the splicing pattern of Rarres2 mRNA is not altered under most tested conditions including a high-fat diet or bacterial, viral, or parasitic infection, nor by cytokine treatment in different mouse organs except BAT, where down-regulation of transcript variant 4 was observed. This may suggest its limited physiological role since total chemerin mRNA levels were elevated. Therefore, these factors are not major determining factors in Rarres2 splice site selection.
We also explored differences in antimicrobial and chemotactic activity between biologically active chemerin isoforms mChem157S and mChem156S since they differ by only a single amino acid, the glutamine at position 128. This change does not directly affect chemerin’s antimicrobial region (p4), which is located in the middle of the protein (positions 66–85 and 68–87 for human and mouse chemerin, respectively) [6]. Our preliminary findings indicate that chemerin isoform mChem157S exhibits slightly increased antibacterial activity than mChem156S (Fig. S3A), but no change in chemotactic activity was observed (Fig. S3B). However, further studies are required to determine whether mChem157S encoded by the mouse Rarres2 variant 1 has any physiological role.
While four transcript variants of mouse Rarres2 encoding three protein isoforms are known, there is only one confirmed transcript variant for human RARRES2, which is translated into the hChem163S precursor protein [42]. Liver has one of the highest levels of alternative splicing among human tissues [43]. However, we did not find any additional transcript variants of human RARRES2 in the liver or adipose tissue based on 3’ and 5’ RACE PCR.
Our study has provided new insight into the mechanisms accounting for chemerin isoforms diversity. We have reported for the first time the identification of rare transcript variant 4 for mouse Rarres2 that encodes mChem153K (protein isoform 3). Rarres2 transcript variants 1 to 4 were present in all investigated mouse tissues, and the most abundant transcript variants encode chemerin isoform mChem162K. Our findings showed that the splicing pattern of RARRES2 mRNA was unaltered by a high-fat diet and bacterial, viral, or parasitic infection, nor by proinflammatory cytokine treatment. We found only one transcript variant of RARRES2 in human tissues. Altogether, our findings indicate that alternative splicing of RARRES2 in human and mouse tissues has a limited role in generating chemerin isoforms diversity under the tested conditions.
## Electronic supplementary material
Below is the link to the electronic supplementary material.
Supplementary Material 1
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|
---
title: Metabolic stone workup abnormalities are not as important as stone culture
in patients with recurrent stones undergoing percutaneous nephrolithotomy
authors:
- Asmaa E. Ahmed
- Hassan Abol-Enein
- Amira Awadalla
- Ahmed A. Shokeir
- Omar A. El-Shehaby
- Ahmed M. Harraz
journal: Urolithiasis
year: 2023
pmcid: PMC10011315
doi: 10.1007/s00240-023-01422-w
license: CC BY 4.0
---
# Metabolic stone workup abnormalities are not as important as stone culture in patients with recurrent stones undergoing percutaneous nephrolithotomy
## Abstract
To investigate the association between metabolic urinary abnormalities and urinary tract infection (UTI) and the stone recurrence status in patients undergoing percutaneous nephrolithotomy (PCNL). A prospective evaluation was performed for patients who underwent PCNL between November 2019 and November 2021 and met the inclusion criteria. Patients with previous stone interventions were classified as recurrent stone formers. Before PCNL, a 24 h metabolic stone workup and midstream urine culture (MSU-C) were done. Renal pelvis (RP-C) and stones (S-C) cultures were collected during the procedure. The association between the metabolic workup and UTI results with stone recurrence was evaluated using univariate and multivariate analyses. The study included 210 patients. UTI factors that showed significant association with stone recurrence included positive S-C [51 ($60.7\%$) vs 23 ($18.2\%$), $p \leq 0.001$], positive MSU-C [37 ($44.1\%$) vs 30 ($23.8\%$), $$p \leq 0.002$$], and positive RP-C [17 ($20.2\%$) vs 12 ($9.5\%$), $$p \leq 0.03$$]. Other factors were mean ± SD GFR (ml/min) (65 ± 13.1 vs 59.5 ± 13.1, $$p \leq 0.003$$), calcium-containing stones [47 ($55.9\%$) vs 48 ($38.1\%$), $$p \leq 0.01$$], median (IQR) urinary citrate levels (mg/day) [333 (123–512.5) vs 221.5 (120.3–412), $$p \leq 0.04$$], and mean ± SD urinary pH (6.1 ± 1 vs 5.6 ± 0.7, $p \leq 0.001$). On multivariate analysis, only positive S-C was the significant predictor of stone recurrence (odds ratio: 9.9, $95\%$ confidence interval [CI] (3.8–28.6), $p \leq 0.001$). Positive S-C, and not metabolic abnormalities, was the only independent factor associated with stone recurrence. A focus on preventing UTI might prevent further stone recurrence.
## Introduction
Urolithiasis represents a prevalent pathology that urologists encounter in everyday practice. Its significance stems from the high volume of cases, costly and painful renal colic episodes, and the requirements for updated healthcare facilities. One important aspect of urinary stones is the high rate of recurrence in both the pediatric and adult populations [1, 2]. The recurrence rates were 11, 20, 31, and $39\%$ at 2, 5, 10, and 15 years, respectively [3]. Various definitions have been proposed for stone recurrence that can be classified as either symptomatic or radiological recurrence [4]. Stone recurrence is a worthy investigation as it adds to the burden of repeated emergency and outpatient visits, frequent imaging, various interventions, and the need for continuous follow-up [5].
Risk factors and prevention of stone recurrence have been the focus of a plethora of published articles. Identified potential risk factors included younger age, male sex, higher body mass index (BMI), positive family history, pregnancy, a history of uric acid, struvite or brushite stones, non-calcium oxalate monohydrate stones, urine pH, and the presence of diabetes mellitus [6, 7]. A nomogram has been previously developed to predict the recurrence risk based on a group of clinical factors [3].
While 24 h urinary metabolic evaluation has been recommended to guide the therapy to prevent stone recurrence [8, 9], recent reports questioned its role [5, 10–12]. In addition, another unforeseen parameter that might potentially affect stone recurrence is the presence of urinary tract infection (UTI) particularly in staghorn stones [13, 14]. Bacterial infection has been shown to promote the growth and aggregation of calcium oxalate crystals [15]. In this context, this study was designed to evaluate the association between the recurrent stone status and the presence of active UTI represented by positive midstream urine culture (MSU-C), renal pelvis culture (RP-C), or stone culture (S-C).
## Study design
A prospective evaluation of patients who underwent percutaneous nephrolithotomy (PCNL) in a tertiary referral center was performed between November 2019 and November 2021. Informed consent was taken before enrollment in the study. The study protocol has been reviewed and approved by the local ethical committee and the institutional review board. Patients with stents or indwelling catheters or those who fail to provide a 24 h urine collection were excluded from the study. Other exclusion criteria included the presence of medical conditions that contribute to stone formation (hyperparathyroidism or renal tubular acidosis), or anatomical abnormalities (UPJ stenosis or horseshoe kidney).
## Measurements and intervention
Upon admission, patients' demographic data were recorded, including gender, associated comorbidities, and BMI. Serum tests included creatinine, sodium, potassium, magnesium, calcium, phosphorus, and albumin. Patients were asked to provide 24 h urinary collection to undergo a full metabolic workup that included 24 h urinary calcium, phosphorus, oxalate, citrate, and uric acid. In addition, urine pH was measured, and the glomerular filtration rate (GFR) was calculated using the 24 h urine volume, serum, and urinary creatinine. Urinary constituents were analyzed based on raw numbers and by laboratory standards. The cut-off values of hypercalciuria, hyperuricosuria, hyperoxaluria and hypocitraturia were 200 mg/day, 750 mg/day, 45 mg/day, and 320 mg/day, respectively.
According to our protocol, any patient with positive pre-operative MSU-C receives the appropriate antibiotic 3–7 days before PCNL to prevent postoperative infectious complications. A routine third-generation cephalosporin was administered one hour before the surgery if MSU-C was negative. All patients underwent PCNL in the prone position after a ureteral catheter fixation. The caliceal puncture was done under fluoroscopic guidance and mechanical dilatation was done using Alkene’s metal dilators. Stone disintegration was accomplished using mechanical or laser disintegration. Postoperative nephrostomy tube placement and ureteral versus JJ stent placement were left to the discretion of the surgeon. Before stone disintegration, a renal pelvis urine sample was obtained and sent separately for culture (RP-C). In addition, fragments of stones were sent for stone culture (S-C) according to Tavichakorntrakool et al. method [16] and biochemical analysis with infrared spectrophotometry (Fourier-transform infrared spectroscopy (FTIR) 2000, Perkin-Elmer Co., U.S.A).
## Outcome
The primary outcome of the study was to identify the relationship between ipsilateral renal stone intervention history and both the metabolic workup, and results of MSU-C, RP-C, and S-C. Any patient with a previous history of PCNL or retrograde intrarenal surgery either in our hospital or elsewhere with documented stone-free status or the presence of clinically insignificant residual fragments (< 3 mm) at the time of hospital discharge and completed at least 6 months free period was considered a recurrent stone former. In addition, patients with no history of any stone intervention were considered primary stone formers. Cohen’s Kappa was used to describe the level of agreement between each pair of RP-C, S-C, and MSU-C. The level of agreement is considered excellent, fair to good, and poor for Kappa levels more than 0.75, 0.4–0.75, and less than 0.4, respectively.
## Statistical analysis
Numeric data were displayed as mean ± SD or median (IQR) according to parametric distribution and the significance level was calculated using the Student t or Mann–Whitney U tests, respectively. Categorical variables were presented as percentages in each category and were compared using the Chi-square test. Factors with a significance level of < 0.05 on univariate analysis were entered into a multivariate logistic regression model to identify the independent predictors of stone recurrence. To avoid multicollinearity in logistic regression model covariates, 3 distinct models were constructed using MSU-C, RP-C, and S-C separately. The area under the curve (AUC) was calculated for each model and compared to select the final model with the best performance. The statistical analysis was performed using R programming language version 4.1.2.
## Demographics
A total of 210 patients were included during the study period of which 84 ($40\%$) patients had a history of stone intervention with a free intervening period. 99 ($47.1\%$) patients had positive findings in either the MSU-C, RP-C, or S-C. Most of our patients were obese with a mean BMI of 32.3 ± 6.7 and 132 ($62.9\%$) were females. The mean ± SD GFR was 61.7 ± 13.4 ml/min with none of our patients had chronic renal failure. The most common stone type detected was uric acid stones in 99 ($47.1\%$) patients while Ca oxalate stones were found in 62 ($29.5\%$) patients. Staphylococcus aureus (S. aureus) was the most common organism found in S-C and RP-C in 33 ($15.7\%$) and 15 ($7.1\%$) patients, respectively. On the other hand, *Escherichia coli* (E. coli) was the most common organism isolated from MSU-C in 39 ($18.6\%$) patients. Table 1 demonstrated the characteristics of patients, stones, and the results of metabolic workup. Table 1Patients and stone characteristics and the results of the metabolic workupVariableValueAge49.6 ± 12.2Gender Female132 ($62.9\%$)Body mass index32.3 ± 6.7DM Yes37 ($17.6\%$)GFR (ml/min)61.7 ± 13.4Hypertension Yes62 ($29.5\%$)Stone density (Hounsfield units)579 (443.5–1004)Stone size (mm)44 (20.9–96)Stone type Ca oxalate62 ($29.5\%$) Ca phosphate12 ($5.7\%$) Uric acid99 ($47.1\%$) *Magnesium ammonium* phosphate6 ($2.9\%$) Ca oxalate and uric acid13 ($6.2\%$) Cystine10 ($4.8\%$) Ca Oxalate and Ca phosphate8 ($3.8\%$)Midstream urine culture (MSU-C) E. coli39 ($18.6\%$) S. aureus13 ($6.2\%$) E. faecalis4 ($1.9\%$) P. aeruginosa3 ($1.4\%$) K. pneumonia8 ($3.8\%$) Negative143 ($68.1\%$)*Renal pelvis* culture (RP-C) E. coli7 ($3.3\%$) S. aureus15 ($7.1\%$) E. faecalis4 ($1.9\%$) P. aeruginosa3 ($1.4\%$) K. pneumonia1 ($0.5\%$) S. epidermidis1 ($0.5\%$) Negative179 ($85.2\%$)Stone culture (S-C) E. coli11 ($5.2\%$) S. aureus33 ($15.7\%$) E. faecalis10 ($4.8\%$) P. aeruginosa10 ($4.8\%$) K. pneumonia2 ($1\%$) S. epidermidis8 ($3.8\%$) Negative136 ($64.8\%$)Serum levels Albumin (g/dL)4.5 ± 0.6 Calcium (mg/dL)9.3 ± 0.9 Creatinine (mg/dL)0.5 ± 0.2 Potassium (mmol/L)4.3 ± 0.5 Magnesium (mg/dL)2.3 ± 0.3 Sodium (mmol/L)138.2 ± 4.1 Phosphate (mg/dL)4.8 ± 0.924 h urine test (mg/day) Calcium230.5 (210–400) Citrate288.6 (122–453.5) Creatinine, mg/dL66.4 ± 8.9 Oxalate22 (15–45.8) Phosphate222 (201–321) Uric acid399 (222.5–607.5)pH5.8 ± 0.9Hypercalciuria (> 200 mg/day) Yes170 ($81\%$)Hyperoxaluria (> 45 mg/day) Yes58 ($27.6\%$)Hyperuricosuria (> 750 mg/day) Yes23 ($11\%$)Hypocitraturia (< 320 mg/day) Yes119 ($56.7\%$)Recurrence Yes84 ($40\%$)Data are described as mean ± SD or median (IQR) based on the parametric distribution
## Multivariate logistic regression models
Urinary tract infection factors that showed significant association with stone recurrence included positive S-C [51 ($60.7\%$) vs 23 ($18.3\%$), $p \leq 0.001$], positive MSU-C [37 ($44.1\%$) vs 30 ($23.8\%$), $$p \leq 0.002$$], positive RP-C [17 ($20.2\%$) vs 12 ($9.5\%$), $$p \leq 0.03$$]. Other factors were mean GFR ± SD (ml/min) (65 ± 13.1 vs 59.5 ± 13.1, $$p \leq 0.003$$), calcium-containing stones [47 ($55.9\%$) vs 48 ($38.1\%$), $$p \leq 0.01$$], median (IQR) urinary citrate levels (mg/day) [333 (123–512.5) vs 221.5 (120.1–412), $$p \leq 0.04$$], and mean ± SD urinary pH (6.1 ± 1 vs 5.6 ± 0.7, $p \leq 0.001$). Data are presented in Table 2.Table 2Univariate analysis for predictors of stone recurrenceNo recurrenceRecurrenceP-valueAge49.7 ± 11.749.3 ± 130.8Gender0.5 Female77 ($61.1\%$)55 ($65.5\%$)DM0.4 Yes20 ($15.9\%$)17 ($20.2\%$)GFR (ml/min)59.5 ± 13.165 ± 13.10.003Hypertension0.1 Yes42 ($33.3\%$)20 ($23.8\%$)Body mass index32.9 ± 6.731.3 ± 6.60.08Obesity (BMI > 30)0.09 Yes89 ($70.6\%$)50 ($59.5\%$)Stone size (mm)41.8 (21.8- 91.5)46.5 (20–105)0.3Stone type0.01 Ca-containing48 ($38.1\%$)47 ($55.9\%$)MSU-C0.002 Positive30 ($23.8\%$)37 ($44.1\%$)RP-C0.03 Positive12 ($9.5\%$)17 ($20.2\%$)S-C< 0.001 Positive23 ($18.2\%$)51 ($60.7\%$)Serum Albumin (g/dL)4.5 ± 0.64.4 ± 0.60.2 Calcium (mg/dL)9.2 ± 0.99.4 ± 0.80.09 Creatinine (mg/dL)0.5 ± 0.10.5 ± 0.20.3 Potassium (mmol/L)4.3 ± 0.54.4 ± 0.50.3 Magnesium (mg/dL)2.4 ± 0.32.3 ± 0.30.1 Sodium (mmol/L)138.3 ± 4.1138.2 ± 4.10.9 Phosphate (mg/dL)4.8 ± 0.84.9 ± 10.424 h urine test (mg/day) Calcium222 (210–350.8)231 (210–462.5)0.5 Citrate221.5 (120.3–412)333 (123–512.5)0.04 Creatinine, mg/dL66.2 ± 8.466.6 ± 9.70.8 Phosphate222 (210–321)222 (200–321)0.6 Uric acid500.0 (224–613.3)281.5 (222–566.3)0.3 Oxalate22 (14.0–44)25.5 (16.5–50.3)0.2Urine pH5.6 ± 0.76.1 ± 1 < 0.001Hypercalciuria (> 200 mg/day)0.7 Yes103 ($81.7\%$)67 ($79.8\%$)Hyperoxaluria (> 45 mg/day)0.2 Yes31 ($24.6\%$)27 ($32.1\%$)Hyperuricosuria (> 750 mg/day)0.7 Yes13 ($10.3\%$)10 ($11.9\%$)Hypocitraturia (< 320 mg/day)0.03 Yes79 ($62.7\%$)40 ($47.6\%$)Data are described as mean ± SD or median (IQR) based on the parametric distributionS-C Stone culture, RP-C *Renal pelvis* culture, MSU-C Midstream culture Because of the presence of a significant association between S-C, RP-C, and MSU-C, three regression models were constructed using one of these factors combined with other significant predictors at once. On multivariate analysis, only positive S-C was the significant predictor of stone recurrence (odds ratio [OR] 9.9, $95\%$ confidence interval [CI] [3.8–28.6], $p \leq 0.001$). For the RP-C model, only urine pH was the significant predictor for stone recurrence (OR 2.008, $95\%$ CI [1.4–2.9], $p \leq 0.001$). Likewise, urinary pH was the only significant predictor in the MSU-C model (OR: 1.9, $95\%$ CI [1.3–2.7], $$p \leq 0.001$$). Table 3 demonstrates the results of multivariate analysis. Table 3Multivariate logistic regression models for the predictors of stone recurrenceOR$95\%$CIP-valueS-C model (Intercept)0.08(0.001–4.9)0.2 Positive S-C9.9(3.8–28.6)< 0.001 GFR1.04(0.9–1.08)0.07 Stone type: non-Ca containing1.08(0.4–3.2)0.8 Urine citrate1.001(0.9–1.003)0.5 Urine pH0.8(0. 5–1.4)0.5The RP-C model (Intercept)0.001(0–0.04)< 0.001 Positive RP-C1.3(0.5–3.3)0.6 GFR1.03(0.91–1.1)0.1 Stone type: non-Ca containing1.2(0.4–3.3)0.7 Urine citrate1.001(0.9–1.004)0.1 Urine pH2.008(1.4–2.9)< 0.001MSU-C model (Intercept)0.002(0–0.06)0.001 Positive MSU-C1.7(0.9–3.3)0.1 GFR1.03(0.9–1.1)0.2 Stone type: non-Ca containing1.1(0.4–3.1)0.8 Urine citrate1.001(0.9–1.004)0.2 Urine pH1.9(1.3–2.7)0.001OR Odds ratio, CI Confidence interval, S-C Stone culture, RP-C *Renal pelvis* culture, MSU-C Midstream culture
## Comparison of the three models
Each model was evaluated using the ROC-derived AUC. The AUC of the S-C model was significantly higher than both the RP-C model (delta AUC 7.8 [2.6;13], $$p \leq 0.004$$), and MSU-C model (delta AUC 6.3 [0.9;11.8], $$p \leq 0.02$$). On the other hand, no significant difference was found between the AUC of RP-C and MSU-C models (delta AUC: – 1.5 [– 3.7;0.7], $$p \leq 0.2$$). Figure 1 shows the AUC with $95\%$ CI of the three models. Fig. 1Receiver operating characteristic (ROC) curves for stone (S-C), renal pelvis (RP-C), and midstream urine (MSU-C) cultures multivariate logistic regression models with the area under the curve (AUC) and its $95\%$ confidence intervals
## The levels of agreement
Cohen’s Kappa levels of agreement are demonstrated in Table 4. Fair to good levels of the agreement were found between RP-C and MSU-C, and between RP-C and S-C in the whole cohort. Likewise, it was also present between RP-C and MSU-C in the recurrence group and RP-C and S-C in the non-recurrence group. Table 4The levels of agreement between the midstream urine, renal pelvis, and the stone culturesPatientsRenal pelvis cultureStone cultureOverall comparisonAll patients0.43 ($p \leq 0.001$) Midstream urine culture0.45 ($p \leq 0.001$)0.37 ($p \leq 0.001$) *Renal pelvis* culture0.51 ($p \leq 0.001$)Recurrence group0.37 ($p \leq 0.001$) Midstream urine culture0.51 ($p \leq 0.001$)0.31 ($p \leq 0.001$) *Renal pelvis* culture0.38 ($p \leq 0.001$)No recurrence group0.44 ($p \leq 0.001$) Midstream urine culture0.35 ($p \leq 0.001$)0.38 ($p \leq 0.001$) *Renal pelvis* culture0.66 ($p \leq 0.001$)The level of agreement is interpreted based on Kappa level as > 0.75 (Excellent), 0.4–0.75 (Fair to good), and < 0.4 (Poor)
## Discussion
The hallmark findings of the current study are that positive S-C was the only independent significant factor associated with recurrent stone formation and that no metabolic stone workup parameter was significantly associated with stone recurrence. In addition, there was a discrepancy between the leading organism in S-C and RP-C (gram-positive S. aureus) compared to gram-negative E. coli in MSU-C.
The role of gram-positive and negative bacteria in promoting stone crystallization has been previously explored. Chutipongtanate and associates have shown that E. coli, S. aureus, K. pneumoniae, and S. pneumoniae dramatically promoted calcium oxalate crystal aggregation and growth to a diameter greater than the lumen of the distal tubules [15]. The authors noticed that this effect is specific to bacterial viability and is dose-dependent. In another report about PCNL for staghorn stones, recurrent episodes of UTIs were an independent predictor of stone recurrence or residual stone enlargement [14]. Likewise, the Staphylococcus spp. has been linked to staghorn stone recurrence [13].
In this report, S-C has achieved the most significant association with stone recurrence when compared to MSU-C or RP-C. To the best of our knowledge, the comparative effect of MSU-C, RP-C, and S-C has not been explored in the context of stone recurrence. The correlation between MSU-C, RP-C, and S-C has been extensively studied in evaluating postoperative sepsis with S-C being considered the most accurate tool [17]. MSU-C does not represent the infection status of the upper tract, especially in the presence of obstruction [18]. In addition, a weak correlation has been found between the lower urinary tract (MSU-C) and upper urinary tract (RP-C and S-C) [19]. Similarly, as a predictor of post-PCNL sepsis, S-C and RP-C have shown superior outcomes in the prediction of infectious complications in a recent meta-analysis [17].
The pivotal role of 24 h metabolic stone evaluation is to identify patients with urinary abnormalities that could specifically benefit from specific dietary recommendations and targeted medical therapy. This is why metabolic evaluation is recommended by the American Urological Association (AUA) as well as the European Association of Urology (EAU) [20]. On the other hand, contradictory results questioning this approach are emerging in concordance with our study. In a recent study using a propensity score matching analysis, $61.2\%$ of patients who completed 24 h metabolic testing developed recurrent stone events compared to $54\%$ who had not any metabolic evaluation ($p \leq 0.001$) [5]. Further analysis of patients with the recurrent stone disease showed that $57.1\%$ of patients with metabolic evaluation developed a third stone-related episode compared to $53.3\%$ of patients who had no metabolic evaluation ($p \leq 0.001$). More interestingly, testing the hypothesis of the significance of metabolic evaluation revealed that patients who had performed the metabolic evaluation and consequently received a new prescription of thiazide or alkali salt were more likely to develop another stone event compared to those who did not undergo metabolic evaluation or receive a prescription. In another report, Samson et al. examined the association between 24 h urine and stone recurrence in a large population of patients [10]. The authors reported that there was an annual decline in the usage of 24 h testing and that there was no significant association between performing the test and stone recurrence in either the total population or the high-risk groups.
Several factors could help interpret these results. Initially, problems related to the completion and accuracy of performing 24 h urine collection were cumbersome and exhausting, and some patients might not exhibit urinary abnormalities during the period of collection. In addition, there is no consensus on how clinicians interpret and treat the abnormalities if found. Furthermore, this metabolic evaluation should be followed by a strict diet regimen and a specific prescription which are less likely to be complied with by the patients [5].
Although our study shed the light on a new parameter that might affect stone recurrence and lessens the importance of another well-known factor, several drawbacks need to be acknowledged. Initially, the retrospective nature of the history of stone disease with the inability to document the primary stone burden, the inaccurate determination of the number of previous episodes, and the inability to investigate the history of UTI are potential factors that could have affected the outcome. Obese females constituted the main portion of our study compared to obese males (73.5 vs $53.8\%$, $$p \leq 0.006$$) which might not reflect the higher incidence of stone formation in males. The possible cause of this might be related to the geographical origin of this work where obesity is likely predominant and that the population demographics are changing with the growing incidence of obesity and metabolic syndrome. In addition, the examined metabolic evaluation and culture analyses were at the endpoint of the study. Therefore, the conclusion is better described as an association rather than a prediction. Furthermore, our study is missing the initial stone composition, stone density, and the initial metabolic evaluation and cultures which could have a significant impact on stone recurrence.
## Conclusion
Our results suggest that positive S-C outweighs the significance of metabolic stone abnormalities in patients with recurrent kidney stones. This highlights the significant role of UTI in the pathogenesis of stone recurrence. Our study suggests exerting all efforts to prevent further UTI that might prevent further stone recurrence. Prospective studies are highly encouraged to foresee the effect of preventing UTI on the chances of stone recurrence and to determine the actual role of metabolic workup.
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|
---
title: Independent risk factors for an increased incidence of thromboembolism after
lung transplantation
authors:
- Isabelle Moneke
- Ecem Deniz Ogutur
- Johannes Kalbhenn
- Ina Hettich
- Bernward Passlick
- Wolfgang Jungraithmayr
- Omer Senbaklavaci
journal: Journal of Thrombosis and Thrombolysis
year: 2022
pmcid: PMC10011327
doi: 10.1007/s11239-022-02748-9
license: CC BY 4.0
---
# Independent risk factors for an increased incidence of thromboembolism after lung transplantation
## Abstract
### Background
Thromboembolism (TE) after lung transplantation (LTX) is associated with increased morbidity and mortality. The aim of this study is to analyze the incidence and outcome of venous and arterial thromboembolic complications and to identify independent risk factors.
### Patients and methods
We retrospectively analyzed the medical records of 221 patients who underwent LTX at our institution between 2002 and 2021. Statistical analysis was performed using SPSS and GraphPad software.
### Results
74 LTX recipients ($33\%$) developed TE. The 30-days incidence and 12-months incidence were $12\%$ and $23\%$, respectively. Nearly half of the patients ($48\%$) developed pulmonary embolism, $10\%$ ischemic stroke. Arterial hypertension ($$p \leq 0.006$$), a body mass index (BMI) > 30 ($$p \leq 0.006$$) and diabetes mellitus ($$p \leq 0.041$$) were independent predictors for TE. Moreover, a BMI of > 25 at the time of transplantation was associated with an increased risk for TE ($43\%$ vs. $32\%$, $$p \leq 0.035$$). At the time of LTX, $65\%$ of the patients were older than 55 years. An age > 55 years also correlated with the incidence of TE ($$p \leq 0.037$$) and these patients had reduced overall post-transplant survival when the event occurred within the first postoperative year ($59\%$ vs. $72\%$, $$p \leq 0.028$$).
### Conclusions
The incidence of TE after LTX is high, especially in lung transplant recipients with a BMI > 25 and an age > 55 years as well as cardiovascular risk factors closely associated with the metabolic syndrome. As these patients comprise a growing recipient fraction, intensified research should focus on the risks and benefits of regular screening or a prolonged TE prophylaxis in these patients.
Trial registration number DKRS: 00021501.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s11239-022-02748-9.
## Highlights
The incidence of arterial and venous thromboembolism after lung transplantation is high, notably in recipients with a BMI > 25 and an age > 55 years. Moreover, cardiovascular risk factors closely associated with the metabolic syndrome are independent risk factors for venous thromboembolism. Intensified research should focus on the risks and benefits of regular screening or a prolonged thromboembolism prophylaxis in patients at risk, especially in the first postoperative year.
## Introduction
Lung transplantation is the most efficient treatment option for selected patients with end stage chronic lung disease [1], such as idiopathic pulmonary fibrosis or chronic obstructive pulmonary disease (COPD) [1]. Although the median survival rates have improved over the last decade, chronic lung allograft dysfunction (CLAD)—the hallmark of chronic lung allograft rejection—is responsible for a 5-years survival of only $55\%$ [2]. It occurs in about half of the patients within 5 years after transplantation and up to now, there is no effective treatment available.
Since the introduction of the Lung Allocation Score (LAS) in 2005, the recipient population has changed, and older patients with age-related comorbidities are now more likely to receive an organ [3]. According to the 2013 report of the Registry of the International Society for Heart and Lung Transplantation, the median age at the time of LTX has gradually increased from 45 to 55 years over the preceding decade [4]. This implies the need to adjust to a growing fraction of patients of more than 60 or even 65 years of age with a higher risk of morbidity and mortality after lung transplantation [5].
Aside from postoperative infections, lung transplant recipients may experience cardiovascular complications, such as thromboembolism [6–8]. Thromboembolism (TE) is a well-known complication after surgery and the level of risk mainly depends on the surgery performed [9]. Acute pulmonary embolism is the most dangerous form of venous TE and can be fatal if left untreated [10]. It is associated with cardiac arrhythmia and right ventricular dysfunction and has an overall mortality rate of up to $10\%$ [10, 11]. The reported incidence of thromboembolic complications after LTX is considered higher compared to other cardiothoracic surgeries [12] but varies widely between studies (6–$44\%$) [6, 8, 13–15]. In our study, as in most of the others, only clinical symptoms prompted further investigation, so thromboembolic events might have been missed. This is supported by studies that implemented a regular screening protocol, where a higher incidence of TE was reported compared to studies without such a protocol [12]. Solid organ transplantation itself is recognized as an independent risk factor for thromboembolic events [16]. There are several underlying factors promoting TE: the surgical trauma itself induces inflammation leading to a prothrombotic state, immobilisation und fluid imbalance results in decreased venous flow. Side effects from immunosuppressive medication such as calcineurin inhibitors or corticosteroids, which impair glucose tolerance and induce post-transplant diabetes [17] further enhance the risk to develop thrombosis. Moreover, bacterial and viral infections have been shown to increase the risk for thrombotic events [9, 12, 18]. With increasing age, a growing number of patients develop traits of the metabolic syndrome, a heterogeneous clinical entity which includes the co-occurrence of overweight, impaired glucose tolerance, dyslipidaemia and hypertension leading to cardiovascular disease and diabetes mellitus [19, 20]. Two additional components underlined by the conference paper on the definition of the metabolic syndrome of the American Heart Association are a proinflammatory and a prothrombotic state [21]. A growing understanding is that venous TE is a chronic process which shares similar risk factors and pathophysiology, e.g., endothelial dysfunction, with atherothrombosis and coronary artery disease [9, 22]. Thus, a higher age at the time of transplantation, often accompanied by metabolic and cardiovascular comorbidity, makes a growing fraction of the lung transplant recipients particularly vulnerable to thromboembolic complications.
Clinical trials in general -, urologic -, and orthopaedic surgery have shown that the incidence of venous postoperative TE can be significantly reduced by interventions like early mobilization and adequate pharmacologic thrombosis prophylaxis to a range of 1.1–$10.6\%$ [9, 23]. While TE in general is associated with increased morbidity and hospital length of stay as well as reduced overall survival [24], there is limited data regarding the optimal management of these patients.
The aim of this study is to analyse the incidence of arterial and venous thromboembolic events in our lung transplanted patient cohort and identify independent risk factors. Furthermore, we make an attempt to discuss a regular screening during follow-up for patients at risk and the potential need for a personalised prophylaxis regime after surgery for a growing number of our patients.
## Design and study population
We performed a retrospective single centre analysis of patients who underwent LTX at the Department of Thoracic Surgery, Medical Centre—University of Freiburg between March 2003 and June 2021. A total of 221 patients were identified (115 males and 106 females). Patients with combined transplantations, such as heart–lung transplantations, were excluded.
All patients underwent regular clinical follow-ups, including bronchoscopy, blood values, and lung function analysis. Data were collected by checking electronic medical records, discharge reports and autopsy reports.
The study was approved by the Medical Centre—University of Freiburg’s local ethics committee and conducted in accordance with the guideline proposed in the Declaration of Helsinki. A waiver of consent was granted due to the retrospective nature of the study and the associated minimal risk. It is registered at the German Registry for Clinical Trials (DRKS) under the trial registration number 00021501.
## Follow-up schedule after lung transplantation
In the first year after the lung transplantation, clinical examination, lung function test and surveillance bronchoscopies with bronchoalveolar lavage and lung biopsies are scheduled for 1, 2, 3, 4, 6, and 12 months. From the second postoperative year on, patients are seen every 3 months for clinical examination, lab and lung function testing (Supplemental Fig. 2). In case of conspicuous results, such as infection or an otherwise not explainable decline in lung function, further testing/imaging/bronchoscopy to rule out/confirm CLAD is performed. If clinical symptoms for TE are present, further testing as described under the “definitions” section is initiated.
Immunosuppression medication levels as well as blood and kidney parameters were initially checked weekly after discharge and once stable, the interval was extended to every 4 weeks (Table 1).Table 1Basic demographic patient characteristicsVariableAll patients [221]Sex Male115 ($52\%$) Female106 ($48\%$)Age at transplantation Minimal age17 years Maximal age69 years Median age56 years < 18 years1 ($0.5\%$) Between 18 and 29 years9 ($4\%$) Between 30 and 39 years6 ($3\%$) Between 40 and 49 years25 ($11\%$) Between 50 and 59 years85 ($39\%$) ≥ 60 years96 ($43\%$)BMI at transplantation Male23.9 kg/m2 Female22.5 kg/m2 Median23.2 kg/m2Operation Double-lung196 ($89\%$) Single-lung25 ($11\%$)Underlying disease Idiopathic fibrosis84 ($38\%$) COPD77 ($35\%$) Mucoviscidosis12 ($5\%$) Extrinsic allergic alveolitis12 ($5\%$) Alpha-1 antitrypsin deficiency12 ($5\%$) Other autoimmune disorders8 ($4\%$) Sarcoidosis7 ($3\%$) LAM3 ($1\%$) Re-transplantation3 ($1\%$) GvHD2 ($1\%$) Other1 ($0.5\%$)Cardiovascular diseases Arterial hypertension65 ($30\%$) Diabetes mellitus40 ($18\%$) Coronary heart disease36 ($16\%$) Coronary Stent16 ($7\%$) Peripheral artery disease5 ($2\%$) Hypercholesterolemia96 ($43\%$) Atrial fibrillation pre LTX14 ($6\%$) Thromboembolism pre LTXTBACardiovascular therapy pre LTX Antiplatelet therapy31($14\%$) Anticoagulants27($12\%$) Statins47($21\%$)Basic patient characteristics before LTXCOPD chronic obstructive pulmonary disease, LAM Lymphangioleiomyomatosis, GvHD Graft-versus-host disease, ECMO extracorporeal membrane oxygenation, BMI body mass index, LTX lung transplantation
## Definitions
We defined TE as the main event. TE includes every event attributed to either thrombotic arterial occlusion (e.g., myocardial infarction, stroke) and venous thrombosis or embolism as listed in Table 2. Thromboembolism was detected mostly by clinical symptoms during regular follow-ups in the transplant outpatient centre. Since asymptomatic patients were not routinely screened, some events might have been missed, especially after the first postoperative year. The diagnosis was established by ultrasound. In case of suspected pulmonary embolism ventilation/perfusion scintigraphy or CT angiography were performed. Diagnostic measures for other venous and arterial events were initiated as appropriate upon clinical presentation. If a patient suffered from multiple thromboembolic events, they were listed separately, each counting as one event. However, for the calculation of survival and risk factors, patients were divided into 2 groups, one with and the other without thromboembolic events. Thromboembolic events that occurred before transplantation were excluded. Table 2Thromboembolic events ≤ 1. month2.–12. monthsAll eventsThromboembolism35 ($16\%$)36 ($16\%$)113 ($51\%$) At least one thromboembolic event26 ($12\%$)24 ($11\%$)74 ($34\%$) More than one thromboembolic event8 ($4\%$)10 ($5\%$)32 ($15\%$)Venous thromboembolism19 ($9\%$)29 ($13\%$)73 ($33\%$) Pulmonary embolism7 ($3\%$)18 ($8\%$)34 ($15\%$) Deep vein thrombosis3 ($1\%$)8 ($4\%$)21 ($10\%$) Jugular vein thrombosis7 ($3\%$)1 ($0.5\%$)8 ($4\%$) Thrombi in axillary vein/ subclavian vein2 ($1\%$)0 ($0.0\%$)2 ($1\%$) CVST0 ($0.0\%$)1 ($0.5\%$)1 ($0.5\%$) Venous retinal vascular occlusion0 ($0.0\%$)0 ($0.0\%$)1 ($0.5\%$) TM0 ($0.0\%$)1 ($0.5\%$)4 ($2\%$) Atrial thrombi0 ($0.0\%$)0 ($0.0\%$)2 ($1\%$)Arterial thromboembolism16 ($7\%$)7 ($3\%$)40 ($18\%$) Stroke13 ($6\%$)1 ($0.5\%$)23 ($10\%$) Abdominal Vascular occlusion0 ($0\%$)2 ($1\%$)5 ($2\%$) Vascular occlusion in extremities1 ($0.5\%$)2 ($1\%$)7 ($3\%$) Arterial retinal vascular occlusion0 ($0\%$)2 ($1\%$)2 ($1\%$) Myocardial infarction2 ($1\%$)0 ($0\%$)3 ($1\%$)Significance of bold means that $p \leq 0.05$Incidence and classification of thromboembolic eventsCVST *Cerebral venous* sinus thrombosis, TM Thrombotic Microangiopathy The body mass index (BMI) was used to define if patients are underweight (BMI < 18.5), of normal weight (18.5–24.9), overweight (25.0–29.9) or obese (BMI > 30.0).
## Thrombosis prophylaxis and ICU management
The standard pharmacological thrombosis prophylaxis consisted of 40 mg enoxaparin or 5000 IE unfractionated heparin every 12 h in intensive care unit (ICU) and 4500 IE tinzaparin in intermediate care unit (IMC) every 24 h. This regimen was initiated in ICU as soon as possible after ruling out postoperative active bleeding. Additionally, medical compression bandages were used in ICU and medical compression stockings in IMC and regular wards until discharge. Generally, patients discharged from the hospital did not receive further thrombosis prophylaxis in accordance with the current guidelines. Patients with the indication for therapeutic anticoagulation, e.g., atrial fibrillation or pulmonary embolism, received unfractionated heparin or enoxaparin in therapeutic doses while being at the hospital. Therapeutic anticoagulation therapy was continued after the patients were discharged. After an arterial thromboembolic event patients were placed on aspirin therapy in accordance with current guidelines. Physical therapy was available to all patients starting from the first postoperative day in ICU.
Intravenous as well as intraarterial catheters were removed as soon as possible at the discretion of the treating physician in ICU or IMC.
## Statistical analysis
The Kaplan–Meier-Method was used to estimate overall survival and the log rank test was used for comparison of survival curves of patients with and without TE. To evaluate connections between different parameters the Fischer’s exact test, the Chi-squared test and the Mann–Whitney U test were used when appropriate. Univariate and multivariate logistic regression models were used to select independent predictors of TE and survival in our cohort. All tests were two-tailed. A p-value < 0.05 was considered statistically significant. All statistical analyses were conducted using SPSS software (Version 27, IBM Corporation, New York, NY, USA) and GraphPad Prism (Version 9, GraphPad Software, San Diego, CA 92108, USA).
## Results
Overall, 221 patients (115 male, 106 female) underwent LTX at our institution between March 2002 and June 2021. 196 ($89\%$) patients underwent a double lung transplantation, 25 ($11\%$) patients a single lung transplantation. 68 ($31\%$) patients underwent LTX before implementation of the lung allocation score (LAS)—based distribution system at the end of 2011. From 2012 until 2021, another 153 transplantations ($69\%$) were performed. At the time of data analysis, 146 patients [66] were still alive. The 1-year-, 5-years- and 10-years-survival-rate in our patient collective is $80\%$, $66\%$ and $59\%$ respectively (Fig. 1a).Fig. 1a Overall post-transplant survival. Kaplan–Meier Analysis of survival after lung tranplantation. 1 month: $89\%$, 6 months: $82\%$, 1 year: $80\%$, 5 years: $66\%$ and10 years $59\%$. b overall survival and survival after thromboembolic events within the first month after LTX. Log rank t test $$p \leq 0.027.$$ Survival after lung tranplantation. Thromboembolic event in the first month (1 year: $74\%$, 3 years: $60\%$, 5 years: $30\%$ and 10 years: $30\%$) vs. no thromboembolic event in the first month (1 year: $81\%$, 3 years: $75\%$, 5 years: $70\%$ and 10 years: $61\%$). c overall survival and survival after thromboembolic events within the first year after LTX of patients ≥ 55 years. Long rank t test $$p \leq 0.028.$$ Survival after lung tranplantation. Thromboembolic event in the first year (1 year: $82\%$, 3 years: $67\%$, 5 years: $45\%$ and 10 years: $37\%$) vs. no thromboembolic event in the first year (1 year: $85\%$, 3 years: $78\%$, 5 years: $72\%$ and 10 years: $66\%$) in patients ≥ 55 years of age The average waiting time for a transplantation was 14 months (range 2 days to 11 years). The main underlying pulmonary diseases leading to LTX were idiopathic fibrosis ($38\%$), or chronic obstructive pulmonary disease (COPD) ($35\%$). Concomitant cardiovascular diseases, e.g., arterial hypertension ($29\%$) or one-vessel coronary artery disease ($17\%$), as well as diabetes mellitus ($18\%$), were present in a substantial fraction of the patients (Table 3). Patients transplanted after 2011 were more often aged > 55 years compared to patients that underwent surgery before the implementation of the LAS score ($72\%$ vs. $49\%$, $p \leq 0.001$). Overall, 143 patients ($65\%$) were over 55 years old, and 96 patients ($43\%$) were aged 60 years or older at the time of transplantation (Table 1).Table 3Factors associated with thromboembolism (TE)VariableAll patients ($$n = 221$$)Patients without TE ($$n = 147$$)Patients with TE (overall) ($$n = 74$$)pVenous TE (all events) ($$n = 73$$)pArterial TE (all events) ($$n = 40$$)pPrimary lung disease Idiopathic fibrosis84 ($38\%$)58 ($40\%$)26 ($35\%$)0.56021($28\%$)0.5138($20\%$)0.117 COPD77 ($35\%$)45 ($31\%$)32 ($43\%$)0.07319($26\%$)0.13216($40\%$)0.070 Mucoviscidosis12 ($5\%$)10 ($7\%$)2 ($3\%$)0.3451($1\%$)0.3061($3\%$)1.000 EAA12 ($5\%$)8 ($5\%$)4 ($5\%$)1.0002($3\%$)1.0001($3\%$)1.000 Alpha-1 antitrypsin deficiency12 ($5\%$)10 ($7\%$)2 ($3\%$)0.3451($1\%$)0.3061($3\%$)1.000 Sarcoidosis7 (%)4 ($3\%$)3 ($4\%$)0.6891 ($1\%$)1.0001($3\%$)1.000Operation Double-lung196 ($88.7\%$)133 ($91\%$)63 ($85\%$)0.26441($56\%$)0.12529($73\%$)1.000 Single-lung25 ($11.3\%$)14 ($10\%$)11 ($15\%$)0.2649($12\%$)0.1253($8\%$)1.000 ECMO78 ($35.3\%$)59 ($40\%$)19 ($26\%$)0.03715($21\%$)0.4059($23\%$)0.427Age ≥ 55 years143 ($65\%$)88 ($60\%$)55 ($74\%$)0.03740($55\%$)0.01123($58\%$)0.427 ≥ 60 years96 ($43\%$)56 ($38\%$)40 ($54\%$)0.03130($41\%$)0.00918($45\%$)0.126BMI* < 18,5 kg/m233 ($15\%$)28 ($19\%$)5 ($7\%$)0.0160($0\%$) < 0.0015($13\%$)1.000 18,5–24,9 kg/m2111 ($50\%$)75 ($51\%$)36 ($49\%$)0.77622($30\%$)0.33617($43\%$)0.849 ≥ 25 kg/m276 ($34\%$)43 ($30\%$)33 ($45\%$)0.03528($38\%$) < 0.00110($25\%$)0.841 ≥ 30 kg/m211 ($5\%$)2 ($1\%$)9 ($12\%$)0.0017($10\%$)0.0032($5\%$)0.661Cardiovascular diseases Arterial hypertension65 ($29\%$)34 ($23\%$)31 ($42\%$)0.00524($33\%$)0.0039($23\%$)1.000 Diabetes mellitus40 ($18\%$)19 ($13\%$)21 ($28\%$)0.00916($22\%$)0.0069($23\%$)0.136 Coronary heart disease3622 ($15\%$)14 ($19\%$)0.42710($14\%$)0.4996($15\%$)0.595 PAVK5 ($2\%$)1 ($1\%$)4 ($5\%$)0.0433($4\%$)0.0754($10\%$) < 0.001 Hypercholesterolemia96 ($43\%$66 ($45\%$)30 ($41\%$)0.45221($29\%$)0.73711($28\%$)0.230 Atrial fibrillation pre LTX14 ($6\%$)13 ($9\%$)1 ($1\%$)0.0391($1\%$)0.2021($3\%$)0.698 Atrial fibrillation post LTX66 ($30\%$)47($31\%$)19($26\%$)0.35311($15\%$)0.2199($23\%$)1.000Significance of bold means that $p \leq 0.05$Patient characteristics and concomitant diseases in patients with and without thromboembolismCOPD chronic obstructive pulmonary disease, ECMO extracorporeal membrane oxygenation, BMI body mass index, LTX lung transplantation*The BMI at the time of transplant is unknown for one patient (external LTX) We identified 74 patients ($33\%$) who experienced at least one thromboembolic event after lung transplantation. Most of the events ($68\%$) took place within the first postoperative year, whereas $35\%$ already occurred within the first postoperative month (Table 1). 34 patients ($15\%$) developed pulmonary embolism and 23 patients ($10\%$) were diagnosed with ischemic stroke. Hemiparesis or hypaesthesia persisted in 11 patients ($4.8\%$) of the latter group. Most pulmonary embolisms occurred during the first year ($53\%$) while the majority of strokes took place in the first month ($57\%$). However, approximately $\frac{1}{3}$ of all thromboembolic events were diagnosed after the first postoperative year (Table 1).
Thromboembolic events within the first postoperative month were associated with reduced survival after transplantation ($56\%$ vs. $68\%$, $$p \leq 0.027$$) (Fig. 1b). Notably, patients over 55 years of age at the time of transplantation, which comprise $65\%$ of our cohort, had not only an increased incidence of thromboembolic events in the first postoperative month ($$p \leq 0$$,025), but also a reduced survival rate when they experienced at least one event within the first year ($59\%$ vs. $72\%$, $$p \leq 0$$,028) (Fig. 1c). In accordance with that, a recipient age of > 55 years correlated with the incidence of TE ($$p \leq 0.037$$) (Table 3). Moreover, arterial hypertension ($$p \leq 0.004$$), peripheral artery disease ($$p \leq 0.019$$) and diabetes mellitus ($$p \leq 0.017$$) were independent predictors for TE (Table 4). These factors are closely related to the metabolic syndrome, and fittingly, a body mass index (BMI) of > 25 at the time of transplantation also contributed significantly to the risk for TE ($$p \leq 0.035$$) (Tables 3 and 4). Particularly venous TE correlated with the above-mentioned factors with few exceptions when arterial TE was more predominant (Tables 3 and 4). While several independent risk factors for TE could be identified, they had no effect on long-term survival (Table 5).Table 4Logistic regression analysis of factors associated with TEVariablesWaldExp(B)$95\%$CI Exp(B)pOverallMin. Max. Peripheral artery disease2.31310.1031.04497.7380.046 BMI ≥ 30 kg/m27.419.2151.86245.5980.006 Diabetes mellitus4.3152.2131.0464.6810.038 Hypertension7.2892.4121.2734.5710.007Venous TE Peripheral artery disease3.8166.8820.99447.6660.051 BMI ≥ 30 kg/m27.0646.1861.61423.7150.008 Diabetes mellitus4.4612.3561.0645.2180.035 Hypertension9.312.9871.4976.0320.002Arterial TE Peripheral artery disease8.50927.5562.968255.7950.004Significance of bold means that $p \leq 0.05$Forward stepwise logistic regression analysis of factors associated with TEBMI body mass indexTable 5Univariate and multivariate Cox regression analyses to identify predictors of survivalVariables ($$n = 221$$)Univariate, HR ($95\%$ CI)pMultivariate, HR ($95\%$ CI)pAge < 55 years1.270 (0.800–2.017)0.311 ≥ 55 years0.787 (0.496–1.251)0.311Sex Gender (female)1.825 (1.150–2.897)0.0111.898 (1.193–3.019)0.007Underlying lung disease Fibrosis1.441 (0.914–2.271)0.116 COPD0.530 (0.315–0.893)0.0170.574 (0.329–1.002)0.051Operation Double lung1.033 (0.515–2.075)0.927 Single lung0.968 (0.482–1.944)0.927 ECMO1.901 (1.207–2.993)0.0061.579 (0.969–2.573)0.067BMI < 18.5 kg/m21.313 (0.733–2.351)0.360 ≥ 25 kg/m21.191 (0.737–1.924)0.475Concomitant diseases PAD2.221 (0.698–7.065)0.177 Diabetes mellitus1.306 (0.741–2.304)0.356 CHD0.813 (0.409–1.613)0.553 Arterial hypertension0.869 (0.520–1.450)0.591 Hypercholesterolemia0.974 (0.599–1.586)0.917 Autoimmune disease1.506 (0.828–2.742)0.180 Atrial fibrillation0.516 (0.162–1.638)0.261 mPAP ≥ 25 mmHG0.923 (0.550–1.550)0.763Thromboembolism ≤ 30 days1.994 (1.067–3.728)0.0311.860 (0.985–3.511)0.056 ≤ 1 year1.292 (0.747–2.235)0.359 All events1.030 (0.640–1.657)0.904Pulmonary embolism ≤ 30 days1.815 (0.568–5.804)0.315 ≤ 1 year0.633 (0.255–1.574)0.326 All events0.538 (0.247–1.172)0.119Stroke ≤ 30 days1.453 (0.584–3.612)0.422 ≤ 1 year1.336 (0.537–3.324)0.533 All events1.343 (0.689–2.616)0.387Significance of bold means that $p \leq 0.05$Predictors of survivalCOPD chronic obstructive pulmonary disease, ECMO extracorporeal membrane oxygenation, BMI body mass index, PAD Peripheral artery disease, CHD coronary heart disease, mPAP mean pulmonary artery pressure At the time of transplantation, about half of patients ($51\%$) had a normal weight with a BMI between 18.5 and 24.9 kg/m2 (Supplementary Fig. 1). In our cohort, 36 ($32\%$) patients of normal weight had a thromboembolic event and $42\%$ of them were diagnosed with pulmonary embolism. The incidence of pulmonary embolism further increased to $61\%$ in patients with a BMI of > 25. In contrast, only 5 ($15\%$) underweight patients were diagnosed with TE and none of them had pulmonary embolism. This indicates that specifically the occurrence of pulmonary embolism was closely related to the patients’ weight ($p \leq 0.001$).
## Incidence and timing of TE
We found a 30-day and 12-month incidence of TE of $12\%$ and $23\%$ respectively in our lung transplant recipients. Overall, about one third of them had at least one thromboembolic event during the postoperative course. Current evidence indicates that the incidence of venous TE after LTX is higher [6] compared to other cardiothoracic surgeries, but varies between studies ($8\%$–$43\%$) [6, 12, 13, 24, 25]. A possible explanation is that the lung transplant cohorts are heterogeneous due to different underlying diseases and preexisting conditions. Moreover, there are also differences in screening protocols and thrombosis prophylaxis regimes as well as in time schedules of follow-up appointments between institutions. Notably, many cases of TE occur during hospital stay despite the use of thrombosis prophylaxis. It is widely accepted that pharmacologic prophylaxis with unfractionated heparin and low molecular weight heparin should be monitored with appropriate tests such as anti-factor-Xa-activity, for example. This may help to tailor individual doses for every patient. Although most thromboembolic events in our cohort took place in the first postoperative year, about one third was detected later than that. It can only be assumed that with a structured screening in place, the incidence would be even higher. In accordance with this, a high risk for recurrence was shown in a study by Prandoni et al., who followed a cohort of 1626 consecutive patients with venous TE in Padua, Italy for up to 10 years and found a high rate of recurrent events: $11\%$ after 1 year, $20\%$ after 3 years, $29\%$ after 5 years, and $40\%$ after 10 years [26].
## Risk factors and distribution of TE
We identified both, arterial and venous thromboembolic events. Both share common risk factors [27], which can be found in an increasing fraction for TE in our cohort. Although they share similar risk factors, they are different diseases and, in our cohort, venous events are more common than the arterial ones. Interestingly, most of the risk factors seem to be statistically relevant for venous TE which makes a prolonged thrombosis prophylaxis even more relevant.
We had a surprisingly high number of patients suffering from stroke, not only in the first month after surgery but also after the first postoperative year. Transplantation surgery itself along with the above-mentioned risk factors have been described to play a role in the early cases of those occurring within 30 days [28]. The later ones, however, may be a combined result of preexisting condition and e.g. the immunosuppression or other factors in the aftermath of the transplantation. Patients with newly diagnosed atrial fibrillation after surgery were placed on anticoagulation therapy according to current guidelines, and there is no statistically significant increase in the incidence of stroke in these patients (Table 3). We may, however, have missed some cases of atrial fibrillation if it occurred later or only paroxysmal and without symptoms.
Perioperative ECMO support itself did not increase the risk for TE in our cohort, however, this may be due to the large time span of observation and the fact that 30-days-mortality in patients on ECMO support as ‘bridge to transplant’ was much higher in the earlier years compared to the last decade.
There are some reports describing thrombi arising from the surgical suture lines, which is a possible, although rare source for TE [29, 30]. The venous and pulmonary arterial anastomoses are checked for patency and flow pattern intra- and, if needed, also postoperatively by transesophageal echography. Moreover, a precise suture technique with an endothelium-to-endothelium junction to occlude the muscle from the blood-contacting surfaces is employed for the venous cuff anastomoses [29]. All patients are checked for an atrial septum defect before transplantation and occlusion therapy is initiated for patients at need.
As described in previous studies, weight plays an important role in the incidence of thromboembolic events [31–33]. In our cohort, almost half of the patients with a BMI > 25 had a thromboembolic event during the postoperative course, most of them within the first year after transplantation. In recent years, our patients who initially presented with a BMI > 30 had to reduce weight before transplantation as this reduces not only the risk for TE and other cardiovascular complications but also for the development of primary graft dysfunction [32].
## Prophylaxis and treatment of venous and arterial TE
All patients at our center received thrombosis prophylaxis and regular physiotherapy with the goal of ambulation starting from ICU until the day of discharge. Furthermore, all patients with TE were prescribed anticoagulation therapy or antiplatelet drugs according to current guidelines. Pre-existing antiplatelet therapy was not interrupted for transplantation and pre-existing anticoagulation therapy was continued as soon as possible after surgery with low molecular weight heparin in therapeutic doses. Patients with atrial fibrillation known prior to LTX were on anticoagulation therapy and none of them suffered postoperative stroke.
Gastrointestinal bleeding occurred in 6 ($3\%$) patients on anticoagulation after transplantation, while no other major bleeding events were recorded. However, individual risks for bleeding complications need to be taken into consideration when thinking about a prolonged thrombosis prophylaxis for a certain amount of time to reduce the incidence of TE for patients at risk.
## Limitations of the study
A limiting factor of this retrospective single-centre study is that it covers a period of nearly 20 years and thus our findings may not apply to all lung transplant recipients. Moreover, due to the absence of clinical signs, we might have missed some cases of TE or complications thereof in our patient cohort. This is especially true for events that occurred after the first postoperative year. That said, a strength of the study is that one single protocol regarding postoperative management and TE prophylaxis applied to all patients at the respective time of transplantation, despite the arguably long follow-up time. The latter, however, allowed us to detect the events that occurred years after transplantation.
## Conclusions
About $\frac{2}{3}$ of all TE events occur in the first postoperative year, therefore an extended thrombosis prophylaxis for lung transplant patients with risk factors for TE such as age of more than 55 years, cardiovascular risk factors or diabetes mellitus, seems beneficial, particularly within the first year after surgery. However, whether this reduces the incidence of TE, or whether therapeutic anticoagulation is beneficial for selected patients for a certain amount of time after LTX needs to be analysed in randomised controlled studies. Considering the individual risk of the patient, also with regards to potential bleeding complications, is essential when making a decision.
In any case, it is very important to be aware of the increased risk of TE and to improve early detection, particularly in patients with pre-existing or new-onset cardiovascular comorbidity. The implementation of a regular screening, possibly integrated in the follow-up schedule at the outpatient transplant centre, as described for example by Zheng et al. [ 12] or Jorge et al [34], seems to be of great value and should be subject to further investigation.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (SVG 71 KB)—Supplementary Fig. 1 Distribution of patients’ weight at the time of lung transplantationSupplementary file2 (PNG 214 KB)—Supplementary Fig. 2 Follow-up schedule after lung transplantation
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|
---
title: The Wnt/β-catenin pathway regulates inflammation and apoptosis in ventilator-induced
lung injury
authors:
- Zongyu Chen
- Shuang He
- Siyu Lian
- Yi Shen
- Wenqing Jiang
- Lihua Zhou
- Leilei Zhou
- Xianming Zhang
journal: Bioscience Reports
year: 2023
pmcid: PMC10011329
doi: 10.1042/BSR20222429
license: CC BY 4.0
---
# The Wnt/β-catenin pathway regulates inflammation and apoptosis in ventilator-induced lung injury
## Abstract
Ventilator-induced lung injury (VILI) may be caused by incorrect mechanical ventilation (MV), and its progression is mainly related to inflammatory reaction, apoptosis, and oxidative stress. The Wnt/β-catenin pathway can modulate inflammation and apoptosis; however, its role in VILI is unknown. This research aims to explore the role of the Wnt/β-catenin pathway in VILI. VILI models were established using rats and type II alveolar epithelial (ATII) cells. Glycogen synthase kinase 3β (GSK-3β), β-catenin, and cyclin D1 were determined using western blotting and immunofluorescence. Apoptosis of lung tissues was evaluated using TUNEL, flow cytometry, Bax, and Bcl2 protein. Interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) were detected via enzyme-linked immunosorbent assay (ELISA). Lung pathological injury was evaluated through hematoxylin and eosin (H&E) staining. Lung permeability was evaluated by the ratio of dry to wet weight of lung tissue and the total protein level of bronchoalveolar lavage fluid (BALF). The results showed that GSK-3β expression was enhanced and β-catenin expression was diminished in lung tissue under MV. SB216763 increased β-catenin and cyclin D1 expression by inhibiting GSK-3β expression and inhibited the inflammatory response and apoptosis of lung, alleviated pulmonary edema and lung tissue permeability, and significantly mitigated lung injury. However, inhibition of β-catenin expression by MSAB attenuated the anti-inflammatory and antiapoptotic effects of SB216763 in VILI. Overall, the present study demonstrates that the Wnt/β-catenin pathway activation in MV may play an anti-inflammatory and antiapoptotic role, thereby alleviating lung injury and delaying VILI progression, which may be a key point of intervention in VILI.
## Introduction
In modern medicine, invasive mechanical ventilation (MV) is an effective respiratory maintenance for critical patients [1]. However, due to the repeated action of mechanical stress, alveoli are repeatedly expanded and stretched, may cause complex changes in alveolar structure and lung tissue molecular omics, and further aggravate or induce new lung inflammation, thereby leading to ventilator-induced lung injury (VILI) [2]. Inflammatory reaction and apoptosis are important pathophysiological changes in VILI. Incorrect MV may aggravate lung tissue inflammation and apoptosis, destroy alveolar-capillary barrier and alveolar integrity, cause pulmonary tissue congestion and edema, result in lung injury, and greatly reduce the prognosis and quality of life of patients [2,3]. Despite various protective ventilation strategies are used in clinical practice [4], there is still no effective method to alleviate VILI. Thus, it is significant to explore the key pathogenesis of VILI.
The Wnt signaling pathway is a complex multibranch regulatory network, which is divided into canonical and noncanonical. The canonical pathway is mainly the Wnt/β-catenin pathway. The noncanonical pathway mainly includes the Wnt/planner cell polarity pathway and the Wnt/Ca2+ pathway [5]. To date, the Wnt pathway is still the focus of scientific research and is considered as an underlying regulatory target for various tumor diseases [5]. Additionally, researchers have found that it also acts an critical role on other diseases except neoplastic diseases, such as glaucoma, lung-related diseases, wound healing, skeletal diseases, etc. [ 6–8].
In particular, the present study pays attention to the Wnt/β-catenin pathway mediated by β-catenin. β-catenin is a multifunctional and evolutionarily conserved molecule, which is an important biomarker for judging whether this pathway is activated and is a pivotal nuclear molecule for signaling of the pathway [9]. In the cytoplasm, axis inhibitor (AXIN), glycogen synthase kinase 3β (GSK-3β), and adenomatous polyposis coli (APC) jointly promote β-catenin ubiquitination and degradation, thereby inhibiting signaling [10]. The Wnt/β-catenin pathway is critical in lung development and repair [7,11,12], and its abnormal regulation is closely correlated with lung diseases progression, such as lung cancer, bronchopulmonary dysplasia, idiopathic pulmonary fibrosis (IPF), asthma, and acute respiratory distress syndrome (ARDS) [10,13–16]. Its roles include regulating inflammation, oxidative stress, cell differentiation, and apoptosis [12,17,18]. However, no studies have elucidated whether this pathway impacts on the development of VILI.
A study has found that during the early phase of harmful tidal volume ventilation, the Wnt/β-catenin pathway was regulated in lung tissue of rats with extrapulmonary sepsis, but the specific role has not been elucidated [19]. Therefore, we hypothesized that this pathway may be correlated with the progression of VILI. In this research, the roles of this pathway in lung injury during MV were investigated in VILI models with rats and type II alveolar epithelial (ATII) cells, which may provide a strong reference for the search for VILI treatment.
## Cell experiment
Rat ATII cell line was from the Cell Bank of Chinese Academy of Sciences (Shanghai, China). High-glucose Dulbecco’s-modified Eagle’s medium (DMEM, Thermo Fisher Scientific, U.S.A.) containing $10\%$ fetal bovine serum and $1\%$ penicillin/streptomycin mixture provides nutrients to cells. ATII cells were cultured in an incubator at 37°C with $5\%$ CO2. Experimental grouping: the control group (without cyclic stretching), high cyclic stretching with $18\%$ mechanical stress (HCS), HCS+dimethyl sulfoxide (DMSO), HCS+DMSO+SB216763. DMSO as placebo and SB216763 as GSK-3β inhibitor (MedChemExpress, Shanghai, China). They were transfected into cells by Lipofectamine 3000 reagent (Thermo Fisher Scientific, U.S.A.) before cyclic stretching with Flexcell Tension PluFX-4000TM (Flexcell International Corporation, Burlington, U.S.A.) [20].
## Animal experiment and ethic
Thirty-six Sprague–Dawley rats (male, 6–8 weeks, weighing 240–260 g) were obtained from Beijing Hufukang Biotechnology Co., Ltd. (SCXK(Jing)2019-0008). The rats were raised with water and food in the animal room (Guizhou, China) and light/dark cycle. Then, they were grouped: the control group (preserved spontaneous breathing), low tidal volume ventilation group (LVT), high tidal volume ventilation group (HVT), HVT+DMSO, HVT+DMSO+SB216763, HVT+DMSO+SB216763+methyl 3-{[(4-methyl phenyl) sulfonyl] amino} benzoate (MSAB), MSAB as β-catenin inhibitor (MedChemExpress, Shanghai, China). There were six rats in each experimental group. Rats were intraperitoneally injected with pentobarbital (50 mg/kg, Narcoren, Merial, Germany) plus fentanyl (0.05 mg/kg, Janssen-Cilag, Neuss, Germany) to induce anesthesia; anesthesia is supplemented every hour: pentobarbital (5–10 mg/kg per hour) and fentanyl (2.5–5μg /kg per hour). After full anesthesia, the rats underwent tracheotomy for MV 4 h and were euthanized by overanesthesia. The ventilation parameters [21] were as follows: LVT was 7 ml/kg, HVT was 40 ml/kg, breathing frequency was 60 times per minute. MSAB (20 mg/kg), DMSO, and SB216763 (5 mg/kg) were injected into the abdominal cavity, respectively [22]. All animal experiments were conducted in specific pathogen free Animal Experimental Center of the Clinical Research Center of Affiliated Hospital of Guizhou Medical University. The Experimental Animal Ethics Committee of Guizhou Medical University permitted this animal experiment (number 2101324, Guizhou, China). Experiments were conducted with the National Institutes of Health’s (NIH) Guidelines for the Care and Use of Experimental Animals, and followed the Guidelines for ARRIVE.
## Lung appearance, bronchoalveolar lavage fluid, and lung wet/dry weight ratio
First, lung appearance was photographed after lung was removed. Second, using normal saline to lavaging the left lobe and collected bronchoalveolar lavage fluid (BALF), centrifuged (4°C, 1500 rpm, 10 min), and collected the supernatant to determine the total protein levels by bicinchoninic acid assay (Solarbio, Beijing, China). The right upper lobe was weighed as wet weight. Then baked it at 65°C for 72 h and weighed dry weight, and calculated W/D ratio.
## Enzyme-linked immunosorbent assay
The content of proinflammatory factor in BALF and cell culture medium was detected by enzyme-linked immunosorbent assay (ELISA) with interleukin-1β kit (IL-1β, CUSABIO, Wuhan, China), interleukin-6 kit (IL-6, CUSABIO, Wuhan, China) and tumor necrosis factor-α kit (TNF-α, CUSABIO, Wuhan, China). All procedures were follow the reagent’s instructions. Using a 450-nm spectrophotometer to determine the optical density value within 5 min.
## Hematoxylin and eosin staining
Using $4\%$ paraformaldehyde to fix the inside and outside of the right middle lobe and stored it at 4°C for 24 h. Then used paraffin to embed the dehydrated lung tissue and sectioned it. Using hematoxylin and eosin (H&E, Solarbio, Beijing, China) to stain lung tissue sections to observe lung pathological injury by the microscope.
## TUNEL staining
The paraffin section of lung tissue was stained with a TUNEL of Apoptosis Detection Kit by the manufacturer’s instructions (Elabscicge, U.S.A.). Using the ZEN Blue system to assess the extent of apoptosis under a forward fluorescence microscope (Carl Zeiss Axio, Jena, Germany). All images were quantified by ImageJ.
## Immunofluorescence analysis
First, antigen repair was performed on lung tissue sections. Then, the tissues were washed using PBS and sealed using goat serum, and the specific primary antibodies of GSK-3β and β-catenin were instilled and deposited at 4°C for one night. Next used the fluorescent secondary antibody to incubate it in the dark and used DAPI to stain the nuclei. Fluorescence was captured under a fluorescence microscope using a ZEN blue system. All images were quantified using ImageJ software.
## Western blotting
The lower lobe and posterior lobe of lung were preserved at −80°C and analyzed by western blotting for molecular biology. Lung tissues were ground thoroughly using RIPA lysis buffer (Solarbio, Beijing, China) under an ultrasonic crusher to extract the proteins, and added loading buffer. The proteins were separated using gel electrophoresis and moved onto membranes, which was sealed with $5\%$ skim milk for 1 h and specific primary antibodies (Proteintech, Wuhan) were added to soak membranes at 4°C for overnight, including GAPDH (1:10000), GSK-3β (1:5000), cyclin D1 (1:8000), β-catenin (1:10000), Bax (1:8000), and Bcl2 (1:1500). Then, using secondary antibody (IgG, 1:5000, Proteintech, Wuhan) to incubate the membrane and observe the imprinting via an ultrasensitive ECL chemiluminescence kit (Beyotime, P0018AM, Shanghai, China). Finally, blots were quantified by ImageJ software.
## Flow cytometry
Following the instructions, the apoptosis rate was analyzed using the Annexin V-FITC/PI Apoptosis Detection Kit (Meilunbio, Dalian, China). In a nutshell, ATII cells were digested and collected using EDTA-free trypsin and rinsed using PBS. Then, it was suspended with 1× binding buffer, added respectively Annexin V-FITC 5 μl and PI 10 μl and incubated for 15 min in the absence of light. The percentage of apoptosis was measured via flow cytometry (Beckman Navios, U.S.A.) and FlowJo software.
## Statistical analysis
Data were analyzed by GraphPad Prism 8.0.2 (GraphPad, La Jolla, CA, U.S.A.). The experiments were all repeated at least three times. Mean ± standard deviation (SD) was used to express data. Two groups were compared via Student’s t test, and three or more groups were compare through an ANOVA followed by a Tukey post hoc test. P-values<0.05 were significant.
## GSK-3β expression was increased and β-catenin expression was reduced in rat VILI models
To understand whether the Wnt/β-catenin pathway is regulated in VILI, we established rat VILI models under HVT ventilation. Compared with the spontaneously breathing rats, obvious pulmonary congestion was observed after HVT ventilation, mainly manifested as lung surface was dark red color, and patchy congestion was also observed (Figure 1A). The total protein levels in BALF and lung W/D ratio were increased significantly in HVT group (Figure 1B,C). After H&E staining of lung tissue, it was observed under the microscope that the interstitial exudation of lung in the HVT group was significantly increased, with increased infiltration of inflammatory cells, alveolar rupture, and poor structural integrity of lung tissue (Figure 1D). Moreover, in the HVT group, the ELISA consequences showed that the expression of proinflammatory factor (IL-1β, IL-6, and TNF-α) in BALF were obviously raised (Figure 1E–G); TUNEL staining results indicated that the TUNEL(+)/DAPI(+) rate was obviously increased in lung tissues (Figure 1H), and the results of western blotting also indicated that Bax expression was distinctly enhanced and Bcl2 was significantly diminished (Figure 1I), lung tissue apoptosis of the HVT group was obvious. Thus, rat VILI models were successfully established in the present study. Meanwhile, western blotting results performed that GSK-3β expression was increased and β-catenin expression was reduced (Figure 1I). This indicated that this pathway is inhibited in the early stage of MV, which may be related to the development of VILI.
**Figure 1:** *GSK-3β expression was increased and β-catenin expression was reduced in lung tissue of rats with HVT ventilation(A) Naked eyes directly observed lung appearance, lung was obviously congested in the HVT group. (B,C) Lung tissue permeability was assessed by total protein levels in BALF and lung W/D ratio (n=6/group). (D) Lung pathological injury was evaluated by H&E staining (200x and 400x). (E–G) The expression level of IL-1β, IL-6, and TNF-α was measured via ELISA (n=3/group). (H) Lung tissue apoptosis was examined through TUNEL staining (200x). (I) The expression level of Bax, Bcl2, GSK-3β, and β-catenin was examined via western blotting. GAPDH is used as an internal reference (n=3/group). Control vs LVT, HVT. *P<0.05, **P<0.01, ***P<0.001.*
## Inhibition of GSK-3β could activate the Wnt/β-catenin pathway attenuates lung injury in rat VILI model
SB216763 was used to inhibit GSK-3β expression. The Wnt/β-catenin pathway-related protein expressions were measured through immunofluorescence and western blotting. Compared with the HVT group, DMSO as a solvent did not significantly affect GSK-3β, β-catenin, and cyclinD1 expression under MV (Figure 2A,B). However, GSK-3β expression was significantly inhibited with SB216763 and β-catenin and cyclin D1 expressions were significantly raised (Figure 2A,B). In addition, pulmonary congestion and bleeding were significantly mitigated by macroscopic observation of lung appearance in SB216763 group (Figure 2C), the total protein level in BALF and the W/D ratio were significantly reduced (Figure 2D,E). After H&E staining, compared with the HVT group, the lung interstitial exudation and alveolar rupture were significantly reduced in SB216763 group (Figure 2F). These results demonstrated that inhibition of GSK-3β expression by SB216763 could activate this pathway and alleviate lung injury.
**Figure 2:** *The Wnt/β-catenin pathway activation attenuated lung injury in MV. SB216763 is a GSK-3β inhibitor and DMSO as the solvent(A,B) The expression level of GSK-3β and β-catenin was measured through immunofluorescence (200x) and western blotting (n=3/group). GAPDH is used as an internal reference. (B) Cyclin D1 protein was detected through western blotting (n=3/group). (C) Lung appearance was visualized directly by the naked eye. (D,E) Lung tissue permeability was evaluated by total protein levels in BALF and lung W/D ratio (n=6/group). (F) Lung pathological injury was assessed by H&E staining (200x and 400x). HVT vs HVT+DMSO, HVT+DMSO+SB216763. ns: no statistical significance, *P<0.05, **P<0.01.*
## The Wnt/β-catenin pathway activation in VILI was anti-inflammatory and antiapoptotic
Previous results have revealed that the alleviation of VILI may be correlated with the Wnt/β-catenin pathway activation, but its specific role has not been elucidated. The expression of inflammatory and apoptosis-related factors was further measured in the present study. The consequences showed that DMSO did not significantly further aggravate lung tissue inflammation and apoptosis (Figure 3A–E). Importantly, compared with the HVT group, ELISA results revealed that proinflammatory factor levels such as IL-1β, IL-6, and TNF-α were significantly reduced after SB216763 inhibited GSK-3β expression (Figure 3A–C); TUNEL staining results performed that TUNEL (+)/DAPI (+) rate was significantly reduced (Figure 3D); western blotting results demonstrated that Bax protein was obviously diminished and Bcl2 was increased (Figure 3E). The results demonstrated that the Wnt/β-catenin pathway activation under HVT ventilation plays an anti-inflammatory and antiapoptotic role, thereby alleviating lung injury and delaying VILI progression.
**Figure 3:** *The Wnt/β-catenin pathway activation had anti-inflammatory and antiapoptotic effects in MVSB216763 is a GSK-3β inhibitor, DMSO as the solvent. (A–C) The expression level of IL-1β, IL-6, and TNF-α in BALF was detected through ELISA (n=3/group). (D) The degree of lung tissue apoptosis was evaluated using TUNEL staining (200x). (E) Bax and Bcl2 proteins were determined by western blotting, GAPDH is used as an internal reference (n=3/group). HVT vs HVT+DMSO, HVT+DMSO+SB216763. ns: no statistical significance, *P<0.05, **P<0.01, ***P<0.001.*
## Inhibition of β-catenin attenuated the anti-inflammatory and antiapoptotic effects of SB216763
To clarify whether GSK-3β inhibitor SB216763 plays a lung protective role through β-catenin in VILI, the present study further inhibited β-catenin expression in VILI using MSAB. The results showed that compared with HVT+DMSO+SB216763 group, in HVT+DMSO+SB216763+MSAB group, the contents of IL-1β, IL-6, and TNF-α in BALF were significantly increased (Figure 4A–C), Bax expression was increased and Bcl2 expression was decreased in lung tissue (Figure 4D). In short, Figure 4 showed that after MSAB inhibited β-catenin, the anti-inflammatory and antiapoptotic effects of SB216763 in VILI were significantly weakened. Therefore, the results suggest that SB216763 plays a lung protective role at least in part by up-regulating β-catenin expression.
**Figure 4:** *Inhibition of β-catenin attenuated the anti-inflammatory and antiapoptotic effects of SB216763SB216763 is a GSK-3β inhibitor, MSAB is a β-catenin inhibitor, DMSO as the solvent. (A–C) The expression level of IL-1β, IL-6, and TNF-α in BALF was detected through ELISA (n=3/group). (D) Bax and Bcl2 proteins were determined by western blotting, GAPDH is used as an internal reference (n=3/group). HVT+DMSO+SB216763: GSK-3β expression inhibition group, HVT+DMSO+SB216763+MSAB: GSK-3β and β-catenin expression inhibition group. **P<0.01, ***P<0.001.*
## The Wnt/β-catenin pathway activation was anti-inflammatory and antiapoptotic in ATII cell VILI model
To further clarify whether the Wnt/β-catenin pathway is correlated with VILI progression, VILI cell models were established by using ATII cells. Western blotting analysis indicated that GSK-3β protein was increased and β-catenin and cyclin D1 expression was down-regulated in ATII cells under high mechanical stress cyclic stretching compared with normal control ATII cells (Figure 5A). In addition, in ATII cells under high mechanical stress cyclic stretching, western blotting results also showed that Bax protein level was raised and Bcl2 was reduced (Figure 5A); flow cytometry analysis indicated that ATII cell apoptosis was obviously increased (Figure 5B,C); ELISA results indicated that proinflammatory factor levels were raised (Figure 5D–F). However, after transfection of SB216763 into ATII cells, GSK-3β expression was significantly inhibited and it reversed the low expression of β-catenin and cyclin D1 in the HCS group (Figure 5A). After inhibiting GSK-3β expression, western blotting results suggested that Bax was diminished and Bcl2 was increased (Figure 5A); flow cytometry results showed that ATII cell apoptosis was significantly decreased (Figure 5B,C). The expression levels of proinflammatory factor were significantly reduced (Figure 5D–F). Overall, the present study demonstrated that inhibition of GSK-3β expression could activate the Wnt/β-catenin pathway to reduce apoptosis and inflammation of ATII cells under cyclic stretching with high mechanical stress.
**Figure 5:** *The Wnt/β-catenin pathway activation inhibited inflammation and apoptosis in ATII cell VILI modelSB216763 is a GSK-3β inhibitor, DMSO as the solvent. DMSO and SB216763 were transfected into ATII cells and then cyclically stretched under 18% mechanical stress. (A) GSK-3β, β-catenin, cyclin D1, Bax, and Bcl2 proteins were measured via western blotting. GAPDH is used as an internal reference. (B,C) The apoptosis rate of ATII cell was measured through flow cytometry. (D–F) IL-1β, IL-6, and TNF-α was detected via ELISA. Control vs HCS, HCS vs HCS+DMSO, HCS+DMSO+SB216763. ns: no statistical significance, *P<0.05, **P<0.01, ***P<0.001.*
## Discussion
MV can maintain lung gas exchange, improve body oxygen saturation, relieve ventilator fatigue, and supply oxygen to systemic organs, which is an advanced support treatment to save the life of severe patients. However, MV can cause severe complications of VILI. Although clinically, protective ventilation strategies have been used for MV to reduce VILI, such as low tidal volume MV, high PEEP, prone position, and neuromuscular block, VILI still cannot be avoided and prevented [2]. It is a pity that there is still no effective treatment to avoid and reduce VILI, and alleviate and improve the prognosis of MV patients.
Under MV, the harmful high tidal volume causes the alveoli to be repeatedly expanded and stretched, which drives the pathophysiological processes of lung tissue such as inflammatory reaction, apoptosis, and oxidative stress, leading to the structural damage and dysfunction of lung tissue, thereby resulting in lung injury and even death of MV patients. Among them, the common harmful mediators are proinflammatory factors (IL-11β, IL-6, TNF-α) and apoptosis-related regulators (Bax, caspase-3, Bcl2). Therefore, VILI is also considered as a biological injury [2,23]. Relevant studies have shown that JAK2-STAT3, PI3/Akt, NF-κB, p38/MAPK, and other signaling pathways can mediate inflammation and/or apoptosis and act on VILI progression [20,24–27]. However, it is still necessary to explore the key points in the pathogenesis network of VILI.
The Wnt/β-catenin pathway is a intercellular signaling system and is a pivotal regulator of cell differentiation, apoptosis, and renewal, which is a key regulatory pathway for human body growth, development, repair, and maintenance of tissue homeostasis [28]. Abnormally regulated the Wnt/β-catenin pathways can aggravate or alleviate various diseases, such as glaucoma, alopecia, and fibrosis-related diseases [8,29,30]. Moreover, several researches have indicated that the Wnt/β-catenin pathway activation can regulate downstream genes to inhibit apoptosis and promote cell proliferation, thereby accelerating ovarian cancer development [31]; excessive activation of this pathway can aggravate osteoarthritis [32]; loss of this pathway during neural development can lead to diseases related to neurological dysfunction [33]. Thus, the Wnt/β-catenin pathway is regarded as a hopeful target for the traement of human diseases.
In the respiratory system, the Wnt/β-catenin pathway is a major regulator of the physiological process of lung disease, and a crucial signaling pathway throughout lung development, growth, injury, and repair, which is related to its changes or its transition to the noncanonical Wnt pathways. Stewart et al. proposed that this pathway activation can enhance resistance to chemotherapy and radiotherapy and accelerate tumor aggressiveness in non-small-cell lung cancer [13]. In a mouse model of COPD, blocking lymphotoxin β-receptor can induce regeneration through activating this pathway, thereby inhibiting apoptosis of alveolar epithelial cells [34]. Moreover, inhibiting GSK-3β expression to activate this pathway can exert an anti-inflammatory role in neonatal rats with ARDS, and promote lung repair and regeneration [16,35]. Another study has also performed that reversing the inactivation of this pathway can attenuate lipopolysaccharide (LPS)-induced apoptosis and vascular permeability of pulmonary vascular endothelial cells (EC) [36].
Given the versatility of the Wnt/β-catenin pathway, we believe that this pathway may exert a key role in VILI progression. Therefore, in the present study, animal models and cell models of VILI were built using high tidal volume and high mechanical stress, which main manifestations were pulmonary congestion and edema, increased lung permeability, significant inflammatory reaction, and apoptosis. Meanwhile, the present study performed that GSK-3β expression was increased and β-catenin was reduced in VILI, suggesting that the Wnt/β-catenin pathway was inhibited at the early phase of MV. To clarify whether its activation affects VILI progression, GSK-3β inhibitor (SB216763) was used to activate this pathway, and increase β-catenin and cyclin D1 expression. Furthermore, the Wnt/β-catenin pathway activation significantly decreased the content of IL-1β, IL-6, TNF-α, and proapoptotic protein Bax, increased Bcl2 protein, improved lung barrier function, reduced lung tissue permeability, mitigated lung congestion and edema, and significantly alleviated lung injury.
Finally, To clarify whether GSK-3β inhibitor SB216763 plays a lung protective role through β-catenin in VILI, MSAB was used to inhibit β-catenin expression. As an effective and selective inhibitor of Wnt/β-catenin pathway signaling, MSAB can bind to β-catenin and promote its degradation, thereby reducing the high expression level of active β-catenin, inhibiting its nuclear translocation, and specifically down-regulating the Wnt/β-catenin pathway target genes [37]. Our study found that inhibited β-catenin expression attenuated the anti-inflammatory and antiapoptotic effects of SB216763 in VILI. Therefore, we confirmed that SB216763 plays a lung protective role at least in part by up-regulating β-catenin expression.
However, the present study has some limitations. The molecular mechanism affecting VILI is extremely complex, in this study, it was not further elucidated whether the Wnt/β-catenin pathway interacts with JAK2-STAT3, NFκB, PI3/Akt, p38/MAPK, and other signaling pathways in VILI. In addition, whether this pathway has other roles in VILI remains to be explored. Most importantly, more attention needs to be paid to the upstream regulatory mechanisms regulating this pathway in the future.
All in all, the study shows that the Wnt/β-catenin pathway is inhibited during early MV, and activation of this pathway may exert anti-inflammatory and antiapoptotic effects, protect lung barrier function and attenuate VILI. This study may provide important reference value for VILI treatment.
## Data Availability
All data are contained within the article.
## Competing Interests
The authors declare that there are no competing interests associated with the manuscript.
## Funding
The study was supported by the Guizhou Provincial Department of Science and Technology [grant number Qian Ke He support [2021] General 061/Qian Ke He foundation ZK [2022] General 450]; the Science and Technology Fund Project of Guizhou Health Committee [grant number gzwkj2021-090]; the Cultivate project 2021 for National Natural Science Foundation of China, Guizhou Medical University [grant number 20NSP038].
## CRediT Author Contribution
Zongyu Chen: Conceptualization, Data curation, Software, Formal Analysis, Validation, Visualization, Methodology, Writing—original draft. Shuang He: Conceptualization, Data curation, Software, Investigation. Siyu Lian: Software, Supervision, Validation, Investigation, Methodology. Yi Shen: Conceptualization, Resources, Data curation, Formal Analysis, Funding acquisition, Writing—original draft, Project administration. Wenqing Jiang: Conceptualization, Resources, Data curation, Formal Analysis, Validation, Investigation. Lihua Zhou: Resources, Formal Analysis, Validation, Investigation, Project administration, Writing—review & editing. Leilei Zhou: Resources, Supervision, Validation, Investigation, Visualization, Project administration, Writing—review & editing. Xianming Zhang: Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Writing—original draft, Project administration, Writing—review & editing.
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|
---
title: Enabling fully automated insulin delivery through meal detection and size estimation
using Artificial Intelligence
authors:
- Clara Mosquera-Lopez
- Leah M. Wilson
- Joseph El Youssef
- Wade Hilts
- Joseph Leitschuh
- Deborah Branigan
- Virginia Gabo
- Jae H. Eom
- Jessica R. Castle
- Peter G. Jacobs
journal: NPJ Digital Medicine
year: 2023
pmcid: PMC10011368
doi: 10.1038/s41746-023-00783-1
license: CC BY 4.0
---
# Enabling fully automated insulin delivery through meal detection and size estimation using Artificial Intelligence
## Abstract
We present a robust insulin delivery system that includes automated meal detection and carbohydrate content estimation using machine learning for meal insulin dosing called robust artificial pancreas (RAP). We conducted a randomized, single-center crossover trial to compare postprandial glucose control in the four hours following unannounced meals using a hybrid model predictive control (MPC) algorithm and the RAP system. The RAP system includes a neural network model to automatically detect meals and deliver a recommended meal insulin dose. The meal detection algorithm has a sensitivity of $83.3\%$, false discovery rate of $16.6\%$, and mean detection time of 25.9 minutes. While there is no significant difference in incremental area under the curve of glucose, RAP significantly reduces time above range (glucose >180 mg/dL) by $10.8\%$ ($$P \leq 0.04$$) and trends toward increasing time in range (70–180 mg/dL) by $9.1\%$ compared with MPC. Time below range (glucose <70 mg/dL) is not significantly different between RAP and MPC.
## Introduction
Closed-loop systems for automated insulin delivery are now the standard of care in type 1 diabetes (T1D) management, helping people living with diabetes better manage their glucose while reducing burden1,2. However, currently available systems are hybrid in nature and still require the person to count carbohydrates and manually announce meals to the system. Postprandial glucose control is substantially improved when meal insulin is delivered before meal intake.
Carbohydrate counting is challenging and people living with T1D are oftentimes inaccurate in estimating carbohydrate intake. Current carbohydrate counting methods require a level of numeracy and literacy that might be a barrier for some people with diabetes3. Gillingham et al. showed that $49\%$ percent of meals with <30 g of carbohydrates are overestimated while the majority ($64\%$) of large carbohydrate meals (≥60 g) are underestimated4. Inaccurate carbohydrate estimations that are used for calculation of prandial insulin are associated with high prevalence of postprandial hyper- and hypoglycemia, even with hybrid insulin delivery systems5,6.
Several approaches to automated meal detection have been described in the literature, which generally use continuous glucose measurements (CGM) and insulin delivery data, and in some cases physical activity data. Some of the approaches of previously published work include fuzzy logic7, Kalman filtering8–10, super-twisting observer combined with Kalman filtering11, probabilistic models12, quantification of the difference between predicted glucose using an autoregressive or other models vs. measured CGM values13, and glucose increase detection14. Smartwatch gesture-based meal reminders have also been proposed for improved postprandial glycemic control15. Sensitivity varies greatly depending on the datasets used and the study protocols, reaching values greater than $90\%$ in some cases, though the sensitivity and specificity of many of previously published algorithms have been validated only in silico. For example, the automated meal detection algorithm presented by Corbett et al.16 used a clustering method to estimate the probability of a meal occurring based on prior meal patterns. The algorithm recognizes a meal pattern and doses a priming dose, demonstrating an improvement in time in range from 52 to $57\%$; however, these results are only provided for an in silico trial. Several prior manuscripts have reported on the effectiveness and safety of using machine-learning-based automated meal detection within automated insulin delivery systems in clinical trials. Recently, Tsoukas et al. reported on a fully automated system that utilized a Kalman filter model-based automated meal detection and multiple hormone delivery, including pramlintide and insulin in response to meal detections. They showed that a fully automated pramlintide plus insulin delivery system was not inferior to an insulin-only hybrid automated insulin delivery system17. In this work, we contribute a new machine learning model for meal detection and meal size estimation that is incorporated into a robust insulin delivery system and tested in humans to assess feasibility and safety of semi-automated meal insulin delivery with minimal user intervention. The algorithm has a sensitivity of $83.3\%$, false discovery rate of $16.6\%$, and mean meal detection time of 25.9 min. When comparing the benefit of dosing bolus insulin in response to a detected meal with the robust insulin delivery system vs. adjusting insulin infusion rate using a hybrid insulin delivery system with no meal announcement, there is no significant difference in incremental area under the curve of glucose. However, the robust insulin delivery system with automated meal detection significantly reduces time above range (glucose >180 mg/dL) by $10.8\%$ ($$P \leq 0.04$$) without significantly increasing risk of postprandial low glucose (glucose <70 mg/dL).
## Participants information
Fifteen adults were enrolled to participate in this study between December 2021 and March 2022. Table 1 summarizes participants’ characteristics and Fig. 1 shows the study CONSORT diagram. The carbohydrate content of the study meals chosen by participants ranged from 45 to 66 g. Two participants withdrew from the study due to [1] high glucose (CBG > 250 mg/dL) and [2] high ketone levels (3.4 mM) following a meal during the model predictive control (MPC) algorithm arm. The markedly elevated ketone level was attributed to the participant’s ketogenic diet and the participant was withdrawn from the study. These participants were not included in the analysis. There were no serious adverse events during the study. Table 1Baseline characteristics of eligible participants ($$n = 15$$).DemographicsAge, years37.6 ± 10.4Biological sex, N (%)Female: 9 (60.0)Male: 6 (40.0)Weight, Kg85.0 ± 18.8Ethnicity (self-identified), N (%)White: 13 (86.6)Black: 1 (6.7)American Indian: 1 (6.7)Clinical dataHbA1c, %6.8 ± 0.5Duration of diabetes, years24.8 ± 9.0CGM use, N (%)Dexcom: 10 (66.6)Medtronic: 3 (20.0)Libre: 1 (6.7)None: 1 (6.7)Fig. 1CONSORT flow diagram. Flow diagram of the progress through the phases of the randomized, single-center crossover trial to compare OHSU’s MPC vs. RAP insulin delivery systems.
## Study outcomes
Supplementary Table 2 shows meal insulin recommended by the robust artificial pancreas (RAP) system for true positive meal detections and the adjustments made by participants or clinical team after discussing the safety of the proposed adjustment and reaching an agreement about the final insulin amount to be delivered. The study participant or the investigator made small adjustments to the insulin bolus $54.5\%$ of the time when a true positive detection occurred. However, the recommended meal insulin amount was reduced only once, by an amount of 1.5 units. The average absolute change in bolus insulin across all participants was very low at 0.35 units.
Mean postprandial incremental area under the curve (iAUC) of glucose was lowest with RAP but not significantly different from the MPC automated insulin delivery algorithm (−23.6, $95\%$ CI: −120.6 to 73.4 mg h/dL; $$P \leq 0.63$$). Time above range (TAR) 70–180 mg/dL was significantly reduced with RAP by $10.8\%$ compared with MPC (time above range $95\%$ CI: 0.02 to $24.4\%$; $$P \leq 0.04$$). Time in range (TIR) 70–180 mg/dL was higher with RAP by $9.1\%$ compared with MPC, but the observed difference was not statistically significant (TIR $95\%$ CI: −1.5 to $22.9\%$; $$P \leq 0.09$$). Time below range (TBR) at less than 70 mg/dL and time below 54 mg/dL were slightly higher with RAP, but not statistically different from MPC (TBR < 70 mg/dL $95\%$ CI: −0.7 to $2.3\%$; $$P \leq 0.52$$ and TBR < 54 mg/dL $95\%$ CI: −0.4 to $1.3\%$; $$P \leq 0.46$$).
Postprandial CGM and insulin (median and interquartile range) during the four hours after meal are shown in Fig. 2. Glucose traces for MPC and RAP were similar for the first two hours after the meal. After two hours, glucose traces were lower during the RAP arm compared with the MPC arm such that at four hours after the meal, the median glucose was substantially lower for RAP compared with MPC (148.5 vs. 191.0 mg/dL, Fig. 2). RAP dosed more insulin (8.7 ± 3.2 vs. 7.6 ± 3.3 units) and delivery occurred sooner after the meal compared with MPC, which tended to deliver over a longer period after the meal (Fig. 2, lower panel).Fig. 2Comparative postprandial sensor glucose (top) and insulin infusion rate (IIR)(bottom) during the MPC and RAP study arms following breakfast meal from the 13 participants who completed the study. Median and interquartile range are shown.
The machine learning model automatically detected $83.3\%$ of the 24 meals consumed during the RAP studies ($95\%$ CI 62.6 to $95.2\%$). The false detection rate was $16.6\%$ ($95\%$ CI 4.7 to $37.4\%$). The reported false detection rate includes a meal detection corresponding to when a rescue carbohydrate was consumed by the participant. In future versions of the RAP algorithm, the RAP algorithm will ignore rapid glucose rises caused by rescue carbohydrates that were reported to the system. Overall meal detection time was 25.9 ± 0.9 minutes. The accuracy of the meal detection and classification algorithm was close to the predictions made in the pre-study simulations. The average in silico sensitivity for meals with carbohydrate content between 40-80 g, which were the categories of self-selected study meals during the clinical trial, ranged from 79.0 to $90.0\%$ with a probability detection threshold PTH = 0.86 used in the study (see Supplementary Fig. 2). Similarly, in silico meal detection time was calculated to be 27.5 ± 4.8 min. The in silico false discovery rate was $10.0\%$ when we did not include false detection caused by low glucose rescue carbohydrate intake as false detections. Thus, the meal detection accuracy results obtained in this study matched almost exactly pre-clinical in silico validation results demonstrating that in silico metabolic simulators based on ordinary differential equations can be effectively used to estimate performance prior to a real-world clinical study in humans.
## Discussion
The use of RAP which includes an automated meal detection and meal size estimation machine learning algorithm resulted in a statistically significant reduction in postprandial time above range following unannounced meals in adults with T1D, and a modest (though not statistically significant) improvement in TIR. These results are relevant given that time below range was not significantly increased by RAP when compared with an MPC algorithm. Two carbohydrate treatments were administered during the RAP arm of the study in response to the system’s predicted low glucose alert while no rescue carbohydrate treatments were given during the MPC arm. There was one event in which a participant experienced glucose below 70 mg/dL following a meal bolus during the RAP arm. For this event, glucose at meal detection time was 200 mg/dL and rising, and the low blood glucose index (LBGI)18 values ranged from 0.0 within the 0–2 h period following the meal bolus to 4.3 over the 0–4 h after the bolus.
The performance of the meal detection algorithm was very close to the performance observed in silico during pre-clinical algorithm validation. The algorithm had high sensitivity and low false discovery rate. There were four false detections, but one of them occurred due to a sharp glucose rise in the morning at about 8:30 AM, which is a pattern with a high likelihood of being associated with a breakfast meal. Another false detection was associated with a sharp rise in glucose following the consumption of a rescue carbohydrate consumed in response to a hypoglycemia treatment. Given the possibility that a false meal detection could increase the risk of hypoglycemia, we calculated LBGI following false meal detections to see whether delivering insulin in response to a false alarm led to increased risk of low glucose(LBGI ≥ 5.0)11. The calculated LBGI was zero in three out of the four false detection cases indicating that no low glucose events had occurred. For the fourth false detection case, the LBGI ranged from 0.94 to 3.3. For this case, the false detection occurred in response to a rescue carbohydrate that was consumed by the participant, which was misinterpreted as a meal by the RAP algorithm. In future usage, the RAP automated meal detection will be disabled following the report of rescue carbohydrate consumption.
The results of this study indicate that if a meal is accurately detected within 25 to 30 min of the meal occurring and dosed a percentage of the nominal required prandial insulin, time in hyperglycemia can be significantly reduced and there is no significant increased risk of postprandial hypoglycemia. Delayed detection is a consequence of delays in carbohydrate absorption and inherent delay in glucose measured in the interstitium relative to blood glucose. We found that, on average, a detectable glucose rise due to meal intake only occurs after 20 minutes of consuming a meal, and this delay is also dependent on the meal composition. For instance, one of the meals that the algorithm was unable to detect had a high fat content which caused a slower rise in glucose and a delayed glucose peak. It’s unlikely that insulin could be dosed for a meal using a CGM-based automated detection any sooner than 20 min after the meal was consumed because of the delay in rise in glucose. Results presented here show that dosing meal insulin approximately 25 min after the meal using a missed meal detection and classification algorithm provides benefit compared with depending on the control algorithm to naturally respond to a meal.
One limitation of this study was the small sample size. However, even with this small sample size, a benefit was shown in terms of reducing high glucose levels for the RAP compared with MPC. Another limitation of this study is that the meal detection required participant confirmation. The participant or investigator were allowed to modify the amount of insulin recommended by the RAP algorithm; thus, these adjustments might have impacted the glucose control performance metrics. The modifications made by the participant or investigator were small (0.35 units on average) indicating that the participant may have been ‘fine tuning’ the insulin bolus rather than correcting a grossly inaccurate recommendation. The insulin was reduced by the participant or investigator only once indicating that in $90.9\%$ of the cases, the RAP was being more conservative and delivering slightly less than what the participant wanted to deliver. This indicates that the system is performing in a safe way to avoid over-delivery of insulin in response to the RAP meal detection algorithm. One reason why participants may have chosen to dose more insulin than recommended by the RAP algorithm could have been a result of the fact that the RAP algorithm was designed to dose only a fraction of meal insulin (up to $75\%$), since it was being dosed approximately 25 min after the meal event occurred. Participants may have wanted to dose the full $100\%$ of the meal insulin dosage, even though it was being dosed late. A larger study without meal confirmation by participants is needed to assess whether a fully automated insulin delivery system provides significant benefits in terms of reduction in postprandial incremental area under the glucose curve and increase in time in target range, which were defined as primary outcomes of this feasibility study.
This study provides evidence that a machine-learning-based model for meal detection and carbohydrate content estimation can be integrated into automated insulin delivery systems to improve postprandial glucose control. Moreover, we demonstrated that a meal detection model trained on in silico data obtained from validated T1D simulators achieves nearly identical performance in this clinical study thereby providing further evidence in the support of use of metabolic simulators for developing and evaluating tools in advance of clinical studies.
## Meal detection and carbohydrate estimation model development
We obtained datasets from simulations performed using two validated T1D simulators: [1] The UVA-Padova simulator, which is approved by the Food and Drug Administration (FDA) for in silico pre-clinical validation; and [2] a published open-source simulator developed by Oregon Health & Science University (OHSU)19. We simulated 199 virtual subjects for 14 days using real-world meal scenarios collected from previous studies (low-carbohydrate diet: 46.6 ± 27.1 g, high-carbohydrate diet: 72.5 ± 29.3 g)13,20. Glucose control was simulated using OHSU’s MPC automated insulin delivery algorithm21. Thirty three percent of the meals were given without a corresponding insulin bolus.
A total of 32 features were derived from the two-hour history of CGM and insulin measurements obtained prior to a meal prediction. Examples of features used included average glucose, glucose rate of change, insulin availability22–24 one hour before prediction time, and prediction time (i.e., hour of day). Since the prediction hour (0–23) is a cyclical feature, we transformed it into two dimensions using cosine and sine operations as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\cos \left({2\pi \frac{{\rm{hour}}}{{24}}} \right),\;\sin \left({2\pi \frac{{\rm{hour}}}{{24}}} \right)$$\end{document}cos2πhour24,sin2πhour24. A descriptive list of input features used for meal detection and carbohydrate content estimation is provided in Supplementary Table 1. We used these features as inputs to a multioutput neural network with fully connected layers (Supplementary Fig. 1 shows high-level architecture of the model). The designed network has three shared hidden layers for processing the input features with 512, 32, and 16 nodes, and two dedicated branches for meal detection (i.e., binary classification) and carbohydrate estimation (i.e., multiclass classification) with a 16-node hidden layer per branch. For multiclass classification, meal sizes were categorized into five groups as follows: [0,20) g, [20,40) g, [40,60), [60,80), and 80+ g. Hyperparameters of the model including number of layers and nodes per layer were determined through cross-validation. L1 regularization with penalty constant of 1e−6 was used in all hidden layers. All weights were randomly initialized using Xavier uniform initializer25, and bias were initially set to zero. Adam optimizer with constant learning rate of 1e−4 and recommended values for the rest of parameters26 was used to minimize binary and categorical cross-entropy losses for detection and classification outputs, respectively. Training was done with mini batches of size 128. Detection loss and classification loss were equally weighted. For carbohydrate content estimation, the samples in the training dataset were weighted to account for imbalance in the dataset and for penalizing overestimation. Early stopping was implemented to help prevent overfitting. Meal detection was determined to have occurred if it exceeded a probability threshold of PTH = 0.86 as determined through simulations. The size of the meal was determined by the meal class node that yielded the highest probability from the neural network. This study evaluates the first version of the meal detection algorithm.
## Automated insulin delivery systems
The OHSU’s MPC algorithm21 uses a glucoregulatory model to predict glucose outcomes over a predicted horizon, and mathematically solves for the optimal insulin dose schedule across the control horizon to bring a person to a target glucose level. The MPC algorithm includes a Kalman filter, which uses the difference between sensor-measured glucose levels and model predictions to update the physiologic model states at each timestep for personalized predictions. The MPC strategy has been previously described in people living with T1D21,27–33.
The RAP system uses a modified MPC algorithm that includes the machine leaning model described herein for missed meal insulin detection. The missed meal insulin detection alert notified the participant through a smartphone app if the probability of a meal detection exceeded a threshold of 0.86 as determined through simulations (see Supplementary Fig. 2). The screens in the app used to notify the participant of a missed meal insulin detection are shown as Supplementary Fig. 3. These screens provide a recommended amount of insulin to dose in response to the detected meal. The recommended amount of meal insulin to deliver is based on [1] the output of the missed meal insulin detection algorithm, [2] the size of the meal estimation as determined by the missed meal insulin detection algorithm, [3] the person’s carbohydrate ratio, and [4] the time when the person responded to the alert. Since the meal insulin is being dosed after the meal was consumed, only a fraction of the person’s typical meal insulin is recommended to be dosed in response to the missed meal insulin detection algorithm. The percentage of the meal insulin recommended to be dosed is a function of the expected time after which the meal was consumed such that the meal insulin dosed is reduced by $1\%$ for every 1 min after which the meal was presumed to occur. We determined through simulations (see “Results”) that the missed meal detection algorithm triggered on average about 25 min after a meal was consumed. Therefore, if the missed meal insulin detection algorithm detected the meal and notified the study participant and they acknowledged the alert immediately, then the meal insulin dosed would be $75\%$ of the meal insulin determined by the person’s carbohydrate ratio and the expected meal amount as determined by the missed meal insulin detection algorithm. However, if the participant waited 20 min to respond to the alert, then the amount of recommended insulin would be further reduced by $20\%$. For instance, if the missed meal detection probability exceeds the threshold of 0.86, and the meal size predicted by the algorithm is 30 g, and the person’s carbohydrate ratio is 1:10 g, and the participant responded to the alert immediately, then the person would be recommended to receive 0.75 × 30 g/10 g/unit = 2.25 units of insulin. Additional features of the RAP system include automated physical activity detection and classification as measured from a smart fitness watch (Polar M600); however, these features were not used in this study.
Both MPC and RAP systems have safety features including predicting low glucose suspend insulin delivery using a long- short-term memory (LSTM) neural network24, maximum insulin dosing based on users’ total daily insulin requirement, switch to background basal insulin if CGM or pump communications are disrupted, and smartwatch on/off wrist detection algorithm. Both control systems run on a smartphone app called iPancreas that has been used to evaluate other automated multi-hormone delivery systems20,34.
## Study design and participants
This paper reports on the outcomes of a single-center, crossover trial designed to compare the glucose control following unannounced meals achieved using OHSU MPC vs. RAP automated insulin delivery systems. Individuals with diagnosis of T1D for at least one year, aged 18–65 years, current use of an insulin pump for at least three months with stable insulin pump settings for longer than 2 weeks, HbA1c ≤ $10.5\%$ at screening, and total daily insulin requirement less than 139 units/day were eligible for inclusion. Exclusion criteria included pregnancy or intention to become pregnant, current use of a glucose lowering medication other than insulin, and use of oral or parenteral corticosteroids.
Pre-clinical validation of the MPC and RAP automated insulin delivery systems was done using computer simulations. The accuracy of the machine learning model in detecting meals was also retrospectively validated on a real-world large dataset from 150 closed-loop participants (age 29 ± 16 years; 66 females, 47 males, 37 records with unknown biological sex; 15 ± 12 years since T1D diagnosis) from the Tidepool Big Data Donation Program (Tidepool.org, Palo Alto, CA) that contains more than 115,000 meals.
This study was conducted under U.S. Food and Drug Administration–approved investigational device exemption, approved by the OHSU Institutional Review Board, and registered on ClinicalTrials.gov (NCT05083559, first posted on October 19, 2021).
## Procedures
Written informed consent was obtained from all participants during the study screening visit. Participants underwent two treatment visits at OHSU for evaluating glucose control following unannounced meals with OHSU MPC and RAP insulin delivery systems in a randomized order. For each intervention visit, participants arrived at approximately 7:00 AM and were monitored through the afternoon and discharged before dinner. Participants wore an Omnipod pump (Omnipod Insulet Corporation, Acton, MA, USA) to deliver insulin and a Dexcom G6 CGM to measure glucose (DexCom, Inc., San Diego, CA, USA). The RAP system captured activity data (i.e., heart rate and accelerometry) through a Polar M600 watch (Polar Electro Inc., Bethpage, NY, USA) worn by the participants. After a run-in period of two hours whereby the participants used the automated insulin delivery systems, participants ate self-selected meals at 10:00 AM for breakfast. The carbohydrate content of the meals allowed per protocol was 45–120 g. During the RAP study visit only, participants were given a second meal four hours later at 2:00 PM when the study staff considered it appropriate to further evaluate the accuracy of the meal detection algorithm. Self-selected breakfasts were identical across both study sessions. A meal bolus was not given before any meals. CBG and blood ketone measurements were taken every 30 min until discharge for safety purposes only and not to inform control decisions of the automated insulin delivery systems under evaluation. During the RAP study visit, the meal detection algorithm was used to identify a missed meal bolus. If the RAP system detected a missed meal, the system sent an alert to the participant indicating that a meal was detected, and the system also provided an estimate of the carbohydrate content of the detected meal. Participants were required to acknowledge the alert. Participants could modify the carbohydrate content that was estimated by the RAP system. The modified carbohydrate estimation was used to calculate the meal bolus, which was dosed by the RAP system via an Omnipod insulin pump. A study investigator evaluated the meal insulin dose prior to delivery, considering insulin availability (I), time since last meal, and glucose trend, and modified the dose if appropriate for participants safety.
All data collected during the study including glucose sensor data, insulin data, physical activity data, and meal data, were aggregated in real time by the OHSU iPancreas app and stored for remote monitoring and further analysis on a cloud-based database hosted on an OHSU-managed secure server called iPancreas Guidance Remote Monitoring (GRM) hosted by Amazon Web Services.
## Outcomes
The primary endpoints prespecified for this study were [1] the incremental area under the curve iAUC of glucose (calculated using the trapezoidal rule) and [2] percent of time with glucose sensor in target range between 70 and 180 mg/dL in the four hours following unannounced breakfast meals. Post-prandial iAUC is defined as the area of the post-prandial glucose response curve above the baseline glucose (i.e., sensor glucose at the start of the meal). iAUC is useful to control variations in baseline glucose across participants. iAUC is different from the typical area under the curve (AUC) calculation in which the area is calculated relative to zero rather than relative to the baseline glucose. As secondary clinical outcome metrics, we assessed postprandial glucose control metrics including percent time with sensor glucose below range (<70 mg/dL) and percent time with sensor glucose above range (>180 mg/dL).
We also analyzed the performance of the meal detection machine learning model in detecting unannounced meals using sensitivity, false discovery rate, and meal detection time. These metrics were calculated based on participant confirmation of meal detection alerts and data on mealtime records entered by a study investigator to the GRM. The primary and secondary endpoints reported here were calculated only using glucose and insulin data collected over the four hours following the breakfast meal. However, the accuracy of the meal detection algorithm was evaluated using both breakfast and lunch meals.
To contextualize the performance of the machine learning model, we compared the accuracy results from this study with pre-clinical in silico results obtained using the FDA approved UVA/Padova and OHSU T1D simulators.
## Statistical analysis
We estimated the differences between the MPC and RAP systems in postprandial glucose control using a fixed effect in a standard crossover model with a random participant effect. We used mixed effects linear regression for the analysis of postprandial iAUC; and mixed effects beta regression for TIR, TAR, and TBR. A P value <0.05 was considered statistically significant.
We used the Clopper–Pearson method to calculate the exact $95\%$ confidence interval of the sensitivity and false discovery rate of the machine learning model in detecting meals. Other reported metrics, including the meal detection time, are expressed as mean ± standard deviation (SD) unless otherwise noted.
Data processing and statistical analyses were carried out using Python 3.7 and R 4.1.3
## Supplementary information
Supplementary Material The online version contains supplementary material available at 10.1038/s41746-023-00783-1.
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|
---
title: Long-term high-fat diet increases glymphatic activity in the hypothalamus in
mice
authors:
- Christine Delle
- Neža Cankar
- Christian Digebjerg Holgersson
- Helle Hvorup Knudsen
- Elise Schiøler Nielsen
- Celia Kjaerby
- Yuki Mori
- Maiken Nedergaard
- Pia Weikop
journal: Scientific Reports
year: 2023
pmcid: PMC10011420
doi: 10.1038/s41598-023-30630-y
license: CC BY 4.0
---
# Long-term high-fat diet increases glymphatic activity in the hypothalamus in mice
## Abstract
Obesity affects millions of people worldwide and is associated with an increased risk of cognitive decline. The glymphatic system is a brain-wide metabolic waste clearance system, dysfunction of which is linked to dementia. We herein examined glymphatic transport in mice with long-term obesity induced by a high-fat diet for 10 months. The obese mice developed hypertension and elevated heart rate, neuroinflammation and gliosis, but not apparent systemic inflammation. Surprisingly, glymphatic inflow was globally unaffected by the high-fat diet except for the hypothalamus, which displayed increased influx and elevated AQP4 vascular polarization compared to the normal weight control group. We propose that a long-term high-fat diet induced metabolic alteration of hypothalamic neurons and neuroinflammation, which in turn enhanced glymphatic clearance in the effected brain region.
## Introduction
The prevalence of obesity is rising yearly in children and adolescents around the world1. Obesity is the leading risk factor for developing hypertension2, type II diabetes1 and is further linked to increased risk for developing certain neurodegenerative diseases3–5. It is well-established that the brain glymphatic system is suppressed in a broad range of neurodegenerative diseases, including Alzheimer’s disease6, which may suggest a causal pathway proceeding from obesity.
Being devoid of lymphatic vessels, the brain utilizes a unique brain-wide fluid system for export of metabolic waste, called the glymphatic system7. In this fluid transport pathway, periarterial cerebrospinal fluid (CSF) influx into the parenchyma is facilitated by the water channel aquaporin-4 (AQP4), which is highly expressed in astrocytic endfeet plastered around blood vessels. Extracellular fluid carrying metabolic waste products is exported along the perivenous space and cranial and spinal nerves before being collected by meningeal and cervical lymph vessels and returned to the general circulation8.
Glymphatic fluid flow is driven in part by arterial pulsation9–11 such that its transport correlates inversely with heart rate11,12. The flow rate and arterial pulsation are both compromised by hypertension, which is a common complication of obesity2. The development of obesity-related hypertension is linked to increased lipolysis and elevated secretion of adipokines such as angiotensin II and renin that adversely impact vascular tone13. Furthermore, the adipokine leptin, which normally functions to suppress appetite, is chronically elevated in obesity, and overstimulates the sympathetic nervous system, thereby increasing the risk for hypertension14. Hypertension is further promoted by impaired endothelial cell function arising due to the chronic metabolic, oxidative, and inflammatory stresses evoked by obesity15–17. In addition, chronic fluid accumulation due to sodium retention within tissue18,19 may create a systemic fluid disbalance20 that could possibly affect glymphatic function. Indeed, impaired glymphatic clearance was previously described in a spontaneously hypertensive rat model21. However, no studies have investigated the effect of obesity on glymphatic flow. We hypothesized that obesity-associated hypertension would impair glymphatic activity in mice. To test this hypothesis, we measured markers of glymphatic function in mice with obesity and hypertension evoked by long-term high-fat diet (HFD)22,23 as compared to a lean control group.
## High-fat diet induces obesity, elevated blood pressure, increased heart rate, and hyperglycemia
We first studied relevant phenotypic changes to confirm the robustness of our obesity model. Starting at 6 weeks of age, groups of C57BL/6JRj mice received either normal chow or a HFD for the next 44 weeks to induce long-term obesity (Fig. 1a, b). HFD mice showed relatively elevated body weight within two weeks compared to controls (HFD: 22.36 g ± 1.37 g, control: 21.27 g ± 1.36 g; $$p \leq 0.0228$$, Fig. 1c). The mean body weight of the HFD group continued to rise over the course of the study (Fig. 1c), exceeding by 46 ± $4.9\%$ the weight of chow-fed controls at 40 weeks after the start of HFD (Fig. 1e).Figure 1Long-term high-fat diet (HFD) causes severe obesity in C57BL/6JRj mice. ( a) Schematic timeline of experimental setup. ( b) Representative control (standard chow fed) and high-fat diet (HFD) mouse freely moving in the home cage (40 weeks of diet). ( c) Body weight development ($$n = 9$$–18 per time point). Grey and blue shaded areas display the time of body weight adaptation. ( d) Weight gain per timepoint compared to previous weekly body weight ($$n = 9$$–18 mice per time point). ( e) Weight gain compared after 40 weeks of diet, normalized to average control weight. ( f) Representative liver images and total liver weight in grams (g) ($$n = 6$$–8). ( g) Liver weights normalized to the animal's body weight ($$n = 7$$–8). ( h) Blood glucose measurements in awake non-fasted ($$n = 8$$–9) and (i) fasted mice ($$n = 12$$–16). ( j) Systolic and diastolic blood pressure measured in awake mice (2-way ANOVA) and (k) heart rate in beats per minute (bpm) measured in awake mice (n (control) = 4 mice, 4 measurements per mouse; n (obese) = 3 mice, 4 measurements per mouse, color coded by mouse). ( l) Averaged water intake in grams (g) per mouse per day ($$n = 4$$ cages, water consumption averaged per mouse). Unless otherwise indicated all graphs are analyzed via unpaired t-test. Graphs created with GraphPad software (version 9.0, https://www.graphpad.com/scientific-software/prism/). Schematic created in Adobe Illustrator 2022 (version 26.3.1, https://www.adobe.com/products/illustrator/free-trial-download.html) by Dan Xue.
Notably, the control mice reached a plateau of maximal body weight after 9–12 weeks of standard chow diet (15–18 weeks of age) (Fig. 1c,d; gray shaded area), in contrast to the continuous weight gain until 20 weeks of HFD (26 weeks of age) (Fig. 1c,d; blue shaded area). Drastic morphological changes in HFD mouse livers manifested after 40 weeks, when the liver tissue appeared pale and the organ weight had nearly doubled (HFD: 3.57 g ± 0.59 g; control: 1.67 g ± 0.16 g; $p \leq 0.0001$, Fig. 1f). Thus, ratio of liver to body weight was significantly higher in HFD mice as compared with controls (HFD: 6.50 ± $0.86\%$; control: 4.42 ± $0.38\%$; $p \leq 0.0001$, Fig. 1g). At 40 weeks into the diet, there was no significant increase in blood glucose baseline levels (HFD: 9.76 ± 1.21 Mmol/L; control: 8.83 ± 1.53 Mmol/L; $$p \leq 0.18$$, Fig. 1h), but upon 7 h of fasting the HFD mice showed mild hyperglycemia (HFD: 10.15 ± 2.26 Mmol/L; control: 8.53 ± 0.96 Mmol/L; $$p \leq 0.0289$$, Fig. 1i). HFD also increased systolic and diastolic blood pressure (systolic: HFD: 171 ± 8 mmHg; control: 153 ± 20 mmHg; $$p \leq 0.0047$$; diastolic: HFD: 142 ± 8 mmHg; control: 118 ± 15 mmHg; $$p \leq 0.0002$$, Fig. 1j) after 12–14 weeks, with increased heart rate in trained mice (HFD: 673 ± 20 bpm; control: 605 ± 68 bpm; $$p \leq 0.0023$$, Fig. 1k, see Suppl. Fig. S1 for blood pressure training). The water intake of HFD mice, which could be a sign of pre-diabetes, remained unchanged (Fig. 1l).
## High-fat diet impairs behavior but not overall mobility
Long-term HFD has been reported to alter behavior24,25. We first assessed the natural digging behavior of mice using the marble burying test26 (Fig. 2a). At 10 weeks HFD mice already showed a trend towards reduced burying activity (21 ± 7; control: 29 ± 7, $$p \leq 0.1151$$), and after 30 weeks of diet, the HFD mice demonstrated significantly reduced activity compared to age-matched controls (10 ± 7; control: 20 ± 9; $$p \leq 0.0004$$). Interestingly, control mice also revealed reduced digging activity after 30 weeks of standard chow compared to their 10-week time point (10-week: 29 ± 7; 30-week: 20 ± 9; $$p \leq 0.0131$$). Yet, the decline in this activity due to diet was greater for HFD mice compared to the standard chow group (10-week: 21 ± 7; 30-week: 10 ± 7; $$p \leq 0.0038$$). An open field test at 30 weeks showed no group differences in the distance traveled or mean velocity (Fig. 2b). Interestingly, there was a trend for HFD mice approaching less often the center area of the open field arena. Figure 2Long-term high-fat diet (HFD) impairs behavior but not mobility of mice. ( a) Schematic of marble burying test and quantification of marble burying score (2-way ANOVA, $$n = 10$$ (10 weeks) and 24–25 (30 weeks)). ( b) Schematic of open field test followed by quantification of moved distance (meter) (left panel), velocity (cm/s) (second panel), time spent in the center zone (percent) (green shaded left panel) and center approaches per minute (green shaded right panel). $$n = 10$$ – 12. Unless otherwise indicated all graphs are analyzed via unpaired t-test. Graphs created with GraphPad software (version 9.0, https://www.graphpad.com/scientific-software/prism/). Schematics created in Adobe Illustrator 2022, https://www.adobe.com/products/illustrator/free-trial-download.html) by Dan Xue. Representative tracking traces created in MATLAB R2022a and Adobe Illustrator (version 26.3.1, https://www.adobe.com/products/illustrator/free-trial-download.html).
## Long-term high-fat diet accelerates hypothalamic glymphatic activity
To examine CSF transport, we injected a mixture of two tracers with different molecular sizes (3 and 45 kDa) into the cisterna magna and allowed circulation for 30 min much as previously described6,7,10,12,27,28, comparing mice after 40 weeks HFD diet with controls (Fig. 3a). The brains were harvested and six selected coronal vibratome sections of 100 µm thickness (1.2 to −1.8 from bregma) where microscopically examined for tracer infiltration (Fig. 3b). Interestingly, we found no brain-wide changes in tracer signal for either the small or large CSF tracers (OVA: HFD: 7.0 ± 1.1 a.u.; control: 5.0 ± 1.8 a.u., FITC: HFD: 11.6 ± 1.6 a.u., control: 8.2 ± 3.7 a.u., Fig. 3c). Additionally, there were no changes of total brain volume between groups according to magnet resonance imaging (MRI) in vivo (Suppl. Table S1).Figure 3Increased glymphatic activity and AQP4 polarization in hypothalamus after 40 weeks of high-fat diet. ( a) Intracisternal CSF tracer injection and brain sectioning. ( b) Representative coronal brain sections (bregma 1.2 mm) with tracer signal. Scale bars: 200 µm. ( c) Quantification of 45 kDa Ovalbumin-647 tracer ($$n = 4$$–5, ns = 0.0947) and 3 kDa FITC-tracer ($$n = 4$$–5, ns = 0.127) for 6 brain slices per animal shown as % total area. ( d) Schematic of regional analysis for tracer fluorescent signal in hypothalamus region (HT), hippocampus (HC), ventral, lateral and dorsal cortex (VC, LC, DC respectively) at bregma -1.80 and corresponding regional quantification of the 45 kDa Ovalbumin-647 tracer ($$n = 4$$–5, 2-way ANOVA, **** < 0.0001). ( e) *Representative hypothalamus* slice showing region of interest (bregma -1.80), red arrows = line ROIs used for tracer penetration analysis, assigned as a distance. Line ROIs positioned perpendicular to the brain surface. ( f) Quantification of tracer intensity in the hypothalamus (AU = arbitary units). Increased tracer signal in HFD mice at 100 and 200 µm (2-way ANOVA, **** < 0.0001, ** = 0.0024). ( $$n = 4$$–5 mice). ( g) Left; Representative hypothalamic fluorescent signal of ovalbumin (45 kDa). Right; total fluorescent tracer signal shown as area under the curve ($$n = 4$$–5, ** = 0.0044), AU = arbitary units. ( h, i) Representative AQP4 staining with line ROIs drawn across blood vessels (yellow dotted lines) in (h) hypothalamus and (i) hippocampus. Scale bars: 50 µm. Line ROI traces were plotted for each animal. Lines represent average fluorescent trace of 12–36 blood vessels per mouse ($$n = 4$$–5). Summarized traces are shown as group averages ± SD. Right: Polarization index of AQP4 in the (h) hypothalamus (* = 0.0388) and (i) hippocampus (ns = 0.7677) ($$n = 4$$–5). ( j) Representative AQP4 staining in hypothalamus for quantification of vessel density in (left) hypothalamus (* = 0.0399) and (right) hippocampus (ns = 0.9457) ($$n = 5$$). ( k) Illustration of microdialysis sampling of extracellular fluid (ECF) in hippocampus and quantification of norepinephrine (NE) levels in extracellular fluid (left) and hippocampus homogenates (right) ($$n = 5$$–7; ECF: ns = 0.1810, homogenates: ns = 0.7183). Graphs show mean ± SD. Unpaired t-tests if not otherwise indicated. Graphs created with GraphPad software (version 9.0, https://www.graphpad.com/scientific-software/prism/). Analysis of line ROI traces analyzed with ImageJ software (version $\frac{2.1.0}{1.53}$c, https://imagej.nih.gov/ij/download.html). Schematics created in Adobe Illustrator 2022 (version 26.3.1, https://www.adobe.com/products/illustrator/free-trial-download.html) by Dan Xue.
Regional analysis of tracer distribution in dorsal, lateral, and ventral cortex, hippocampus and hypothalamus depicted no difference in cortex or hippocampus between the groups (Fig. 3d). Strikingly however, we noted increased tracer distribution area (%) in the hypothalamus of HFD mice (2-way ANOVA, main effect of region, F[4, 32] = 82.1, $p \leq 0.0001$, Fig. 3d) and increased tracer penetration into the hypothalamic parenchyma at distances 100 and 200 µm from the brain surface (2-way ANOVA, main effect of distance, F[3, 28] = 63.0, $p \leq 0.0001$, Fig. 3f).
## Long-term high-fat diet increases AQP4 vascular polarization and density in the hypothalamus
AQP4 is expressed on astrocytic endfeet surrounding blood vessels, where it facilitates glymphatic fluid flow28. Coronal brain sections were next immunolabeled for AQP4 and its vascular polarization was evaluated by placing line ROIs through the vessel structures to map the profile of AQP4 signal28 in hypothalamus and hippocampus (Fig. 3h,i). The AQP4 polarization index was significantly increased around blood vessels in the hypothalamus of HFD group compared to aged-matched controls (HFD: 0.83 ± 0.11, control: 0.59 ± 0.16, Fig. 3h) but not in hippocampus (HFD: 0.76 ± 0.19, controls: 0.71 ± 0.25, Fig. 3i). We next assessed the vascular density in hypothalamus and hippocampus based on the AQP4 immune marker. There was a significant increase with HFD in the density of blood vessels per square mm in hypothalamus (HFD: 296.8 ± 31.46 control: 242.1 ± 38.77, Fig. 3j). In contrast, we found no difference between groups in vessel density in the hippocampus (HFD: 278.0 ± 34.85 control: 279.5 ± 33.92, Fig. 3j).
## Consistent norepinephrine levels support unaltered brain wide glymphatic activity
Earlier studies have shown that norepinephrine (NE) is an inhibitor of glymphatic flow29. Lower levels of NE generally promote glymphatic flow while higher levels decrease it29. Quantification of NE in hippocampus of HFD and standard chow mice showed no group difference in extracellular fluid collected via microdialysis (HFD: 0.67 ± 0.75, control: 0.26 ±0.24) or in hippocampus homogenates (HFD: 348.0 ± 48.47, control: 358.5 ± 32.97, Fig. 3k).
## Long-term high-fat diet is linked to neuroinflammation, but not systemic inflammation
Previous studies reported low-grade neuroinflammation and systemic inflammation induced by obesity30–32. Since the hippocampus and hypothalamus are affected by obesity-related neuroinflammation in humans and rodents, we focused on these two regions31–33 evated in the median eminence (ME), supraoptic nucleus (SON) and periventricular nucleus (PEVN) in both sexes of diabetic rats. We measured the cytokine profile in blood plasma and in samples of extracellular fluid collected via microdialysis in the hippocampus in mice receiving HFD or standard chow diet for 44 weeks. Elevated extracellular fluid concentrations of the cytokines IL-6 (HFD: 16.65 ± 8.81 pg/mL, control: 1.58 ± 2.88 pg/mL) and TNFα (HFD: 0.52 ± 0.29 pg/mL, control: 0.018 ± 0.035 pg/mL) and the chemokines CXCL1 (HFD: 228.8 ± 111.6 pg/mL, control: 23.16 ± 41.29 pg/mL) and CXCL2 (HFD: 29.91 ± 9.60 pg/mL, control: 0.46 ± 0.93 pg/mL) were detected in HFD mice (Fig. 4a; left). In contrast, blood plasma of HFD mice showed no elevation of any cytokines/chemokines compared to the control group (Fig. 4a; right).Figure 4Chronic obesity induces brain inflammation but not peripheral inflammation. ( a) Cytokine and chemokine profiles of extracellular fluid (green shaded panels) and blood plasma (red shaded panels) for IL-6, TNFα, CXCL1 and CXCL2 of HFD or control mice ($$n = 4$$–6; unpaired t-test, IL-6 * = 0.0174; TNFα * = 0.0120; CXCL1 * = 0.0135; CXCL2 *** = 0.009). ( b, c) Immunohistochemistry of coronal brain slices of HFD and control mice after 44 weeks of diet. Representative images of (b) hypothalamus and (c) hippocampus regions for the microglial marker CD68 and astrocytic marker GFAP. Quantification of % total area stained for microglial CD68 and astrocytic GFAP marker in (b) hippocampus and (c) hypothalamus. Scale bars: 50 µm. Graphs shows mean ± SD ($$n = 6$$, unpaired t-test, hypothalamus % area GFAP * = 0.0254, hippocampus % area GFAP * = 0.0340). Graphs created with GraphPad software (version 9.0, https://www.graphpad.com/scientific-software/prism/). Figure design in Adobe Illustrator 2022 (version 26.3.1, https://www.adobe.com/products/illustrator/free-trial-download.html) by Dan Xue.
Of note, MRI relaxometry revealed no differences between HFD and control mice for T1 and T2 relaxation times (Suppl. Table S1), which indicated no apparent global neuroinflammation. On the other hand, T1 and T2 relaxation times were both reduced in the ventricular system (T1: HFD: 2277 ± 56.60 ms, control: 2577 ± 90.45 ms, $$p \leq 0.0182$$; T2: HFD: 59.57 ± 2.956 ms, control: 70.82 ± 3.816 ms, $$p \leq 0.0420$$, Suppl. Table S1).
To evaluate cellular markers of glial reactivity, we analyzed the CD68-positive (microglial cells) and GFAP-positive (astrocytic cells) in vibratome sections. Interestingly, there were no detectable differences in total fluorescence coverage of the microglial marker (Fig. 4b,c) either in hypothalamus (HFD: 0.48 ± $0.15\%$area, control: 0.44 ± $0.13\%$area) or hippocampus (HFD: 1.44 ± $0.65\%$area, control: 1.06 ± $0.58\%$area). However, we found an increased GFAP fluorescence coverage in hypothalamus (HFD: 4.99 ± $1.62\%$ area, control: 2.97 ± $1.43\%$area) and hippocampus (HFD: 6.22 ± $2.20\%$ area, control: 3.85 ± $0.87\%$area), indicating that astrogliosis was induced by chronic HFD in both regions (Fig. 4b,c). The observed differences in astrocytic but not microglial reactivity could indicate that astrocytes are the key drivers of diet-induced neuroinflammation. Overall, neuroinflammation, but not systemic inflammation, was evident in this mouse model of chronic obesity.
## Discussion
We here utilized a HFD mouse model to study the impact of obesity on the glymphatic system. Mice receiving the HFD developed severe obesity as well as hypertension, increased heart rate, and signs of neuroinflammation but no systemic inflammation within the blood compartment (Figs. 1, 4). Elevated fasting blood glucose levels in HFD mice was also noted (Fig. 1i), which indicates the development of insulin resistance, a hallmark of type II diabetes34. The marble burying test revealed progressively impaired behavior of HFD mice (Fig. 2a), while open field assessment showed no difference in total walked distance, but a trend for obese mice to approach the center area less frequently (Fig. 2b). Previous studies have shown cognitive decline in both human and rodents with obesity24,25, and obese humans show an increased risk of developing Alzheimer’s disease35. Although control mice showed declining performance with age in the marble burying test, the HFD group exhibited accelerated age-related behavioral impairment (Fig. 2a).
Surprisingly, we detected no changes in global glymphatic flow, even though the HFD mice developed a severe obesity phenotype (Fig. 1a–c). The structural MRI examination displayed no changes in brain volume (Suppl. Table S1), but T1 and T2 relaxation times in the ventricles were reduced in HFD. An earlier study has shown declining ventricular T1 and T2 relaxation times in association with increased CSF protein levels in patients with type II diabetes36.
The rate of glymphatic fluid transport is inversely proportional to heart rate and vascular pulsation9–12. A previous study in spontaneously hypertensive rats (SHR) revealed significant glymphatic impairments to dynamic contrast-enhanced MRI imaging21. However, the SHR model was obtained by selective inbreeding, resulting in considerably greater increases in systolic blood pressure (relative increase of 60–70 mmHg) as compared to our HFD-induced obesity mice. In agreement with the current study, Weisbrod et al. reported comparable relative changes in systolic blood pressure (an increase of 10–20 mmHg) in mice after 7–12 months of high-fat-high-sugar (HFHS) diet. Furthermore, the same study showed that arterial stiffness and hypertension were reversable within 2 weeks after changing HFHS diet back to standard chow37.
Our study is the first to provide insight into glymphatic flow after HFD lasting 10 months, a duration far exceeding that in most previous experimental models38–40. Hence, the lacking effect of HFD on glymphatic flow (Fig. 3a–c) may reflect adaptations to maintain brain homeostasis. Adaption to diet-induced obesity was previously reported to occur for the microvasculature of the blood brain barrier via suppression of metabolic alterations41. Although a high-fat diet induces pro-inflammatory cytokine release and astrogliosis, such changes appear to be time-dependent and can subside over time42. Neuroprotective adaptations in glymphatic flow may also develop if mice are chronically exposed to a HFD. The present finding of unaltered glymphatic activity in HFD mice is further supported by the absence of changes in the NE level in the extracellular fluid and hippocampal homogenates (Fig. 3k). As a key modulator of the glymphatic system, pharmacological manipulation of NE impacts glymphatic activity29. Reduction of NE levels resulted in increased glymphatic flow, mirroring the onset of glymphatic activity observed during sleep or with certain anesthetics29. Thus, the lack of significant changes in brain volume and NE level changes supports the absence of brain-wide glymphatic alterations in response to HFD.
Nonetheless, we uncovered focally increased glymphatic activity, revealed by elevated total tracer uptake and increased tracer penetration at the hypothalamic surface of HFD mice (Fig. 3d–g). Among many functions, the hypothalamus controls energy homeostasis and regulates appetite43. Chronic HFD increased excitatory synaptic transmission of hypothalamic neurons44. Whereas cytoskeletal proteomic changes and increased synaptic plasticity were reported in HFD mice, hypothalamic energy metabolism may be elevated, giving rise to increased demand for extracellular metabolite removal45. In keeping with this, we observed increased vascular density in the hypothalamic region of HFD mice (Fig. 3j). Angiogenesis was previously observed in hypothalamus of type 2 diabetic patients and after exposure to high-caloric diet in mice46. Particularly striking is the present observation of increased AQP4 polarization in hypothalamic cerebral vasculature of the HFD group (Fig. 3h,i). In accordance with regional glymphatic tracer influx analysis, the AQP4 polarization was identified only in hypothalamus and not in hippocampus (Fig. 3h,i). Glymphatic transport is driven by AQP4 vascular polarization, and AQP4 knockout (KO) mice exhibit lower CSF tracer influx27. We speculate that long-term HFD increased the need for fluid trafficking in the hypothalamus, which was underpinned by locally enhanced AQP4 polarization and increased vessel density, and a resultant increase in glymphatic tracer influx24,44.
Obesity impairs lymphatic function and shrinks lymph nodes47. An earlier study in HFD mice indicated reduced fluid uptake and transport via peripheral lymphatic vessels 47. Lymphatic alterations are driven by chronic inflammation, immune cell activation, and decreased vascular endothelial growth factor (VEGF) expression24,48. Cytokines IL-6, TNFα, IL-10 and chemokine CXCL1 are the key plasma inflammatory markers in obesity49–51. We analyzed several pro-inflammatory cytokines and chemokines, including mediators that can signal a Th1 and Th2 response, via a multiplex panel. Most parameters were undetectable despite a very low detection limit and measurable markers showed no differences between groups which indicated no apparent systemic inflammation within the blood compartment of the HFD group (Fig. 4a). There, however, was a trend towards increased TNFα, IL-10, IL-5, IL-17α, CXCL1 and CXCL2 levels, which was driven by a subpopulation of HFD mice. Yet, that observation may be in accordance with previous reports of low-grade peripheral inflammation52,53, which was localized within the adipose tissue, rather than in the blood circulation54. It is possible that peripheral proinflammatory mediators could directly affect brain perivascular fluid transport by signaling across the BBB55.
We saw increased levels of pro-inflammatory markers in extracellular fluid of hippocampus, which was confirmed by cellular markers of glia reactivity in our HFD model. In particular, there were elevated brain levels of the cytokines IL-6 and TNFα and the chemokines CXCL1 and CXCL2 (Fig. 3a). Obesity-related insulin and leptin resistance is reported in hypothalamus30,56, together with neuroinflammation31,32 and gliosis48. Mutual interactions of metabolic hypothalamic adaptation and neuroinflammatory responses are presently under investigation56–58, with the implication that chronic HFD gliosis paired with TNFα secretion may arise after disruption of neuronal metabolic homeostasis. As such, an obesity-related shift in brain energy and metabolic expenditure may promote glymphatic flow changes in the hypothalamus.
Since microdialysis sampling in the hypothalamus region is technically challenging, we undertook additional immuno-staining in hippocampus and hypothalamus. In both regions, there was a significant increase in the astrocytic marker GFAP (Fig. 4b,c). Reactive astrocytes that produce pro-inflammatory mediators have been reported to arise with long term HFD in humans and rodent models59–61. On the other hand, the microglial marker CD68 did not exhibit any changes in our model (Fig. 4b,c). While astrocyte reactivity develops gradually, the microglial cells contribute to pathometabolic changes principally by acute secretion of pro-inflammatory signaling molecules62. Indeed, microglial activation in hypothalamus was previously shown to be evident as early as 6 h after HFD consumption58. While our study is limited to the use of CD68 as single microglia marker, it still indicates the absence of a reactive phenotype. CD68 particularly shows microglia activation since it labels lysosomes63 and was in a recent study clearly shown to be upregulated in mice after a long-term 9-months HFD64.Therefore, we suppose that astrocytes may be the dominant factor in the hypothalamic alterations caused in our chronic model of diet-induced obesity.
In summary, we provide the first evidence for regionally accelerated glymphatic activity in the hypothalamus of mice after long-term HFD, in the absence of major changes in glymphatic flow in other brain regions. This could reflect an increased demand for hypothalamic fluid exchange and metabolic waste removal in diet-induced obesity in mice.
## Animals and diets
6-week-old male C57BL/6JRj mice (Javier, France) were housed in groups of five and received either a standard chow diet (SAFE, Germany, D30) or a high-fat diet (Research Diets, Inc., USA, D12492) for up to 44 weeks to induce obesity (Suppl. Table S2). Food and water were provided ad libitum. Mice were housed in a $\frac{12}{12}$ light/dark cycle (lights on 7am) at room temperature (22 ± 2 °C) and a relative humidity of 55 ± $10\%$. Experimental data was collected at 10, 30, 40 and 44 weeks after chow/HFD.
All experiments were performed at the Center for Translational Neuromedicine at the University of Copenhagen with national approval from the Danish Animal Inspectorate and local approval by the Department of Animal Experimental Medicine of the University of Copenhagen. The study was carried out in compliance with the ARRIVE guidelines.
## Monitoring of body weight, water intake and blood glucose
Mice were weighed weekly to monitor weight gain over the entire experimental period of 40 weeks. To estimate potential changes in water consumption, we weighed the water bottles before and after refill of control and obese mice home cages in week 40. Individual water intake was calculated as the average consumption per day divided by the number of mice per cage and plotted by day. To examine whether the high-fat diet increased blood glucose baseline levels in obese mice, we measured the blood glucose in week 40 in awake non-fasting mice. Here, blood was obtained from the tail tip after needle puncture and measured with a BAYER *Countour apparatus* and test stripes (Ascensia). Blood glucose measurements were also performed at 7 h of food deprivation (mice received fresh cages), to compare the response of blood glucose levels to fasting in control and obese mice.
## Marble burying and open field test
A marble burying test was performed as previously described26 to compare general activity and natural burying behavior of obese and control mice. A thick layer of fresh bedding was provided in an open standard housing cage (Tecniplast 1284, Blue line type 2) and 28 marbles were spread evenly across the surface. Each mouse was allowed 15 min to explore the cage and was then returned to its home cage. A blinded researcher afterwards entered the room and counted the number of marbles fully buried (score = 2 points), half buried (score = 1 point), and not buried/unmoved (score = 0 points). This simple scoring system to quantify the natural activity behavior of control and obese mice was plotted to compare activity between both groups. To test whether the results of the marble burying test were influenced by body weight and general mobility, we performed an open-field test, where the mice were allowed to explore an empty arena (40 × 40 cm) with a defined center area (20 × 20 cm). Video recording was performed using Synapse (Tucker Davis Technologies) and Basler cameras. Afterwards, the total distance, velocity, and time spent and number of entries to the center area were analyzed using EthoVision XT.
## Blood pressure and heart rate monitoring
To examine systolic and diastolic blood pressure and heart rate in awake control and obese mice, we used a non-invasive tail-cuff method (CODA® Monitor, Kent Scientific). Mice were trained weekly for up to 12 weeks for the procedure prior to conducting the experimental measurements (Suppl. Fig. S1). Mice that did not habituate well and showed visual signs of distress were excluded from this analysis of recordings. For monitoring blood pressure and heart rate, mice were placed into a CODA® animal holder of appropriate size (small: 0–25 g mice; medium: 25–50 g mice; large: 50–75 g mice) and habituated 15–20 min prior to recordings. A CODA X-Small Occlusion Cuff and VPR Cuff were placed on the base of the tail and 25 measurements taken per mouse (Mode: Mouse; cycle interval 10 s, deflation time 15 s). The collected measurements were checked for stable readouts throughout the measurement. Mice that showed fluctuations indicating stress or cases of failure due to movements of the animal were excluded. The final four measurements were used for further analysis if stable readouts were obtained throughout the recording. A minimum of three mice per group mice were used for this analysis.
## Intracisternal CSF tracer injection
To investigate glymphatic dynamics, tracers were injected intracisternal as previously described11,27. In brief, anesthetized mice (ketamine (100 mg/kg) and xylazine (20 mg/kg)) were head-fixed in a stereotactic frame and a dental needle (SOPIRA®Carpule®30G-0.30 × 12 metric, Kulzer GmbH, Germany) attached to a P10 tubing (Scandidact ApS, Denmark) was inserted into the cisterna magna. Over a 10-min period 10 µl of mixture of 45 kDa Alexa Fluor 647-conjugated ovalbumin (O34784, ThermoFisher Scientific) and 3 kDa fluorescein-conjugated dextran (FITC, ThermoFisher, D3305) (both to equal parts, Suppl. Table S3) at a final $1\%$ concentration (w/v, in artificial cerebral spinal fluid (155 mM NaCl, 3.5 mM KCl, 1 mM CaCl2, 1 mM MgCl2, and 2 mM NaH2PO4, pH 7.4, 300 mOsm)) was infused at a rate of 1 µl/min utilizing a 100 µL syringe (81,020, Hamilton Bonaduz AG, Bonaduz, Switzerland) mounted on a motorized pump (Havard Apparatus Pump 11 Elite (USA). Physiological body temperature was maintained with a heating pad (Rodent Warmer X2, Stoelting Europe, Ireland) placed below the body. All glymphatic-related experiments were conducted in the sleeping period of the animals between 9 am and 5 pm. Tracer was allowed to circulate 30 min before the animals were sacrificed.
## Analysis of tracer distribution in the brain
After tracer circulation, brains were harvested and fixed by immersion for 24 h in $4\%$ paraformaldehyde in phosphate-buffered saline. Coronal vibratome (Leica VT1200 S) slices (100 μm) were cut and mounted (Prolong® Gold Antifade Mountant, P36934, Thermo Fisher Scientific). Tracer distribution in the brain was acquired on whole-slice brain sections using a standard fluorescence microscope (Nikon ECLIPSE Ni-E) and a digital camera (Mono-Camera Nikon DS-Fi3) controlled by an imaging software (NIS-Elements Imaging software AR 4.60.00) at constant exposure times throughout the study. Images were analyzed in ImageJ software (version $\frac{2.1.0}{1.53}$c), keeping a uniform threshold (pixel intensity 50 out of 255) and subtracting background fluorescence. The thresholded area was expressed as percentage of the entire brain slice area. To obtain a single biological measure per animal, the mean tracer area coverage for seven brain slices was averaged. For regional analysis of hypothalamus, ventral cortex, lateral cortex and dorsal cortex, the section –1.8 mm from bregma was divided into corresponding regions of interests. Regional tracer analysis of hippocampus was performed on the section –2.4 mm from bregma, due to larger hippocampal area.
## In vivo magnet resonance imaging
After 40 weeks of either chow or HFD, mice underwent MRI under isoflurane anesthesia ($3\%$ induction, and 1–$1.5\%$ maintenance) in a $\frac{1}{1}$ mixture of air/oxygen. The body temperature was maintained at 37 ± 0.5 °C with a thermostatically controlled waterbed and monitored, along with the respiratory monitoring by an MR-compatible remote monitoring system (SA Instruments, NY, USA). MRI was performed in a 9.4 T animal scanner (BioSpec $\frac{94}{30}$ USR, Paravision 6.0.1 software, Bruker BioSpin, Ettlingen, Germany). Imaging was acquired using a 1H cryogenically-cooled quadrature-resonator Tx/Rx coil (CryoProbe) and 240 mT/m gradient coil (BGA-12S, Bruker). The MRI protocol comprised a T2-weighted rapid acquisition with relaxation enhancement (RARE) sequence with the following settings: repetition time (TR) = 7000 ms, effective echo time (TE) = 35 ms, RAREfactor = 16, number of average (NA) = 6, field of view (FOV) = 14.4 mm × 14.4 mm, matrix = 192 × 192, slice thickness = 0.3 mm, bandwidth (BW) = 75 kHz. Fifty coronal slices were acquired to cover the entire brain. Total experiment time = 8 min for T2-weighted imaging. For T1 relaxometry, rapid acquisition with relaxation enhancement at variable TR (RARE-VTR) was used for saturation recovery sequence. The imaging parameters were TE = 10 ms, TR array = 27, 30, 40, 60, 80,100, 120, 200, 300, 400, 600, 800, 1000, 2000, 4000, and 8000 ms, RARE factor = 4, NA = 2, slice thickness = 1 mm, FOV = 14.4 mm × 14.4 mm, matrix = 128 × 128, BW = 100 kHz, and total experiment time = 19 min for each T1-map. In addition, T2 relaxation time was measured with multi-slice multiecho (MSME) sequence. The imaging parameters were: TR = 3000 ms, number of echoes = 40 with echo spacing = 7 ms, NA = 2, slice thickness = 1 mm, FOV = 14.4 mm × 14.4 mm, matrix = 128 × 128, BW = 100 kHz, and total experiment time = 13 min for each T2-map. T1- and T2-maps were generated using an image sequence analysis tool package (Paravision 6.0.1, Bruker), which uses a fit function:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{For the T1}} - {\text{map}}:{\text{ M}}\left({\text{t}} \right) \, = {\text{ A }} + {\text{ M}}0 \, * \, \left({{1 } - {\text{ exp}}\left({{\text{t}}/{\text{T1}}} \right)} \right),$$\end{document}For the T1-map:Mt=A+M0∗1-expt/T1,where A = absolute bias, M0 = the equilibrium magnetization, and T1 = longitudinal relaxation time.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{For the T2}} - {\text{map}}:{\text{ y }} = {\text{ A }} + {\text{ C }}*{\text{ exp}}\left({ - {\text{t}}/{\text{T2}}} \right),$$\end{document}For the T2-map:y=A+C∗exp-t/T2,where A = absolute bias, C = signal intensity, and T2 = transverse relaxation time.
MRI analysis was performed in ITK-SNAP (version 3.8.0)65. The image bias field was removed using Advanced Normalization Tools (ANTs N4 bias correction)66,67. The brain volume was automatically segmented by using the region-growing function with ITK-SNAP. In addition, pixel intensity-factorized semi-automatic thresholding was performed to segment hippocampus and the lateral ventricle in each hemisphere. The volume measurement of each compartment was performed in ITK-SNAP.
## Immunohistochemistry
Coronal brain slices (100 µm) were stained for the glial markers GFAP, the water channel AQP4 and the microglial marker CD68 (Suppl. Table S3). Tissue sections were blocked at room temperature for two hours in normal goat serum ($5\%$ in PBS, and $0.3\%$ Triton-X 100 (Sigma Aldrich). The primary antibodies were diluted in the same blocking solution and the tissue incubated overnight at 4 °C on an orbital shaker. After a PBS wash, primary antibodies were conjugated with the appropriate secondary antibodies coupled to fluorophores (Alexa Fluor, 1:500; Invitrogen™ Molecular Probes™; Thermo Fisher Scientific), in PBS for 2 h at room temperature while protected from light. After a 5 min nuclear counterstaining with DAPI (4’,6-diamidino-2-phenylindole, Thermo Fisher Scientific, 62,248, 1:1000), tissue sections were mounted on glass slides with Prolong Gold Antifade Reagent (Invitrogen/Thermo Fisher Scientific, Carlsbad, CA, USA). Images were then taken with a confocal microscope (Nikon Eclipse Ti, Tokyo, Japan) using Plan Fluor 20X/0.75, 40X/1.30 and 60x/1.40 oil objectives. Obtained images were analyzed using FIJI/ImageJ for MAC (ver. $\frac{2.3.0}{1.53}$q).
## Quantification of immuno-stained brain sections
We acquired 50 µm confocal z-stacks of CD68 and GFAP markers in the hypothalamus and hippocampus regions (at 40 × magnification). The maximum projection intensities were then assessed in FIJI/ImageJ software. The immunohistochemical signal was thresholded for each marker and the signal expressed as percent area coverage. For each animal we acquired 3 confocal images in each brain region and averaged the signal to obtain a single biological replicate.
For assessing AQP4 polarization towards blood vessels, we performed an analysis much as previously described28. Herein, multiple line ROIs were drawn across the blood vessel structures in hypothalamus. To get a representative trace for each mouse, 12–36 blood vessels per animal were analyzed. Obtained traces were aligned using cross-correlation, with the first trace serving as a reference. Polarization index was calculated by subtracting 5 µm of background signal to the vascular peak fluorescent value. All values were subsequently normalized to the highest fluorescent. Vessel density analysis was done by creating a mask on the thresholded image with the AQP4 signal using MaxEntropy. The Fiji/ImageJ tool Analyse Particles was used to count number of blood vessels per mm2. Only the particles bigger than 20 µm2 were included for quantification.
## Microdialysis
To investigate NE levels in diet-induced obesity mice underwent microdialysis surgery as previously described68. Microdialysis of NE was performed in ventral hippocampus (A/P: − 3.0 mm, M/L: −3.0 mm and D/V: −3.0 mm), whereas samples for K+ measurement were collected in striatum (AP: + 1.0 mm, M/L: + 1.5 mm, D/V: −2.0 mm) In brief, the microdialysis guide canula was implanted stereotaxically at 24 h prior to sampling. Under isoflurane anesthesia (induction $4\%$, maintenance 1.6–$2\%$) mice were mounted to a stereotactic frame and received lidocaine (0.1 mL, 1 mg/kg; Accord) subcutaneously at the site of the scalp incision and trepanation, whereupon the microdialysis guide canula was implanted (CMA 8 Microdialysis Probe Guide, Harvard Apparatus). After wound closure, mice were housed in their home cage and received carprofen (5 mg/kg; ScanVet) for pain relief. Microdialysis sampling was performed using a syringe (CMA syringe type I, Harvard Apparatus) mounted on a pump (CMA 402 Syringe Pump, Harvard Apparatus). The syringe filled with aCSF (119 mM NaCl, 3.5 mM KCl, 1.0 mM CaCl2, 0.8 mM MgCl2, HEPES 10 mM, pH 7.2, dissolved in dH2O) was connected to a 6 kDa cut-off microdialysis probe (CMA 7 CMA Microdialysis AB, Sweden). Sampling was performed in the natural sleeping phase of the mice (approximately 10:00–14:30). Constant flow rate (0.1 µL/min) was applied throughout the experiment. To minimize variation of sampling results due to technical differences, an obese and a control mouse were always yoked to a shared injection pump. Collected ISF samples were immediately snap frozen and stored at −80 °C until analysis for NE content by high-performance liquid chromatography with electrochemical detection (HPLC-ECD). After termination of the experiments, the mice were decapitated, and the brains were quickly removed and stored at −80 °C. The correct placement of microdialysis probes was histologically verified. Only results from mice with correct probe placements are reported. The contralateral brain hemisphere was used for preparation of tissue homogenates. The striatum and hippocampus were dissected and homogenized in 150 µL 0.1 M perchloride acid and thereafter analyzed for NE as described above.
## Cytokine analysis of microdialysis and plasma samples
To assess hippocampal neuroinflammatory changes due to diet-induced obesity, we performed microdialysis sampling of cytokines after 44 weeks of diet. Here we perfused high cut-off microdialysis probes (CMA 8 High Cut-Off 100 kDa, Harvard Apparatus) with aCSF + $3\%$ BSA mixture. Sampling was performed during animal resting phase (9 am to 3 pm) at a constant flow rate of 0.8 µL/min. Mice were killed by decapitation and trunk blood collected in sterile EDTA-coated 1.5 mL centrifuge tubes and centrifuged at 1000 g for 10 min at 4 °C. The blood plasma supernatant was separated and snap frozen on dry ice. Cytokine levels in microdialysis samples and plasma were then analyzed using the Multiplex Laser Bead Assay by Eve Technologies (MDHSTC18, Calgary, Canada) as previously described y69. The multiplexing technology was performed using the Bio-Plex TM 200 system (Bio-Rad Laboratories, Inc., Hercules, CA, USA) and Mouse-High-Sensitivity-18-Plex Discovery Assay panel (Millipore, St. Charles, MO, USA) to quantify different biomarkers in the same samples, which were measured in duplicate. Here, we present only data of cytokines that were detectable by the assay and displayed significant differences between the groups (IL-6, TNFα, CXCL1, CXCL2, Fig. 4). Detectible cytokines without significant differences are attached in Supplementary Fig. S2.
## Statistical analysis
Statistical analysis was performed using GraphPad Prism version 9.0 (GraphPad Software). For comparison of the mean values following a normal distribution, we used an unpaired t-test when analyzing two groups. For datasets with more than two groups, we applied a 2-wayANOVA to compare the means, followed by Šídák's multiple comparisons test. The significance level of all performed analysis in this study was $a = 0.05.$ If not otherwise stated, all values are expressed as mean ± SD.
## Ethical approval
The animal experimental procedures were approved by the local research ethics committee (Department of Experimental Medicine, University of Copenhagen) and conducted in accordance with the Danish Animal Experiments Inspectorate.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30630-y.
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|
---
title: 'Comparison of weight change between face-to-face and digital delivery of the
English National Health service diabetes prevention programme: An exploratory non-inferiority
study with imputation of plausible weight outcomes'
authors:
- Antonia M. Marsden
- Mark Hann
- Emma Barron
- Jamie Ross
- Jonathan Valabhji
- Elizabeth Murray
- Sarah Cotterill
journal: Preventive Medicine Reports
year: 2023
pmcid: PMC10011422
doi: 10.1016/j.pmedr.2023.102161
license: CC BY 4.0
---
# Comparison of weight change between face-to-face and digital delivery of the English National Health service diabetes prevention programme: An exploratory non-inferiority study with imputation of plausible weight outcomes
## Abstract
Worldwide evidence suggests face-to-face diabetes prevention programmes are effective in preventing and delaying the onset of type 2 diabetes by encouraging behaviour change towards weight loss, healthy eating, and increased exercise. There is an absence of evidence on whether digital delivery is as effective as face-to-face. During 2017–18 patients in England were offered the National Health Service Diabetes Prevention Programme as group-based face-to-face delivery, digital delivery (‘digital-only’) or a choice between digital and face-to-face (‘digital-choice’). The contemporaneous delivery allowed for a robust non-inferiority study, comparing face-to-face with digital only and digital choice cohorts.
Changes in weight at 6 months were missing for around half of participants. Here we take a novel approach, estimating the average effect in all 65,741 individuals who enrolled in the programme, by making a range of plausible assumptions about weight change in individuals who did not provide outcome data. The benefit of this approach is that it includes everyone who enrolled in the programme, not restricted to those who completed. We analysed the data using multiple linear regression models.
Under all scenarios explored, enrolment in the digital diabetes prevention programme was associated with clinically significant reductions in weight which were at least equivalent to weight loss in the face-to-face programme. Digital services can be just as effective as face-to-face in delivering a population-based approach to the prevention of type 2 diabetes. Imputation of plausible outcomes is a feasible methodological approach, suitable for analysis of routine data in settings where outcomes are missing for non-attenders.
## Introduction
Diabetes is a chronic health condition associated with numerous adverse outcomes including microvascular disease, cardiovascular disease, and premature death. Both the incidence and prevalence of Type 2 diabetes is increasing globally, and prevention has become a major international public health objective (Bergman et al., 2012, Saeedi et al., 2019, World Health Organization, 2016). Many countries have initiated diabetes prevention programmes which target those at highest risk of type 2 diabetes and encourage change in diet and exercise behaviours, with the objective of delaying or preventing onset of disease. Evidence from around the world suggests that face-to-face, group-based diabetes prevention programmes can be effective in reducing the incidence of diabetes (Ashra et al., 2015, Galaviz et al., 2018).
Digital behaviour change interventions have been shown to be as effective as in-person interventions in other healthcare settings (Luo et al., 2020), including programmes for weight loss among people who are overweight (Beleigoli et al., 2019). However, there is less strong evidence supporting digital delivery for diabetes prevention (Bian et al., 2017, Grock et al., 2017, Joiner et al., 2017, Van Rhoon et al., 2020).
The English National Health Service Diabetes Prevention Programme (NHS DPP), “Healthier You”, was based on international evidence, encouraging healthy eating, weight loss and increased exercise in people at high risk of developing Type 2 diabetes, according to national guidelines (HbA1c 42–47 mmol/mol [6.0–$6.4\%$] or fasting plasma glucose (FPG, 5.5–6.9 mmol/L) (Hawkes et al., 2020a, Hawkes et al., 2020b, National Institute for Health and Care Excellence (NICE), 2012). The NHS offered patients face-to-face service delivery, for which there was stronger evidence, and it was rolled out in stages across England between 2016 and 2018. There was an NHS experimental pilot in 2017–18 which offered a digital service to patients in selected areas of England (Ross et al., 2022). The digital service was introduced in two ways: (i) in some localities face-to-face delivery was not yet available and patients were only offered a digital service (digital-only) (ii) in other localities, patients were offered a choice between digital and face-to-face delivery (digital choice). The face-to-face and digital services were commissioned by the same NHS team from external providers, using comparable service specifications and offering similar behaviour change and self-management content. Previous studies have reported that mean weight loss at 6 months was 3.2 kg [$95\%$ confidence interval (CI): 3.1, 3.3] in the face-to-face service (Marsden et al., 2022a) and 3.5 kg [$95\%$ CI: 3.3, 3.7) in the digital service (Ross et al., 2022).
The contemporaneous delivery of face-to-face and digital versions of the same programme content allowed for a robust non-inferiority observational study, comparing face-to-face with the digital only and digital choice cohorts, with adjustment for differences between the participating populations. We have reported elsewhere that weight change on the digital pilot was non-inferior to face-to-face at 6 months. Mean weight loss among those who were offered a choice and chose digital was higher than face to face (difference in weight change: −1.165 kg [$95\%$ CI: −1.841, −0.489], and among those with no choice it was similar (−0.284 kg [$95\%$ CI: −0.712, 0.144])) (Marsden et al., 2022b).
The primary analysis in our previous study was a complete case analysis, comparing change in weight only in those patients who provided weight measures at 6 and 12 months; there was a substantial amount of missing data for people who did not attend to have their weight measured (Marsden et al., 2022b). We considered a multiple imputation approach to be unsuitable because the weights were likely missing ‘not at random’. That is, the probability of a weight measure being missing, even after accounting for covariates, is dependent on the value of the missing weight (Austin et al., 2021). Most of the missing data was from people who had stopped participating in the programme, and it seemed implausible to impute weights for non-participants using data from those who continued to participate.
The aim of the current paper is to estimate the average effect in all individuals who enrolled in the digital or face-to-face cohorts, by making plausible assumptions about the change in weight of individuals who did not provide outcome data. We considered a range of plausible changes in weight in both the face-to-face and digital groups, using a non-inferiority approach, and analysed the data using multiple linear regression models to see what impact this has on the overall conclusion. This was a pre-specified exploratory analysis. The benefit of this approach over the previous analysis is that it includes everyone who enrolled in the programme, rather than being restricted to those who completed the programme and provided outcome data. Unlike multiple imputation and Inverse Probability Weighting, this approach incorporates the fact that the outcome data are missing not at random. It is also a simple and intuitive approach. The study was pre-registered on the Open Science Framework 14 July 2021 (Marsden, 2021).
## Research design
The design was a retrospective observational cohort study, using patient-level data collected by NHS DPP service providers. Details of the diabetes prevention programme and the population inclusion criteria are fully reported elsewhere (Hawkes et al., 2020a, Ross et al., 2022). In summary, the face-to-face service offered a one-to-one initial assessment followed by at least thirteen group sessions, which delivered behaviour change content related to diet, weight loss and increased exercise with regular group education and exercise sessions (Hawkes et al., 2020a, NHS England, 2022). The digital service offered similar content (Ross et al., 2022). There were two digital cohorts in the analysis. The ‘digital only’ cohort were those who lived in areas without any face-to-face DPP, and so their only option was digital. The ‘digital choice’ cohort lived in areas where both digital and face-to face services were operating and they were offered a choice. All participants were referred from primary care. NHS England published a service specification which outlined what the broad content of the programme should look like; the services were delivered by several independent providers, and specific content varied across the providers (NHS England, 2016).
The pre-specified non-inferiority margin for change in weight at 6 months was determined by the NHS DPP Expert Reference Group (NHS England, 2022) as 1 kg. For example, if average change in weight via face-to-face delivery was no greater than 1 kg more than via digital delivery, digital was deemed non-inferior.
## Population
Eligible patients were those with non-diabetic hyperglycaemia who were referred to the digital or face-to-face NHS DPP in 2017–18 and who either attended a first session of the face-to-face service or registered for the digital service, and provided a baseline weight measure.
## Data collection
Data was collected by service providers and compiled by NHS England. This process is described fully elsewhere (Marsden et al., 2022b). In summary, personal characteristics were recorded at baseline. These included age at referral, sex, ethnicity, socioeconomic deprivation (defined by the English Index of Multiple deprivation (IMD) 2015 associated with the individual’s local area, grouped into quintiles), HbA1c (a widely used measure of blood glucose, used to assess diabetes risk) in mmol/mol and body mass index (BMI) in kg/m2. The area in which the participant resided was described via health-administration geographical areas: Clinical Commissioning Group (CCG) and Sustainability and Transformation Partnership (STP). Each STP commissioned a single service provider and CCGs managed the local implementation of referrals from General Practice. Weight was recorded at group sessions (face-to-face), and in General Practice, pharmacies or at home (digital), using Wi-Fi enabled pre-calibrated equipment supplied by the provider (which automatically uploaded the recorded weight). There were no self-reported weight measures. The same mode of measurement was used for a participant’s baseline and follow-up observations. We defined baseline weight as that measured at the first intervention session attended (face-to-face) or registration (digital) and 6-month weight as that closest to 6 months after baseline (and within 4–8 months).
Weight in face-to-face delivery was collected if participants continued to attend sessions. Hence, changes in weight were missing for anyone who stopped attending before the 6-month weight was collected. In the digital pilot, all individuals who registered were invited to provide 6-month data, regardless of whether they were still enrolled. Changes in weight were missing for 29,080 ($47\%$) in the face-to-face cohort, 753 ($42\%$) in the digital only cohort and 1309 ($62\%$) in the digital choice cohort.
## Assumed plausible outcomes
Weight at 6 months was estimated in one of four ways, depending on the data available: For all individuals who did not provide outcome data in the digital cohort, and individuals in the face-to-face cohort who did not provide outcome data and who were not participating in the programme at the time, we imputed a range of plausible assumptions. These were based on clinical opinion, and the thinking underpinning the assumptions was that weight gain is the expected natural progression in untreated people at risk of diabetes, and weight loss could be due to being informed of high risk of T2DM and undertaking behaviour change on their own. The following changes in weight from the first intervention session attended to 6 months were assumed:1.No change2.0.5 kg increase3.1 kg increase4.0.5 kg decrease5.1 kg decrease Since some individuals in the face-to-face cohort who did not provide a 6 m weight did provide one or more weight measures after baseline, scenarios 6–10 incorporate these by assuming the following changes in weight (excluding measures collected within one month of baseline, which we thought was too close to baseline):6.Change from baseline to last observed weight (if available),No change otherwise.7.Change from baseline to last observed weight (if available),0.5 kg increase otherwise.8.Change from baseline to last observed weight (if available),1 kg increase otherwise.9.Change from baseline to last observed weight (if available),0.5 kg decrease otherwise.10.Change from baseline to last observed weight (if available),1 kg decrease otherwise.
## Observed values
For those who provided weight outcome data, their observed values were used.
## Regression mean imputation
For those in the face-to-face cohort who were recorded as still participating in the programme at 6 months, but where a weight measure was not recorded, regression mean imputation was used to impute an estimated outcome. Our justification for this was that it is reasonable to assume that their outcomes, if they had been collected, would be like other participants for whom a 6-month weight had been measured.
## Statistical analyses
Mixed effects linear regression modelling was used to compare change in weight from baseline to 6 months between the face-to-face cohort and each digital cohort separately. An indicator variable (face-to-face/digital-only/digital-choice) was included to convey the estimated adjusted difference in mean change in weight at 6 months between face-to-face and each digital group, with face-to-face as the reference group. We chose a non-inferiority over an equivalence design because our purpose was to estimate whether digital delivery was not inferior (equivalent or possibly superior) to face-to-face, unlike an equivalence study would have considered only whether digital delivery was strictly equivalent (Walker and Nowacki, 2011). Non-inferiority was inferred if the upper bound of the $95\%$ confidence interval for this adjusted difference was lower than the 1 kg non-inferiority limit. The model adjusted for the timing of the outcome measure (months from baseline) as fixed effects and CCG nested within STP as random effects to account for variation across sites. The timing of the weight measure was set to 6 months where this outcome was originally missing.
## Results
A summary of baseline characteristics in those with and without a missing 6-month weight is shown in Table 1. In all delivery modes, the distribution of sex was similar in the missing and non-missing groups. The non-missing groups in face-to-face and digital only were, on average, older, had a lower baseline weight and BMI, and had a higher proportion of individuals with White ethnicity than the missing groups. In the face-to-face and digital choice delivery modes, there was a higher proportion of individuals from the most deprived quintile in the missing group compared to the non-missing group. The mean baseline HbA1c was higher in those with a missing 6-month weight value in the digital-choice group. Table 1Baseline characteristics of adults enrolled in NHS DPP 2017–18, comparing those with and without a missing 6-month weight value. Face-to-faceDigital onlyDigital choiceNot missing ($$n = 32744$$)Missing ($$n = 29122$$*)Not missing ($$n = 1025$$)Missing ($$n = 753$$)Not missing ($$n = 833$$)Missing ($$n = 1324$$)Sex, N(%) Male14865 (45.5)13370 (46.0)475 (46.4)361 (48.1)414 (50.0)645 (49.5) Female17810 (54.5)15691 (54.0)548 (53.6)390 (51.9)414 (50.0)658 (50.5) p-valuea0.2010.5490.822 Age at referral Mean (SD)66.6 (10.4)63.3 (12.9)59.6 (12.2)56.0 (12.0)59.5 (11.5)60.3 (14.6) Median (IQR)68 [61, 74]65 [55, 73]61 [52, 69]57 [48, 65]61 [52, 68]61 [51, 71] p-valuea<0.001<0.0010.191 Ethnicity, N(%) White25543 (84.6)19217 (73.0)781 (78.7)502 (70.5)642 (83.8)699 (84.7) Mixed459 (1.5)591 (2.2)14 (1.41)14 (1.94)19 (2.48)18 (2.18) Asian2328 (7.7)4155 (15.8)141 (14.2)142 (19.9)77 (10.1)72 (8.73) Black1444 (4.78)1845 (7.01)55 (5.54)53 (7.44)23 (3.00)33 (4.00) Other412 (1.36)519 (1.97)2 (0.20)1 (0.14)5 (0.65)3 (0.36) p-valuea<0.0010.0040.606 IMD Quintileb, N(%) 1 (Most deprived)4168 (12.8)6287 (21.6)150 (14.7)110 (14.6)205 (24.7)618 (48.3) 25507 (16.9)5413 (18.6)227 (22.2)196 (26.1)117 (14.1)223 (17.4) 36533 (20.0)5396 (18.6)309 (30.2)198 (26.3)108 (13.1)167 (13.1) 47479 (22.9)5591 (19.2)207 (20.2)156 (20.7)183 (22.1)147 (11.5) 5 (Least deprived)8990 (27.5)6380 (22.0)131 (12.8)92 (12.2)217 (26.1)124 (9.70) p-valuea<0.0010.27<0.001 Weight in kg at baseline Mean (SD)83.23 (18.13)85.18 (19.38)87.43 (19.15)89.56 (21.02)86.98 (19.04)86.10 (21.22) Median (IQR)81.2 (70.4, 93.6)82.8 (71.6, 96.0)85.0 (73.8, 99)88.0 (74.0, 101.5)85.0 (74.0, 98.0)83.9 (71.1, 97.2) p-valuea<0.0010.0260.332 Body Mass Index (BMI) at baseline Mean (SD)29.95 (5.70)30.75 (6.20)31.06 (5.97)31.72 (6.58)30.46 (5.99)30.76 (6.71) Median (IQR)29.1 (26.0, 32.9)29.8 (26.5, 34.0)30.0 (26.6, 34.4)30.5 (27.1, 35.3)29.3 (26.3, 33.4)29.6 (26.3, 34.0) N(%) Normal weight5865 (18.1)4524 (15.8)122 (11.9)97 (12.9)122 (14.7)224 (17.1) Overweight12503 (38.5)10212 (35.7)390 (38.1)241 (32.1)329 (39.6)458 (35.0) Obese12352 (38.0)11616 (40.6)427 (41.7)332 (44.2)321 (38.7)498 (38.0) Severely obese1759 (5.4)2287 (8.0)86 (8.39)81 (10.8)58 (6.99)129 (9.85) p-valuea<0.0010.0270.303 HbA1cdat baseline Mean (SD)40.7 (4.0)40.8 (4.2)43.9 (2.0)43.9 (1.9)43.1 (2.4)43.6 (1.8) Median (IQR)41 [38, 43]41 [38, 43]44 [42, 45]44 [43, 45]43 [42, 45]43 [42, 45] N(%) Normal range10184 (55.6)7571 (55.1)29 (2.83)18 (2.40)71 (8.6)38 (2.92) NDHC7579 (41.4)5736 (41.8)984 (96.2)714 (95.3)757 (91.2)1261 (96.9) T2DM560 (3.06)426 (3.10)10 (0.98)17 (2.27)2 (0.24)3 (0.15) p-valuea0.6630.655<0.001*Explanation for why this is 29,122 instead of the 29,080 reported in the results section: there are 42 people who had a missing 6 month weight value and who were still participating in the programme at the 6 month time point, but regression mean imputation could not be performed as they had a missing ethnicity value.ap-value from a two-sample t-test test for age at referral, weight at baseline, BMI at baseline and HbA1c at baseline, and a chi-square test for sex, ethnicity and IMD quintile.bIndex of multiple deprivation (IMD) score for an individual’s small-area of residence, grouped using quintiles. CNondiabetic hyperglycaemia (NDH) describes adults at high risk of developing type 2 diabetes (T2DM, defined as having HbA1c 42–47 mmol/mol [6.0–$6.4\%$] or fasting plasma glucose (FPG, 5.5–6.9 mmol/L) Barron et al., 2018, Howarth et al., 2020, Valabhji et al., 2020).dHbA1c is a blood glucose test, widely used to assess diabetes risk.
By assuming an outcome for those for whom this was missing, cohort-specific sample sizes increased: from 32,744 to 61,824 for the face-to-face cohort (29080 were replaced ($47\%$)), from 1025 to 1778 for the digital-only cohort (753 were replaced ($42\%$)) and from 830 to 2139 for the digital choice cohort (1309 were replaced ($62\%$)). Baseline characteristics of the sample are reported elsewhere (Marsden et al., 2022b). In summary, in comparison to the face-to-face-cohort, the digital only cohort was younger, with a slightly larger proportion of ethnic minorities, fewer people from the most and least deprived areas and higher baseline weights; the digital choice cohort had a slightly higher proportion of males, was younger, had a slightly lower proportion of ethnic minorities, greater numbers from the most deprived areas and higher baseline weights.
Table 2 shows the raw mean change in weight (with a $95\%$ confidence interval) across the three cohorts under the various assumptions about the missing outcome data. Under all assumptions considered, in all three cohorts, patients lost weight between baseline and 6 months. The estimated weight loss ranged between 1.07 kg and 2.06 kg (face-to-face), 1.30 kg and 2.18 kg (digital only) and 0.86 kg and 2.08 kg (digital choice) depending on the assumptions made (Table 2). Mean weight loss as a % of mean baseline weight ranged between $1.32\%$ and $2.54\%$ (face-to-face), $1.52\%$ to $2.47\%$ (digital only) and $0.98\%$ to $2.36\%$ (digital choice).Table 2Mean change in weight at 6 months of adults enrolled in NHS DPP 2017–18 in the face-to-face cohort and both digital cohorts under missing outcome data assumptions. Mean change (kg) ($95\%$ CI)Assumptions about weight loss in those who did not provide 6-month weigh measuresFace-to-faceDigital onlyDigital choiceN32,7441025830Complete case analysis(Marsden et al., 2022b)−2.85 (−2.89, −2.81)−3.05 (−3.38, −2.73)−3.79 (−4.16, −3.43) N61,82417782139[1] no change a−1.53 (−1.55, −1.50)−1.76 (−1.96, −1.56)−1.47 (−1.63, −1.31)[2] 0.5 kg increase a−1.30 (−1.32, −1.27)−1.54 (−1.75, −1.35)−1.17 (−1.33, −1.00)[3] 1 kg increase a−1.07 (−1.09, −1.04)−1.34 (−1.54, −1.13)−0.86 (−1.03, −0.69)[4] 0.5 kg decrease a−1.76 (−1.79, −1.74)−1.97 (−2.17, −1.78)−1.78 (−1.94, −1.62)[5] 1 kg decrease a−1.99 (−2.01, −1.97)−2.18 (−2.38, −1.99)−2.08 (−2.24, −1.93)[6] Latest observation or no change a−1.87 (−1.89, −1.84)−1.76 (−1.96, −1.56)−1.47 (−1.63, −1.31)[7] Latest observation or 0.5 kg increase a−1.62 (−1.65, −1.60)−1.54 (−1.75, −1.35)−1.17 (−1.33, −1.00)[8] Latest observation or 1 kg increase a−1.48 (−1.50, −1.45)−1.34 (−1.54, −1.13)−0.86 (−1.03, −0.69)[9] Latest observation or 0.5 kg decrease a−1.91 (−1.94, −1.89)−1.97 (−2.17, −1.78)−1.78 (−1.94, −1.62)[10] Latest observation or 1 kg increase a−2.06 (−2.08, −2.03)−2.18 (−2.38, −1.99)−2.08 (−2.24, −1.93)aWhere individuals had no 6-month weight measure recorded, weights collected 1–4 months after baseline were used, but if not observed, the plausible values were imputed.
The average change in weight was lower than the observed change in the main analysis, as was expected from an analysis which includes patients who did not complete the programme and assumes their weight loss was lower than the average of completers. Under the most extreme positive assumption, that those with a missing outcome lost 1 kg in weight (assumption 5), the mean changes were −1.99 ($95\%$ CI: −2.01, −1.97), −2.18 ($95\%$ CI: −2.38, −1.99) and −2.08 ($95\%$ CI: −2.24, −1.93) in the face-to-face, digital only and digital choice cohorts respectively (Table 2).
Table 3, Table 4 show the output from the linear mixed model analyses comparing change in weight at 6 months between the face-to-face and each of the two digital cohorts. Table 3Regression analyses comparing change in weight among adults enrolled in NHS DPP 2017–18 (baseline to 6 months) between the face-to-face cohort and the digital only cohort under missing outcome data assumptions. AssumptionNDifference in weight change between digital only and face-to-face$95\%$ CIp-value[1] No change a58,000−0.368(−0.711, −0.025)0.036[2] 0.5 kg increase a58,000−0.427(−0.783, −0.070)0.019[3] 1 kg increase a58,000−0.486(−0.857, −0.115)0.010[4] 0.5 kg decrease a58,000−0.309(−0.639, 0.022)0.067[5] 1 kg decrease a58,000−0.250(−0.569, −0.069)<0.001[6] Latest observation or no change a58,000−0.014(0.367, 0.338)0.936[7] Latest observation or 0.5 kg increase a58,000−0.010(−0.378, 0.359)0.959[8] Latest observation or 1 kg increase a58,0000.057(−0.326, 0.440)0.772[9] Latest observation or 0.5 kg decrease a58,000−0.138(−0.480, 0.205)0.430[10] Latest observation or 1 kg increase a58,000−0.197(−0.529, 0.134)0.242aModel adjusts for age at referral, sex, ethnicity (white/mixed/black/Asian/other), Index of Multiple Deprivation (IMD) quintile, time since baseline (in months) as fixed effects and Clinical Commissioning Group (CCG) nested within Sustainability and Transformation Partnership (STP) as random effects. Table 4Regression analyses comparing change in weight among adults enrolled in NHS DPP 2017–18 (from baseline to 6 months) between the face-to-face cohort and the digital choice cohort under missing outcome data assumptions. AssumptionNDifference in weight change between digital choice and face-to-face$95\%$ CIp-value[1] No change a57,890−1.006(−1.366, −0.647)<0.001[2] 0.5 kg increase a57,890−1.070(−1.444, −0.697)<0.001[3] 1 kg increase a57,890−1.136(−1.525, −0.747)<0.001[4] 0.5 kg decrease a57,890−0.943(−1.289, −0.597)<0.001[5] 1 kg decrease a57,890−0.882(−1.216, −0.549)<0.001[6] Latest observation or no change a57,890−0.678(−1.049, −0.306)<0.001[7] Latest observation or 0.5 kg increase a57,890−0.747(−1.132, −0.361)<0.001[8] Latest observation or 1 kg increase a57,890−0.711(−1.112, −0.309)0.001[9] Latest observation or 0.5 kg decrease a57,890−0.821(−1.179, −0.464)<0.001[10] Latest observation or 1 kg increase a57,890−0.860(−1.206, −0.515)<0.001aModel adjusts for age at referral, sex, ethnicity (white/mixed/black/Asian/other), Index of Multiple Deprivation (IMD) quintile, time since baseline (in months) as fixed effects and Clinical Commissioning Group (CCG) nested within Sustainability and Transformation Partnership (STP) as random effects.
Under all assumptions (Table 3), weight change in the digital only cohort was found to be non-inferior to that in face-to-face, like in the main analysis: this is indicated in Table 3 by the upper bound of the confidence intervals being under 1 kg. Under assumptions 1–5, weight change was greater in the digital only cohort than the face-to-face cohort, unlike in the main analysis. However, under assumptions 6–10, which incorporate intermediate weight outcomes, the mean change in weight at 6 months, was non-inferior to face-to-face, but was not superior.
Under all assumptions (Table 4), the estimated mean weight change in the digital choice cohort was non-inferior to that in face-to-face, like in the main analysis: this is indicated in Table 3 by the upper bound of the confidence intervals being under 1 kg. Under all assumptions, after adjustment, the mean change was greater in the digital choice cohort compared to the face-to-face cohort, and this was statistically significant, as in the main analysis. Values of mean weight change were like that in the main analysis for assumptions 1–4 but slightly closer to 0 for assumptions 5–10.
## Discussion
In this exploratory analysis including those who provided a baseline weight measure, missing weight values at 6 months post baseline were imputed, using the last observed weight measure, if available, or assumed plausible outcomes ranging from weight loss of 1 kg to weight gain of 1 kg. Under all ten assumptions, face-to-face, digital only and digital choice diabetes prevention cohorts saw an average reduction in weight at 6 m of between 0.9 kg and 2.2 kg. Under all assumptions, after accounting for differences in baseline characteristics of the three cohorts, weight loss in both digital cohorts was non-inferior to that in the face-to-face cohort. Weight loss in the digital only cohort was like face-to-face, and the difference was no greater than 0.5 kg under any assumption. Weight loss in the digital choice cohort was, on average, superior to face-to-face, between 0.7 kg and 1.1 kg greater. Patients who were offered a choice and chose the digital service achieved significantly more weight loss, compared to patients offered face-to-face only.
These results are consistent with the findings of the primary analysis (Marsden et al., 2022b), which reported that weight change on the digital pilot was non-inferior to face-to-face at 6 months: it was similar in the comparison of those not offered a choice and greater in digital when participants were offered a choice. The similarity with the primary analysis, under all assumptions, provides greater confidence in the finding that digital delivery of a diabetes prevention programme is non inferior to face-to-face delivery. Compared to the primary analysis, which was restricted to those who provided weight measures at 6 months, our analysis included a much larger cohort of people who attended a first session of the face-to-face service or registered for the digital service.
A previous complete case analysis of the NHS digital DPP, including only participants who provided weight measures at baseline and 6 months, reported mean weight loss of 3.5 kg ($95\%$ CI: 3.3, 3.7), $$n = 1$$,811 (Ross et al., 2022). By comparison, in our analysis, which includes a larger cohort of everyone who registered and provide a baseline weight measure, mean weight loss was between 1.34 and 2.18 kg (digital only, $$n = 1778$$) and 0.86 and 2.08 kg (digital choice, 2139). Our results were more conservative than the complete case analysis, as expected when the largest plausible assumption for non-completers was lower than the mean weight loss among completers.
We believe that our approach to missing data is novel among studies of real-world DPPs. In their analysis of the face-to-face DPP, Valabhji et al. performed a complete-case analysis but additionally used multiple imputation in a sensitivity analysis (Valabhji et al., 2020). Using similar data, Marsden et al. also undertook a complete-case analysis, with multiple imputation only for patients who were still participating when the outcomes were collected, assuming the data for others was not missing at random (Marsden et al., 2022a). In Ross et al. ’s analysis of the digital DPP, a sensitivity analysis was performed using Inverse Probability Weighting where the observed data was weighted based on the probability of drop-out (Ross et al., 2022). These approaches can be effective for data that is missing at random, but do not remove bias for data missing not at random. They also rely on the relevant models being correctly specified. Ashra et al. performed a meta-analysis of RCTs evaluating diabetes prevention programmes and reported that several studies performed complete case analyses, likely inflating the intervention effect sizes (Ashra et al., 2015).
The missing data in this study was disproportionately from among lower-weight, low-income and non-White participants, which is consistent with engagement challenges in prevention services (McGill et al., 2015). Although statistical methods such as this are helpful to the field, it should also be noted that more effort is needed to promote uptake among participants from all backgrounds, especially those at higher risk for diabetes, during delivery of DPPs.
This study has several strengths. The data allows a contemporaneous comparison of face to face and digital delivery of a diabetes prevention service, which has not previously been done. This analysis using plausible assumptions has produced similar results to compete case analysis based on both matching and regression adjustment, suggesting robustness of the findings. Plausible imputation has allowed inclusion of the whole cohort, unlike the complete case approach, and this has permitted something closer to an intent-to-treat analysis, providing greater confidence in the original findings. As with any study based on observational data, the results may be biased by unmeasured confounding.
Our findings that digital can be as effective as face-to-face delivery, and more effective when participants are offered a choice, add to growing evidence that digital delivery has the potential for wider reach at lower cost, and may be more acceptable and accessible to some sectors of the population (Murray et al., 2019; NHS England, 2022). However, digital health interventions can face low rates of uptake and completion (Beleigoli et al., 2019, Murray et al., 2018, Murray et al., 2017).
## Conclusion
Enrolment in the NHS digital diabetes prevention programme was associated with clinically significant reductions in weight which were (at least) equivalent to the weight loss seen in the face-to-face programme. Digital services can be just as effective as face-to-face in delivering a population-based approach to the prevention of type 2 diabetes. Patients who chose digital after being offered a choice of digital or face-to-face achieved (significantly) better weight loss outcomes, compared to patients offered face-to-face only. Indeed, current operational delivery of the Programme now offers participants the choice of face-to-face group-based delivery or digital delivery, as described in the 2022 version of the Programme Service Specification (NHS England, 2022). Imputation of plausible outcomes is a feasible methodological approach, suitable for analysis of routine data in settings where outcomes are systematically missing for non-attenders.
## Ethics committee approval
The research was approved by the North West Greater Manchester East NHS Research Ethics Committee (Reference: 17/NW/0426, $\frac{01}{08}$/17, amended $\frac{29}{09}$/20).
## Availability of data
The data that support the findings of this study were used under license from NHS England for the current study only and are not publicly available.
## Disclosure of funding
This work is independent research funded by the National Institute for Health and Care Research (Health Services and Delivery Research, $\frac{16}{48}$/07 – Evaluating the NHS Diabetes Prevention Programme (NHS DPP): the DIPLOMA research programme (Diabetes Prevention – Long Term Multimethod Assessment)). The views and opinions expressed in this manuscript are those of the authors and do not necessarily reflect those of the National Institute for Health and Care Research or the Department of Health and Social Care.
## CRediT authorship contribution statement
Antonia M. Marsden: Methodology, Validation, Formal analysis, Data curation, Project administration, Investigation, Writing – original draft. Mark Hann: Methodology, Validation, Investigation, Writing – review & editing. Emma Barron: Resources, Writing – review & editing. Jamie Ross: Resources, Writing – review & editing. Jonathan Valabhji: Conceptualization, Writing – review & editing. Elizabeth Murray: Conceptualization, Funding acquisition, Writing – review & editing. Sarah Cotterill: Conceptualization, Funding acquisition, Supervision, Methodology, Writing – original draft.
## Declaration of Competing Interest
AM, MH, EB, BM and SC declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EM is managing director of a not-for-profit Community Interest Company, HeLP-Digital, which exists to disseminate a digital diabetes self-management programme, HeLP-Diabetes, across the NHS. JV is the national clinical director for diabetes and obesity at NHS England.
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---
title: Placental cell type deconvolution reveals that cell proportions drive preeclampsia
gene expression differences
authors:
- Kyle A. Campbell
- Justin A. Colacino
- Muraly Puttabyatappa
- John F. Dou
- Elana R. Elkin
- Saher S. Hammoud
- Steven E. Domino
- Dana C. Dolinoy
- Jaclyn M. Goodrich
- Rita Loch-Caruso
- Vasantha Padmanabhan
- Kelly M. Bakulski
journal: Communications Biology
year: 2023
pmcid: PMC10011423
doi: 10.1038/s42003-023-04623-6
license: CC BY 4.0
---
# Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences
## Abstract
The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. *We* generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue ($$n = 9$$ biological replicates) at term ($$n = 40$$,494 cells). We deconvoluted eight published microarray case–control studies of preeclampsia ($$n = 173$$ controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 ($37.8\%$, $95\%$ CI [$27.5\%$, $48.8\%$]), LEP ($34.5\%$, $95\%$ CI [$26.0\%$, $44.9\%$]), and ENG ($34.5\%$, $95\%$ CI [$25.0\%$, $45.3\%$]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.
A single-cell RNA-seq analysis of placental villous tissue provides a deconvolution reference atlas of fetal and maternal placental cell types, and indicates that placental cellular heterogeneity in preeclampsia might contribute to differences in bulk gene expression.
## Introduction
The public health burden of adverse pregnancy outcomes is substantial. An important example is preeclampsia, which affected $6.5\%$ of all live births in the United States in 2017 and is characterized by high maternal blood pressure, proteinuria, and damage to other organ systems1. Adverse pregnancy outcomes may lead to myriad health complications including an elevated risk of chronic diseases throughout the life course2. The placenta, a temporary organ that develops early in pregnancy, promotes maternal uterine artery remodeling; mediates transport of oxygen, nutrients, and waste3; secretes hormones to regulate pregnancy; metabolizes various macromolecules and xenobiotics; and can serve as a selective barrier to some, but not all, pathogens and xenobiotics4. The executive summary of the Placental Origins of Adverse Pregnancy Outcomes: Potential Molecular Targets workshop recently concluded that most adverse pregnancy outcomes are rooted in placental dysfunction5. Despite this, the molecular underpinnings of placental dysfunction are poorly understood.
Placenta-specific cell types including cytotrophoblasts, syncytiotrophoblasts, extravillous trophoblasts, and placental resident macrophage Hofbauer cells are all essential for placental development, structure, and function6. Dysfunction of these specific cell types likely plays a role in placental pathogenesis. For example, extravillous trophoblasts are responsible for invading into the maternal decidua early in pregnancy to remodel uterine arteries and increase blood flow to the placenta3. Inadequate or inappropriate invasion of extravillous trophoblasts has previously been implicated in preeclampsia etiology7–9. Despite some knowledge of the roles of specific placental cell types in the development of preeclampsia, relatively little is known about how individual cell types contribute to placental dysfunction.
Existing research models used to investigate the function and dysfunction of individual cell types are limited. Protocols to isolate primary placental cells for experimental research are restricted to one or few cell types10–15. Cell type-specific assays are costly and require special techniques or training resulting in small sample sizes and have not yet been scalable to large epidemiological studies16–18. Furthermore, placental cell lines such as BeWo, derived from choriocarcinoma19, and HTR-8/SVneo, immortalized by SV4020, are typically derived by processes that alter the DNA of the cells, limiting their in vivo translatability. Consequently, the characteristics of even healthy placental cell type function and especially their connections to adverse outcomes such as preeclampsia are incompletely understood.
Measures of gene expression in bulk placental tissue are used to better understand the biological mechanisms underlying adverse pregnancy outcomes21–23 and are common in epidemiological studies24. Gene expression profiles differ systematically by cell type25,26. Thus, bulk placental tissue-level gene expression measurements represent a convolution of gene expression signals from individual cells and cell types27,28. Deconvolution refers to the bioinformatic process of estimating the distribution of cell types that constitute the tissue29,30. Deconvoluting tissue-level gene expression profiles is essential to account for effects introduced by unmodeled cell type proportions31 by disentangling shifts in cell type proportions from direct changes to cellular gene expression32. Reference-based deconvolution boasts biologically interpretable cell type proportion estimates with few modeling assumptions but relies on independently collected cell type-specific gene expression profiles as inputs32. Prior placental cell type-specific gene expression measures from term villous tissue16,17 had a limited number of biological replicates and included neither technical replicates nor benchmarking against physically isolated placental cell types. A robust, accessible, and publicly available gene expression deconvolution reference is currently unavailable for healthy placental villous tissue.
To advance the field of perinatal molecular epidemiology, our goal was to develop an accessible and robust gene expression deconvolution reference for healthy placental villous tissue at term. *We* generated single-cell RNA-sequencing data with technical replicates for integration with existing cell type-specific placental gene expression data16,17. In addition, we benchmarked these single-cell cell type-specific gene expression profiles against placental cell types isolated with more conventional fluorescence-activated cell sorting (FACS) followed by bulk RNA-sequencing. Finally, to assess links between preeclampsia and placental cell types and their proportions, we applied our placenta cell type gene expression reference to deconvolute bulk placental tissues in a secondary data analysis of a case–control study33 of preeclampsia, including a mediation analysis of the preeclampsia-associated genes FLT1, LEP, and ENG that quantifies the role cellular composition plays in explaining bulk gene expression measures.
## Single-cell gene expression map of healthy placental villous tissue
A conceptual layout of the laboratory methods and analyses contained within this manuscript is provided in Supplementary Fig. 1. From healthy term placental villous tissue, 9244 cells across a total of two biological replicates and two technical replicates were sequenced and analyzed (Michigan sample). These data were combined with single-cell RNA-sequencing data of 5911 cells from three healthy term villous tissue samples in a previously published study (Pique-Regi sample)17 and 25,339 cells from four healthy term villous tissue samples in another previously published study, two of which were subsampled with an additional peripheral placental villous tissue sample (Tsang sample)16 (Supplementary Table 1). Cells were excluded if they had low RNA content (<500 unique RNA molecules), few genes detected (<200), or were doublets or outliers in mitochondrial gene expression (Supplementary Figs. 2 and 3). Fetal or maternal origin of cells was determined by genetic variation in sequencing data. Fetal sex was determined by XIST expression (Supplementary Fig. 4). The final analytic sample included 40,494 cells and 36,601 genes across nine biological replicates, two of which had a technical replicate and another two included peripheral subsampling.
Uniform Manifold Approximation and Projection (UMAP)34 was used to visualize sequencing results in two dimensions with mutual nearest neighbor batch correction35 (Fig. 1a). Cells clustered into 19 fetal and 8 maternal cell types with $84.4\%$ of all cells being of fetal origin (Table 1). Cell type clustering decisions balanced cluster stability, resolution, and biologic plausibility with prior knowledge. If desired, downstream analyses could collapse cell subtypes into a single, more general cell type cluster. We observed placenta-specific trophoblast cell types including cytotrophoblasts (KRT7), proliferative cytotrophoblasts (KRT7, STMN1 and other proliferation-related genes)36, extravillous trophoblasts (HLA-G)37, and syncytiotrophoblasts (PSG4 and other pregnancy-specific hormone genes) (Supplementary Fig. 5a)38. Proliferative cytotrophoblasts were distinguished from other cytotrophoblasts by overexpression of genes related to cytoplasmic translation (padj = 8.1 × 10−15) and mitotic sister chromatin segregation (padj = 1.5 × 10−12), indicative of their proliferative phenotype (Supplementary Fig. 6). Other fetal-specific cell types included mesenchymal stem cells (COL1A1lo, TAGLNlo, LUMhi), fibroblasts (COL1A1hi, TAGLNhi, LUMlo)39, endothelial cells (PECAM1)40, and Hofbauer cells (CD163)11 (Fig. 1b).Fig. 1Integrated single-cell gene expression map of healthy placental villous tissue.a Uniform Manifold Approximation and Projection (UMAP) plot of all cells ($$n = 40$$,494), with each cell colored by cell type cluster. b UMAP plot of fetal cells only ($$n = 34$$,165), with each cell colored by cell type cluster. c UMAP plot of maternal cells only ($$n = 6329$$), with each cell colored by cell type cluster. Table 1Number of cells captured by single-cell RNA-sequencing in the final analytic dataset for each cell type by sample source. Distribution of cell types by sample and fetal/maternal originCell type1A1B2A2B345678C8P9C9PCountOverall proportionsFetal originMaternal originFetal B cells$10914919522938271503591118092.0\%$$2.4\%$–Fetal CD14+ monocytes$23218717419745234211130109162.3\%$$2.7\%$–Fetal CD8+ activated T cells$22020514816278766029120129392.3\%$$2.7\%$–Fetal cytotrophoblasts$37003508598412613748220316335935028.6\%$$10.3\%$–Fetal endothelial cells$611221514310581801818423613719384.8\%$$5.7\%$–*Fetal extravillous* trophoblasts$00006222323433362519752415610.3\%$$12.2\%$–Fetal fibroblasts$100325175033524617946755110282.5\%$$3.0\%$–Fetal GZMB+ natural killer$10777481661931041190.3\%$$0.3\%$–Fetal GZMK+ natural killer$71371110690100420002500.6\%$$0.7\%$–Fetal Hofbauer cells$23271151361071142662406079323264917431167.7\%$$9.1\%$–Fetal memory CD4+ T cells$2641424568542811831503320.8\%$$1.0\%$–Fetal mesenchymal stem cells53465460377115011207412718066061633175512,$23630.2\%$$35.8\%$–Fetal naive CD4+ T cells$2222152252133055251062567210772.7\%$$3.2\%$–Fetal naive CD8+ T cells$47301001171737911204003740.9\%$$1.1\%$–Fetal natural killer T cells$30342232514280110002210.5\%$$0.6\%$–Fetal nucleated red blood cells$001110302141110340.1\%$$0.1\%$–Fetal plasmacytoid dendritic cells$3127211271116431071040.3\%$$0.3\%$–Fetal proliferative cytotrophoblasts$101011118528233468014121123145326306.5\%$$7.7\%$–Fetal syncytiotrophoblast$718636730341333954383840.9\%$$1.1\%$–Maternal B cells$11814434733922200622009822.4\%$–$15.5\%$Maternal CD14+ monocytes$27128218121221680122310111082.7\%$–$17.5\%$Maternal CD8+ activated T cells$4494352662773347404129620216094.0\%$–$25.4\%$Maternal FCGR3A+ monocytes$97114404831978255216740111010912.7\%$–$17.2\%$Maternal naive CD4+ T cells$524256661120803171312980.7\%$–$4.7\%$Maternal naive CD8+ T cells$7495181172841501100005871.4\%$–$9.3\%$Maternal natural killer cells$11413968601983056100314811.2\%$–$7.6\%$*Maternal plasma* cells$393832460820710001730.4\%$–$2.7\%$Count2214228022922458162020812210376510,679372616512508301040,$494100.0\%$34,1656329Overall cell composition by cell count provided for each cell type. Proportions represent the overall proportion of that cell type in the dataset or among cells of only fetal or maternal origin. The final analytic sample included 40,494 cells and 36,601 genes across nine biological replicates, two of which had a technical replicate (Samples 1B and 2B) and another two included peripheral subsampling (Samples 8P and 9P).
Fetal and maternal lymphocytes, B cells, and monocytes were also captured (Fig. 1b, c). We observed fetal and maternal B cells (CD79A)41 and maternal plasma cells (XBP1, IGHA and other immunoglobulins)42. We also observed fetal and maternal CD14+ monocytes (CD14+/FCGR3A−), maternal FCGR3A+ monocytes (CD14+/FCGR3A+)43, and a small population of fetal plasmacytoid dendritic-like cells (FLT3+/ITM2C+)44,45. We further observed fetal and maternal natural killer cells (NKG7), fetal GZMB+ or GZMK+ natural killer cell subtypes, and fetal natural killer T cells (NKG7+/CD3E+/CD8A-)46,47. Finally, we observed a variety of T cell subtypes: naïve CD4+ (CCR7, CD3E, CD4), naïve CD8+ (CCR7, CD3E, CD8A), memory CD4+ (S100A4, CD3E, CD4, IL2, CCR7lo), and activated CD8+ T cells (NKG7, CD3E, CD8A) (Supplementary Fig. 5b)48.
To identify upregulated genes in each cell type, we compared the expression of a gene in one cell type against that gene’s average expression in all other cell types (Supplementary Data 2). Consequently, the same genes could be upregulated across several cell types of a similar lineage. FLT1 expression was highly upregulated in extravillous trophoblasts (log2 fold-change (FC) = 3.89, padj < 0.001). Trophoblast cell types had the largest and most diverse transcriptomes, characterized by the largest number of unique RNA transcripts and detected genes per cell (Supplementary Fig. 7). Functional analysis of upregulated genes revealed cell type-specific biological processes (Supplementary Data 3). For example, fetal extravillous trophoblasts were enriched for genes relevant to placental structure and function such as cell migration (padj < 0.001) and response to oxygen levels (padj < 0.001) and syncytiotrophoblasts were enriched for genes involved in steroid hormone biosynthetic process (padj < 0.001). Technical replication in Michigan samples 1 and 2 appeared high in UMAP space (Supplementary Fig. 8a, b). Indeed, the average intra-cluster gene expression between technical replicates had an average Spearman correlation (mean ± standard deviation) of 0.94 ± 0.14 for sample 1 and 0.88 ± 0.20 for sample 2 (p values < 0.001).
## Single-cell RNA-sequencing deconvolution reference exhibits excellent in silico performance
Based on the single-cell data, we created a placental signature gene matrix that incorporated expression information across an algorithmically selected 5229 signature genes to estimate the cellular composition of 27 fetal and maternal cell types from whole tissue gene expression data (Supplementary Fig. 9). To test the performance and robustness of this placental single-cell RNA-sequencing deconvolution reference, we randomly split our analytic single-cell RNA-sequencing dataset into $50\%$ training and $50\%$ testing subsets with balanced cell type proportions49. The same training dataset was used for each comparison; test mixtures were generated from the testing half of the dataset. Using a signature gene expression matrix generated from the training data, we estimated cell type composition in in silico pseudo-bulk testing data mixtures of known cell type composition with varying contributions of fetal vs. maternal origin cells and male vs. female fetal cells (Fig. 2). In all mixtures, the 27 predicted and actual cell type proportions were correlated (p value < 0.001 for each test). In the primary deconvolution analysis of all cell types at their natural rates ($$n = 20$$,242), estimated and actual cell type proportions had a Pearson correlation coefficient of 0.956 ($95\%$ CI [0.904, 0.980]). The worst performance was under the unrealistic scenario that the mixture was composed entirely of maternal cell types ($$n = 3162$$) with a Pearson correlation of 0.734 ($95\%$ CI [0.491, 0.871]) between actual estimated cell type proportions. Our deconvolution reference was also robust to fetal sex when only male fetal cells (Pearson correlation = 0.893, $95\%$ CI [0.776, 0.950]) were included ($$n = 8394$$), or only female fetal cells (Pearson correlation = 0.983, $95\%$ CI [0.964, 0.993]) ($$n = 8394$$). Together, these results show that our reference panel can successfully deconvolute placental tissues, though some maternal cell types common to both mother and fetus may be erroneously labeled fetal in the absence of fetal cells of those cell types. Fig. 2In silico placental deconvolution testing. Scatter plots summarizing the performance of our single-cell deconvolution reference using CIBERSORTx with in silico mixtures of single-cell libraries from a $\frac{50}{50}$ training/test split of the integrated single-cell RNA-seq dataset ($$n = 40$$,494). The same training dataset was used for each comparison; test mixtures were generated from the testing half of the dataset. Predicted deconvoluted cell type proportions for each of the 27 cell types are encoded on the x-axis. Actual cell type proportions from the test dataset are encoded on the y-axis. Correlation coefficients and root mean square error measures are presented for each comparison. A linear line of best fit overlays the results. The gray shaded area represents the $95\%$ confidence intervals around the simple linear regression estimates. a The test mixture is the test half of the single-cell dataset ($$n = 20$$,242). b The test mixture sampled only fetal cells ($$n = 17$$,080). c The test mixture sampled only maternal cells ($$n = 3162$$). d The test mixture sampled only female fetal cells ($$n = 8394$$). e The test mixture sampled only male fetal cells ($$n = 8394$$).
## Fluorescence-activated cell sorting of major placental cell types yielded mixed cell type isolation results
We isolated whole bulk placental villous tissue, enriched syncytiotrophoblasts, and sorted five cell types (Hofbauer cells, endothelial cells, fibroblasts, leukocytes, extravillous trophoblasts, and cytotrophoblasts) via FACS from four healthy term, uncomplicated Cesarean sections for bulk RNA-sequencing, labeled Sorted 1 (same sample source as single-cell RNA-sequencing sample 1), Sorted 2, Sorted 3, and Sorted 4 (Supplementary Fig. 10 and Supplementary Table 2). For analysis, as recommended50, we excluded 19,048 genes that were not present in at least 3 samples and an additional 865 genes that did not have a cumulative library size-normalized count of at least 10. Principal component (PC) analysis of whole-transcriptome sorted-cell bulk RNA-sequencing normalized counts is provided in Supplementary Fig. 11.
To identify upregulated genes in each cell type, we compared the expression of a gene in one cell type against that gene’s average expression in all other cell types (Supplementary Fig. 12). Consequently, the same genes could be upregulated across several cell types of a similar lineage. All 38,468 uniquely mapping genes were tested. A total of 746 genes were algorithmically dropped from the syncytiotrophoblast contrast due to excessively low counts, low variability, or extreme outlier status. Large-scale gene expression differences were observed for each cell type (Supplementary Data 4). Functional analysis of upregulated genes revealed cell type-specific biological processes (Supplementary Data 5). For example, syncytiotrophoblasts were enriched for genes relevant to placental structure and function such as angiogenesis, cell-substrate adhesion, and regulation of epithelial cell proliferation (padj < 0.001). To compare sorted and single-cell differential expression and enrichment results, we tabulated the number of unique genes and pathways overlapping between the two analyses after collapsing the single-cell cell type cluster labels to the seven cell type fractions that we had targeted to isolate for downstream analyses (Supplementary Table 3). On average, $15.0\%$ of single-cell upregulated genes and $5.9\%$ of enriched pathways were also identified among the sorted-cell data. On average, $17.5\%$ of sorted cell type upregulated genes and $39.2\%$ of pathways were also identified among the single-cell data. Sorted endothelial cell results were limited due to the limited number of biological replicates.
We applied the single-cell deconvolution reference to estimate cell proportions in the 4 whole tissue (with 1 additional technical replicate) and 19 sorted or enriched cell type fractions. We collapsed the single-cell cell type cluster labels to the seven cell type fractions we targeted for isolation for downstream analyses (Supplementary Data 6, Sheet 1). All deconvoluted samples exhibited high goodness-of-fit between original bulk mixtures and the estimated cell type proportion mixtures (p values < 0.001). Among the signature genes, original bulk and estimated cell type fractions had a Pearson correlation (mean ± standard deviation) of 0.73 ± 0.11 and root mean square error of 0.88 ± 0.04 (Supplementary Data 6, Sheet 2). Deconvolution results (mean ± standard deviation) suggest we successfully isolated fibroblast- ($$n = 3$$, $74.7\%$ ± $0.6\%$) and leukocyte-enriched ($$n = 4$$, $82.3\%$ ± $24.8\%$) cell type fractions. Other cell type targets were less successful (range 0–$26\%$ estimated purity). The Hofbauer cell fraction was predicted to be mostly leukocytes ($$n = 4$$, $91.5\%$ ± $0.5\%$).
## Cell proportion deconvolution of bulk placental tissue preeclampsia dataset
We applied the single-cell deconvolution reference to estimate cell proportions from bulk placental tissue in 157 preeclampsia cases and 173 controls33 compiled from eight previously published studies33,51–57. Mean gestational age was 2.2 weeks younger in cases than controls (p value < 0.001, Table 2). All deconvoluted samples exhibited high goodness-of-fit between original bulk mixtures and the estimated cell type proportion mixtures (p values < 0.001). Among the signature genes, original bulk and estimated mixtures had a Pearson correlation (mean ± standard deviation) of 0.70 ± 0.04 and root mean square error of 0.73 ± 0.03 (Supplementary Data 7). Fetal naïve CD4+ T cells and fetal GZMB+ natural killer cells were estimated to be at $0\%$ abundance in all samples and were dropped from downstream analyses. Cytotrophoblasts were the most abundant (mean ± standard deviation) estimated fetal cell type ($27.9\%$ ± $4.3\%$) followed by syncytiotrophoblasts ($23.4\%$ ± $5.0\%$) and mesenchymal stem cells ($10.3\%$ ± $3.3\%$). The most common maternal cell types were naïve CD8+ T cells ($2.8\%$ ± $1.5\%$), plasma cells ($1.4\%$ ± $0.7\%$), and B cells ($1.3\%$ ± $0.8\%$). A comparison of deconvoluted whole tissue cell type proportions among healthy individuals (Supplementary Fig. 13) between the microarray dataset GSE75010 ($$n = 173$$ controls), our whole tissue bulk RNA-sequencing samples (sorted samples 1–4), and the single-cell dataset compiled here (single-cell samples 1–9) suggests that syncytiotrophoblasts and endothelial cells are underrepresented in the single-cell data. This is likely due to dissociation bias, which has been commonly observed in single-cell assays of other tissues58. Overall, the Pearson correlation of the average deconvoluted cell type proportion across the 27 cell types between healthy bulk RNA-sequencing and microarray controls was 0.80 ($95\%$ CI: [0.60, 0.91]).Table 2Demographic characteristics of eight previously published bulk microarray placental gene expression case–control studies (accessed through GSE75010) for deconvolution application testing. Descriptive statistics of microarray preeclampsia case–control studiesControl($$n = 173$$)Preeclampsia($$n = 157$$)P valueFetal sex Female78 ($45.1\%$)81 ($51.6\%$)0.28 Male95 ($54.9\%$)76 ($48.4\%$)Gestational age (weeks) Mean (SD)35.2 (3.97)33.0 (3.17)<0.001 Median [min, max]37.0 [25.0, 41.0]33.0 [25.0, 39.0]Study GSE1058826 ($15.0\%$)17 ($10.8\%$)0.39 GSE241298 ($4.6\%$)8 ($5.1\%$) GSE2590637 ($21.4\%$)23 ($14.6\%$) GSE301866 ($3.5\%$)6 ($3.8\%$) GSE439427 ($4.0\%$)5 ($3.2\%$) GSE447118 ($4.6\%$)8 ($5.1\%$) GSE47074 ($2.3\%$)10 ($6.4\%$) GSE7501077 ($44.5\%$)80 ($51.0\%$)Bivariate batch with Kruskal–Wallis ANOVA (regular ANOVA homogeneity of variances violated) for continuous variables and χ2 test for categorical outcomes.
## Differentially abundant cell type proportions in preeclampsia cases versus controls
To test for differences in cell proportions between preeclampsia cases and controls (Supplementary Fig. 14), we fit beta regression models for each cell type proportion adjusted for study source, fetal sex, and gestational age to estimate the prevalence odds ratio for each cell type (Supplementary Data 8). Among fetal cell types, extravillous trophoblasts ($p \leq 0.001$), memory CD4+ T cells ($$p \leq 0.007$$), CD8+ activated T cells ($$p \leq 0.005$$), and natural killer T cells ($$p \leq 0.006$$) were more abundant (Fig. 3) in preeclampsia cases relative to controls. The unadjusted median extravillous trophoblast abundance was $6.4\%$ among cases compared to $2.1\%$ among controls. Mesenchymal stem cells (median percent composition in cases vs. controls, $8.8\%$ vs. $11.0\%$), Hofbauer cells ($2.7\%$ vs. $4.4\%$), and fetal naive CD8+ T Cells ($4.2\%$ vs. $4.5\%$) were all less abundant among preeclampsia cases compared to controls ($p \leq 0.001$). Among maternal cell types, maternal plasma cells ($1.6\%$ vs. $1.2\%$) were more abundant among preeclampsia cases compared to controls ($p \leq 0.001$).Fig. 3Preeclampsia case–control status and cell type proportion differential abundance analysis. Forest plot of multivariate beta regression models’ prevalence odds ratio estimates adjusted for study source, gestational age, and fetal sex tested for a difference in each cell type’s proportions in cases versus controls ($$n = 157$$ cases, 173 controls). Horizontal lines indicate the range of the $95\%$ confidence interval.
## Differential expression between preeclampsia cases and controls attenuated by cell type proportion adjustment
To test whether microarray gene expression differences between preeclampsia cases and controls are partly driven by differences in cell type abundances, we fit linear differential gene expression models adjusted for covariates study source, fetal sex, and gestational age with and without adjustment for deconvoluted cell type proportions. To reduce the number of model covariates and account for dependence between deconvoluted cell type proportions, we applied PC analysis to deconvoluted cell type proportions. The first five PCs accounted for $87.2\%$ of the variance in deconvoluted cell type proportions and were added as additional covariates to form the cell type-adjusted model. Variation in PCs 1 and 2 was largely driven by syncytiotrophoblasts ($33.8\%$), extravillous trophoblasts ($33.5\%$), and cytotrophoblasts ($15.3\%$) proportions and provided some separation between cases from controls (Supplementary Fig. 15a, c). Variation in PC3 was largely driven by cytotrophoblasts ($50.1\%$) and to a lesser extent syncytiotrophoblasts ($16.6\%$), mesenchymal stems cells ($14.5\%$), and extravillous trophoblasts ($13.7\%$) (Supplementary Fig. 15b, d).
In the cell type-naïve base models ($$n = 14$$,651 genes, 173 controls, and 157 cases) adjusted for study source, gestational age, and fetal sex, 550 genes were differentially upregulated and 604 were downregulated in preeclampsia cases versus controls (Fig. 4a and Supplementary Data 9). Gene set enrichment analysis of biological processes identified 41 overrepresented pathways in the base model (Fig. 5a and Supplementary Data 10). Biological process pathways such as aerobic respiration (false discovery adjusted q < 0.001), mitochondrial respiratory chain complex assembly (q < 0.001), glutathione metabolism ($q = 0.003$), and ribosome biogenesis ($q = 0.001$) were overrepresented among downregulated genes. No pathways were overrepresented among upregulated genes, though intermediate filament organization ($q = 0.26$), keratinocyte differentiation ($q = 0.77$), and endothelial cell development ($q = 0.42$) had comparable enrichment scores. Remarkably, when the base model was additionally adjusted for the first five PCs of imputed cell type proportions, there were zero differentially expressed genes between preeclampsia cases and controls (Fig. 4b and Supplementary Data 9). Of the cell type-adjusted results, 19 pathways were overrepresented (Fig. 5b and Supplementary Data 10). Downregulation of mitochondrial respiratory chain complex assembly (q < 0.001), aerobic respiration ($q = 0.001$), ribosome biogenesis ($q = 0.001$), and glutathione metabolism ($q = 0.02$) were overrepresented among downregulated genes. Detection of chemical stimulus involved in sensory perception of smell ($q = 0.04$) and non-coding RNA processing ($q = 0.04$) were also overrepresented pathways among downregulated genes. Neuroepithelial cell differentiation ($q = 0.04$) was overrepresented among upregulated genes. Vascular endothelial growth factor receptor signaling pathway ($q = 0.15$), of which FLT1 is a member, had an enrichment score of 1.77 (up from 1.34, $q = 0.43$ in the base model) but did not meet the q value cutoff. Overall, downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis and related pathways were robust to cell type proportion adjustment. Fig. 4Preeclampsia case–control differential expression analysis. Volcano plots comparing differentially expressed genes in samples from 153 preeclampsia cases versus 173 healthy controls across two models: a the base model adjusted for covariates fetal sex, study source, and gestational age and b the model adjusted for fetal sex, study source, and gestational age and additionally adjusted for the first five principal components of estimated cell type proportions. Dotted line represents a false discovery rate-adjusted q value of 0.05. FLT1, LEP, and ENG are labeled as genes of interest in preeclampsia. Fig. 5Preeclampsia case–control differential expression enrichment analysis. Top Gene Set Enrichment Analysis pathways from the Gene Ontology: Biological Processes database results for the differential expression analysis by preeclampsia case–control status. Results arranged by descending magnitude of the absolute value of the normalized enrichment score. Pathways colored red are significant at a false discovery rate-adjusted (FDR) q value of 0.05 whereas pathways in blue are statistically insignificant. a Top pathways from the cell type-unadjusted analysis. b Top pathways from the cell type-adjusted analysis.
## Differential expression of preeclampsia-associated genes mediated by placental cell type proportions
Overexpression of FLT1 in placental tissue59–62, detection of a soluble isoform of FLT1 in maternal circulation63,64, and fetal genetic variants near FLT165 have implicated FLT1 in preeclampsia etiology. Because we observed cell type-specific expression patterns of FLT1 in trophoblasts, particularly in extravillous trophoblasts, we hypothesized that the observed attenuation of FLT1 differential expression may be due in part to the differences in cell type proportions observed between preeclampsia cases and controls. To test this hypothesis, we applied a unified mediation and interaction analysis to quantify the proportion of FLT1 expression differences mediated by deconvoluted cell type proportions. We did not observe an interaction between preeclampsia status and cellular composition (overall proportion attributable to interaction = −$5.8\%$, $95\%$ CI [−$17.1\%$, $5.0\%$]). We therefore dropped interaction parameters from the model for the final analysis. In the model without interaction, $37.8\%$ ($95\%$ CI [$27.5\%$, $48.8\%$]) of the 1.05 ($95\%$ CI [0.89, 1.21]) log2 signal intensity increase in the association between preeclampsia and FLT1 expression was attributable to differences in placental cell composition between preeclampsia cases and controls (Fig. 6). Overexpression of LEP and ENG have also been associated with preeclampsia59–62. Mediation results were similar for LEP (total effect = 2.62 ($95\%$ CI [2.26, 2.97] log2 signal intensity increase; proportion mediated = $34.5\%$ ($95\%$ CI [$26.0\%$, $44.9\%$]) and ENG (total effect = 0.93 ($95\%$ CI [0.79, 1.07] log2 signal intensity increase; proportion mediated = $34.5\%$ ($95\%$ CI [$25.0\%$, $45.3\%$]).Fig. 6Placental cell composition as a mediator of FLT1 expression. Mediation of FLT1 gene expression by placental cell type composition ($$n = 157$$ cases, 173 controls). Placental cell composition was operationalized as first five principal components of estimated cell type proportions. $95\%$ confidence intervals are provided after effect estimates for each model parameter. The same framework was also applied with LEP or ENG expression as the outcome.
## Discussion
To create the largest publicly available placental RNA deconvolution reference of 19 fetal and 8 maternal cell type-specific gene expression profiles, we newly sequenced placental villous cells, integrated those results with data from previously published studies, and built a signature gene matrix for deconvolution of bulk villous tissue gene expression data. In silico testing of our deconvolution reference demonstrated successful and robust deconvolution. To compare single-cell placental cell type expression profiles to more conventional sorting methods, we created a FACS scheme to enrich and sequence RNA from five important placental cell types as well as syncytiotrophoblasts. Deconvolution of sorted cell type fractions with the single-cell deconvolution reference suggested most conventionally sorted cell types are far less pure than what can be accomplished with clustering and aggregation of single-cell results, and at much lower cell type resolution. We applied the single-cell deconvolution reference to estimate cell type proportions in a previously published epidemiologic microarray study of the pregnancy complication preeclampsia, revealing placental cell type proportion differences between preeclampsia cases and controls at term. We then showed that large gene expression differences between preeclampsia cases and controls were markedly attenuated after adjustment for cell type proportions. Preeclampsia-associated pathways, including downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Finally, to quantify the attenuation of differential expression of the preeclampsia biomarkers FLT1, LEP, and ENG, we applied mediation analysis to show cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1, LEP, and ENG overexpression. Cell type proportions may be an important and often overlooked factor in gene expression differences in placental tissue studies.
By integrating our new single-cell RNA-sequencing results with those from a previously published study, our integrated dataset, to our knowledge, is the largest and possibly only reference available for healthy, term placental villous tissue to date. We document term cell type-specific gene expression patterns for well-characterized placental cell types, including syncytiotrophoblasts10, cytotrophoblasts13, and extravillous trophoblasts14. In addition, we provide gene expression markers for relatively understudied placental cell types such as endothelial cells, mesenchymal stem cells, and Hofbauer cells as well as maternal peripheral mononuclear cells recovered from the maternal-fetal interface. Compared to the previous analysis of the published samples17 which relied on predominately sex-specific gene expression markers to differentiate proliferative from non-proliferative cytotrophoblasts, we show that functional enrichment analysis revealed broad upregulation of proliferation pathways in proliferative cytotrophoblasts. The low representation of some cell types such as trophoblasts in our single-cell RNA-sequencing results from the Michigan study suggests that these cell types may be especially sensitive to dissociation and disintegrate before transcript capture, commonly referred to as dissociation bias58. Michigan samples 1 and 2 also included a cryopreservation step like those employed in large-scale epidemiological studies that may have exacerbated dissociation bias66; this applies to both single-cell and sorted cell type experiments. Future studies may propose alternative approaches to perform unbiased single-cell RNA-sequencing in placental tissues; indeed, single-nucleus RNA-sequencing has been used to characterize an in vitro syncytiotrophoblast model and may be more appropriate to assay such cell types sensitive to dissociation procedures67. We verified that our deconvolution reference exhibited strong performance even with extremely imbalanced and unlikely real-world test mixture distributions by fetal sex and maternal cell type representation.
Our preeclampsia findings are consistent with a prior pathophysiological understanding of the disorder, linking cell type proportion estimates and gene expression data in bulk tissue. Among preeclampsia cases, we observed an elevated proportion of extravillous trophoblasts and underrepresentation of stromal cell types, which may reflect an arrest in conventional placental cell type differentiation and maturation following insufficient uterine spiral artery remodeling implicated in preeclampsia68–70. A recent study of bulk placental gene expression across trimesters suggests that Hofbauer cells may more abundant in the second trimester compared to the third, possibly to support vasculogenesis, though this study involved a small deconvolution reference that contained a limited variety of cell types71. A better understanding of the evolution of temporal placental composition changes may yield greater insight into placental pathologies. In the cell type-naïve differential expression model, consistent with previous findings, placentas from pregnancies with preeclampsia overexpressed FLT1, LEP, and ENG59–62. In our cell type-adjusted model, FLT1 and LEP remained only nominally significant whereas ENG did not meet the nominal significance threshold. Mediation analysis confirmed that a significant proportion of FLT1, LEP, and ENG overexpression was attributable to differences in the cellular composition of the placenta. These results suggest that placental cell type proportion differences may be an overlooked factor in explaining the well-documented association between preeclampsia and FLT1, LEP, and ENG expression59–62. Downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis was robust to cell type adjustment, indicating direct changes to these pathways beyond shifts in cell type abundance. Indeed, disruption of the mitochondrial fission-fusion cycle72, malperfusion73,74, and inhibited protein synthesis secondary to endoplasmic reticulum stress75,76 have all previously been associated with preeclampsia. Interestingly, cell type adjustment increased the enrichment score results of vascular endothelial growth factor receptor signaling pathway, a mechanistic hypothesis in preeclampsia etiology63,73,77,78, from 1.36 to 1.77 ($q = 0.43$ to $q = 0.15$). This approach may reveal the biological mechanisms of other diseases beyond cellular composition differences. Because oxygen tension is a critical factor in trophoblast differentiation, inappropriate oxygenation may partially explain the elevated proportion of extravillous trophoblasts, though regulators of this process such as HIF1A and TGFB379 were not differentially expressed at the tissue level in our analysis. A recent single-cell RNA-sequencing case–control study of preeclampsia, however, identified upregulation of TGFB1 in extravillous trophoblasts, potentially indicative of altered trophoblast differentiation or invasion80,81. A similar study revealed decreased activity of gene network modules regulated by transcription factors ATF3, CEBPB, and GTF2B and decreased expression of CEBPB and GTF2B in preeclamptic extravillous trophoblasts compared to controls; follow-up in vitro experiments suggested CEBPB and GTF2B knockdown reduced extravillous trophoblast viability and invasion82. Consistent with our other findings, this study also observed a similar trend in cell type proportion differences and upregulation of FLT1 in extravillous trophoblasts and ENG in syncytiotrophoblasts between preeclampsia cases and controls80. Future work should consider and account for cell type proportions and the cell type-specific expression patterns of genes that regulate placental development or are associated with preeclampsia to better understand preeclampsia etiology.
This study has several strengths. We profile the parenchymal healthy term villous tissue in the placenta and integrate our dataset with samples from previously published studies to generate the largest, to the best of our knowledge, cell type-specific placental villous tissue gene expression reference to date. Single-cell RNA-sequencing allowed us to agnostically capture diverse placental cell types without a priori knowledge of cell types and their characteristics and tabulate gene expression patterns at high resolution and specificity. Our in silico deconvolution tests demonstrated robust performance to even extreme distributions of maternal or sex of fetal cells. We demonstrate technical replication of single-cell RNA-sequencing in placental villous tissue. We were able to apply our findings to a large target deconvolution dataset of preeclampsia that contained placental measures from hundreds of participants across eight different studies. Most importantly, we evaluate cell type proportion differences in an epidemiological study of placental parenchymal tissue and preeclampsia, and genome-wide gene expression differences accounting for cell type heterogeneity, a critical limitation in bulk tissue assays.
This study also has several limitations. Although our cellular sample size comprised of 40,494 cells is relatively large compared to previous single-cell RNA-sequencing studies of term placental villous tissue, this dataset still represents a limited biologic replicate sample size compared to epidemiologic scale studies. Our newly sequenced samples came from a convenience sample without available demographic information beyond uncomplicated and healthy Cesarean-section status. Similarly, the sample size of FACS-sorted tissues was limited, and some cell type fractions were excluded due to low RNA quality or exhibited poor estimated purity, likely complicated by degradation of cell surface markers from apoptosis characteristic of development and parturition83,84 and sample processing. This study did not include placental tissues for single-cell analysis from preeclamptic patients to confirm intra-cell type gene expression changes. Despite excellent in silico performance, we had no external gold standard to verify deconvolution performance. This deconvolution reference may not be sensitive enough to discriminate between cell subtypes such as proliferative vs. non-proliferative cytotrophoblasts that are clearly delineated in the single-cell analysis; in such cases, investigators may collapse cell type proportions counts into a single major cell type group, such as cytotrophoblasts. Future studies may verify whether cell type proportions estimated in diseased or vaginally delivered tissues are robust to a deconvolution reference generated from healthy villous tissue delivered via Cesarean-section. Residual confounding may remain in our statistical models due to the limited number of common covariates across all eight preeclampsia case–control studies. Due to the nature of villous tissue sampling, our study design is cross-sectional, limiting our ability to establish temporality between exposure and outcome to rule out reverse causation. As with any study conditioned on live birth, selection bias may affect our results. However, the effects of harmful exposures that lead to selection tend to be underestimated in these scenarios85,86. Therefore, our results likely represent a conservative underestimate of the effects of preeclampsia on inappropriate cell composition and preeclampsia status on gene expression.
In summary, we provide a cell type-specific deconvolution reference via single-cell RNA-sequencing in the parenchymal placental term villous tissue. We demonstrated this reference was robust to different distributions of maternal and fetal sex through in silico validation testing. In addition, we benchmarked these single-cell cell type-specific gene expression profiles against placental cell types isolated with more conventional FACS followed by bulk RNA-sequencing. We applied this deconvolution reference to an epidemiologic preeclampsia dataset to reveal biologically relevant shifts in placental cell type proportions between preeclampsia cases and controls. Once cell type proportion differences were accounted for, differential gene expression differences were markedly attenuated between preeclampsia cases and controls. Enrichment analysis revealed downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. A substantial proportion of the overexpression of the FLT1, LEP, and ENG in preeclampsia was mediated by placental cell composition. These results add to the growing body of literature that emphasizes the centrality of cell type heterogeneity in molecular measures of bulk tissues. We provide a publicly available placental cell type-specific gene expression reference for term placental villous tissue to overcome this critical limitation.
## Placental tissue collection and dissociation
Placentas were collected shortly after delivery from healthy, full-term, singleton uncomplicated Cesarean sections at the University of Michigan Von Voigtlander Women’s Hospital. Pregnant women provided written informed consent for research use of discarded tissues. Study protocols for discarded tissue collection and research use were approved by the University of Michigan Institutional Review Board (HUM00017941, HUM00102038). Villous placental tissue biopsies were collected and minced for dissociation after cutting away the basal and chorionic plates and scraping villous tissue from blood vessels13. We subjected approximately 1 g minced dissected villous tissue to the Miltenyi Tumor Dissociation Kit on the GentleMACS Octo Dissociator with Heaters (Miltenyi Biotec) to yield single-cell suspensions of viable placental cells in 5 μM StemMACS™ Y27632 (Miltenyi Biotec) in RPMI 1640 (Gibco) according to manufacturer’s instructions for soft tumor type. Red blood cells were depleted using RBC lysis buffer (BioLegend) according to manufacturer’s protocol A. Single-cell suspensions were size-filtered at 100 μm to remove undigested tissue and subsequently at 40 μm12,13. To collect a syncytiotrophoblast-enriched fraction, the fraction between 40 and 100 μm was washed from the 40-μm strainers, adapting a previous protocol that collected syncytiotrophoblasts throughout this size range10. Single-cell suspensions <40 μm were cryogenically stored in 5 μM StemMACS™ Y27632 $90\%$ heat-inactivated fetal bovine serum (Gibco)/$10\%$ dimethyl sulfoxide (Invitrogen). For each placenta, additional whole villous tissue samples were stored in RNALater (Qiagen).
Previously published single-cell RNA-sequencing raw data of healthy, term placental villous tissue samples came from the Database of Genotypes and Phenotypes (Pique-Regi et al., accession number phs001886.v1.p187) SRR10166478 (Sample 3), SRR10166481 (Sample 4), and SRR10166484 (Sample 5)17. The collection and use of human materials for the study were approved by the Institutional Review Boards of the Wayne State University School of Medicine. All participating women provided written informed consent prior to sample collection17. Additional previously published samples came the European Genome-Phenome Archive (Tsang et al., accession number EGAS0000100244988) (Samples 6–9)16. The study was approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee, and informed consent was obtained after the nature and possible consequences of the studies were explained. Pregnant women were recruited from the Department of Obstetrics and Gynecology, Prince of Wales Hospital, Hong Kong with informed consent; the subjects studied had consented to sequencing data archiving16.
## Placental single-cell RNA-sequencing
Villous tissue single-cell suspensions were thawed and sorted via FACS with LIVE/DEAD Near-IR stain (Invitrogen) for viability and forward-scatter and side-scatter profiles to eliminate cellular debris and cell doublets. Viability- and size-sorted single-cell suspensions were submitted to the University of Michigan Advanced Genomics Core for single-cell RNA-sequencing. Single cells were barcoded, and cDNA libraries constructed on the Chromium platform (10X Genomics, Single Cell 3’ v2 chemistry). Paired-end 110 base pair reads were sequenced on NovaSeq 6000 (Illumina).
## Single-cell RNA-sequencing preprocessing
Raw reads were processed, deconvoluted, droplet filtered, and aligned at the gene level with the Cell Ranger pipeline using default settings (v4.0.0, 10X Genomics) based on the GRCh38 GENCODEv32/Ensembl 98 reference transcriptome with STAR v2.5.1b89. Previously published single-cell RNA-sequencing raw data of healthy, term placental villous tissue samples from the Database of Genotypes and Phenotypes (Pique-Regi et al., accession number phs001886.v1.p1) SRR10166478 (Sample 3), SRR10166481 (Sample 4), and SRR10166484 (Sample 5)17 and from the European Genome-Phenome Archive (Tsang et al., accession number EGAS00001002449) (Samples 6–9)16 were processed identically. The freemuxlet program in the latest version (accessed December 5, 2021) of the “popscle” package was used to assign fetal or maternal origin and identify 736 mosaic doublets for removal based on single nucleotide polymorphisms with minor allele frequency greater than $10\%$ from the 1000 Genomes Phase 3 reference panel (released May 2, 2013)90. Per cell quality control criteria were calculated using the quickQCPerCell() function (scater R package, version 1.18.6) with default settings91 (Supplementary Figs. 2 and 3) and included total unique RNA transcripts (also called unique molecular identifiers), unique genes, and percentage of reads mapping to mitochondrial genes92. According to the current recommended best practice, each batch was quality-controlled separately93. We excluded 6497 low-quality outlier cells defined as cells with less than 500 unique RNA molecules, less than 200 unique genes, or that were outliers in mitochondrial gene mapping rate. Mitochondrial mapping outliers exceeded four median absolute deviations in samples 1 and 2 (mitochondrial reads >$9.2\%$) or three median absolute deviations in samples 3, 4, and 5 (mitochondrial reads >$8.9\%$) and samples 6, 7, 8C, 8P, 9C, and 9P (mitochondrial reads >$9.1\%$). *To* generate normalized gene expression data for visualizations and analyses that required normalization, single-cell gene counts were library size normalized by dividing the number of counts by the total number of counts expressed in that cell, multiplied by a scale factor of 10,000, and log-transformed with the NormalizeData() function (Seurat R package, version 4.1.1).
## Single-cell RNA-sequencing clustering and cluster annotation
Maternal and fetal cells were split into separate datasets for clustering. To integrate data from cells across study sources and visualize clustering results with uniform manifold projection34, we used the mutual nearest neighbor batch correction approach via FastMNN from “SeuratWrapper” with default settings (R package, version 0.3.0)35. Supervised iterative clustering and sub-clustering with “Seurat” (R package, version 4.0.1) function FindClusters at different resolution parameters were evaluated using cluster stability via clustering trees in “clustree”94,95. A priori canonical cell type marker gene expression patterns and cluster marker genes were used to assign cell types to cell clusters (see results). Cells that fell outside cell type clusters or outlying in doublet density calculated with computeDoubletDensity were removed as putative doublets and doublet clusters were identified with findDoubletClusters for removal in “scDblFinder” (R package, version 1.4.0)96. 723 maternal-maternal or fetal-fetal putative doublets were excluded after integration and clustering. Using the manually annotated Michigan (this study) and Pique-Regi (phs001886.v1.p1) cell cluster labels as the reference data, Tsang sample (EGAS00001002449) cells were algorithmically annotated with “SingleR” (R package, version 1.6.1)97 with default settings, followed by manual review. Cells with low prediction certainty (assignment score lower than three median absolute deviations of all cells assigned) were excluded as putative maternal-maternal or fetal-fetal doublets. Fetal sex in Michigan (this study) samples was determined with average normalized XIST expression; fetal sex in Pique-Regi and Tsang samples was determined by annotation and confirmed with average normalized XIST expression (Supplementary Fig. 4). The final analytic sample included 40,494 cells and 36,601 genes across nine biological replicates, two of which had a technical replicate (Samples 1 and 2) and another two included peripheral subsampling (Samples 8 and 9).
## Single-cell RNA-sequencing differential expression and biological pathway enrichment statistical analysis
Technical correlation was assessed by Spearman correlation after averaging the normalized expression for each gene by cluster and by technical replicate. Cluster marker genes were identified in “Seurat” with the FindAllMarkers function with default settings on single-cell gene expression counts92,95. Specifically, including both maternal and fetal cell types, the expression level in each cell type cluster was compared against the average expression of that gene across all other cell types using the two-tailed Wilcoxon rank sum test with significance defined at a false discovery rate-adjusted p value less than 0.05 and a log2 FC cutoff of 0.25. Pairwise cluster markers were identified in “Seurat” with the FindMarkers function with an identical testing regime. *Overexpressed* genes were ranked by decreasing log2 FC for functional enrichment analysis with “gprofiler2” (R package, version 0.2.0, database version e102_eg49_p15_7a9b4d6) using annotated genes as the universe, excluding electronically generated annotations, and with the g:SCS multiple testing correction method applying a significance threshold 0.0598.
## In silico testing of deconvolution performance
To test the performance and robustness of our placental single-cell RNA-sequencing deconvolution reference, we randomly split our analytic single-cell RNA-sequencing dataset into $50\%$ training and $50\%$ testing subsets with balanced cell type proportions49. We applied the test subset with the CIBERSORTx Docker container (accessed December 7, 2021) to create a signature gene expression matrix to test deconvolution performance with default settings99. To evaluate the reference’s robustness to fetal sex and ability to discriminate immune cell types of fetal versus maternal origin, we generated in silico pseudo-bulk test mixtures with known distributions of fetal and maternal cells, as well as male and female placental cells. Test mixtures included all of the $50\%$ testing data, only fetal cells from the test data, only maternal cells from the test data, only female fetal cells from the test data, or only male cells from the test data. For the female and male fetal cell test mixtures, the baseline distribution of maternal cells was maintained by randomly down-sampling the maternal cells and randomly down-sampling the male fetal cells to the number of female fetal cells. We used the signature matrix generated from the training data to estimate constituent cell type proportions in these test mixtures using CIBERSORTx with cross-platform S-mode batch correction and 50 permutations to evaluate imputation goodness-of-fit. Pearson correlations and root mean square error between the test set predicted and actual cell type proportions in the test mixtures were used to assess deconvolution performance.
## Fluorescence-activated cell sorting of major placental cell types from villous tissue
Villous tissue single-cell suspensions were quickly thawed and stained according to manufacturer’s instructions with five fluorescently labeled antibodies (CD9-FITC, CD45-APC, HLA-A,B,C-PE/Cy7, CD31-BV421, and HLA-G-PE) as well as LIVE/DEAD Near-IR stain (Invitrogen) to isolate six viable populations of placental cells by FACS at the University of Michigan Flow Cytometry Core Facility. Initial flow cytometry experiments included fluorescence minus one, single-color compensation, and isotype controls. Isotype controls were found to be the most conservative and were consequently included in all sorting experiments, as well as single-color compensation controls due to the large number of colors used in sorting. The six populations of cells were Hofbauer cells, endothelial cells, fibroblasts, leukocytes, extravillous trophoblasts, and cytotrophoblasts. We developed a five-marker cell surface FACS scheme to sort cytotrophoblasts (HLA-A,B,C-), endothelial cells (CD31+), extravillous trophoblasts (HLA-G+), fibroblasts (CD9+), Hofbauer cells (CD9-), and leukocytes (CD45+/CD9+) from villous tissue (Supplementary Fig. 10)11,12,14,37,100–106. Syncytiotrophoblast fragments were enriched from villous tissue digests. We isolated cell type fractions and whole villous tissue from four healthy term, uncomplicated Cesarean sections, labeled Sorted 1 (same sample source as single-cell RNA-sequencing sample 1), Sorted 2, Sorted 3, and Sorted 4. We subjected 24 cell type fractions with sufficient RNA content to RNA-sequencing, including two cytotrophoblast, one endothelial, three extravillous trophoblast, three fibroblast, four Hofbauer cell, four leukocyte, and two syncytiotrophoblast fractions, and five whole tissue samples (Supplementary Table 2).
Detailed antibody information: FITC, marker CD9: Mouse IgG1-kappa, clone HI9a (2.5 μg/mL), BioLegend #312103, lot B188319, BioLegend #312104, lot B232916; isotype control: clone MOPC-21 BioLegend #400107, Lot B199152 (2.5 μg/mL). APC, marker CD45: Mouse IgG1-kappa, clone 2D1, BioLegend #368511, Lot B215062 (0.125 μg/mL); isotype control: clone MOPC-21, BioLegend #400121, lot B216780 (0.125 μg/mL). PE/CY-7, marker HLA-ABC: Mouse IgG2a-kappa, clone W$\frac{6}{32}$, BioLegend #311429, lot B188649, BioLegend #3111430, lot B238602 (0.44 μg/mL); isotype control: clone MOPC-173, BioLegend #400231, lot B209000 (0.44 μg/mL);. BV421, marker CD31: Mouse IgG1-kappa, clone WM59, BioLegend #303123, lot B204347, BioLegend #303124, lot B232010 (0.625 μg/mL); isotype control: clone MOPC-21, BioLegend #400157, lot B225357 (0.625 μg/mL). PE, marker HLA-G: Mouse IgG2a-kappa, clone 87G, BioLegend #335905, lot B222326, BioLegend #335906, lot B199294 (5 μg/mL); isotype control clone MOPC-173, BioLegend #400211, lot B227641 (5 μg/mL). Mouse IgG1-kappa, clone MEM-G/9, Abcam #24384 Lot GR3176304-1 (2.5 μg/mL); isotype control: monoclonal, Abcam #ab81200, lot GR267131-1 (2.5 μg/mL). Validation information available on the manufacturer’s website under the catalog ID for each antibody.
A cutoff of $0.1\%$ events was used to set a series of gates. Cells were first gated on size and granularity (FSC-HxSSC-H) to eliminate debris, followed by doublet discrimination (FSC-HxFSC-W and SSC-HxSSC-W). Ax750 was used to sort on viability. Extravillous trophoblasts were isolated based on Human Leukocyte Antigen-G (HLA-G) expression (Supplementary Fig. 10a). Cytotrophoblasts are HLA-ABC negative (Supplementary Fig. 10b). HLA-ABC-positive cells were then subjected to a CD45/CD9 gate to isolate Hofbauer cells and a heterogeneous population of leukocytes (Supplementary Fig. 10c). Finally, CD45-/CD9- population is sorted into the endothelial or fibroblast bins based on CD31 expression (Supplementary Fig. 10d).
## Bulk placental tissue and sorted placental cell type RNA extraction and sequencing
Approximately 2 mg of bulk RNALater-stabilized (Qiagen) bulk villous tissue was added to 350 μL $1\%$ β-mercaptoethanol (Sigma-Aldrich) RLT Buffer Plus (Qiagen) to Lysing Matrix D vials (MP Biomedicals). Samples were disrupted and homogenized on the MP-24 FastPrep homogenizer (MP Biomedicals) at 6 m/s, setting MP24x2 for 35 s. For the homogenized bulk villous tissue, syncytiotrophoblast-enriched fraction, and sorted cell types, RNA extraction was completed according to the manufacturer’s instructions using the AllPrep DNA/RNA Mini Kit (Qiagen) and stored at −80 °C. RNA samples were submitted to the University of Michigan Advanced Genomics Core for RNA-sequencing. Ribosomal RNAs were depleted with RiboGone (Takara) and libraries were prepared with the SMARTer Stranded RNA-Seq v2 kit (Takara). Paired- or single-end 50 base pair reads were sequenced on the HiSeq platform (Illumina). Raw RNA reads were assessed for sequencing quality using “FastQC” v0.11.5107 and “MultiQC” v1.7108. Reads were aligned to the GRCh38.p12/ GENCODEv28 reference transcriptome using “STAR” v2.6.0c with default settings89. featureCounts from “subread” v1.6.1 was used to quantify and summarize gene expression with default settings109.
## Sorted placental cell type differential expression analysis and comparison to single-cell results
For visualizations or analyses that required normalized gene counts, sorted cell type gene counts were library size normalized with the median ratio method using the counts() function (DESeq2 R package, version 1.32.0). As recommended50, we excluded genes that were not present in at least three samples and did not have an expression of 10 library size-normalized counts. To visualize broad cell type-specific gene expression patterns, we used “DESeq2’s” (R package, version 1.32.0) plotPCA() function with the regularized logarithm transformation, blinded to experimental design. *Upregulated* genes in each cell type were identified using the negative binomial linear model two-tailed Wald test in “DESeq2” (R package, version 1.32.0) adjusted for biological replicate using default settings with contrasts comparing the expression of a gene in one cell type against the average expression across all other cell types at a false discovery rate-adjusted p value less than 0.05 and a log2 FC cutoff of 1.250. *Overexpressed* genes were ranked by decreasing log-FC for functional enrichment analysis with “gprofiler2” (R package, version 0.2.0, database version e102_eg49_p15_7a9b4d6) using annotated genes as the universe, excluding electronically generated annotations, and with the default g:SCS multiple testing correction method applying significance threshold adjusted p value of 0.0598. To compare sorted and single-cell results, we tabulated unique overlapping differentially expressed genes and overrepresented pathways by cell type (Supplementary Table 3) Peripheral fetal and maternal immune cell types from the single-cell RNA-sequencing data were collapsed to one leukocyte category, cytotrophoblast subtypes to one cytotrophoblast category, and mesenchymal stem cells and fibroblasts to one fibroblast category for this comparison.
We used the CIBERSORTx Docker container (accessed December 7, 2021) to create a signature gene expression matrix for deconvolution from the counts of the single-cell RNA-sequencing data with the following default parameters: differential expression q value < 0.01, no minimum gene expression cutoff, and a 300 gene feature selection floor and a 500 gene feature selection ceiling99. We used the signature matrix to estimate constituent cell type proportions in the 4 whole tissue (with 1 additional technical replicate) and 19 sorted or enriched cell type fractions using CIBERSORTx with cross-platform S-mode batch correction and 50 permutations to evaluate imputation goodness-of-fit. We collapsed the high-resolution single-cell cell type cluster labels to the seven cell type fractions we targeted for comparison with sorted cell type results.
## Application: bulk placenta gene expression dataset and CIBERSORTx deconvolution
Bulk placental tissue microarray gene expression (previously batch-corrected and normalized) from eight preeclampsia case–control studies was downloaded from the NCBI Gene Expression Omnibus (accession number GSE75010) for deconvolution33. We used the CIBERSORTx Docker container (accessed December 7, 2021) to create a signature gene expression matrix for deconvolution from the counts of the single-cell RNA-sequencing data with the following default parameters: differential expression q value < 0.01, no minimum gene expression cutoff, and a 300 gene feature selection floor and a 500 gene feature selection ceiling99. We used the signature matrix to estimate constituent cell type proportions in GSE75010 using CIBERSORTx with cross-platform S-mode batch correction and 50 permutations to evaluate imputation goodness-of-fit.
## Application: preeclampsia case–control differential cell type abundance, differential gene expression statistical analysis, and mediation analysis
To test for differences in estimated cell type proportions between preeclampsia cases and controls, estimated cell type proportions for GSE75010 were regressed on preeclampsia case–control status using beta regression models adjusted for gestational age, sex, and study source110 (Supplementary Data 8). Statistical significance was assessed using the two-tailed Wald test applying a nominal significance threshold of 0.05. Cell types imputed at zero percent abundance across all samples were excluded. For modelling purposes, zero percent abundance estimates were transformed to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{2}/n$$\end{document}12/n where n is the number of observations ($$n = 330$$).
Differential expression analysis was conducted in limma111 with default linear models adjusted for gestational age, fetal sex, and study source with empirical Bayes standard error moderated t-test statistics. A cell type-adjusted model was built on the base model adjusted for gestational age, fetal sex, and study source and additionally adjusted for the first five PCs of deconvoluted cell type proportions (Supplementary Data 9). Statistical significance was assessed at false discovery rate-adjusted q value < 0.05 and a log2 FC cutoff of 0.1. Differentially expressed genes were descending-ranked by value of the moderated test statistic for gene set enrichment analysis in desktop version GSEA 4.1.0 with the GSEAPreranked tool with default settings against the c5.go.bp.v7.5.1.symbols.gmt gene set database112,113 (Supplementary Data 10). PC analysis was performed with prcomp() from “stats-package” (R, version 4.0.5) without scaling and with default settings.
A unified mediation and interaction analysis114 was conducted in “CMAverse” (R package, version 0.1.0)115 via the g-formula approach116 to estimate causal randomized-intervention analogs of natural direct and indirect effects117 through direct counterfactual imputation. The model was operationalized with preeclampsia status as the binary exposure, log2 transformed gene expression intensity as the continuous outcome, and the first five PCs of deconvoluted cell type proportions as continuous mediators. Baseline covariates included fetal sex and study source. Continuous gestational age was included as a confounder of the mediator-outcome relationship affected by the exposure. Confidence intervals were bootstrapped with 1000 boots with otherwise default settings. Statistical tests were two-tailed and interpreted at a p value significance threshold of 0.05.
## Statistics and reproducibility
Technical replication measured by average intra-cluster gene expression between technical replicates was tested via the two-tailed Spearman correlation test within Samples 1 and 2 assessed across all 32,738 common genes. The number of cells contributing expression data for each cell type is available in Table 1. Single-cell cluster marker genes were identified in “Seurat” with the FindAllMarkers function with default settings on single-cell gene expression counts92,95. Specifically, including cells from both maternal and fetal cell types, the expression level in each cell type cluster was compared against the average expression of that gene across all other cell types using the two-tailed Wilcoxon rank sum test with significance defined at a false discovery rate-adjusted p value less than 0.05 and a log2 FC cutoff of 0.25 ($$n = 40$$,494 cells). The final analytic sample included 40,494 cells and 36,601 genes across nine biological replicates, two of which had a technical replicate (Samples 1B and 2B) and another two included peripheral subsampling (Samples 8P and 9P). Pairwise cluster markers were identified in “Seurat” with the FindMarkers function with an identical testing regime ($$n = 6132$$ cells for proliferative vs. non-proliferative cytotrophoblasts). *Overexpressed* genes were ranked by decreasing log-FC for functional enrichment analysis with “gprofiler2” (R package, version 0.2.0, database version e102_eg49_p15_7a9b4d6) using annotated genes as the universe, excluding electronically generated annotations, and with the default g:SCS multiple testing correction method applying significance threshold adjusted p value of 0.0598. *Overexpressed* genes per cell type cluster are available in Supplementary Data 2 and ontology results in Supplementary Data 3. *Overexpressed* genes and related enrichment results comparing proliferative to non-proliferative cytotrophoblasts are available in Supplementary Data 1.
*Upregulated* genes in each cell type were identified using the negative binomial linear model two-tailed Wald test in “DESeq2” (R package, version 1.32.0) adjusted for biological replicate using default settings with contrasts comparing the expression of a gene in one cell type against the average expression across all other cell types at a false discovery rate-adjusted p value less than 0.05 and a log2 FC cutoff of 1.250 ($$n = 19$$ cell type fraction samples with breakdown by cell type available in Supplementary Table 2). *Overexpressed* genes were ranked by decreasing log-FC for functional enrichment analysis with “gprofiler2” (R package, version 0.2.0, database version e102_eg49_p15_7a9b4d6) using annotated genes as the universe, excluding electronically generated annotations, and with the default g:SCS multiple testing correction method applying significance threshold adjusted p value of 0.0598. Differentially expressed genes per cell type available in Supplementary Data 4 and number of differentially expressed genes are summarized in Supplementary Fig. 12. Ontology results are available in Supplementary Data 5.
Bulk placental tissue microarray gene expression (previously batch-corrected and normalized) from eight preeclampsia case–control studies was downloaded from the NCBI Gene Expression Omnibus (GSE75010) for deconvolution ($$n = 330$$)33. We used the CIBERSORTx Docker container (accessed December 7, 2021) to create a signature gene expression matrix for deconvolution from the counts of the single-cell RNA-sequencing data with the following default parameters: differential expression q value < 0.01, no minimum gene expression cutoff, and a 300 gene feature selection floor and a 500 gene feature selection ceiling99. We used the signature matrix to estimate constituent cell type proportions in GSE75010 using CIBERSORTx with cross-platform S-mode batch correction and 50 permutations to evaluate imputation goodness-of-fit.
To test for differences in estimated cell type proportions between preeclampsia cases and controls ($$n = 330$$), estimated cell type proportions for GSE75010 were regressed on preeclampsia case–control status using beta regression models ($$n = 25$$ cell type proportion outcomes) adjusted for gestational age, sex, and study source110. Cell types imputed at zero percent abundance across all samples were excluded ($$n = 2$$ excluded: fetal naïve CD4+ T cells and fetal GZMB+ natural killer cells). Statistical significance was assessed using the two-tailed Wald test applying a nominal significance threshold of 0.05.
Differential expression analysis was conducted in limma111 with default settings using linear models ($$n = 14$$,651 genes) adjusted for gestational age, fetal sex, and study source ($$n = 330$$). A cell type-adjusted model was built on the base model additionally adjusted for the first five PCs of deconvoluted cell type proportions. PC analysis was performed with prcomp from “stats-package” (R, version 4.0.5) without scaling and default settings. Statistical significance was assessed at false discovery rate-adjusted q value < 0.05 and a log2 FC cutoff of 0.1. Differentially expressed genes were descending-ranked by the value of the moderated test statistic for gene set enrichment analysis in desktop version GSEA 4.1.0 with the GSEAPreranked tool with default settings against the c5.go.bp.v7.5.1.symbols.gmt gene set database112,113.
A unified mediation and interaction analysis114 was conducted in “CMAverse” (R package, version 0.1.0)115 via the g-formula approach116 to estimate causal randomized-intervention analogs of natural direct and indirect effects117 through direct counterfactual imputation. The model ($$n = 330$$) was operationalized with preeclampsia status as the binary exposure, normalized log2 gene expression signal intensity as the outcome, and the first five PCs of deconvoluted cell type proportions as continuous mediators. Baseline covariates included fetal sex and categorical study source. Continuous gestational age was included as a confounder of the mediator-outcome relationship affected by the exposure. Confidence intervals were bootstrapped with 1000 boots with otherwise default settings. Statistical tests were two-tailed and interpreted at a p value significance threshold of 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 Data Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Supplementary Data 6 Supplementary Data 7 Supplementary Data 8 Supplementary Data 9 Supplementary Data 10 Reporting Summary Checklist The online version contains supplementary material available at 10.1038/s42003-023-04623-6.
## Peer review information
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---
title: The effect of extended participation windows on attendance at cervical cancer
screening
authors:
- Kelly M. Castañeda
- Grigory A. Sidorenkov
- Jolien de Waard
- Marcel J.W. Greuter
- Bert van der Vegt
- Inge M.C.M. de Kok
- Albert G. Siebers
- Karin M. Vermeulen
- G. Bea A. Wisman
- Ed Schuuring
- Geertruida H. de Bock
journal: Preventive Medicine Reports
year: 2023
pmcid: PMC10011428
doi: 10.1016/j.pmedr.2023.102166
license: CC BY 4.0
---
# The effect of extended participation windows on attendance at cervical cancer screening
## Highlights
•Participation rates in cervical cancer screening are usually estimated using time windows of 15 months or shorter.•The participation rate increases significantly when using a 36-month time window.•Younger age, pregnancy, and higher education are associated with delayed participation.
## Abstract
Research has long since confirmed the benefits of regular cervical cancer screening (CCS) worldwide. However, some developed countries have low participation rates despite well-organized screening programs. Given that studies in Europe typically define participation in 12-month windows from an invitation, we evaluated both whether extending this defined time window could reveal the true participation rate and how sociodemographic determinants affect participation delays. This involved linking data from the Lifelines population-based cohort with CCS-related data from the Dutch Nationwide Pathology Databank and including data for 69 185 women eligible for screening in the Dutch CCS program between 2014 and 2018. We then estimated and compared the participation rates for 15- and 36-month time windows and categorized women by the primary screening window into timely participation (within 15 months) and delayed participation (within 15–36 months) groups, before performing multivariable logistic regression to evaluate the association between delayed participation and the sociodemographic determinants. Participation rates for the 15- and 36-month windows were $71.1\%$ and $77.0\%$, respectively, with participation considered timely in 49 224 cases and delayed in 4047 cases. Delayed participation was associated with age 30–35 years (odds ratio [OR]: 2.88, 95 %CI: 2.67–3.11), higher education (OR: 1.50, 95 %CI: 1.35–1.67), the high-risk human papillomavirus test-based program (OR: 1.67, 95 %CI: 1.56–1.79), and pregnancy (OR: 4.61, 95 %CI: 3.88–5.48). These findings show that a 36-month window for monitoring attendance at CCS better reflects the actual participation rate by accommodating possible delayed uptake among younger, pregnant, and highly educated women.
## Introduction
Implementing cervical cancer screening (CCS) programs in Europe has contributed to reducing the mortality associated with the disease (Jansen et al., 2020, Sung et al., 2021). Nevertheless, many countries with established programs, such as France, Denmark, and the Netherlands, have national coverage rates that fall short of the $70\%$ threshold recommended by the World Health Organization to ensure an efficient CCS program (Maver and Poljak, 2020, Coverage of national cervical cancer screening program, 2022, World Health Organization, 2021). This is problematic when we consider that invasive disease in such countries presents mostly in women who do not take part in screening (Arbyn et al., 2018, Bos et al., 2006), with low participation observed particularly in younger women, immigrants, and those of low socioeconomic status (Aitken et al., 2021, Audiger et al., 2021, Harder et al., 2018).
Implementing robust surveillance and monitoring systems is key to identifying gaps in reducing cervical cancer incidence and mortality (Bruni et al., 2022). The coverage rate, defined as the number of screened women in the total eligible population in a given time interval, is a core indicator that reflects the capacity to provide testing for primary screening at a country level (Bruni et al., 2022). However, the definition of the time interval is arbitrary and can change by country. Most studies in Europe have defined participation using time windows of 12 months after an invitation (Aitken et al., 2021, Audiger et al., 2021, Hermens et al., 2000), whereas the Dutch CCS estimates the participation rate using a 15-month window from the start of the invitation year (National Institute for Public Health and the Environment, 2017). However, evidence from a recent report has indicated that a short time window for monitoring the Dutch CCS might not be enough to capture all participation, especially since implementing the new high-risk human papillomavirus (hrHPV)-based screening program that introduced many organizational changes (Aitken, 2021). Extending the participation window could give a better indication of the true participation rate by reflecting possible delays.
The present study targets these issues with two primary aims. First, we assessed whether extending the time window for participation from 15 months to 36 months could increase the participation rate, based on the trends in 2014–2018. Second, we evaluated the association between sociodemographic determinants and participation delays.
## Study design
This study used a cross-sectional design nested in a population-based cohort. We linked data from the Lifelines cohort (Scholtens et al., 2015) and the Dutch Nationwide Pathology Databank (PALGA) (Casparie et al., 2007) between 2014 and 2018. Lifelines is a multi-disciplinary prospective population-based cohort study examining in a unique three-generation design the health and health-related behaviours of 167 729 persons living in the North of the Netherlands. It employs a broad range of investigative procedures in assessing the biomedical, socio-demographic, behavioural, physical and psychological factors which contribute to the health and disease of the general population, with a special focus on multi-morbidity and complex genetics (Scholtens et al., 2015, Stolk et al., 2008). PALGA provides centralized data from all pathology laboratories in the Netherlands (Casparie et al., 2007). We used data from PALGA to estimate the participation rate in the CCS based on the primary screening date recorded as part of the organized screening program within either 15 or 36 months of the start of the invitation year, and to compare women who had a record within 15 months and 15–36 months after the start of the invitation year, defining these as timely and delayed participants, respectively.
## Study population
The Lifelines cohort included 167 729 people between December 2006 and December 2013. It set out to follow them for at least 30 years, with follow-up questionnaires every 1.5 years and physical examinations every 5 years (Scholtens et al., 2015) Currently, Lifelines has completed the baseline questionnaire and a physical assessment (2007–2013), three follow-up questionnaires (2011–2014, 2012–2015, 2016–2019), and a second physical assessment (2014–2017), with the third physical assessment ongoing (2019–2023) (Lifelines Wiki, 2020).
The CCS-related data from PALGA between January 2000 and December 2020 were linked to all women in the Lifelines cohort based on a combination of the family name, date of birth, sex, and zip code. For this project, we accessed the following data from the PALGA database: cytology, hrHPV positivity, histology records, nature of testing (primary or secondary screening), and the reason for testing (organized screening or another indication). Before 2017, screening organizations invited women aged 30–60 years for primary cytology testing every 5 years (Aitken et al., 2019). Since then, they have invited women for primary hrHPV testing at ages 30, 35, 40, 50, and 60 years, only inviting those aged 45, 55, and 65 years if they are hrHPV positive or missed their last screening (National Institute for Public Health and the Environment, 2021). Because HPV statuses were unknown at the start of the hrHPV-based program, all women aged 30–60 years were invited to the first round (2017–2021) (National Institute for Public Health and the Environment, 2021). Due to these factors and a considerable decline in the participation rate since introducing the hrHPV-based program (Aitken et al., 2021), we selected a screening round of 5 years to reflect both versions of the program when evaluating delays in participation. Therefore, women who turned 30, 35, 40, 45, 50, 55, and 60 years old in each year between 2014 and 2018 were considered eligible for CCS and included in the analyses. We excluded women with any hysterectomy (based on self-report in the Lifelines questionnaire before 2000 and PALGA records from 2000 onwards) or who had died before the invitation year (based on Lifelines questionnaires).
Ethical approval Informed consent was obtained from all participants. The Lifelines study complies with the principles of the Declaration of Helsinki and it received approval from the Medical Ethics Committee of the University Medical Center Groningen, the Netherlands (no. $\frac{2007}{152}$).
## Determinants
The assessed sociodemographic determinants comprised the invitation year, age that year, country of birth, ethnicity, educational level, income level, marital status, and pregnancy during the invitation year. All determinants (except pregnancy) were derived from the self-reported baseline questionnaires for the Lifelines cohort. The invitation year was categorized as 2014–2016 and 2017–2018 to compare the cytology-based and hrHPV-based programs, respectively. Age in the year of invitation was estimated by the year of birth. Educational level was measured according to the highest academic level achieved and was categorized according to the standard categorization of educational level in the Netherlands, as follows: low (no education, primary education, lower or preparatory vocational education, lower general secondary education), middle (intermediate vocational education or apprenticeship, higher general senior secondary education or pre-university secondary education), and high (higher vocational education, university) (van Zon et al., 2018). Income was recorded as low, medium, and high when the net income per month was less than €1500, between €1500 and €2500, and higher than €2500, respectively, or as unknown (“I don’t know,” “I don’t want to say,” or did not respond or missing response) (Faruque et al., 2021). Due to the substantial number of missing entries for country of birth and ethnicity, we combined the data as follows: those reported as white-European ethnicity with missing values for the country of birth were considered born in the Netherlands ($$n = 425$$); and those reported as white Mediterranean, Arabic, black, and Asian were considered born in other countries ($$n = 6$$). Only $0.2\%$ ($\frac{158}{69}$ 185) of women had missing values for both ethnicity and country of birth. If the invitation year included one of the following two distinct periods based on a biological child’s birth date, we assumed the woman was pregnant that year: [1] from 9 months before the birth date until 3 months after the birth date, including the dates women reported being pregnant in the follow-up questionnaires of the Lifelines study; and [2] from the questionnaire date to 3 months after.
## Statistical analysis
To assess whether extending the time for the definition of CCS participation significantly increased the participation rate, we estimated the proportion of eligible women who participated per year with the respective $95\%$ confidence intervals. Rates were calculated as the number of women with a primary screening record in PALGA within either 15 months or 36 months of the start of the invitation year, divided by the total number of women eligible in the invitation year. Paired-sample t-tests were used to estimate possible statistically significant differences between the participation rates at 15 months and 36 months. To evaluate the association between sociodemographic determinants and participation timeliness, we first presented the sociodemographic determinants by the timely and delayed participant groups from among all eligible women. Secondly we included all women who had a primary screening record in PALGA within 36 months after the start of the invitation year in the univariable and multivariable logistic regression analyses, using the previously mentioned determinants as covariables and participation as the outcome (delayed compared with timely). Before carrying out the multivariable analysis, we performed a Spearman correlation test to evaluate whether including education and income could generate collinearity in the multivariable model. All analyses were conducted using IBM SPSS Version 25.0 (IBM Corp., Armonk, NY, USA).
## Results
In total, 69 185 of 89 176 women from the Lifelines cohort were eligible for primary CCS between 2014 and 2018. The average participation rate was higher using a 36-month window ($77.0\%$; 95 %CI: 76.7–77.3) compared with a 15-month window ($71.1\%$; 95 %CI: 70.8–71.5) ($P \leq 0.001$; Table 1). When using the 15-month window, the participation rate decreased over time from $73.1\%$ in 2014 to $68.4\%$ in 2018, but it increased again to $69.9\%$ in 2019. However, when using a 36-month window, the participation rate changed less markedly ($79.0\%$ in 2014; $77.9\%$ in 2015; $76.0\%$ in 2016; stable at approximately $76\%$ in subsequent years; Table 1).Table 1CCS participation in the Lifelines cohort from 2014 to 2018 by time window. Invitation yearWomen eligible for screening anParticipation rateDifference in participation rate (P value)d15-month window b% [95 %CI] (n)36-month window c% [95 %CI] (n)201413 80273.1 [72.3–73.8] [10 088]79.0 [78.3–79.7] [10 901]5.9 (<0.001)201513 85073.0 [72.2–73.7] [10 104]77.9 [77.2–78.6] [10 795]4.9 (<0.001)201614 14471.4 [70.7–72.2] [10 100]76.0 [75.3–76.7] [10 746]4.6 (<0.001)201713 81368.4 [67.6–69.2] [9 445]75.9 [75.2–76.6] [10 481]7.5 (<0.001)201813 57669.9 [69.1–70.7] [9 487]76.2 [75.5–76.9] [10 348]6.3 (<0.001)Total69 18571.1 [70.8–71.5] [49 224]77.0 [76.7–77.3] [53 271]5.9 (<0.001)aWomen eligible for screening: Number of women in Lifelines aged $\frac{30}{35}$/$\frac{40}{45}$/$\frac{50}{55}$/60 after excluding women who had a hysterectomy and died before the Invitation year.bNumber of women with at least one primary screening record in PALGA from January first of the year of eligibility till April first of the following year (15 months) divided the total number of women eligible in the year of eligibility.cNumber of women with at least one primary screening record in PALGA from January first of the year of eligibility till 36 months after divided the total number of women eligible in the year of eligibility.dPaired-sample T-Test.
Table 2 presents the sociodemographic determinants by participation window. In this cohort, $5.9\%$ of women had delayed participation in the CCS, but with $14\%$ of women aged 30–35 years having delays compared with only $4.8\%$ of those aged 40–50 years and $2.4\%$ of those aged 55–60 years. Around $7\%$ of women invited to CCS in 2017–2018 had delayed participation compared with about $5\%$ invited in 2014–2016. By the country of birth, delays occurred in $5.9\%$ and $4.7\%$ of the women born in the Netherlands and in other countries, respectively ($11.4\%$ who did not report country of birth and/or ethnicity had delays). About $8\%$ of highly educated women took part with delays, while fewer than $6\%$ of those with middle and low education levels had delays. More single women had delayed participation compared with women in the other marital status categories. The distribution of delayed participation was similar by income category. Finally, $32.4\%$ of women who were pregnant during the invitation year had delayed participation, compared with only $5.5\%$ of those who were not pregnant. Table 2Sociodemographic determinants by timely and delayed participation in the CCS for the 2014–2018 period. DeterminantsTotal women eligibleTimely participantsn (%)Delayed participantsn (%)Total69 18549 224 (71.1)4047 (5.9)Age at the Invitation year 30–3513 0878134 (62.2)1829 (14.0) 40–45–5036 39526 433 (72.6)1735 (4.8) 55–6019 70314 657 (74.4)483 (2.5)Invitation year 2014–2016 (cytology)41 79630 292 (72.5)2150 (5.1) 2017–2018 (HPV)27 38918 932 (69.1)1897 (6.9)Country of birth The Netherlands66 41247 586 (71.7)3907 (5.9) Other country26151553 (59.4)122 (4.7) Missing15885 (53.8)18 (11.4)Educational level Low16 29211 703 (71.8)549 (3.4) Middle29 46921 239 (72.1)1651 (5.6) High22 24515 466 (69.5)1774 (8.0) Missing1179816 (69.2)73 (6.2)Income Low97546478 (66.4)630 (6.5) Medium16 12011 288 (70.0)983 (6.1) High29 92221 861 (73.1)1839 (6.1) Unknown13 3899597 (71.7)595 (4.4)*Marital status* Married/relationship39 13028 321 (72.4)2137 (5.5) Single3 9352 337 (59.4)306 (7.8) Divorced352250 (71.0)<10 (<2.0) Widow18951328 (70.1)58 (3.1)Pregnancy during invitation year No68 38648 860 (71.4)3 788 (5.5) Yes799364 (45.6)259 (32.4)We categorized women by the primary screening window into timely participation (within 15 months) and delayed participation (within 15–36 months) groups. Missing are not reported for marital status to protect the confidentiality of the participants.
Table 3 shows the associations between participant characteristics and participation window for only those women with a screening record within 36 months after the start of invitation year ($$n = 53$$ 271). Univariable analysis revealed that all putative covariables were significant. Spearman’s correlation coefficient between education level and income was 0.059, indicating a low probability of collinearity. Therefore, all determinants could be included as variables in the multivariable model. Multivariable logistic regression showed associations between delayed participation and age, invitation year, education level, and pregnancy. Women aged 30–35 years were almost three times as likely to have delayed participation as women aged 40 years or older. Those invited in 2017–2018 were also more likely to be included after a delay than those invited in 2014–2016. Having a middle or higher educational level was also associated with delayed participation compared a low educational level. Finally, pregnancy during the invitation year had the largest effect on delayed participation, with these women being 4.6 times more likely to have delays than women who were not pregnant. Table 3Univariable and multivariable analyses of the determinants of delayed versus timely participation in the CCS for the 2014–2018 period. DeterminantsUnivariable modelMultivariable model aOR (95 %CI)P valueOR (95 %CI) aP valueAge<0.001<0.001 30–353.43 (3.20–3.68)2.88 (2.67–3.11) 40–45–50Ref. Ref. 55–600.50 (0.45–0.56)0.50 (0.45–0.56)Invitation year<0.001<0.001 2014–2016 (Cyt)Ref. Ref. 2017–2018 (HPV)1.41 (1.32–1.51)1.67 (1.56–1.79)Country of birth0.0010.014 The NetherlandsRef. Ref. Other country0.96 (0.79–1.15)1.03 (0.85–1.25) Missing2.583 (1.55–4.30)2.41 (1.33–4.37)Educational level<0.001<0.001 LowRef. Ref. Middle1.66 (1.50–1.83)1.20 (1.08–1.33) High2.45 (2.22–2.70)1.50 (1.35–1.67) Missing1.91 (1.48–2.46)1.30 (0.97–1.74)Income<0.0010.119 LowRef. Ref. Medium0.90 (0.81–0.99)1.02 (0.92–1.14) High0.87 (0.79–0.95)1.00 (0.90–1.11) Unknown0.64 (0.57–0.72)0.90 (0.79–1.02)Marital status<0.0010.258 Married/relationshipRef. Ref. Single1.74 (1.53–1.97)1.08 (0.94–1.25) Divorced0.37 (0.18–0.79)0.67 (0.32–1.43) Widow0.58 (0.44–0.76)0.80 (0.61–1.06) Missing1.20 (1.12–1.29)1.02 (0.95–1.09)Pregnancy during Invitation year<0.001<0.001 NoRef. Ref. Yes9.18 (7.80–10.80)4.61 (3.88–5.48)Data are shown as odds ratios (ORs) and $95\%$ confidence intervals ($95\%$ CIs). P-values are based on Wald test.a. Model. Age + Invitation year + country of birth + Educational level + Income + *Marital status* + Pregnancy during invitation year.
## Discussion
In this representative cohort from the north of the Netherlands, we show that changing the definition of participation by extending the time window for considering CCS participation increased the estimated participation rate from $71.1\%$ to $77.0\%$ between 2014 and 2018. Moreover, we identified age 30–35 years, middle or high educational level, pregnancy during the invitation year, and invitation year as determinants of delayed participation.
This study shows a decrease in participation from 2014 to 2015 to 2016–2018 for both the 15-month and 36-month time windows. This is consistent with the known decrease in participation since introducing the hrHPV-based program (Aitken et al., 2021). However, the participation rate in this study was much higher among inhabitants from the north of the Netherlands than in earlier reports. At a national level, the participation rate decreased by $7.8\%$ from $64.8\%$ to $57\%$ over the 2014–2017 period (Centrum et al., 2016, Integraal Kankercentrum Nederland, 2020), whereas in the present study, it decreased by $4.7\%$ from $73.1\%$ to $68.4\%$ for the same period and 15-month window. Extending the participation window to 36-months led to a reduction of $3.1\%$ (from $79\%$ in 2014 to $75.9\%$ in 2017) in the participation rate for 2014–2017 in the north of the Netherlands. Such a minor change might not have important implications for the CCS in this region because the total participation rate, including delays, exceeded $75\%$ for the 2014–2018 period. The population distribution in the Netherlands might explain these differences, with the north having a lower number of immigrants than other regions (Centraal Bureau voor de Statistiek, 2022). In 2018, for example, only $5\%$ of all immigrants to the Netherlands lived in the north, compared with $21\%$ in the east, $53\%$ in the west, and $21\%$ in the south (Centraal Bureau voor de Statistiek, 2022). Studies tend to report lower participation rates in immigrants due to language barriers and cultural differences (Bongaerts et al., 2020, Chorley et al., 2017, Idehen et al., 2020, Marques et al., 2020). Thus, the Lifelines cohort probably lacks these barriers, and if so, will present a higher participation rate. Another explanation could be that women from Lifelines are more willing to take part in a population-based study, which may make them more inclined to take part in the CCS. Further research is needed to evaluate the impact of delayed participation on national data.
For this study, an extension of a 15 to 36-month window was used to assess whether the time definition could significative increase the participation rate. However, as the program is every five years, a 60-months window would cover the actual total participation rate. Therefore, we used a 36-month window for two main reasons. 1) Since we only had data until December 2020, the maximum time to follow women invited in 2018 is 36 months. 2) When we compared the participation rate using a 36 and 60-month window, the difference was only 0,01 from 2014 to 2016.
Although we are aware of no research that has discussed the determinants of delayed participation, research has mentioned the possible impact of the definition of participation. Indeed, a short time window for monitoring the Dutch CCS was considered potentially insufficient, especially since introducing the new hrHPV-based screening program that required many organizational changes (Aitken, 2021). Our project shows that extending the time window when defining participation may produce a more accurate estimate of the actual participation rate, especially since 2017.
Regarding the determinants for delayed participation, since women with delays are, by time definition, wrongly counted as non-participants, in this study, we included determinants for non-participation as well as pregnancy. Our analysis showed that younger women were more likely to delay participation in the CCS compared with older women. Even though age and pregnancy during the invitation was independently associated with delayed participation, a reason for delayed participation at a younger age could be that the average age when women have their first child has been increasing in the Netherland since 2015, with a current average of 30 years (Centraal Bureau voor de Statistiek, 2013). In fact, pregnancy had the largest effect on delayed participation, which is a direct consequence of the way the cervical cancer screening is organized. During pregnancy there is no screening for cervical cancer in the Netherlands and the invitation for pregnant women will be sent six months after the delivery (National Institute for Public Health and the Environment, 2021). As a result, women are still considered non-participants in a short time window when they may not have even completed the time to be screened. In addition, the number of highly educated women in the Netherlands has increased in recent year, such that around $50\%$ of women aged 25–35 years were deemed highly educated in 2018 (Centraal Bureau voor de Statistiek, 2019). These sociodemographic changes in education, pregnancy, and age might affect the time of participation because young, highly educated, or pregnant women might not have the time to take part in the CCS within the current 15-month window, but eventually, they do. Although country of birth does not appear to have a major role in participation delay, women who did not report their country of birth or ethnicity were at higher odds of delay. Nevertheless, only $0.2\%$ ($\frac{158}{69}$ 185) of women had missing data on this variable, making this association trivial.
This study benefited from being conducted in a large population-based cohort with extensive follow-up data from the north of the Netherlands, ensuring representativeness for the general population only in this region (Klijs et al., 2015). The CCS data were also retrieved from PALGA, an automated pathology databank with national coverage (Casparie et al., 2007), so this study could rely on a complete history of cervical diagnostics, including cytology, hrHPV test results, and histological diagnoses from colposcopies and treatment procedures. This allowed us to define the population at risk more precisely by identifying and excluding women with hysterectomies. PALGA also provides accurate and standardized pathology data for the annual monitoring of the Dutch CCS program (Casparie et al., 2007). Even though it may be widely accepted that pregnancy affects participation in CCS, this study confirms the actual role of pregnancy based on data collected in Lifelines.
This study has some important limitations. First, lack of knowledge of the exact day on which an invitation was sent meant that we used a period from January 1st of the invitation year based on the usual procedure for monitoring CCS in the Netherlands (National Institute for Public Health and the Environment, 2017). This decision might have led to information bias because women could have had more or less time to take part in the screening. However, we expect a random distribution of this information bias, with it not significantly affecting the results because screening organizations sent invitations either randomly during the year (before 2017) or a few days after a birthday (since 2017). Considering the 36-month time window, all women had a minimum time of 2 years to participate, regardless of the exact invitation date. Second, the use of self-sampling might have played a role in participation delay because patients were only offered a self-sampling test if they did not take part by 4 months after the first invitation. Nonetheless, only $2.5\%$ ($\frac{1729}{69}$ 185) of the women in the current study attended through self-sampling; given that $68\%$ of these were in the timely participant group, this may not have affected the overall results. Third, we used baseline measurements collected in 2006–2013 from the Lifelines cohort to determine educational level, income, and marital status (Scholtens et al., 2015), yet this study evaluated participation for the 2014–2018 period. Because the mean age of participants in Lifelines was 41 years at baseline (Scholtens et al., 2015), we expect neither substantial changes in these variables after inclusion nor a significant impact on the study findings. Fourth, when evaluating pregnancy during the invitation year, we only considered pregnancies that resulted in live births (according to the birth date of the children) or those reported during follow-up in the Lifelines cohort (self-reported current pregnancy when completing the questionnaire). By not considering any miscarriages or abortions that occurred between assessments, we may have underestimated the effect size of pregnancy on participation delays in the CCS. Fifth, although we combined the country of birth with ethnicity to reduce missing data, we do not expect this to affect the result drastically because *Lifelines is* a very homogenous white European cohort (Scholtens et al., 2015, Klijs et al., 2015). Finally, the linkage between the PALGA and Lifelines databases could have introduced bias by linking based on the family name, date of birth, sex, and zip codes initials especially for women with the same family names. Nevertheless, in a large cohort that also includes the zip code initials, these errors are unlikely.
Our study shows that a 15-month window to monitor the screening program might not cover the actual participation rate in the CCS because delays in participation are likely to occur in younger, pregnant, highly educated women. Since non-participation is an outcome of high importance for the effectiveness of an organized screening program, the time definition should be carefully sett-up to identify precisely the gaps to reduce cervical cancer mortality. Future studies should consider extending the time window to get a precise estimation of the participation rate, and, therefore, a better definition of non-participants.
## Conclusion
Using a time window of 36 months instead of 15 months to monitor participation in the Dutch CCS program resulted in a higher participation rate. Determinants for delayed participation in the CCS were younger age, pregnancy, middle/higher education, and invitation year. Even though participation in the north of the Netherlands was higher than at the national level, extending the time window in the Netherlands could more accurately reflect the true participation rate.
## CRediT authorship contribution statement
Kelly M. Castañeda: Conceptualization, Data curation, Methodology, Formal analysis, Writing – original draft. Grigory A. Sidorenkov: Conceptualization, Methodology, Writing – review & editing. Jolien de Waard: Conceptualization, Writing – review & editing. Marcel J.W. Greuter: Writing – review & editing. Bert van der Vegt: Writing – review & editing. Inge M.C.M. de Kok: Writing – review & editing. Albert G. Siebers: Methodology, Writing – review & editing. Karin M. Vermeulen: Conceptualization, Writing – review & editing. G. Bea A. Wisman: Supervision, Conceptualization, Writing – review & editing. Ed Schuuring: Supervision, Conceptualization, Writing – review & editing. Geertruida H. de Bock: Conceptualization, Methodology, Project administration, Supervision, Writing – review & editing.
## Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## Data availability
The data used in this study are available through Lifelines biobank (www.lifelines.nl). Restrictions apply to the availability of these data, which were used under license for this study.
## Acknowledgments
This work was supported by The Ministry of Science, Technology, and Innovation of Colombia [PhD call 860 to KMC] and ZonMw [to JW and KMV]. The Lifelines initiative has been made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), Groningen University and the Provinces in the North of the Netherlands (Drenthe, Friesland, Groningen). The authors wish to acknowledge the services of the Lifelines Cohort Study, the contributing research centers delivering data to Lifelines, and all the study participants. Doctored Ltd (www.doctored.org.uk) provided manuscript editing services for the final drafts.
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|
---
title: 'COVID-19 pandemic phases and female precocious puberty: The experience of
the past 4 years (2019 through 2022) in an Italian tertiary center'
authors:
- Laura Chioma
- Mariangela Chiarito
- Giorgia Bottaro
- Laura Paone
- Tommaso Todisco
- Carla Bizzarri
- Marco Cappa
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011474
doi: 10.3389/fendo.2023.1132769
license: CC BY 4.0
---
# COVID-19 pandemic phases and female precocious puberty: The experience of the past 4 years (2019 through 2022) in an Italian tertiary center
## Abstract
### Objective
Since the outbreak of COVID-19 pandemic, several centers of pediatric endocrinology worldwide have observed a significant increase in the number of girls presenting with precocious or early puberty. We aimed to compare the incidence rates of female precocious puberty before and during the different phases of COVID-19 pandemic.
### Methods
We have retrospectively analyzed all the consultations recorded in the outpatient clinic database of the Endocrinology Unit of Bambino Gesù Children’s Hospital, Rome, Italy, from the lockdown start in March 2020 up to September 2020, in comparison with the consultations recorded in the same months of 2019, 2021 and 2022. Age, height, weight, body mass index, Tanner’s pubertal stage and bone age at presentation, birth weight, ethnicity, family history of central precocious puberty (CPP), maternal age at menarche, history of adoption were retrieved from clinical records. Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH) both at baseline and after gonadotropin-releasing hormone (GnRH) stimulation, and basal estradiol levels were collected.
### Results
In 2019, 78 girls with suspected precocious puberty were referred for endocrinological consultation, compared to 202 girls in 2020, 158 girls in 2021 and 112 girls in 2022. A significant increase in the proportion of girls diagnosed with rapidly progressive CPP was observed in 2020, compared to 2019 ($\frac{86}{202}$ vs. $\frac{18}{78}$, $p \leq 0.01$). In the following periods of 2021 and 2022, a gradual decrease in the number of cases of progressive CPP was evident, so much that the number of cases was not significantly different from that observed in 2019 ($\frac{56}{158}$ in 2021 and $\frac{35}{112}$ in 2022, $$p \leq 0.054$$ and $$p \leq 0.216$$ respectively, compared to 2019).
### Conclusions
Our research suggests that drastic lifestyle changes, such as those imposed by COVID-19 lockdown, and the consequent stress may affect the regulation of pubertal timing. The remarkable increase in CPP cases observed during the 2020 first pandemic wave seems to be reduced in 2021 and 2022, concurrently with the progressive resumption of daily activities. These data seem to support the hypothesis of a direct relationship between profound life-style changes related to the pandemic and the rise in precocious puberty cases.
## Introduction
Puberty is the crucial transition process between childhood and adulthood, leading to full reproductive capacity [1]. Female central precocious puberty (CPP) is defined as the onset of breast development before the age of eight years, due to the activation of the hypothalamic-pituitary-ovarian (HPO) axis [2]. Puberty is a complex phenomenon, and factors modulating timing and/or tempo of puberty are not fully understood. It has been assumed that genetic, epigenetic and environmental factors, such as energy imbalance, exposure to endocrine disruptors or stressful events may trigger an earlier pubertal development [3, 4].
In the last century a trend toward earlier puberty was already observed (5–7). This phenomenon, known as “secular trend of puberty”, has described a progressive reduction in the age at menarche, dropping from 17 years in the early-1800s to 13 years by the mid-1900s, with a further minor decline through the last three decades [5].
Recently, several centers of pediatric endocrinology worldwide, including ours, have observed a further significant increase in the number of girls presenting with precocious or early puberty since mid-2020 (8–21). During this period, corresponding to the first wave of COVID-19 pandemic, the Italian government imposed a strict lockdown across the country, in order to reduce the transmission rate and to avoid hospital bed saturation. Consequently, profound changes in everyday life occurred, such as school closures and the restriction of outdoor and team sports activities. Families were forced to stay at home, except for emergency reasons, with more opportunities for hypercaloric food consumption and overnutrition and the worsening of sedentary lifestyle. There was a significant rise of e-learning, extremely uncommon in primary schools before the pandemic. All these changes led to a larger daily use of electronic devices among children.
Given the growing worldwide evidence of an increase in female precocious puberty since the outbreak of COVID-19 pandemic, we aimed to investigate the evolution of this phenomenon before and during the different phases of the pandemic, from 2019 to 2022.
## Subjects
We retrospectively analyzed all the consultations for suspected precocious or early puberty recorded in the outpatient clinic database of the Endocrinology Unit of Bambino Gesù Children’s Hospital, Rome, Italy from lockdown start in March 2020 to September 2020, in comparison with the consultations recorded in the same period of 2019, 2021 and 2022.
Consultations for premature thelarche in girls younger than 3 years were excluded. All subjects with suspected precocious puberty were observed for up to three months in order to reach the final diagnosis.
For each year, the subjects were further divided into subgroups based on the final diagnosis: transient thelarche (TT), non-progressive precocious puberty (NPP), CPP, or early puberty (EP). Subjects presenting with thelarche that disappeared during the 3-month observation period were assigned to the TT group. EP was defined as pubertal signs first appearing between 8 and 9 years, these girls were not further investigated.
The Institutional Review Board of ‘Bambino Gesù Children’s Hospital approved the study protocol.
## Anthropometric data and medical history
Age, height (H), weight (W), body mass index (BMI), pubertal stage and bone age (BA) at presentation, birth weight, ethnicity, CPP family history, maternal age at menarche, history of adoption were retrieved from clinical records. H (cm) and W (kg) were also expressed as age and sex specific standard deviation score (SDS) according to the standard growth charts for the Italian population [22]. Body mass index (BMI) was calculated as the ratio between W and H2 and expressed as SDS. Birth weight was expressed also as SDS according to the Italian Neonatal Anthropometric Charts [23]. Tanner’s method was used to assess pubertal stages [24]. Questionnaires concerning physical activity, screen time and eating habits at the onset of pubertal signs were administered to all groups.
## Laboratory measurements
Serum levels of follicle-stimulating hormone (FSH), luteinizing hormone (LH) both at baseline and after gonadotropin-releasing hormone (GnRH) stimulation, and basal estradiol levels were collected, when available, among all subgroups except EP. GnRH stimulation test was performed by the i.v. administration of GnRH (Lutrelef; Ferring) at a dosage of 100 µg, with FSH and LH measurement at baseline and 30, 60 and 90 minutes after the injection. A basal LH level above 0.2 IU/l and/or a LH peak after GnRH infusion above 5 IU/l were considered diagnostic for CPP [2, 25]. In the absence of one or both these criteria, subjects with slow pubertal progression were assigned to the NPP group.
## Imaging
All subjects underwent pelvic ultrasound to assess uterine and ovarian characteristics. A uterine longitudinal diameter above 36 mm and the presence of the endometrium echo-pattern were considered signs of estrogenic stimulation, suggestive of precocious puberty.
An X-ray of the left hand and wrist was performed in all subjects to assess BA, according to the Greulich & Pyle method [26]. Bone age advancement (years) was assessed as the difference between BA and chronological age.
Most subjects diagnosed with CPP (152 girls, $78\%$) underwent a magnetic resonance of the hypothalamus-pituitary area to rule out intracranial pathologies.
## Statistical analysis
Data were expressed as mean ± SD when normally distributed and as median (interquartile range or IQR) for parameters with non-normal distribution, unless otherwise specified. Categorical variables were reported as number and percentage. The observed subjects were divided into four groups according to the year of evaluation (2019, 2020, 2021 or 2022). Each group was further divided in four subgroups according to the final diagnosis (TT, NPP, CPP or EP). Categorical variables were compared using chi-square (χ²) test. ANOVA was applied to compare variables with normal distribution between more than two groups, while Kruskal–Wallis test was applied for variable with non-normal distribution.
Statistical analysis was performed with the statistical package SPSS v23 for Windows (SPSS Inc, Chicago, IL, USA) and a probability value of $p \leq 0.05$ was considered statistically significant.
## Results
The sharpest increase of consultations was observed in 2020, with 208 subjects referred for suspected precocious or early puberty among a total number of 747 consultations in the period March-September 2020 ($27.8\%$), in comparison with 85 subjects/1260 consultations in the same period of 2019 ($6.7\%$). In 2021 there was still an increase in consultations for suspected precocious puberty, even if less pronounced than in 2020, with 166 subjects/1190 consultations ($13.3\%$). A further reduction of consultations was observed in 2022, with 120 subjects/1380 consultations ($8.7\%$).
Given the similarity in the number of boys observed throughout the years (7 subjects in 2019, 6 subjects in 2020, 8 subjects in 2021 and 8 subjects in 2022), we decided to further analyze only the female population of each considered period. Thirty-one girls were excluded because they were lost at follow-up after the first observation.
The study population consisted of 550 girls, divided as follows: 78 girls evaluated in 2019, 202 girls in 2020, 158 girls in 2021 and 112 girls in 2022. Figure 1 summarizes the design of the study and the results of data collection.
**Figure 1:** *Flowchart summarizing the study design.*
The number of consultations for suspected precocious or early puberty in girls was confirmed significantly higher in 2020 than in 2019 ($\frac{202}{747}$ equivalent to $27\%$ in 2020 vs. $\frac{78}{1260}$ equivalent to $6.2\%$ in 2019, $p \leq 0.01$). In 2020, the most evident increase in consultations was observed during the months following the lockdown ($\frac{139}{202}$ between June and September, equivalent to $72.8\%$ vs. $\frac{63}{202}$ between March and May, equivalent to $27.2\%$). In 2021 an initial downward trend was observed ($\frac{158}{1190}$, equivalent to $13.3\%$, $p \leq 0.01$ vs. 2020), that became even further evident in 2022 ($\frac{112}{1380}$ equivalent to $8.1\%$, $p \leq 0.01$ vs. 2020). This progressive downward trend led to a number of consultations in 2022 that was not significantly different from the number observed in 2019 ($8.1\%$ vs. $6.2\%$ respectively, $$p \leq 0.06$$) (Table 1).
**Table 1**
| Unnamed: 0 | Visit for suspected precocious puberty between March-September | Visit for suspected precocious puberty between March-September.1 | Visit for suspected precocious puberty between March-September.2 | Total visit March-September |
| --- | --- | --- | --- | --- |
| | March-May (%) | June-September (%) | Total (%) | |
| 2019.0 | 37 (47.4) | 41 (52.6) | 78 (6.2)* | 1260.0 |
| 2020.0 | 63 (27.2) | 139 (72.8) | 202 (27)* | 747.0 |
| 2021.0 | 77 (48.7) | 81 (51.3) | 158 (13.3)* | 1190.0 |
| 2022.0 | 48 (42.9) | 64 (57.1) | 112 (8.1)* | 1380.0 |
CPP family history was positive in $28.7\%$ of girls in 2020, in $24.1\%$ in 2021 and in $31.3\%$ in 2022, without significant differences with the 2019 population ($35.9\%$). In total, ten girls had been adopted, 3 of them belonged to the NPP group and 7 to the CPP group.
The proportion of girls with rapidly progressive CPP was significantly higher in 2020, compared to 2019 ($\frac{86}{202}$ vs. $\frac{18}{78}$, equivalent to $42.6\%$ vs. $23.1\%$, $p \leq 0.01$). In 2021, the number of cases of progressive CPP slightly decreased, compared to 2020 ($\frac{56}{158}$ vs. $\frac{86}{202}$, equivalent to $35.4\%$ vs. $42.6\%$, $$p \leq 0.17$$). In 2022, a further significant reduction in the number of cases of progressive CPP was observed compared to 2020 ($\frac{35}{112}$ vs. $\frac{86}{202}$, equivalent to $31.3\%$ vs. $42.6\%$, $$p \leq 0.04$$). The number of cases observed in 2022 was not statistically different from the number of cases observed in 2019 ($\frac{35}{112}$ vs. $\frac{18}{78}$, equivalent to $31.3\%$ vs. $23.1\%$, $$p \leq 0.22$$) (Figure 2).
**Figure 2:** *Subgroup distribution according on the final diagnosis in the different years. TT, transient thelarche; NPP, non-progressive precocious puberty; CPP, central precocious puberty; EP, early puberty. *p<0.01; °p<0.05.*
Table 2 shows patients’ characteristics according to the year of observation and final diagnosis.
**Table 2**
| Unnamed: 0 | Unnamed: 1 | Number (%) | Age (years) | Height SDS | Weight SDS | BMI SDS | BW SDS | BMI SDS – BW SDS |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 2019 | TT | 22 (28.2) | 6.72 ± 0.81 | 0.57 ± 1.10 | 0.62 ± 1.07 | 0.55 ± 1.03* | -0.05 ± 1.12 | 0.57 ± 1.49 |
| 2019 | NPP | 24 (30.8) | 7.34 ± 0.67° | 0.99 ± 1.10 | 0.71 ± 1.15 | 0.42 ± 1.20 | 0.07 ± 1.05 | 0.30 ± 1.39 |
| 2019 | CPP | 18* (23.1) | 7.02 ± 0.94 | 1.15 ± 1.01 | 0.87 ± 0.82 | 0.64 ± 0.61 | -0.06 ± 0.85 | 0.65 ± 0.80 |
| 2019 | EP | 14 (17.9) | 8.28 ± 0.39 | 0.84 ± 0.94 | 0.45 ± 1.01 | 0.20 ± 1.09 | -0.09 ± 0.82 | 0.27 ± 1.41 |
| 2020 | TT | 48 (23.8) | 6.89 ± 0.98 | 0.45 ± 0.98 | -0.03 ± 0.86 | -0.29 ± 0.90* | 0.04 ± 1.29 | -0.25 ± 1.38 |
| 2020 | NPP | 53 (26.2) | 6.83 ± 0.84° | 0.58 ± 1.01 | 0.50 ± 0.96 | 0.38 ± 0.96 | -0.17 ± 1.08 | 0.58 ± 1.20 |
| 2020 | CPP | 86 (42.6)*° | 7.05 ± 0.70 | 0.88 ± 0.94 | 0.40 ± 1.08 | 0.01 ± 1.80 | -0.15 ± 1.01 | 0.24 ± 2.02 |
| 2020 | EP | 15 (7.4) | 8.15 ± 0.35 | 0.52 ± 1.13 | 0.18 ± 1.10 | 0.02 ± 1.10 | 0.09 ± 1.09 | -0.20 ± 0.99 |
| 2021 | TT | 15 (9.5) | 6.55 ± 0.97 | -0.01 ± 1.21 | -0.12 ± 1.11 | -0.08 ± 1.02 | -0.01 ± 1.23 | -0.27 ± 1.38 |
| 2021 | NPP | 70 (44.3) | 7.13 ± 0.83 | 0.62 ± 0.93 | 0.69 ± 1.08 | 0.58 ± 1.15 | -0.17 ± 0.99 | 0.77 ± 1.60 |
| 2021 | CPP | 56 (35.4) | 7.27 ± 0.53 | 0.85 ± 0.99 | 0.57 ± 0.76 | 0.37 ± 0.77 | -0.25 ± 1.23 | 0.64 ± 1.34 |
| 2021 | EP | 17 (10.8) | 8.14 ± 0.44 | 1.00 ± 0.72 | 0.35 ± 0.69 | -0.05 ± 0.84 | -0.37 ± 1.12 | 0.12 ± 1.00 |
| 2022 | TT | 26 (23.2) | 6.43 ± 0.94 | 0.41 ± 0.88 | 0.13 ± 0.94 | -0.02 ± 0.89 | -0.12 ± 0.94 | 0.08 ± 1.17 |
| 2022 | NPP | 38 (33.9) | 7.26 ± 0.60° | 0.70 ± 0.84 | 0.43 ± 0.91 | 0.22 ± 1.04 | -0.05 ± 1.13 | 0.20 ± 1.22 |
| 2022 | CPP | 35 (31.3)° | 7.30 ± 0.47 | 1.05 ± 1.17 | 0.60 ± 1.04 | 0.28 ± 1.11 | -0.09 ± 1.40 | 0.28 ± 1.38 |
| 2022 | EP | 13 (11.6) | 8.04 ± 0.67 | 0.66 ± 0.95 | 0.60 ± 0.88 | 0.54 ± 0.82 | -0.22 ± 1.29 | 0.61 ± 1.04 |
No significant differences in anthropometric characteristics and laboratory parameters were found comparing the CPP subgroups of the four different years. The exceptions to this finding were a lower basal LH level in 2020 compared to 2022 (0.7 ± 0.98 IU/L in 2020 vs. 1.88 ± 1.99 IU/L in 2022, $p \leq 0.01$) and a less evident BA advancement in 2020 compared to 2021 (1.32 ± 0.92 years in 2020 vs. 1.85 ± 1.17 years in 2021, $$p \leq 0.02$$) (Table 3).
**Table 3**
| CPP | 2019 | 2020 | 2021 | 2022 |
| --- | --- | --- | --- | --- |
| Number (%) | 18 (23.1)* | 86 (42.6)*° | 56 (35.4) | 35 (31.3)° |
| Age (years) | 7.02 ± 0.94 | 7.05 ± 0.70 | 7.27 ± 0.53 | 7.30 ± 0.47 |
| Birth Weight SDS | -0.06 ± 0.85 | -0.15 ± 1.01 | -0.25 ± 1.23 | -0.09 ± 1.40 |
| Height SDS | 1.15 ± 1.01 | 0.88 ± 0.94 | 0.85 ± 0.99 | 1.05 ± 1.17 |
| Weight SDS | 0.87 ± 0.82 | 0.40 ± 1.08 | 0.57 ± 0.76 | 0.60 ± 1.04 |
| BMI SDS | 0.64 ± 0.61 | 0.01 ± 1.80 | 0.37 ± 0.77 | 0.28 ± 1.11 |
| BMI SDS – BW SDS | 0.65 ± 0.80 | 0.24 ± 2.02 | 0.64 ± 1.34 | 0.28 ± 1.38 |
| Basal LH (IU/L) | 1.00 ± 1.51 | 0.52 ± 0.98* | 1.13 ± 1.22 | 1.88 ± 1.99* |
| LH peak (IU/L) | 19.49 ± 16.79 | 17.01 ± 14.02 | 22.48 ± 17.35 | 21.28 ± 14.27 |
| 17-beta-estradiol (pg/mL) | 8.50 ± 10.02 | 16.54 ± 19.25 | 16.18 ± 18.26 | 24.23 ± 20.45 |
| BA - CA | 1.69 ± 0.75 | 1.32 ± 0.92° | 1.85 ± 1.17° | 1.82 ± 1.06 |
| Uterine longitudinal diameter (mm) | 42.04 ± 6.85 | 38.83 ± 8.02 | 41.30 ± 9.23 | 41.88 ± 7.65 |
The majority of CPP girls ($78\%$) underwent brain MRI study, none of them showed organic lesions related to CPP.
As regards to lifestyle, a significantly lower weekly physical activity was reported in the 2020 group compared to the 2019 and 2022 groups (median 1-2 h/week, IQR [0] in 2020 vs. 3-4 h/week, IQR (1-2 h/week to 5-6 h/week) in both 2019 and 2022, $p \leq 0.01$) (Figure 3). In addition, the overall weekly time spent on electronic devices (as tablet, PC or smartphone) was considerably greater in the 2020 group than in 2019 and 2022 groups (median >20 h/week, IQR [0] in 2020 vs. 10-15 h/week, IQR [0] in 2019 and 5-10 h/week, IQR (1-5 h/week to 10-15 h/week) in 2022; $p \leq 0.01$) (Figure 4). No significant difference in eating habits were evident among the groups.
**Figure 3:** *Overall weekly physical activity in 2019, 2020 and 2022 populations.* **Figure 4:** *Overall weekly use of electronic devices (as tablet, PC and smartphone) in 2019, 2020 and 2022 populations.*
## Discussion
Our current data confirms the repeatedly reported, sharp increase in endocrinological consultations for suspected precocious or early puberty in girls, during the first waves of COVID-19 pandemic (8–21). As previously described, the increase in consultations was also reflected in an increase in CPP cases in 2020 compared to pre-pandemic values. Supporting the assumption of a different etiology between early and true precocious puberty, no difference in the number of cases of EP was observed throughout the four years.
The number of consultations for suspected precocious or early puberty in 2020 could have been affected by a selection bias due to the home confinement with elevated health anxiety that characterized the first phase of the pandemic. On the other hand, the significant increase of CPP cases in 2020 is supported by an objective diagnosis formulated by the same medical personnel among the different years.
For the first time, a gradual tendency towards a decrease of consultations and CPP cases during the evolution of the pandemic has been revealed, suggesting a downward trend of this phenomenon in concert with waning of the pandemic such that cases observed in 2022 were similar to the number of cases seen in 2019.
During 2021, the restrictive measures previously put in place to contain the pandemic were progressively relaxed and daily life activities returned to normal. Distant learning was gradually abandoned, at least in the primary schools, and children resumed face-to-face school activities. Group activities in leisure time and outdoor physical exercise resumed.
In a previous study [19], we correlated the increase of precocious puberty cases with home confinement, lack of physical exercise and the significant increase of daily screen time (both for studying and for leisure activities). These profound changes could have acted as stressors triggering the onset of puberty. The results of the present study seem to confirm the impact of lifestyle changes on pubertal timing.
Although there is no conclusive data on the association between poor physical activity and precocious puberty, a recent meta-analysis has confirmed that regular exercise training substantially increases adiponectin levels in obese children [27]. Adiponectin, one of the most relevant adipokines secreted by mature adipocytes, has been demonstrated to suppress kisspeptin gene transcription and GnRH secretion by hypothalamic neurons, playing an inhibitory role in the onset of puberty [28, 29].
Beyond the physical benefits of exercise, several studies reported a positive association between physical activity and psychological well-being in children and adolescents. A sedentary lifestyle has been related to both depression and lower life satisfaction and happiness, while promoting physical activity and decreasing sedentary behavior might protect mental health [30]. Early studies [31, 32] suggested that psychological stress itself (due to insecure bonds with parents or parental conflicts) might modify pubertal timing. A recent study reported that anxiety and other internalizing symptoms in pre-pubertal girls are associated with early pubertal onset, independently from maternal education anxiety, BMI, and ethnicity [33].
Several studies have recently investigated the effects of exposure to electromagnetic fields on melatonin (34–37). Exposure to electromagnetic fields has been associated with decreased melatonin production in vitro, as well as with a decreased pineal and plasma melatonin and its urinary metabolites [35]. Nighttime serum melatonin levels are highest in infants and young children and decrease progressively by $80\%$ throughout childhood and adolescence, nocturnal melatonin levels drop in parallel with sexual maturation [38, 39]. Animal models have also shown that a reduction in melatonin may accelerate pubertal development [40] and that the administration of melatonin suppress GnRH secretion [41]. A recent study performed on immature female rats differentially exposed to a light spectrum predominantly emitted by LED (light-emitting diode) screens, showed a faster pubertal maturation in rats bathed with the blue-tinged light for longer bouts [42]. The combination of this data suggests that a greater use of electronic devices leads to a reduction in melatonin levels, which in turn triggers the endocrine changes culminating in the earlier onset of puberty [43].
Another study reported more frequent late bedtime, sleep disturbances, excessive somnolence, sleep breathing disorders and sleep-wake transition disorders in girls diagnosed with CPP during the Italian lockdown [15].
Published data analyzing the impact of overweight and obesity on the rise of CPP cases are conflicting [8, 13, 44, 45]. Interestingly, we did not find any significant difference in BMI SDS at CPP diagnosis across the four years of observation, suggesting that overnutrition and overweight do not represent determining factors in this context.
All the mentioned factors (inactivity, increased screen time, sleep disturbances, and stress) may have contributed to the sharp increase in CPP cases, acting directly on the HPO axis. The retrospective design of the study does not allow identifying which factor predominates over the others. Indeed, the speed and reversibility of the phenomenon and the absence of differences in the anthropometric characteristics of the groups (in particular, BMI unchanged over the years) allows us to rule out already known risk factors for CPP (such as endocrine disruptors, obesity, or epigenetic factors). In support of this hypothesis, we observed lower basal LH levels and the less evident BA advancement in the 2020 CPP cases, compared to 2019. This could suggest that life-style changes can only act as weaker triggers of GnRH secretion with a transient effect on pubertal timing.
A single study from Korea described an almost doubled CPP incidence in 2021, in comparison with 2016, with a concurrent increase in the proportion of boys ($19.55\%$ vs. $9.21\%$) [11]. As in the majority of the published studies, we reported an increase of CPP cases uniquely in girls. This fact seems to confirm that male CPP, in its rarity, is mostly related to organic disorders and/or genetic factors and less influenced by environmental changes.
We are aware that the major limitation of this study is its retrospective design, which did not allow us to obtain more data on factors potentially influencing GnRH secretion, but to our knowledge, this is the first study that describes a progressive downward trend in CPP cases during the post-pandemic period in 2022 to near pre-pandemic levels.
In conclusion, the sharp increase of CPP cases in girls during the first pandemic wave in mid-2020 seems to give way to a gradual downward trend, concurrently with the easing of the restrictive measures, returning to the pre-pandemic incidence of CPP in 2022. This suggests that the drastic lifestyle changes, as lack of physical exercise, increased screen time, sleep disturbances, and stress, may represent weak and reversible triggers on the central “biological clock” controlling timing and tempo of puberty.
## 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 Bambino Gesù Children’s Hospital. Written informed consent from the participants’ legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.
## Author contributions
CB and MCa conceptualized and designed the study. GB, LC, LP, TT, and MCh collected data. LC and MCh performed statistical analysis. CB, LC, and MCh drafted the initial manuscript, and reviewed the manuscript. MCa and CB revised 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: 'Early detection of the impact of combined taxane and carboplatin treatment
on autonomic nerves in patients with cervical cancer: Measurement of heart rate
variability'
authors:
- Jian Liu
- Weizheng Guan
- Yilin Sun
- Yuling Wang
- Guangqiao Li
- Sai Zhang
- Bo Shi
journal: Frontiers in Physiology
year: 2023
pmcid: PMC10011481
doi: 10.3389/fphys.2023.1126057
license: CC BY 4.0
---
# Early detection of the impact of combined taxane and carboplatin treatment on autonomic nerves in patients with cervical cancer: Measurement of heart rate variability
## Abstract
Background: Previous studies have shown that heart rate variability (HRV) analysis is a sensitive indicator of chemotherapy-induced cardiotoxicity. However, most studies to date have observed long-term effects using long-term analyses. The main purpose of this study was to evaluate the acute effect of chemotherapy on the cardiac autonomic nervous system (ANS) in patients with cervical cancer (CC) by examining short-term HRV.
Methods: Fifty patients with CC admitted to the Department of Gynecology and Oncology of the First Affiliated Hospital of Bengbu Medical College were enrolled in the study. Based on their chemotherapy regimens, the patients were divided into a DC group (docetaxel + carboplatin) and a TC group (paclitaxel + carboplatin). A 5-min resting electrocardiogram (ECG) was collected before and the day after chemotherapy: the time domain (standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive differences (RMSSD)) and frequency domain (low-frequency power (LF), high-frequency power (HF), and (LF/HF)) parameters were analyzed, and the differences before and after chemotherapy were compared.
Results: The results showed that SDNN, RMSSD and HF were significantly higher in the DC and TC groups after chemotherapy than before ($p \leq 0.05$, Cohen’s d > 0.5). In addition, LF was significantly higher after TC than before chemotherapy ($p \leq 0.05$, Cohen’s d > 0.3), and LF/HF was significantly lower after DC than before chemotherapy ($p \leq 0.05$, Cohen’s d > 0.5).
Conclusion: Chemotherapy combining taxane and carboplatin can increase the HRV of CC patients in the short term, and HRV may be a sensitive tool for the early detection of chemotherapy-induced cardiac ANS perturbations.
## Introduction
Cervical cancer (CC) is a common gynecological cancer (Sung et al., 2021). Chemotherapy, an important means of cancer treatment, chemotherapy can effectively reduce the tumor burden of cancer patients and prolong their survival time (Monsuez et al., 2010; Regalado Porras et al., 2018; Gabani et al., 2021). Nevertheless, the toxic side effects of chemotherapy cannot be ignored. Cardiotoxicity is one of the clinically recognized side effects of chemotherapy. Chemotherapy-induced cardiotoxicity can develop over time in an acute, subacute or chronic manner, with acute or subacute cardiotoxicity appearing at any time from the start of treatment to 2 weeks after the end of treatment (Madeddu et al., 2016). A large cohort study of 2,625 cancer patients who received chemotherapy showed that the overall incidence of cardiotoxicity was $9\%$ during a median follow-up of 5.2 years (Cardinale et al., 2015). Thus, the early detection of cardiac function in patients with chemotherapy can help to prevent and identify the occurrence of cardiotoxicity and guide and improve the direction of subsequent treatment to maximize patient cardiac safety.
Electrocardiogram (ECG) is a routine method to identify cardiotoxicity. However, the abnormalities found on ECG may be interfered with by numerous factors, and the specificity of cardiotoxicity detection is low. Imaging methods, such as echocardiography, cardiac magnetic resonance (CMR), and multigated radionuclide angiography (MUGA), are also commonly used to evaluate cardiotoxicity in clinical practice (Tan and Scherrer-Crosbie, 2012; Reuvekamp et al., 2016; Jordan and Hundley, 2019; Tak et al., 2020; Siddiqui et al., 2022). However, the accuracy of ultrasound examination is affected by the physician’s technology and image quality (Jerusalem et al., 2019), there are many contraindications in magnetic resonance examination (Christian et al., 2012), and MUGA can cause radiation damage to patients (Bikiewicz et al., 2021). As a result, imaging methods still have limitations in clinical use. Some cardiac biomarkers, such as B-type natriuretic peptide and cardiac troponin I, have also been considered for the detection of early cardiac injury during chemotherapy, but there is no consensus on the optimal time for biomarker measurement (Pudil et al., 2020; Semeraro et al., 2021). Therefore, determining the best way to assess early cardiotoxicity is still in the exploratory stage.
Currently, the mechanism of cardiac dysfunction is thought to involve abnormalities in autonomic nervous system (ANS) function (Teng et al., 2021). Heart rate variability (HRV), as an objective index to evaluate cardiac ANS regulation, is convenient and noninvasive (Patural et al., 2022; Rajendra Acharya et al., 2006). Several studies have suggested that HRV may be a sensitive indicator for evaluating chemotherapy-induced cardiotoxicity. For example, Frye et al. found that carotid artery stiffness was significantly higher and cardiovascular baroreflex sensitivity (cBRS) along with time- and frequency-domain HRV indices were significantly lower in cancer patients receiving chemotherapy compared to healthy controls; furthermore, cBRS correlated significantly with the low-frequency power of HRV ($r = 0.66$, $p \leq 0.001$) (Frye et al., 2018). Caru et al. compared HRV in acute lymphoblastic leukemia patients with different cumulative doses of doxorubicin. The results showed that the patients in the high-risk group had significantly altered HRV time-domain, frequency-domain and nonlinear indicators compared to the patients in the standard-risk group, suggesting that HRV is a sensitive indicator for detecting changes in cardiac ANS in patients (Caru et al., 2019).
At present, the use of HRV to detect chemotherapy-induced cardiotoxicity has been involved in a variety of cancers. However, few scholars have conducted research in this direction for CC patients. Therefore, this study used the traditional time- and frequency-domain indices of HRV to assess whether HRV can detect perturbations of the cardiac ANS in CC patients with chemotherapy and to provide new ideas for the evaluation of cardiotoxicity in CC patients receiving chemotherapy.
## Subjects
The study subjects were 52 CC patients who received taxane combined with carboplatin adjuvant chemotherapy admitted to the Department of Gynecology and Oncology, the First Affiliated Hospital of Bengbu Medical College (Anhui, China), from December 2021 to October 2022.
The inclusion criteria were as follows: [1] patients with CC (pathological type of squamous and adenocarcinoma) confirmed by pathological examination; [2] patients who received chemotherapy and the chemotherapy regimen was taxane (docetaxel/paclitaxel) combined with carboplatin.
The exclusion criteria were as follows: patients with ectopic heartbeats >$5\%$ of all beats who were unsuitable for HRV analysis (2 patients were excluded).
This study was approved by the Clinical Medical Research Ethics Committee of The First Affiliated Hospital of Bengbu Medical College (Bengbu, Anhui, China) (registration number: 2021KY010). The experiments were performed in strict accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All patients were informed of the details of the purpose, procedures, risks and potential adverse effects of the experiment and signed an informed consent form.
## Data collection
ECG data collection was performed in a quiet temperature-controlled room (23°C ± 1C). The subjects were prohibited from consuming caffeine, alcohol or other autonomic nervous system stimulants within 24 h before the test. A single-lead miniature ECG recorder (version 2.8.0, Healink-R211B, Healink Ltd., Bengbu, China) with a V6 lead was used to collect a 5 min supine resting ECG before and the day after chemotherapy. The signal bandwidth of the equipment was set to 0.67–40 Hz, and the sampling rate was 400 Hz. The patients were asked to keep quiet and breathe smoothly during the period of ECG collection.
## HRV analysis
The Pan-Tompkins algorithm was used to extract the time series of the RRI. The artifacts caused by interference and ectopic heartbeat were corrected by a time-varying threshold algorithm, and then HRV time and frequency domain analysis was carried out.
The time-domain indices included the standard deviation of all normal-to-normal intervals (SDNN) and the root mean square of successive differences (RMSSD).
The frequency-domain indices included low-frequency power (LF, 0.04–0.15 Hz), high-frequency power (HF, 0.15–0.4 Hz), and the ratio of LF to HF (LF/HF).
The above analysis was performed with Kubios HRV Premium software (version 3.1.0, https://www.kubios.com, Kubios Oy, Kuopio, Finland).
## Statistical analysis
The normality of all data was checked by the Shapiro-Wilk test, and the normally distributed data are expressed as the mean ± standard deviation. The nonnormally distributed data are expressed as the median (first quartile, third quartile). Independent sample t tests and chi-square tests were used to assess the differences in basic clinical information between the two groups; Mann‒Whitney U tests were used to compare the group differences in each HRV index before chemotherapy between the two groups; and paired samples t tests and Wilcoxon signed rank tests were used to examine the differences in HRV indices before and after chemotherapy between the two groups. Cohen’s d value was used to characterize the effect size of the difference in each HRV index before and after chemotherapy, with $d = 0.2$ considered a small effect, $d = 0.5$ considered a moderate effect, and $d = 0.8$ considered a large effect (Cohen, 1992). SPSS Statistics 26.0 (IBM Corp., Chicago, Illinois, United States of America) software was used for the above statistical analysis, and $p \leq 0.05$ (two-tailed) was considered statistically significant.
## Results
A total of 50 patients met the inclusion criteria and were divided into two groups based on chemotherapy regimen: 19 patients in the DC group (docetaxel combined with carboplatin) and 31 patients in the TC group (paclitaxel combined with carboplatin). Table 1 summarizes the basic clinical data of the two groups, and Table 2 shows the baseline HRV of the two groups. There were no significant differences in age, BMI, mean heart rate, diabetes, hypertension, histological type or baseline HRV between the two groups ($p \leq 0.05$), and there was a significant difference in whether the patients were receiving chemotherapy for the first time ($$p \leq 0.019$$).
Figure 1 shows the comparison of HRV parameters before and after chemotherapy in the DC and TC groups. In the DC group, SDNN, RMSSD, and HF were significantly higher after chemotherapy than before ($p \leq 0.05$); LF/HF was significantly lower than before ($p \leq 0.05$); and LF was not significantly different before and after chemotherapy ($p \leq 0.05$). In the TC group, SDNN, RMSSD, LF, and HF were significantly higher after chemotherapy than before ($p \leq 0.05$); LF/HF was not significantly different before and after chemotherapy ($p \leq 0.05$).
**FIGURE 1:** *Differences in HRV before and after chemotherapy in the DC and TC groups.*
The effect sizes of the differences in each HRV parameter before and after chemotherapy in the two groups are shown in Figure 2.
**FIGURE 2:** *Effect sizes of HRV indicators with differences before and after chemotherapy in the DC and TC groups.*
## Discussion
This is a study to evaluate the effects of combined taxane and carboplatin chemotherapy on early cardiac ANS function in patients with CC. In this study, the traditional time-domain (SDNN, RMSSD) and frequency-domain (LF, HF, LF/HF) indices were used to analyze the HRV of 50 CC patients before and after chemotherapy. The results showed that significant increases in SDNN, RMSSD and HF were observed in both groups. In addition, LF increased significantly in the TC group and LF/HF decreased significantly in the DC group. The alteration of HRV predicts changes in cardiac ANS function, and the results of this study suggest that combined taxane and carboplatin chemotherapy may affect the early ANS status in CC patients.
The mechanism of the effect of taxane on cardiac function in patients has not been clarified. Because it belongs to the anti-microtubule class of drugs, it acts as an anticancer agent by promoting polymerization of tubulin, forming stable microtubules, and inhibiting cell division. However, it can damage the cytoskeleton due to its anticancer properties and impair the basic functions of cardiac endothelial cells, which in turn leads to myocardial injury (Morbidelli et al., 2016; Rosa et al., 2016). Several studies have shown that the occurrence of multiple cardiac disturbance events (including arrhythmias, bradycardia, and different degrees of atrioventricular block) can be observed during the administration of taxane (Rowinsky et al., 1991; Markman and Nazarian, 2016). The altered HRV observed in our study may be an early sign of cardiac disturbance events. There are few reports on the cardiotoxicity of carboplatin, and its major toxic side effect manifests as myelosuppression (Oun et al., 2018). However, it cannot be determined whether some of the HRV alterations observed in this study originated from the combination of carboplatin.
RMSSD is highly correlated with HF, both representing vagal activity (Picard et al., 2021). The significant increase in RMSSD and HF indicates enhanced cardiac vagal activity, whereas vagal activity stimulation shortens the atrial effective refractory period, increases spatial electrophysiological heterogeneity, and promotes early after-depolarization at the end of action potential phase 3, which may be a trigger for arrhythmias (Shen and Zipes, 2014). SDNN is generally thought to reflect the overall activity of sympathetic and vagal nerves, but the main cause of its variability in short-time recordings comes from respiratory sinus arrhythmias (RSA) (Shaffer et al., 2014; Shaffer and Ginsberg, 2017). RSA is a physiological phenomenon resulting from the regulation of the cardiac system by the vagus nerve and could be used as an indicator of vagal activity (Porges, 2007; Lubocka and Sabiniewicz, 2021). It has been found that decreased RSA is associated with reduced cardiac vagal activity in patients with paroxysmal atrial fibrillation after undergoing pulmonary vein isolation (Jungen et al., 2019). Increasing the biofeedback training of RSA can effectively enhance vagal regulation (Munafò et al., 2016). Moreover, various studies have noted that RSA is highly correlated with SDNN (Smith et al., 2013; Vieira et al., 2016). Consequently, in our study, the significant increase in SDNN after chemotherapy was most likely attributable to an increase in vagal activity rather than an alteration in overall ANS activity.
Compared to time-domain indices, the study of HRV frequency-domain indices contains a great deal of uncertainty. The physiological significance of LF has been controversial. Initially, LF was considered to reflect sympathetic activity (Pagani et al., 1997). Later, it was reported that LF represents the comodulation of the sympathetic-vagal system (Vanderlei et al., 2009). In recent years, other researchers have found that LF primarily reflects baroreflex activity (Goldstein et al., 2011; Rahman et al., 2011). This challenges the notion that LF/HF represents sympathetic-vagal balance (Billman, 2013). LF and LF/HF may not accurately reflect the state of the ANS. We observed a significant increase in LF in the TC group and a significant decrease in LF/HF in the DC group, which probably relates to the complex interaction between sympathetic and parasympathetic nerves, as well as mechanical effects caused by respiration (Billman, 2011). Additionally, in contrast to the DC group, the TC group required Cremophor EL as a solvent for the formulation of paclitaxel; evidence suggests that this solvent induces histamine release and thus causes cardiovascular stimulation (Rowinsky et al., 1991; Madeddu et al., 2016, Al-Mahayri et al., 2021), which may also be a possible reason for the elevated LF we observed in the TC group. It is worth mentioning that in our research, the effect sizes of the time-domain indicators (SDNN, RMSSD) before and after chemotherapy were greater than 0.8 in both groups. We consider the time domain is more sensitive than the frequency domain in reflecting the physiological state of the body.
In previous studies examining the effects of chemotherapeutic agents on the HRV of patients, some researchers have found no significant change in HRV in patients before and after chemotherapy. For example, Ekholm et al. included 24 BC patients previously pretreated with anthracyclines and evaluated their HRV by 24-h ambulatory ECG before and after three to four courses of docetaxel treatment. The results showed that the HRV time-domain (SDNN, NN50, RMSSD) and frequency-domain (LF, HF, VLF, LF/HF) parameters of the patients after chemotherapy were not significantly altered (Ekholm et al., 2002). In contrast to their findings, the patients in this study had significantly higher SDNN, RMSSD, and HF after chemotherapy. We suppose that, owing to the different chemotherapeutic agents from the previous studies and the heterogeneity of the study population, there will be some differences regarding the effects of HRV. Additionally, most of the present studies have used 24-h ambulatory ECGs to observe HRV in patients after several courses of treatment, and the series of changes observed may contribute to long-term cumulative drug effects. In contrast, the present study used short-term ECGs for short-term observation, and we collected 5-min ECGs before and the day after chemotherapy to analyze the HRV of patients. We considered the relatively subtle effect of chemotherapy drugs on the cardiac ANS in the short term; that is, chemotherapy drug use stimulates the patient’s cardiac vagus nerve in the short term, resulting in elevated SDNN, RMSSD and HF.
## Limitations
This explorative study included a relatively small number of patients, and there was heterogeneity in the dosage of chemotherapeutic drugs. Furthermore, our study merely observed short-term changes in HRV in patients and did not indicate whether these changes are permanent. Therefore, the results of this study need to be confirmed in a prospective study with a larger sample size, homogeneous drug doses and a long-term follow-up period.
## Conclusion
Our findings demonstrate that combined taxane and carboplatin chemotherapy can increase HRV in the short term for CC patients. HRV may be a sensitive tool for the early detection of cardiac ANS perturbations caused by chemotherapy with taxane combined with carboplatin. Early changes in cardiac function can be monitored clinically based on HRV alterations to prevent cardiotoxicity and myocardial injury in patients.
## 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 This study was approved by the Clinical Medical Research Ethics Committee of The First Affiliated Hospital of Bengbu Medical College (Bengbu, Anhui, China). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
Conceptualization, BS and JL; methodology, YS, WG, SZ, and YW; resources, BS and JL; data curation, JL; writing—original draft preparation, WG and GL; writing—review and editing, BS and JL; supervision, JL; project administration, BS; funding acquisition, BS and SZ. All authors have read and agreed to the published version of the manuscript.
## Conflict of interest
An immediate family member of BS owns stock in HeaLink Ltd., Bengbu, China.
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: Serine/threonine-protein kinase STK24 induces tumorigenesis by regulating the
STAT3/VEGFA signaling pathway
authors:
- Senyan Lai
- Dao Wang
- Wei Sun
- Xiaonian Cao
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10011487
doi: 10.1016/j.jbc.2023.102961
license: CC BY 4.0
---
# Serine/threonine-protein kinase STK24 induces tumorigenesis by regulating the STAT3/VEGFA signaling pathway
## Body
Globally, lung cancer is the most common cause of cancer-related deaths, with almost 1.6 million deaths reported annually [1]. Nonsmall cell lung cancer (NSCLC) accounts for 85 % of all the lung cancers. Further, it has been found that the most common histological subtypes are lung adenocarcinoma and lung squamous cell carcinoma accounting for approximately 25 to $30\%$ as well as $40\%$, respectively [2]. Surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy are the main therapeutic strategies for treatment of patients with lung cancer [1, 3]. Although anti-angiogenesis therapies, such as use of bevacizumab and ramucirumab, are effective for treatment of advanced stages of lung cancer and significantly improves prognosis of the patients, primary and secondary drug-resistance is a common problem with the strategy [4, 5, 6]. To overcome the challenge, there is need for further studies toward understanding of tumor angiogenesis.
Tumor progression is often accompanied by tumor angiogenesis [7], which was first suggested by Folkman in 1971. It has been reported that anti-angiogenesis therapy greatly improves the outcomes of patients with cancer [8]. Solid tumor vessels are immature, tortuous, and disorganized as well as excessively leaky. Excess leaking of the vessels results in high interstitial fluid pressure and a reduction in blood perfusion as well as oxygenation [4]. During cancer progression, upregulation of multiple pro-angiogenic factors, such as vascular endothelial growth factor A (VEGFA), PDGF, and IGF-1, and downregulation of anti-angiogenic factors, such as ENS and PF-4, contributes to pathological angiogenesis [9, 10]. Recent studies have shown that the redox state of cancer cells is a critical regulatory factor of tumor angiogenesis [11]. The imbalance between pro-angiogenic and anti-angiogenic factors leads to development of immature vascular supply. Several classical signaling pathways, such as IL-6/signal transducer and activator of transcription (STAT3), HIF1/VEGFA, and PI3K/AKT, have been shown to regulate tumor angiogenesis [4, 8].
Serine/threonine-protein kinase 24 (STK24), also known as MST3, belongs to the subfamily germinal center kinase III, together with other members including STK4 (MST1), STK3 (MST2), andSTK26 (MST4) [12]. STK24 is associated with cellular proliferation, differentiation, death, polarity, and exocytosis [12]. For solid tumors, STK24 positively regulates ERK$\frac{1}{2}$ activation and cooperates with STK26 and YSK1 to promote migration and metastasis of the cancer cells [13, 14]. The expression of STK24 is negatively correlated with the overall survival in patients with breast cancer [15]. Furthermore, overexpression of STK24 is associated with chemoresistance to cisplatin, carboplatin, paclitaxel, etoposide, and erlotinib [16]. Besides, STK24 can also regulate excitatory synaptic transmission in epileptic hippocampal neurons, inhibit cavernoma development, control kidney water reabsorption by regulating Aqp2 membrane targeting, and protect the obesity-associated metabolic disorders by disrupting the NLRP3 inflammasome [13, 14, 17, 18]. However, the biological function of STK24 in NSCLC tumorigenesis and tumor angiogenesis is still unclear.
The present study demonstrated that STK24 acts as an oncogene in NSCLC tumorigenesis and positively regulates the proliferation, migration, and invasion potential of NSCLC cells. In addition, in vivo xenograft assays showed that loss of STK24 inhibited tumorigenesis. For mechanism, of action, the present study evidently revealed that overexpression of STK24 stabilizes and upregulates expression of STAT3 by decreasing the ubiquitination of STAT3. Moreover, it was evident that STK24-mediated regulation of tumor angiogenesis and proliferation was dependent on expression of STAT3. Therefore, results of the current study enhances the available understanding on tumor angiogenesis and shows that STK24/STAT3/VEGFA signaling pathway may be a novel therapeutic target for treatment of patients with NSCLC.
## Abstract
Lung cancer is the most common cause of cancer-related death. Although anti-angiogenesis therapy has been effective in the treatment of nonsmall cell lung cancer (NSCLC), drug-resistance is a common challenge. Therefore, there is a need to develop new therapeutic strategies for NSCLC. Serine/threonine-protein kinase 24 (STK24), also known as MST3, belongs to the germinal center kinase III subfamily, and the biological function of STK24 in NSCLC tumorigenesis and tumor angiogenesis is still unclear. In this study, we demonstrated that STK24 was overexpressed in lung cancer tissues compared with normal lung tissues, and lung cancer patients with higher STK24 expression levels had shorter overall survival time. In addition, our in vitro assays using A549 and H226 cell lines revealed that the STK24 expression level of cancer cells was positively correlated with cancer cells proliferation, migration, invasion, and tumor angiogenesis ability; in vivo assays also demonstrated that silencing of STK24 dramatically inhibited tumor progress and tumor angiogenesis. To investigate a mechanism, we revealed that STK24 positively regulated the signal transducer and activator of transcription 3 (STAT3)/vascular endothelial growth factor A (VEGFA) signaling pathway by inhibiting polyubiquitin-proteasomal–mediated degradation of STAT3. Furthermore, we performed in vivo assays in BALB/c nude mice and in vitro assays to show that STK24-regulated tumor angiogenesis depends on STAT3. These findings deepened our understanding of tumor angiogenesis, and the STK24/STAT3/VEGFA signaling pathway might be a novel therapeutic target for NSCLC treatment.
## STK24 functioned as an oncogene in NSCLC
The role of STK24 in tumorigenesis was investigated by first comparing the expression of STK24 between normal and NSCLC tissues using data obtained from The Cancer Genome Atlas (TCGA) database. Remarkably, it was found that STK24 was upregulated in both lung adenocarcinoma and lung squamous cell carcinoma (Fig. 1A). RT-PCR assays was then used to measure the mRNA expression levels of STK24 in 22 paired clinical NSCLC tissues and matched adjacent normal lung tissues. Figure 1STK24 acted as an oncogene in NSCLC.A, analysis of STK24 expression levels in normal lung tissues and NSCLC tissues using TCGA database (LUAD: normal = 59; tumor = 483; LUSC: normal = 51; tumor = 500); Left $p \leq 0.0001$, Right $p \leq 0.0001$, by Student’s t test. B, expression level analysis of STK24 in 22 pairs of NSCLC tissues and matched adjacent normal tissues using RT-PCR. C, immunoblots analysis of 12 pairs of NSCLC tissues and matched adjacent normal tissues. D, showing statistical analysis of C ($$n = 12$$); $$p \leq 0.0001$$, by Student’s t test. E, representative images of IHC assays for 69 pairs of NSCLC tissues and matched adjacent normal tissues; scale bar =100 μm. F and G, statistical analysis for IHC assay of NSCLC tissues and matched adjacent normal tissues in E ($$n = 69$$); F for $p \leq 0.0001$, by unpaired Student’s t test, G for $p \leq 0.0001$, by paired Student’s t test. H, statistical analysis for IHC assay of NSCLC tissues from patients grouped by clinical stage in E ($$n = 69$$); $$p \leq 0.0085.$$ I, representative images of IHC assays for 69 NSCLC tissues; scale bar =100 μm. J, Kaplan-Meier curve of NSCLC patients grouped by IHC scores (high = 39 cases; low = 30 cases); $$p \leq 0.0044$$, by log-rank (Mantel-Cox) test. K, Kaplan-Meier curve of NSCLC patients grouped by STK24 expression levels using TCGA database (high = 481 cases; low = 481 cases); $$p \leq 0.0032$$, by Log-rank (Mantel-Cox) test. All immunoblots were conducted three time, and consistent results were found. IHC, immunohistochemistry; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24; TCGA, The Cancer Genome Atlas.
Results of the study showed that NSCLC tissues had higher STK24 expression level as compared with the matched adjacent normal lung tissues (Fig. 1B). The RT-PCR results were consistent with that of the Western blot and immunohistochemistry (IHC) (Fig. 1, C–G). Furthermore, patients with higher clinical stages had higher STK24 expression level, whereas the expression level of STK24 had no correlation with age or gender of the patients (Figs. 1H and S1, A and B). The STK24 expression levels were also higher in tissues of other cancers types as compared with the matched normal tissues (Fig. S1C).
The present study also analyzed the STK24 expression levels of 69 NSCLC tumor tissues using IHC assays. The patients were hence grouped according to the levels of STK24 expression, and survival analysis was carried out using Kaplan–Meier curves. Results of the analysis showed that patients with higher STK24 expression level was associated with poor overall survival time as compared with those with lower STK24 expression level (Fig. 1, I and J). Similar results were obtained after analysis of data for NSCLC and pancreatic cancer in the TCGA and Human Protein Atlas database (Figs. 1K and S1D). Overall, the findings of the current study showed that STK24 acts as an oncogene in NSCLC tumorigenesis.
## High STK24 expression promoted proliferation, migration, and invasion of NSCLC cells
Two NSCLC cell lines: lung adenocarcinoma cancer cell line (A549) and lung squamous carcinoma cell line (H226) were selected for further experiments and assessment of the role of STK24 in NSCLC tumorigenesis. Lentiviruses was used to construct A546 and H226 cell lines with stable ectopic expression of vector or STK24, and Western blot analysis was performed to confirm the stable expression (Fig. 2A). The CCK8 assays were carried out in the present study to assess the proliferation potential of vector or STK24 NSCLC cell lines. It was found that the stable expression of STK24 significantly enhanced proliferative potential of NSCLC cells (Fig. 2B). Results of cell cycle analysis revealed that cells expressing STK24 had a higher S and G2-M phase and a lower G0-G1 phase as compared with cells transfected with the vector (Fig. 2, C and D). The findings showed that overexpression of STK24 enhances the proliferation ability of NSCLC cells. Figure 2STK24 overexpression promoted proliferation, migration, and invasion ability of NSCLC cells. A, immunoblot analysis of protein expression levels in NSCLC cells stably expressing vector or STK24. B, cell proliferation analysis of cells stably expressing vector or STK24 using CCK8 assays ($$n = 3$$); $p \leq 0.0001$, by Student’s t test. C, representative image of cell cycle analysis for cells stably expressing Vector or STK24. D, statistical analysis of cell cycle analysis in C ($$n = 3$$); p for left = 0.0095, by Student’s t test, p for right = 0.0009, by Student’s t test. E, representative images for transwell assays for NSCLC cells stably expressing vector or STK24; scale bar = 100 μm. F, statistical analysis of transwell assays in E ($$n = 3$$); $p \leq 0.0001$, by Student’s t test. G, representative images for wound healing assays for NSCLC cells stably expressing vector or STK24; scale bar =100 μm. H, statistical analysis of wound healing assays in G ($$n = 3$$); $p \leq 0.0001$, by Student’s t test. All immunoblots were conducted three times, and consistent results were found. NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
Tumor progression is associated with increased potential of cancer cells metastasis. Therefore, transwell assays were performed to assess the metastasis potential of cells with stable expression of STK24. It was found that STK24 overexpression promotes invasion and migration of cancer cells (Fig. 2, E and F), which was consistent with the results obtained in the wound healing assays (Fig. 2, F and G). Furthermore, we constructed a kinase activity-deficient STK24 mutant (STK24 K53R) [19, 20] and proved that the kinase activity of STK24 was required for its effects on invasion, migration, and proliferation of cancer cells (Fig. S2, A–D) Overall, the results of the current study demonstrated that cancer cells with higher STK24 expression have a higher proliferation, migration, and invasion potential.
## Loss of STK24 expression inhibits proliferation, migration, and invasion of cancer cells
Expression of STK24 in NSCLC cell lines was knocked out using Crisp-Cas9 system to verify the findings obtained in the present study, and Western blot was used to confirm successful deletion of the gene (Fig. 3A). The CCK8 and cell cycle analysis were conducted in the current study to investigate the proliferation potential of the cells. Results showed that silencing of STK24 inhibited the proliferation of NSCLC cells. Further, it was evident that sgSTK24 NSCLC cells had a higher G0-G1 phase and a lower S and G2-M phase (Fig. 3, B–D). In addition, the findings of the transwell and wound healing assays showed that loss of STK24 significantly inhibited the migration as well as invasion potential of NSCLC cells (Figs. 3, E and F and S3, A–D). Moreover, it was revealed that loss of STK24 inhibited expression of cyclin D and vimentin and promoted expression of E-cadherin in the cancer cells (Fig. S4A).Figure 3Loss of STK24 inhibited proliferation, migration, and invasion ability of NSCLC cells. A, immunoblot analysis of protein expression levels in NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2. B, cell proliferation analysis of cells stably expressing control, sgSTK24#1, or sgSTK24#2 using CCK8 assays ($$n = 3$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. C, representative image of cell cycle analysis for cells stably expressing control, sgSTK24#1, or sgSTK24#2. D, statistical analysis of cell cycle analysis in C ($$n = 3$$); p for Left = 0.0019 (control versus sgRNA#1), 0.0022 (control versus sgRNA#2), by Student’s t test, p for right = 0.0004 (control versus sgRNA#1), 0.0003 (control versus sgRNA#2), by Student’s t test. E, statistical analysis of transwell assays (shown in Fig. S3, A and B) for NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2 ($$n = 3$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. F, statistical analysis of wound healing assays (shown in Fig. S3, C and D) for NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2; $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. G, mice xenograft experiments were conducted using NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2, and representative tumor image shown. H, tumor growth curve shown in G ($$n = 5$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. I, statistical analysis of tumor weight in G ($$n = 5$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. All immunoblots were conducted three times, and consistent results were found. NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
In the mouse xenograft transplantation assay, A549 cells expressing control, sgSTK24#1, or sgSTK24#2 were subcutaneously injected into 4-week-old male nude BALB/c mice. Tumor volume was measured every after 4 days and after 28 days. All the mice were sacrificed, and their tumors were separated and weighed. Results showed that A549 cells without STK24 had slower growth rate as compared with control A549 cells (Fig. 3, G–I). Overall, the findings of the present study confirmed that STK24 was an oncogene in tumorigenesis.
## STK24 promoted tumor angiogenesis
The formation of tumor vessels enables supply of nutrients for growth of tumor, but the hypoxic environment in tumors leads to the formation of immature vessels [8]. The formation of immature vessels in the tumors is called tumor angiogenesis. VEGFA is a key factor in the formation of the vessels [21]. The formation of immature vessels has been shown to significantly promote tumor progression [4]. Further, the anti-VEGF-VEGFRs axis has also been shown to be an efficient strategy for tumor treatment [22, 23].
The present study investigated the role of STK24 in tumor angiogenesis by assessing the density of microvessel in subcutaneous tumors when induced by A549 cells expressing control, sgSTK24#1, or sgSTK24#2. Notably, it was found that the sgSTK24 tumors had less microvessels as compared with control tumors (Fig. S4 B, and C). This implies that STK24 regulates tumor angiogenesis. Moreover, Matrigel plug assays were also conducted, and hemoglobin measurement indicated a decreased blood perfusion with loss of STK24 (Fig. S4 D, and E). The current study also investigated if STK24 regulates the secretion of VEGFA by cancer cells because VEGFA is an essential factor of tumor angiogenesis. Results of Western blot and enzyme-linked immunosorbent assay (ELISA) analysis revealed that overexpression of STK24 promotes secretion of VEGFA by cancer cells, whereas the loss of STK24 decreased the secretion of VEGFA by the cells (Fig. 4, A–D). Conditional media (CM) were then collected from control, sgSTK24#1, or sgSTK24#2 cancer cells and used them to culture human umbilical vein endothelial cells (HUVECs).Figure 4STK24 regulated tumor angiogenesis. A and C, immunoblot analysis of protein expression levels in indicated NSCLC cells. B and D, statistical analysis of the expression levels of VEGFA using ELISA in indicated NSLCL cells ($$n = 3$$); $p \leq 0.0001$, by Student’s t test. E, cell proliferation analysis of HUVECs cultured with different CMs using CCK8 assays ($$n = 3$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. F, representative images of trans-well assays for HUVECs cultured with different mediums; scale bar =100 μm. G, statistical analysis for trans-well assays in F ($$n = 3$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. H, representative images of tube formation assays for HUVECs cultured with different mediums; scale bar =100 μm. I, statistical analysis for tube formation assays in H ($$n = 3$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. All immunoblots were conducted three times, and consistent results were found. CM, conditional media; NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
Results of CCK8 assays showed that HUVECs cultured with CMs from sgSTK24 cells had lower proliferation potential as compared with HUVECs cultured with CMs from control cells (Fig. 4E). Furthermore, results of transwell and tube formation assays revealed that HUVECs cultured with CMs from sgSTK24 cells had lower migration and tube formation ability as compared with those cultured with CMs from control cells (Fig. 4, F–H). Overall, the findings of the current study showed that STK24 positively regulates tumor angiogenesis.
## STK24 regulated STAT3/VEGFA signaling pathway
Three signaling pathways (STAT3/VEGFA, HIF1/VEGFA, and AKT) have been reported to regulate tumor angiogenesis [24]. To unravel the detailed mechanism of STK24-mediated tumor angiogenesis, the present study investigated whether STK24 could regulate the three signaling pathways. It was notable that loss of STK24 only decreased STAT3 expression but had no influence on HIF1A, HIF1B, AKT, and AKT phosphorylation (Figs. 5A, 4F, and S4G). The results were also confirmed by the analysis findings of the IHC assays (Fig. S4B).Figure 5STK24 regulated STAT3/VEGFA signaling pathway. A and B, immunoblot analysis of protein expression levels in the indicated NSCLC cells. C and D, STAT3 mRNA expression levels using RT-PCR assays in the indicated NSCLC cells ($$n = 3$$), p = ns (control versus sgRNA#1 or sgRNA#2), by Student’s t test. E, investigating the binding of STAT3 and STK24 using immunoprecipitation and immunoblot assays in indicated NSCLC cells. F, denaturing immunoprecipitation and immunoblot analysis of NSCLC cells transfected with HA-tagged ubiquitin plasmid. Cells were treated with MG132 before transfection to inhibit endogenous protein degradation. G, immunoblot analysis of NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2, and all cells were treated with CHX to inhibit endogenous protein generation at indicated time points. H, statistical analysis for G ($$n = 3$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. All immunoblots were conducted three time, and consistent results were found. CHX, cycloheximide; NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
Consistently, it was noted that the gain of STK24 increased expression levels of STAT3 in A549 and H226 cancer cells (Fig. 5B). Besides, STK24 kinase activity was required for STK24-mediated STAT3 and VEGFA expression upregulation (Fig. S4H). STAT3 protein expression levels are regulated at the transcriptional and posttranscriptional levels. Therefore, RT-PCR was first carried out to detect the mRNA expression levels of STAT3 in indicated cancer cells. Remarkably, it was found that the gain or loss of STK24 had no effect on the mRNA expression levels of STAT3 (Fig. 5, C and D). Results obtained from co-IP analysis revealed that there was a physical interaction between STK24 and STAT3 (Fig. 5E). Moreover, GST pull-down analysis proved that there was a direct physical interaction between STK24 and STAT3 (Fig. S4I). Upon these findings, we assumed that STK24 regulates STAT3 stabilization. Since ubiquitylation-dependent degradation is the main mode of posttranscriptional regulation of proteins, the ubiquitylation levels of STAT3 in control, sgSTK24#1, and sgSTK24#2 cancer cells were also investigated, and denaturing immunoprecipitation assays were performed under denaturing conditions to eliminate interacting proteins. MG132 was used to inhibit endogenous STAT3 degradation, and then, all cells were transfected with ha-ubiquitin plasmid to increase the ubiquitylation level of all endogenous proteins. Results of the immunoblot analysis showed that loss of STK24 significantly increased the ubiquitylation level of endogenous STAT3 (Fig. 5F). When cycloheximide was used to inhibit the generation of endogenous protein, it was noted that the degradation rate of STAT3 in sgSTK24 cells exceeded that in the control cells (Fig. 5, G and H). Since STK24 belonged to serine/threonine-protein kinase family, we further detect whether STK24 could regulate STAT3 phosphorylation level. MG132 was used to inhibit endogenous STAT3 degradation. Results showed that STK24 could regulate STAT3 serine/threonine phosphorylation level, but not tyrosinase phosphorylation level. In conclusion, the results of the present study demonstrated that loss of STK24 destabilizes STAT3 and further inhibits the STAT3/VEGFA signaling pathway.
## STK24-mediated tumor angiogenesis relied on STAT3/VEGFA signaling pathway in vitro
To investigate the correlation between STK24 expression and STAT3/VEGFA signaling pathway during tumor angiogenesis, the present study constructed NSCLC cell lines stably expressing vector+shnc, STK24+shnc, STK24+shSTAT3#1, or STK24+shSTAT3#2 and used immunoblot analysis to confirm the stable expression (Fig. 6A). Results of the Western blot and ELISA assays showed that STK24-mediated VEGFA upregulation was reversed by silencing of STAT3 (Fig. 6, A and B). The CMs that were obtained from cancer cells stably expressing vector+shnc, STK24+shnc, STK24+shSTAT3#1, or STK24+shSTAT3#2 were used to maintain HUVECs. Results of cell cycle analysis, CCK8, transwell, wound healing, and tube formation assays showed that HUVECs cultured with CMs from STK24+shnc cells had higher proliferation, migration, and tube formation potential as compared with HUVECs cultured with CMs from the vector+shnc cells. However, this effect was abrogated by silencing the expression of STAT3 (Figs. 6, C–H and S5). The results of the current study revealed that STK24-mediated tumor angiogenesis was dependent on STAT3/VEGFA signaling pathway. Figure 6STK24-mediated regulation of tumor angiogenesis was dependent on STAT3 in vitro. A, immunoblot analysis of indicated protein expression levels in NSCLC cells stably expressing vector+shnc, STK24+shnc, STK24+shSTAT3#1, or STK24+shSTAT3#2. B, statistical analysis of the VEGFA expression levels using ELISA ($$n = 3$$); $p \leq 0.0001$ (STK24+shnc versus STK24+shSTAT3#1 or STK24+shSTAT3#2), by Student’s t test. C, cell proliferation analysis of HUVECs cultured with different CMs using CCK8 assays ($$n = 3$$); $p \leq 0.0001$ (STK24+shnc versus STK24+shSTAT3#1 or STK24+shSTAT3#2), by Student’s t test. D, statistical analysis of cell cycle analysis in Fig. S5A ($$n = 3$$); p for Left = 0.0002 (STK24+shnc versus STK24+shSTAT3#1), 0.0001 (STK24+shnc versus STK24+shSTAT3#2), by Student’s t test, p for right = 0.0024 (STK24+shnc versus STK24+shSTAT3#1), 0.0025 (STK24+shnc versus STK24+shSTAT3#2), by Student’s t test. E, statistical analysis of trans-well assays in Fig. S5B ($$n = 3$$); $p \leq 0.0001$ (STK24+shnc versus STK24+shSTAT3#1 or STK24+shSTAT3#2), by Student’s t test. F, statistical analysis of wound healing assays in Fig. S5C ($$n = 3$$); $p \leq 0.0001$ (STK24+shnc versus STK24+shSTAT3#1 or STK24+shSTAT3#2), by Student’s t test. G, representative images for tube formation assays for HUVECs cultured with different conditional mediums; scale bar = 100 μm. H, statistical analysis of tube formation assays in G ($$n = 3$$); $p \leq 0.0001$ (STK24+shnc versus STK24+shSTAT3#1 or STK24+shSTAT3#2), by Student’s t test. All immunoblots were conducted three times, and consistent results were found. CM, conditional media; NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
## Silencing of STAT3 inhibited STK24-induced tumor angiogenesis in vivo
Mouse xenograft transplantation assays was conducted to validate the obtained findings on the relationship between STK24 and STAT3/VEGFA signaling pathway in tumor angiogenesis. Cancer cells stably expressing vector+shnc, STK24+shnc, STK24+shSTAT3#1, or STK24+shSTAT3#2 were subcutaneously injected into 4-week-old male nude BALB/c mice (Fig. 7A). Tumor volume was measured every 4 days, and after 20 days, all mice were sacrificed, tumors isolated and weighed. It was evident that tumors induced by cancer cells stably expressing STK24+shnc grew faster than tumors induced by cancer cells stably expressing vector+shnc. This effect was abolished by knocking down STAT3 expression (Fig. 7B). Similarly, tumors induced by cancer cells stably expressing STK24+shnc were much bigger than tumors induced by cancer cells stably expressing vector+shnc, with the loss of STAT3 reversing STK24-mediated tumor progression (Fig. 7, C and D). Results of IHC assays used to determine the density of microvessels in tumors showed that silencing of STAT3 abrogated STK24-mediated tumor angiogenesis (Fig. 7, E and F). Immunoblot analysis of protein expression level in tumors showed that tumors induced by cancer cells stably expressing STK24+shnc had higher STAT3 and VEGFA expression levels as compared with to tumors induced by cancer cells stably expressing vector+shnc. However, it was evident that silencing of STAT3 inhibited STK24-mediated VEGFA upregulation (Fig. 7G). In conclusion, the findings of this study demonstrated that STK24-mediated tumorigenesis, and tumor angiogenesis was dependent on STAT3/VEGFA signaling pathway. Figure 7STK24-mediated regulation of tumor angiogenesis is dependent on STAT3 in vivo. A, immunoblot analysis of indicated protein expression levels in A549 cells stably expressing vector+shnc, STK24+shnc, STK24+shSTAT3#1, or STK24+shSTAT3#2. B, shown tumor growth curve in A ($$n = 5$$); $$p \leq 0.0079$$ (STK24+shnc versus STK24+shSTAT3#1), 0.0079 (STK24+shnc versus STK24+shSTAT3#2), by Mann–Whitney test. C, shown representative tumor image in A ($$n = 5$$). D, statistical analysis of tumor weight in A ($$n = 5$$); $$p \leq 0.0079$$ (STK24+shnc versus STK24+shSTAT3#1), 0.0079 (STK24+shnc versus STK24+shSTAT3#2), by Mann–Whitney test. E, representative images for IHC analysis of tumors induced by indicated NSCLC cells in A; scale bar =100 μm. F, statistical analysis of microvessels density of indicated tumors in A ($$n = 5$$); $p \leq 0.0001$ (STK24+shnc versus STK24+shSTAT3#1 or STK24+shSTAT3#2), by Student’s t test. G, immunoblot analysis of protein expression levels of indicated tumors in A. H, the mechanism through which STK24 regulated the STAT3/VEGFA signaling pathway. All immunoblots were conducted three times, and consistent results were found. IHC, immunohistochemistry; NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
## Discussion
Angiogenesis is a critical event for tumor progression and metastasis. In the current study, the mechanism of STK24-mediated regulation of angiogenesis in NSCLC was elucidated for the first time. It was found that STK24 activated STAT3 signaling pathway by inhibiting polyubiquitin–proteasomal–mediated degradation of STAT3. On the other hand, activation of STAT3 signaling pathway promoted tumor progression and angiogenesis through the upregulation of VEGFA expression.
High expression of STK24 is correlated with more aggressive breast cancer subtypes and poor prognosis [12]. The STK24 regulates cell cycle by phosphorylating nuclear BDF2-related kinase two kinase at T442 [25]. In addition, STK24 regulates cell migration through tyrosine phosphatase PTP-PEST and the adhesion complex molecule paxillin [14, 16, 18, 26]. Further, STK24 enhances proliferation and tumorigenicity in a kinase-independent manner by regulating the VAV2/Rac1 signaling pathway [15]. However, the expression of STK24 has been shown to be lower in gastric cancer as compared with matched normal tissues whereas loss of STK24 was found to promote tumorigenicity and distant metastasis in gastric cancer [26, 27]. Nevertheless, the biological function of STK24 in lung cancer progress is still unclear. Besides, the role of STK24 in tumor angiogenesis is not explored in any tumors. In the present study, it was demonstrated that STK24 was upregulated in NSCLC tissues as compared with normal lung tissues, and NSCLC cells with higher expression of STK24 had stronger proliferation, migration, and invasion potential, which was on contrary to the function of STK24 in gastric cancer. These contradictory results may be attributed to the different experimental conditions and the different cancer subtypes.
STAT3 dysfunction appears to promote the proliferation, migration, and invasion of malignant tumors [28]. Increased IL-6/JAK/STAT3 signaling has been observed in different types of tumors and tumor infiltrating immune cells, as well as that this has been associated with lack of response to immunotherapy [29]. The IL-6/STAT3 signaling pathway upregulates factors involved in cancer cell proliferation (MYC, cyclin D1), angiogenesis (VEGFA, PDGF), and apoptosis (Bcl-XL) [30]. Therefore, a deeper understanding of the IL-6/STAT3 signaling pathway is needed to promote the overall survival of patients with cancer. The IL-6/STAT3/VEGFA signaling pathway is activated in different cancers [31, 32, 33]. Several pathways regulate the IL-6/STAT3/VEGFA signaling pathway, for instance, PARK2 inhibits tumor proliferation and angiogenesis by decreasing IL-6/STAT3/VEGFA signaling; Splice variant ΔNp63 activates the signaling pathway to upregulate hypoxia-inducible factor 1α, the secretion of VEGF and angiogenesis; IL-8 and IL-35 induce STAT3 phosphorylation; PTPN dephosphorylates STAT3 and inhibits STAT3 activation; MAGEC2/PDLIM2/Fbw7 interacts with STAT3 and inhibits its polyubiquitination and proteasomal degradation; TRAF6 negatively regulates the STAT3 signaling pathway by binding to STAT3 and mediating its ubiquitination [32, 34, 35, 36]. In the current study, it was notably revealed the underlying function of SKT24 in tumor angiogenesis. Further, it was found that STK24 can positively regulate tumor angiogenesis. Furthermore, a novel regulation mechanism for STAT3, whereby STK24 interacts with STAT3 to prevent polyubiquitin–proteasomal–mediated degradation of STAT3, was also demonstrated. In vitro xenograft tumor assays confirmed that STK24 can positively administer STAT3/VEGFA signaling pathway. Furthermore, it was illustrated that STK24 mediated tumor proliferation and angiogenesis in a STAT3-dependent manner using in vivo and in vitro experiments. Remarkably, it was found that although STAT3 has been reported to regulate the levels of HIF1α expression, STK24 did not regulate levels of HIF1α expression [36]. This discrepancy could be attributed to different experimental conditions and tumor models.
In conclusion, findings of the current study showed that STK24 acts as an oncogene, and the expression of STK24 in cancer cells is positively correlated with the proliferation, migration, invasion, and tumor angiogenesis potential of cancer cells. For mechanism of STK24 action, it was evident that STK24 prevents STAT3 from polyubiquitin–proteasomal–mediated degradation and STK24 regulated tumor angiogenesis through STAT3/VEGFA signaling pathway. The findings hence provide a novel insights into the mechanism of STK24 action and reveal the underlying mechanism of tumor angiogenesis, as well as provide the expected guidance to the future development of therapeutic strategies for cancer treatment.
## Cell lines and antibodies
Three cell lines (H226, A549, and HUVECs) were obtained from the American Type Culture Collection (ATCC). Cell lines A549 and HUVECs were maintained with F-12K medium (21127022; Thermo Fisher Scientific), whereas H226 cells were cultured with 1640 medium (11875168; Thermo Fisher Scientific). Ten percent (v/v) of fetal bovine serum (12483012; Thermo Fisher SCIENTIFIC) was added into all media. As for F12K medium (500 ml), 50 mg stock heparin (H3393; Sigma) for a final concentration of 0.1 mg/ml was added, and 15 mg endothelial cell growth supplementary was also added. All cells were cultured in $95\%$ air, $5\%$ CO2 at 37 °C. Cells lines used in this study had been authenticated by cell line sequence report on December, 2020, December, 2021, and December, 2022. Cells used in this study were confirmed to be free from mycoplasma. All cells were cultured at good status. The growth rate of cells was fast, the cells were homogeneous and transparent, the intracellular particles were few, the vacuoles were not visible, the cell edges were clear, the suspended cells and fragments were not visible in the medium, and the culture medium was clear and transparent. All cells were cultured in $5\%$ (v/v) CO2 at 37 °C. Antibodies for STK24 (3723; Cell Signaling Technology), B-actin (3700; Cell Signaling Technology), VEGFA (65373; Cell Signaling Technology), STAT3 (9239; Cell Signaling Technology), HIF1A (36169; Cell Signaling Technology), HIF1B (5537; Cell Signaling Technology), p-AKT308 (13038; Cell Signaling Technology), p-AKT473 (4060; Cell Signaling Technology), AKT (ab8805; Abcam), and CD31 (ab28364; Abcam), ubiquitin (ab134953; Abcam) were all commercially procured for the present study. The specificity of the antibody was validated by knockout testing.
## Real-time PCR
Total mRNAs were isolated from the indicated cancer cells using TRIzol (Invitrogen). The cDNAs were synthesized using ABScript II Reverse Transcriptase kit (RK21400; ABclonal). Quantitively real-time PCR was carried out using Genious 2X SYBR Green Fast qPCR Mix (RK21204; ABclonal). The primers used in the current study were: STAT3 forward: 5-CAGCAGCTTGACACACGGTA-3; STAT3 reverse: 5-AAACACCAAAGTGGCATGTGA-3; STK24 forward: 5-AGGCATTGACAATCGGACTCA-3; and STK24 reverse: 5- CTGACTCAGCACTGTGATTTCT-3.
## Cell lines construction
For STK24 knock-out cell lines construction, the three SgRNAs (bought from Shanghai Genechem Co, Ltd) (#1: 5-GGTCCATTGAAGAGCTGCGA-3; #2: 5-GCCACTCTACCTCATCCTGG-3; #3: 5-GAGAAGAGCCAGGCGTGCGG -3;) were cloned into pSpCas9(BB)-2A-Puro (PX459) V2.0 plasmids (62988; Addgene). Then, 2 μg sgRNAs-plasmids were transfected into A549 or H226 cell lines. After 48 h, 2 μg/ml puromycin (HY-B1743A; MedChemExpress) was applied to screen the infected cells until all the control cells died. 96-plate-wells were used for monoclonal screening. For Stat3 knock-down cell lines construction, the three shRNAs-Stat3 (bought from Shanghai Genechem Co, Ltd) (#1: F: 5- CCGG GCAGGGTTTGTCATTAATAATCTCGAGATTATTAATGACAAACCCTGC-3; R: 5-AAAAAGCAGGGTTTGTCATTAATAATCTCGAGATTATTAATGACAAACCCTGC-3 #2: F: 5- CCGGGACAGGTACAAGAGATGGAAGCTCGAGCTTCCATCTCTTGTACCTGTC-3; R: 5-AAAAAGACAGGTACAAGAGATGGAAGCTCGAGCTTCCATCTCTTGTACCTGTC-3; #3: F: 5- CCGGGTGGACAGAAATAAGATGAAAGCTCGAGCTTTCATCTTATTTCTGTCCA-3; R: 5-AAAAATGGACAGAAATAAGATGAAAGCTCGAGCTTTCATCTTATTTCTGTCCAC-3) were cloned into pLKO.1 plasmids (8453; Addgene). Then pLKO.1-shRNAs, MD2-G, and PPAX three packing system was applied to generate lentivirus in HEK293 T cells. The lentivirus subsequently was added into A549 or H226 cell lines. After 48 h, 2 μg/ml puromycin was applied to screen the infected cells until all the control cells died. For STK24 overexpression cell lines construction, a cDNA coding STK24 was cloned into pLVX-Vector plasmids (bought from Shanghai Genechem Co,Ltd). Then pLVX-STK24, MD2-G, and PPAX three packing system was applied to generate lentivirus in HEK293T cells. The lentivirus subsequently was added into A549 or H226 cell lines. After 48 h, 2 μg/ml puromycin was applied to screen the infected cells until all the control cells died. Mut Express MultiS Fast Mutagenesis Kit V2 (C215-01; Vazyme) was used for pLVX-STK24 K53R plasmid construction. The successful construction of identified cells was confirmed by RT-PCR or Western blot assays.
## Western blot and immunoprecipitation
Cultured cells were collected and washed using cold PBS (C0221A; Beyotime). The cells were then lysed using NP-40 lysis buffer (P0013F; Beyotime). Protein concentration was measured using bicinchoninic acid assay kit (23225; Thermo). The proteins were separated through electrophoresis in premade sodium dodecyl sulfate-polyacrylamide minigels and were then transferred to PVDF membranes (88520; Thermo). The membranes were incubated overnight with primary antibodies (dilution 1:1000) at 4 °C before incubation with the secondary antibodies (dilution 1:1000). Protein bands were detected using chemiluminescence (GelView 6000 Pro; BLT). For immunoprecipitation, primary antibodies were incubated with Protein L Magnetic Beads (HY-K0205; MCE) at room temperature for 2 h. The whole cell lysates were then incubated overnight with beads at 4 °C, followed by Western blot analysis. For denaturing immunoprecipitation, cells were lysed in $1\%$ SDS lysis buffer and boiled for 20 min, and the lysates were centrifuged and diluted 1:10 with lysis buffer. The diluted lysates were followed by immunoprecipitation.
## GST pull-down assay
GST-STK24 fusion protein was purified from HEK293T cells using Anti-GST Magnetic Beads (HY-K0222; MedChemExpress), and His-STAT3 fusion protein was isolated from BL21 using Ni bead (C650033; Sangon), purified recombinant His-STAT3 protein was co-incubated with purified GST or recombinant GST-STK24 protein over night at 4 °C, followed by immunoprecipitation using Anti-GST Magnetic Beads.
## Immunohistochemistry
This study was approved by the Huazhong University of Science and Technology Ethics Committee. The formed consents had been obtained from NSCLC patients. We strictly obeyed Helsinki principles in this study. Formalin-fixed paraffin-embedded tissues were cut into 4 mm section. Primary antibodies (dilution 1:100) were applied for IHC assays. Two experienced pathologists independently evaluated the obtained immunostaining results. IHC staining was quantified by multiplying the proportion score with the intensity score. Whereby the proportion score reflected the fraction of positively stained tumor cells: 1 (<$10\%$); 2 (10–$50\%$); 3 (50–$75\%$); 4 (>$75\%$), whereas the intensity score indicated the staining intensity (0, no staining; 1, weak; 2, intermediate and 3, strong). The obtained staining score ranged between 0 and 12.
## CCK8 assays
A total of between 1000 and 3000 pretreated cells were seeded in 96-well plates. HUVECs were treated with CM. The CCK8 assays were carried out using Cell Counting Kit-8 (RM02823; ABclonal) using automatic microplate reader (synergy 2; BioTek) according to instructions provided by the manufacturer.
## Cell cycle analysis
The cells were washed with cold PBS and then fixed overnight with $80\%$ ethanol at −20 °C. Thereafter, the cells were washed with cold PBS and stained with PI (40710ES03; YEASEN) for 15 min at room temperature. The distribution analysis of the cell cycle was carried out using the Becton-Dickinson FACScan System.
## Transwell assays
The transwell chambers (CLS3450-24EA) were purchased from Merck company. The cells were seeded in serum-free medium at a density of between 1 and 4∗10ˆ4 per well into the upper chamber with or without Matrigel (354234; CORNING). A 500 μl conditional medium was added into the lower chamber. Further, Mitomycin C (10 μg/ml; M5353; Merck) was used to inhibit cell proliferation. After 10 to 20 h, the cells were washed thrice with PBS, fixed with 4 % formaldehyde (1004965000; Merck) for 30 min, and then stained with $0.1\%$ crystal violet (C6158; Merck). Cell images were obtained using a microscope (BX53; OLYMPUS).
## Wound healing assays
A linear wound was made using a 200-μl sterile plastic pipette tip after cells reached 95 % confluency. Mitomycin C (10 μg/ml) was used to inhibit cell proliferation. After 12 h (Fig. 2) or 24 h (Figs. S3 and S5), cells were washed twice using PBS, and then the wound sizes were observed as well as measured at the indicated times (BX53; OLYMPUS).
## Enzyme-linked immunosorbent assay
ELISA assays were carried out using ELISA kits (RK00023; ABclonal) using automatic microplate reader (synergy 2; BioTek) and according to the instructions provided by the manufacturer.
## Tube formation assay
Matrigel (100 μl) (354234; CORNING) was added into the 96-well plates. 1 to 4∗10ˆ4 HUVECs were seeded into each well and cultured with conditional mediums for between 10 and 16 h. Images were captured using a microscope.
## Mouse xenograft experiments
The mouse xenograft assays were approved by the Animal Care and Use Committee of Tongji Hospital. Four-week-old male BALB/c mice were purchased from Beijing Huafukang Bioscience Company. Husbandry of mice obeyed the rules of Breeding and management of SPF mice. We designed the mouse experiments according to the triple blind method. Before mouse experiments, mice were grouped randomly. A total of 1 to 2 × 106 indicated cells were subcutaneously injected into the back of each 5- to 6-week-old male BALB/c. Each mouse was injected one tumor at the same place. Tumor volume was also measured at the indicated time using formulae: 0.5 × Length (L) × Width (W)2. After indicated time, all mice were sacrificed, and all tumor xenografts were isolated and weighed. Then, all xenografts were cut in half. One half of tumor was fixed in 4 % formaldehyde for IHC assays, and the other was preserved in −80 °C for further assays.
## Database
Databases for The Cancer Genome Atlas (TCGA; http://xena.ucsc.edu/welcome-to-ucsc-xena/) and Human Protein Atlas database (https://www.proteinatlas.org) were used for statistical analysis in the present study.
## Statistical analysis
All the data obtained in the present study were analyzed using SPSS 20.0 software and specificity. Data were shown as mean ± SD. Kaplan–Meier curve was tested by Log-rank (Mantel–Cox) test. Correlation analysis was calculated by Pearson's r test. Differences between two groups were compared using the Student’s t test or Mann–Whitney test, while the differences among multiple groups were compared using ANOVA. p value <0.05 was considered statistically significant.
## Data availability
Data are available within the article or its supplementary materials.
## Supporting information
This article contains supporting information.
Supplemental Fig. S1STK24 was a prognostic risk factor for NSCLC patients. A, IHC analysis of NSCLC tissues of patients grouped by gender ($$n = 69$$), p = ns, by Student’s t test. B, the correlation of STK24 expression level of NSCLC with age of patients ($$n = 69$$) p = ns, by Pearson's r test. C, analysis of STK24 expression level in different types of cancer and normal tissues using GEPIA database. D, Kaplan–Meier curve of patients with pancreatic cancer grouped by STK24 expression level using TCGA database (low = 43; high = 133); $$p \leq 0.00058$$ by Log-rank (Mantel–Cox) test. IHC, immunohistochemistry; NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24. Supplemental Fig. S2The kinase activity of STK24 was required for its effects on invasion, migration, and proliferation. A, immunoblot analysis of indicated protein expression levels in NSCLC cells ectopic expressing vector, STK24 WT, or STK K53R. B, cell proliferation analysis of cells ectopic expressing vector, STK24 WT, or STK K53R ($$n = 3$$); $p \leq 0.0001$, by Student’s t test. C, representative images of transwell assays for NSCLC cells ectopic expressing vector, STK24 WT, or STK K53R; scale bar = 100 μm. D, statistical analysis of transwell assays in Fig. S2C ($$n = 3$$); $p \leq 0.0001$ (vector versus STK24 WT) or ns (vector versus STK24 K53R), p = ns, (STK24 WT versus STK24 K53R), by Student’s t test. All immunoblots were conducted three times, and consistent results were found. NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24. Supplemental Fig. S3Loss of STK24 inhibited NSCLC cell migration and invasion ability. A and B, representative images of transwell assays for NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2; scale bar =100 μm. C and D, representative images of wound healing assays for indicated NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2; scale bar =100 μm. NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24. Supplemental Fig. S4STK24 regulated tumor angiogenesis. A, immunoblot analysis of indicated protein expression levels in NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2. B, representative images for IHC analysis of tumors induced by NSCLC cells stably expressing control, sgSTK24#1, or sgSTK24#2; scale bar = 100 μm. C, statistical analysis of micro-vessel density in identified tumors ($$n = 5$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. D, representative images for Matrigel plug assays. E, statistical analysis of hemoglobin measurement of indicated Matrigel in D ($$n = 5$$); $p \leq 0.0001$ (control versus sgRNA#1 or sgRNA#2), by Student’s t test. F–H, immunoblot analysis of indicated protein expression levels in pretreated NSCLC cells. I, the direct binding of GST-STK24 and His-STAT3 was investigated by GST-pull down assays. J, immunoprecipitation and immunoblot analysis of indicated protein expression level of NSCLC cells ectopic expressing vector, STK24 WT, or STK K53R. All immunoblots were conducted three time, and consistent results were found. NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24. Supplemental Fig. S5STK24-mediated tumor angiogenesis relied on STAT3.A, representative image of cell cycle analysis for indicated cells. B, representative images of transwell assays for indicated NSCLC cells; scale bar =100 μm. C, representative images of wound healing assays for indicated NSCLC cells; scale bar = 100 μm. NSCLC, nonsmall cell lung cancer; STK24, serine/threonine-protein kinase 24.
## Conflict of interest
The authors declare that they have no conflict of interest with the contents of the article.
## Author contributions
S. L. and X. C. methodology; S. L. investigation; D. W. and W. S. resources; S. L., X. C., D. W., and W. S. writing–review and editing.
## Funding and additional information
This study is approved by $\frac{10.13039}{501100001809}$Natural Science Foundation of China (No. 81702264).
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|
---
title: Adipocyte YTH N(6)-methyladenosine RNA-binding protein 1 protects against obesity
by promoting white adipose tissue beiging in male mice
authors:
- Sujun Yan
- Xiaoling Zhou
- Canlan Wu
- Yunyi Gao
- Yu Qian
- Jingyu Hou
- Renxiang Xie
- Bing Han
- Zhanghui Chen
- Saisai Wei
- Xiangwei Gao
journal: Nature Communications
year: 2023
pmcid: PMC10011519
doi: 10.1038/s41467-023-37100-z
license: CC BY 4.0
---
# Adipocyte YTH N(6)-methyladenosine RNA-binding protein 1 protects against obesity by promoting white adipose tissue beiging in male mice
## Abstract
Obesity, one of the most serious public health issues, is caused by the imbalance of energy intake and energy expenditure. N[6]-methyladenosine (m6A) RNA modification has been recently identified as a key regulator of obesity, while the detailed mechanism is elusive. Here, we find that YTH RNA binding protein 1 (YTHDF1), an m6A reader, acts as an essential regulator of white adipose tissue metabolism. The expression of YTHDF1 decreases in adipose tissue of male mice fed a high-fat diet. Adipocyte-specific Ythdf1 deficiency exacerbates obesity-induced metabolic defects and inhibits beiging of inguinal white adipose tissue (iWAT) in male mice. By contrast, male mice with WAT-specific YTHDF1 overexpression are resistant to obesity and shows promotion of beiging. Mechanistically, YTHDF1 regulates the translation of diverse m6A-modified mRNAs. In particular, YTHDF1 facilitates the translation of bone morphogenetic protein 8b (Bmp8b) in an m6A-dependent manner to induce the beiging process. Here, we show that YTHDF1 may be an potential therapeutic target for the management of obesity-associated diseases.
Activation of white adipose tissue (WAT) thermogenesis alleviates obesity-associated metabolic disorders in rodents. Here the authors report that the m6 A RNA modification reader YTHDF1 promotes WAT thermogenesis in a study with male mice, and may be a potential target for the treatment of obesity.
## Introduction
Adipose tissue is traditionally categorized into white adipose tissue (WAT) and brown adipose tissue (BAT), depending on its morphology and function1. WAT possesses unilocular, large lipid droplets and is involved in lipid storage. Excessive lipid storage in WAT results in obesity and related metabolic disorders, including insulin resistance, hepatic steatosis, diabetes, and cardiovascular disease2,3. By contrast, BAT possesses multilocular, small lipid droplets and a large number of mitochondria, in which uncoupling protein 1 (UCP1) is expressed, specializing in energy production in the form of heat4. Emerging evidence has identified a third type of adipose tissue, termed “beige” adipose tissue, which can be induced from WAT in cases of cold exposure, exercise, diet, and various activators5,6. Beige adipose tissue has unique origins and molecular characteristics compared with classic BAT7. Activation of thermogenesis in brown or beige adipose tissues increases systemic energy expenditure and alleviates obesity-associated metabolic diseases8,9. Thus, a deeper understanding of the mechanisms regulating energy storage and expenditure may lead to the development of therapeutic strategies that improve metabolic health.
N[6]-methyladenosine (m6A), the most abundant internal modification on mRNA, is catalyzed by the RNA methyltransferase complex methyltransferase-like (METTL) 3/METTL14/WT1-associated protein10–12. As a reversable modification, m6A methylation can be removed by AlkB homolog 5 or fat mass and obesity-associated protein (FTO)13,14. The YTH domain-containing ‘reader’ proteins, bind m6A and differentially regulate mRNA metabolism, including mRNA splicing, maturation, degradation, and translation. YTHDF1 enhances the translation of m6A-modified mRNAs, while YTHDF2 promotes the degradation of m6A-modified mRNAs15,16. YTHDF3 was reported to regulate both mRNA translation and degradation17.
Recent studies have highlighted an essential role of m6A modification in adipocytes metabolism. Single-nucleotide polymorphisms in FTO are closely related to the occurrence of obesity18,19, and knockout of Fto decreases HFD-induced obesity20. Coordinately, FTO inhibitors can increase thermogenesis, improve glucose tolerance, and ultimately inhibit obesity21. Moreover, METTL3 is essential for controlling postnatal development and energy homeostasis in BAT22. Because the fates of methylated mRNAs, ranging from degradation to translation, are determined by their reader proteins15,16,23,24, deciphering the functions of m6A reader proteins may help elucidate the mechanisms of m6A function in adipocyte metabolism.
In this work, we assessed the role of YTHDF1 in adipose tissue using WAT-specific Ythdf1-knockout male mice and adeno-associated virus25-mediated Ythdf1 overexpression. We demonstrated that YTHDF1 promotes mRNA translation to induce WAT beiging and alleviate obesity. Our results highlighted an important role of YTHDF1 in preventing obesity and provided potential targets for the treatment of obesity-associated metabolic diseases.
## Adipose YTHDF1 expression is reduced in obesity
Dysregulated m6A modification results in obesity in both mice and human beings18,19. To investigate the functions of “reader” proteins in this process, we detected the expression of YTH proteins (YTHDF$\frac{1}{2}$/3) in mice fed a standard chow diet (CD) or obese mice induced by a high fat diet (HFD). We observed a dramatic increase in body weight, iWAT weight, eWAT weight and BAT weight, indicating the successful induction of obesity (Supplementary Fig. 1A). Interestingly, YTHDF1 was dramatically downregulated in inguinal WAT (iWAT) in obese mice compared with that in control mice (Fig. 1a, c, Supplementary Fig. 1B, C). The expression of YTHDF$\frac{2}{3}$ did not change in both iWAT and BAT of HFD-induced mice (Fig. 1a–d, Supplementary Fig. 1B–E). Notably, HFD-fed mice exhibited lower expression of thermogenic genes (e.g., Ucp1, Ppargc1a, Cidea, Pparg, Adrb3, and Cox8b) in iWAT, implying impaired thermogenesis (Fig. 1c). Importantly, the published sequencing data revealed decreased YTHDF1 mRNA expression in WAT of individuals with obesity compared with that in nonobese individuals (Fig. 1e). The negative correlation of YTHDF1 expression in WAT with obesity implies a role of this protein in adipose tissue metabolism. Fig. 1Adipose YTHDF1 expression is reduced in obesity.a, b *Immunoblot analysis* of YTH family proteins in iWAT a and BAT b of mice fed with CD or HFD for 12 weeks. c, d mRNA levels of Yth family genes and thermogenesis-related genes in iWAT c and BAT d of CD- and HFD-fed mice. Data were presented as mean ± SEM ($$n = 10$$). ns, not significant, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001$, two-sided t-test. e Expression of YTHDF1 in human white adipose tissues from GSE156906 (nLean = 14, nObesity = 27, two-sided t-test), GSE162653 ($$n = 10$$, two-sided t-test), and GSE110729 (nLean = 15, nObesity = 13, F-test). Source data are provided as a Source Data file.
## Adipocyte-specific knockout of Ythdf1 predisposes mice to HFD-induced obesity
To study the function of YTHDF1 in adipose tissue, we generated adipocyte-conditional Ythdf1-knockout mice (Ythdf1cKO mice, termed Y1cKO or cKO), which were produced by crossing Ythdf1flox/flox mice (Ythdf1CTL mice, termed Y1CTL or CTL) with Adipoq-Cre transgenic mice (Supplementary Fig. 2A, Fig. 2a). Knockout of Ythdf1 did not affect the expression of YTHDF$\frac{2}{3}$ in iWAT and BAT (Supplementary Fig. 2B, C). CD-fed Ythdf1cKO mice and Ythdf1CTL littermates showed similar weight gain and similar iWAT, BAT, and eWAT weights (Supplementary Fig. 2D, E). Consistent with these results, there were no difference in triglyceride (TG), total cholesterol (CHO), or low-density lipoprotein (LDL) levels between Ythdf1CTL and Ythdf1cKO mice after CD feeding (Supplementary Fig. 2F). Analyses of intraperitoneal glucose tolerance and insulin resistance, as measured using glucose tolerance tests (GTTs) and insulin tolerance tests26, showed no significant difference between CD-fed Ythdf1CTL and Ythdf1cKO mice (Supplementary Fig. 2G, H).Fig. 2Adipocyte-specific knockout of Ythdf1 predisposes mice to HFD-induced obesity.a Immunoblotting of adipose tissues from Y1CTL and Y1cKO mice. b Schematic of the mouse treatment regimen. c–n The Y1CTL and Y1cKO littermates were fed with HFD. c Daily food intake by Y1CTL and Y1cKO mice. d Gross view of Y1CTL and Y1cKO mice. Scale bar, 0.5 cm. e Body weights of Y1CTL and Y1cKO mice. f Gross view and weights of iWAT, eWAT, and BAT from Y1CTL and Y1cKO mice. Scale bar, 0.5 cm. g H&E staining of iWAT eWAT, and BAT from Y1CTL and Y1cKO mice. Scale bar, 50 μm. The red line indicated the average size. No adjustments. h–j O2 consumption h, CO2 generation i, and energy heat generation j of Y1CTL and Y1cKO mice. White and gray areas in the graphs indicate day and night, respectively. k Rectal temperature in Y1CTL and Y1cKO mice. l Serum concentrations of TG, CHO, LDL, and HDL in Y1CTL and Y1cKO mice fed with HFD. m, n glucose tolerance m and Insulin tolerance n fed with HFD. Data in c, e–n were presented as mean ± SEM ($$n = 4$$ biologically independent mice). ns, not significant, *$P \leq 0.05$, **$P \leq 0.01$, ****$P \leq 0.0001$, two-sided t-test. Source data are provided as a Source Data file.
To clarify the function of YTHDF1 in obesity, Ythdf1CTL and Ythdf1cKO mice were fed an HFD starting at 10 weeks of age to induce obesity (Fig. 2b). Although CD-fed Ythdf1cKO mice and Ythdf1CTL littermates showed similar weight gain (Fig. 2e), HFD-fed Ythdf1cKO mice showed significantly higher body weights compared with Ythdf1CTL littermates, despite comparable food intake (Fig. 2c–e). Examination of dissected fat tissues confirmed that epididymal and inguinal but not brown fat pads were larger in Ythdf1cKO mice (Fig. 2f). Moreover, hematoxylin and eosin (H&E) analysis revealed that the WAT in Ythdf1cKO mice showed enhancement of obesity-related features, including larger unilocular lipid droplets (Fig. 2g). Taken together, these data suggested that Ythdf1cKO mice were more prone to HFD-induced obesity.
## Adipocyte-specific knockout of Ythdf1 aggravates obesity-induced metabolic disorders
The observed increase in body weight after HFD feeding, independent of food intake, suggested increased energy storage and reduced thermogenesis by Ythdf1cKO mice. Indeed, metabolic cage experiments demonstrated that deletion of Ythdf1 inhibited oxygen consumption, carbon dioxide generation, and energy heat generation during dark cycles (Fig. 2h–j). The core temperature was significantly reduced in Ythdf1cKO mice (Fig. 2k). HFD feeding can elevate circulating levels of TG, CHO, and LDL27. We observed a further increase in these parameters in HFD-fed Ythdf1cKO mice (Fig. 2l). Notably, GTTs and ITTs showed that Ythdf1cKO mice fed an HFD developed glucose intolerance and insulin resistance (Fig. 2m, n). Collectively, our data demonstrated that adipocyte-specific knockout of Ythdf1 aggravated the detrimental effects of obesity.
Furthermore, we fed the Ythdf1CTL and Ytdhf1cKO mice HFD under thermoneutral conditions. Deletion of Ythdf1 exacerbated obesity, reduced rectal temperature, and downregulated the thermogenic genes (Supplementary Fig. 3A–E). Metabolic cage experiments showed that deletion of Ythdf1 inhibited oxygen consumption, carbon dioxide generation, and energy heat generation during both light and dark cycles (Supplementary Fig. 3F–H). These data implied that the metabolic changes in Ythdf1cKO mice might be caused by impaired thermogenesis of iWAT.
## Adipocyte-specific knockout of Ythdf1 inhibits the beiging of WAT
Studies have demonstrated that beige adipose tissue plays active roles in lipid metabolism and has potential therapeutic relevance for weight loss8,28. Beiging is induced by chronic exposure to external cues, such as cold treatment or adrenergic stimulation5,6. To investigate whether YTHDF1 modulates the functions of beige adipose tissues, we induced the beiging process. YTHDF1, but not YTHDF$\frac{2}{3}$, was upregulated in cold-treated iWAT (Fig. 3a and Supplementary Fig. 4A, B). β3-Adrenergic receptor (AR) is the main signaling protein involved in the beiging process. Intraperitoneal administration of the β3-AR agonist CL-316,243 (CL) resulted in the upregulation of YTHDF1 (Fig. 3b). Histological and immunohistochemical analyses showed that the lipid droplets were larger in Ythdf1cKO mice than that in Ythdf1CTL mice after cold stimulation (Fig. 3c). UCP1 expression was also dramatically downregulated in iWAT of Ythdf1cKO mice (Fig. 3d). Overexpression of YTHDF$\frac{2}{3}$ could not rescue the phenotype of Ythdf1cKO (Supplementary Fig. 4C, D), implying differential roles of YTHDF$\frac{1}{2}$/3 in adipose tissue. Interestingly, UCP1 in BAT was unchanged in Ythdf1cKO mice after cold stimulation (Fig. 3e). Other thermogenesis- and lipolysis-related genes were both downregulated in iWAT rather than BAT (Fig. 3f, g and Supplementary Fig. 4E, F), suggesting that YTHDF1 may have a major role in WAT rather than BAT. Consistently, the rectal temperatures of Ythdf1cKO mice remained lower after cold exposure (Fig. 3h). The primary preadipocytes were further isolated from Ythdf1CTL and Ythdf1cKO mice and the thermogenic program was determined. Deletion of Ythdf1 dramatically reduced the O2 consumption rate (OCR) in primary preadipocytes (Fig. 3i).Fig. 3Adipocyte-specific knockout of Ythdf1 inhibits beiging of iWAT.a *Immunoblot analysis* of YTH family proteins expression in iWAT and BAT from mice housed at room temperature (RT) or 4 °C. b *Immunoblot analysis* of YTHDF1 and UCP1 expression in iWAT from mice treated with or without CL-316,243 (CL). c–g Y1CTL and Y1cKO littermates were exposed to cold conditions for 7 days. c H&E staining of iWAT. Scale bar, 50 μm. The white line indicated the average size. No adjustments. d, e *Immunoblot analysis* of UCP1 in iWAT d and BAT e. f, g mRNA levels of thermogenesis- and lipolysis-related genes in iWAT (f) and BAT (g) Data in f, g were presented as mean ± SEM ($$n = 3$$ biologically indipendent mice). No adjustments. h Rectal temperature measured after exposure to cold conditions for up to 8 h, as indicated. Data were presented as mean ± SEM ($$n = 3$$ biologically independent mice). i The O2 consumption rate (OCR) in primary preadipocytes isolated from Y1CTL and Y1cKO mice determined by Seahorse. j The average basal and maximal respiration rates. Data in i, j were presented as mean ± SEM ($$n = 10$$,000 cells exmined over 3 independent experiments). ns, not significant, *$P \leq 0.05$, **$P \leq 0.01$, ***$P \leq 0.001.$ All of the P-values are determined by unpaired two-sided t-test. Source data are provided as a Source Data file.
To exclude BAT function, we surgically removed BAT. Without BAT, Ythdf1cKO mice still had lower OCR and lower Ucp1 expression than Ythdf1CTL mice under RT, cold, and thermoneutral conditions (Supplementary Fig. 4G-I). Taken together, these data suggested that YTHDF1 promoted the thermogenesis of adipocytes in WAT.
## Transcriptome-wide identification of YTHDF1-regulated transcripts in beige adipose tissue
Because YTHDF1 is an m6A reader protein, we performed m6A-seq in cold treated iWAT (Fig. 4a). As expected, our m6A data revealed that the distribution of m6A was enriched around stop codons within transcripts (Fig. 4b). The m6A peaks were characterized by the canonical RGACH motif (Fig. 4c). YTHDF1 is known to affect mRNA translation15. To identify potential targets regulated by YTHDF1, we assessed changes in mRNA levels and translational efficiency by RNA-seq and ribosome profiling (Ribo‐seq) in wild-type and Ythdf1-knockout iWAT under cold stimulation (Fig. 4a). As expected, our data revealed a notable decrease in translational efficiency for m6A‐marked transcripts in Ythdf1-knockout mice compared with that in wild-type mice (Fig. 4d).Fig. 4Transcriptome‐wide identification of YTHDF1‐regulated transcripts in beige adipose tissue.a Schematic description of the m6A-seq and RNA-seq, and Ribo-seq experiments. b Metagene plot of m6A peak distribution in iWAT at 4 °C. c Consensus motif of m6A sites in beige adipose tissue. d Violin plots showing TE changes between Ythdf1CTL and Ythdf1cKO mice for nonmethylated (non‐m6A) and methylated (m6A) transcripts. **** $P \leq 0.001$, Mann–Whitney test. No adjustments. e Top KEGG and GO analysis terms enriched for transcripts during beiging and downregulated translation levels. f Overlap of m6A targeted transcripts with translationally downregulated or upregulated genes. g Volcano plot of fold changes in translation levels from iWAT of cold-treated Y1CTL and Y1cKO mice. The upregulated (red) and downregulated (blue) genes are highlighted. Green dot indicates the Bmp8b gene. The other BMP genes were highlighted in yellow. Mann–Whitney test. Source data are provided as a Source Data file.
To identify the functional pathways associated with YTHDF1‐targeted mRNAs, we analyzed genes that were translationally altered by Ythdf1 knockout. In total, we detected 595 downregulated genes and 560 upregulated genes from Ribo-seq data. Kyoto Encyclopedia of Genes and Genomes pathways analyses showed that the top enriched pathways of downregulated genes were associated with a series of metabolism-related pathways such as PPAR signaling pathway and fatty acid metabolism. Gene *Ontology analysis* revealed that cAMP catabolic process, TG homeostasis, and lipoprotein metabolic process were enriched (Fig. 4e). The upregulated genes were enriched in nicotine addiction and cardiac muscle contraction (Supplementary Fig. 5A). By overlapping differentially expressed genes with m6A-modified mRNAs, we identified 59 downregulated genes and 49 upregulated genes with m6A modification, which are potential YTHDF1 targets. ( Fig. 4f). Among the 59 genes, bone morphogenetic protein 8b (Bmp8b) was the most dramatically downregulated one (Fig. 4g). Therefore, we picked BMP8B for further validation.
## YTHDF1 regulates BMP8B in an m6A-dependent manner
The elevated expression of BMP8B in beige adipose tissue was confirmed, implying a role of BMP8B in iWAT beiging (Fig. 5a). The Bmp8b mRNA was almost unchanged, while BMP8B protein level was downregulated in Ythdf1cKO iWAT, implying that YTHDF1 regulated BMP8B expression at the translational level (Fig. 5b). We analyzed BMP8B expression in BAT from Ythdf1CTL and Ythdf1cKO mice. Knockout of Ythdf1 did not affect BMP8B protein level and mRNA translation in BAT (Supplementary Fig. 5B–D), suggesting that YTHDF1 regulates Bmp8b translation mainly in iWAT.Fig. 5YTHDF1 regulates BMP8B in an m6A-dependent manner.a The mRNA and protein levels of BMP8B in iWAT from mice treated at RT or 4 °C. b The mRNA and protein levels of BMP8B in iWAT from Y1CTL and Y1cKO mice treated at 4 °C. c The mRNA level of Ythdf1 and Bmp8b in 3T3-L1 cells with or without YTHDF1 knockdown. d The protein levels of BMP8B expression in 3T3-L1 cells with or without Ythdf1 knockdown. e Polysome profiles of 3T3-L1 cells with or without YTHDF1 knockdown. f The distributions of Gapdh and Bmp8b in polysome fractions. g m6A peak distribution within Bmp8b transcripts. * indicates the predicted m6A peak. h Schematic of luciferase constructs with the predicted m6A sites in the CDS and 3′UTR of Bmp8b mRNA. i Luciferase activity of the Bmp8b reporter in 3T3-L1 cells with or without YTHDF1 knockdown. j Schematic of the mouse treatment regimen. k–m Mice were treated at 4 °C. k Oxygen consumption rates (OCR) of iWAT from Y1CTL and Y1cKO mice with or without BMP8B overexpression. Data were presented as mean ± SEM ($$n = 6$$ biologically independent mice). **** $P \leq 0.0001.$ l The expression of BMP8B and UCP1 in iWAT from Y1CTL and Y1cKO mice with or without BMP8B expression. m H&E staining of iWAT depots from Y1CTL and Y1cKO mice with or without BMP8B expression. Scale bar, 50 μm. The white line indicated the average size. No adjustments. Data in a–c, f, and i were presented as mean ± SEM ($$n = 3$$). ns, not significant, *$P \leq 0.05$, ****$P \leq 0.0001.$ All of the P-values are determined by unpaired two-sided t-test. Source data are provided as a Source Data file.
To elucidate the regulatory mechanism through which YTHDF1 affected BMP8B expression, we generated Ythdf1-knockdown pre-adipocyte 3T3-L1 cells (Fig. 5c), which also exhibited decreased BMP8B protein level but no change in Bmp8b mRNA level (Fig. 5c, d). Polysome profiling revealed reduced translation of Bmp8b in Ythdf1‐silenced cells (Fig. 5e, f). Knockdown of YTHDF2 or YTHDF3 did not affect BMP8B expression (Supplementary Fig. 5E, F), implying that BMP8B expression is specifically regulated by YTHDF1. Three m6A peaks were identified in the coding sequence (CDS) and 3’ untranslated region (UTR) of Bmp8b mRNA (nt 683–1282, 1391–1840, and 2314–2766 in the transcript ENSMUSG00000002384; Fig. 5g). To investigate the regulation of BMP8B by YTHDF1 through m6A, we cloned each of the m6A peaks into a luciferase reporter (Fig. 5h). Knockdown of Ythdf1 decreased Bmp8b−3’UTR-1 luciferase activity but not Bmp8b-CDS or Bmp8b−3’UTR-2 luciferase activity (Fig. 5i), suggesting that YTHDF1 mediated the translation of the Bmp8b 3’UTR. The binding of YTHDF1 with Bmp8b mRNA was confirmed by RIP-qPCR analysis (Supplementary Fig. 5G). Collectively, these findings suggested that the translation of Bmp8b was directly regulated by YTHDF1.
To clarify the functional relationship between BMP8B and YTHDF1, we performed rescue experiments by expressing FLAG-tagged BMP8B in Ythdf1-depleted iWAT using an AAV system (Fig. 5j). Overexpression of BMP8B largely reversed the impaired beiging, changes in UCP1 expression, and OCR elicited by Ythdf1 deletion (Fig. 5k–m). These results indicated that BMP8B was a key effector promoting WAT beiging downstream of YTHDF1.
## WAT-specific YTHDF1 overexpression promotes beiging
We hypothesized that WAT-specific YTHDF1 overexpression may promote the beiging process and ameliorate HFD-induced obesity. Direct unilateral injection of AAVs expressing YTHDF1 (Y1OE) or yellow fluorescent protein (YFP) (Y1CTL) into the inguinal fat pads of mice was performed. All mice in this cohort were housed in individual cages at room temperature to reduce variability in the individual degree of WAT beiging. Mice were sacrificed after 3 weeks of recovery from surgery (Fig. 6a). Our findings showed that the expression of UCP1 and BMP8B was evaluated in Y1OE side than Y1CTL side, with a dramatical increase in YTHDF1 overexpression (Fig. 6b). The mRNA levels of thermogenesis- and lipolysis-related genes were elevated (Fig. 6c). We observed an increase in iWAT beiging in Y1OE fat pads (Fig. 6d). To achieve adipocyte-specific expression, we used adiponectin-promoter to drive YTHDF1 expression and got similar results (Supplementary Fig. 6A, B).Fig. 6WAT-specific YTHDF1 overexpression promotes beiging.a Schematic of the mouse treatment regimen. b *Immunoblot analysis* of YTHDF1, UCP1 and BMP8B in iWAT. c mRNA levels of thermogenesis-related genes. Data were presented as mean ± SEM ($$n = 4$$). ns, not significant, *$P \leq 0.$ 05. d H&E staining of iWAT. Scale bar, 50 μm. The white line indicated the average size. No adjustments. e Schematic of the mouse treatment regimen. f Rectal temperature of Y1CTL and Y1OE littermates. t-test. g The OCR of iWAT from CTL and OE mice. f, g Data were presented as mean ± SEM ($$n = 6$$). ** $P \leq 0.01$, ***$P \leq 0.001.$ h The OCR in primary preadipocytes isolated from CTL and OE mice determined by Seahorse. i The average basal and maximal respiration rates in primary preadipocytes. Data in h, i were presented as mean ± SEM ($$n = 10$$,000 cells exmined over 3 independent experiments). * $P \leq 0.$ 05, **$P \leq 0.01.$ All of the P-values are determined by unpaired two-sided t-test. Source data are provided as a Source Data file.
In another group, bilateral injection of YTHDF1- or YFP-AAV into the inguinal fat pads of mice was performed (Fig. 6e). The rectal temperature, OCR, Ucp1 expression, and lipid droplets in iWAT of Y1OE mice were dramatically increased under cold, RT, thermoneutral condition, and NE stimulation (Fig. 6f, g and Supplementary Fig. 6C–E), supporting the enhanced thermogenic capacity. The OCR of preadipocytes isolated from Y1OE mice was dramatically increased (Fig. 6h). Overall, these findings indicated that overexpression of YTHDF1 in iWAT caused spontaneous WAT beiging and adipocyte thermogenesis.
## WAT-specific YTHDF1 overexpression ameliorates HFD-induced obesity
To explore whether YTHDF1 overexpression affects obesity, direct bilateral injection of YTHDF1- or YFP-AAV into the inguinal fat pads of mice was performed, followed by HFD feeding for 8 weeks after 2 weeks of CD feeding (Fig. 7a). As expected, YTHDF1 expression alleviated HFD-induced obesity (Fig. 7b, c). A reduction in the weight of adipose tissue was observed in YTHDF1 expressing mice, resulting in smaller, multilocular adipocytes containing multiple lipid droplets (Fig. 7d, e). Metabolic cage experiments demonstrated that overexpression of YTHDF1 increased oxygen consumption, carbon dioxide generation, and energy heat generation during both light and dark cycles (Fig. 7f–h). The core temperature was much higher in YTHDF1-expressing mice (Fig. 7i). YTHDF1-expressing mice showed blunting of HFD-induced increases in TG, CHO, and LDL levels (Fig. 7j). Moreover, GTTs and ITTs demonstrated that glucose tolerance and insulin sensitivity were improved in YTHDF1-expressing mice (Fig. 7k, l). Collectively, these results demonstrated that WAT-specific YTHDF1 overexpression protected against HFD-induced obesity and metabolic disorders. Fig. 7WAT-specific YTHDF1 overexpression ameliorates HFD-induced obesity.a Schematic of the mouse treatment regimen. b–l The AAV-CTL (CTL) and AAV-Y1 (OE) littermates were fed with HFD. b Gross view of CTL and OE mice. Scale bar, 0.5 cm. c Body weights of CTL and OE mice. d Weights and gross view of iWAT, BAT, and eWAT from AAV-CTL and AAV-Y1 mice. Scale bar, 0.5 cm. e H&E staining of adipose tissue. Scale bar, 50 μm. The white line indicated the average size. f–h O2 consumption f, CO2 generation g, and energy heat generation h of CTL and OE mice. White and gray areas in the graphs indicate day and night, respectively. i Rectal temperature in CTL and OE mice. j Serum concentrations of TG, CHO, LDL, and HDL in CTL and OE mice. k, l Glucose tolerance k and insulin tolerance (l) of CTL and OE mice. Data in c–l were presented as mean ± SEM ($$n = 4$$ biologically independent mice). ns, not significant, *$P \leq 0.$ 05, **$P \leq 0.01$, ***$P \leq 0.001$, ****$P \leq 0.0001$, two-sided t-test. Source data are provided as a Source Data file.
## Discussion
The m6A “writer” METTL3 promotes while the m6A “eraser” FTO inhibits thermogenesis of adipocytes20–22. In this study, we discovered that YTHDF1, an m6A “reader” protein, contributed to adipose tissue thermogenesis and systemic metabolism. The expression of YTHDF1, rather than other YTH family members, was reduced in obese mice and upregulated in beiging adipose tissue. Ythdf1cKO mice gained more weight and showed aggravated insulin resistance and glucose intolerance after consumption of an HFD. By contrast, WAT-specific YTHDF1 overexpression tempered obesity-related symptoms. Moreover, YTHDF1 directly regulated the expression of BMP8B, an important stimulator of adipose tissue thermogenesis. Together, these findings demonstrated that YTHDF1 played important roles in promoting beiging and antagonizing HFD-induced obesity (Supplementary Fig. 7).
Both beige and brown adipose tissue can be activated to produce heat and take UCP1 as the main marker gene. However, the distinct feature of beige adipocytes, which distinguishes them from brown adipocytes, is their inducible and reversible thermogenic capacity in response to environmental stimuli. Emerging evidence suggests that an alternative thermogenic pathway may exist in beige fat and could contribute to the regulation of systemic energy expenditure and glucose homeostasis7. Intriguingly, studies showed that the metabolically active cells are mostly beige adipocytes in humans29,30. Gene expression analyses of multiple human fat depots suggest that the majority of UCP1+ fat cells in humans show beige fat characteristics instead of brown30, implying that beige adipocytes play an essential role in thermogenesis. Interestingly, YTHDF1 was shown to have different regulatory effects on UCP1 expression in various adipose tissues. Under cold stimulation, or HFD feeding, adipocyte-specific deletion of Ythdf1 slightly affected UCP1 expression in BAT, but dramatically downregulated UCP1 in iWAT. This may be attributed to the fact that beige adipose tissue exhibits a more rapid response to stimulation, whereas BAT expresses constitutively high levels of UCP1. These findings proved that YTHDF1 is indispensable for beige adipose tissue, indicating its potential function in regulating systemic metabolism. Therefore, we used AAV to inject iWAT in situ for Ythdf1 overexpression. Mice with Ythdf1 overexpression in iWAT showed significant alleviation of obesity-related symptoms, including glucose intolerance, insulin resistance, and TG synthesis. These findings demonstrated that YTHDF1 specifically controlled the beiging of iWAT to regulate systemic metabolism.
As an m6A reader protein, YTHDF1 has been reported to enhance the translation of m6A-modified mRNAs15. Consistent with this, our data revealed a notable decrease in translational efficiency for m6A‐marked transcripts in Ythdf1-knockout mice compared with that in wild-type mice (Fig. 4d), suggesting that the effect of YTHDF1 on the translation of m6A-modified transcripts might be general for most m6A genes in adipocytes. Of note, not all m6A-modified mRNAs are YTHDF1 targets15, while some m6A-modified mRNAs are bound by YTHDF2. That is, YTHDF1 only regulates a group of m6A-modified mRNAs.
We observed decreased Leptin mRNA level in Ythdf1-KO iWAT (Supplementary Fig. 4E). Leptin is hypothesized to function as a negative feedback signal in the regulation of energy balance. It activates lipolysis and promote thermogenesis of adipose tissue31,32. Downregulation of Leptin mRNA might reflect the reduced thermogenesis in Ythdf1-KO iWAT. However, *Leptin is* not among the YTHDF1-regulated transcripts (Fig. 4g). That is, the translation of *Leptin is* not regulated by YTHDF1. Downregulation of Leptin might be an indirect effect of Ythdf1-KO. The regulation and function of Leptin in YTHDF1-promoted thermogenesis needs further investigation.
Bmp8b shows dramatical alteration after YTHDF1 knockout. Our data showed that YTHDF1 promotes Bmp8b translation mainly in iWAT, which could be explained by the finding that the percentage of m6A-modified Bmp8b mRNA is much lower in BAT than iWAT (Supplementary Fig. 5H). The detailed mechanism needs further investigation. BMP8B is an important ligand regulating adipose tissue thermogenesis and energy balance33. Autocrine signaling by BMP8B produced by beige/brown adipocytes enhance the energy dissipation of the cells34. Furthermore, adipocyte-specific Bmp8b overexpression enhances adipose tissue browning and thermogenesis33,34. Consistent with these reports, BMP8B expression was increased after cold stimulation. Importantly, BMP8B can rescue Ythdf1-deletion-induced impaired thermogenesis and beiging. Of note, we used the CDS region of Bmp8b for overexpression, which does not have m6A site and cannot be regulated by YTHDF1. Therefore, in some specific Ythdf1cKO + BMP8B mice, the expression of BMP8B was much higher than Ythdf1CTL mice, which induces higher UCP1 expression and thermogenesis. BMP8B acts as a pan-BMP/transforming growth factor β-receptor ligand and activates SMADs to regulate transcription35,36. BMP8B stimulation has also been demonstrated to activate SMAD$\frac{1}{5}$/8 in brown and beige adipocytes33. We also observed altered transcription in Ythdf1-KO tissue, which may be a secondary effect regulated by BMP8B/SMAD signaling. Thus, YTHDF1 may have applications in therapeutic strategies for the management of obesity-associated metabolic diseases.
## Ethics statement
All animal studies were performed in compliance with the Guide for the Care and Use of Laboratory Animals by the Medical Experimental Animal Care Commission of Zhejiang University. All animal studies used the protocol that has been approved by the Medical Experimental Animal Care Commission of Zhejiang University (ZJU20220512).
## Mouse experiments
Mice were maintained and bred in specific pathogen-free conditions at the Animal Center of Zhejiang University. All animal studies were performed in compliance with the Guide for the Care and Use of Laboratory Animals by the Medical Experimental Animal Care Commission of Zhejiang University. Only adult male mice were used in our experiments. For each experiment, about 3–6 mice were used for each group. Randomization and blinding were used for animal studies. All mice were housed in a pathogen-free and climate-controlled environment (22–25 °C, 40–$60\%$ humidity) with a 12-h light–dark cycle that provided free access to food and water unless stated otherwise.
Conditional Ythdf1 KO allele (Ythdf1CTL) was generated by Cyagen Biosciences (China). The fourth exon of Ythdf1 was targeted with flanking LoxP sites. AdipoQ-Cre mice were obtained from the Jackson Laboratory. All of the primers for PCR genotyping were listed in Supplementary Table1.
For obese mice model, C57BL/6 J male mice at 8–10-week-old were fed with HFD ($60\%$ fat as kcal, D12492, ResearchDiet Inc, New Brunswick, NJ) for 8–12 weeks as indicated in Figures. Chow diet ($10\%$ fat as kcal, D12450J, ResearchDiet Inc) was used as control. For cold exposure, mice were individually housed in plastic cages at 4 °C for 7 days. For thermoneutral condition, mice were individually housed in plastic cages at 29 °C. Mice were intraperitoneally injected with CL316,243(Sigma) at 1 mg/kg/day for seven consecutive days. To exclude the BAT function, interscapular BAT was surgically removed37,38. The OCR of adipose tissues was performed using Clark-type oxygen electrodes (Strathkelvin Instruments)39. For histology analysis, adipose tissues were rinsed with DPBS and fixed in $10\%$ formalin. Hematoxylin and eosin (H&E) staining and immunohistochemistry were performed.
## Cells and reagents
3T3-L1 pre-adipocytes (CL-173) and HEK293T cells (CRL-3216) are obtained from ATCC and cultured in DMEM medium (HyClone) supplemented with $10\%$ fetal bovine serum (Thermo Fisher Scientific). The primary preadipocytes were digested in Dispase II (Sigma-Aldrich) and cultured in DMEM/F12 medium (Gibco)40. The seahorse assay was performed using Seahorse XF96 Analyzer (Agilent).
## Plasmids construction
To construct shRNA for YTHDF1-shRNA virus, shRNA oligos of YTHDF1 were cloned to the lentiviral vector pLKO.1. Transgenes encoding YFP or YTHDF1 were inserted into the multiple cloning sites of rAAV2 vector. To achieve adipose tissue-specific expression, the promoter in the vector was replaced with AdipoQ promoter. The oligos for shRNA construction are listed in Supplementary Table 1.
## RNA isolation and quantitative RT-PCR
Total RNA was isolated from various adipose tissues of mice or cells using TRIzol reagent (Life Technologies). Reverse transcription of RNA sample with M-MLV reagent (Takara) using random primers. Real-time PCR was performed using the SYBR Green I master mix (Takara) on a Light Cycler 480 real-time PCR system (Roche). *Relative* gene expression was normalized for the ACTB or GAPDH reference gene and was calculated using the 2(−ΔΔCT) method. RT-qPCR primers are listed in Supplementary Table 2.
## Immunoblotting
Cells or adipose tissues were lysed in RIPA lysis buffer (Beyotime, China). Total protein under denaturing conditions was separated by sodium dodecyl sulfate-polyacrylamide gel (SDS-PAGE) and transferred to PVDF membranes (Millipore). Membranes were blocked and incubated with primary antibodies, followed by incubation with the secondary antibody and chemiluminescent detection system (Bio-Rad). Anti-YTHDF1 (Proteintech, 17479-1-AP), Anti-YTHDF2 (Proteintech, 24744-1-AP), Anti-YTHDF3 (Proteintech, 25537-1-AP), Anti-UCP1 (Proteintech, 23673-−1-AP), Anti-MCP1 (Abcam, ab214819), Anti-BMP8B (Shanghai Huzhen, HZ-12837R) were used for immunoblotting at 1:1000 dilution. Anti-Alpha-Tubulin (Abclonal, A6830) and anti-ACTB (Proteintech, 66009-1-Ig) were used for immunoblotting at 1:3000 dilution.
## Polysome Profiling
Cells or tissues were lysed in polysome lysis buffer (10 mM HEPES, pH 7.4, 100 mM KCl, 5 mM MgCl2, 100 μg/ml CHX, 5 mM DTT, and $1\%$ Triton X‐100) and centrifuged. The supernatant was centrifuged in gradient sucrose (Beckman, rotor SW41Ti), fractioned (BioCamp), and collected (FC203B, Gilson). Total RNA from the indicated fractions were isolated by TRIzol reagent for RT-qPCR analysis41.
## Virus packaging
The lentivirus was packaged in HEK293T cells with helper vector pMD2G and psPAX2 by transfecting cells with indicated constructs. The AAV was packaged with pAAV-RC2 and pHelper in AAV293 cells.
## rAAV injection to inguinal WAT
A total of 6-week-old mice were anesthetized with $2\%$ isoflurane in O2. After anesthesia was fully induced, mice were injected with rAAV (1.0 × 1012 vg per 20 μl phosphate buffered saline) with a 0.3 cc, 30 G insulin syringe42. Finally, the wound was closed with 4-0 PDS II FS-2 suture.
## Metabolic analysis
Body weight and food intake were measured weekly. For GTT, mice were intraperitoneally injected of D-glucose (2 g/kg body weight, 1 g/kg body weight for obese mice) after overnight starvation. For ITT, mice were intraperitoneally injected with insulin (0.7 U/kg body weight, 1.5 U/kg body weight for obese mice) after 5 hr fasting. Serum glucose levels were determined in tail blood samples at 0, 15, 30, 60, 90, and 120 min after glucose or insulin injection using a glucometer (Accu-Chek, Roche). Core body temperature was measured intra-rectally at around 4 p.m. The energy expenditure, including O2 consumption, CO2 generation, and energy heat generation, were monitored using Promethion High-Definition Behavioral Phenotyping System for Mice (Sable Systems International).
## Biochemical analysis of plasma
Blood samples were obtained from cardiac puncture, and plasma was collected after centrifugation for 15 min at 3000 rpm at 4 °C. Total cholesterol, triglyceride, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol were measured by using the assay kits were from HaoKe Biotech Co., Ltd. (Shanghai, China).
## RNA-seq
The polyadenylated RNA was enriched from total RNA using the Dynabeads Oligo(dT)25 (Invitrogen, USA) and fragmented into ~100-nucleotide-long fragments using RNA fragmentation reagent (Ambion, AM8740). Fragmented RNA samples were used for library construction and high‐throughput sequencing.
## m6A-seq
Fragmented mRNA was incubated with m6A antibody-coated beads for 6 h at 4 °C. The immunoprecipitation complex was digested with Proteinase K at 55 °C for 1 h. RNA was then extracted using TRIzol reagent and used for library construction.
## Ribo-seq
iWAT tissue was treated with polysome lysis buffer. After centrifugation, the supernatant was digested with E. coli RNase I (Ambion) for 1 h. Ribosome-protected fragments were collected by ultracentrifugation. RNA was extracted using TRIzol reagent. The library was constructed with small RNA library construction kit (NEB)41.
## Statistical analysis
Illumina Casava1.7 software used for basecalling. Sequenced reads were trimmed for adaptor sequence, and masked for low-complexity or low-quality sequence, then mapped to mm8 whole genome using Hisat2 v2.2.141. Statistical analysis was performed using GraphPad Prism 8 software (GraphPad Software, Inc.). Data were presented as mean ± standard errors of the means (SEM). P values were calculated using a two‐tailed t‐test unless stated otherwise. Asterisks denote statistical significance (*$P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$; ****$P \leq 0.0001$).
## Statistics and reproducibility
All of the experiments had repeated at least 3 times independently with similar results.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary information Reporting summary The online version contains supplementary material available at 10.1038/s41467-023-37100-z.
## Source data
Source data
## Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
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|
---
title: De novo lipogenesis fuels adipocyte autophagosome and lysosome membrane dynamics
authors:
- Leslie A. Rowland
- Adilson Guilherme
- Felipe Henriques
- Chloe DiMarzio
- Sean Munroe
- Nicole Wetoska
- Mark Kelly
- Keith Reddig
- Gregory Hendricks
- Meixia Pan
- Xianlin Han
- Olga R. Ilkayeva
- Christopher B. Newgard
- Michael P. Czech
journal: Nature Communications
year: 2023
pmcid: PMC10011520
doi: 10.1038/s41467-023-37016-8
license: CC BY 4.0
---
# De novo lipogenesis fuels adipocyte autophagosome and lysosome membrane dynamics
## Abstract
Adipocytes robustly synthesize fatty acids (FA) from carbohydrate through the de novo lipogenesis (DNL) pathway, yet surprisingly DNL contributes little to their abundant triglyceride stored in lipid droplets. This conundrum raises the hypothesis that adipocyte DNL instead enables membrane expansions to occur in processes like autophagy, which requires an abundant supply of phospholipids. We report here that adipocyte Fasn deficiency in vitro and in vivo markedly impairs autophagy, evident by autophagosome accumulation and severely compromised degradation of the autophagic substrate p62. Our data indicate the impairment occurs at the level of autophagosome-lysosome fusion, and indeed, loss of Fasn decreases certain membrane phosphoinositides necessary for autophagosome and lysosome maturation and fusion. Autophagy dependence on FA produced by *Fasn is* not fully alleviated by exogenous FA in cultured adipocytes, and interestingly, imaging studies reveal that Fasn colocalizes with nascent autophagosomes. Together, our studies identify DNL as a critical source of FAs to fuel autophagosome and lysosome maturation and fusion in adipocytes.
The function of de novo lipogenesis (DNL) in adipocytes has been a mystery as it contributes little to fat storage in these cells. Here, the authors show that DNL is a critical source of fatty acids for membrane-expanding processes like autophagy.
## Introduction
Adipose tissues are major regulators of whole-body metabolism, and disruptions to adipose tissue function can drastically alter overall health1. White adipose tissue (WAT) sequesters energy in the form of triglycerides (TGs), which are derived from two main sources of lipid: exogenous circulating fatty acids and endogenously synthesized fatty acids arising from de novo lipogenesis (DNL). Through DNL, fatty acids are synthesized from acetyl-CoA derived from carbohydrates, amino acids, and other sources. Specifically, acetyl-CoA is converted to malonyl-CoA by acetyl-CoA carboxylase (ACC), and these substrates combine to form palmitate through the activities of fatty acid synthase (Fasn)2. Palmitate is in turn the precursor for synthesis of many other fatty acid types. In adipose tissue, DNL is thought to serve mostly for storing excess energy from carbohydrates, amino acids and other carbon sources as fatty acids, which become esterified and sequestered into TGs3. This TG storage function of WAT is hypothesized to enhance whole body metabolic health by enhancing glucose disposal and sequestering toxic lipids away from metabolic tissues such as liver and skeletal muscle4–7.
Although the above pathways are well established, adipocyte DNL surprisingly accounts for <$2\%$ of adipose TG content; instead, most adipocyte TGs are derived from circulating TGs found in lipoproteins4,8. Supporting the notion that DNL contributes minimally to TG in fat stores, loss of adipocyte Fasn, the rate-limiting enzyme of DNL, has little effect on total lipid accumulation in adipose tissues in mice under normal feeding conditions9. Instead, adipocyte specific-Fasn knockout mice exhibit browning of white adipose tissue and improved glucose homeostasis9,10. These data raise the idea that DNL and Fasn may be playing important roles in adipocytes beyond merely being a source of fatty acids for storage in lipid droplets. In fact, in a myriad of cell types, *Fasn is* often necessary for cell survival or proper cellular function. Some of these functions include maintenance of native sarcoplasmic reticulum membrane composition in muscle and plasma membrane cholesterol in macrophages and retina, Schwann cell myelination, and production of ligands for PPAR transcription factors9,11–14. The main cellular role of Fasn in adipocytes, however, has remained a mystery.
The studies described here identify a critical role for Fasn in adipocyte autophagy. Autophagy is a degradative process in which cellular components are broken down and recycled by lysosomes15. Autophagy can promote survival under stress conditions, such as a means of recycling nutrients in response to starvation, but also serves homeostatic roles in the clearance of damaged or unneeded cellular components15,16. During autophagy, cellular contents destined for degradation are encompassed and sequestered by autophagosomes, double-membrane vesicles that ultimately fuse with lysosomes. Autophagosomes form on-demand, and it’s estimated their growth requires an astonishing ~4000 phospholipids per second, but the source of these membrane lipids has largely remained elusive17. Our results presented here demonstrate that fatty acids produced by Fasn are a critical source of lipids for these remarkable autophagosome dynamics. This conclusion is consistent with studies in both yeast and mammalian cells that have suggested a link between de novo lipid synthesis and the growing phagophore18–21. Indeed, we show that adipocytes require localized fatty acid synthesis for efficient autophagy, as exogenous fatty acid supplementation does not fully rescue the defects observed in Fasn-deficient cells. These new data demonstrate a key role of adipocyte DNL in autophagy, as opposed to its relatively minor contribution to adipocyte TG storage.
## Fasn-deficient adipocytes display impaired autophagy
In contrast to adipocytes in vivo, cultured adipocytes that are differentiated from progenitor cells (preadipocytes) in vitro rely almost exclusively on DNL for TG synthesis and lipid droplet formation due to the low fatty acid content of cell culture media. Therefore, we reasoned that investigating Fasn function might be most productively achieved in cultured adipocytes. Although *Fasn is* required for adipocyte differentiation, Fasn-deficient adipocytes can be obtained from preadipocytes derived from FasnFl/Fl mice harboring adiponectin promoter-driven Cre recombinase. In such cells, Fasn deletion is delayed until after onset of adipocyte differentiation, enabling adipocyte formation. Indeed, preadipocytes from FasnFl/Fl and FasnFl/Fl; Adiponectin-Cre+ (cAdFasnKO) mice differentiate competently into adipocytes (Fig. 1a). However, whereas cAdFasnKO adipocytes initially accumulate lipid droplets in a manner similar to Fasn-expressing control cells, they lose most of their lipids by Day 7 post-differentiation (Fig. 1a), due to an almost complete lack of fatty acid synthesis (Supplementary Fig. 1A). Interestingly, this loss of lipids does not appear to be accompanied by a de-differentiation of the cells, as they maintain their rounded adipocyte morphology and express genes known to be abundant in adipocytes (Fig. 1b).Fig. 1Impaired autophagy in adipocytes deficient in Fasn.a Light microscopy of in vitro differentiated FasnFl/Fl and cAdFasnKO primary adipocytes. Scale bar = 50 μm. b qPCR of DNL genes and adipocyte markers. $$n = 3$$ samples, similar data obtained in 2 independent experiments. c Electron micrographs of same adipocytes as in (a). Arrows indicate autophagic vesicles. Scale bar = 1 μm. d Western blot and (e) quantification of autophagic markers of FasnFl/Fl and cAdFasnKO adipocytes. Proteins normalized to Gapdh. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. f qPCR of autophagy-related genes in FasnFl/Fl and cAdFasnKO primary adipocytes. Map1lc3a = LC3A, Map1lc3b = LC3B, Sqstm1 = p62. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. g Western blots of LC3II turnover assay in FasnFl/Fl and cAdFasnKO adipocytes. Cells were starved (in HBSS) and/or treated with CQ for 4 h. HBSS = Hanks’ balanced salt solution, CQ = 50 μM chloroquine. h Synthesis ratio of LC3II calculated as the ratio of LC3II with HBSS to the no HBSS condition. The ratio was calculated with and without autophagy inhibition with CQ as indicated. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. i Degradation ratio of LC3II calculated as the ratio of LC3II in the presence of CQ to no CQ treatment. The ratio was calculated with and without autophagy induction with HBSS as indicated. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. j Western blots for p62 of Triton X-100-soluble and -insoluble protein fractions. Ponceau S staining provided for loading control. k Quantification of blots in (j). $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. All data are means ± SE. Two-tailed t tests: P values from left to right (b) 0.000268, 0.215699, 0.00448, 0.151227, 0.020024, 0.008245, 0.032821, (e) 0.000359, 0.029211, 0.015753, 0.004466, (f) 0.213048, 0.27397, 0.910372, 0.293104, 0.014782, 0.005682, 0.007374, (h) 0.2892, 0.6414, (i) 0.0182, 0.0065, (k) 0.000002, 0.000008. * <0.05, **<0.01, ***<0.001, ****<0.0001. Source data are provided as a Source Data file.
Imaging of Fasn-deficient adipocytes by electron microscopy revealed a striking abundance of autophagic vesicles, characterized by a double membrane surrounding cytoplasmic content/organelles in various states of degradation (Fig. 1c). Accumulation of autophagic vesicles correlated as expected with a clear increase in levels of the autophagosome marker LC3 (Fig. 1d, e). We also assessed levels of p62, a mediator of selective autophagy and an autophagy substrate. Levels of this protein were strongly increased (~6-fold) in cAdFasnKO adipocytes (Fig. 1d, e), which could not be fully attributed to a much smaller increase (~2-fold) in levels of the p62/Sqstm1 transcript (Fig. 1f), suggesting a potential stabilization of p62 or a decrease in its degradation. Immunofluorescence labeling also showed strong increases in LC3 and p62 protein in cAdFasnKO adipocytes (Supplementary Fig. 1B). Gene expression analysis showed that Becn1 and Mitf transcripts were slightly elevated in KO adipocytes, whereas expression of other autophagy-related genes was unaffected (Fig. 1f).
After proteolytic processing from a common precursor (proLC3), the LC3 protein exists in two forms in the cell: LC3I and LC3II. LC3II differs from LC3I in that upon autophagy activation, the LC3I protein becomes conjugated to phosphatidylethanolamine, forming LC3II. LC3II is then anchored into the autophagosome membrane and thus serves as a reliable marker of both growing and completed autophagosomes. Because an increase in LC3II could indicate an increase in autophagosome formation but also a decrease in autophagosome degradation, measuring changes in LC3II in response to stimuli that activate or block autophagy is necessary to adequately assess autophagic flux22. The increases in LC3 and p62 proteins in cAdFasnKO adipocytes suggesting altered autophagy flux prompted us to investigate further with an LC3II turnover assay. Adipocytes were treated with HBSS to activate autophagy and chloroquine (CQ) to block autophagic degradation. Shown in Fig. 1g and quantified in Fig. S1C, activation of autophagy with HBSS had no effect on LC3II levels in either control or KO adipocytes in the absence of CQ. Blockade of autophagic degradation by CQ, however, increased LC3II in WT and KO adipocytes (Fig. 1g and Supplementary Fig. 1C). This large increase in LC3II accumulation with CQ but not with HBSS indicates these adipocytes likely already have a high rate of autophagic flux under basal conditions. Since LC3II levels differ between FasnFl/Fl and cAdFasnKO adipocytes under basal conditions (Fig. 1d, g), it is necessary to calculate the ratios of LC3II in the presence of HBSS and CQ to accurately determine flux22. Surprisingly, the LC3II synthesis ratio, a measure of autophagosome formation rate, is not significantly reduced by FasnKO (Fig. 1h). However, calculation of the LC3II degradation ratio, a measure of autophagic degradation, showed a significant reduction by FasnKO (Fig. 1i). These data indicate that autophagic flux is impaired in cAdFasnKO adipocytes due to reductions in autophagic degradation and not autophagy activation.
Since p62 is an autophagy substrate, p62 degradation can be another useful way to assess autophagy flux in cells. Under conditions in which autophagy is inhibited, p62 degradation is impaired, and accumulated p62 condenses to form detergent-insoluble aggregates22. We assessed the presence of these insoluble aggregates by fractionating adipocyte protein lysates into Triton X-100-soluble and Triton X-100- insoluble forms. In the soluble fraction of Fasn KO cells, p62 is significantly increased similar to the data shown in Fig. 1d, in concert with a massive accumulation of p62 in the insoluble fraction (Fig. 1j and k). These data further confirm autophagic degradation is strongly impaired in cAdFasnKO adipocytes. Furthermore, the abundance of enclosed autophagosomes (Fig. 1c) as well as the increase in LC3 lipidation in response to autophagy modulation (Fig. 1g) in Fasn deficient adipocytes indicate they are not defective in early stages of autophagy but rather have compromised fusion with the lysosome leading to impaired degradation.
## Protein malonylation is increased in cAdFasnKO adipocytes
While loss of Fasn eliminates the production of palmitate via DNL, it can also affect levels of earlier pathway intermediates, such as acetyl-CoA and malonyl-CoA23,24 (Fig. 2a). The role of malonyl-CoA in autophagy has not been determined, whereas acetyl-CoA is a well established negative regulator of autophagy. Reductions in cytosolic acetyl-CoA activate, while increases in acetyl-CoA inhibit autophagy25. For these reasons, it was important to establish whether the impairment of autophagy in cAdFasnKO adipocytes might be linked to reduced palmitate production or altered levels of acetyl-CoA or malonyl-CoA (Fig. 2a).Fig. 2DNL metabolites acetyl-CoA and malonyl-CoA do not mediate the impaired autophagy in cultured FasnKO adipocytes.a Diagram of de novo lipogenesis and its proposed role(s) in autophagy. Figure created with BioRender. b Relative acetyl-CoA and malonyl-CoA levels in FasnFl/Fl and cAdFasnKO adipocytes determined by LC-MS/MS. $$n = 3$$ samples. Two-tailed t test: P value (Acetyl-CoA) = 0.0075, **<0.01. c Western blot of malonylated proteins in FasnFl/Fl and cAdFasnKO adipocytes in the presence of 15 μM ND-630 (ACC inhibitor, ACCi) for 72 h and 1 μM insulin for the indicated time points. d Light microscopy of in vitro differentiated adipocytes treated with and without 10 μM ACCi for 48 h. Scale bar = 50 μm. e Relative triglyceride content and (f) Lipogenesis index calculated as the ratio of the percentage of C16:0 to the percentage of C18:2 in triglycerides in control and ACCi-treated adipocytes. $$n = 3$$ samples. Two-tailed t test: P values = (E) 0.0336, (f) 0.0245, *<0.05. g Western blot of soluble p62 and LC3 in FasnFl/Fl and cAdFasnKO adipocytes treated with ACCi and insulin as shown in (c). h Western blot for p62 and LC3 of Triton X-100-soluble protein fractions from adipocytes treated with 10 μM ACCi for 48 h with and without 4-h treatment with 50 μM CQ. i LC3II degradation ratio calculated from Fig. 2H. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. Two-tailed t test: P Value = 0.0038, **<0.01. j Western blot for p62 of Triton X-100-insoluble protein fractions from adipocytes treated with 10 μM ACCi for 48 h with and without 4-h treatment with 50 μM CQ. k Quantification of insoluble p62 from 2 J. Ponceau provided as loading control. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. 2-way ANOVA with Tukey post hoc: P values: Main effects: CQ = 0.0042, ACCi = 0.0001, Interaction = 0.2040, Control vs. ACCi = 0.0173, CQ control vs. CQ ACCi = 0.0016, ACCi control vs. CQ ACCi = 0.0225, *<0.05, **<0.01. All data are means ± SE. Source data are provided as a Source Data file.
To determine whether acetyl-CoA or malonyl-CoA levels were affected by Fasn deletion, LC-MS/MS was performed on cell extracts. Surprisingly, total acetyl-CoA levels were significantly reduced, indicating elevated acetyl-CoA is unlikely to play a role in the autophagy inhibition observed in cAdFasnKO adipocytes. In contrast, malonyl-CoA was slightly but not significantly elevated in cAdFasnKO adipocytes (Fig. 2b), and immunoblot analysis of total malonylated proteins demonstrated a dramatic increase in cAdFasnKO adipocytes (Fig. 2c). This discrepancy between malonyl-CoA levels and malonylated proteins may suggest that protein malonylation serves as a sink for excess malonyl-CoA. These data suggest elevated malonyl-CoA or protein malonylation could play a role in the autophagy inhibition observed in cAdFasnKO adipocytes.
## Protein malonylation in cAdFasnKO adipocytes does not mediate autophagy inhibition
To determine whether protein malonylation or malonyl-CoA impairs autophagy, we used firsocostat/ND-630, a pharmacological inhibitor of ACC$\frac{1}{2}$, the enzyme responsible for malonyl-CoA production and immediately upstream of Fasn (Fig. 2a). Inhibition of ACC in fully differentiated cultured adipocytes caused a slight diminution of lipid droplets as observed by light microscopy (Fig. 2d), accompanied by a reduction in cellular triglycerides (Fig. 2e). Quantification of the lipogenesis index, the ratio of C16:0 to C18:2, in triglycerides and a measure of DNL26,27, indicated that DNL was effectively inhibited by ND-630 treatment (Fig. 2f). ND-630 treatment also successfully reduced protein malonylation in control cells and returned protein malonylation levels to control levels in cAdFasnKO adipocytes (Fig. 2c). Together, these data demonstrate that pharmacological inhibition of ACC in fully differentiated adipocytes effectively reduces protein malonylation and DNL.
Western blotting for autophagy markers LC3 and p62 showed that ACC inhibition alone is sufficient to increase LC3II and p62 content, while ACC inhibition in cAdFasnKO adipocytes does not restore LC3II or p62 to control levels, indicating increased protein malonylation does not contribute to autophagy impairment (Fig. 2g). Since we noticed an effect of ND-630 alone on autophagy markers, we measured autophagy flux in the ND-630-treated adipocytes in the presence of CQ. Similar to the findings with cAdFasnKO adipocytes, ACC inhibition significantly reduces autophagy flux measured by the LC3II degradation ratio (Fig. 2h, i, and Supplementary Fig. 2A). ACC inhibition also significantly increases both soluble and insoluble p62 levels (Fig. 2h and j, quantified in Supplementary Fig. 2B and K, respectively). Combined, these data show that like cAdFasnKO, ACC inhibition in adipocytes significantly impairs autophagic degradation, providing independent evidence that inhibition of fatty acid synthesis is sufficient to impair autophagy.
## Fatty acids produced by Fasn are essential for autophagy
If fatty acid synthesis via *Fasn is* required for autophagy, we hypothesized that the provision of exogenous fatty acids would restore autophagy function in cAdFasnKO adipocytes. Supplementation of cAdFasnKO adipocytes with a mixture of palmitate and oleate indeed restored soluble p62 levels, though insoluble p62 levels were only partially rescued (Fig. 3a, b). Likewise, LC3II was not restored by the addition of fatty acids to cAdFasnKO adipocytes (Fig. 3a and c). Similar results were obtained when fatty acids were provided for a longer period and throughout the differentiation process, where lipid droplets were plentiful in KO adipocytes (Supplementary Fig. 3A–D). Thus, the autophagy impairment in cAdFasnKO adipocytes could not be ascribed simply to a lack of adipocyte lipid content. Importantly, p62/Sqstm1 mRNA levels were not reduced by fatty acids (Fig. 3d), though other autophagy gene expression changes as a result of cAdFasnKO were restored by fatty acids (Fig. 3e). Immunofluorescent labeling similarly showed a partial reduction of p62 levels in cAdFasnKO adipocytes by fatty acids (Fig. 3f). These data suggest that the partial rescue of p62 protein levels was a direct consequence of fatty acids restoring p62 degradation and thus autophagy flux in FasnKO adipocytes. Fig. 3Fatty acids produced by Fasn are essential for autophagy. FasnFl/Fl and cAdFasnKO adipocytes were supplemented with 200 μM each of palmitate and oleate for 48 h after they were fully differentiated (day 5). a Western blots of Triton X-100-soluble and -insoluble protein fractions. b Quantification of soluble and insoluble p62 in Fig. 3A normalized to Gapdh. c Quantification of LC3II normalized to Gapdh from Fig. 3A. d qPCR of Sqstm1/p62 and (e) autophagy-related genes in adipocytes supplemented with fatty acids (FAs) throughout differentiation, beginning on day 0. $$n = 3$$ samples, similar data obtained in at least 2 independent experiments. ( b–e) *All data* are means ± SE. All datasets (b–e) analyzed with two-way ANOVA with Tukey post hoc: P values = (B, soluble p62) FasnFl/Fl vs. cAdFasnKO 0.0009, FasnFl/Fl vs. FasnFl/Fl FA 0.5464, FasnFl/Fl FA vs. cAdFasnKO FA 0.4807, cAdFasnKO vs. cAdFasnKO FA 0.0010, (b, insoluble p62) FasnFl/Fl vs. cAdFasnKO <0.0001, FasnFl/Fl vs. FasnFl/Fl FA 0.9998, FasnFl/Fl FA vs. cAdFasnKO FA 0.0010, cAdFasnKO vs. cAdFasnKO FA 0.0122, (c) FasnFl/Fl vs. cAdFasnKO <0.0001, FasnFl/Fl vs. FasnFl/Fl FA 0.9948, FasnFl/Fl FA vs. cAdFasnKO FA < 0.0001, cAdFasnKO vs. cAdFasnKO FA 0.9092, (d) FasnFl/Fl vs. cAdFasnKO 0.0239, FasnFl/Fl vs. FasnFl/Fl FA 0.9784, FasnFl/Fl FA vs. cAdFasnKO FA 0.0298, cAdFasnKO vs. cAdFasnKO FA 0.9952, (e, Becn1) FasnFl/Fl vs. cAdFasnKO 0.0491, FasnFl/Fl vs. FasnFl/Fl FA 0.9067, FasnFl/Fl FA vs. cAdFasnKO FA 0.9845, cAdFasnKO vs. cAdFasnKO FA 0.0123, (e, Atg7) FasnFl/Fl vs. cAdFasnKO 0.0061, FasnFl/Fl vs. FasnFl/Fl FA 0.9898, FasnFl/Fl FA vs. cAdFasnKO FA 0.2938, cAdFasnKO vs. cAdFasnKO FA 0.0532, (e, Mitf) FasnFl/Fl vs. cAdFasnKO 0.0022, FasnFl/Fl vs. FasnFl/Fl FA 0.9958, FasnFl/Fl FA vs. cAdFasnKO FA 0.7404, cAdFasnKO vs. cAdFasnKO FA 0.0009, (e, Tfe3) FasnFl/Fl vs. cAdFasnKO 0.0014, FasnFl/Fl vs. FasnFl/Fl FA 0.9655, FasnFl/Fl FA vs. cAdFasnKO FA 0.4359, cAdFasnKO vs. cAdFasnKO FA 0.0177, *<0.05, **<0.01, ***<0.001, ****<0.0001. f Immunofluorescence of FasnFl/Fl and cAdFasnKO adipocytes supplemented with FAs beginning on Day 2 or Day 5 of differentiation and labeled with p62. Scale bar = 50 μm. g Immunofluorescence imaging of wild-type adipocytes starved for 2 h in HBSS and double-labeled for LC3B and Fasn. Scale bar = 10 μm. h Quantification of colocalization of LC3B and Fasn in Fig. 3F as measured by Manders’ coefficient = fraction of LC3B colocalized with Fasn. $$n = 14$$ (Control) and $$n = 19$$ (HBSS) images from single Z slices. Data are means ± SE. Two-tailed t test, P value = <0.0001 ****<0.0001. i Manders’ coefficient for adipocytes treated with 30 µM CQ for 24 h (CQ condition) or starved in HBSS with 50 µM CQ for 5 h (CQ HBSS condition). $$n = 7$$ (Control), $$n = 11$$ (CQ) and $$n = 9$$ (HBSS) images from single Z slices. Data are means ± SE. One-way ANOVA with Sidak post hoc, P values = Control vs. CQ 0.9913, Control vs. CQ HBSS 0.0327, CQ vs. CQ HBSS 0.0076, *<0.05, **<0.01. Source data are provided as a Source Data file.
Because exogenous fatty acids and lipid droplet restoration were not sufficient to fully rescue autophagic flux in knockout adipocytes, we hypothesized that local fatty acid synthesis is required for proper autophagosome dynamics. To test this, we performed immunofluorescence colocalization analyses to determine if Fasn colocalized with the autophagosome marker, LC3B, shown in Fig. 3g. As expected, LC3B puncta formation was increased by starvation in HBSS (Fig. 3g). Fasn immunolabeling showed diffuse cytoplasmic staining but also appeared in punctate structures, which were increased by autophagy-activating conditions; i.e. starvation. Interestingly, when autophagy was activated by starvation, the amount of LC3B colocalized with Fasn significantly increased (Fig. 3h). Importantly, increasing LC3B puncta formation by autophagy inhibition (CQ) did not increase Fasn/LC3B colocalization; whereas, autophagy activation under these conditions did increase colocalization (Fig. 3i). These data indicate Fasn localizes with nascent autophagosomes and likely contributes to the growing autophagosome membrane. Together, these data are consistent with the hypothesis that endogenous fatty acid synthesis directly supplies lipids to autophagosome membranes and is required for effective autophagic flux.
## Impaired autophagy in adipocytes of cAdFasnKO mice
We next asked whether the autophagy defect in Fasn-deficient adipocytes could be observed in vivo. For these experiments, we used tamoxifen-inducible, adipocyte specific-FasnKO (iAdFasnKO) mice, as well as the constitutively active Adiponectin-Cre (cAdFasnKO) mice that were used for the previous studies on cultured adipocytes. In subcutaneous white adipose tissue of iAdFasnKO mice, we observed increased p62 and LC3II (Fig. 4a), consistent with our in vitro experiments. Also observed was the characteristic beiging of iAdFasnKO mice evident by increased Ucp1 expression. Similar results were found after mice were fasted, an autophagy-activating condition (Fig. 4a). These changes in autophagic markers occurred specifically in adipocytes, shown by fractionation of adipose tissue (Fig. 4b) and immunohistochemical staining of p62 in WAT (Fig. 4c). Increased p62 protein was observed in all adipose tissues but was not due to increased Sqstm1 transcription (Fig. 4d and e, respectively). An ex vivo autophagy flux assay assessing LC3II and p62 changes in the presence of CQ showed significantly increased LC3II and p62 in both basal and CQ-treated conditions of cAdFasnKO WAT (Fig. 4f, g). Calculation of the degradation ratio for LC3II indicated a tendency for a reduced autophagosome degradation in KO explants (Fig. 4h), although there was a significant reduction in p62 degradation (Fig. 4h). These results suggest an impairment in autophagosome degradation in cAdFasnKO adipose tissue in vivo, consistent with our findings in cultured cAdFasnKO adipocytes in vitro. Fig. 4Adipocyte Fasn deficiency impairs autophagy and induces accumulation of p62 protein in vivo.a Western blot of subcutaneous white adipose tissue (WAT) from FasnFl/Fl and inducible AdFasnKO (iAdFasnKO) mice under fed and 24 h-fasted conditions. Ponceau (Ponc) provided for loading control. $$n = 3$$ mice (fed, FasnFl/Fl and iAdFasnKO), $$n = 2$$ mice (fasted, FasnFl/Fl), $$n = 4$$ mice (fasted, iAdFasnKO). b Western blot of pooled ($$n = 4$$ mice/group) subcutaneous WAT (iWAT) from FasnFl/Fl (WT) and iAdFasnKO (KO) mice separated into adipocyte (Adipo) and stromal vascular fractions (SVF). c Immunohistochemical staining of p62 in iWAT of FasnFl/Fl and iAdFasnKO mice; scale bar = 25 µm. Representative image from $$n = 4$$ mice. d p62 Western blot of adipose tissue depots from FasnFl/Fl and cAdFasnKO mice. Histone H3 (H3) and vinculin (Vcl) provided as loading controls. eWAT = epididymal adipose tissue, BAT = brown adipose tissue. $$n = 3$$ mice. e qPCR comparing p62 (Sqstm1) mRNA expression across adipose tissue depots. $$n = 3$$ mice. f Ex vivo autophagy flux assay in subcutaneous WAT explants harvested from FasnFl/Fl and cAdFasnKO mice. WAT explants were cultured in serum-free DMEM/F12 media with or without 50 µM CQ for 2 h. Western blot results are shown. g Quantification of LC3II and p62 in western blot in Fig. 4F. Proteins were normalized to mouse IgG light chain levels. For LC3II: Mixed-effects model, with Sidak post hoc, P values = FasnFl/Fl vs. cAdFasnKO 0.0238, FasnFl/Fl CQ vs. cAdFasnKO CQ 0.0101, For p62: Mixed-effects model, Sidak post hoc, P values = FasnFl/Fl vs. cAdFasnKO <0.0001, FasnFl/Fl CQ vs. cAdFasnKO CQ < 0.0001, $$n = 4$$ explants for FasnFl/Fl and cAdFasnKO CQ, $$n = 5$$ explants for cAdFasnKO and FasnFl/Fl CQ. h Degradation ratios of LC3II and p62 were determined by calculating the change in protein amount in the CQ condition divided by the control condition. Two-tailed t tests, P values (left to right) = 0.03215, 0.0276 *<0.05, $$n = 4$$ mice. i FasnFl/Fl and iAdFasnKO mice fed a chow diet or high fat diet. Western blot of subcutaneous WAT. j qPCR of Ucp1 and Sqstm1 (p62) in samples from 4I. Two-way ANOVA: a = main effect of FasnKO. $$n = 4$$ mice. All data are means ± SE. Source data are provided as a Source Data file.
Based on the partial restoration of autophagy flux by fatty acid supplementation in vitro, mice were fed a high fat diet (HFD) rich in saturated fatty acids to determine if this would restore autophagy flux in vivo. Shown in Fig. 4i, HFD feeding reduced p62 protein levels and interestingly also blunted Ucp1 protein levels in iAdFasnKO iWAT. Again, these p62/Sqstm1 changes were not due to transcriptional effects (Fig. 4j). Changes in LC3 lipidation were not as clear in this cohort of chow-fed iAdFasnKO mice. We hypothesize these variations are due to the cellular heterogeneity of the adipose tissue in vivo as we see a significant amount of LC3 in the SVF fraction (Fig. 4b). These data in mice reinforce our findings with cAdFasnKO adipocytes in culture indicating *Fasn is* required for proper autophagy flux in adipocytes, and exogenous fatty acids can only partially restore autophagic function.
## General proteostasis is unaltered by Fasn deficiency
The autophagic degradation system and the ubiquitin-proteasome system are the major pathways for protein degradation and maintenance of proteostasis, thus blockade of either arm can lead to an accumulation of misfolded and damaged proteins. In addition, crosstalk between these systems has been established, illustrated by the fact that impairment of proteasomal degradation can activate autophagy as a compensatory mechanism, though it’s not clear if the alternative is true28,29. To determine if general proteostasis was altered by Fasn deletion or inhibition, we measured total ubiquitinated protein levels. Inhibition of Fasn in cultured adipocytes using the potent inhibitor, TVB3664, increased soluble p62 and lipidated and unlipidated LC3B, as found with FasnKO adipocytes, though total ubiquitinated proteins were not affected (Supplementary Fig. 4A). Treatment with the proteasomal inhibitor MG132 increased protein ubiquitination similarly in vehicle and Fasn-inhibited adipocytes (Supplementary Fig. 4A). Western blotting of the Triton X-100 insoluble fraction of these lysates showed an increase in insoluble p62 with Fasn inhibition, as expected, accompanied by a mild increase in protein ubiquitination (Supplementary Fig. 4B). MG132 treatment, however, dramatically increased protein ubiquitination in the insoluble fraction in both groups (Supplementary Fig. 4B). The protein p62 contains a ubiquitin binding domain and mediates the turnover of some ubiquitinated proteins, namely insoluble aggregates30, thus reduced autophagic turnover by Fasn inhibition prevents the degradation of p62-bound ubiquitinated cargo. Compared to the accumulation of ubiquitinated proteins by proteasomal inhibition (MG132); however, the increase with Fasn inhibition is minor and found only in the insoluble fraction. Moreover, western blotting for total ubiquitinated proteins in WAT lysates from cAdFasnKO mice showed no change (Supplementary Fig. 4C).
Lastly, to ensure proteasomal activity was not altered by FasnKO, we performed a proteasomal activity assay on the WAT samples shown in Supplementary Fig. 4C. We found no differences in activity with any proteasomal substrate (Supplementary Fig. 4D). These findings suggest that while autophagic protein degradation is impaired by Fasn deficiency, general proteostasis is not significantly disrupted.
## Deletion of Fasn alters the cellular lipidome in vitro and in vivo
Given that deletion of Fasn appears to interfere with the degradation of autophagic vesicles but not their formation, Fasn may function to ensure proper membrane lipid composition for autophagosome maturation and lysosome fusion/function. We therefore performed lipidomics analysis on Fasn deficient adipocytes to investigate the effect of Fasn on cellular lipid composition. In vitro, loss of adipocyte Fasn led to significant remodeling of phospholipid composition. Phosphatidylinositol, palmitic acid, cardiolipin, and all measured lysophospholipid species were reduced, while phosphatidylethanolamine, phosphatidylglycerol, and ceramides were increased (Fig. 5a and Supplementary Data 1). In vivo, however, only sphingomyelin was significantly reduced in adipose tissue (Fig. 5b and Supplementary Data 1).Fig. 5Deletion of Fasn alters the cellular lipidome in vitro and in vivo. Lipidomics analyses from in vitro differentiated FasnFl/Fl and cAdFasnKO primary adipocytes and subcutaneous WAT from FasnFl/Fl and iAdFasnKO mice. a Relative amounts of different lipid classes from in vitro differentiated adipocytes and (b) in vivo iWAT. Two-tailed t tests: P Values = (a) from left to right <0.00001, 0.000738, 0.061515, 0.004430, 0.061515. 0.054831, 0.001855, 0.000028, 0.00042, 0.013057, 0.016585, <0.00001, (b) from left to right 0.178372, 0.820148, 0.057631, 0.284882, 0.041275, 0.580865, 0.50506, 0.457181, 0.184524, 0.90473, 0.141867, 0.885965, *<0.05, **<0.01, ***<0.001, ****<0.0001. Fatty acyl chain composition of different lipid classes in vitro (c) and in vivo (d). Percent of indicated lipid containing fatty acid chains ≤ 16 carbons vs. lipids with fatty acid chains ≥ 18 carbons. $$n = 3$$ samples for in vitro analyses. $$n = 4$$ mice for in vivo analyses. All data are means ± SE. Two-way ANOVA with Sidak post hoc, P values = (c) from left to right <0.00001/<0.00001, <0.00001/<0.00001, <0.00001/<0.00001, <0.00001/<0.00001, (d) from left to right <0.00001/<0.00001, <0.00001/<0.00001, <0.00001/<0.00001, $\frac{0.0375}{0.0375}$, *<0.05, ****<0.0001. PE phosphatidylethanolamine, PI phosphatidylinositol, PS phosphatidylserine, PG phosphatidylglycerol, SM sphingomyelin, PC phosphatidylcholine, PA phosphatidic acid, CL cardiolipin, Cer ceramide, LPE lysophosphatidylethanolamine, LPC lysophosphatidylcholine, LCL lysocardiolipin. Source data are provided as a Source Data file.
In mice, the effect of FasnKO on membrane lipids is likely obscured by a constant delivery of fatty acids from the circulation. This prompted us to examine the fatty acyl composition of the lipidome to determine if *Fasn is* contributing specific fatty acids to each lipid species. We found that Fasn deficiency consistently caused a shift in fatty acyl composition toward longer chain (>18 C) fatty acids in various lipid classes both in vitro (Fig. 5c) and in vivo (Fig. 5d), reflecting a reduction in palmitate (C16:0) synthesis. We also found that loss of Fasn results in shifts in double bond composition in vitro and in vivo, where FasnKO adipocytes contain fatty acyl chains with greater numbers of double bonds, which arise from essential fatty acids taken up from culture media or dietary intake (Supplementary Fig. 5A and B).
Phosphatidylinositols (PI) are critical for multiple steps of the autophagy pathway, from autophagosome formation to fusion with the lysosome31,32. Investigation of the specific phosphatidylinositol species altered by Fasn deficiency in vitro and in vivo showed that PI(16:0-18:2), PI(16:1-20:4), and PI(16:0-20:4) were significantly reduced and PI(18:0-20:4) was significantly increased (Fig. S5C) suggesting these particular PI species may play specific roles in autophagy. Collectively, these data show that loss of Fasn can significantly alter the cellular lipidome and can alter specific phospholipids both in vitro and in vivo.
## Lysosomal activity is impaired by Fasn deficiency
Lysosomal function is also exquisitely sensitive to perturbations in lipid composition32–34. Particularly, phosphoinositides and their phosphorylated derivatives regulate the biogenesis and maturation of lysosomes, including their fusion capacity with autophagosomes34–37. Since FasnKO showed effects on multiple adipocyte lipids, including PI, we sought to determine whether lysosomal function was also altered by Fasn inhibition or deletion. Lysosomal content is generally estimated by assessing the specific lysosomal membrane protein Lamp1 by western blotting. Shown in Fig. S6A, under nutrient replete conditions, Lamp1 is similar between control and cAdFasnKO adipocytes, despite increased p62 and LC3B. Similarly, inhibition of Fasn by TVB3664 does not affect Lamp1 levels (Supplementary Fig. 6B).
We next examined lysosomal activity using the Magic Red Cathepsin B substrate. Under normal fed conditions lysosomal activity is low and comparable between TVB3664- and vehicle-treated adipocytes (Supplementary Fig. 6C). Upon starvation, vehicle-treated adipocytes display a strong increase in lysosomal activity, indicated by increased fluorescence. However, TVB3664-treated adipocytes show almost no increase in fluorescence, indicating low lysosomal activity (Supplementary Fig. 6C). This defect appears to be at least partially the result of reduced mature Cathepsin B (CTSB) in TVB3664-treated adipocytes, while pro-CTSB appears unaffected (Supplementary Fig. 6D). Lysosomal proteases, such as CTSB, are synthesized as pro-proteins and transported to acidic endosomes then lysosomes, where they are cleaved into their mature form38. This difference only in mature CTSB suggests a defect in lysosomal maturation, and together, these data indicate that lysosomal function and membrane dynamics are impaired by Fasn deficiency.
## Fasn inhibition in non-adipocytes impairs autophagy
Fasn and other DNL enzymes are likely universally expressed across mammalian cell types. We thus asked whether inhibition of Fasn in a cell type other than adipocytes would have similar effects on autophagy. In cultured HepG2 cells treated with TVB3664, soluble p62 and lipidated LC3B are indeed significantly increased (Supplementary Fig. 7A–C), as is insoluble p62 (Supplementary Fig. 7D). In addition, an LC3 turnover assay revealed the LC3 degradation ratio is significantly reduced by Fasn inhibition, as found with FasnKO adipocytes, indicating autophagic degradation is compromised (Supplementary Fig. 7E). Notably, calculation of the LC3 synthesis ratio indicated autophagosome synthesis is also significantly reduced by Fasn inhibition (Supplementary Fig. 7F). This is in contrast to FasnKO adipocytes, which do not show a significant impairment in autophagosome synthesis (Fig. 1h). Consistent with our hypothesis, this suggests the large lipid content of adipocytes, even those that are DNL-deficient, can compensate, in part, for the lack of fatty acid synthesis during autophagosome synthesis. In non-adipocytes such as HepG2 cells, where lipid droplets are minimal, the effect of an inhibition of fatty acid synthesis on autophagosome synthesis becomes apparent in addition to the effect on the degradative pathway.
## Discussion
The major finding of this study is the identification of de novo lipogenesis as an essential source of lipids for autophagy in adipocytes. It has previously been appreciated that autophagosome synthesis is an extremely lipid-demanding process, yet the precise source(s) of these lipids has not been fully elucidated16,17. Data presented here demonstrate that endogenous de novo fatty acid synthesis via Fasn funnels fatty acids into autophagosomes and lysosomes, and that loss of this pathway disables autophagic degradation via disruption of autophagosome-lysosome fusion and lysosomal function. Bioenergetically, our findings are compatible with the concept that it is more favorable to synthesize new membranes than to reuse old membranes, which require extensive processing such as protein removal20. In addition, preferential usage of de novo lipogenesis for membrane lipids rather than depleting preexisting membranes or lipid droplets would appear to confer a benefit to cells, especially during periods of stress. Autophagy serves critical cytoprotective and hormetic roles in virtually all cell types. Thus, our findings answer a fundamental biological question with wide-ranging implications.
Adipose tissue function is paramount to overall health, and the importance of autophagy in adipose tissue function has recently come to light39,40. Excessive autophagy induced by adipocyte-selective deletion of the negative autophagy regulator, Rubicon, results in lipodystrophy, glucose intolerance, and fatty liver. Also, downregulation of Rubicon and the associated increase in autophagy has been linked to the age-dependent decline in adipose tissue and metabolic health41. Conversely, autophagy inhibition has been associated with improvements in metabolic health. Knockout of Atg7, an early autophagy factor, in adipose tissue blocked autophagy and led to reduced body fat, increased insulin sensitivity, and beiging of white adipose tissue42. In addition, several studies have shown that blocking mitophagy, a specialized form of autophagy, prevents the turnover, or “whitening,” of beige and brown adipose tissue, leading to beneficial metabolic effects43–45.
Interestingly, in addition to impaired autophagy, iAdFasnKO and cAdFasnKO mice also exhibit increased white adipose beiging (Fig. 4a, i) and improved glucose tolerance9,10. One of the more prominent effects of Fasn knockout on adipocytes was the substantial increase in p62 protein (Fig. 4a, i). In addition to its role in autophagy, p62 serves as a signaling hub that participates in multiple pathways, but most notably, p62 regulates beige and brown adipocyte formation. Knockout of p62 in adipose tissue results in obesity, glucose intolerance, and reduced energy expenditure, effects attributed to reductions in brown and beige adipose tissue thermogenesis46–48. Mechanistically, p62 directs phospho-ATF2 to promoters of thermogenic genes and also functions as co-activator of PPARγ46,48. It is tempting to speculate that FasnKO-mediated increases in adipose tissue p62 as a result of autophagy dysfunction regulate the beiging and metabolic improvements observed in these mice, but FasnKO in vitro does not upregulate Ucp1 under our experimental conditions. Thus, further investigation is required to determine whether the autophagy impairment in cAdFasnKO mice contributes to development of the beiging phenotype.
A notable finding from these studies was that FasnKO did not impair autophagosome formation in adipocytes, suggested by autophagy flux assays (Fig. 1h) and the presence of apparently fully formed autophagosomes in the electron micrographs (Fig. 1c). Lipid droplets and preexisting membranes have been shown to contribute to autophagosome membranes, and in the absence of de novo lipogenesis, it’s likely that multiple membrane lipid sources are utilized16,49. Moreover, even though cAdFasnKO adipocytes are largely lipid droplet-depleted, some lipid droplets remain and could serve as lipid sources for autophagosome membranes. Consistent with this hypothesis, inhibition of Fasn in HepG2 cells, a cell line with minimal lipid content, impairs autophagosome synthesis as well as degradation (Supplementary Fig. 7E and F). Current studies are aimed at more precisely identifying the role of Fasn in autophagy in non-adipocyte cell types where lipid droplets do not accumulate.
Despite the fact that autophagosomes can form in the absence of Fasn, our data strongly suggest that these autophagosomes are not fully functional, i.e., do not fuse with the lysosome for degradation. Autophagic membrane composition must be tightly regulated to ensure proper protein recruitment and membrane dynamics, including autophagosome-lysosome fusion events31,32,50–52. Our lipidomics analyses from both cultured adipocytes and adipose tissue showed that Fasn deficiency significantly alters cellular lipid composition, including membrane lipids (Fig. 5). In vitro, knockout of Fasn dramatically alters phospholipid levels, including total PI, PA, PG, PE, among others (Fig. 5a). Phosphatidylinositol, in particular, plays significant roles in autophagy. Its phosphorylated derivatives, PI3P, PI4P, PI[3,5]P2, serve as recognition and docking sites for autophagy effector proteins on both autophagosome and lysosomal membranes31,32. Accordingly, reductions in PI synthesis result in impaired autophagy turnover19. In addition, alterations to PA content have been shown to affect autophagic flux53. In vivo, however, these same changes to total phospholipids were not evident (Fig. 5b) likely due to compensation from uptake of fatty acids from the circulation, though individual species were significantly changed (Supplementary Fig. 5C). Nonetheless, since these lipidomics analyses were performed on whole cells and tissues, we cannot rule out significant alterations to phospholipid profiles at the subcellular level, i.e., in autophagosome or lysosome membranes, where these changes would be impactful. On the other hand, a common theme between the data obtained in Fasn-deficient adipocytes in both cultured cells and in mouse adipose tissue is the significant reduction in C16 and saturated acyl chains across most lipid species, reflecting the reduction in palmitate synthesis (Fig. 5c and Supplementary Fig. 5). Alterations to acyl chain length and saturation can have significant effects on membrane fluidity, thickness, and packing density, which can affect fusion with the lysosome20,51,54. Together, these data suggest de novo lipogenesis and Fasn equip the autophagosome membranes with a precise lipid composition that ensures their proper fusion, trafficking, and degradation.
In addition to autophagosome membranes, phospholipid composition is vital to the function and proper maturation of lysosomes32–34,37. During autophagy, lysosomes are degraded along with their cargo and must be repopulated to maintain cellular homeostasis. The processes of autophagic lysosome reformation and lysosome repopulation are essential for the maintenance of the lysosomal population and are critically dependent on phosphoinositides35,36. Our data showing defective lysosomal function in Fasn-deficient adipocytes (Supplementary Fig. 6) are consistent with the idea that altered lipid, and in particular, PI composition of lysosomal membranes impairs the proper maturation and repopulation of lysosomes.
A prominent finding in this study was that Fasn directly localizes near nascent autophagosomes (Fig. 3f, g). Supportive of this, our results revealed that fatty acids could not fully restore autophagic function of cultured cAdFasnKO adipocytes (Fig. 3a, b), and even fully lipid-replete cAdFasnKO adipose tissue exhibited autophagy impairment (Fig. 4f–h). These results indicate that Fasn may be acting locally to supply fatty acids to growing autophagosomes. Interestingly, Fasn was found to copurify with autophagosomes in a proteomics screen in human cancer cells55. In addition, our findings are in line with studies in yeast that showed the fatty acyl CoA synthetase, Faa1, directly localized on growing autophagosome membranes18. The phosphatidylinositol (PI) synthesizing enzyme, PI Synthase (PIS) was also shown to localize to sites of autophagosome biogenesis in mammalian cells19. The presence of Fasn and lipid synthesizing enzymes on nascent autophagosomes supports the conclusion that one of the primary functions of *Fasn is* to directly funnel fatty acids into autophagosome membranes.
In summary, our studies provide the first demonstration of a direct contribution of de novo fatty acid synthesis to autophagosome and lysosome membrane dynamics in mammalian cells. Our findings are in line with studies implicating the lipid synthesis enzymes Acc1 and Faa1 in yeast and the fatty acid desaturase, Scd1, in mammalian cells in autophagosome membrane synthesis18,21,56. These findings could have far-reaching therapeutic implications as inhibitors of de novo lipogenesis enzymes, particularly ACC and Fasn, are currently in development for the treatment of fatty liver and cancers57. Our studies identify an important new function of these enzymes that may impact the usage of these inhibitors. Moreover, dysregulation of autophagy has been implicated in various diseases58. Thus, identification of the de novo lipogenesis pathway as a critical component of autophagy opens new opportunities to modulate autophagy and disease progression.
## Mice
All mice were raised in standard housing conditions (12 h light/12 h dark cycle, 21–23 °C, 30-$35\%$ humidity) and fed a standard chow diet (Prolab IsoPro 3000 #5P76, LabDiet) ad libitum under approval by the University of Massachusetts Chan Medical School Institutional Animal Care Use Committee (IACUC). C57Bl/6 J (WT) mice were obtained from Jackson Laboratory and bred for primary preadipocyte isolation. FasnFlox/Flox mice were generated as previously described23 and bred with constitutively expressed Adiponectin-Cre59 (Jax stock #028020) mice to obtain adipocyte-specific Fasn knockout (KO) mice (cAdFasnKO). For inducible knockout of Fasn in adult mice, FasnFlox/Flox mice were bred with Adiponectin-Cre-ERT2 mice, as previously described10. To induce Fasn knockout, FasnFlox/Flox or FasnFlox/Flox; Adiponectin-Cre-ERT2+ mice (iAdFasnKO) were administered via i.p. injection 1 mg tamoxifen dissolved in corn oil once per day for 5 days. For high fat diet experiments, mice were fed a $60\%$ kcal from fat diet (Research Diets, D12492i) ad libitum for 4 weeks prior to tamoxifen administration, then remained on the diet for another 12 weeks. Male and female mice were used throughout the study.
## Primary preadipocyte isolation and differentiation
In total, 2–4 week old C57Bl/6 J (WT), FasnFlox/Flox or cAdFasnKO pups were euthanized and subcutaneous inguinal fat pads were dissected and placed in Hanks’ Balanced Salt Solution (HBSS). The fat pads were digested with 1.5 mg/ml Collagenase (Sigma #C6885) with $2\%$ BSA for 40 min at 37 °C with shaking. The digested solution was filtered through a 100μm filter, centrifuged, and the pellet resuspended in red blood cell lysis buffer. After 2–3 min, the solution was centrifuged again and the pellet resuspended in growth media consisting of DMEM/F12 with $10\%$ fetal bovine serum and $1\%$ (v/v) penicillin/streptomycin. The cells were filtered through a 40 μm filter and grown to confluency. Preadipocytes were used no later than passage 1 for these experiments. To induce differentiation (day 0), preadipocytes were grown to full confluency and cultured in induction medium containing growth media supplemented with 5 μg/mL insulin, 1 μM dexamethasone, 0.5 mM 3-isobutyl-1-methylxanthine, 60 μM indomethacin and 1 μM rosiglitazone. Forty-eight hours later the media was changed to Day 2 media consisting of growth media with 5 μg/mL insulin. The same media was replaced 48 h later (Day 4). Adipocytes were considered fully differentiated at Day 5 and were maintained in regular growth media thereafter. Adipocytes were harvested on Day 6 or Day 7. For fatty acid (FA) supplementation experiments, a 20X stock solution containing 4 mM sodium palmitate, 4 mM oleic acid, and $10\%$ fatty acid free BSA dissolved in DMEM/F12 was prepared. The FAs were conjugated to BSA by heating at 56 °C for ~1 h and the pH was adjusted to ~7.4. The FA mixture was added to the media at 1X concentration (200 μM sodium palmitate, 200 μM oleic acid, $0.5\%$ BSA) on Day 0, Day 2, or Day 5 of differentiation and replaced every other day.
For ACC inhibition experiments, WT primary preadipocytes were differentiated as described above. On day 5, 10 μM firsocostat/ND-630 (S8893, Selleck Chemicals) dissolved in DMSO was added for 48 h. In experiments with co-treatment with insulin, 1 μM insulin and 15 μM firsocostat were added for 24, 48, or 72 hours beginning on day 5.
For Fasn inhibition experiments, WT primary adipocytes were differentiated and treated with 100 nM TVB3664 (S8563, Selleck Chemicals) or vehicle for 48 hours starting on day 5. For proteostasis experiments, a subset of cells were treated with 20 μM MG132 for 18 hours prior to harvest.
## Lipogenesis assay
Measurement of lipogenesis in cultured cells were performed as described10,60. Briefly, differentiated FasnFl/Fl and cAdFasnKO primary adipocytes were incubated with labeling media containing $0.2\%$ FA-free BSA, 0.5 mM d-glucose, 2 mM sodium pyruvate, 2 mM glutamine and 0.8 mCi/mL 3H-[H2O]. 1 mM insulin was added to insulin-stimulated conditions. Adipocytes were incubated at 37 °C with $5\%$ CO2 for 4.5 hours before lipid extraction. 3H-[H2O] incorporation into fatty acids was then determined as previously described10,60,61.
## Autophagy flux Assay/LC3II turnover assay
Cultured adipocytes: For autophagy activation, cultured adipocytes were washed with PBS and incubated in HBSS for 4 h. For autophagy inhibition, adipocytes were cultured in growth media supplemented with 50 μM chloroquine (CQ) (Sigma #6628) for 4 hours. For the combination treatment, 50 μM CQ was added to HBSS and the cells were incubated for 4 h. Control cells were incubated in growth medium. The synthesis ratio, described in22 was calculated as the change in LC3II in HBSS vs. control or HBSS + CQ vs. CQ for each genotype. Similarly, the degradation ratio was calculated as the change in LC3II in the CQ vs. control or HBSS + CQ vs. HBSS condition.
Adipose Tissue Explants: Freshly dissected subcutaneous adipose tissue depots from individual FasnFlox/Flox or cAdFasnKO male mice were washed in PBS, minced into ~20 mg pieces, and divided into two culture dishes. One dish contained DMEM/F12 and the other contained DMEM/F12 with 50uM CQ, thus each mouse/fat pad was its own control. The explants were incubated at 37 °C with $5\%$ CO2 for 2 hours. After 2 hours, the explants were washed briefly in PBS and protein was isolated as described.
## HepG2 cells
HepG2 cells were grown in DMEM with $10\%$ fetal bovine serum and $1\%$ (v/v) penicillin/streptomycin. Cells were plated in 12-well plates at 2×105 cells/well and grown overnight. The next day, the cells were treated with 100 nM TVB3664 or vehicle and cultured for 48 hours. For the LC3II turnover assay, 18 hours prior to harvest, subsets of cells were treated with 5 μM Rapamycin and/or 50 μM CQ.
## Isolation of adipocytes vs. stromal vascular fraction (SVF)
Subcutaneous white adipose tissue fat pads were dissected and placed in Hanks’ Balanced Salt Solution. The fat pads were digested with 1.5 mg/ml Collagenase (Sigma #C6885) for 30 min at 37 °C with shaking. The digested solution was filtered through a 100 μm filter and centrifuged for 5 min at 500 g. The resulting top layer (fat cake) was transferred to a new tube and processed for western blotting as described below. The pellet (SVF) was resuspended in protein homogenization buffer and similarly processed for western blotting.
## Electron microscopy
FasnFlox/Flox and cAdFasnKO preadipocytes were differentiated and on day 7 were fixed by first removing half the plate media and then adding an equal volume of $2.5\%$ glutaraldehyde (v/v) in 1 M Na phosphate buffer (pH 7.2) for 10 min before being transferred to pure $2.5\%$ glutaraldehyde (v/v) in 1 M Na phosphate buffer (pH 7.2) for 1 hour. Next, the cell plates were briefly rinsed (3 × 10 min) in 1 M Na phosphate buffer (pH 7.2) and post-fixed for 1 hr in $1\%$ osmium tetroxide (w/v) in dH2O. Samples were then washed three times with dH2O for 10 mins and then cells were scraped off the bottom of the wells with a soft plastic spatula, collected in a microfuge tube, and pelleted by centrifugation. Samples were washed three times with dH2O for 10 min and dehydrated through a graded series of ethanol (10, 30, 50, 70, 85, $95\%$ for 20 min each) to three changes of $100\%$ ethanol. Samples were infiltrated first with two changes of $100\%$ Propylene Oxide and then with a $50\%$/$50\%$ propylene oxide/SPI-Pon 812 resin mixture overnight. The following morning the cell pellets were transferred through four changes of fresh SPI-pon 812-Araldite epoxy resin and finally embedded in tubes filled with the same resin and polymerized for 48 hr at 70 °C. The epoxy blocks were then trimmed, and ultrathin sections were cut on a Reichart-Jung ultramicrotome using a diamond knife. The sections were collected and mounted on copper support grids and contrasted with lead citrate and uranyl acetate. The samples were examined on a Philips CM 10 using 100 Kv accelerating voltage. Images were captured using a Gatan TEM CCD camera.
## Real-time quantitative PCR
RNA was isolated from cultured adipocytes or tissue with Trizol following the manufacturer’s instructions. cDNA was synthesized from 1μg RNA using Bio-Rad iScript cDNA kit. qPCR was performed using Bio-Rad iTaq SYBR Green Supermix on a BioRad CFX97 thermocycler and analyzed using the ΔΔCt method. For cultured adipocytes, 18s was used for normalization, and for tissues, the average of 18s and B2m was used for normalization. Primer sequences are listed in Supplementary Table 1.
## Western blotting
Cultured adipocytes or HepG2 cells were harvested in 50 mM Tris-HCl, pH 7.6, 150 mM NaCl, 5 mM EDTA, $1\%$ Triton X-100 supplemented with Halt protease and phosphatase inhibitors (ThermoScientific). The homogenate was spun at 6000 × g for 15 min at 4 °C. The resulting supernatant was considered the “Triton X-100 soluble fraction,” and the pellet considered the “Triton X-100 insoluble fraction.” To solubilize the pellet, 2.5X Laemmli buffer with β-mercaptoethanol was added. For adipose tissue, fat pads or explants were suspended in 50 mM Tris-HCl, pH 7.6, 150 mM NaCl, 5 mM EDTA with Halt protease and phosphatase inhibitors and homogenized with the Qiagen TissueLyser. Homogenates were spun at 6000 × g for 15 min at 4 °C and the resulting infranatant, between the pellet and overlying fat cake was collected. Protein concentrations were determined by BCA (Pierce). Proteins were prepared for electrophoresis by adding Laemmli buffer with β-mercaptoethanol and heating to ~90 °C for 10 min. Proteins were resolved by SDS-PAGE and blotted with the following antibodies: anti-Fasn (1:1000, CST #3180), anti-Ucp1 (1:1000, Abcam #10983), anti-p62 (1:1000, CST #23214), anti-p62 (1:1000, R&D #MAB8028), anti-alpha-tubulin (1:1000, Sigma #T5168), anti-LC3B (1:1000, CST #83506), anti-LC3A/B (1:1000, CST #12741), anti-Gapdh (1:1000, CST #8884), anti-malonylated lysines (1:1000, PTM Biolabs #901), anti-vinculin (1:1000, CST #18799), anti-Gabarap (1:1000, CST #13733), anti-ubiquitin (1:1000, Proteintech #10201-2-AP), anti-Lamp1 (1:1000, BD Biosciences #553792), anti-Cathepsin B (1:1000, Proteintech #12216-1-AP), anti-β-actin (1:1000, Sigma #A5316).
## Histology
Freshly dissected adipose tissue pieces were fixed in $4\%$ paraformaldehyde overnight and embedded in paraffin. Sections were stained with H&E and anti-p62 (1:500, CST #23214) at the UMass Chan Medical School Morphology Core. Images were taken with a Leica DM2500 LED microscope equipped with a Leica MC170 HD camera.
## Immunofluorescence
Preadipocytes were grown on glass coverslips and differentiated as described. On day 6 or 7, differentiated adipocytes were fixed in $4\%$ paraformaldehyde for 10 min at room temperature, washed, and permeabilized with ice-cold $100\%$ methanol for 10 min. The cells were incubated with blocking solution containing $2\%$ normal goal serum, $1\%$ BSA, $0.1\%$ Triton X-100, and $0.05\%$ Tween 20. Primary antibodies were diluted in blocking solution and incubated overnight at 4 °C. Secondary antibodies were diluted in blocking solution and incubated at room temperature for 1 hour. Nuclei were stained with 1 µg/ml DAPI (Invitrogen), and coverslips were mounted on slides with Prolong Glass mountant (Invitrogen).
Images were taken with a Leica TCS SP8 confocal microscope. Colocalization analysis was performed with the JACoP plugin in ImageJ. Antibodies: rabbit anti-Fasn (5 ug/mL, Abcam #ab22759); rabbit anti-p62 (1:500, CST #23214); mouse anti-p62 (8 μg/mL, R&D #MAB8028); rabbit anti-LC3A/B (1:200, CST #12741); mouse anti-LC3B (1:200, CST #83506); Alexa Fluor-594 goat anti-rabbit (1:500) and Alexa Fluor-488 goat anti-mouse (1:500, Invitrogen).
For the Magic Red Cathepsin B assay, differentiated WT adipocytes were grown on coverslips and treated with 100 nM TVB3664 or vehicle. After 48 hours, the adipocytes were either fed with regular growth media or starved with HBSS and loaded with Magic Red Cathepsin B substrate (#937, Immunochemistry Technologies) for ~40 min according to the manufacturer’s instructions. The adipocytes were washed and fixed in $4\%$ paraformaldehyde for 15 min. Nuclei were stained with DAPI, and coverslips were mounted on slides with Prolong Glass mountant, as described above. Images were taken with the Leica TCS SP8 confocal microscope.
## Lipidomics
Lipid species were analyzed using multidimensional mass spectrometry-based shotgun lipidomic analysis62. In brief, homogenates of adipocytic samples containing 0.2 mg of protein as determined by the Pierce BCA assay were accurately transferred to disposable glass culture test tubes. A pre-mixture of lipid internal standards (IS) was added prior to conducting lipid extraction for quantification of the targeted lipid species. Lipid extraction was performed using a modified Bligh and Dyer procedure63, and each lipid extract was reconstituted in chloroform:methanol (1:1, v-v) at a volume of 500 µL/mg protein.
For shotgun lipidomics, individual lipid extract was further diluted to a final concentration of ~500 fmol total lipids per µL. Mass spectrometric analysis was performed on a triple quadrupole mass spectrometer (TSQ Altis, Thermo Fisher Scientific, San Jose, CA) and a Q Exactive mass spectrometer (Thermo Scientific, San Jose, CA), both of which were equipped with an automated nanospray device (TriVersa NanoMate, Advion Bioscience Ltd., Ithaca, NY) as described64. Full and tandem MS scans were automatically acquired by a customized sequence subroutine operated under Xcalibur software (Thermo Fisher 4.2.47). Identification and quantification of lipid species were performed using an automated software program65,66. Data processing (e.g., ion peak selection, baseline correction, data transfer, peak intensity comparison and quantitation) was performed as described66. The results were normalized to the protein content (nmol or pmol lipid/mg protein). The full lipidomics dataset can be found in Supplementary Data 1.
## Acetyl-CoA and malonyl-CoA measurements
Malonyl- and acetyl-CoA were extracted with 0.3 M perchloric acid and analyzed by LC-MS/MS using a method based on a previously published report by67. The extracts were spiked with 13C2-Acetyl-CoA (Sigma, MO, USA), centrifuged, and filtered through the Millipore Ultrafree-MC 0.1 µm centrifugal filters before being injected onto the Chromolith FastGradient RP-18e HPLC column, 50 × 2 mm (EMD Millipore) and analyzed on a Waters Xevo TQ-S triple quadrupole mass spectrometer coupled to a Waters Acquity UPLC system (Waters, Milford, MA).
## Proteasome activity assay
Proteasome activity was measured using the Proteasome Activity Fluorometric Assay Kit II (#J4120) from UBPBio, according to the manufacturer’s instructions. Briefly, lysates from female FasnFl/Fl and cAdFasnKO subcutaneous adipose tissues were prepared in buffer containing 50 mM Tris-HCl, pH 7.6, 150 mM NaCl, 5 mM EDTA. 30 μg lysates were used for the assay. Samples were run in duplicate for each substrate with and without MG132 proteasome inhibition. Data presented are the rates without MG132 minus the MG132-inhibited rate.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37016-8.
## Source data
Source Data
## Peer review information
Nature Communications thanks David James, Meilian Liu and Huiyong Yin for their contribution to the peer review of this work. Peer reviewer reports are available.
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title: Renal toxicity and biokinetics models after repeated uranium instillation
authors:
- Laurie De Castro
- Annabelle Manoury
- Olivier Claude
- Bastien Simoneau
- Virginie Monceau
- David Suhard
- Christelle Elie
- Victor Magneron
- Laurence Roy
- Céline Bouvier-Capely
- Chrystelle Ibanez
- Estelle Davesne
- Yann Guéguen
journal: Scientific Reports
year: 2023
pmcid: PMC10011524
doi: 10.1038/s41598-023-31073-1
license: CC BY 4.0
---
# Renal toxicity and biokinetics models after repeated uranium instillation
## Abstract
During nuclear fuel processing, workers can potentially be exposed to repeated inhalations of uranium compounds. Uranium nephrotoxicity is well documented after acute uranium intake, but it is controversial after long-term or protracted exposure. This study aims to analyze the nephrotoxicity threshold after repeated uranium exposure through upper airways and to investigate the resulting uranium biokinetics in comparison to reference models. Mice (C57BL/6J) were exposed to uranyl nitrate (0.03–3 mg/kg/day) via intranasal instillation four times a week for two weeks. Concentrations of uranium in urines and tissues were measured at regular time points (from day 1 to 91 post-exposure). At each exposure level, the amount of uranium retained in organs/tissues (kidney, lung, bone, nasal compartment, carcass) and excreta (urine, feces) reflected the two consecutive weeks of instillation except for renal uranium retention for the highest uranium dose. Nephrotoxicity biomarkers, KIM-1, clusterin and osteopontin, are induced from day 4 to day 21 and associated with changes in renal function (arterial fluxes) measured using non-invasive functional imaging (Doppler-ultrasonography) and confirmed by renal histopathological analysis. These results suggest that specific biokinetic models should be developed to consider altered uranium excretion and retention in kidney due to nephrotoxicity. The threshold is between 0.25 and 1 mg/kg/day after repeated exposure to uranium via upper airways.
## Introduction
The biodistribution and health effects of uranium depend on the speciation of uranium compounds, the isotopic composition, duration and route of exposure1–3. Among known nephrotoxic agents, uranium is a radio element with known chemical (as a heavy metal) and radiological (α-emitting radionuclide) toxicities, which accumulates preferentially in the kidneys and more specifically in proximal convoluted tubules causing kidney toxicity at high dose. Abundant existing literature, including our previous studies, showed that acute uranium exposure induces renal tubular damage associated with kidney function impairment in animals4–8 and humans9–11. But, the level of uranium induced nephrotoxicity is controversial after chronic or protracted exposure, particularly after inhalation12–15. Repeated inhalation of uranium compounds can occur in several situations including nuclear fuel processing for workers, military activities using depleted uranium munitions, and non-war situations such as crashed aircraft16,17.
The kidney is a key organ in maintaining ion and fluid homeostasis, eliminating metabolic degradation products, detoxification by elimination of xenobiotics, and the biosynthesis of some hormones. Renal dysfunction can therefore lead to severe disorders that cause the individual's general health status to deteriorate due to renal failure that can lead to death. Most damage caused by xeno-induced renal disease affects the proximal tubular epithelial cells (PTECs)18, an area of maximum transport, secretory, and metabolic activity. They are the most sensitive target cells affected by uranium toxicity as reported by experimental studies showing oxidative stress, inflammation, DNA damage, and induced cell death. This alteration to the molecular pathway induces histological and tubular biomarkers2,15,19–21 including the transmembrane protein Kidney Injury molecule-1 (KIM-1), a sensitive biomarker of proximal convoluted tubule injury4,22.
It is essential to correlate knowledge of how a toxic element like uranium is retained and excreted with biological effects. A biokinetic model after occupational uranium intake has recently been updated by the International Commission on Radiological Protection (ICRP)23. However most experimental data either consider acute inhalation, chronic ingestion via drinking water, or wound contamination24–26. Repeated acute intake is assumed in order to evaluate uranium kidney concentration after protracted inhalation, but few experimental data are available to support this assumption27,28. The procedure for the single or repeated intra-nasal instillation of chemicals in rodents is well described in the literature for modeling upper airway exposure29–31, including uranium exposure32.
This study aims to define the dose–response relationship of kidney impairment due to uranium induced nephrotoxicity in an exposure model representative of occupational or military exposure to uranium via inhalation. The objective of this work is therefore to verify [1] if the uranium biokinetic model developed from data gathered after acute inhalation is consistent with data obtained after repeated exposure of animals via intra-nasal instillations, [2] if nephrotoxicity modifies uranium retention and excretion [3] if the nephrotoxicity threshold can be predicted by the models.
C57BL/6J mice were exposed to the repeated instillation of uranium in a dose–response study (0.25, 1 or 3 mg/kg/day) and groups of animals were euthanized at regular time points (2, 4, 7, 11, 21 and 91 days after the first treatment). Uranium biokinetics were monitored in the kidneys, lungs, bones, urine, feces, nasal compartments, gastrointestinal tract, and the remaining carcass. Nephrotoxicity was evaluated by (i) high resolution ultrasonography (US) with Doppler mode to identify morphological and functional changes, (ii) renal anatomopathological scoring of lesions induced by uranium exposure, (iii) biomarker measurements in urines or renal tissues by KIM-1 ELISA, immunostaining or gene expression RT-PCR assay.
## Uranium biokinetics
Specific groups of mice were used to evaluate the biokinetics of uranium on days 2, 4, 7, 11, 21 and 91 after the first treatment day. The renal, lung, bone femur and carcass retention of uranium and urine and feces excretion of uranium were measured by Inductively Coupled Plasma—Mass Spectrometry (ICP-MS) analysis to determine the biokinetics of uranium (Figs. 1 and 2).Figure 1Uranium content in lungs, bones, kidneys, urine, feces and remaining carcass after repeated intranasal instillation (0.25–3 mg/kg/day) from Day 2 to Day 91 after the first instillation. Uranium concentration was evaluated in the tissue by ICP-MS and expressed in ng/g tissue wet weight. The values are expressed as mean ± SD. NE non-exposed. * $p \leq 0.05$/**$p \leq 0.01$/***$p \leq 0.001$, comparison with unexposed animals, Holm-Sidak test. Figure 2Uranium tissue content of mice exposed to intranasal instillation. ( a) Uranium retention in lungs, kidneys, gastrointestinal tract, and nasal compartments 1 h after the first instillation to uranium of 0.25 mg/kg. Each point represents an independent animal, and the square represents the SAAM II (Simulation, analysis, and modeling software for tracer and pharmacokinetic studies) biodosimetric model, $$n = 4$.$ GIT gastro-intestinal tractus, NC nasal compartment. ( b) Biodosimetric model of uranium content in the kidneys after repeated intranasal instillations (0.03–3 mg/kg/day) according to the quantity of uranium administered to mice. Each point represents an independent animal; $$n = 4$$–12 for each time and dose.
The initial dose incorporated by mice after the first instillation was measured 1 h after exposure by collecting tissues from the kidneys, lungs, gastrointestinal tracts, nasal compartment, and remaining carcasses (Fig. 2a). High uranium retention was recorded in the nasal compartment (NC, 4624 ng/g of tissue), 20-fold more than in control animals ($p \leq 0.001$), whereas the lung compartment only retains 21 ng/g at this time (tenfold more than the control group, $p \leq 0.001$). Gastro-intestinal tract (GIT) content reaches 1388 ng/g but represents only 1.6-fold the control group uranium content. From 1 h after exposure, uranium reaches the kidneys with no difference between the right and left kidneys (77 vs 78 ng/g) at a level 13-fold higher than control animals ($p \leq 0.001$) (data not shown).
Overall, a dose-dependent accumulation ($p \leq 0.001$ between each animal group per dose) is observed with a double peak of tissue or excreta (urine, feces) content corresponding to the two weeks of uranium treatment for all the tissues (kidneys, lungs, bones, carcasses) analyzed for uranium content (Fig. 1).
The pulmonary biokinetics of uranium are consistent with the established ICRP model for inhalation33. Pulmonary passage is proportional to the dose administered by repeated instillations, but a smaller proportion reaches the lung than expected (Fig. 1a). Uranium accumulation peaks are observed at D4 and D11, 24 h after the first series of exposure and 24 h after the second series of exposure respectively. A significant uranium level is detected in the lungs of mice 91 days after first exposure for doses 0.25 and 3 mg/kg/day ($p \leq 0.001$, Control vs uranium exposed), although it decreases by a factor of 10 ($p \leq 0.05$) between day 11 and day 91 for mice exposed to the highest uranium concentration (3 mg/kg/day), and by a factor of 5 for the group exposed to 0.25 mg/kg/day.
Renal retention is also proportional to the uranium dose administered for doses between 0.03 and 0.25 mg/kg/day (Fig. 2b), whereas the level of uranium in the tissue increases exponentially ($p \leq 0.001$) for doses of 1 to 3 mg/kg/day (Fig. 1c). The renal retention of uranium is thus modified as a function of the dose administered ($p \leq 0.001$ between D2 and D4, D4 or D7 and D11, D21, D91), probably influenced by the nephrotoxicity of uranium. Two accumulation peaks (D4 and D11) are observed for concentrations up to 1 mg/kg/day whereas only one peak on day 4 is observed for animals exposed to 3 mg/kg/day. The decrease in uranium concentration between the two peaks of uranium accumulation is less marked for 1 mg/kg/day than for 0.25 mg/kg/day. Retention is observed over the longer term (D91) as shown on Fig. 1c: kidney uranium content remains around 0.1 µg/g at D91 for the lowest uranium exposed group ($p \leq 0.001$). The level of uranium for the group exposed to the highest dose also decreases 7-times from D4 to D91 ($p \leq 0.001$), but remains at around 10 µg/g of kidney weight (a renal concentration known to be generally nephrotoxic). The data acquired over several experimental procedures were grouped together in the same data set and normalized with respect to the uranium administered dose for the renal retention model shown in Fig. 2b. Renal retention is proportional to the dose administered up to 1 mg/kg/day with a good superposition of the normalized level and retention increases exponentially for the highest uranium dose. This confirms that the renal retention of uranium is probably altered at high doses due to renal impairment.
Uranium retention in bone and whole body (carcass) varies in proportion to the uranium dose administered. The first accumulation peak was observed on D4 and the second on D11 (Fig. 1b,d). Interestingly, bone retention of uranium on D91 is higher than in other tissues. For the uranium exposed groups, the bone uranium content is diminished between D11 and D91 by 3.8-fold ($p \leq 0.001$) for the group exposed to 3 mg/kg/day and threefold for the group exposed to 0.25 mg/kg/day ($p \leq 0.001$) respectively. The remaining carcass uranium level is similar to bone uranium content: uranium concentration increases significantly ($p \leq 0.001$) from D2 to D91 for mice exposed to 1–3 mg/kg/day compared to non-exposed animals whereas the augmentation is only significantly increased ($p \leq 0.05$ for D7, $p \leq 0.001$ for D2, D4, D11) from D2 to D11 for animals exposed to 0.25 mg/kg/day compared to non-exposed animals.
Finally, the excretion of uranium in the feces and urine reflects tissue content with an increase in the quantity of uranium during the 2 exposure periods. A significant increase in urinary level is observed from D2 to D91 for the group exposed to 3 mg/kg/day ($p \leq 0.05$), from D2 to D11 for the group exposed to 1 mg/kg/day and on D2, 4 and 11 for the lowest dose ($p \leq 0.05$).The fecal uranium level is significantly higher than the controls on D2, D4 and D11 for animals exposed to 1 mg/kg/day ($p \leq 0.05$) and 3 mg/kg/day ($p \leq 0.001$).
The level of uranium was quantified by ICP-MS (iCAP Q, Thermo Fisher Scientific) in the kidneys, lungs, bone femurs, urine, feces, gastrointestinal tracts, nasal compartment, and carcasses. Beforehand, carcasses and feces were ashed at 500 °C. The organs were mineralized in nitric acid $69\%$ and hydrogen peroxide $30\%$. The organs were mineralized using a microwave digestion furnace (Ethos One®, Milestone). The samples were then evaporated until dry and dissolved in nitric acid $20\%$. After appropriate dilution, uranium was quantified with bismuth as an internal standard and a uranium external calibration curve. The detection limit of uranium was determined by ICP-MS: 0.5 ng/L for 238U and 0.01 ng/L for 235U.
## Clinical follow-up
Body weight, urine volume, feces weight, and creatinine are monitored at each time point (D2, 4, 7, 11, 21 and 91) as shown in Table 1. Regular monitoring of the animals revealed time- and dose-dependent weight loss. Body weight does not vary significantly for the control group and the group exposed to 0.25 mg/kg/day between D0 and D91. Between D2 and D11, the body weight of animals exposed to 1 mg/kg/day decreased by up to $10\%$ compared to control non-exposed mice ($p \leq 0.01$). The body weight loss is more pronounced (up to $16\%$) for those exposed to 3 mg/kg/day ($p \leq 0.001$) compared to unexposed mice and occurred also from D2 to D11, the 2 weeks of repeated exposure to uranium (Table 1). From D21, body weight is similar for all groups of exposed or non-exposed mice, which return to a non-pathological body weight. Table 1Body weight and weight gained or lost since day 1 of exposure to uranium. Dose (mg/kg/day)Days post instillations0247112191NEAverage weight (g)24.4 ± 1.524.1 ± 1.824.2 ± 1.424.6 ± 1.524.3 ± 1.223.7 ± 2.229.8 ± $2.1\%$ Gain/lost since day 010098.5 ± 3.599.1 ± 1.6100.7 ± 2.698.3 ± 4.398.0 ± 3.8120.5 ± 4.9Number (n)2823111581280.25Average weight (g)24.0 ± 1.223.6 ± 1.423.4 ± 1.123.9 ± 1.023.2 ± 1.124.6 ± 0.928.1 ± $1.8\%$ Gain/lost since day 010098.1 ± 3.697.6 ± 1.998.4 ± 2.397.5 ± 2.397.8 ± 2.5118.3 ± 6.6Number (n)2824111581281Average weight (g)24.2 ± 1.422.7 ± 1.422.9 ± 0.522.9 ± 0.823.0 ± 0.723.1 ± 1.1ND% Gain/lost since day 010093.9 ± 2.7 ***90.4 ± 2.4**94.4 ± 3.9***90.3 ± 2.2***100.3 ± 4.2Number (n)201648443Average weight (g)24.3 ± 1.521.7 ± 1.620.2 ± 1.520.9 ± 2.321.1 ± 1.923.8 ± 1.727.9 ± $2.5\%$ Gain/lost since day 010088.7 ± 3.5***83.6 ± 2.4***86.7 ± 3.6***87.0 ± 4.6***93.5 ± 1.2115.0 ± 7.7Number (n)282412168128The percentage of weight gain or loss is calculated for each animal and is related to its initial weight at D0. The number of animals decreases overall over time due to the successive animals’ euthanasia apart from the occasional absence of weighting due to technical constraints. NE non-exposed, uranium doses = 0.25, 1 or 3 mg/kg/day. Days = Time in days since the first day of exposure to uranium. * $p \leq 0.05$/**$p \leq 0.01$/***$p \leq 0.001$, comparison with non-exposed animals, Two-way ANOVA.
Diuresis is increased almost 4 times in animals treated with 3 mg/kg/day compared to unexposed animals on D7 ($p \leq 0.01$) while the amount of feces decreases 2 to 5 times between D4 and D11 for the same dose of uranium ($p \leq 0.05$) (Table 2). The amounts of diuresis and feces content do not differ for animals exposed to 1 mg/kg/day or lower compared to the control group. Glomerular filtration rate (GFR) does not vary significantly as a function of uranium dose. Nevertheless, a significant increase in GFR (threefold, $p \leq 0.05$) is observed between D4 and D7 for animals exposed to 3 mg/kg/day of uranium. Table 2Measurement of urine volumes, feces and glomerular filtration rate after uranium exposure. Dose (mg/kg/day)Days post instillations247112191NEDiuresis (mL)0.83 ± 0.360.84 ± 0.290.83 ± 0.220.62 ± 0.350.68 ± 0.440.99 ± 0.55GFR (mL/min/kg)2.9E−6 ± 1.4E−62.9E−6 ± 1.0E−62.8E−6 ± 0.2E−62.6E−6 ± 1.5E−61.7E−6 ± 1.1E−62.1E−6 ± 0.7E−6Feces (g)1.07 ± 0.271.15 ± 0.331.29 ± 0.150.94 ± 0.230.90 ± 0.380.75 ± 0.36Number (n)4443480.25Diuresis (mL)0.59 ± 0.360.87 ± 0.170.98 ± 0.160.74 ± 0.300.58 ± 0.260.83 ± 0.28GFR (mL/min/kg)2.4E−6 ± 0.8E−63.9E−6 ± 1.4E−62.9E−6 ± 0.7E−63.7E−6 ± 2.1E−62.4E−6 ± 0.9E−62.8E−6 ± 0.7E−6Feces (g)1.13 ± 0.281.23 ± 0.261.29 ± 0.051.15 ± 0.181.23 ± 0.160.49 ± 0.18Number (n)4443481Diuresis (mL)0.63 ± 0.250.76 ± 0.271.11 ± 0.490.51 ± 0.380.65 ± 0.23NDGFR (mL/min/kg)2.4E−6 ± 0.4E−62.9E−6 ± 1.7E−62.0E−6 ± 1.4E−61.2E−6 ± 0.4E−62.6E−6 ± 0.6E−6Feces (g)1.09 ± 0.220.73 ± 0.541.14 ± 0.260.87 ± 0.191.22 ± 0.35NDNumber (n)444443Diuresis (mL)0.53 ± 0.500.96 ± 0.701.95 ± 0.57 **1.88 ± 1.411.03 ± 0.491.39 ± 0.81GFR (mL/min/kg)0.9E−6 ± 0.8E−60.8E−6 ± 0.4E−62.6E−6 ± 2.0E−61.1E−6 ± 0.3E−62.4E−6 ± 0.7E−63.7E−6 ± 2.3E−6Feces (g)0.36 ± 0.42 *0.17 ± 0.05 *0.61 ± 0.33 **0.26 ± 0.18 **1.17 ± 0.210.74 ± 0.29Number (n)444448NE non-exposed, GFR glomerular filtration rate*$P \leq 0.05$/**$P \leq 0.01$/***$P \leq 0.001$, comparison with unexposed animals, Two-way ANOVA.
## Doppler-ultrasonography (Doppler-US)
Mice underwent a Doppler-US examination on D0, D4, D11 and D91 to evaluate morphological and functional changes to the kidneys. During the follow-up by Doppler-US, morphological changes were assessed: the left and right kidneys were screened for any change in kidney tissue echogenicity or kidney length (Fig. 3a). The kidneys did not show any visible morphological changes nor visible lesions (hypoechoic cysts or hyperechoic fibrotic scars) regardless of the analysis period. Intrarenal arteries are visible in color mode and vascular parameters are obtained from at least 3 different peaks from the expiration phase with a heart rate of 400–500 BPM (Fig. 3b,c) Vascular parameters are monitored and the PI and RI calculated (Fig. 3d,e respectively) are similarly altered as a function of uranium exposure dose and time. A time-dependent change is observed independently of the dose for PI and RI levels (not shown): PI and RI are significantly altered ($p \leq 0.001$) between D0 (before exposure) and D4 or D11 after the beginning of the experiment (corresponding to the final days of exposure on each week). Moreover, dose-dependent changes are apparent on D4: PI decreased by $30\%$ ($p \leq 0.001$) for the highest dose (3 mg/kg/day) compared to the control non-exposed group and a return to physiological level is observed on D91. Similarly, the lower dose (0.25 mg/kg/day) induces a decrease of $20\%$ ($p \leq 0.01$) on D11 compared to D0 and a return to physiological values on D91. This could highlight the cumulative effect of repeated exposure to uranium: 8 times exposure to 0.25 mg/kg/day results in a slight change in PI and RI on D11, whereas 4 doses of 3 mg/kg/day induce greater changes in PI and RI, as early as D4.Figure 3Renal morphological and functional follow-up by ultrasound. A-C: Representative ultrasound images of the left kidney in mice exposed to uranium at 3 mg/kg/day for 91 days. B-mode (a), Color-mode (b) and corresponding Pulse-Wave doppler of the intrarenal artery (c) on Vevo-Lab® software (V5.6.1). D-E: Resistive index (RI) (d) and Pulsatile index (PI) (e) levels of intrarenal arteries obtained for mice before exposure (green), during exposure (D4), just after exposure (D11) and 3 months after exposure (D91) at 0.25 or 3 mg/kg/day. Each point represents an independent animal. The pre-instillation group (D0) includes all animals that were monitored for 4, 11 or 91 days after the first instillation to uranium or the vehicle solution. The non-exposed group (NE) received the vehicle solution (sodium bicarbonate) at the same time point as uranium exposed animals. $$n = 8$$ for each time and dose. The values are expressed as mean ± SEM. * $P \leq 0.05$/**$P \leq 0.01$/***$P \leq 0.001$, comparison with unexposed animals, Holm-Sidak test.
## Renal anatomopathology
Representative microphotographs of renal lesions induced by repeated uranium exposure in mice are shown in Fig. 4a–e. Global scoring of the damage induced in the kidney, and the extent of this damage were quantified. No lesions were observed for animals exposed to the lowest uranium concentration and control groups over any observation period. Seven and eleven days after the first treatment at 1 mg/kg/day, global damage had significantly increased in mice kidneys compared to control animals (Fig. 4f), resulting in the transient appearance of tubular necrosis and tubular regeneration/dilatation respectively (Fig. 4g). Finally, the total damage score is higher (2–threefold) for the 3 mg/kg/day group. Significant structural damage was observed on D7, D11 and D91 ($p \leq 0.05$) and non-significant damage on D4 ($$p \leq 0.06$$) and D21 ($$p \leq 0.07$$) (Fig. 4f). In the short term after first exposure, necrosis and tubular dilatation were induced while beyond D21, glomerulosclerosis, fibrosis and interstitial inflammation were observed (Fig. 4h). The extent and type of renal lesions induced by uranium is dose and time dependent. Figure 4Assessment of renal damage by histopathological examination after exposure to uranium (0.25–3 mg/kg/day) by intranasal instillation. ( a–e) Kidney longitudinal section microphotographs (200×) representative of damage observed after HES staining, scale bar = 100 µm. ( a) Normal kidney. ( b) Glomerulosclerosis and interstitial fibrosis. ( c) Interstitial inflammation. ( d) Tubular regeneration. ( e) Tubular necrosis. ( f) Total damage score (all criteria combined) according to exposure dose and time since first instillation. Each point represents an independent animal. ( g,h) Percentage distribution of the different impairments for animals exposed to 1 mg/kg/day (g) or 3 mg/kg/day (h). The values are expressed as mean ± SD, $$n = 3$$–4 for each time and dose. * $P \leq 0.05$, comparison with unexposed animals, Two-way ANOVA.
## Urinary and renal protein KIM-1 levels
KIM-1, a known damage marker for proximal convoluted tubules, was measured in kidneys and in urine using different methods. ELISA urine testing shows no variation over time for control animals or exposed to 0.25 mg/kg/day of uranium. A significant increase in KIM-1 level was observed on D7 (2.5-fold, $p \leq 0.05$) and a non-significant increase on D11 (sevenfold, $$p \leq 0.057$$) for the 1 mg/kg/day group (Fig. 5a). For the 3 mg/kg/day animals, a significant tenfold increase occurred on D4 ($p \leq 0.05$), a peak was recorded on D7 (30-fold, $$p \leq 0.057$$) followed by the beginning of decrease in level which remained significant on D21 (sixfold, $p \leq 0.05$). KIM-1 content in renal tissue was measured by IHC on longitudinal kidney sections. Ten images per section were scored by semi-quantification from 0 to 4 according to the number of tubules labelled, the extent and the intensity of staining (Fig. 5c–f). For animals exposed to uranium doses lower than 1 mg/kg/day, the evaluated tubules were marked at 0 or 1. This immunostaining showed a persistent increase in KIM-1 expression from D7 to D91 for animals exposed to 3 mg/kg/day of uranium compared to mice exposed to lower doses (Fig. 5b).Figure 5Urine assay and immunostaining for KIM-1 after 0.25–3 mg/kg/day uranium exposure. Urine is collected by passing through a metabolic cage 16 h before the euthanasia of the animals. The kidneys are collected after the euthanasia. KIM-1 expression is measured in urine using ELISA (a) and by immunohistochemistry on a longitudinal kidney section (b–f). ( b) Mean score over 10 fields according to exposure dose and time since first instillation. ( e,f) Representative microphotographs corresponding to each score from 0 to 4, scale bar = 100 µm. NE non-exposed group. The values are expressed as mean ± SD, $$n = 3$$–4 for each time and dose. * $P \leq 0.05$/**$P \leq 0.01$/***$P \leq 0.001$, comparison with unexposed animals, Two-way ANOVA.
## Renal gene expression of nephrotoxicity markers
Exposure to uranium significantly altered the expression of KIM-1 over time ($p \leq 0.05$) for doses higher than 1 mg/kg/day between D2 and D4 (Fig. 6). Similarly, from D11 to D91, KIM-1 mRNA level returned to a basal level of expression. The dose-dependent effect is noticeable from D2, for animals exposed to the highest dose (5 to 275-fold, $p \leq 0.05$), and from D4 to D11 for the 1 mg/kg/day dose (2 to 34-fold, $p \leq 0.01$). No significant differences were observed for lipocalin-2 (NGAL), a very early diagnostic marker of proximal convoluted tubule injury, regardless of exposure dose or time. Osteopontin (OPN) is naturally expressed in renal tissue but may increase in case of glomerulonephritis or tubulointerstitial nephritis. A significant decrease (threefold, $p \leq 0.05$) in its expression was recorded on D4 for animals exposed to 0.25 mg/kg/day of uranium. A time-dependent effect was noticeable for the highest dose between D11 and D91 ($p \leq 0.05$). Between D4 and 21, a significant increase in the expression of OPN compared with unexposed animals was detected in mice treated with 3 mg/kg/day ($p \leq 0.01$). Conversely, a decrease equal to half of the kallikrein (KLK) expression, a marker that decreases in case of renal injury caused by a nephrotoxic agent, was visible at D7 for this same dose. Its expression decreased significantly between D2 and D7 and then returned to the basal expression level from D7 to D91 ($p \leq 0.05$). After exposure to 3 mg/kg/day of uranium, β2 microglobulin (B2M) and cystatin C (CST) expression ware doubled on D91 compared to unexposed animals ($p \leq 0.05$). Both markers increase in case of tubular and glomerular dysfunction or damage. Clusterin (CLU) is an early marker of acute tubular damage, whose expression was decreased (threefold) after treatment with 0.25 mg/kg/day of uranium on D4, whereas it was increased (2 to fivefold) from D4 to D21 with a peak of expression on D7 for the 3 mg/kg/day dose. The time effect was observed from D7 to D91 for this same dose ($p \leq 0.01$).Figure 6Gene expression of nephrotoxicity biomarkers in the renal tissue after uranium exposure (0.25–3 mg/kg/day). Results are expressed as a ratio to the expression of the housekeeping gene HPRT. AU arbitrary unit, B2M β-2 microglobulin, CLU clusterin, CST cystatin, KIM-1 kidney injury molecule 1, KLK kallikrein, NGAL lipocalin 2, OPN osteopontin. The values are expressed as mean ± SD, $$n = 3$$–4 for each time and dose. * $P \leq 0.05$/**$P \leq 0.01$/***$P \leq 0.001$, comparison to unexposed animals, Two-way ANOVA.
## Discussion
The kidney and more specifically the proximal convoluted tubules are the primary site of uranium accumulation34, but knowledge of the nephrotoxicity threshold is inadequate depending on the route and mode of exposure, particularly after inhalation35. The objectives of our study were to experimentally investigate the biokinetics of uranium after repeated exposure via the upper airway in mice and determine a nephrotoxicity threshold and then test whether this threshold affects uranium retention and excretion. Then we experimentally modeled exposure to uranium via the upper airways, that can concern nuclear cycle workers. It addresses, for the first time, the monitoring of uranium accumulation as a function of time and dose during and after exposure period to uranium. The biokinetic modeling of uranium under these conditions of exposure by repeated instillation (exposure via the upper respiratory tract) in mice was established from published models25,33,36 and adjusted based on the exposure protocol: repeated acute exposure by intranasal-instillation23.
The retention of uranium in the kidneys, lungs, bones, and carcass showed a double peak of accumulation as a function of time, corresponding to the end of each week of instillation (Fig. 1). There is a linear relationship between exposure dose and body accumulation for doses below 1 mg/kg/day, while it accumulates exponentially between doses of 1 and 3 mg/kg/day. Previous works on repeated exposure via the upper airways studied biokinetics at the end of the treatment period without following the critical period of repeated exposure27,37. Thus, we showed that the renal biokinetics covered by our model are similar to a sum of acute subcutaneous or intraperitoneal exposures to 2 mg/kg uranium in rats38,39 or to repeated exposure of rats by inhalation of UO227. Unlike exposure by intratracheal instillation40,41, intranasal instillation makes it possible to more closely mimic exposure by inhalation, as it also includes the upper airways (extra-thoracic compartment) and the digestive tract. One hour after exposure by intranasal instillation, uranium is mainly found in the gastro-intestinal tract, the extra-thoracic compartment, and the lung (Fig. 2a). Uranium also reaches the kidneys without any differences between the right and left kidneys (data not shown). Exposure by instillation offers the advantages of being reproducible, the dose administered is controllable, and it can be used to treat a large number of animals at the same time. This exposure model has been previously validated for uranium32 and aluminum exposure30. The elimination time of uranium is very similar to that described during the inhalation of uranium, namely, rapid elimination by urine and feces in the first days after stopping treatment and significantly slower elimination after this point27,33,37.
Uranium lung retention is consistent with the model established by the ICRP and proportional to the dose administered23, although a lower than expected dose reached the lung. The renal retention of uranium is also proportional if the dose administered is less than 1 mg/kg/day, while uranium renal accumulation increases exponentially for a concentration above 1 mg/kg/day. For the highest exposure dose, the renal accumulation peak was observed on D4, whereas it was observed on D11 for lower doses (Fig. 1). After chronic exposure to 0.3 g of uranium (35 µg/g of kidney) via a gastrocnemius implant in rats, it takes 3 months to reach $50\%$ of this concentration in the organ42. In case of chronic exposure through drinking water at 600 mg/L, a renal accumulation 10 times lower is found after 9 months of exposure12. On D21, i.e. 10 days after the end of exposure to 1 mg/kg/day, the renal retention reaches 5 µg of uranium/g of kidney, a value close to that of43 for acute intramuscular exposure of rats to 1 mg/kg of uranium or39 for acute exposure of mice to 2 mg/kg intraperitoneally. Thus, the elimination kinetics obtained during this study differ from those of chronic exposure, but are similar to repeated acute contamination. However, the renal retention of uranium is modified for high concentrations during repeated exposure (> 1 mg/kg/day) probably due to the functional renal impairment induced by this uranium exposure.
Kidney damage is the first symptom of uranium poisoning8,34. The risk can be better assessed using the relationship between the concentration in the organ and the harmful effects observed. In fact, in the event of tubular and glomerular damage, functional and morphological analyses are needed. Ultrasound, as well as urinary, tissue and plasma diagnostic markers, such as the measurement of creatinine or the KIM-1 assay, CLU and OPN are good sensitive markers used in humans and animals, and representative of histological damage that can be altered by uranium2,7. Short-term tubular damage (dilatation and necrosis) from the end of the first week of instillation for the 3 mg/kg/day dose (i.e. a uranium content in the kidneys of 72 µg/g) and from D7 for the 1 mg/kg/day (5.6 µg/g of uranium in the kidneys) are actually observed according to the histological analysis (Figs. 1 and 4). The onset of this morphological damage corresponds to peak uranium accumulation in the kidneys observed for each of the doses (Fig. 1). Similar tubular damage was observed in kidneys greater than 10 µg/g after a single administration of uranium in rats by subcutaneous injection44, greater than 8.2 µg/g after intraperitoneal injection39 or greater than 22 µg/g after intramuscular injection43. Tissue regeneration from D7 for the highest dose (Fig. 4) can be observed by monitoring over time up to 91 days after exposure by intranasal instillation, whereas it is necessary to wait 28 days after a single intraperitoneal administration of 4 mg/kg in Swiss mice39 or 15 days after 10 mg/kg administered subcutaneously in rats5. On D91, the integrity of the renal tissue appears to be globally restored, but inflammation and fibrosis persist for the dose of 3 mg/kg/day (10 µg of uranium /g of kidney), resembling long-term renal damage (Figs. 1 and 4)5. The appearance of dilated tubules and necrosis is mainly influenced by the dose and mode of exposure (acute/repeated VS chronic) to uranium, while regeneration is mainly influenced by time and route of exposure. Nevertheless, we failed to observe any major morphological changes in renal ultrasound monitoring (Fig. 3). The renal ultrasound study was previously completed during a single study of exposure to uranium in rats (intraperitoneal administration of 5 mg/kg of uranium)45. The authors describe the appearance of fibrotic zones containing atrophied or dilated tubules but after exposure to a higher single dose of uranium, and results were not correlated with histological analyses, which makes comparison difficult.
Tubular and glomerular morphological damage was also assessed by in-situ gene expression analysis. The overexpression of inflammatory markers (OPN) and tubular damage (CLU and KIM-1)46 were observed from D4 to D21 for the highest exposure dose (Fig. 6). At the lower dose (1 mg/kg/day) only KIM-1 increased from D4 to D11, i.e. for uranium content in kidneys greater than 5.5 µg/g. KIM-1 is indeed a very sensitive marker and can be used to evaluate renal damage. It has previously been proven that a link exists between overexpression of KIM-1 and uranium-induced renal failure22,47. Inflammation is a uranium toxicity mechanism involving the recruitment of inflammatory cells4,48. KIM-1, CLU and OPN are increased after chronic exposure to uranium through drinking water followed by treatment with gentamicin49 or after a single intraperitoneal injection of uranium4,50. Repeated exposure to uranium by intranasal instillation at doses less than or equal to 1 mg/kg/day induces morphological damage similar to transient acute renal failure, whereas the highest dose induces acute damage followed by chronic renal failure, in particular because of the persistent inflammation it induces (Figs. 4h and 6).
The functional consequences of this renal morphological damage induced by uranium are evaluated by clinical biochemistry assays, measuring the urinary excretion of KIM-1, and thanks to long-term longitudinal monitoring (3 to 91 days after exposure) by high-resolution Doppler-ultrasound. A decrease in GFR associated with an increase in urinary excretion of KIM-1 was observed on D4, i.e. at peak uranium accumulation in kidneys for animals exposed to 3 mg/kg/day (Table 2 and Fig. 5). Decreasing GFR is a classic clinical parameter indicating a significant loss of function, particularly at the level of the glomeruli. Urinary excretion of KIM-1 shows functional impairment of the proximal convoluted tubules, the main site of uranium accumulation in the kidneys34,46. Renal blood supply represents $25\%$ of cardiac output and it can be quantified by measuring the renal vascular resistance indices by Doppler ultrasound (Fig. 3). No previous work deals with the study of renal blood flows in the context of radiochemical exposure. The *Doppler analysis* carried out in our study makes it possible to detect renal functional impairment over time after uranium exposure (< 3 mg/kg/day). The effect of uranium is transient since we observed changes in PI and RI between D4 and D11 then a return to a basal state on D91, probably correlating with the tubular regeneration observed in histopathology. Interestingly, our data showed a reduction in PI & RI in the kidneys, indicating a reduction in renal blood flow resistance. These results assume a slight functional impairment from a dose of 0.25 mg/kg/day without morphological impairment as described earlier. A reduction in RI can be attributed to pre-glomerular vasodilation mediated by myogenic vasoconstriction51, part of the autoregulation of the renal system. Other studies in humans have associated increased RI (resistive index) with shock (cardiogenic, hypovolemic, septic, etc.)52 or persistent acute renal injury (AKI)53. In mice, chronic pathologies such as diabetes54,55 or persistent AKI induced by a nephrotoxic agent such as cisplatin56 are associated with an increase in RI in these studies. The latter authors also noted that the RI does not vary in patients with transient AKI. By contrast45, described a decrease in RBF (renal blood flow) in the presence of uranium, but the latter is not quantified in their study. A reduction in RBF reflects reduced glomerular filtration capacity due to less efficient blood perfusion.
## Conclusion
Repeated intranasal instillation of uranium is a biokinetic model that mimics exposure through the upper airways. The morphological and functional renal damage observed from exposure to a 1 mg/kg/day dose therefore means that the nephrotoxicity threshold of our exposure model is between 0.25 and 1 mg/kg/day, which is similar to the thresholds described for other routes of acute uranium exposure. The adapted biokinetic model of acute exposure is consistent with the data obtained in mice after repeated exposure. We also show that this model is no longer valid for exposure to a highly nephrotoxic dose due to the induced renal failure.
Thanks to this work, we have shown that our experimental model could be used in other studies to mimic occupational exposure to uranium. Indeed, recent epidemiological studies show an excess risk of the development of renal cancer for nuclear fuel workers exposed to uranium57,58 and raise the question of the potential link between renal cancer and uranium exposure59,60. This question could be answered using an experimental model of exposure like the one we developed in this study with a good knowledge of uranium biokinetics and nephrotoxicity thresholds.
## Animals
Experiments were performed on 8-week year old male C57BL/6 J mice provided by Charles River (France). Animals were housed at a constant room temperature (21 °C ± 1) with a 12 h:12 h light–dark cycle. Water and food were supplied ad libitum. Body weight, urine volume and feces were monitored at regular time points from the first day of exposure to the last time point.
## Animal uranium exposure
Mice were subdivided into groups of 4 animals per dose per time until D21, and into groups of 8 animals for D91. Animals in the contaminated group were exposed to uranyl nitrate (UO2(NO3)2; U238: $99.74\%$, U235: $0.26\%$, U234: $0.001\%$, Merck-Prolabo) dissolved in 100 mM of sodium bicarbonate at different uranium solution concentrations to deliver the desire amount of uranium to mice (0.03, 0.125, 0.25, 1 or 3 mg/kg/day) with the same volume administered (15 µl). Control animals were instilled with a solution of 100 mM of sodium bicarbonate. The animals were instilled once a day for 4 days, followed by a 3-day break and then another 4 days of contamination by intranasal-instillation of 15 µL as previously described32.
## Small animal functional imaging
The VEVO 3100 High resolution Ultrasound (US) imaging system (Fujifilm Visualsonic Inc) with the MX550D probe (40 MHz) was used to acquire kidney sagittal images and Doppler measurements. Eight-week old C57BL/6 J mice were monitored by renal echography before (D0) and after uranyl nitrate instillation, at regular time points (D4, D7, D21 and D91). A US examination was performed on both animal kidneys. All animals were anaesthetized with isoflurane (induction $2\%$; maintenance: 0.75–$1\%$ to keep heart rate above 400 BPM) (Aerrane) and held on a platform heated to 37 °C (Fujifilm Visualsonic Inc), designed to monitor physiological parameters (ECG and respiration rate). The animals' abdomens were then depilated with a shaver and depilatory cream (Cosmia). An ultrasound transmission gel (Centravet) is applied to clean skin. B-mode images were acquired of the kidney to detect any morphological alteration to the whole kidney using the three-dimensional motor to scan the left and right kidneys. Images were recorded every 0.76 mm to reconstitute a three-dimensional image of the kidney. PW-Doppler images were acquired for the intra-renal arteries for the left and right kidneys at angles between 41 and 53°, PRF was fixed at 20 kHz, the transducer (MX550D) was positioned to acquire sagittal sections of the kidneys. All data acquired were then analyzed using Visualsonic’s Vevolab software: for each animal and at each time-point, both kidneys' arterial fluxes were analyzed with three different peaks to acquire intra-renal flow velocity. Renal pulsatile index and resistive index (PI and RI respectively) were automatically determined by the Vevolab® software using the following constructor formulas: PI = (PSV-EDV)/MV, RI = (PSV-EDV)/PSV; where PSV = Peak Systolic Velocity, EDV = End Diastolic Velocity and MV = Velocity Time Integral (VTI) Mean Velocity.
## Urine and feces collection
During each collection period, animals were placed individually in standard metabolic cages (Techniplast) for a 16 h period to collect urine and feces samples. Urine was centrifuged at 3000×g for 10 min and supernatants were isolated and stored at −80 °C.
## Plasma and tissue collection
At each time point, mice were euthanized by terminal exsanguination (intracardiac puncture) and cervical dislocation under isoflurane anesthesia. Both kidneys were collected and sagitally cut: half of the left kidney was placed in formaldehyde $4\%$ for preservation for 24 h, the other half of a kidney was flash-frozen in liquid-nitrogen and stored at − 80 °C. The right kidney, both lungs, back legs bones, nasal compartment, gastrointestinal tracts and carcass were stored at − 20 °C for quantification by Inductively-Coupled Plasma—Mass Spectrometry (ICP-MS). Blood samples were centrifuged at 3000×g for 10 min to obtain plasma which was then stored at − 80 °C.
## Glomerular filtration rate (GFR)
Plasma creatinine was measured with the fluorometric Creatinine assay kit (ab65340, Abcam), and urine creatinine with the colorimetric Creatinine assay kit (ab204537, Abcam).
## Renal biodosimetric model
A specific biodosimetric model, SAAM II (Simulation, analysis, and modeling software for tracer and pharmacokinetic studies), was developed from published ones in order to model the data: the pulmonary model is based on the ICRP model for inhalation33, the gastro-intestinal tract model is derived from25 and the systemic model was determined by Leggett and Pellmar36. The fraction deposited in the nasal compartment was adjusted from observed pulmonary retentions to implement the specific deposition pattern corresponding to intra-nasal instillations.
## Histopathology
The half kidney preserved in $4\%$ paraformaldehyde was dehydrated before being embedded in paraffin and cut with a microtome (5 µm section), stained with hematoxylin, eosin and saffron, and examined under brightfield microscope. Damage was assessed blindly by an external expert pathology laboratory (Vebio) according to standard criteria61. Glomerular damage was estimated using glomerulosclerosis and glomerular cystic dilatation. Tubule-interstitial damage was estimated based on tubule necrosis, regeneration and dilatation, and interstitial inflammation and fibrosis. The different kinds of lesions were scored from 0 to 4 for each animal (0: no damage/1: slight/2: moderate/3: marked/4: severe). The total sum of all lesions corresponds to the global lesion scoring. The percentage of lesion distribution represents the scope of the different types of impairment in relation to the total score.
## Immunostaining
Paraffin-embedded slices were deparaffinized and hydrated in descending gradations of ethanol and in $3\%$ H2O2 to block endogenous peroxidase activity. Antigen retrieval was achieved with a pH6 citrate buffer. Sections were incubated overnight with anti-KIM-1 (Ab47635, Abcam) diluted to 1:200. After washing, slices were incubated with an Alexa-488 secondary antibody (ab150061, Abcam) diluted to 1:1000 and assembled with mounting medium (Vectashield, VWR). Ten microphotographs per animal were collected with a fluorescence microscope (Zeiss AxioImager). The fluorescence intensity of each image was manually scored from 0 to 4, depending on the number and area of labeled tubules.
## Real time RT-PCR
Total RNA was extracted from 20 to 30 mg of renal tissue using the RNeasy total RNA isolation kit (74106, Qiagen) and reverse-transcribed into cDNA using the High-capacity cDNA reverse transcription kit (4368814, Thermo Fisher Scientific). Real-time PCR was used to analyze the mRNA level of nephrotoxicity biomarkers: B2M (CACTGACCGGCCTGTATGCT/GGTGGGTGGCGTGAGTATACTT), CLU (TCGGGCATCTGGCATCA/AAGCTCACGGGCGAAGAAC), KLK (GCCCAACACCGGCTTGT/TGCTCATTCAGGAGGCTCATG), CST (GCGTTGGACTTCGCTGTGA/GGCTGTGGTACGCATCGTT), OPN (CCCTCGATGTCATCCCTGTT/TTCCGTTGTTGTCCTGATCAGA), KIM-1 (TTTCAGGCCTCATACTGCTTCTC/TGACCCACCACCCCCTTT), NGAL (CGGGACCTGGTACCTCGAT/ CCATTTTCTTCAATGCGAGTCA). Samples were prepared at a final concentration of 1 ng/µL cDNA per well. A mix containing $2.5\%$ v/v primers (Fisher Scientific), $83\%$ v/v SYBR (4367659, Thermo Fisher Scientific) and $14.5\%$ v/v sterile water to yield a final volume of 10 µL was used. Samples were normalized to hypoxantine-guanine phosphoribosyl-transferase (HPRT) and fold induction calculated relative to the unexposed controls.
## KIM-1 assay in urine
KIM-1 was measured in urine using an ELISA kit according to the manufacturer’s instructions (DY1817, R&D). Urine was diluted to a concentration 1:10 to 1:50 to comply with the concentration intervals required for the assay.
## Statistical analysis
To compare the nephrotoxicity induced by different doses of uranium exposure, statistical analysis used two-way analysis of variance (ANOVA), in case of absence of normality Wilcoxon signed-rank testing, or Holm-Sidak testing had been used with uranium exposure and time as the two factors (Prism, R studio, Sigma plot). The level of signification was set to 0.05. The n value was specified in the legends of each figure.
## Approval for animal experiments
The Animal Care Committee #81 C2EA-IRSN of the Institute approved the experiments under the reference APAFIS#16305-2018072616221896 v2 delivered on December 13th, 2018, which were conducted in accordance with French regulations on animal testing (Ministry of Agriculture Act No. 2001-464, May 2001) and which complied with the ARRIVE guidelines.
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|
---
title: VEGF dose controls the coupling of angiogenesis and osteogenesis in engineered
bone
authors:
- Andrea Grosso
- Alexander Lunger
- Maximilian G. Burger
- Priscilla S. Briquez
- Francesca Mai
- Jeffrey A. Hubbell
- Dirk J. Schaefer
- Andrea Banfi
- Nunzia Di Maggio
journal: NPJ Regenerative Medicine
year: 2023
pmcid: PMC10011536
doi: 10.1038/s41536-023-00288-1
license: CC BY 4.0
---
# VEGF dose controls the coupling of angiogenesis and osteogenesis in engineered bone
## Abstract
Vascular endothelial growth factor-A (VEGF) physiologically regulates both angiogenesis and osteogenesis, but its application in bone tissue engineering led to contradictory outcomes. A poorly understood aspect is how VEGF dose impacts the coordination between these two processes. Taking advantage of a unique and highly tunable platform, here we dissected the effects of VEGF dose over a 1,000-fold range in the context of tissue-engineered osteogenic grafts. We found that osteo-angiogenic coupling is exquisitely dependent on VEGF dose and that only a tightly defined dose range could stimulate both vascular invasion and osteogenic commitment of progenitors, with significant improvement in bone formation. Further, VEGF dose regulated Notch1 activation and the induction of a specific pro-osteogenic endothelial phenotype, independently of the promotion of vascular invasion. Therefore, in a therapeutic perspective, fine-tuning of VEGF dose in the signaling microenvironment is key to ensure physiological coupling of accelerated vascular invasion and improved bone formation.
## Introduction
Large bone defects due to trauma, surgery or other pathological conditions cannot be repaired by spontaneous regeneration1 and their treatment is an unmet challenge in clinical practice2. The use of tissue-engineered bone grafts is promising for the repair of clinical-size defects but has yet to make a significant impact for patients. One of the critical issues to be solved towards this goal is the need to coordinate the formation of a vascular supply within the engineered grafts to form adequate amounts of physiological bone tissue. The role of the vasculature in bone formation is two-fold: 1) the supply of oxygen and nutrients, which requires a rapid establishment of perfusion3, and 2) the exchange of a complex crosstalk of paracrine signals that coordinate bone formation, as well as osteoprogenitor differentiation and behaviour, i.e. a so-called angiocrine function4–7. Therefore, under physiological conditions, the bone repair is a rapid, well-orchestrated and efficient process that involves a tight coupling of osteogenesis and angiogenesis.
An attractive strategy to drive vascular growth into osteogenic grafts is the supply of specific signals that regulate physiological angiogenesis. Vascular endothelial growth factor-A (VEGF) is the master regulator of vascular growth both in normal and pathological angiogenesis and is therefore the target for inducing the therapeutic growth of new blood vessels8. However, simple provision of VEGF to osteogenic grafts has been met with limited success, due in part to the inherent complexity of the biological connections between angiogenesis and osteogenesis9–13. There is therefore a need to better elucidate how VEGF regulates the coupling of the two processes, in order to exploit its biological potential to drive bone vascularization.
One key aspect that needs to be precisely understood is how VEGF dose impacts the coordination between vascular growth and bone formation. Several lines of evidence indicate that VEGF levels of expression must be carefully controlled during osteogenesis in vivo. In fact, while physiological levels are required to maintain bone homeostasis, loss of VEGF can impair osteoblast differentiation and bone deposition14,15, whereas over-expression can stimulate excessive bone resorption and likewise lead to bone loss3. However, very little is known about how VEGF dose regulates the therapeutic regeneration of vascularized bone, due to the difficulty of precisely controlling the spatiotemporal distribution of VEGF available in osteogenic grafts (reviewed by Martino et al.16). In this regard, it should be also considered that: a) the physiological presentation of VEGF to its target cells requires the interaction with extracellular matrix (ECM), which orchestrates its activity by regulating its local concentration, bioavailability and signalling17, and b) tissue regeneration after damage starts in all cases with the deposition of a provisional fibrin-based matrix derived from blood clotting and fibrin is also widely used for tissue engineering approaches18. Therefore, we took advantage of a protein engineering approach to decorate fibrin matrices with tunable and homogeneous concentrations of VEGF and recapitulate its physiological matrix-bound presentation19,20. An engineered version of mouse VEGF164 was fused to the octapeptide substrate sequence for the transglutaminase coagulation Factor XIII (TG-VEGF), whereby upon cross-linking of fibrinogen monomers into a fibrin hydrogel, TG-VEGF is covalently linked to the fibrin network21–23. Thus, TG-VEGF is presented to embedded osteoprogenitors and invading endothelial cells in the context of ECM, allowing the engineering of a specific signalling microenvironment. Here we investigated whether and how VEGF dose regulates the effective coupling of angiogenesis and osteogenesis for the therapeutic regeneration of vascularized bone.
## Increasing VEGF dose delays vascular invasion and determines the distribution of vascular growth
First, we addressed the effects of VEGF dose on angiogenesis in osteogenic grafts. Invasion by host blood vessels needs to occur rapidly within the first week after in vivo implantation to ensure progenitor survival. Therefore, osteogenic constructs were generated with fibrin decorated with increasing doses of TG-VEGF (0.1, 1, 10 and 100 µg/ml) or without recombinant factor as control, together with 1 × 106 human bone marrow-derived stromal cells (BMSC) per construct (Fig. 1). To control the rate of fibrin degradation and ensure it would take longer than 4 weeks, a TG-version of the fibrinolysis inhibitor aprotinin was included at a concentration of 51 µg/ml, as previously determined23. After 1 week, in vivo vascular ingrowth was assessed by quantifying areas invaded by host blood vessels on whole-section microscopy reconstructions (yellow tracings of CD31 + areas in Fig. 2a). As expected, vascular growth started from the surrounding tissue and remained confined at the periphery of the control constructs, while the presence of 0.1 µg/ml of TG-VEGF significantly increased invasion up to about $25\%$ of the construct area (Fig. 2b). However, higher doses of TG-VEGF not only did not further increase invasion, but actually prevented it, as vascularized areas were similar in size to the control condition with no VEGF (Fig. 2b; TG-VEGF 0.1 = 24.9 ± $4.8\%$ vs Control = 7.7 ± $4.9\%$, $p \leq 0.01$; and TG-VEGF 1 = 6.7 ± $1.9\%$, TG-VEGF 10 = 5.9 ± $0.9\%$, TG-VEGF 100 = 4.5 ± $1.2\%$; p = n.s. vs Control).Fig. 1Study design for the ectopic model of bone formation. Osteogenic constructs were generated by combining in vitro expanded human BMSC with calcium phosphate granules in a fibrin hydrogel decorated with different doses of TG-VEGF, ranging from 0 to 100 µg/ml; scale bars = 2 mm. Fig. 2Blood vessel invasion of osteogenic constructs after 1 week of in vivo implantation.a Reconstruction of graft sections under fluorescence microscopy to elucidate the areas of blood vessel growth (in yellow), with TG-VEGF doses ranging from 0 to 100 µg/ml; b Quantification of the areas invaded by blood vessels (expressed as % of total section area); c Assessment of angiogenesis in the invaded areas. Immunostaining of endothelium (CD31, in red) and nuclei (DAPI, in blue) of areas invaded by blood vessels after 1 week of in vivo implantation, with TG-VEGF doses ranging from 0 to 100 µg/ml; d Quantification of induced angiogenesis, after 1 week in vivo, expressed as VLD (vessel length density), calculated as millimeters of vessel length per square millimeter of tissue area (mm/mm2). Values are expressed as mean ± s.e.m.; ** $p \leq 0.01$, ***$p \leq 0.001.$ $$n = 6$$–8. Scale bars (a) = 500 µm; (c) = 100 µm.
In order to determine whether increasing TG-VEGF doses actually impaired vascular growth or rather vessel migration into the constructs, vascular density was assessed within the invaded areas by quantification of vessel length density (VLD), obtained by tracing individual vascular structures and defined as millimeters of vessel length per square millimeter of tissue area (Fig. 2c, d). Interestingly, 0.1 µg/ml of TG-VEGF did not increase VLD compared to controls, whereas higher doses progressively increased vascular density within the invaded areas (Control = 4.6 ± 0.3 mm/mm2, TG-VEGF 0.1 = 8.1 ± 0.7 mm/mm2, TG-VEGF 1 = 14.5 ± 3.2 mm/mm2, TG-VEGF 10 = 27.4 ± 3.7 mm/mm2, TG-VEGF 100 = 43.4 ± 6.6 mm/mm2). Therefore, these results suggest that VEGF dose determines the distribution of induced vessels within the construct, with a low dose favoring rapid ingrowth while maintaining density similar to controls, and higher doses slowing effective ingrowth, thereby increasing vessel density in the periphery.
## Optimal vascular invasion by 0.1 µg/ml TG-VEGF promotes human progenitor proliferation and survival throughout the grafts
To investigate the functional effects of the rapid vascular invasion provided by a low dose (0.1 μg/ml) of TG-VEGF, proliferation and apoptosis of the implanted human cells were systematically analyzed 1 week after implantation at different levels of depth inside the graft, by drawing 3 concentric layers, each spanning a depth of 500 µm, and a central core covering the last 1 mm till the center (Fig. 3a). Human cells were identified by staining for a specific human nuclear protein (HuNu), while proliferating and apoptotic cells were recognized by Ki67 or Cleaved-Caspase3 (Cas3) staining, respectively (Fig. 3b, c). A clear trend of decreasing proliferation was observed at increasing depths towards the centre of the graft in each condition, with the notable exception of the 0.1 µg/ml dose of TG-VEGF, which significantly increased the proportion of proliferating human progenitors in each layer compared to all other conditions (Fig. 3d). Interestingly, even deep inside the core, grafts containing 0.1 µg/ml of TG-VEGF enabled human progenitors to proliferate similarly to the middle layer of all other conditions (TG-VEGF 0.1 = 2.0 ± $0.4\%$ Ki67+ human cells vs 1–$2\%$ for all other conditions in the middle layer), despite being about 1 mm deeper (Core depth = 1.5–2.5 mm; Middle layer depth = 0.5–1 mm).Fig. 3Human BMSC survival and proliferation.a Reconstruction of graft sections under fluorescence microscopy (layer subdivision in yellow); b Example of immunostaining of human nuclei (HuNu, in red), proliferating cells (Ki67, also in the nucleus, in green) and all nuclei (DAPI, in blue) of constructs (representative picture of the outer layer of TG-VEGF 0.1 μg/ml condition) after 1 week of in vivo implantation; c Immunostaining of human nuclei (HuNu, in red), dying cells (CAS3, also in the nucleus, in green) and all nuclei (DAPI, in blue) of constructs (representative picture of the middle layer of TG-VEGF 10 μg/ml condition) after 1 week of in vivo implantation d Quantification of proliferating human cells (%) in each layer. e Quantification of dying human cells (%) in each layer. Values are expressed as mean ± s.e.m.; * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$ $$n = 4$$–6. Scale bars (a) = 500 µm; (b, c) = 50 µm.
Conversely, quantification of Cas3+ human cells showed a clear trend of increasing apoptosis, for each condition, from the surface towards the core of the graft (Fig. 3e). While no significant difference between the conditions was observed in the outer layer of the grafts, only 0.1 μg/ml of TG-VEGF significantly promoted human cells survival in all deeper layers down to the core, never exceeding the frequency of apoptotic cells of the outer layer (TG-VEGF 0.1 Core = 3.8 ± $0.7\%$ Cas3+ human cells vs 2–$3\%$ for all other conditions in the outer layer).
## Steady-state vascular density increases directly with VEGF dose
Four weeks after in vivo implantation, constructs were completely invaded by blood vessels in all conditions, as expected. At this late time-point quantification of VLD (Fig. 4a, b) showed that global vascular density was progressively increased by increasing TG-VEGF doses starting from 1 μg/ml compared to controls (Control = 1.3 ± 0.1 mm/mm2, TG-VEGF 0.1 = 2.1 ± 0.3 mm/mm2 p = n.s., TG-VEGF 1 = 2.5 ± 0.3 mm/mm2 $p \leq 0.05$; TG-VEGF 10 = 3.8 ± 0.3 mm/mm2 $p \leq 0.001$; TG-VEGF 100 = 5.9 ± 0.5 mm/mm2 $p \leq 0.001$). After 8 weeks, vascular density remained similar to what was observed at the 4 week time-point (Fig. 4c, d; Control = 1.6 ± 0.1 mm/mm2, TG-VEGF 0.1 = 1.6 ± 0.1 mm/mm2, TG-VEGF 1 = 2.7 ± 0.3 mm/mm2; TG-VEGF 10 = 2.4 ± 0.3 mm/mm2; TG-VEGF 100 = 4.7 ± 0.6 mm/mm2 $p \leq 0.001$). These results show that the steady-state had been reached by 4 weeks and that the induced vasculature was stable. Fig. 4Long term vascularization of osteogenic constructs.a, c Immunostaining of endothelium (CD31, in red) and nuclei (DAPI, in blue) of constructs, with TG-VEGF doses ranging from 0 to 100 µg/ml, after 4 (a) and 8 (c) weeks of in vivo implantation. b, d Quantification of vessel length density (VLD), expressed as millimeters of vessel length per square millimeter of tissue area (mm/mm2) after 4 (b) and 8 (d) weeks of in vivo implantation. Values are expressed as mean ± s.e.m.; * $p \leq 0.05$, ***$p \leq 0.001.$ $$n = 4$$–6. Scale bars = 100 μm.
## Increasing VEGF dose progressively impairs bone tissue formation
Next, we investigated the effects of VEGF dose on bone formation. Bone matrix deposition was assessed with Masson’s trichrome staining, which shows the presence of compact collagen fibers as well as of elastic fibers characteristic of mature bone. After 4 weeks in vivo, fibrin was almost completely degraded, but still present, and initial formation of a dense collagenous matrix could be observed at the interface with the hydroxyapatite granules (dark green stain in Fig. 5a top panels). However, grafts with TG-VEGF doses higher than 1 µg/ml contained almost only fibrous tissue. Quantification of the areas occupied by dense collagenous matrix (Fig. 5b) showed that 0.1 µg/ml TG-VEGF significantly promoted matrix deposition compared to controls (Control = 1.8 ± $0.2\%$, TG-VEGF 0.1 = 4.0 ± $0.6\%$; $p \leq 0.01$). However, 1 µg/ml TG-VEGF negated this improvement (TG-VEGF 1 = 2.0 ± $0.5\%$; p = n.s. vs Control and $p \leq 0.01$ vs TG-VEGF 0.1) and higher TG-VEGF doses almost completely prevented dense matrix deposition (TG-VEGF 10 = 0.1 ± $0.1\%$, TG-VEGF 100 = 0.2 ± $0.1\%$; $p \leq 0.05$ vs Control).Fig. 5Bone tissue formation and maturation.a Representative images of Masson’s trichrome staining of constructs 4 weeks (top panels) and 8 weeks (bottom panels) after in vivo implantation (dense collagenous tissue in green, elastic fibers in red), with TG-VEGF doses ranging from 0 to 100 µg/ml (Scale bars = 200 µm). Higher magnification images are shown in the bottom row (Scale bars = 100 µm). Asterisks = hydroxyapatite granules; arrowheads = osteocyte lacunae. b Quantification of areas occupied by dense collagenous matrix at 4 weeks (expressed as % of construct area). c Quantification of areas occupied by bone tissue at 8 weeks (expressed as % of construct area). d Quantification of areas occupied by mature bone at 8 weeks (expressed as % of total bone tissue). Values are expressed as mean ± s.e.m.; * $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001.$ $$n = 4$$–6.
After 8 weeks, frank bone tissue could be observed, characterized by dense collagenous matrix with organized collagen fibers and the presence of osteocyte lacunae (Fig. 5a, bottom panels; arrowheads indicate some lacunae). Bone formation was severely and dose-dependently impaired by TG-VEGF doses higher that 1 µg/ml (Fig. 5c: Control = 6.4 ± $0.8\%$, TG-VEGF 10 = 0.8 ± $0.3\%$, TG-VEGF 100 = 0.5 ± $0.1\%$; $p \leq 0.001$ vs Control), whereas the 0.1 and 1 µg/ml doses supported bone tissue formation similarly to the controls (TG-VEGF 0.1 = 8.5 ± $1.3\%$, TG-VEGF 1 = 4.1 ± $0.8\%$; p = n.s. vs Control). Interestingly, although the differences were not statistically significant due to intrinsic variability, a clear trend could be observed also at 8 weeks for 0.1 µg/ml to enable better bone formation than controls, while 1 µg/ml caused already an initial loss.
The degree of maturation of bone tissue can be evaluated by the presence of elastic fibers, shown in red by Masson’s trichrome staining. Quantification of the areas occupied by elastic fibers (Fig. 5d, expressed as percentage of total bone tissue formed) showed that that the 0.1 µg/ml TG-VEGF dose could significantly increase the amount of mature bone tissue compared to all other conditions (TG-VEGF 0.1 = 5.4 ± $1.4\%$ vs Control = 1.2 ± $0.3\%$, TG-VEGF 1 = 1.1 ± $0.5\%$, TG-VEGF 10 = 1.3 ± $0.9\%$, TG-VEGF 100 = 0.7 ± $0.4\%$; $p \leq 0.05$).
## Increasing VEGF dose stimulates osteoclastic bone resorption
The amount of bone tissue depends on the balance between deposition by osteoblasts and resorption by osteoclasts. Therefore, osteoclast recruitment was assessed by staining for the osteoclast-specific enzyme tartrate-resistant acid phosphatase (TRAP). Few TRAP + cells were detected in the constructs with BMSC alone or in combination with 0.1 µg/ml TG-VEGF, in close proximity to the bone matrix or at the interface with the hydroxyapatite granules, both at 4 and 8 weeks (Fig. 6a, c). Quantification of the number of TRAP+ multinucleated cells per tissue area (TRAP+ MNC/mm2) showed that TG-VEGF doses higher than 1 µg/ml significantly increased osteoclast recruitment at both time points (Fig. 6b, d; 4 weeks: Control = 15.3 ± 3.3 TRAP+ MNC/mm2, TG-VEGF 10 = 38.8 ± 4.3, TG-VEGF 100 = 40.3 ± 7.1, $p \leq 0.01$ vs Control; 8 weeks: Control = 24.1 ± 1.5 TRAP+ MNC/mm2, TG-VEGF 10 = 42.4 ± 2.1, TG-VEGF 100 = 44.4 ± 6.7, $p \leq 0.05$ and <0.01 vs Control, respectively). Constructs containing 0.1 µg/ml of TG-VEGF, instead, did not increase osteoclast recruitment compared to controls, whereas 1 µg/ml showed a non-significant trend towards increase (TG-VEGF 0.1 = 13.5 ± 2.8, TG-VEGF 1 = 26.3 ± 4.9 at 4 weeks and TG-VEGF 0.1 = 24.2 ± 1.8 and TG-VEGF 1 = 38.2 ± 1.6 at 8 weeks, p = n.s. vs Control).Fig. 6Osteoclast recruitment.a, c Histochemical stain for TRAP activity (red) and nuclear counterstaining with hematoxylin (blue) of constructs 4 weeks (a) and 8 weeks (c) after in vivo implantation, with TG-VEGF doses ranging from 0 to 100 µg/ml; b, d Quantification of Trap+ multinucleated cells (MNC) per tissue area (mm2) after 4 (c) and 8 (d) weeks. Values are expressed as mean ± s.e.m.; * $p \leq 0.05$, **$p \leq 0.01.$ $$n = 3$$–6. Scale bar = 200 µm.
## A minimum dose of 0.1 µg/ml TG-VEGF is required to couple accelerated vascular invasion and improved bone formation
Based on the observed negative effects of increasing TG-VEGF doses on both vascularization and bone formation, we investigated whether further improvements could be achieved with an even lower dose. Constructs decorated with 0.01 µg/ml of TG-VEGF were implanted and analyzed as above after 1 and 4 weeks in vivo. As shown in Fig. 7a, b, after 1 week 0.01 µg/ml of TG-VEGF significantly increased invasion of the construct area compared to controls and similarly to 0.1 µg/ml (Fig. 7b; Control = 5.9 ± $1.8\%$, TG-VEGF 0.01 = 16.3 ± $4.0\%$, TG-VEGF 0.1 = 17.7 ± $1.9\%$; $p \leq 0.05$ both vs Control). On the other hand, after 4 weeks the gain in osteogenic matrix deposition observed with 0.1 µg/ml of TG-VEGF was completely lost by lowering the dose to 0.01 µg/ml (Fig. 7c, d; Control = 1.4 ± $0.5\%$, TG-VEGF 0.01 = 2.1 ± $0.7\%$, TG-VEGF 0.1 = 4.4 ± $0.8\%$; p = n.s. TG-VEGF 0.01 vs Control). Lastly, osteoclast recruitment was not affected by 0.01 µg/ml of TG-VEGF compared to either controls or 0.1 µg/ml of TG-VEGF (Fig. 7e, f; Control = 9.7 ± 1.3 TRAP+ MNC/mm2, TG-VEGF 0.01 = 9.0 ± 1.9, TG-VEGF 0.1 = 7.6 ± 0.8; p = n.s.). Therefore, these data show that a minimum dose of TG-VEGF (0.1 µg/ml) is required to stimulate bone formation in association with accelerated vascular invasion. Fig. 7Early vascular invasion, bone formation and osteoclast recruitment by low VEGF doses.a Reconstruction of graft sections under fluorescence microscopy to elucidate the areas of blood vessel growth (in yellow), with TG-VEGF doses ranging from 0 to 0.1 µg/ml; b Quantification of the areas invaded by blood vessels (expressed as % of total section area); c Bone tissue formation. Representative images of Masson’s trichrome staining of constructs 4 weeks after in vivo implantation (dense collagenous tissue in green), with TG-VEGF doses ranging from 0 to 0.1 µg/ml. d Quantification of areas occupied by dense collagenous matrix at 4 weeks (expressed as % of construct area). e Osteoclast recruitment. Histochemical stain for TRAP activity (red) and nuclear counterstaining with hematoxylin (blue) of constructs 4 weeks after in vivo implantation, with TG-VEGF doses ranging from 0 to 0.1 µg/ml; f Quantification of Trap+ multinucleated cells (MNC) per tissue area (mm2) after 4 weeks. Values are expressed as mean ± s.e.m.; * $p \leq 0.05.$ $$n = 5$.$ Scale bars = 200 µm.
## VEGF dose specifically regulates osteogenic differentiation of human progenitors in vivo and induction of pro-osteogenic endothelium
We also investigated the influence of VEGF dose on in vivo osteogenic differentiation of human BMSC. Since bone differentiation is a progressive process, requiring progenitor commitment and osteoblast expansion before the deposition of bony matrix in vivo, BMSC commitment was assessed by measuring expression of the early osteogenic transcription factor Osterix (Osx) in human cells after 1 week in vivo. Based on the results of bone formation, constructs decorated with 0.01, 0.1 and 100 µg/ml of TG-VEGF were selected for the comparison. As shown in Fig. 8a, a significant proportion of human progenitors already underwent osteogenic commitment after 1 week and they were specifically localized in a few concentric layers around the invading vascular front. In agreement with the bone formation data above, 0.1 µg/ml significantly increased the amount of Osx+ human progenitors, whereas 0.01 µg/ml did not provide any improvement compared to controls (Fig. 8b). On the other hand, a high dose of 100 µg/ml already showed a trend towards loss of osteogenic commitment (Control = 11.3 ± $1.4\%$, TG-VEGF 0.01 = 10.3 ± $0.7\%$, TG-VEGF 0.1 = 27.9 ± $2.8\%$, TG-VEGF 100 = 6.1 ± $2.0\%$; $p \leq 0.001$ TG-VEGF 0.1 vs Control). We sought to determine whether VEGF specifically impaired the osteogenic differentiation of human BMSC or their survival. The number of human cells was quantified and it was found to remain similar among all conditions (Fig. 8c; Control = 316.2 ± 26.0 cells/mm2, TG-VEGF 0.01 = 244.4 ± 22.4, TG-VEGF 0.1 = 293.9 ± 18.5 cells/mm2, TG-VEGF 100 = 315.5 ± 52.1 cells/mm2; p = n.s.).Fig. 8Human osteoprogenitor differentiation in vivo.a, d Immunostaining of osteogenic constructs 1 week (a) and 4 weeks (d) after in vivo implantation with TG-VEGF doses ranging from 0 to 100 µg/ml for Osterix (OSX, white), human nuclei (HuNu, red) and blood vessels (CD31, green). b, f Quantification of the number of Osx positive human cells after 1 week (b) and 4 weeks (f) expressed as % of the total number of human cells. c, h Quantification of the number of human cells per tissue area (mm2) after 1 week (c) and 4 weeks (h) ($$n = 5$$–6); e Immunostaining of osteogenic constructs 4 weeks after in vivo implantation for human BSP (hBSP, red) and nuclei (DAPI, blue). g Quantification of human BSP positive matrix after 4 weeks expressed as percentage of total tissue area ($$n = 5$$–6). ( i) Gene expression of human Runx2, OSX, BSP and OCN was quantified by qRT-PCR and expressed as relative expression to human GAPDH (2−ΔCt), with TG-VEGF doses ranging from 0 to 100 µg/ml. Data represent the values of individual samples and the mean (black bar, $$n = 8$$); dotted line and grey area = expression range of undifferentiated human BMSC in vitro. Values are expressed as mean ± s.e.m.; * $p \leq 0.05$, ***$p \leq 0.001.$ Scale bars = 200 µm.
In order to investigate whether high VEGF might simply delay osteogenic commitment rather than blocking it, we measured the expression of both Osx and the later osteogenic marker Bone Sialoprotein (BSP) in constructs decorated with 100 µg/ml of TG-VEGF 4 weeks after implantation and compared it to the osteogenic conditions 0.1 and 0.01 µg/ml and controls. Immunostaining showed that 100 µg/ml of TG-VEGF caused a significant loss of both osteogenic proteins, whereas their expression was preserved with both 0.01 and 0.1 µg/ml (Fig. 8d–g; hOsx+ cells: Control = 48.3 ± $5.4\%$, TG-VEGF 0.01 = 45.3 ± $2.7\%$, TG-VEGF 0.1 = 43.5 ± $6.1\%$, TG-VEGF 100 = 6.4 ± $2.0\%$ of human cells, $p \leq 0.001$; hBSP+ matrix: Control = 3.3 ± $0.9\%$, TG-VEGF 0.01 = 4.7 ± $0.9\%$, TG-VEGF 0.1 = 4.7 ± $1.2\%$, TG-VEGF 100 = 0.2 ± $0.1\%$, $p \leq 0.05$). Also at this later time-point the number of human cells was found to remain similar among all conditions (Fig. 8h; Control = 1220 ± 143.6 cells/mm2, TG-VEGF 0.01 = 1066 ± 169.1 cells/mm2, TG-VEGF 0.1 = 1186 ± 123.7 cells/mm2, TG-VEGF 100 = 1036 ± 212.5 cells/mm2; p = n.s.). Lastly, we expanded these data by in vivo gene expression analysis of 2 early osteogenic transcription factors (Runx2 and Osx) and 2 later bone matrix proteins (BSP and Osteocalcin, OCN). As shown in Fig. 8i, all osteogenic genes were strongly upregulated in constructs containg BMSC alone as well as with 0.1 µg/ml of TG-VEGF, indicating robust differentiation. However, 100 µg/ml of TG-VEGF significantly impaired the upregulation of all osteogenic gene expression, which was barely greater than the undifferentiated controls.
Therefore, a high VEGF dose did not affect initial osteoprogenitor survival and engraftment, but specifically interfered with their osteogenic commitment, providing a further mechanism for the observed impairment of bone tissue formation by high VEGF doses. On the other hand, a minimum amount of VEGF (0.1 µg/ml) was required to specifically promote osteogenic commitment of human progenitors.
As shown in Fig. 8a, Osx+ cells could only be found in the vicinity of the invading vascular front. Interestingly, both 0.01 and 0.1 µg/ml of VEGF caused similar vascular invasion, but only 0.1 µg/ml improved osteogenic differentiation. This suggests a functional difference in the endothelium stimulated by the two doses. The pro-osteogenic function of blood vessels has been shown to depend on the production of angiocrine signals by a specialized endothelial phenotype named Type H24, which is mechanistically induced by activation of Notch1 signaling25. Therefore, we investigated whether VEGF dose regulated endothelial Notch1 activation in osteogenic constructs. For this, we used an antibody specifically recognizing the cleaved form of Notch1 intracellular domain (NICD1), which upon activation is translocated into the nucleus where it initiates its specific transcriptional response, together with a co-staining for CD31 to positively identify endothelial cells of invading vascular structures. As shown in Fig. 9a, after 1 week the ingrowing endothelium at the invasion front in both controls and constructs with 0.01 µg/ml of TG-VEGF showed very rare Notch1+ nuclei, whereas 0.1 µg/ml increased Notch1 activation by almost 4-fold. Instead, this increase was lost with a high dose of 100 µg/ml (Fig. 9b; Notch1+ endothelial cells: Control = 4.8 ± $0.5\%$, TG-VEGF 0.01 = 4.1 ± $1.1\%$, TG-VEGF 0.1 = 17.4 ± $1.9\%$, TG-VEGF 100 = 7.1 ± $2.4\%$; $p \leq 0.05$ TG-VEGF 0.01 vs control). We further assessed the degree of Notch1 activation in the positive endothelial nuclei by quantification of the mean signal intensity and no significant differences among conditions were observed (Fig. 9c).Fig. 9Induction of Notch1 + pro-osteogenic endothelium.a Immunostaining of osteogenic constructs 1 week after in vivo implantation with TG-VEGF doses ranging from 0 to 100 µg/ml, showing activated Notch1 (intranuclear white signal), blood vessels (CD31, red) and nuclei (DAPI, blue). The yellow dashed lines indicate the limit of the vascular invasion fronts. The bottom panels show high-magnification images of the areas indicated in the middle panels and white arrows indicate endothelial nuclei with activated Notch1. b Quantification of the number of endothelial nuclei with activated Notch1, expressed as % of the total number of endothelial cells. c Quantification of the mean intensity of intranuclear activated Notch1 signaling (in arbitrary units, a. u.). d Cartoon summarizing the regulation of vascular invasion, Notch1 activation (green nuclei) and osteogenic commitment (Osx+) of progenitors by VEGF dose. In the underlying table, - means no improvement and + means significant improvement compared to control. Values are expressed as mean ± s.e.m.; * $p \leq 0.05.$ Scale bars = 50 µm.
## Discussion
Taking advantage of a unique and highly tunable platform, here we could dissect how VEGF dose over a 1’000-fold range controls the intimately connected processes of angiogenesis and osteogenesis in the generation of engineered bone. We found that increasing VEGF dose not only impairs bone formation, but surprisingly also delays vascular invasion of the engineered constructs. However, stimulation of early osteogenic commitment and bone formation required a minimum VEGF dose in the graft microenvironment (0.1 µg/ml), which promoted the specific induction of a pro-osteogenic endothelial phenotype, marked by activation of Notch1 signaling. Therefore, the effective coupling of optimal osteogenesis and rapid vascular invasion is finely regulated by VEGF dose (Fig. 9d). The underlying mechanism is complex and comprises distinct and opposing effects on vessel migration and endothelial function, bone resorption through osteoclast recruitment and bone formation through osteoprogenitor differentiation.
An explanation for the unexpected effects of VEGF dose on vascular invasion lies in the dynamic nature of construct vascularization. In fact, upon in vivo implantation, blood vessel in-growth must be stimulated initially from the surrounding pre-existing vascular network, while later the vascular density in the invaded areas is regulated by the metabolic needs of the tissue. Angiogenesis is commonly quantified as steady-state vascular density within the implant, without taking in consideration the kinetics of growth. What we found here is that, although the final vascular density is indeed positively regulated by VEGF dose, the speed of vascular invasion is instead impaired by increasing VEGF dose. This is a very important distinction. In fact, we found that rapid vascularization is the key parameter to improve progenitor survival and proliferation, as shown in Fig. 3. On the other hand, doses that increased vascular density, but not early invasion, failed to do so, showing that stimulating vessel density beyond its physiological values is not required to support the metabolic needs of this developing tissue. Therefore, for the therapeutic purpose of promoting net bone formation, it is important to target rapid vascular ingrowth with a low VEGF dose rather than increasing vessel density with a higher one.
The observation that higher VEGF dose impaired active vascular migration inside the graft and rather promoted vessel expansion at the graft surface, leading to high vascular densities in limited areas, is intriguing. A consideration of the different mechanisms by which new vessels may grow suggests an explanation for this phenomenon. In fact, rapid invasion of an avascular tissue, such as an implanted graft, takes place by the process of vascular sprouting26. Sprouting angiogenesis is guided by the formation of VEGF concentration gradients and requires coordinated endothelial migration and proliferation to enter the tissue27,28. Even though the constructs were decorated with homogeneous concentrations of VEGF, it is interesting to note that micro-gradients do actually form with low VEGF doses upon fibrin degradation. In fact, VEGF164 has an intermediate degree of affinity for ECM and, when released from fibrin, is able to undergo limited diffusion29. However, vascular growth can also take place by the alternative mechanism of intussusception, or splitting angiogenesis. In this case, endothelial cells proliferate without migrating, leading to circumferential enlargement of pre-existing vessels, which subsequently split longitudinally to form new structures30–32. This mode of vascular growth is highly efficient to expand pre-existing networks, but does not have an intrinsic directional component, leading to increases in vessel density rather than to invasion. Recent unpublished findings by our group suggest that splitting angiogenesis results from higher doses of VEGF, which saturate the extracellular matrix of the vessel microenvironment and therefore present endothelial cells with a flat concentration profile rather than a gradient (Gianni-Barrera, R.; Banfi, A. et al. manuscript in preparation). This could explain the observed delay in vascular invasion of grafts decorated with concentrations greater than 1 µg/ml of TG-VEGF.
The initial discrepancy in vascular invasion was a transient phenomenon, as all grafts were fully vascularized with all VEGF doses after 4 weeks. A likely explanation is related to the transient nature of the fibrin-bound delivery of VEGF: as fibrin degrades over this time-frame, the “barrier” of high VEGF concentration also is lost and the endothelium is again exposed to moderate VEGF concentrations and microenvironmental gradients conducive to migration inside the tissue. Therefore, high VEGF doses do not stably prevent vascular invasion, but rather delay it over the crucial initial time window of 1 week, nevertheless leading to detrimental effects on progenitor survival/proliferation and bone formation. A similar consideration likely underpins the observation that VEGF-dependent stimulation of osteogenic differentiation of embedded human progenitors is also transient (Fig. 8), consistently with the progressive release of the fibrin-bound VEGF in the localized microenvironments. It should be noted that, although transient, this boost translated into more effective long-term bone formation at both 4 and 8 weeks (Fig. 5).
The effects of different VEGF doses on net bone formation were particularly interesting. In fact, while a dose of 0.1 µg/ml significantly promoted initial bone matrix deposition by 4 weeks compared to controls, both lower and higher doses failed to do so. This observation is strikingly reminiscent of the roles of VEGF during bone development and repair, where its loss causes skeletal deficits and malformations33,34, but excessive levels also lead to bone failure35. Hu and Olsen35 also reported that excessive VEGF delivery can impair endogenous bone repair. In fact, it was found that a concentration of 1’000 µg/ml of VEGF loaded in collagen sponges inhibited intramembranous bone formation in a tibial cortical defect. This was ascribed mainly to reduced collagen I accumulation and BSP expression in the area of the defect, suggesting a possible impairment of endogenous osteoprogenitors differentiation. This non-linear relationship between VEGF dose in the microenvironment and net bone formation likely reflects the complex biological roles of VEGF in both bone anabolism and catabolism. Both osteoprogenitors and osterix-positive osteoblasts express VEGF receptors (especially VEGF-R2) and VEGF can directly regulate their differentiation and activity maintaining bone homeostasis, through paracrine and intracrine mechanisms36–38. Here we found that the harmonious association of vascular growth and bone formation is exquisitely dependent on VEGF dose and that different processes are stimulated by distinct dose ranges. In fact, vascular invasion is already stimulated with a very low dose of VEGF (0.01 µg/ml), but promotion of progenitor differentiation requires a higher dose (0.1 µg/ml) and osteoclast recruitment only increases with still higher doses (greater than 1 µg/ml), which also impair vascular invasion (Fig. 9d).
The mechanism by which VEGF regulates both vascular invasion and osteogenic commitment of BMSC is unclear and likely complex. However, a few observations reported here allow some speculation on the nature of this relationship. First, the data reported here suggest that the beneficial effect of angiogenesis on osteogenesis does not depend simply on the physiological function of blood vessels in supplying oxygen and nutrients and removing waste products. In fact, both 0.01 and 0.1 µg/ml of TG-VEGF promoted a similarly effective early vascular invasion of grafts, but osteogenic progenitor commitment could be stimulated only by 0.1 µg/ml. On the other hand, we found evidence against a direct effect of VEGF on the differentiation of implanted BMSC, since progenitors are exposed to the same dose of TG-VEGF throughout the constructs, whereas Osx expression only appears in the vicinity of the vascular front (Fig. 8). Furthermore, Osx induction does not depend on vessel quantity, as higher TG-VEGF doses progressively increase vascular density, but this is actually accompanied by a significant impairment of osteogenic commitment. Therefore, taken together these observations suggest that vessels induced by different doses of VEGF are qualitatively different from each other, beyond the effects on their quantity and kinetics of ingrowth.
These features are highly suggestive of an angiocrine function for the blood vessels induced by different VEGF doses. A specialized Type H endothelial phenotype has been described to regulate bone formation during development and repair24,25. Mechanistically, the pro-osteogenic function of Type H endothelium depends on activation of Notch1 signaling that induces the secretion of angiocrine factors such as Noggin5,25. Consistently, we found that the pro-osteogenic dose of VEGF (0.1 µg/ml) specifically stimulates Notch1 activation in the endothelium of the invading vascular front (Fig. 9), and the Osx+ osteogenic cells also specifically localize around the invading vessels (Fig. 8). Taken together, these data suggest that an optimal VEGF dose promotes coupled angiogenesis and osteogenesis through the induction of “Type H-like” vessels. However, positive identification will require the discovery of unequivocal Type H markers, which are currently lacking.
In conclusion, here we uncovered how VEGF signaling controls several processes at the crossroads of angiogenesis and osteogenesis in a dose-dependent manner. In a therapeutic perspective, the factor-decorated matrix platform described here provides the ability to precisely control the signaling microenvironment and the translation of these biological findings will need to be tested in an orthotopic critical-size defect model. The molecular players mediating the angiocrine cross-talk between blood vessels and bone stimulated by optimal doses of VEGF remains to be elucidated and could provide further therapeutic targets.
## BMSC isolation and culture
Human primary bone marrow mesenchymal stromal cells (BMSC) were isolated from marrow aspirates. The aspirates were obtained from the iliac crest of 10 healthy donors (7 males and 3 females, ranging in age from 22 to 48 years old) during routine orthopaedic surgical procedures according to established protocols, after written informed consent by the patients. The methods were performed in accordance with relevant guidelines and regulations and approved by the local ethical committee Ethik Kommission Beider Basel (Ref. $\frac{78}{07}$). Cells were isolated and cultured as previously described19,39. Briefly, after centrifugation, the cell pellet was washed in PBS (GibcoTM, Thermo Fisher Scientific, Waltham, Massachusetts, USA), resuspended in α-MEM medium (GibcoTM, Thermo Fisher Scientific, Waltham, Massachusetts, USA) containing $10\%$ bovine serum (HyClone, South Logan, Utah, USA), 1 mM Sodium Pyruvate (GibcoTM, Thermo Fisher Scientific, Waltham, Massachusetts, USA), 10 mM HEPES Buffer Solution (GibcoTM, Thermo Fisher Scientific, Waltham, Massachusetts, USA) and 5 ng/ml FGF-2 (R&D System Minneapolis, Minnesota, USA) and plated at a density of 105 nucleated cells/cm2. BMSC were cultured in $5\%$ CO2 at 37 °C.
## Recombinant TG-VEGF production and purification
The engineered cross-linkable form of mouse VEGF-A 164 was produced as previously described19. Briefly, before insertion into the expression vector pRSET (Invitrogen, Carlsbad, California, USA), the cDNA for mouse VEGF-A 164 was amplified by PCR using primers designed to add the transglutaminase substrate sequence NQEQVSPL, including the 8 N-terminal residues of α2-plasmin inhibitor (α2PI 1–8), onto the N-terminus of the amplified cDNA. The engineered protein was expressed in *Escherichia coli* strain BL21 (D ε3) pLys (Novagen, Merck, Darmstadt, Germany). The recombinant α2 PI 1–8 -VEGF-A 164 (TG-VEGF) was isolated from inclusion bodies, processed and refolded following a modified version of a previously published protocol40. Briefly, the bacteria were lysed in triton X-100 by sonication and the inclusion bodies were collected from the lysate by centrifuging. A washing with Tri- ton X114 was used to remove membrane proteins and endotoxins. Proteins were extracted with urea buffer overnight at 4 °C under magnetic stirring. Urea concentration was slowly reduced by dialysis and further dimerization of TG-VEGF was obtained with a redox system (0.5 mM oxidized glutathione, 5 mM reduced glutathione) under stirring for 48 h at 4 °C. After, 24 h dialysis against Tris- buffered saline (TBS) was used to remove glutathione and urea. Proteins were then concentrated using a 10-kDa Amicon tube (Millipore, Merck, Darmstadt, Germany) and further filtered with 0.22-μm filters. TG-VEGF monomers and dimers were separated using size exclusion with a HiLoad $\frac{16}{60}$ Superdex 75-pg column (GE healthcare, Chicago, Illinois, USA). Fractions corresponding to TG-VEGF dimers were pooled together, concentrated with Amicon tubes, and filtered through a 0.22-μm filter. SDS/PAGE was used to assess TG-VEGF dimers purity (>$99\%$). Endotoxin level was verified to be under 0.05 EU/mg of protein using the human embryonic kidney (HEK)-Blue mTLR4 assay (Invivogen, San Diego, California, USA).
## Recombinant TG-Aprotinin production and purification
The engineered cross-linkable form of aprotinin was produced as previously described19. Briefly, the cDNA for bovine aprotinin was modified at its N-terminus to add an 6x histidine tag and a thrombin cleavage site, and at its C-terminus with the cDNA of the transglutaminase substrate sequence NQEQVSPL. The cDNA was then subcloned into a pXLG vector for expression in HEK-293F mammalian cells. HEK-293F cells were transiently transfected using polyethylenimine (PEI) and cultured in suspension for 7 days, after what the supernatant was collected and purified via immobilized metal affinity chromatography (HisTrap columns, GE healthcare, Chicago, Illinois, USA) using an Akta Pure FPLC system (GE healthcare, Chicago, Illinois, USA). The purified proteins were then dialyzed and the histidine tag was cleaved using thrombin (50 U/mg) for 24 h at room temperature. The proteins were purified again using HisTrap and benzamidine columns (GE healthcare, Chicago, Illinois, USA) to remove the cleaved his-tag and thrombin. The purified TG-aprotinin was dialyzed against TBS, concentrated using Amicon tubes, filtered through a 0.22-μm filter and stored at −80 °C.
## Generation and in vivo implantation of osteogenic constructs
60 mm3 of silicate-substituted calcium phosphate granules of 1–2 mm size (Actifuse®; Apatech-Baxter, Elstree, UK) where mixed with 1 × 106 BMSC at first passage after initial confluence (P1) and embedded in a 60-µl fibrin gel prepared by mixing 25 mg/ml human fibrinogen (plasminogen-, von Willebrand Factor-, and fibronectin-depleted; Milan Analytica AG, Rheinfelden, Switzerland), 3 U/mL factor XIII (CSL Behring, King of Prussia, Pennsylvania, USA), and 6 U/ml thrombin (Sigma-Aldrich, St. Louis, Missouri, USA) with 2.5 mM Ca2+ in 4-(2-hydroxyethyl)-1- piperazineethanesulfonic acid (Hepes, Lonza, Basel, Switzerland). The final constructs were roughly spherical with a diameter of about 6 mm. Fibrin gels decorated with 51 µg/ml of aprotinin-α2PI1–8 and 0.01, 0.1, 1, 10 or 100 µg/ml of α2PI1–8-VEGF-A164 were obtained by adding the engineered proteins to the cross-linking enzymes solution before mixing with fibrinogen. Osteogenic grafts were allowed to polymerize at 37 °C for 10 min after mixing before in vivo implantation. The resulting constructs were implanted subcutaneously (4 constructs/animal) in 5-weeks old female nude mice (CD1-Foxn1nu, Charles-River, Sulzfeld, Germany). Studies were performed in age- and sex-matched young animals in order to reduce sources of variability in the efficiency of bone formation, and not to investigate the influence of age and gender. Animals were treated in agreement with Swiss legislation and according to a protocol approved by the Veterinary Office of Canton Basel-Stadt (permission #1797). Four to ten constructs were implanted for each condition ($$n = 4$$–10 samples/group), generated with cells from 5 independent donors (at least 2 replicates/donor) and in a minimum of 2 independent experiments per condition. After 1, 4 and 8 weeks, mice were sacrificed by inhalation of CO2 and constructs were explanted.
## Histological processing and immunofluorescence tissue staining
Explanted constructs were washed with PBS and fixed overnight at +4 °C with freshly prepared $1\%$ paraformaldehyde (Sigma-Aldrich, St. Louis, Missouri, USA) in PBS. Subsequently, the samples were decalcified in a PBS-based solution containing $7\%$ w/v EDTA (0.5 M, pH 8, Sigma-Aldrich, St. Louis, Missouri, USA) and $10\%$ w/v sucrose (Sigma-Aldrich, St. Louis, Missouri, USA) and incubated at 37 °C on an orbital shaker. The solution was renewed daily for about 20 days, until the samples were fully decalcified, as estimated by the degree of sample stiffness. Finally, the samples were embedded in OCT compound (CellPath LTD, Newtown, UK), frozen in freezing 2-methylbutane (isopentane) (Sigma-Aldrich, St. Louis, Missouri, USA) and 10 µm-thick sections were obtained with a cryostat.
Immunofluorescence staining was performed with the following primary antibodies and dilutions: rat anti-mouse CD31 (clone MEC 13.3, BD Bioscience, San Jose, California, USA) at 1:100; mouse anti-Human nuclei (clone 235-1, Merk Millipore, Darmstadt, Germany) at 1:200; rabbit anti-Ki67 (Abcam, Cambridge, UK) at 1:100; rabbit anti-Cleaved Caspase 3 (Asp175; Cell Signaling Technology, Danvers, Massachusetts, USA) at 1:200; mouse anti-human BSPII (Clone LFMb-24, Santa Cruz Biotechnology, California, USA) at 1:50; rabbit anti-Osterix (SP7; Abcam, Cambride, UK) at 1:200; rabbit anti-cleaved NICD1 (Cell Signaling Technology, Danvers, Massachusetts, USA) at 1:100. Fluorescently labeled secondary antibodies (Invitrogen, Thermo Fisher Scientific, Waltham, Massachusetts, USA) were used at 1:200.
Fluorescence images were acquired with an Olympus BX63 (Olympus, Münster, Germany) and a Nikon Ti2 Eclipse microscope (Nikon, Tokyo, Japan). All image measurements were performed with cellSens software (Olympus, Münster, Germany), NIS-Elements (Nikon, Tokyo, Japan) and FIJI software (ImageJ, http://fiji.sc/Fiji).
All subsequent quantifications were performed by 3 independent observers who were blinded to the sample identities.
## Angiogenesis
Invasion of osteogenic constructs by blood vessels, as well as vascular density were assessed after 1 week in vivo by immunostaining for CD31. Complete images of whole sections from the central part of each sample were acquired ($$n = 6$$ samples/group) and the area of invasion was measured by tracing the area occupied by blood vessels (CD31+ structures) and expressed as percentage of the total graft area. To quantify vessel density at week 1, at least 15 images were acquired per sample within the invaded areas ($$n = 6$$ samples/group) and vessel length density (VLD) was measured tracing the total length of vessels in the fields and by normalizing it to the field area (mm/mm2). Total vessels length (mm) was obtained multiplying the measured VLD by the area invaded by blood vessels. VLD at 4 and 8 weeks was quantified on 15 randomly acquired images, covering all the area of the tissue section, since constructs were completely invaded by these time-points.
## Proliferation and apoptosis
Proliferation and apoptosis of implanted human progenitor cells were quantified after 1 week in vivo by immunostaining for Ki67 or Cleaved-Caspase3, respectively, together with anti-Human nuclei. Images of whole sections for each condition ($$n = 6$$ samples/group) were divided in three concentric layers, each spanning a depth of 500 µm from the external surface, and the remaining central part was considered the core (Fig. 3a). Ki67+ or Caspase3+ human cells were manually counted in 6–8 fields of 300 µm2-area within each layer and expressed as percentage of the total number of human cells in the field.
## Bone formation
Bone tissue was detected by Masson’s trichrome staining (Réactifs RAL, Martillac, France), performed according to manufacturer’s instructions. Twenty whole-section reconstructions per sample ($$n = 6$$ samples/group) were acquired with transmitted light and bone tissue was quantified tracing the area occupied by mineralized matrix (dark green staining) and normalizing it by the total area of the section. In addition, the presence of mature bone (red staining) matrix was measured and normalized by the total amount of bone.
## Osteoclast detection
In order to detect osteoclasts, sections were stained for tartrate-resistant acid phosphatase (TRAP) activity. Briefly, after rinsing with water, slides were incubated for 20 min with 0.1 M Acetate Buffer (0.2 M Sodium Acetate, 0.2 M Acetic Acid, 50 mM Sodium L-tartrate dibasic dihydrate, pH 5.0) and then stained with 1 mg/ml of Fast Red LB salt (Sigma-Aldrich, St. Louis, Missouri, USA) and 1 mg/ml of naphtol AS-MX phosphate (Sigma-Aldrich, St. Louis, Missouri, USA) dissolved in 0.1 M acetate buffer for 1 h at 37 °C. After TRAP staining, nuclear counter staining was performed with Haematoxylin for 1 min at room temperature. TRAP-positive cells were quantified on 15 randomly-chosen fields per construct in 6 constructs/condition ($$n = 6$$ samples/group). Multinucleated TRAP + cells in the fields were counted manually and the total number was normalized by the field area.
## Quantification of osterix (OSX) positive human cells
The number of osteogenic committed human cells was assessed after 1 and 4 weeks in vivo by immunostaining for OSX in combination with an anti-human nuclei antibody (HuNu) and a blue-fluorescent DNA dye (DAPI). For the first week time point, images of whole sections for each condition ($$n = 5$$ samples/group) were acquired. A concentric layer of 1 mm depth from the external surface was traced and the OSX- positive human cells were automatically detected and counted using a custom-made macro for FIJI Software. Briefly, a region of interest (ROI) was traced within the 1 mm layer, thresholding was applied to the nuclei (DAPI), human nuclei and OSX channel; the number of OSX + human cells was obtained by colocalization of the three individual channels and expressed as percentage of the total number of human cells present inside the ROI.
Quantification of the number OSX- positive human cells at 4 weeks was performed as described above but on 10 randomly acquired fields per conditions throughout the entire construct ($$n = 6$$ samples/group).
## Quantification of bone sialoprotein II (BSPII)
To quantify the amount of human BSP, immunofluorescence staining was performed and 5–7 random fields ($$n = 6$$ samples/group) were acquired per each condition. The amount of human BSP was quantified using a custom-made macro in FIJI software. Briefly, a region of interest (ROI) was traced, thresholding was applied to the human BSP channel and the number of pixels above the threshold was normalized by the total number of the pixels of the ROI. The number of human cells (detected by immunofluorescence staining for an anti-human nuclei antibody) was quantified automatically on a whole section per each sample ($$n = 6$$ samples/group) using FIJI software and normalized by the tissue area.
## Quantitative real-time PCR
For RNA extraction from osteogenic grafts, constructs were immediately frozen in liquid nitrogen after harvesting ($$n = 8$$–10 samples/group). Tissues were disrupted and homogenized using a Qiagen Tissue Lyser (Qiagen, Basel, Switzerland) in 1 ml TRIzol Reagent (Thermo Fisher Scientific, Waltham, Massachusetts, USA) for every 100 mg of tissue. Total RNA from lysed tissues was isolated with a RiboPure RNA purification kit (Thermo Fisher Scientific, Waltham, Massachusetts, USA) according to manufacturer’s instruction. Total RNA from in vitro-cultured human BMSC ($$n = 6$$, from 3 independent donor) was isolated with a Quick-RNA *Miniprep plus* kit (Zymo Research Europe GbmH, Freiburg im Breisgau, Germany) according to manufacturer’s instruction.
RNA from tissues and human BMSC was reverse-transcribed into cDNA with the SuperScript III Reverse Transcriptase (Thermo Fisher Scientific, Waltham, Massachusetts, USA). Quantitative Real-Time PCR (qRT-PCR) was performed on an ABI 7300 Real-Time PCR system (Applied Biosystems, Foster City, California, USA). Expression of genes of interest was determined using the following human-specific TaqMan gene expression assays (Thermo Fisher Scientific, Waltham, Massachusetts, USA): Runx2 (Hs01047973_m1); SP7/Osterix (Hs01866874_s1); BSP (Hs00913377_m1); BGLAP/Osteocalcin (Hs01587814_g1). Reactions were performed in duplicate for each template, and normalized to expression of the GAPDH housekeeping gene (Hs02786624_g1). Relative mRNA expression was defined as 2−ΔCt, where ΔCt = Ct Target Gene – Ct GAPDH. The significance of differences was calculated on ΔCt values.
## Statistics
Data are presented as mean ± standard error of the mean (SEM). The significance of differences was assessed with the GraphPad Prism 9 software (GraphPad Software, San Diego, California, USA). The normal distribution of all data sets was tested and, depending on the results, multiple comparisons were performed with the parametric one‐way analysis of variance (ANOVA) followed by the Bonferroni test, or with the nonparametric Kruskal–Wallis test followed by Dunn’s post‐test. Percentage of proliferating and dying human cells were first normalized by log2‐transformation and then analyzed by one‐way ANOVA followed by Bonferroni test for multiple comparisons. Differences were considered statistically significant if $p \leq 0.05.$
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---
title: Nigella sativa seeds mitigate the hepatic histo-architectural and ultrastructural
changes induced by 4-nonylphenol in Clarias gariepinus
authors:
- Mahmoud Abd-Elkareem
- Alaa El-Din H. Sayed
- Nasser S. Abou Khalil
- Mohamed H. Kotob
journal: Scientific Reports
year: 2023
pmcid: PMC10011539
doi: 10.1038/s41598-023-30929-w
license: CC BY 4.0
---
# Nigella sativa seeds mitigate the hepatic histo-architectural and ultrastructural changes induced by 4-nonylphenol in Clarias gariepinus
## Abstract
Due to its prevalence in aquatic environments and potential cytotoxicity, 4-nonylphenol (4-NP) has garnered considerable attention. As a medicinal plant with numerous biological activities, *Nigella sativa* (black seed or black cumin) seed (NSS) is widely utilized throughout the world. Consequently, this study aimed to examine the potential protective effects of NSS against 4-NP-induced hepatotoxicity in African catfish (Clarias gariepinus). To achieve this objective, 18 fish (351 ± 3 g) were randomly divided into three equal groups for 21 days. The first group serves as a control which did not receive any treatment except the basal diet. The second and third groups were exposed to 4-NP at a dose of 0.1 mg L−1 of aquarium water and fed a basal diet only or supplemented with $2.5\%$ NSS, respectively. The histological, histochemical, and ultrastructural features of the liver were subsequently evaluated as a damage biomarker of the hepatic tissue. Our results confirmed that 4-NP was a potent hepatotoxic agent, as 4-NP-intoxicated fish exhibited many lesions. Steatohepatitis, ballooning degeneration, sclerosing cholangitis, and coagulative necrosis of melanomacrophagecenters (MMCs) were observed. Hemosiderin, lipofuscin pigments, and proliferation of fibroblasts, kupffer cells, and telocytes were also demonstrated in the livers of 4-NP-intoxicated fish. In addition, decreased glycogen content and increased collagen deposition were observed in the hepatic tissue. Hepatocytes exhibited ultrastructural alterations in the chromatin, rough endoplasmic reticulum, smooth endoplasmic reticulum, mitochondria, lysosomes, and peroxisomes. Co-administration of $2.5\%$ NSS to 4-NP-intoxicated fish significantly reduced these hepatotoxic effects. It nearly preserved the histological, histochemical, and ultrastructural integrity of hepatic tissue.
## Introduction
4-NP is an endocrine-disrupting element derived from a nonionic surface-active agent, 4-NP ethoxylates. The latter is widely used for domestic, agricultural, and industrial purposes1. Due to its lipophilic nature, 4-NP has a strong tendency to deposit in aquatic organisms2 until it reaches the human consumer, causing wide spread concern1.
4-NP is a highly hepatotoxic substance that triggers the release of hepatic enzymes into the bloodstream by causing multiple histopathological alterations3. It resulted in the up-regulation of apoptotic mediators, the acceleration of reactive oxidant production, and the suppression of redox stabilizer activity4. Numerous histopathological abnormalities in the liver following 4-NP exposure had been reported in the scholarly articles. These include hepatitis, lymphocytic cell infiltration, coagulative necrosis, nuclear changes, fatty degeneration, hepatic steatosis, disappearance of cell borders, glycogen depletion, increase in lipofuscin and hemosiderin pigments, and necrosis of endothelial cells5–7. Another report reveals fibrosis surrounding the vasculature and bile ductules, a dramatic increase in the size and number of MMCs, and the presence of necrotic macrophages8.
African catfish (Clarias gariepinus, Teleostei: Clariidae) is one of the most abundant species in the River Nile and its tributaries9. As a result of its exceptional physiological and economic characteristics from the perspectives of breeders and consumers10, commercial farming is a cost-effective opportunity, and investments in this field are rapidly increasing. In order to ensure safe feeding practices, significant efforts are made to enhance its health and prevent xenobiotics from reaching humans through their consumption3,11.
Black cumin (Nigella sativa, Ranun culaceae) is cultivated extensively in the tropical and subtropical zones12. NSS possesses a variety of redox stabilizers and cytoprotective phytochemicals, making it an excellent candidate for combating the environmental toxins7,13. Thymoquinone (TQ), thymol, and α-hederin are effective hepato-protective phytochemicals in *Nigella sativa* by limiting the overgeneration of reactive oxidants and inflammatory mediators, suppressing the lipid peroxidation cascade, and stimulating antioxidant network14. Most of the literature revealed that dietary inclusion of NSS in fish is used as a valuable strategy to reduce the hepatotoxicity of environmental pollutants by enhancing the cell membrane integrity, reductive/oxidative balance, and histo-architectural characteristics15. Therefore, this study aims to investigate the cytoprotective effect of NSS on the hepatic histoarchitecture, cytochemistry, and ultrastructure of 4-NP-burdened Clarias gariepinus.
The findings of this investigation may shed light on the importance of limiting the use and safe disposal of 4-NP as an emerging environmental toxicant, as well as the efficacy of natural products as a shield against its adverse health effects.
## Fish
Eighteen adults male Clarias gariepinus, weighing 351 ± 3 g, were utilized in this experiment. They appeared typical and healthy (AFS-FHS, 2003). The fish were acclimated for two weeks in aerated glass tanks containing dechlorinated tap water. Fish were fed commercial feed pellets at a rate of $5\%$ of their body weight per day for two feedings16.
## Experimental design
The pre-acclimatized fish were divided into three groups ($$n = 6$$ fish per group) using a randomization technique. The first group serves as a control which did not receive any treatment except the basal diet. However, the second groups were exposed only to 4-NP (purity, $99.3\%$; Sigma-Aldrich, Schnelldorf, Germany) at a dose of 0.1 mg L−1 of aquarium water17. While the third group were fed a basal diet supplemented with $2.5\%$ NSS (purchased from the Ministry of Agriculture Selling Port, Giza, Egypt) along with the same dose of 4-NP18. The water quality, composition of the basal diet, preparation of NSS, and the method of addition of NSS to the diet were described in our previous study16.
## Histological analysis
Twenty-one days after the beginning of the experiment, the fish were anesthetized using ice19 and livers were collected from all fish for further analysis. Pieces of liver were fixed in $10\%$ neutral buffered formalin and Bouin’s fluid. The samples were then dehydrated in ascending ethanol concentrations, clarified in methyl benzoate, and embedded in paraffin wax. The following histological stains were applied to 5 µm-thick paraffin sections:Hematoxylin and eosin for general histological examination20.Periodic acid Schiff (PAS) stain to detect neutral mucopolysaccharides21.Perls’ Prussian blue for ferric iron and MMCs detection8.Crossmon’s trichrome stain for collagen fiber detection22.Acridine orange stain for identification of necrotic hepatocytes 23.
## Transmission electron microscopy
Two millimeter-thick pieces of freshly sacrificed fish liver were fixed in $2.5\%$ glutaraldehyde in phosphate buffer (pH 7.2). The fixed specimens were then washed in 0.1 M phosphate buffer and postfixed with$1\%$ osmium tetroxide. The specimens were then dehydrated in an ascending alcohol series and encased in araldite resin. Using a Reichert ultra-microtome, semi-thin sections were cut and stained with $1\%$ toluidine blue. The ultrathin sections were then stained with uranyl acetate and lead nitrate24,25 and examined with a JeolJem 1200 EX Transmission Electron Microscope at the Electron Microscope Unit of Assiut University.
## Negative image analysis
Negative image analysis was carried out so that the transmission electron photomicrographs could provide greater clarity26.
## Ethical approval
All methods were carried out following the relevant regulations and ARRIVE guidelines. Studies were approved by the Research Ethics Committee of the Molecular Biology Research and Studies Institute (VET-22-04-R), Assiut University, Assiut, Egypt.
## The protective effects of NSS against hepatic histopathological changes in 4-NP-intoxicated Clarias gariepinus
The liver of the control fish was composed of normal hexagonal cords of hepatocytes arranged around the central vein. Hepatocytes were large in size, polygonal in shape, and contained vesicular central nuclei in homogeneous acidophilic cytoplasm. The hepatic cords were separated by blood sinusoids, which appeared to be communication channels occupied by blood cells and lined by endothelial cells and Kupffer cells (Figs. 1A1,A2, 2A,D).Figure 1Photomicrograph of paraffin sections in the liver of control, 4-NP-intoxicated, and $2.5\%$ NSS treated groups. ( A1, A2) Control liver section depicts the normal histological architecture, which consists of a central vein (CV), hepatocytes (H), and a triangular portal area containing bile ductule (BD), portal vein (V), and melanomacrophage centers (MMC). ( B1, B2) 4-NP-intoxicated liver section. ( B1) *Exhibiting a* rise in melanomacrophage centers (MMC) and congested veins (V). ( B2) Connective tissue proliferation (CT) and inflammatory cell infiltration around bile ductules (BD) (Sclerosing cholangitis). ( C1, C2) Liver sections of the $2.5\%$ NSS treated group demonstrate partially restored histological architecture and normal appearance of the portal area, portal veins (V), hepatocytes (H), and bile ductules (BD) with a reduction in the number of melanomacrophage centers (MMC) in comparison to the 4-NP-intoxicated group. Scale bar in (A1–C1) = 200 μm; (A2–C2) = 100 μm, Hematoxylin and Eosin stain. Figure 2Photomicrograph of paraffin (A–C) and semi-thin (D–F) sections in the liver of control, 4-NP-intoxicated, and $2.5\%$ NSS treated groups. ( A) Control liver sections displaying healthy hepatocytes arranged in hepatic cords radiating from the central vein (CV) and were separated by blood sinusoids (BS), which were lined by endothelial cells (arrow) and kupffer cells (arrowhead). Hepatocytes (H) appeared as polyhedral cells with acidophilic cytoplasm and a vesicular, spherical nucleus in the center. ( B) 4-NP-intoxicated liver section showing hepatocytes with widespread fatty degeneration as clear, small vacuoles filling the majority of hepatocytes’ cytoplasm (yellow arrow). Other hepatocytes exhibited stages of coagulative necrosis, which are characterized by deeply stained acidophilic cytoplasm and pyknotic, karyorrhexis, or nuclei loss (CN). A mononuclear inflammatory cell infiltration (infl) confirms the presence of acute hepatitis (black arrow). ( C) The hepatic structure, hepatocytes (H), and central vein (CV) of the $2.5\%$ NSS treated group were comparable to those of the control group, with no inflammatory cell infiltration or vacuolar fatty degeneration (arrow). ( D) Semi-thin section of liver of control *Clarias gariepinus* displayed healthy hepatocytes (H) with a centralized nucleus and distinct cell outlines (arrow). ( E) Semi-thin section of 4-NP-intoxicated *Clarias gariepinus* displaying cloudy swelling in the hepatocytes (H) with clear pale cytoplasm (arrow) and area of ballooning degeneration (star) containing hepatocytes with faintly stained cytoplasm, absence of cell outlines, and eccentric, pyknotic, or absent nuclei. ( F) Semi-thin section of the liver of $2.5\%$ NSS treated *Clarias gariepinus* demonstrates an improvement in the histological appearance of the liver, with more healthy, deeply stained hepatocytes (H) containing round vesicular nuclei and distinct cell outlines (arrow). Scale bar in (A–C) = 50 μm and stained with Hematoxylin and Eosin stain; (D–F) = 20 μm and stained with Toluidine blue stain.
Following exposure to 0.1 mg L−1 4-NP for 21 days, liver sections exhibited a loss of hexagonal architecture. Most hepatocytes displayed coagulative necrotic changes as the disintegration of most cytoplasmic contents with faintly stained cytoplasm and pyknotic nuclei or loss of nuclei (Figs. 1B2, 2B). Other hepatocytes exhibited vacuolar fatty degeneration with eccentric nuclei (macro and microvesicular steatosis) in their cytoplasm (Fig. 2B). Blood stagnation was also observed in the dilated sinusoids, central veins, and portal veins (Fig. 1B1). The infiltration of mononuclear inflammatory cells was also observed in the necrotic areas, indicating the presence of hepatitis (Fig. 2B). Coadministration of $2.5\%$ NSS improved and partially restored the histological structures of the liver of 4-NP-intoxicated fish, Most of the hepatocytes had pink-stained cytoplasm and lacked clear fat vacuoles (Figs. 1C1,C2 & 2C).
Examining semi-thin sections stained with toluidine blue revealed that the liver of the 4-NP-exposed group contained large areas of ballooning degeneration (cloudy swelling) that consisted of hepatocytes with faintly stained cytoplasm, absence of cell outlines, and nuclei that are eccentric, pyknotic, or absent (Fig. 2E) compared to the liver of the unexposed group (Fig. 2D). Coadministration of $2.5\%$ NSS improved the histological appearance of hepatocytes manifested by the presence of stained cytoplasm and nuclei in the center, resembling the control group (Fig. 2F).
## The protective effects of NSS on the amount of the hepatic MMCs in 4-NP-intoxicated Clarias gariepinus
The amount (size and number) of Sudan black, Nile blue, Prussian blue, and PAS-positive hepatic MMCs increased in the 4-NP-intoxicated group (Fig. 3B,E,H, respectively). These hepatic MMCs were primarily gathered around the clogged central veins, bile ductules, and portal veins in the portal regions. The hepatocytes of *Clarias gariepinus* intoxicated with 4-NP also displayed abundant Sudan black and Nile blue-positive lipofuscin pigments (Fig. 3B,E, respectively) as well as large numerous Prussian blue-positive hemosiderin pigments (Fig. 3H).Figure 3Photomicrograph of paraffin-embedded sections illustrating the hepatoprotective effects of NSS against 4-NP-induced hepatic MMCs disturbance in Clarias gariepinus. ( A) Liver of control *Clarias gariepinus* showing a less amount of Sudan black B-positive MMCs (arrow). ( B) Liver of 4-NP-intoxicated *Clarias gariepinus* showing an increase in the amount of Sudan black B-positive MMCs (arrow). ( C) Liver of $2.5\%$ NSS treated *Clarias gariepinus* showing a fewer Sudan black B-positive MMCs. Note the central vein (CV) and the abundant Sudan black-positive lipofuscin pigments (arrowheads) that filled the hepatocytes of 4-NP-intoxicated *Clarias gariepinus* compared to the control group and the NSS + 4-NP treated group. ( D) The liver of control *Clarias gariepinus* contains a fewer Nile blue-positive MMCs (arrow). ( E) The liver of 4-NP-intoxicated *Clarias gariepinus* demonstrates an increase in Nile blue-positive MMCs (arrow) surrounding the central vein (CV). ( F) Liver of $2.5\%$ NSS treated *Clarias gariepinus* showing a decline in the amount of Nile blue-positive MMCs (arrow) similar to control liver. Note the central vein (CV) and the abundant Nile blue-positive lipofuscin pigments (arrowheads) that filled the hepatocytes of 4-NP-intoxicated *Clarias gariepinus* compared to the control and NSS + 4-NP treated groups. ( G) The liver of the control group displayed a small number of Prussian blue-positive MMCs of small size. ( H) The 4-NP-intoxicated group’s liver contains numerous large Prussian blue-positive MMCs. ( I) The liver of the $2.5\%$ NSS + 4-NP-treated group reveals a small number of Prussian blue-positive MMCs of small size. Note the central vein (CV) and the abundant Prussian blue-positive hemosiderin pigments (arrowheads) that filled the hepatocytes of 4-NP-intoxicated *Clarias gariepinus* compared to the control group and the NSS + 4-NP treated group. Scale bar in (A–C, G–I) = 100 μm; (D–F) = 200 μm. ( A–C) were stained with Sudan black B and Haematoxylin stain, (D–F) were stained with Nile blue stain, and G, H and I were stained with Perls’ Prussian blue stain.
Coadministration of $2.5\%$ NSS improved these characteristics (Fig. 3C,F,I), which resembled those of the control group (Fig. 3A,D,G).
## The protective effects of NSS on the hepatic collagenous fiber proliferation and glycogen content in 4-NP-intoxicated Clarias gariepinus
Fibrous tissue proliferation around the congested veins and bile ductules in the portal area and inflammatory cell infiltration around the fibrotic bile ductules indicated the presence of sclerosing cholangitis in the liver of 4-NP-intoxicated *Clarias gariepinus* (Figs. 1B2 and 4B). Conversely, fibrous connective tissue proliferation around the central veins and bile ductules in the NSS + 4-NP treated group (Figs. 1C2, 4C) was less than in the 4-NP-intoxicated group and resembled the normal non-exposed group (1A1, 1A2& 4A).Figure 4Photomicrograph of paraffin sections in the liver of control, 4-NP-intoxicated, and $2.5\%$ NSS treated groups. ( A, D) Liver of control Clarias gariepinus. ( A) Displaying normal hepatic structure and hepatocytes (H) and minimal connective tissue around the central veins (Arrow), (D) Showing the presence of numerous fine PAS-positive glycogen granules in the cytoplasm of hepatocytes. ( B, E) Liver of 4-NP-intoxicated Clarias gariepinus. ( B) Showing proliferation of connective tissue around the bile ductule (BD) (sclerosing cholangitis) in the portal area (black arrow) and around the central veins (white arrow). ( E) Demonstrating fatty change (macrovesicular steatosis) of hepatocytes with depletion in the PAS-positive glycogen granules (arrow). Note the PAS-positive MMCs. ( C, F) Liver of $2.5\%$ NSS treated Clarias gariepinus. C: Showing partial decrease in the amount of connective tissue in the portal area (arrow) and around the central veins (arrowheads) compared to 4-NP-treated Clarias gariepinus. ( F) Showing hepatocytes partially restored PAS-positive glycogen granules in the cytoplasm (arrow). Scale bar in (A) = 100 μm; (B, C) = 200 μm; (D, E), $F = 50$ μm. ( A–C) were stained with Crossmon’strichrome stain. ( D–F) stained with periodic acid Schiff reagent (PAS) and Hematoxylin stain.
Numerous glycogen granules in the cytoplasm of hepatocytes were diminished in the 4-NP-exposed group compared to the non-exposed control group (Fig. 4D,E). Coadministration of $2.5\%$ NSS partially restored hepatocyte glycogen content (Fig. 4F).
## The protective effect of NSS against 4-NP-induced hepatic DNA damage
The liver of the 4-NP-intoxicated group exhibited necrotic hepatocytes and damaged nuclei (Fig. 5B). While the liver of NSS + 4-NP treated group exhibited hepatocytes with healthy nuclei (Fig. 5C), nearly identical to the control liver (Fig. 5A). When stained with acridine orange and viewed through a fluorescence microscope, the damaged DNA appeared as orange spots. Figure 5Fluorescence photomicrograph of paraffin-embedded sections illustrating the hepatoprotective effects of NSS against 4-NP-induced hepatic damage in Clarias gariepinus. ( A) Hepatocytes of the control group’s liver have healthy nuclei (arrowheads). ( B) Numerous hepatocytes are visible in the liver of the 4-NP-intoxicated group containing necrotic and broken nuclei (arrowheads) C: The NSS + 4-NP-treated group’s liver contains hepatocytes with healthy nuclei (arrowheads). The necrotic nuclei (DNA) stained with acridine orange appeared as orange spots under a fluorescence microscope. Scale bar = 50 μm, acridine orange stain.
## The protective effects of NSS against the hepatic ultrastructural changes in 4-NP-intoxicated Clarias gariepinus
The hepatocytes of the control group had euchromatic nuclei with distinct nucleoli, well-developed rough and smooth endoplasmic reticulum, abundant mitochondria, few lysosomes, and peroxisomes. Few fibroblasts were observed in the hepatic tissue of the control group (Figs. 6A,D, 7A). On the other hand, many of the hepatocytes in the 4-NP-intoxicated group exhibited decreasing heterochromatin and degenerated nucleoli, rough endoplasmic reticulum, and smooth endoplasmic reticulum. A few mitochondria and lysosomes were also demonstrated in the hepatocytes of the 4-NP-intoxicated group (Figs. 6B,E, 7B). In addition, kupffer cells, fibroblast proliferation and telocytes were observed in the liver tissue of 4-NP-intoxicated fish (Figs. 6B,E, 7B). In contrast, the hepatocytes of the NSS + 4-NP-treated group exhibited euchromatic nuclei, nearly healthy rough and smooth endoplasmic reticulum and mitochondria. In addition, the liver of the NSS + 4-NP-treated group contained lymphocytes, peroxisomes, phagocytic vacuole, and a significant number of lysosomes (Figs. 6C,F, 7C).Figure 6Colored transmission electron photomicrographs illustrating the hepatoprotective effects of NSS against 4-NP-induced hepatotoxicity in *Clarias gariepinus* using colored transmission electron micrographs. ( A) Hepatocyte of the control group displaying euchromatic nucleus (N) with distinct nucleolus (Nu), well-developed rough endoplasmic reticulum (rER), well-developed smooth endoplasmic reticulum (sER), abundant mitochondria (M), and few lysosomes (Ly) and peroxisomes (P). ( B) Hepatocyte of the 4-NP-intoxicated group showing nucleus (N) with decreasing amount of heterochromatin and degenerated nucleolus (Nu), degenerated rough endoplasmic reticulum, degenerated smooth endoplasmic reticulum, Kupffer cells (K), few mitochondria (M), and lysosomes (Ly). ( C) Hepatocyte of the NSS + 4-NP-treated group displaying euchromatic nucleus (N), nearly healthy rough endoplasmic reticulum (rER), smooth endoplasmic reticulum (sER), mitochondria (M), phagocytic vacuole, and lysosomes (Ly). Note the lymphocyte (Lym). ( D) Liver of the control group showing few fibroblasts (Fb). ( E) Liver of 4-NP-intoxicated group showing numerous fibroblasts (Fb). Note the telocyte (TC). ( F) Liver of NSS + 4-NP treated group showing few fibroblasts (Fb). Note the numerous lysosomes (Ly) and peroxisomes (P) on the hepatocytes. Figure 7Colored transmission electron photomicrographs illustrating the hepatoprotective effects of NSS against 4-NP-induced hepatotoxicity in Clarias gariepinus. ( A) Hepatocyte of the control group with euchromatic nucleus (N), well-developed rough endoplasmic reticulum (rER), well-developed smooth endoplasmic reticulum (sER), abundant mitochondria (M), and few lysosomes (Ly). ( B) Hepatocyte of the 4-NP-intoxicated group displaying euchromatic nucleus (N), a small amount of rough endoplasmic reticulum (rER), smooth endoplasmic reticulum (sER), small sized mitochondria (M), and a small number of lysosomes (Ly). Note the telocyte (TC). ( C) Hepatocyte of the NSS + 4-NP-treated group displaying euchromatic nucleus (N), rough endoplasmic reticulum (rER), smooth endoplasmic reticulum (sER), mitochondria (M), and myelin figure (MF), as well as numerous lysosomes (Ly).
The colored transmission electron photomicrographs (Figs. 6, 7) and negative images (Fig. 8) of Fig. 6 were employed to determine the cytoprotective effect of NSS against 4-NP-induced hepatocellular damage in Clarias gariepinus. Figure 8Negative images of the photomicrographs shown in Fig. 6.
## Discussion
Nigella sativa is fully armed with redox stabilizers and cytoprotective constituents11 giving a driving force for the scientific community to utilize it as an effective approach against several aquatic contaminants. One of the most dangerous compounds agents is 4-NP owing to resistance to biodegradation, wide prevalence in the ecosystem, and high probability to reach to the consumers27,28. It represents a main risk hazard as it induces a battery of toxicological aspects, including reproductive dysfunction29, immunosuppressive, and hepato- and nephrotoxic impacts3. Thus, this study is designed to highlight the potential protective effects of *Nigella sativa* against the histo-architectural and ultrastructural changes in the liver of 4-NP-intoxicated Clarias gariepinus.
The histological changes in the livers of 4-NP-exposed *Clarias gariepinus* are consistent with those described in previous scholarly works7,30. By increasing pro-inflammatory cytokines31 and chemotactic proteins, 4-NP could be associated with inflammatory cell infiltration in the hepatic tissues32. The activation of pro-apoptotic markers may be one of the negative effects of inflammation33. Abd-Elkareem et al.7 reported that the hepatocytes of *Clarias gariepinus* contain vacuolated cytoplasm under 4-NP stress. This outcome can be explained by the overexpression of the lipogenic enzyme genes and the downregulation of the transcription factors involved in fatty acid oxidation33. These metabolic changes result in the deposition of triglycerides, which are removed during tissue processing by organic solvents, leaving hollow spaces unstained7. Aggregation of misfolded proteins and expansion of the endoplasmic reticulum lumen occurred when the hepatocytes exposed to peroxidative insult34.
The morphology and content of pigments in MMCs are sensitive to various internal and external factors35; therefore, we used staining to monitor their differential responses to 4-NP contamination. As previously observed, the content of Nile blue-positive MMCs increased significantly36,37, reflecting its ability to neutralize toxicants38 and quench reactive oxidants39. The increase in melanin is a morphological response triggered by reactive species overloading36, to counteract them, as well as the harmful derivatives that emerged from the breakdown of cellular components40. In multiple ecotoxicological studies, hepatic melanin is thus a sensitive biomarker for aquatic pollution8,41. Numerous Sudan black and Nile blue-positive lipofuscin pigments were present in the hepatocytes of 4-NP-intoxicated Clarias gariepinus, similar to recent findings37. Lipofuscin is the final consequence of the accumulation of highly oxidized cross-linked proteins, indicating that the cellular proteolytic capacity falls below the sub-threshold level required to manage the redox disturbance42. Overproduction of free radicals and induction of cell death7,36 may account for the increased lipofuscin amount in MMCs following the exposure to 4-NP. Lipofuscin triggers a vicious redox instability and apoptosis cycle by increasing caspase-3 activity43 and producing free radicals44. Numerous hepatocytes with necrotic and damaged nuclei were discovered in the 4-NP group, confirming this fact. As an adaptive response to 4-NP-induced tissue destruction, the abundance of lipofuscin reflects the upregulation of phagocytic activity in MMCs7.
Depletion of intrahepatic glycogen content in response to 4-NP exposure is comparable to that observed in Clarias gariepinus7 but not in Italian newt (Lissotrito nitalicus)36. This contradiction results from differences in fish species, doses of 4-NP, and duration of the intervention. Long-term exposure to 4-NP disrupts the insulin signaling downstream pathway triggering insulin resistance and alters the carbohydrate metabolizing machinery due to oxidative damage to the liver45.
The upregulation of collagen expression is responsible for the excessive fibrosis surrounding the central veins and bile pathway in the 4-NP-exposed group46. The impeded intrahepatic perfusion, secondary to fibrosis, stimulates advanced fibrogenesis and subsequent portal hypertension47.
The genotoxicity of 4-NP is caused by the promotion of redox disequilibrium48, increase in the transcript level of pro-apoptotic regulators33, inhibition of the endoplasmic reticulum Ca2+ pump49, and loss of mitochondrial membrane electricity50.
Bernabò et al.36 found that the ultrastructural changes in hepatocytes following the 4-NP intoxication are similar to those found in Lissotrito nitalicus. It is common knowledge that the nucleolus is the RNA processing center and ribosome factory51. Thus, the degeneration in the nucleoli of hepatocytes caused by 4-NP supplementation reveals a slowdown in translating genetic codes into polypeptide sequences. This slowdown in translating genetic codes resulting in a decrease in the cell’s ability to produce structural and functional proteins, ultimately leading to alterations in the hepatic microenvironment.
Kupffer cells appeared in the 4-NP group, indicating a trial of the hepatic protective device to stimulate the phagocytic activity of sinusoidal cells in order to detoxify 4-NP and its degradation intermediates52. The hypertrophy and hyperplasia in the MMC of 4-NP group are consistent with previous observations in 4-NP-intoxicated *Clarias gariepinus* and goldfish (Carassius auratus)8,37. This response is considered a compensatory adaptation8 to a suppressed innate immunity, as it is closely linked to pollutant loading53.
Bernabò et al.36 described the degeneration in the rough endoplasmic reticulum in response to exposure to various chemical toxins, including 4-NP. The remarkable appearance of an increased number of cytosolic lipid bodies is associated with damage to the rough endoplasmic reticulum, indicating a potential causative link54. This link may result from a decrease in protein synthesis, which inhibits the consumption of lipids in lipoprotein aggregation36.
The lack of mitochondria indicates that 4-NP can directly inhibit ATP synthesis, resulting in bioenergetic deficiency55. Due to their lipophilic nature, the endocrine disrupting chemicals interact with the hydrophobic lipid matrix of membranes disrupting the phospholipid vesicles56. The decrease in mitochondrial biogenesis may be attributable to a change in peroxisome proliferator-activated receptor-γ coactivator-1α33; a co-transcriptional regulation factor responsible for this process by interacting with numerous transcription proteins. As mitochondria are especially susceptible to oxidative stress, an excess of free radicals could be a leading cause of mitochondrial damage57. DNA mutations, respiratory chain damage, membrane permeability disruption, and mitochondrial defense suppression are induced by mitochondrial stress58.
The presence of telocytes in 4-NP-intoxicated liver tissue indicates attempts to promote tissue regeneration and repair, slow down abnormal stimulation of immune cells and fibroblasts, and reduce the matrix architecture transformation during fibrosis59.
According to a previous report, the normalization of hepatic histo-architecture following the administration of NSS to 4-NP-intoxicated fish is attributable to an improvement in the hepatic antioxidant defensive network15. NSS has an abundance of redox stabilizers, including thymoquinone, flavonoids, and terpenoids11. NSS boosts the transcript level of redox stabilizers60, and shifts the cell fate decisions to pro-survival events61. By elevating proliferating cell nuclear antigen, TQ stimulates cell multiplication, thereby enhancing the ability of cell to regenerate after tissue injury62,63.
In our study, NSS supplementation protected the liver from excessive fibrosis and restricted the inflammatory infiltration similar to that observed in the glomeruli of 4-NP-intoxicated Clarias gariepinus13 and the myocardium of lipopolysaccharide-intoxicated rats64. This result can be explained by the ability of 4-NS to reduce the fibrogenic and proinflammatory mediators65,66.On the genetic level, TQ inhibits the expression of profibrotic and nuclear factor Kappa-B67,68.
The restoration of hepatic glycogen content in the NSS + 4-NP-treated group is in the same line as that observed in Rohu (Labeorohita) fingerlings exposed to diethyl phthalate15, secondary to stimulation of insulin release which promotes glycogenesis69.
NSS intervention decreased the number of hepatic MMCs and the characteristics of autophagy in 4-NP-intoxicated fish, paralleling the reduction observed in the glomeruli of nephrotoxic Clarias gariepinus13. The immune response of MMCs was normalized due to the motivation in the detoxification and biotransformation pathways of xenobiotics70 and the limitation in generating reactive oxidants11.
The appearance of euchromatic nuclei indicates the restoration of active transcription, paving the way for the resumption of normal cellular synthetic apparatus71. This action is necessary to restore hepatocyte viability and repair damaged hepatocytes72. The presence of peroxisome in the 4-NP + NSS group contributes to the maintenance of redox homeostasis73, elimination of oxidizing proteins74, and decrease in the likelihood of lipid peroxidation75. The presence of phagocytic vacuoles in the hepatic tissue of the 4-NP + NSS group indicates the activation of immune defensive mechanisms to counteract the cytotoxicity of 4-NP metabolites through electrophile and oxidant detoxification76. The lysosome is a necessary prerequisite for autophagy. This process promotes cell survival by removing damaged organelles and protein aggregates and promoting bioenergetic balance77. Thus, the abundance of lysosomes may be associated with the effort to repair and regenerate the attacked cell and remove cell debris caused by 4-NP-induced oxidative damage. This response was first observed in the epithelial cells of proximal tubule23.
The role of NSS as a useful alternative in preventing mitochondrial degeneration62 and endoplasmic reticulum stress78 signifies the restoration of the healthy characteristics of these organelles. This result may be due to down-regulation of apoptotic cascade and quenching of lipid peroxidation products, maintaining the normal membrane permeability of the cell organelles78.
The hepatocytes of 4-NP + NSS group displayed healthy nuclei. The genoprotective potential of NSS may be mediated by TQ, which increases the transcript levels of Bcl2, decreases the transcript levels of caspase-3 and Bax79, and inhibits oxidative stress-induced DNA fragmentation80.
In conclusion, NSS is an effective hepato-protective agent against the cytotoxicity of 4-NP in *Clarias gariepinus* by preserving the histological, histochemical, and ultrastructural integrity of the hepatic tissue. In order to evaluate the effects of these cytological improvements on the liver functions, additional research are required.
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|
---
title: Mangiferin relieves CCl4-induced liver fibrosis in mice
authors:
- Lijun Zhang
- Chuhe Liu
- Liufang Yin
- Cheng Huang
- Shengjie Fan
journal: Scientific Reports
year: 2023
pmcid: PMC10011547
doi: 10.1038/s41598-023-30582-3
license: CC BY 4.0
---
# Mangiferin relieves CCl4-induced liver fibrosis in mice
## Abstract
Hepatic fibrosis is a late stage process of many chronic liver diseases. Blocking the fibrosis process will be beneficial to the treatment and recovery of the diseases. Mangiferin has many pharmacological activities. Recently, it has been reported that mangiferin may relieve tissue fibrosis, including renal, myocardial, pulmonary fibrosis via anti-inflammatory and anti-oxidative effects in animal models. Here, we investigate the effects of mangiferin on CCl4-induced liver fibrosis and the underlying mechanism in mice. Thirty-two male C57BL/6 mice were randomly divided into 4 groups ($$n = 8$$ in each group), injected with carbon tetrachloride ($10\%$ CCl4) for 8 weeks, and oral administrated with mangiferin (50 mg/kg or 100 mg/kg) from the fifth week. The serum levels of ALT, AST were analyzed to evaluate liver function. H&E, Masson’s trichrome and Sirius red staining were used to assess liver morphology and the degree of liver fibrosis. Quantitative RT-PCR and Western blot were used to assay the gene expression and protein levels. The results showed that mangiferin alleviated the serum levels of AST, ALT, ALP, TBA and TBIL, reduced liver lesions, prevented hepatic parenchymal necrosis, and ameliorated collagen accumulation in the liver of CCl4-treated mice. Meanwhile, mangiferin inhibited the expression of inflammatory genes IL-6 and IL-1β, fibrogenic genes α-SMA, TGF-β and MMP-2 and bile acid metabolism genes ABCB4, ABCB11, SULT2A1 in the liver of CCl4-treated mice. Furthermore, mangiferin reduced collagen accumulation and HSCs activation, inhibited the p-IκB and p-p65 protein levels. Our results suggest that mangiferin could alleviate liver fibrosis in CCl4-treated mice through inhibiting NF-κB signaling, and mango consuming may have beneficial effects to hepatic fibrosis.
## Introduction
Fibrosis is a pathological condition caused by an abnormal process of tissue regeneration in response to chronic injury, which leads to continuous activation of fibroblasts and permanent tissue damage1,2. Liver fibrosis is an important global health problem, characterized by the excessive deposition of extracellular matrix (ECM) and the structural disorder of the liver3, which eventually leads to the end stage of liver diseases such as liver cirrhosis and hepatocellular carcinoma. There are many causes for liver fibrosis, such as viral hepatitis, alcoholic liver, fatty liver disease and autoimmune diseases and so on. Among the activated fibrous cells, hepatic stellate cells (HSCs) play the most important role in the development of hepatic fibrosis4, which could transform into proliferative and contractile myofibroblast-like cells, one of the sources of the accumulation of ECM5. In addition, hepatocytes, portal fibroblasts, and bone marrow derived myofibroblasts precursors are the source of ECM6. At present, the therapeutic intervention of liver fibrosis involves in preventing the stimulus or harmful cause, inhibiting hepatic inflammation, interfering in the activation of stellate cells and promoting the deterioration of extracellular matrix7.
In animals, various causes of fibrogenesis have been studied, but the main hepatic fibrosis model is based on repeated application of carbon tetrachloride (CCl4) over a period of several weeks8. CCl4 is a hepatotoxin that causes lobular central hepatic necrosis, proinflammatory and profibrotic cytokine release, and the metabolic activation in the liver9, consequently, results in liver fibrosis and even cirrhosis after long-term exposure.
Mangiferin (1, 3, 6, 7-tetrahydroxyxanthone-C2-β-D-glucoside), a naturally occurred C-glucosyl xanthone, is primarily isolated from mango (Anacardiaceae), and exists in at least 16 plant families, including Iridaceae and Gentianaceae10. It has been reported that mangiferin has strong antioxidant11 and anti-inflammatory12 effects and may exert beneficial properties to acute inflammation of the organs such as lung13, kidney14 and cardiovascular system. Moreover, it has also been reported that mangiferin has a wide range of pharmacological roles, including immunomodulatory15, anticancer16, antibacterial, antiviral and neuroprotective17 effects. The emerging evidence has shown that mangiferin could alleviate organ fibrosis18, including renal fibrosis19,20, myocardial fibrosis21, pulmonary fibrosis22 via restraining myofibroblast activation, anti-oxidant and anti-inflammatory functions. Furthermore, Hou et al. found that oral administration of mangiferin to adults did not cause significant side effects, suggesting that there is no toxicity to human23. Therefore, mangiferin has been shown as potential candidate for anti-fibrosis agent. In the present study, we investigate whether mangiferin has a therapeutic effect in CCl4-induced hepatic fibrosis in mice.
## Mangiferin attenuates CCl4 induced liver fibrosis in mice
The administration of CCl4 is known to induce toxicity in the liver by producing highly reactive metabolites, resulting in severe damage to liver cells and subsequently developing into fibrosis24. In order to determine whether mangiferin could relieve liver injury, mice were intraperitoneally injected with $10\%$ CCl4 for 8 weeks, and mangiferin was given intragastrically every day from the fifth week (Fig. 1A). At the end of the experiment, the liver/body weights were calculated, and the levels of AST, ALT, ALP, TBA and TBIL were measured. We found that mangiferin reduced the liver/body weight rational increased by CCl4, but did not changed body weight (Fig. 1B,C). The levels of serum ALP, ALT and AST, important markers reflecting liver function25, were increased in CCl4-treated group compared to those of CCl4-untreated group, indicating the liver function was impaired by the CCl4 administration. Mangiferin treatment significantly reduced the ALT, AST and ALP level both in low and high dose mangiferin treated groups (Fig. 1D–F). The levels of serum TBA and TBIL, representing the accumulation of bile acids in liver fibrosis, were all increased in CCl4-treated group. Mangiferin treatment also significantly attenuated TBA and TBIL content between CCl4-treated group and mangiferin-treated group (Fig. 1G–H). These data suggest that mangiferin could alleviate liver injury and improve liver function caused by the administration of CCl4.Figure 1Mangiferin relieves liver injury induced by CCl4. ( A) The diagram of treatment of mouse model. ( B) Body weight. ( C) The liver/body weight. ( D) Serum ALT level. ( E) Serum AST level. ( F) Serum ALP level. ( G) Serum TBA level. ( H) Serum TBIL level. The mice were injected intraperitoneally $10\%$ CCl4 (CCl4 was dissolved in olive oil) at dose of 2 mg/kg of body weight for 8 weeks, the control group mice were injected with an equal amount of olive oil. The mangiferin treatment group were injected intraperitoneally $10\%$ CCl4 and orally administrated mangiferin at the dose of 50 mg/kg and 100 mg/kg (0.1 ml/10 g of body weight). Date are presented as means ± S.E.M. ($$n = 8$$). ### $p \leq 0.001$, compared with control group; *$p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001$, compared with CCl4 group. Ctrl, control group; CCl4, carbon tetrachloride treated group; L-MF, low dose of mangiferin (50 mg/kg); H-MF, high dose of mangiferin (100 mg/kg).
## Mangifein alleviates liver pathological damages and fibrosis scores in CCl4 treated mice
Next, we investigated whether mangiferin could relieve liver fibrosis and protect the liver damage from CCl4 toxicity. H&E, Masson’s trichrome and Sirius red staining were used to analyze histology and collagen deposit in the liver section. As shown in Fig. 2A, the livers of control group mice had normal lobular structure with central vein and radial hepatic cord, however, there was necrosis in the center of the hepatic lobules, deposition of lipid droplets in hepatocytes, inflammatory cell infiltration, and lipid degeneration in CCl4-treated group following the administration of CCl4 for 8 weeks, whereas mangiferin treatment inhibited these pathological changes, as showed by the decrease in hepatocytes degeneration, inflammatory cell accumulation and lipid deposition, indicating that mangiferin may relieve hepatic steatosis in CCl4 induced mice (Fig. 2D). Masson's trichrome staining revealed that the administration of CCl4 resulted in connective tissue proliferation, notably structure distorted and fibrous collagen deposition between the portal vein and lobules in the liver of the mice compared with the control group, suggesting that liver fibrosis was established (Fig. 2B). On the contrary, mangiferin treatment significantly decreased collagen fiber accumulation, suggesting that mangiferin has protective effect on hepatic fibrosis (Fig. 2E). In Sirius red staining, we found collagen accumulated obviously in the liver of CCl4-treated mice, and mangifeirn reversed those changes (Fig. 2C). Taken together, these data indicate that mangiferin could attenuate liver fibrosis and improve liver function in CCl4 treated mice. Figure 2Effects of mangiferin on histology of the liver in CCl4 induced mice. ( A) H&E staining. ( B) Masson’s trichrome staining. Red arrows indicated damaged liver tissue and fiber cords. ( C) Sirius red staining. ( D) *The analysis* of inflammation in H&E staining. ( E) *The analysis* of fibrosis grade in Masson’s trichrome staining. Date are presented as means ± S.E.M. ($$n = 8$$). ## $p \leq 0.01$ and ###$p \leq 0.001$, compared with control group. * $p \leq 0.05$ and **$p \leq 0.01$, compared with CCl4 group. Ctrl, control group; Ctrl, control group; CCl4, carbon tetrachloride treated group; L-MF, low dose of mangiferin (50 mg/kg); H-MF, high dose of mangiferin (100 mg/kg).
## Mangiferin regulates the mRNA expression of inflammation, bile acid metabolism and fibrotic genes
Mangiferin could play anti-fibrosis roles through NF-κB, Smad/TGF-β signaling pathways, which is related to the anti-inflammatory and anti-fibrotic properties26. To investigate the molecular mechanisms regarding the mangiferin protecting against CCl4 induced liver fibrosis, we measured the mRNA expression of proinflammatory cytokines, bile acid metabolism and profibrotic related genes by quantitative RT-PCR. As shown in Fig. 3A, the expression of α-SMA, a marker of hepatic stem cell activation, MMP-2 and TGF-β, the fibrogenesis related genes, were increased in CCl4-treated group, whereas these genes were significantly decreased with the treatment of mangiferin. In contrast, there was no significant different in the mRNA expression of COL1A1, COL3A1, PDGF and TIMP-2. The data indicated that mangiferin may inhibit CCl4 induced fibrogenesis via the suppression of the expression of α-SMA, MMP-2 and TGF-β in the liver. The expression of proinflammatory cytokines IL-6 and IL-1β were increased in the liver of CCl4-treated mice, while these were decreased by mangiferin treatment, however, the expression level of TNF-α and MCP-1 were not significantly changed between the groups, indicating that mangiferin may inhibit inflammatory cytokines IL-6 and IL-1β induced by CCl4 (Fig. 3B). CCl4-treatment inhibited the mRNA level of ABCB4, ABCB11, SULT2A1, NTCP and CY7A1, the bile acid metabolism related genes, in the liver of the mice. Compared with that in the CCl4-treated mice, the expression of ABCB4, ABCB11, SULT2A1 was increased in mangiferin treated group (Fig. 3C), although NTCP and CY7A1 remained unchanged. The data indicate that the suppression of the expression of bile acid metabolism and proinflammatory related genes may also involve in the liver protective effects of mangiferin. These findings collectively demonstrated that mangiferin may relieve liver fibrosis through regulating proinflammatory cytokines, bile acid metabolism and pro-fibrotic related pathways. Figure 3Mangiferin regulates the mRNA expression of proinflammatory cytokines, bile acid metabolism and pro-fibrotic related genes. Real time quantitative PCR were performed to detect the mRNA expression. ( A) The mRNA expression levels of α-SMA, MMP2, TGF-β, COL1A1, COL3A1, PDGF and TIMP-2. ( B) The mRNA expression levels of IL-1β, IL-6, MCP-1 and TNF-α. ( C) The mRNA expression levels of ABCB4, ABCB11, SULT2A1, NTCP, SHP and CYP7A1. Date are presented as means ± S.E.M. ($$n = 8$$). # $p \leq 0.05$, ##$p \leq 0.01$ and ###$p \leq 0.001$, compared with control group. * $p \leq 0.05$, **$p \leq 0.01$ and ***$p \leq 0.001$, compared with CCl4 group. Ctrl, control group; CCl4, carbon tetrachloride treated group; L-MF, low dose of mangiferin (50 mg/kg); H-MF, high dose of mangiferin (100 mg/kg).
## Mangiferin reduces collagen accumulation, HSCs activation and inhibits NF-κB signaling
IHC experiments showed that α-SMA levels were markedly increased in CCl4 treated mice compared to those in the control group, while mangiferin treatment repressed α-SMA levels (Fig. 4A). Next, western blot was used to verify the protein levels of COL1 and α-SMA. As shown in Fig. 4B and C, CCl4 administration upregulated COL1 and α-SMA protein expression, mangiferin decreased the protein contents of COL1 and α-SMA induced by CCl4, suggesting that mangiferin may alleviate the formation of fibrosis through the reduction of collagen accumulation and hepatic stem cell activation. Furthermore, we determined the p-IκB, p-p65 protein levels, and found that mangiferin inhibited the p-IκB protein contents (Fig. 4D,E). Meanwhile, it also inhibited p-p65 levels (Fig. 4F). Taken together, the data indicate that mangiferin may improve the liver fibrosis through inhibiting NF-κB signaling. Figure 4Mangiferin reduces CCl4-evoked hepatic α-SMA and COL1 protein levels and inhibits NF-κB pathway. ( A) Immunohistochemistry staining of α-SMA. ( B) Western blot analysis of α-SMA and COL1 protein. ( C) Quantitation of western blot analysis of α-SMA and COL1 protein levels in (B). ( D) Western blot analysis of p-IκB and p-p65 protein. ( E) Quantitation of western blot analysis of p-IκB protein. ( F) Quantitation of western blot analysis of p-p65 protein. GAPDH and β-Actin were assayed as an internal control respectively. All experiments repeated 3 times. The blots were cut prior to hybridisation with antibodies during blotting. # $p \leq 0.05$, ##$p \leq 0.01$ and ###$p \leq 0.001$, compared with control group. * $p \leq 0.05$ and ***$p \leq 0.001$, compared with CCl4 group. Ctrl, control group; CCl4, carbon tetrachloride treated group; H-MF, high dose of mangiferin (100 mg/kg).
## Discussion
CCl4-induced liver fibrosis animals have been widely used as a model to simulate the pathogenesis of human liver fibrosis27. The current study demonstrated that mangiferin treatment had beneficial effects on liver fibrosis induced by CCl4. Mangiferin administration reduced CCl4-induced inflammatory cell infiltration and the release of pro-inflammatory factors, inhibited CCl4-induced NF-κB pathway activation in mice.
Overabundance of ECM deposition in the liver tissue and the activation of HSCs are the key factors involved in the formation of fibrosis and pro-fibrotic cytokines28,29. Previous reports have shown that mangiferin could improve renal, pulmonary, myocardial fibrosis by inhibiting the collagen deposition and the ECM aggregation in the lung, kidney and heart tissues19–22. In this study, we found that mangiferin reduced collagen accumulation and inhibited the mRNA and protein levels of α-SMA, the marker of the HSC activation. In addition, mangiferin also inhibited the protein level of COL1 and the mRNA levels of TGF-β, the key factor involved in the formation of fibrosis and pro-fibrotic cytokines. These data indicate that mangiferin may suppress fibrogenesis in the liver via the activation of HSCs.
Liver cell injury accounts for increasing inflammatory cell infiltration and the release of pro-inflammatory cytokines, which is a major inducer of liver fibrosis. By measuring the levels of ALT and AST, the liver injury markers, we found that mangiferin reduced CCl4-induced liver injury. The bile acid metabolism disorder usually accompanies the liver injury. Thus, we tested the expression of bile acid metabolism related genes. We found that mangiferin could reduce the bile acids accumulation in CCl4-induced mice, supporting that mangiferin may improve the liver injury induced by CCl4. Previous studies have shown that mangiferin has anti-inflammatory effects in the lung and kidney fibrosis animals19,22. The present study found that mangiferin alleviated inflammatory cell infiltration, and inhibited the expression of pro-inflammatory factors IL-6 and IL-1β in the liver of CCl4-induced mouse model. Considering these cytokines are the inducers of pro-fibrotic pathogenesis, we speculate that mangiferin may inhibit the hepatic fibrogenesis partly through the suppression of inflammatory signaling.
We further explored the underlying mechanism of mangiferin anti-inflammatory effect in liver fibrosis. It has been demonstrated that NF-κB is an important regulator in the series of inflammatory response and linked to the regulation of liver injury, liver fibrosis, hepatocellular carcinoma and other diseases30. Inhibition of NF-κB signaling could ameliorate liver fibrosis31. Previous studies have reported that mangiferin ameliorated renal and pulmonary inflammation by inhibiting NF-κB pathway20,32, and mangiferin-riched mango peel powder supplementation inhibited fibrosis and inflammatory cell infiltration in the liver of CCl4-induced hepatic fibrotic rats33. In this study, we showed that mangiferin reduced the p-IκB and p-p65 protein levels in the liver of CCl4-induced mice. Thus, the alleviating effect of mangifeirn on CCl4-induced liver fibrosis may via downregulating NF-κB signaling pathway.
## Conclusion
In conclusion, we showed that mangiferin could alleviate liver injury and inflammation, suppress the accumulation of collagen, regulate the mRNA levels of bile acid metabolism and pro-fibrotic genes in the liver of CCl4 induced mice, furthermore, mangiferin also inhibit the protein levels in NF-κB pathway. Our data suggest that mangiferin may exert anti-fibrosis effect through inhibiting NF-κB pathway and may be a potential choice for the treatment of hepatic fibrosis.
## Materials
Mangiferin was obtained from ALFA (Chengdu, China). Carbon tetrachloride (CCl4) was purchased from Aladdin (Shanghai, China).
## Animals and treatments
The procedures for this study were approved by Shanghai University of Traditional Chinese Medicine (PZSHUTCM190912014). All animal experiments in this study were conducted in accordance with the ARRIVE guidelines for reporting experiments involving animals34. All methods were carried out in accordance with relevant guidelines and regulations. The thirty-two male C57BL/6 mice (Six-week-old), weighing 23–29 g, were purchased from the SLAC Laboratory (Shanghai, China). All mice were housed under the controlled temperature (22–23 °C) and on a 12 h light and 12 h dark cycle with food and water ad libitum. All animals were adapted to their new housing conditions for one week before the experiments.
The mice were randomly divided into 4 groups ($$n = 8$$ in each group) as follows: control group, CCl4-treated model group, high and low dose (50 mg/kg and 100 mg/kg) of mangiferin co-treated group. Diagram of this research showed in Fig. 1A. CCl4-treated mice were injected intraperitoneally with $10\%$ CCl4 (2 ml/kg of body weight) diluted in olive oil every other day for 8 weeks. From the 5th week, the mice were orally administered with mangiferin or same volume of vehicle for 4 weeks. At the end of the animals experiment, mice were fasted overnight. Using $20\%$ urethane anesthetized mice and collected cardiac blood and liver tissue.
## Serum biochemical
After anesthetizing, the vascular blood was taken from the heart, and the supernatant was collected after 3000 rpm centrifugation for 15 min at the room temperature and stored at − 80 °C for further experimental analysis. The serum levels of ALT, AST, TBA, ALP and TBIL were measured by an automatic biochemical analyzer (Hitachi 7020, Japan).
## Liver histology
The liver tissues were fixed in $4\%$ formalin solution, embedded in paraffin, cut at 5 µm and stained with haematoxylin–eosin (HE), Masson trichrome and Sirius red staining according to standard procedures. The morphology was observed with a microscope (Zeiss, Germany). The degree of fibrosis was graded by METAVIR scoring system35.
## Immunohistochemistry analysis
For α-SMA staining, paraffin-embedded sections were incubated with goat α-SMA polyclonal antibody (cat. no. ab5694, Lot: GR3183259-37Abcam, Cambridge, MA) overnight at 4 °C after antigen removal using sodium citrate repair solution. All slides were then incubated with goat anti-rabbit HRP secondary antibody (mp-7,452, Vector, Burlingame, CA) for 30 min at room temperature. Slides were incubated with DAB for 2 min, counterstained with hematoxylin for 2 min, and counterstained with blue reagent for 10 s.
## RNA extraction and RT-qPCR analysis
Real-time quantitative PCR was performed as previously described36. Briefly, Total RNA was extracted from the liver tissues using the Trizol reagent (Vazyme, Nanjing, China) according to the manufacturer's instructions. RT-qPCR was first performed using a cDNA kit (Vazyme, Nanjing, China) with 1 μg of total RNA as the template to synthesize cDNA under the following conditions: 42 °C for 2 min, 50 °C for 15 min, and 85 °C for 5 s. Quantitative real-time PCR was carried out using ChamQ Universal SYBR qPCR Master mix (Vazyme, Nanjing, China) on an ABI StepOne Plus real-time PCR system (Applied Biosystems, USA). β-actin was used as the internal reference for the expression level of mRNA of all genes. Statistical analysis was carried out by using 2−∆∆Ct method. The sequences of all primers were listed in Table 1.Table 1List of primers in PCR amplification. GeneForward primerReverse primerβ-ActinTGTCCACCTTCCAGCAGATGTAGCTCAGTAACAGTCCGCCTAGAABCB4CGGCGACTTTGAACTAGGCACAGAGTATCGAACAGTGTCAACABCB11CGGACCTGTATTGTCATTGCCCCTTCTGGTCCATCAGTTTCOL1A1CAAGGTCACGGTCACGAATGGCAAAGACGGACTCAACOL3A1GTAGTCTCATTGCCTTGCTCCAGAACATTACATACCCYP7A1GTGGTAGTGAGCTGTTGCATATGGCACAGCCCAGGTATGGAATCAIL-1βTCGTGCTGTCGGACCCATATGGTTCTCCTTGTACAAAGCTCATGIL-6AACCACGGGCTTCCCTACTTTCTGTTGGGAGTGGTATCCTCTGTMCP-1AGGTCCCTGTCATGCTTCGTGCTTGAGGTGGTTGTGMMP2CTGTCCGCCAAATAAACCCCCCGATGCTGATACTGANTCPTATCAGCCCCCTTCAATTTCGTGAGCCTTGATCTTGCTGAPDGFTTCCTGTCTCCTCCTCCCTAACACCAGCAGCGTCAASHPGGAGTCTTTCTGGAGCCTTGATCTGGGTTGAAGAGGATCGα-SMATCGGATACTTCAGCGTCAGGGAGTAATGGTTGGAATGSULT2A1GAACTGGCTGATTGAGATAGGTTAGAGTCGTGGTCTGF-βGGGAGTAATGGTTGGAATGGGGAGTAATGGTTGGAATGTIMP2TGACCCAGTCCATCCAGAGCACGCTTAGCATCACCCATNF-αATGGATCTCAAAGACAACCAACTAGACGGCAGAGAGGAGGTTGACTT
## Protein extraction and Western Blot analysis
The protein was extracted from the liver tissues using RIPA buffer (Beyotime, Shanghai, China) containing protease inhibitor, phosphatase inhibitor and Phenylmethylsulfonyl fluoride (PMSF). 30 μg proteins were separated using sodium dodecyl sulfate polyacrylamide (SDS-PAGE) gel, and transferred to polyvinylidene fluoride (PVDF) membranes. Blocked in $5\%$ BSA for 2 h at the room temperature, and incubated in primary antibody for α-SMA (cat. no. ab5694, Lot: GR3183259-37, Abcam, Cambridge, MA), COL1 (cat. no. ab34710, Abcam, Cambridge, MA), IκB α (cat. no. 4814; Cell Signaling Technology), p-IκB α (cat. no. 2859; Cell Signaling Technology), p65 (cat. no. 8242; Cell Signaling Technology), p-p65 (cat. no. 3033; Cell Signaling Technology), GAPDH and β-Actin (Huabio, Hangzhou, China), with 1:1000 dilution in $3\%$ BSA at 4 °C overnight. Then, the membranes were washed three times with TBST each 10 min, and the membranes were incubated in the secondary antibody at room temperature for 2 h. The blots were visualized using ECL chemiluminescence kit (Beyotime, Shanghai, China). GAPDH was used as loading control and Image J software (National Institutes of Health, Bethesda, MD, USA) was used for densitometric analysis of the bands.
## Statistics analysis
Statistical analyses were performed using GraphPad Prism V.7.00 (La Jolla, CA, USA). All date were presented as means ± standard error of the mean (S.E.M). Comparisons were carried out via One-way analysis of variance (ANOVA) followed by Dunnett’s tests. Differences were considered statistically significant when p was < 0.05.
## Supplementary Information
Supplementary Information 1. The online version contains supplementary material available at 10.1038/s41598-023-30582-3.
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|
---
title: Quantitative proteomic landscape of unstable atherosclerosis identifies molecular
signatures and therapeutic targets for plaque stabilization
authors:
- Yung-Chih Chen
- Meaghan Smith
- Ya-Lan Ying
- Manousos Makridakis
- Jonathan Noonan
- Peter Kanellakis
- Alin Rai
- Agus Salim
- Andrew Murphy
- Alex Bobik
- Antonia Vlahou
- David W. Greening
- Karlheinz Peter
journal: Communications Biology
year: 2023
pmcid: PMC10011552
doi: 10.1038/s42003-023-04641-4
license: CC BY 4.0
---
# Quantitative proteomic landscape of unstable atherosclerosis identifies molecular signatures and therapeutic targets for plaque stabilization
## Abstract
Atherosclerotic plaque rupture leading to myocardial infarction is a major global health burden. Applying the tandem stenosis (TS) mouse model, which distinctively exhibits the characteristics of human plaque instability/rupture, we use quantitative proteomics to understand and directly compare unstable and stable atherosclerosis. Our data highlight the disparate natures and define unique protein signatures of unstable and stable atherosclerosis. Key proteins and pathway networks are identified such as the innate immune system, and neutrophil degranulation. The latter includes calprotectin S100A8/A9, which we validate in mouse and human unstable plaques, and we demonstrate the plaque-stabilizing effects of its inhibition. Overall, we provide critical insights into the unique proteomic landscape of unstable atherosclerosis (as distinct from stable atherosclerosis and vascular tissue). We further establish the TS model as a reliable preclinical tool for the discovery and testing of plaque-stabilizing drugs. Finally, we provide a knowledge resource defining unstable atherosclerosis that will facilitate the identification and validation of long-sought-after therapeutic targets and drugs for plaque stabilization.
Protein signatures of unstable and stable atherosclerosis are defined by quantitative proteomics using a preclinical mouse model of plaque instability/rupture.
## Introduction
Cardiovascular diseases (CVD) are the leading cause of death worldwide1. The typical sequence of events starts with the abrupt rupture of unstable atherosclerotic plaques leading to occlusive thrombi and consequent myocardial infarction (MI). The molecular mechanisms contributing to the development of unstable plaques and, ultimately, plaque rupture remain largely unknown2. Difficulties in obtaining human unstable plaque tissue and, in particular, a lack of clinically relevant animal models, which adequately capture human plaque instability and rupture, so far limited the study of plaque instability.
Previously we developed a mouse model of hemodynamically driven atherosclerotic plaque instability (the Tandem Stenosis [TS] model; Fig. 1), which uniquely reflects the central features of plaque instability and rupture clinically observed in humans, such as thin fibrous caps, intraplaque hemorrhage, large necrotic cores, expansive vascular remodeling, neovascularization, and high inflammatory burden3–9. Utilizing this unique model, we now define the proteomic composition of unstable atherosclerosis in direct comparison to stable atherosclerosis and healthy artery tissue. We identify the networks that drive plaque instability (as distinct from stable atherosclerosis and vascular tissue), provide a comparative analysis of human plaque instability/rupture and identify potential therapeutic targets for plaque stabilization. The power of this approach is illustrated in the identification of fundamental neutrophil-derived molecular networks that drive plaque instability, including S100A8/A9. The role of S100A8/A9 in plaque instability was confirmed in human plaques. Its suitability as a therapeutic target for plaque stabilization was confirmed in the TS mouse model. Overall, our proteomic atlas of plaque instability provides unique insights into the pathology of unstable atherosclerotic plaques and represents a reliable preclinical platform for the discovery and testing of much-needed plaque-stabilizing drugs. Fig. 1Workflow of quantitative proteomic comparison of unstable and stable atherosclerosis as well as healthy vessels using the tandem stenosis (TS) mouse model.a Schematic workflow of proteomic analyses of vascular segments representing unstable, stable atherosclerosis (aortic arch, red), and healthy artery. Each segment ($$n = 15$$) was isolated and pooled for protein extraction, gel electrophoresis, and in-gel tryptic digestion. Single-shot label-free quantitative mass spectrometry was performed. The schematic drawing was created by somersault18:24 BV. b Principal component analysis and sample correlation matrix of distinct tissue groups (healthy arteries, stable plaques, unstable plaques). ( c/d/e) Heat map, pathway/network enrichment analysis of unstable and stable plaque proteomes, and distributions of proteins identified across plaque compositions. Molecular target identification for plaque stabilization and validation in human plaques and the TS model.
## The proteomic landscape of unstable and stable atherosclerotic plaques
We performed a comparative proteome analysis of TS-derived unstable atherosclerotic, stable atherosclerotic, and plaque-free healthy arteries using a label-free quantitative MS approach (Fig. 1a, Supplementary Fig. 1). Principal component analysis (PCA) demonstrated distinct proteomes in each tissue subsection (Fig. 1b). The left carotid artery was used as a healthy control as it does not develop atherosclerotic plaques despite systemic hypercholesterolemia as shown previously8 and in Supplementary Fig. 2a, b. To reveal the similarity of biological replicates ($$n = 8$$ from 15 mice pooled per sample per phenotype), we used a sample-to-sample correlation matrix and confirmed correlations based on protein expression using hierarchical clustering (Fig. 1c). This finding points to an effective pooling strategy for acquiring sufficient tissue for proteome analysis. A total of 1130 quantifiable proteins were identified across unstable plaques (857 proteins), stable plaques [710], and healthy arteries [511]. Moreover, for proteins uniquely identified in healthy arteries [61], stable plaques [193], and unstable plaques [325], these proteins were combined in downstream network/pathway analyses (Fig. 1d, e, Supplementary Data 1–2).
Hierarchical proteome clustering analysis of all artery phenotypes (proteins detected in at least $\frac{5}{8}$ samples; one-way ANOVA, significance FDR < 0.05) shows 32 proteins abundantly expressed in diseased arteries (stable and unstable) compared to the healthy artery, while 53 proteins were minimally expressed in diseased arteries (Fig. 2a, Supplementary Data 3). Based on pathway enrichment analysis, compared to healthy arteries, diseased vessels (combined stable and unstable plaques) were enriched in several key processes and functions associated with “extracellular matrix (ECM) organization”, “response to elevated platelet cytosolic calcium”, “platelet activation, signaling and aggregation, integrin signaling”, “fibrin clot formation”, “platelet degranulation”, and “RAF signaling” (Fig. 2b, Supplementary Data 3). Further unbiased analysis of the healthy vessel proteome (based on Supplementary Data 2) is provided in Supplementary Data 4, highlighting networks enriched ($p \leq 0.01$) in “metabolism,” “muscle contraction,” “ECM organization,” “platelet activation, signaling, and aggregation,” and “EPH–ephrin and ROBO receptor signaling.” We show that, while all arteries are affected by systemic hypercholesterolemia, divergent pathways lead to plaque-free healthy arteries and diseased atherosclerotic arteries. Fig. 2Differential proteome analyses of atherosclerotic (unstable and stable) arteries versus healthy, plaque-free arteries.a Hierarchical clustering of differential protein expression across all tissue regions, comparing diseased plaque (stable and unstable) to the healthy vessels ($$n = 8$$). Proteins identified in at least $70\%$ within each tissue group (at least $\frac{5}{8}$ samples), FDR < 0.05, 32 proteins highly expressed in plaque (purple), 53 proteins lowly expressed in plaque (orange). b Enrichment map of Reactome pathway terms overrepresented in atherosclerotic plaques (stable and unstable) in comparison to healthy arteries.
To understand specific pathological differences in the proteomic landscape between stable and unstable atherosclerosis, we combined uniquely identified and significantly differentially expressed proteins for further unsupervised hierarchical cluster analysis (unpaired Student’s t-test, FDR < 0.05); this analysis included 253 stable plaque proteins (stable plaque proteomes) and 443 unstable plaque proteins (unstable plaque proteomes) (Fig. 3a & c, Supplementary Data 5). Specific pathways downregulated in unstable plaques compared to stable plaques included “ECM organization,” “TCA cycle and electron transport chain,” “RHO GTPase activity,” “cell–ECM interactions,” and “smooth muscle contraction” (Fig. 3b, Supplementary Data 6). Further, pathway analysis using Reactome revealed upregulation of “translation,” “immune response” (including neutrophil degranulation, platelet degranulation), “complement cascade,” “interleukin signaling,” “IGF transport and uptake,” and “dissolution of fibrin clots” in unstable plaques (Supplementary Data 6). This comparative enrichment analysis reveals a distinct proteome landscape at the tissue level for unstable and stable plaques, including proteins associated with immune/inflammatory activity, neutrophils, and platelet degranulation in unstable plaques in contrast to a reduction in smooth muscle cell (SMC) contraction and ECM remodeling in stable plaque; such networks directly function in the regulation of structural integrity and plaque rupture. Fig. 3Defining the unstable and stable atherosclerotic plaque proteome.a Global heatmap expression analysis between unstable and stable plaque (FDR < 0.05). b *Enrichment analysis* of Reactome pathways overrepresented in differential stable and unstable plaque proteome. c *Comparative analysis* of unstable and stable plaque proteomes with previous proteome analyses of human plaques by Vaisar et al., Hansmeier et al., Hao et al., and Liang et al.10–13. Proteins co-identified in stable plaques (blue) and unstable plaques (red) are highlighted. d Pathway involvement of indicated proteins is asterisk color-coded. Direct Reactome pathway analyses of proteins co-identified in stable plaques (blue) and unstable plaques (red) with human plaque data are shown, # denoting pathways associated with S100A8 and S100A9. e GeneMANIA-based radial interaction map of Reactome pathway enrichment analyses of stable and unstable plaque proteomes. Nodes represent proteins, and edges represent evidence-based direct physical interactions. S100A8/A9 is located in the center.
## Differential TS plaque proteome reveals correlation with other mouse model’s plaque proteomes
To gain specific insights into the mechanism of plaque rupture, we compared our differential plaque tissue proteomes with existing mouse model plaque proteome studies (Supplementary Data 7)10–13. Firstly, we assessed the co-enrichment of proteins between our TS unstable plaques and plaques from transgenic mice with macrophage-specific overexpression of urokinase (SR-uPA+/0), which also display several markers of plaque instability13. This investigation revealed that 10 proteins co-enriched in SR-uPA+/0 plaque were also identified in TS unstable plaque proteomes. These proteins function in apoptosis and senescence. ( Supplementary Data 7). In addition, for proteins minimally expressed in murine aortic plaques (SR-uPA+/0) relative to healthy arteries (TS mice), we identified 11 proteins attributed to hemostasis function, and at a subcellular/cell-type level, associated with basal membranes and common fibroblast. Even though the genetic backgrounds of the two mouse models are different, the priming of plaque development was similar in both. The healthy arteries in our TS model displayed chronic endothelial activation and systemic hypercholesterolemia, but no plaque development.
## Human plaques correlate with TS unstable and stable plaque proteomes
To gain a further understanding of the differential stable and unstable plaque proteomes in the crucial context of translating our findings to human atherosclerotic disease and plaque instability/rupture, we correlated our data with reported human ruptured carotid endarterectomy proteome signatures13. Here, 252 proteins were uniquely identified in the stable plaque proteome, while 443 proteins were uniquely identified in the unstable plaque proteome (Fig. 3c, Supplementary Data 7). Using these uniquely expressed proteins, we highlight that 199 (90 + 109) out of 443 co-identified proteins ($44\%$) of the unstable plaques correlate with human ruptures, whereas 124 (109 + 15) out of 443 co-identified proteins ($27\%$) were correlated with human plaque combined proteome profiles. Further, 71 proteins out of 252 were co-identified proteins ($28\%$) from stable plaques which correlated with human plaques combined, while 114 proteins out of 252 were co-identified proteins ($45\%$) associated with human plaque ruptures (Fig. 3c, Supplementary Data 7)10–13. This study has discovered a substantial overlap in the proteomes of mouse and human plaques, emphasizing the relevance of using animal models to study human atherosclerosis. Because human carotid plaques are typically composed of a mix of unstable and stable plaques, Vaiser et al.13 were able to dissect those with intraplaque hemorrhage as unstable plaques. Thus, we were particularly interested in the 109 proteins that were found in both the human plaque proteome signatures and the unstable (TS) plaque proteomes. These protein-generated pathways were associated with “platelet activation, signaling, and aggregation,” “innate immune system,” “immune system,” “neutrophil degranulation,” and “ECM organization” (Fig. 3d, Supplementary Data 7–8). In contrast, 59 proteins from stable plaques co-identified in both the human plaque proteomes and the pathways were associated with “smooth muscle contraction,” “elastic fiber formation/function,” “ECM organization/function/interaction,” and “vasopressin” (including various collagens/myosins) (Supplementary Data 7-8). In this context, proteomics might be utilized to dissect/differentiate the unique contributions of both stable and unstable plaque proteome signatures, as well as to identify factors from human atherosclerotic plaques.
Supporting key changes at a molecular level of stable plaque included annotations associated with “smooth muscle contraction”, “laminin interactions”, “cell–ECM interactions”, and “elastic fiber formation,”; highlighting the ECM for essential scaffolding and maintaining structural integrity contributing to plaque stability (Supplementary Data 8). Furthermore, the pathways associated with unstable mouse and human plaques include “platelet activation, signaling, and aggregation”, “neutrophil degranulation”, “immune system”, “dissolution of fibrin clots”, “ECM organization/degradation”, “hemostasis,” and “lipoprotein remodeling” (Fig. 3d, Supplementary Data 8). Despite different differentially expressed proteins for each tissue subset, several pathways were co-identified for unstable and stable plaque (and in human plaque, Fig. 3d). These include collagen formation, hemostasis, ECM organization, and platelet activation, signaling, and aggregation in both phenotypes of atherosclerotic mouse plaques (Supplementary Data 8). The importance of these pathways for atherosclerosis is obvious and individual proteins are up or downregulated, depending on their inhibiting or activating role in the particular pathway. For example, for collagen formation, we identified components specific to stable plaque (Col18a1, Col4a1, Col6a1, Col6a3, Ctss, Lox, Loxl1) in comparison to different components in unstable plaque (Col14a1, Col4a2, Ctsb, P4hb, Plec, Ppib, Serpinh1). These findings are consistent with the central but differential role of the various collagen types and differences in abundance in the pathogenesis of stable and particularly unstable atherosclerosis.
We have cross-validated the proteins differentially expressed in plaque stability/instability from mouse and human samples using this unbiased approach, supporting the concept that stable and unstable plaques have widely disparate proteomic signatures and that the underlying pathways of inflammation are the driving force of plaque instability/rupture across species.
## Pro-inflammatory response proteins S100A8/S100A9 in the unstable plaque proteome
A salient finding between the unstable plaque with murine and human plaque composition was the unique identification of S100 family members S100A8 and S100A9 in unstable plaques of TS mice and human carotid plaques (based on fragmented spectra), and S100A8 in ruptured human plaques (Fig. 3e, Supplementary Fig. 3, Supplementary Data 7–8). During inflammation, S100A8/A9 is actively released and plays a critical role in modulating inflammatory responses14. We show S100A8/A9-associated functional enrichment categories included “neutrophil degranulation”, “innate immune system”, “immune system”, “regulation of TLR by endogenous ligand”, and “diseases of the immune system” (Fig. 3d, Supplementary Data 8).
## Immunostaining of S100A8/A9 in human and mouse plaque tissues
Expression of S100A9 was validated using immunofluorescence staining and Western blot analysis of mouse plaques (Fig. 4a–c). Notably, S100A9 was only detectable in unstable plaques but not in stable plaques or healthy arteries, validating the tissue analysis at the proteome level (Fig. 4b, d, Supplementary Data 1, Supplementary Fig. 7). Further, using immunofluorescence S100A9 predominantly co-localized with myeloperoxidase supporting the finding that neutrophils are likely the primary source of these proteins as we have shown in other inflammatory disorders15,16. Moreover, we confirmed the expression of S100A8 and S100A9 in histological samples of human carotid atherosclerotic plaques ($$n = 14$$) (Fig. 4e–h).Fig. 4Validation of S100A8, S100A9 protein expression in TS plaques and human carotid plaques.a S100A9, MPO, and DAPI immunofluorescence staining in unstable TS mice plaques ($$n = 10$$) and b stable plaques ($$n = 10$$). As a negative control, c IgG Isotype control antibodies for S100A9 and MPO were deployed. d Western blots were performed in three TS mice, and each artery segment was collected for protein identification. S100A9 is only found in the segments that are unstable. e S100A8 and g S100A9 immunohistochemistry in human carotid plaques ($$n = 14$$). As a negative control, IgG Isotype control antibodies for f S100A8 and h S100A9 were deployed. Bars indicate 100 μm. H/E: Hematoxylin and eosin, L: Lumen, Ath: Atherosclerosis. Dashed lines indicate endothelium.
## Inhibition of S100A9 stabilizes vulnerable plaques in the TS mouse model
To further confirm the contribution of S100A9 to plaque instability/rupture, we applied a pharmacological approach using the S100A9 inhibitor ABR-25757 (paquinimod) (Supplementary Fig. 4)15,17. Based on the histological assessment, we show that ABR215757 did not influence lesion size (Supplementary Fig. 5a–c), collagen content (Supplementary Fig. 5d–f), necrotic core size (Supplementary Fig. 5g–i), or lipid accumulation (Supplementary Fig. 5j–l) in stable atherosclerotic plaques and therefore does not contribute to stable atherosclerosis.
Next, we examined the effect of S100A9 inhibition on plaque instability, assessing whether the local inflammatory status in unstable plaques in the TS mice was altered. We identified a significant decrease in CD68 staining, used to identify macrophages, in unstable plaques (Fig. 5a–c). Furthermore, the collagen content within unstable plaques was significantly higher in mice treated with ABR-215757 compared to the vehicle control (Fig. 5d–f). While the treatment with ABR-215757 resulted in a trend toward decreased necrotic core size in unstable plaques, the reduction was not statistically significant ($$p \leq 0.09$$) (Fig. 5g–i). Also, ABR-215757 did not change the plasma levels of total cholesterol, triglycerides, HDL and VLDL/LDL, or blood glucose levels in treated mice (Supplementary Fig. 6a–e). These findings indicate that ABR-215757, a pharmacological inhibitor of S100A9, has the potential to stabilize vulnerable atherosclerotic plaques by reducing macrophage infiltration and increasing collagen content. Fig. 5Inhibition of S100A9 stabilizes vulnerable atherosclerotic plaques in the TS mouse model. ApoE−/− mice were given ABR-215757 (S100A9 inhibitor, 25 mg/kg, intraperitoneal injection) three times weekly for 7 weeks after TS surgery. a, b In mice treated with ABR-215757, TS mouse plaques showed a considerable reduction in CD68 foam cells. c CD68 quantification in percent of the atherosclerotic area. d, e Plaque collagen was detected with Picro Sirius red staining under polarized light, and f treatment with the inhibitor dramatically increased plaque collagen content. g, h H/E staining was utilized to define the necrotic core relative to the total plaque area. i Treatment with the S100A9 inhibitor showed a trend of lowering the necrotic core size. Data are presented as means and standard error of the mean (S.E.M.). The total plaque area refers to the plaque from the internal elastic lamina to the endothelium. Each dot represents the mean value of six sections from one mouse. L: Lumen. Student’s t-test was used to test for statistical significance. Bars represent 50 μm.
## Discussion
The insidious nature of atherosclerosis renders it a major and typically unpredictable contributor to the high rates of CVD-associated mortality and morbidity globally18. Identification and, importantly, effective stabilization of vulnerable atherosclerotic plaques, which are at risk of rupturing and causing thrombotic events, are crucial issues in primary care and remain so for secondary prevention. In this study, we used a unique preclinical model of plaque instability/rupture, the TS mouse model8, to identify the proteome signatures of unstable plaques in direct comparison to stable plaques and healthy artery tissues. Unlike traditional animal models of atherosclerosis, which typically develop stable atherosclerosis, the TS mouse model develops both stable and unstable, rupture-prone plaques at predefined areas in the vasculature. The unstable plaques in the TS model resemble the pathology seen in humans, including the development of thin fibrous cap ruptures, intraplaque hemorrhage, and intraluminal thrombosis4–6. These predefined areas of unstable and stable atherosclerosis in the TS model allowed the pooling of tissues originating from mice and building a comparable sample-to-sample correlation matrix and demonstrating the consistency of each individual pool sample cohort.
Importantly, unique proteins and pathways identified in the mouse plaque proteomes demonstrate significant similarity with human carotid plaque proteomes (a combination of four individual studies; $70\%$ correlation across species)10–13. We focused on the 109 co-identified proteins that were more abundant in the TS mouse model of unstable plaques and in human plaque proteomes, including human rupture plaques13. These proteins constitute several protein families, including calprotectin, cathepsin, serine protease inhibitors, and components of the coagulation system. Calprotectin (S100A8/A9) is a known marker of neutrophil activation, degranulation, and NETosis19. The amount of serum S100A8/A9 correlates to the number of circulating neutrophils, carotid artery disease, and other classic CV risk factors in middle-aged healthy individuals15. There are several proteases, including cathepsin B and cathepsin D, which are well-known potent collagenases and elastases that function through the degradation of the ECM and have been shown to cause plaque instability20. Interestingly, cathepsin D, in combination with S100A8/A9 and two other proteins, formed a 4-biomarker signature for CVD risk prediction over a 10-year follow-up20. In conjunction with the upregulation of serine protease inhibitors ITIH2 and ITIH4, our data indicate that unstable plaques contain an imbalance of proteolytic activity, potentially accelerating ECM degradation. Other imbalances, such as in the coagulation pathway, were also found in the unstable plaques. Proteins such as fibrinogen, prothrombin, and plasminogen were overexpressed in the unstable plaques, indicating the presence of (micro)thrombi at the time of sample collection. Microthrombi and intraplaque hemorrhage are well known to be strong makers of plaque instability in patients21,22. Our findings indicate that unstable plaques are dynamic in proteome composition in proteolytic, coagulative, and ECM degradation functions, the combination of these ultimately contributing to plaque rupture.
Using such co-identified proteins, our functional enrichment analyses revealed pathways downregulated in unstable plaques, including “smooth muscle contraction,” and “ECM organization,” The largest of these networks, “smooth muscle contraction,” consists of the downregulated node proteins ACTA2. Not surprisingly, the protein ACTA2 is a marker of contractile SMCs in plaques. Shear stress may cause contractile SMCs to convert into synthetic SMCs, which can produce new collagen. A high ratio of ACTA2+ to CD68+ cells, along with thicker ECM, are more stable in late-stage atherosclerotic plaques23. Our findings are consistent with the following statement published by the PlaqOmics Transatlantic Network: atherosclerotic plaques are destabilized by deleterious reprogramming of SMCs and other ACTA2 + fibrous cap cells, and these are critical biological differences determining susceptibility for coronary artery disease23.
A potential limitation in our comparative proteomics approach is the fact that we cannot fully exclude the difference in baseline proteome between the carotid artery (source of the unstable plaque in the TS model) and the aortic plaque (source of the stable plaque in the TS model) confounds the comparison of the plaque stability state. However, the following three points will mitigate this risk. [ 1] In our analysis, we include a comparison with the plaque-free left carotid artery as a control. [ 2] We include a comparison to human plaques. [ 3] In the comparison between the carotid artery and aortic plaques, we do not see a major difference in the baseline proteomes, as the protein expression distribution and coefficient variations are not very different. We normalized our data at several levels to allow for comparison. Protein extracted from tissue was normalized (protein yield), and peptides used for sample analysis were further normalized following high-sensitivity peptide generation and cleanup, in addition to single batch post-processing informatic (TIC-based normalization using MaxQuant; maxLFQ). We provide protein expression distributions for all sample groups, where we did not observe baseline proteomic differences in individual samples between the aortic arch, carotid arteries with unstable plaque, and carotid arteries without plaque (Supplementary Fig. 1a). This is further supported by cross-group analysis of coefficient variation (Supplementary Fig. 1b), which did not differ within our group, indicating that baseline proteomic variation (~20–$22\%$ coefficient variation) was similar across groups. In conclusion, the risk of the comparison of different localizations representing different phenotypes of plaques is a potential inherent confounding factor. However, we mitigated this risk as much as possible.
The major pathways upregulated in unstable plaques are associated with “neutrophil degranulation,” “innate immune system,” and “immune system”. Using GeneMANIA functional interaction networks, we identified that S100A8 and S100A9, together with CD68, play a significant role in plaque inflammatory pathways. S100A8 and S100A9 are members of danger-associated molecular patterns which are known to initiate and promote inflammation and are predominantly expressed by neutrophils24. They form a heterodimer complex, which has been reported as a systemic biomarker for the detection of acute coronary syndrome25,26. However, contradictory results were reported in ApoE−/− and LDLR−/− mice27,28. In LDLR−/− mice, bone marrow S100A9 deficiency does not reduce atherosclerosis27. In ApoE−/− mice, whole-body S100A9 deficiency lowers atherosclerosis28. Nevertheless, neither animal model was used to evaluate the therapeutic potential of S100A9 inhibition in plaque stabilization. Importantly, in histological analyzes of unstable plaques in the TS model as well as in human carotid endarterectomy samples, S100A9 is highly expressed and co-localizes with neutrophils. Our findings are also in accordance with a previous report on a high abundance of neutrophils associated with rupture-prone plaques in humans29.
We employed the small-molecule S100A9-inhibitor ABR-215757 in the TS model to validate our findings. Inhibition of S100A9 reduced CD68+ macrophage infiltration and increased collagen content in the fibrous cap of unstable plaques in the TS model. Our investigation is consistent with a prior report showing that ABR-215757 reduced diabetes-accelerated atherosclerosis in STZ-treated ApoE−/− mice, associated with reduced macrophage and lipid content17. Collectively, these promising effects on plaque stabilization support the development of ABR-215757 or similar drugs as therapeutics for the stabilization of atherosclerotic plaques, prevention of plaque rupture, and, ultimately, prevention of MI. The successful application of ABR-215757 as a plaque-stabilizing drug also supports the suitability of the TS model as a preclinical discovery tool for the identification of molecular targets and drug testing. The TS model has recently also been successfully applied to establish diagnostic technologies in relation to the identification of unstable plaques5 and to demonstrate plaque stabilization via an MPO inhibitor6 or SGLT2 inhibitor30.
In conclusion, using quantitative proteomics in a preclinical TS mouse model, we established disparate natures and define the protein signatures of unstable and stable atherosclerosis. Integrating a robust proteome of unstable atherosclerosis derived from the TS model with existing murine data and in particular human plaque rupture, we define the core pathways contributing to unstable atherosclerosis, including neutrophil and platelet degranulation and inflammatory response pathways, while showing the core networks in stable plaques include SMC contraction and ECM remodeling. Confirming the TS model as a preclinical tool for drug discovery, with S100A8/A9, we identify a molecular target, validate this target in mice and humans, and demonstrate its suitability for therapeutic plaque stabilization. Finally, this study describes an atlas of numerous pathways and protein candidates to be tested for their suitability to identify/diagnose unstable plaques and, ultimately, develop long-sought-after plaque-stabilizing drugs.
## Tandem stenosis (TS) surgery of the carotid artery and vessel segment dissection
Male ApoE−/− mice from a C57BL/6 J background were obtained from the Animal Resource Center in Western Australia. The ApoE-/- were backcrossed to C57BL/6J for at least 10 generations. Mice at 6–7 weeks of age were fed on a high-fat diet (HFD) containing $22\%$ fat and $0.15\%$ cholesterol (SF00-219, Specialty Feeds, Western Australia) prior to TS surgery. After 6 weeks on an HFD, mice were anesthetized by intraperitoneal injection of 100 mg/kg ketamine and 20 mg/kg xylazine, and TS surgery was performed on all mice following procedures as previously described in detail.8 *The stenosis* was achieved by ligating the vessel with needles of specific diameters and then removing the needles.9 Post-surgical care and monitoring were provided for 48 h post-procedure, and mice were continued on an HFD for an additional 7 weeks before being culled by an overdose of ketamine and xylazine, and then perfused with 15 ml PBS buffer. The unstable plaque segments either with or without intraplaque hemorrhage, were identified under a dissecting microscope and recorded. All segments (unstable plaque: right carotid artery; healthy vessel: plaque-free left carotid artery; and stable plaque: aortic arch; Supplementary Fig. 2a, b) were isolated by dissection and snap-frozen in liquid nitrogen. The samples were stored at −80 °C until further use. All animal work was approved by the AMREP Animal Ethics Committee (E/$\frac{1581}{2015}$/B and E/$\frac{1904}{2019}$/B).
## Proteomics: tissue sample preparation
Detailed methods of protein extraction and proteomic analysis have been described previously31 In brief, approximately 10 mg (net weight) of vessel samples from each of the pooled segment groups were homogenized in a lysis buffer (0.1 M Tris-HCl, pH 7.6) supplemented with $4\%$ sodium dodecyl sulfate (SDS) and 0.1 M dithioerythritol (DTE), pulse centrifuged (16,000 g, 10 min), and the supernatant analyzed for protein concentration using a Bradford assay. Proteins (10 μg for each sample) were separated using SDS=polyacrylamide gel electrophoresis (PAGE, $4\%$ stacking/$12\%$ separating gel), and the entire in-gel fraction isolated for analysis32 Samples were reduced with 10 mM DTE in 100 mM NH4HCO3 at room temperature (RT) for 20 min, alkylated with 54 mM iodoacetamide for 20 min (in the dark) at RT, and digested with trypsin (600 ng/sample) at RT for 18 h (in the dark). Subsequently, peptides were extracted using 50 mM NH4HCO3 for 15 min at RT, followed by dilution with $10\%$ formic acid (FA) and acetonitrile (I) (1:1), filtered (polyvinylidene fluoride, Merck Millipore), and lyophilized to dryness (SpeedVac centrifugal vacuum concentrator, Thermo Fisher Scientific). Peptides were acidified with a buffer containing $0.1\%$ FA, pH < 3.
## Proteomics: liquid chromatography–tandem mass spectrometry
Peptides were analyzed using nanoscale liquid chromatography coupled to tandem mass spectrometry (nanoLC-MS/MS), where the peptides were loaded onto a nanoflow ultraperformance liquid chromatography (UPLC) instrument (Ultimate 3000 RSLS nano, Dionex) coupled online to an Orbitrap Velos FT mass spectrometer (Thermo Fisher Scientific) with a Proxeon nanoelectrospray ion source (Thermo Fisher Scientific)33. Peptides were loaded (0.1 × 20 mm 5 μm C18 beads, nano-trap column, Dionex; 5 μL/min in $0.1\%$ FA, $2\%$ ACN) and separated (Acclaim PepMap C18 nano-column 75 μm × 50 cm, 2 μm 100 Å, Dionex) at a flow rate of 300 nL/min at 35 °C. Liquid chromatography (LC) parameters: 480-min gradient from 1 to $65\%$ (v/v) phase B ($0.1\%$ (v/v) FA in $80\%$ (v/canACN); phase A ($0.1\%$ FA) (1–$5\%$ from 0 to 10 min, 10–$25\%$ from 10 to 360 min, and 25–$65\%$ from 360 to 480 min). The mass spectrometer was operated in MS/MS mode scanning from 350 to 2000 amu. The resolution of ions in MS1 was 60,000 and 15,000 for high-field collision-induced dissociation (HCD) MS2. The top 20 multiply charged ions were selected from each scan for MS/MS analysis using HCD at $35\%$ collision energy. Automatic gain control (AGC) settings were 1,000,000 for full scans in Fourier transform mass spectrometry and 200,000 for MSn. Resolution in MS2 at m/z 115 was 16,30034. An MS1 scan was acquired from 350–2000 m/z (60,000 resolution, 1 × 106 AGC), 50 ms injection time) followed by MS/MS data-dependent acquisition of the top 20 most intense MS/MS ions from each scan, with HCD and detection in the orbitrap (resolution in MS2 at m/z 115 was 16,300, $35\%$ normalized collision energy, 1.6 m/z quadrupole isolation width). Dynamic exclusion was enabled with a repeat count of 1, an exclusion duration of 30 s. Proteomic experiments were performed in biological replicates ($$n = 8$$ from 15 mice pooled per sample). Proteomic data (RAW and processed/search files) for each tissue region (healthy, stable, unstable) and comparisons between mouse tissue regions were uploaded to the Proteome Xchange *Consortium via* the PRIDE partner repository with the dataset identifier PXD030857.
## Proteomics: data processing and informatic analysis
Peptide identification and quantification were performed using MaxQuant (v1.6.6.0) with its built-in search engine Andromeda35–37. Tandem mass spectra were searched as a single batch against the *Mus musculus* reference proteome (UniProt; UP000000589, 59,345 entries, Feb-2019; canonical protein sequence) supplemented with common contaminants. Search parameters included carbamidomethylated cysteine as a fixed modification and oxidation of methionine and N-terminal protein acetylation as variable modifications. Enzyme specificity was set (C-terminal to arginine and lysine) using trypsin protease, with a maximum of two missed cleavages allowed. Peptides were identified with an initial precursor mass deviation of up to 7 ppm and a fragment mass deviation of 20 ppm. Protein identification was performed with at least one unique or razor peptide per protein group. Contaminants and reverse identifications were then excluded from further data analysis. “ Match between run algorithm” in MaxQuant38 and label-free protein quantification (maxLFQ) was performed, with proteins/ peptides matching the reversed database filtered out. The original MaxLFQ study by Cox et al.39, is based on extracted ion current (XIC)-based approach and not spectral counting. This intensity-based approach takes into consideration the entire XIC for each sample for comparative purposes. The specifics of this algorithm are protected by the maxLFQ algorithms. However, the normalization approach has two main advantages (i) “delayed normalization,” which makes label-free quantification fully compatible with any up-front separation, and (ii) extracts the maximum ratio information from peptide signals in arbitrary numbers of samples to achieve the highest possible accuracy of quantification. Perseus (v1.6.14)40 was used to analyze proteins whose expression was identified across multiple biological replicates (i.e., in at least $70\%$ in at least one group). Statistical analyses were performed using Perseus, R programming, and GraphPad Prism, with unpaired two-sample Student’s t-test or one-way ANOVA performed (statistical significance defined at FDR < 0.05). Pathway enrichment map analysis was performed using Cytoscape (v3.7.1)41, Reactome42, and DAVID functional annotation43 software; significance $p \leq 0.05.$ Unique proteome profile compositions for each vessel segment were graphically visualized using Venny software44. Protein–protein interaction networks were described using StringApp incorporated into Cytoscape (v3.7.1)45.
## Protein quantification and Western blotting
To obtain sufficient quantities of proteins from each segment, identical vessel segments from 4 mice were pooled together for each sample. To extract proteins from the vessels, the tissues were firstly homogenized in 1× RIPA lysis buffer (Cat# 20–188, Merck) with 1× cOmplete™ Protease Inhibitor Cocktail (Cat# 11697498001, Roche). Homogenized tissue suspension was then centrifuged for 20 min at 12,000 rpm at 4 °C. The supernatant containing the proteins was removed and placed in a fresh tube. A small amount of the sample was used to determine the protein concentration using a Pierce™ BCA Protein Assay Kit (Cat# 23225, Thermo Fisher Scientific) according to the manufacturer’s protocol. SDS-PAGE was performed to separate the proteins. Firstly, $15\%$ separating gel with $4\%$ stacking gel was prepared. Protein samples were denatured in Laemmli loading buffer (Cat #1610747, Bio-Rad) by boiling at 95 °C for 5 min. A total of 15 μg of proteins were loaded and separated on SDS-PAGE, then transferred onto a PVDF membrane. The membrane was blocked with $5\%$ milk at RT for 1 h. Subsequently, the membrane was probed for anti-S100a9 primary antibody ($\frac{1}{5000}$, Cat # PA1-46489, Thermo Fisher Scientific) overnight at 4 °C, followed by incubation with HRP-conjugated anti-rabbit ($\frac{1}{5000}$, Cat# 205718, Abcam). The membrane was incubated with Pierce™ ECL Western Blotting Substrate (Cat# 32106, Thermo Fisher Scientific) for 5 min, and the protein bands were visualized using the ChemiDoc™ Gel Imaging System (Bio-Rad). After detection of S100a9, the membrane was stripped using Restore™ Western Blot Stripping Buffer (Cat# 21059, Thermo Fisher Scientific) and reprobed for β-actin ($\frac{1}{1000}$, Cat# 4970 S, Cell Signaling) as a loading control. Image J was used as the software to determine the densitometry of protein bands. The relative expression of protein candidates was normalized to the loading control β-actin.
## Immunofluorescence staining
TS vessel segments were extracted and embedded in Tissue-Tek ® optimal cutting temperature compound (Sakura Finetek) and stored at −80 °C. Following the thawing of samples for 20 min at RT, samples were fixed in acetone at 20 °C for 10 min, followed by air drying. Samples were permeabilized in $0.1\%$ Triton-X-100 in PBS for 10 min, followed by two PBS washes for 5 min at RT. Samples were blocked using normal serum blocker for 30 min at RT and then incubated in the primary antibody at 4 °C overnight. Samples were washed twice in PBS for 5 min and subsequently incubated in secondary antibodies Goat anti-Rat Alexa Fluor 488 (cat#A1106, Invitrogen) and Donkey Anti-Goat Tritc (cat#A16004, Invitrogen) for 30 min at RT. Samples were twice washed with PBS for 5 min, counterstained with Dapi (cat#D1306, Invitrogen), and mounted with Vectashield Mounting Medium (cat#H-1000, Vector). Negative isotype controls Rat IgG (cat#1-4000 Vector), and Goat IgG (cat#02-6202, Thermo Fisher) were performed in concurrence. Refer to antibodies in Supplementary Data 9.
## Pharmacological inhibition of S100A9 in the TS mouse model of plaque instability
This study was designed to evaluate the effect of S100A9 inhibition using ABR-215757 on atherosclerosis plaque instability. ABR-215757 is a small-molecule inhibitor of S100A9. ABR215757 was dissolved in alkaline water at pH 9 and provided in solution at a stock concentration of 5 mg/ml. Vehicle control was prepared as alkaline water at pH 9 and autoclaved before administration in treatment. Male ApoE-/- mice at 6 to 7 weeks of age were fed an HFD containing $22\%$ fat and $0.15\%$ cholesterol (SF00-219, Specialty Feeds) for 6 weeks, as previously described. Following TS surgery, the mice were continued on an HFD and received either 25 mg/kg ABR-215757 (Active Biotech) or vehicle control via intraperitoneal injection 3 times weekly for a total period of 7 weeks. The mice were euthanized as previously described. At the termination of the study, body weights were recorded. Plasma was collected for lipid profile analysis as follows. Whole blood from the mice was collected using heparin as the anticoagulant and centrifuged at 300×g for 10 min. Plasma was isolated from the mice for lipid profile analysis. Plasma was diluted with double distilled water in $\frac{1}{5}$ dilution before analysis. Total plasma cholesterol and glucose concentrations were measured using an LX20PRO Analyzer (Beckman Coulter) in combination with the following kits: Cholesterol (Cat # 467825, Beckman Coulter), HDL Cholesterol (Cat # 650207, Beckman Coulter), Triglycerides GPO (Cat # 445850, Beckman Coulter), and Glucose (Cat # 442640 Beckman Coulter). The kits performed are based on a series of enzymatic colorimetric reactions. Colorimetric changes were measured at 520 nm for cholesterol and triglycerides, at 560 nm for HDL, and at 340 nm for glucose. Following perfusion, aortic sinuses and TS vessel segments of carotid arteries were collected for atherosclerosis histological assessment.
## Immunohistochemical quantification and assessment
Mouse plaque tissue was embedded in Tissue-Tek O.C.T. compound (Sakura) and cryosectioned into 6 µm thick sections using a Cryostat Microm HM525 (Thermo Fisher Scientific). Sections were stained with primary antibody, rat anti-mouse CD68 (Cat#: MCA1957GA, Bio-Rad), followed by secondary antibody, mouse absorbed biotinylated rabbit anti-rat (Cat#: BA-4001, Vector Laboratories), followed by DAB substrate incubation (Supplementary Data 10) and counter-stained in hematoxylin before mounting in dibutyl-phthalate polystyrene xylene (DPX). Stained sections ($$n = 6$$ per mouse) were imaged using an Olympus BX61 microscope. The positively stained area of dark brown color was quantified using Fiji (Image J). Each image was the first color deconvoluted into H/DAB mode. Then the brown picture was selected, and the threshold was adjusted that matches the positive dark brown staining of the original image. The proportion of the total plaque area was indicated after drawing the region of interest from the internal elastic lamina to the vascular endothelium.
## Hematoxylin and eosin
Frozen cryosections of aortic sinus and carotid segments I were thawed for 30 min prior to staining and subsequently rehydrated in distilled water for 5 min. The slides were incubated with Harris Haematoxylin (HH-500, Amber Scientific) for 15 s and immediately washed in running tap water. Cryosections were incubated in alkaline water (sodium bicarbonate in tap water) for 10 s and then washed in distilled water, followed by staining with Eosin $1\%$ alcoholic (EOS1-500, Amber Scientific) for 2 min. The slides were dehydrated with $95\%$ ethanol for 3 min and then with $100\%$ ethanol twice for 3 min each. Finally, the slides were cleared twice with xylene for 5 min each and mounted with DPX mounting medium (Thermo Fisher Scientific).
## Picro Sirius red (PSR) staining
Frozen cryosections of aortic sinus and TS segments were thawed for 30 min prior to staining and then fixed in $10\%$ neutral buffered formalin (Sigma-Aldrich) for 10 min. The slides were then washed twice in PBS for 5 min each, followed by staining with $0.1\%$ picrosirius red staining solution conta12iriussirius red powder (Cat # 365548, Sigma Aldrich) in picric acid solution (Cat # FNNFF004, Fronine) for 1 h. The slides were then differentiated in 0.01 M hydrochloric acid, and adequate levels of differentiation were checked under a microscope. The slides were subsequently dehydrated with ethanol and cleared with xylene, as described above. Finally, the slides were mounted with DPX mounting medium (Thermo Fisher Scientific).
## Oil Red O staining
Frozen cryosections were fixed in $10\%$ neutral buffered formalin (Sigma-Aldrich) for 5 min and washed in PBS for 4 min, followed by washing in $60\%$ isopropanol for 30 s. The slides were then stained with $0.6\%$ Oil Red O staining solution containing Oil Red O powder (Cat # O0625, Sigma Aldrich) in $60\%$ isopropanol for 1 h. Tissue sections were differentiated in $60\%$ isopropanol and then washed in distilled water for 2 min, followed by counterstaining with Harris Haematoxylin (HH-500, Amber Scientific). The slides were washed and finally mounted with Aquatex (Cat # 108562, Merck).
## Human carotid plaque candidate protein validation by immunohistochemistry
Human carotid plaques were collected from patients who presented with symptoms such as stroke or transient ischemic attack and underwent endarterectomy in the operating theater in the Alfred Hospital, Melbourne, Australia (Ethics approval number: $\frac{130}{110}$). Plaque tissue was embedded in the Tissue-Tek O.C.T. compound (Sakura) and cryosectioned using Cryostat Microm HM525 (Thermo Fisher Scientific). Cryosections were thawed at RT for 30 min and then fixed in acetone at −20 °C for 20 min. Subsequently, slides were treated with $3\%$ hydrogen peroxide in methanol for 30 min to block endogenous peroxidase activity. Sections were incubated with $10\%$ normal goat serum (Cat# S-1000, Vector Laboratories) for 30 min, followed by avidin and biotin blocking according to the manufacturer’s requirement (Cat# SP-2001, Vector Laboratories). After the blocking steps, the sections were treated with primary antibodies (Supplementary Data 11) at 4 °C overnight. Sections were subsequently incubated with goat biotinylated anti-rabbit secondary antibodies (Cat# BA-1000, Vector Laboratories) for 30 min at RT, followed by the use of an avidin-biotin-peroxidase complex system (Cat#PK-4000, Vector Laboratories). Positive staining was developed using a 3,3′-diaminobenzidine (DAB) substrate kit (Cat#SK4100, Vector Laboratories) and visualized as a brown color on the sections. Then sections were counterstained with hematoxylin. Negative controls, including rabbit IgG isotype (Cat# I-1000-5, Vector Laboratories) control and primary antibody omission, were performed in parallel with the experiments.
## Statistics and reproducibility
Label-free protein quantification intensities were log2 transformed. In vivo data were quantified using Fiji (Image J). Statistical analyses were applied using Student’s t-tests or one-way ANOVA for parametric data and the Mann–Whitney U test or Kruskal–Wallis test for non-parametric data. Normality testing was performed using the D’Agostino & Pearson normality test in GraphPad Prism. The experimental numbers (n) are listed in the figure legends. n represents the number of biological replicates. Data are presented as mean ± SEM, with FDR and p-values less than 0.05 (as indicated) considered statistically significant.
## 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 1-11 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04641-4.
## Peer review information
Communications Biology thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Madhumita Basu and Joao Valente. Peer reviewer reports are available.
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|
---
title: SAPS3 subunit of protein phosphatase 6 is an AMPK inhibitor and controls metabolic
homeostasis upon dietary challenge in male mice
authors:
- Ying Yang
- Michael A. Reid
- Eric A. Hanse
- Haiqing Li
- Yuanding Li
- Bryan I. Ruiz
- Qi Fan
- Mei Kong
journal: Nature Communications
year: 2023
pmcid: PMC10011557
doi: 10.1038/s41467-023-36809-1
license: CC BY 4.0
---
# SAPS3 subunit of protein phosphatase 6 is an AMPK inhibitor and controls metabolic homeostasis upon dietary challenge in male mice
## Abstract
Inhibition of AMPK is tightly associated with metabolic perturbations upon over nutrition, yet the molecular mechanisms underlying are not clear. Here, we demonstrate the serine/threonine-protein phosphatase 6 regulatory subunit 3, SAPS3, is a negative regulator of AMPK. SAPS3 is induced under high fat diet (HFD) and recruits the PP6 catalytic subunit to deactivate phosphorylated-AMPK, thereby inhibiting AMPK-controlled metabolic pathways. Either whole-body or liver-specific deletion of SAPS3 protects male mice against HFD-induced detrimental consequences and reverses HFD-induced metabolic and transcriptional alterations while loss of SAPS3 has no effects on mice under balanced diets. Furthermore, genetic inhibition of AMPK is sufficient to block the protective phenotype in SAPS3 knockout mice under HFD. Together, our results reveal that SAPS3 is a negative regulator of AMPK and suppression of SAPS3 functions as a guardian when metabolism is perturbed and represents a potential therapeutic strategy to treat metabolic syndromes.
Inhibition of AMPK leads to metabolic perturbations yet how AMPK is inactivated is not fully understood. Here the authors show protein phosphatase 6 subunit SAPS3 is a negative regulator of AMPK and loss of SAPS3 activates AMPK and protects male mice against overnutrition.
## Introduction
Metabolic syndromes, cancer, and ageing, which are all associated with deterioration in the maintenance of metabolic homeostasis, are fundamental health problems worldwide. The AMP-activated protein kinase (AMPK) senses metabolic stress and is a central mediator in maintaining metabolic homeostasis within cells and at the whole-organism level. AMPK relays the state of the cells’ AMP: ATP and ADP: ATP ratios and its activation promotes restoration of cellular energy through the rapid reduction in macromolecular synthesis, enhancement of glucose uptake, and fatty acid oxidation1. Systemically, AMPK has adapted to integrate hormone signals to balance organismal energy intake and expenditure at the whole-body level2. Thus, AMPK activation has become an attractive target for treating diseases associated with metabolic perturbations, such as diabetes, obesity, fatty liver disease, cancer, and ageing3–5. However, little is known about regulators of AMPK that antagonize the AMPK activators. These factors are critical to efficiently target AMPK activation for therapies.
While it has been demonstrated that AMPK activity is tightly regulated by reversible protein phosphorylation, how AMPK is dephosphorylated and inactivated upon recovery from metabolic stress is much less clear. We and others have indicated that the protein phosphatase 2 A (PP2A) family phosphatases may contribute to AMPK dephosphorylation6–8. However, a specific phosphatase complex among the hundreds of possible PP2A family phosphatase complexes that directly dephosphorylates AMPK and regulates metabolism in vivo has not been identified. Unlike kinases, serine/threonine phosphatase activity is promiscuous and phosphatase specificity is governed largely by associated proteins. The PP2A family protein phosphatases (including PP2A, PP4, and PP6) play an important role in many cellular functions9,10. The PP2A family catalytic subunits (C) are among the most conserved enzymes in eukaryotic cells and dephosphorylate serine and threonine residues without specificity for substrates. Thus, the specificity of the phosphatase is conferred by association with different regulatory subunits9–11. To identify specific protein phosphatase complexes that bind to AMPK, we performed protein mass spectrometric analysis using Flag-tagged AMPKα and found components of the PP6 phosphatase complex, including the regulatory subunit SAPS3 and the catalytic subunit of PP6 in association with AMPK. SAPS3 is one of the three PP6 regulatory subunits and plays a critical role in determining PP6 substrate specificity12,13. However, the biological functions of SAPS3 are largely unknown. Interestingly, it has been reported in yeast the Sit4 phosphatase (the catalytic subunit of PP6 homolog) and Snf1 kinase (AMPK homolog) have opposing functions in regulating the transcriptional response during nutrient-sensing14, suggesting PP6 complex could be an evolutionally conserved negative regulator of AMPK.
Here, we show SAPS3 is upregulated upon over nutrition and facilitates PP6C binding to and dephosphorylates AMPK. To evaluate the role of SAPS3 in vivo, we generated SAPS3 whole-body knockout mice and observed loss of SAPS3 leads to AMPK activation in vivo and maintains glucose homeostasis under high fat diet. Moreover, specific deletion of SAPS3 in the liver is sufficient to reverse the adverse effects precipitated by HFD. Following metabolomic and gene expression profiling, we identify global metabolic and transcriptional responses under HFD are reversed in liver-specific SAPS3 knockout mice. Furthermore, blocking AMPK diminishes the protective effects found in SAPS3 knockout mice under HFD. In summary, we demonstrate targeting SAPS3 might be an efficient strategy to restore metabolic homeostasis and treat diseases associated with metabolic perturbations.
## SAPS3 brings the PP6 catalytic subunit to dephosphorylate AMPK
AMPK is rapidly phosphorylated upon glucose deprivation and resupplying glucose causes massive dephosphorylation of AMPK, suggesting the activity of nutrient-mediated phosphatases (Supplementary Fig. 1a, b). To identify specific protein phosphatase complexes that bind to AMPK, we performed protein mass spectrometric analysis using Flag-tagged AMPKα expressed in 293 T cells following glucose deprivation and resupply. Among the identified proteins, AMPK subunits β and γ were identified, thus validating the approach (Fig. 1a). Besides most identified candidates being reported AMPK binding proteins15–17, we observed the most abundant peptides, other than AMPK subunits, were from SAPS3 (Fig. 1b). The catalytic subunit PP6C and the scaffolding subunit of PP6, ANKRD28, were also identified in the complex, suggesting the SAPS3-containing PP6 holoenzyme may play a role in AMPK dephosphorylation. We further validated the interaction of SAPS3 and AMPK in 293 T cells using co-immunoprecipitation/co-transfection (Fig. 1c, d). Moreover, we found that endogenous SAPS3 colocalized with AMPK in HT1080 cells (Supplementary Fig. 1c). Using recombinant proteins, we found that SAPS3 can directly bind to AMPK in vitro (Fig. 1e).Fig. 1SAPS3 brings the PP6 catalytic subunit to dephosphorylate AMPK.a Schematic model of top binding proteins to AMPK by mass spectrometry. The bigger sphere represents more binding peptides to AMPK. Colored lines represent previously reported interactions. PRKDC, DNA-dependent protein kinase catalytic subunit; HSP90α1, heat shock protein 90 alpha 1; HSP70, heat shock protein 70; UBE2O, ubiquitin-conjugating enzyme E2 O; CDC37, cell division cycle 37; ANKRD28, ankyrin repeat domain 28. b The top binding proteins to AMPK by mass spectrometry. c 293 T cells were transfected with Flag-AMPK and treated with glucose deprivation and addback. Cell lysates were purified with anti-Flag beads and followed by silver staining. The result is representative of three independent experiments. d Flag-SAPS3 and Myc-AMPK were co-expressed in 293 T cells. Cell lysates were immunoprecipitated with anti-Myc or anti-Flag beads followed by Western blotting. The results are representative of three independent experiments. e His-tag pull down of AMPK was performed followed by Western blotting using antibodies indicated. The results are representative of three independent experiments. f Flag-AMPK was overexpressed in 293 T cells that were then treated with glucose deprivation (−), addback (−/+) 1 h, or addback (−/+) 3 h. Cell lysates were immunoprecipitated using anti-flag beads followed by Western blotting. The results are representative of three independent experiments. g His-tag pull down of pAMPK or AMPK was performed, followed by Western blotting. The results are representative of three independent experiments. h Purified protein PP6C was used to pull down SAPS3 and pAMPK. The results are representative of three independent experiments. i HT1080 cells expressing either control (Ctrl) or SAPS3 shRNA were cultured in glucose-free medium and addback (−/+) and cell lysates were analyzed by Western blotting. The results are representative of three independent experiments. j HT1080 cells were transfected with siControl, siSAPS1, siSAPS2, or siSAPS3 respectively. Cells were cultured in the glucose-free medium, and glucose was added back (−/+) and cell lysates were analyzed by Western blotting. The results are representative of three independent experiments.
We next sought to determine if the SAPS3-containing PP6 phosphatase complex dephosphorylates AMPK. First, we examined whether the association of SAPS3/PP6C with AMPK is responsive to glucose. Interestingly, binding of the SAPS3/PP6 complex with AMPK started to accumulate after glucose withdrawal and reached maximal level upon short-term glucose addback. The complex dissociated after the return of glucose for 3 h indicating the phosphatase complex is assembled when AMPK is phosphorylated, perhaps in preparation for nutrient recovery and subsequent dephosphorylation (Fig. 1f). We next tested whether SAPS3 had a preference for the phosphorylated form of AMPK and found that SAPS3 preferably associated with phospho-AMPK (Fig. 1g). To gain more insight into the dynamics of this complex, we used recombinant protein binding assays and found that SAPS3 was required for the association of PP6C and AMPK (Fig. 1h). These data suggest that SAPS3 is essential for bringing the PP6 catalytic subunit to AMPK.
Next, we investigated whether SAPS3 affects AMPK signaling. We found that knockdown of SAPS3 using shRNA significantly decreased dephosphorylation of both AMPK and its downstream substrate, ACC, upon glucose addback (Fig. 1i and Supplementary Fig. 1d, e). SAPS3 is part of a family that includes three members, SAPS1, SAPS2, and SAPS3. To identify whether these other family members play a role in the mediation of AMPK, we knocked down SAPS1, SAPS2, or SAPS3 and found only knockdown of SAPS3 was able to modulate AMPK dephosphorylation upon glucose addback (Fig. 1j and Supplementary Fig. 1f).
## SAPS3 deletion in mice reverses HFD-induced detrimental effects
To evaluate the biological functions of SAPS3 on glucose homeostasis in vivo, we generated SAPS3 whole-body knockout mice (SAPS3 KO). Loss of SAPS3 gene expression was confirmed by PCR (Fig. 2a), and loss of protein expression was confirmed in a variety of tissues by Western blotting (Fig. 2b). Interestingly, knockout of SAPS3 had no significant effects on mice under a control balanced diet. However, when we put these mice on an HFD with 45 kcal% fat for 16 weeks, SAPS3 KO mice significantly reduced the increase in body weight to a similar level of mice fed with the control diet (CD) (Fig. 2c) without change in food intake (Supplementary Fig. 2a). HFD-fed SAPS3 KO mice had less fat mass and more lean mass compared to WT mice using MRI analysis (Fig. 2d), indicating loss of SAPS3 prevents mice from HFD-induced obesity. To assess glucose homeostasis in these mice, we performed glucose tolerance test (GTT) and insulin tolerance test (ITT). SAPS3 knockout mice maintained lower levels of blood glucose when subjected to both GTT and ITT compared to WT mice under HFD (Fig. 2e and Supplementary Fig. 2b). Interestingly, we did not observe a significant difference in blood glucose levels between SAPS3 KO and WT mice under CD during GTT and ITT, indicating the loss of SAPS3 affects the response to over-nutrition but has no discernible effect on a balanced diet. Moreover, HFD-induced liver hypertrophy was almost completely prevented in SAPS3 KO mice (Fig. 2f, g). Unlike the liver from WT mice fed with HFD, SAPS3 KO mice showed no signs of hepatic steatosis characterized by large lipid droplets. The enlarged adipocytes from epididymal white adipose tissue (eWAT) under HFD were also reversed in SAPS3 KO mice (Fig. 2h, i). We didn’t find a significant difference in muscle cells in SAPS3 KO mice compared to WT under both CD and HFD (Fig. 2h). Consistent with our in vitro data, the deletion of SAPS3 maintained AMPK activity in liver tissues under HFD (Fig. 2j and Supplementary Fig. 2c). In addition, we found HFD substantially increased SAPS3 expression, but not SAPS1 or SAPS2, along with reducing AMPK activity (Supplementary Fig. 2d, e). Taken together, these results indicate that the loss of SAPS3 substantially reverses the detrimental phenotypes that are induced by HFD.Fig. 2SAPS3 deletion in mice reverses HFD induced detrimental effects.a Schematic strategy of ppp6r3 knockout. WT: ppp6r3+/+; FF: ppp6r3 fl/fl; HE: ppp6r3+/-; KO: ppp6r3-/-. Colored arrows indicated the positions of primers designed to determine the genotype of wildtype (WT) and SAPS3 knockout (KO) mice. PCR analysis showed a complete loss of SAPS3 expression. The results are representative of at least three independent experiments. b Indicated tissues were collected from SAPS3 KO mice. Tissue lysates were analyzed by Western blotting. $$n = 3$$ mice per group. c 8 weeks old male WT and KO mice were fed with HFD (45 kcal% fat) for 16 weeks. Bodyweight was measured (CD, ctrl diet; HFD, high-fat diet). Mean ± s.d., $$n = 8$$ mice per group analyzed by one-way ANOVA, ****$p \leq 0.0001.$ d Fat mass and lean mass composition of mice were measured by echo-MRI. Mean ± s.d., $$n = 7$$ mice per group analyzed by two-tailed t-test. Fat mass ***$$p \leq 0.0005$$, lean mass ***$$p \leq 0.0004$$; n.s., not significant. e Glucose tolerance test and insulin tolerance test were performed at the endpoint. Mean ± s.d., CD, $$n = 7$$ mice per group; HFD, $$n = 8$$ mice per group analyzed by one-way ANOVA, ****$p \leq 0.0001.$ f Representative pictures of WT and KO mice and their livers. Liver weight was measured and shown in g. Mean ± s.d., $$n = 7$$ mice per group analyzed by two-tailed t-test, **$$p \leq 0.002$$; n.s., not significant. h Representative images of H&E staining of the liver, epididymal white adipose tissue (eWAT), and muscle. Scale bar, 100 µm. i The quantification of fat percentages from the H&E staining images shown in h. Mean ± s.d., CD, $$n = 4$$ mice per group; HFD, $$n = 6$$ per group analyzed by two-tailed t-test, ***$$p \leq 3.06$$E-06; n.s., not significant. j Liver samples were collected from WT and SAPS3 KO mice under CD and HFD. Tissue lysates were analyzed by Western blotting. The results are representative of two independent experiments.
## Liver-specific deletion of SAPS3 is sufficient to improve the systemic response to high fat diet
The liver plays a key role in the regulation of whole-body energy metabolism, and targeting the liver attenuates HFD-induced obesity and diabetes18–20, we next crossbred ppp6r3 fl/fl (FF) mice with a mouse line expressing Cre under the control of the liver-specific albumin promoter to generate liver-specific SAPS3 knockout mice (LKO) (Fig. 3a). To more closely model human intakes, we fed the mice using a moderately high-fat diet (32.5 kcal% fat). Deletion of SAPS3 in the liver had no noticeable effect on mice and the bodyweights of FF and LKO mice were similar over time under CD (Fig. 3b). Mirroring our results from the whole-body knock-out, we found the body weights of LKO mice were significantly lower than FF mice under HFD (Fig. 3b) with no change in food intake (Supplementary Fig. 2f), suggesting that SAPS3 deletion in the liver mitigates the effects induced by HFD. In addition, we tracked fat mass and lean mass composition during the course of the HFD and found that there was less fat and more lean mass composition in LKO mice compared to FF mice (Fig. 3c). Moreover, liver weights and tissue staining showed HFD-induced hepatic steatosis was almost completely blocked in LKO mice (Fig. 3d,e). HFD-induced increases in serum triglycerides were also significantly reduced in LKO mice (Fig. 3f).Fig. 3Liver specific deletion of SAPS3 is sufficient to improve the systemic response to high fat diet.a Schematic showing of the generation of SAPS3 liver specific knockout mice. b 8 weeks old male FF and LKO mice were fed with CD or HFD (32.5 kcal% fat) for 16 weeks (FF, ppp6r3 fl/fl mice; LKO, SAPS3 liver specific knockout mice). Bodyweight was measured. Mean ± s.d., CD $$n = 8$$ mice per group; HFD $$n = 10$$ mice per group analyzed by one-way ANOVA, ****$p \leq 0.0001.$ The SAPS3 protein expression in the liver was tested by Western blotting. The results are representative of three independent experiments. c Fat mass and lean mass composition of mice under HFD were measured by echo-MRI every 4 weeks. Mean ± s.d., $$n = 6$$ mice per group analyzed by two-tailed t-test. Fat mass, week: 4, *$$p \leq 0.042$$; 8, **$$p \leq 0.009$$; 12, **$$p \leq 0.005$$; 16, **$$p \leq 0.002.$$ Lean mass, week: 8, *$$p \leq 0.039$$; 12, **$$p \leq 0.006$$; 16, **$$p \leq 0.009.$$ d Liver weight was compared. Mean ± s.d., CD $$n = 8$$ mice per group; HFD $$n = 10$$ mice per group analyzed by two-tailed t-test, *$$p \leq 0.026$$; ***$$p \leq 0.0006$$; n.s., not significant. e Representative pictures of livers, H&E staining and quantification of liver samples after H&E staining. Scale bar, 100 µm. Mean ± s.d., $$n = 3$$ mice per group analyzed by two-tailed t-test, ***$$p \leq 0.0003$$; n.s., not significant. f Serum triglyceride level was measured. Mean ± s.d., CD $$n = 7$$ mice per group; HFD $$n = 10$$ mice per group analyzed by two-tailed t-test. FF (CD/HFD), **$$p \leq 0.007$$; HFD (FF/LKO), **$$p \leq 0.009$$; n.s., not significant. g Fasting blood glucose level was measured. Mean ± s.d., CD $$n = 7$$ mice per group; HFD $$n = 10$$ mice per group analyzed by two-tailed t-test. FF (CD/HFD), ***$$p \leq 0.0004$$; HFD (FF/LKO), ***$$p \leq 0.0008$$; n.s., not significant. h Glucose tolerance test and insulin tolerance test were performed at the endpoint. Mean ± s.d., CD $$n = 7$$ mice per group; HFD $$n = 10$$ mice per group analyzed by one-way ANOVA. GTT, ****$p \leq 0.0001$; ITT, ***$$p \leq 0.0005.$$ i The concentration of C-peptide was compared between groups. Mean ± s.d., CD $$n = 7$$ mice per group; HFD $$n = 10$$ mice per group analyzed by two-tailed t-test, **$$p \leq 0.008$$; n.s., not significant. j Liver samples were collected from FF and LKO mice under CD and HFD. Tissue lysates were analyzed by Western blotting. The results are representative of two independent experiments.
One of the main functions of the liver is to respond to low glucose levels by triggering gluconeogenesis, a response in part mediated by AMPK21. Thus, we asked whether loss of SAPS3 in the liver would affect the systemic response to fasting. We found that LKO mice were not affected by fasting glucose levels under normal dietary conditions (Fig. 3g). Interestingly, LKO mice had significantly lower levels of fasting blood glucose compared to FF mice under HFD (Fig. 3g). Moreover, LKO mice under HFD cleared blood glucose more efficiently than FF mice and performed significantly better in ITT (Fig. 3h and Supplementary Fig. 2g), again suggesting SAPS3 is important for maintaining metabolic homeostasis under HFD but not required when the metabolic environment is balanced. LKO mice also showed increased levels of the plasma C-peptide, a proportional readout for insulin sensitivity (Fig. 3i). Next, we investigated whether the function of SAPS3 is through regulating AMPK activation and found that SAPS3 deletion led to an increase in phospho-AMPK and phospho-ACC compared to FF under HFD (Fig. 3j and Supplementary Fig. 2h). Therefore, liver-specific SAPS3 knockout recapitulates the phenotype observed in SAPS3 whole body knock-out and is sufficient to hinder the onset of diet-induced liver steatosis, obesity, and insulin resistance.
## Loss of SAPS3 reverses metabolic and transcriptional alterations induced by HFD
To systemically examine the effects of SAPS3 deletion on liver metabolism, we performed metabolic profile analysis using liver samples from both FF and LKO mice fed with control and high-fat diets. Interestingly, SAPS3 knockout did not exhibit much effect on the metabolic landscape under a control diet further supporting the phenotypic data reported above. In contrast, HFD induced significant changes to metabolite pools in WT mice and most of the metabolites increased were reversed in LKO mice (Fig. 4a and Supplementary Data 1). We next used unbiased KEGG pathway analysis to understand the effect of HFD on metabolic alterations and observed many metabolic pathways are affected by HFD (Supplementary Fig. 3a). Among those altered pathways, we found most of them are impacted by the loss of SAPS3 (Supplementary Fig. 3b). To further determine the effects of SAPS3 deletion on metabolic regulations, we distinguished upregulated metabolites and downregulated metabolites in SAPS3 LKO mice compared to FF mice. Interestingly, we found that fatty acid oxidation was significantly upregulated in LKO mice, while fatty acid biosynthesis was the most downregulated pathway in LKO mice, suggesting loss of SAPS3 reverses the fatty acid metabolism altered by HFD in the liver (Fig. 4b and Supplementary Fig. 3c). Notably, LKO mice exhibited more evidence of fatty acid breakdown than synthesis compared to FF under HFD. We observed a significantly higher concentration of the FAO-related metabolites, including coenzyme A, carnitine, and NAD+, in LKO mice compared to FF under HFD (Supplementary Fig. 3d). Moreover, pools of palmitoylcarnitine, the direct product of the FAO initializing enzyme carnitine palmitoyltransferase 1 (CPT1) were increased further suggesting FAO is increased in the livers of these mice (Supplementary Fig. 3d). In contrast, the long-chain fatty acids, palmitate, stearate, and oleate were reduced in LKO mice under HFD, showing a lower level of fatty acid synthesis (Supplementary Fig. 3e). Moreover, it has been reported that HFD leads to decreased glucose uptake and glycolysis22. Consistently, we found decreased glycolytic intermediates upon HFD in FF mice, such as pyruvate and phosphoenolpyruvate, which can be reversed in LKO mice (Supplementary Fig. 3f). AMP is an important regulator of AMPK, therefore, we further compared the AMP levels between WT and KO samples and found no significant difference in AMP levels between WT and KO cells, indicating regulation of AMPK activity in SAPS3 KO cells is independent of AMP concentrations (Supplementary Fig. 3g). Thus, loss of SAPS3 causes a metabolic landscape in the liver that mirrors increased fatty acid oxidation and decreased fatty acid synthesis. Fig. 4Loss of SAPS3 reverses metabolic and transcriptional alterations induced by HFD.a Heatmap of significantly changed metabolites in liver samples from FF and LKO mice fed with different diets was analyzed by LC-MS. $$n = 3$$ mice per group analyzed by one-way ANOVA. b *Pathway analysis* of upregulated metabolites and downregulated metabolites from FF and LKO mice respectively. $$n = 3$$ mice per group analyzed by unpaired t-test. c Transcriptional profile heatmap denoting significantly changed genes in liver samples from FF and LKO mice fed with different diets. CD $$n = 4$$ mice per group; HFD, $$n = 5$$ per group. Fold change > 1.5, $p \leq 0.05$ and FDR < 0.05. d Distribution in genes that were changed in FF mice by HFD and completely reversed ($78\%$), partially reversed ($3\%$), no difference ($18\%$), or augmented ($1\%$) by the knockout of SAPS3. e KEGG pathway analysis of the significantly reversed genes by SAPS3 knockout. CD $$n = 4$$ mice per group; HFD, $$n = 5$$ per group. Fold change > 1.5, $p \leq 0.05$, and FDR < 0.05. f, g GSEA analysis of the genes from FF and LKO mice under HFD. Adipogenesis and long-chain fatty acid synthesis were identified and shown in g.
Metabolic syndromes bring about diverse and severe changes to the transcriptional landscape. Indeed, HFD is reported to induce changes in gene expression profiles. With this in mind, we investigated the gene expression profile of the livers from mice lacking SAPS3 and fed HFD. We performed RNA-seq from liver tissues collected from FF and LKO mice following 16 weeks on HFD. The liver samples from FF and LKO mice under CD or HFD clustered by genotype, confirming that genotype-dependent transcriptional changes dominated the dataset. Interestingly, there was almost no difference in gene expression between FF and LKO livers under CD, again enforcing the idea that SAPS3 may be a metabolic stress-responsive protein. In contrast, HFD-induced differential gene expression is significant in SAPS3 knockout mice (Fig. 4c). Among 2205 genes that were significantly altered by HFD in FF mice, 1724 ($78\%$) of these genes were completely reversed by the deletion of SAPS3, and 56 ($3\%$) of these genes were partially reversed by SAPS3 knockout (Fig. 4d and Supplementary Fig. 4a). Together, we identified 1780 ($81\%$) out of 2205 genes that were altered by HFD but reversed by SAPS3 deletion, suggesting that SAPS3 deletion hinders the transcriptional changes caused by HFD. To better understand the networks of genes regulated by SAPS3 during HFD, we used unbiased KEGG pathway analysis on the set of 1780 genes and found the most affected pathways by loss of SAPS3 were metabolic pathways that involve lipid handling, including Sphingolipid metabolism, biosynthesis of unsaturated fatty acids and fatty acid metabolism (Fig. 4e). Additionally, the PPAR pathway and adipocytokine pathway, which are activated by HFD23,24, were decreased by SAPS3 knockout. Moreover, we used gene set enrichment analysis (GSEA) to unbiasedly explore the differential pathways in FF and LKO mice fed HFD. We found that CYP450 metabolism, the reported top-upregulated pathway in mice in response to HFD25, was reversed by the deletion of SAPS3 (Fig. 4f). One of the most well-known consequences of HFD is cardiovascular diseases. Remarkably, the pathway involving pathogenesis of cardiovascular diseases was decreased by SAPS3 knockout (Fig. 4f). Importantly, and in line with our metabolomics analysis, adipogenesis and long-chain fatty acid synthesis were impaired by the loss of SAPS3 (Fig. 4g). When we investigated the expression of the gatekeeper enzymes for fatty acid synthase in the LKO and FF mice following HFD, we found decreased expression of fatty acid synthase (FAS) and acetyl-CoA carboxylase 2 (ACC2) further supporting the loss of SAPS3 blocks lipogenesis in the liver (Supplementary Fig. 4b). To further confirm the fatty acid synthesis was inhibited by the loss of SAPS3, we performed experiments using stable isotope-labeling. Using labeled palmitate tracing in WT and KO MEF cells, we found less stearate and oleate were synthesized from U-13C16 palmitate when SAPS3 was lost, indicating synthesis of both saturated and unsaturated fatty acids is affected by SAPS3 deletion (Supplementary Fig. 4c). Thus, RNA expression profiling data demonstrate that the deletion of SAPS3 has a significant effect on gene expression under HFD and a limited influence on gene expression during balanced nutrition.
## SAPS3 regulates cellular metabolism via AMPK
Consistent with in vivo results, we found knockout of SAPS3 in MEFs leads to significantly increased phospho-AMPK and phospho-ACC in 25 mM and 10 mM glucose culture (Fig. 5a and Supplementary Fig. 5a). Moreover, we were not able to detect PP6C in complex with AMPK in these SAPS3 knockout cells (Fig. 5b). As AMPK has been implicated in the regulation of a number of metabolic pathways, including activation of glycolysis and fatty acid oxidation (FAO)21,26,27, and inhibition of fatty acid synthesis (FASN)28, we asked whether SAPS3 affects AMPK mediated metabolic functions. We found glucose uptake was significantly increased in SAPS3 KO MEFs (Fig. 5c). To better understand how glucose in cells was being stored in the absence of SAPS3, we used 13C-glucose stable isotope tracer-labeling and identified metabolites for incorporation of heavy glucose. Consistently, the key labeled glycolytic intermediates were increased when SAPS3 is deleted in cells (Fig. 5d). To examine whether increased glucose consumption is channeled to produce lactate or into the TCA cycle, we measured the lactate and TCA intermediates from labeled glucose. The data showed that loss of SAPS3 led to the increase in both lactate and TCA intermediates, suggesting both pathways increase their consumption of glucose carbon (Supplementary Fig. 5b). Using an oxygen consumption rate (OCR) based palmitate oxidation stress assay, a metric of long-chain fatty acid oxidation, we found FAO was significantly increased in SAPS3 KO MEFs (Fig. 5e). Moreover, these cells also displayed decreased levels of labeled palmitate, stearate, oleate, and palmitoleate (Fig. 5f), indicating inhibition of de novo lipogenesis in SAPS3 KO MEFs compared to WT MEFs. Fig. 5SAPS3 regulates cellular metabolism via AMPK.a pAMPK and downstream pACC were tested by Western blotting in WT and SAPS3 KO MEF cultured in 25 mM and 10 mM glucose medium. The results are representative of three independent experiments. b WT and SAPS3 KO MEF cell lysates were immunoprecipitated with anti-PP6C antibody followed by Western blotting. The results are representative of three independent experiments. c Glucose uptake was measured using the Nova Bioprofile 100 analyzer. Mean ± s.d., $$n = 4$$ biological replicates analyzed by two-tailed t-test, ***$$p \leq 0.0006.$$ d U-13C6 glucose- derived pyruvate and glyceraldehyde-3-phosphate (GADP) levels in WT and SAPS3 KO MEF cells. Mean ± s.d., $$n = 3$$ biological replicates analyzed by two-tailed t-test. Pyruvate, *$$p \leq 0.018$$; GADP, *$$p \leq 0.028.$$ e Fatty acid oxidation rate was measured using the XF-24 Seahorse system. Mean ± s.d., $$n = 5$$ biological replicates. f U-13C6 glucose-derived palmitate, stearate, palmitoleate, and oleate levels in WT and SAPS3 KO MEF cells. Mean ± s.d., $$n = 3$$ biological replicates analyzed by two-tailed t-test. Palmiate, *$$p \leq 0.049$$; Stearate, *$$p \leq 0.022$$; Palmitoleate, **$$p \leq 0.005$$; Oleate, **$$p \leq 0.005.$$ g AMPKα1 and α2 expression in Crispr AMPK knockout MEF cells. Mean ± s.d., $$n = 3$$ biological replicates analyzed by one-way ANOVA, **$$p \leq 0.0015$$; ****$p \leq 0.001.$ h U-13C6 glucose-derived pyruvate and phosphoenolpyruvate (PEP) levels in WT and SAPS3 KO MEF cells with or without AMPK. Mean ± s.d., $$n = 3$$ biological replicates analyzed by two-tailed t-test. Pyruvate, **$$p \leq 0.008$$; ***$$p \leq 0.001$$; PEP, WT, *$$p \leq 0.039$$; KO, *$$p \leq 0.032.$$ i U-13C6 glucose derived palmitate, stearate, palmitoleate, and oleate levels in WT and SAPS3 KO MEF cells with or without AMPK. Mean ± s.d., $$n = 3$$ biological replicates analyzed by two-tailed t-test. Palmitate, *$$p \leq 0.02$$; Stearate, **$$p \leq 0.003$$; Palmitoleate, *$$p \leq 0.025$$, ***$$p \leq 0.001$$; Oleate, WT, *$$p \leq 0.025$$; KO, *$$p \leq 0.024.$$
To further confirm if SAPS3 deletion regulates metabolic pathways through activation of AMPK, we knocked out AMPK α1 and α2 in WT and SAPS3 KO MEF cells using CRISPR- based gene editing (Fig. 5g). As above, we used 13C-glucose isotope tracer labeling to follow the fate of glucose to fatty acids. Deletion of AMPK significantly reduced glycolytic intermediates in WT cells as previously shown29. As expected, the knockout of SAPS3 increased the concentration of glycolytic intermediates, which can be completely blocked by the deletion of AMPK (Fig. 5h). Meanwhile, loss of AMPK reversed the levels of labeled fatty acids that were reduced by SAPS3 knockout (Fig. 5i). Taken together, these results demonstrate that metabolic alterations in SAPS3 knockout cells were largely through activation of AMPK.
## SAPS3 deficiency-mediated metabolic homeostasis in vivo under HFD is AMPK dependent
To investigate whether the metabolic effect induced by SAPS3 deletion is via activation of AMPK in vivo, we treated LKO mice with Compound C, a non-selective AMPK inhibitor, after 16 weeks on HFD (Fig. 6a). As observed before, the HFD-induced increases in fat mass and liver weights observed in FF mice were significantly reduced in SAPS3 LKO mice. However, treatment with Compound C largely diminished the effect of SAPS3 deletion on fat mass and liver weights (Fig. 6b, c). In addition, reduced phosphorylation of the canonical AMPK substrate ACC was found in both FF and LKO livers upon compound C treatment, confirming the deactivation of AMPK by Compound C (Fig. 6d and Supplementary Fig. 6a). Importantly, Compound C substantially increased the lipid droplets in SAPS3 LKO liver samples compared to control-treated mice (Fig. 6e,f). We found normal levels of alanine transaminase (ALT) and aspartate transaminase (AST) in mice livers excluding the possibility that Compound C impairs liver functions (Supplementary Fig. 6b). These data suggest the inhibition of AMPK largely reverses the phenotype in SAPS3 knockout mice and further demonstrate these two work together to modulate the response to HFD in vivo. Fig. 6SAPS3 deficiency-mediated metabolic homeostasis in vivo under HFD is AMPK dependent.a Experimental strategy for feeding 8 weeks old male LKO mice with HFD (45 kcal% fat) for 16 weeks and then treating mice with compound C for two weeks. b Fat mass composition of FF and LKO male mice under HFD before compound C treatment and after compound C treatment for two weeks. Mean ± s.d., $$n = 6$$ mice per group analyzed by two-tailed t-test. Before treatment, FF/LKO, **$$p \leq 0.006$$, **$$p \leq 0.0017$$; After treatment, FF/LKO, *$$p \leq 0.033$$; n.s., not significant.; LKO, before treatment/comC, *$$p \leq 0.048.$$ c Liver weights from mice treated with ctrl or compound C. Mean ± s.d., $$n = 8$$ mice per group analyzed by two-tailed t-test, ***$$p \leq 0.0005$$; n.s., not significant. d AMPK mediated ACC phosphorylation was tested by Western blotting. The results are representative of two independent experiments. e Representative images of H&E staining of liver slides from FF and LKO mice. Scale bar, 100 µm. f The quantification of the H&E staining images shown in e. Mean ± s.d., $$n = 6$$ mice per group analyzed by two-tailed t-test, ****$$p \leq 8.7918$$E-08; n.s., not significant. g Experimental strategy for injecting AAV-AMPK-DN in 6 weeks old male LKO mice and, after two weeks, followed by feeding mice with HFD (45 kcal% fat) for 16 weeks. h The inhibition of AMPK activity by AAV-AMPK-DN was evaluated by the level of phospho-ACC from liver samples after HFD. The results are representative of two independent experiments. i Body weight was measured. Mean ± s.d., $$n = 6$$ mice per group analyzed by two-tailed t-test, n.s., not significant; or one-way ANOVA, ****$p \leq 0.0001.$ j Fat compositions were measured by echo-MRI. Mean ± s.d., $$n = 6$$ mice per group analyzed by two-tailed t-test, ****$$p \leq 5.9683$$E-06; n.s., not significant. k Representative pictures of mice and livers from FF and LKO mice. And the liver weights were compared. Mean ± s.d., $$n = 6$$ mice per group analyzed by two-tailed t-test, ***$$p \leq 0.00018$$; n.s., not significant. l Representative images of H&E staining of liver slides from FF and LKO mice with or without AAV. Scale bar, 100 µm. CD, AAV (−) $$n = 3$$ mice per group; CD, AAV (+) $$n = 4$$ mice per group; HFD, AAV (−) $$n = 6$$ mice per group; HFD, AAV (+) $$n = 6$$ mice per group. m Glucose tolerance test and insulin tolerance test were performed at the end of the experiment. Mean ± s.d., $$n = 6$$ per group analyzed by one-way ANOVA or two-tailed t-test. **** $p \leq 0.0001$; ***$$p \leq 0.0001$$; n.s., not significant.
To further determine whether SAPS3 functions directly through hepatic activation of AMPK, we used Cre-dependent adeno-associated virus (AAV) expressing a dominant negative (DN) kinase dead (K45R) form of AMPK in the liver30, then fed with HFD (Fig. 6g). Since LKO mice have liver-specific expression of Cre protein, AMPK-DN was only expressed in LKO mice, not in FF mice. The AMPK-DN expression was confirmed via Western blotting by reduced phospho-ACC in liver samples after HFD (Fig. 6h and Supplementary Fig. 6c). As a control, we found there was no effect on phospho-ACC in lung tissue (Supplementary Fig. 6d). We found the body weights of LKO mice with AAV-AMPK-DN were similar to FF mice under HFD (Fig. 6i) with similar food intakes (Supplementary Fig. 6e), suggesting that AMPK-DN suppresses the beneficial effects found in SAPS3 LKO mice in response to HFD. Moreover, AMPK-DN reverses the effect of SAPS3 knock-out on fatty acid synthesis and lipid accumulation in vivo (Fig. 6j). We also observed a similar level of liver hypertrophy and liver steatosis in LKO mice treated with AAV-AMPK-DN compared to FF mice (Fig. 6k, l and Supplementary Fig. 6f). When we investigated glucose and insulin sensitivity in these mice, we found under HFD, LKO mice with AMPK-DN reversed the sensitivity exhibited by SAPS3 LKO (Fig. 6m and Supplementary Fig. 6g) and also reversed the levels of fasting blood glucose (Supplementary Fig. 6h).
## Discussion
It has been well studied that AMPK activity is tightly and dynamically regulated by reversible protein phosphorylation31,32. However, the specific protein phosphatase complex that can directly dephosphorylate AMPK and shut down AMPK-mediated metabolic responses is still unknown. Despite studies that have shown many catalytic subunits of serine-threonine protein phosphatases, including protein phosphatase 2 C and protein phosphatase 2 A, can dephosphorylate AMPK in vitro, they may not function as an AMPK phosphatase in vivo since catalytic subunits of serine-threonine protein phosphatase are constitutively active with promiscuous activity towards substrates6,33. The substrate specificity of the serine-threonine protein phosphatase is largely determined by the regulatory subunits of serine-threonine protein phosphatases. Here, we found that protein phosphatase 6 regulatory subunit, SAPS3, was the top binding protein to AMPK and functions as a central regulator of metabolism. Deletion of SAPS3 in mice leads to AMPK activation and protects against the HFD-induced detrimental metabolic phenotypes by improving glucose homeostasis and insulin sensitivity. Interestingly, PPP6R3, the gene that encodes SAPS3, is located on the type 1 diabetes susceptibility locus, IDDM4, on chromosome 11q1334. SAPS3 was also reported as being among obesity or diabetes-related genes in the human gene database GeneHancer and there is a significant association of higher expression level of SAPS3 with T2D based on sequencing data and genome-wide association studies (GWAS)35–37. A recent report indicates that in yeast, PP6 family phosphatase Ppe1 is the primary phosphatase for Ssp2/AMPKα dephosphorylation, suggesting a conserved role for the PP6 phosphatase complex in AMPK dephosphorylation and links SAPS3 to the genesis of nutrient-sensing38. Recent data indicate that AMPK plays a global role in regulating aspects of whole-body energy balance including appetite and body weight. Metabolically, AMPK is well known to be a major mediator of glucose and lipid homeostasis39–41 and activation of hepatic AMPK is essential for whole-body metabolism, such as control of blood glucose levels and plasma triglyceride levels42–44. Consistent with this, we found that liver-specific deletion of SAPS3 is sufficient to affect whole-body metabolism under a high-fat diet. These results support the notion that activation of the AMPK pathway in the liver is sufficient to regulate whole-body metabolism. Interestingly, we found no significant difference in food intake between WT and SAPS3 KO mice under HFD. Besides food intake, the resistance to body weight increase might be caused by the impact of SAPS3 deletion on energy expenditure since recent publications indicate that AMPK plays a role in energy expenditure45,46.
Interestingly, we found that deletion of SAPS3 in mice had minimal effects on mice development and biological functions during balanced nutritional intake. Consistently, the metabolomic and genomic analysis also confirmed that SAPS3 deletion issued no significant changes on metabolism and transcription under the control diet. These data suggest that SAPS3 mainly functions under metabolic stress conditions, induced by high-fat diet, which are critical causes for metabolic perturbations. Thus, SAPS3 deficiency functions as a guardian to maintain metabolic homeostasis upon stress conditions. We also noticed there are increased levels of many metabolites in LKO mice under HFD compared to mice under control diet. After statistical analysis by Metaboanalyst, three pathways were shown to be affected by the knockout of SAPS3 under HFD compared to control diet: glycine metabolism, citric acid cycle, and β oxidation of long-chain fatty acids (Supplementary Fig. 2b). Glycine and serine metabolism are related to glutathione synthesis that protects and assists fatty acid oxidation47, the TCA is also a source for fatty acid oxidation48. Therefore, the three pathways might cooperatively boost the reduction of fatty acids in vivo.
AMPK is known to be a major mediator of energy homeostasis. By regulating tissues such as liver, muscle, and adipose tissues, AMPK controls whole-body glucose homeostasis41. Meanwhile, lipid synthesis and oxidation in the liver, and lipolysis and lipogenesis in adipose tissue are also tightly regulated by AMPK. Thus, using genetic expression of activated AMPK in the liver could lower blood glucose levels and improve lipid profile49,50. As a result, many different AMPK activators were developed to control blood glucose levels and plasma triglyceride levels in metabolic syndromes51,52. However, the role of hepatic AMPK in regulating blood glucose levels is still controversial. Studies using liver-specific AMPK knockout mouse models or pharmaceutical AMPK activators that target hepatic AMPK failed to show significant impact on blood glucose levels53,54. On the other hand, genetic expression of activated AMPK in liver is sufficient to lower blood glucose levels and improve lipid profile49,50. Consistently, our data show that liver-specific deletion of SAPS3, which leads to AMPK activation, is sufficient to improve blood glucose levels under HFD.
In summary, our data provide a more complete understanding of the biological response to unbalanced nutrition. We identify SAPS3 containing PP6 complex as an AMPK phosphatase that plays a central role in the response to metabolic perturbations upon dietary challenges. Thus, inhibition of SAPS3 function provides a strong potential for the development of novel AMPK activators to treat metabolic syndromes.
## Mice and ethics statement
All studies involving mice were performed according to the Institutional Animal Care and Use Committee (IACUC)-approved protocols at the University of California, Irvine (IACUC protocol: AUP-20-159). We have complied with the relevant ethical considerations for animal research overseen by this committee. Ppp6r3fl/fl C57BL/6 mice were generated by inGenious Targeting Laboratory. Ppp6r3+/- mice were generated by microinjecting Cre recombinase RNA to ppp6r3wt/fl embryos55. WT and SAPS3 KO mice were generated by breeding ppp6r3+/- mice. LKO mice were generated by breeding ppp6r3fl/fl mice with C57BL/6Alb-Cre mice obtained from the Jackson Laboratory. The following diets were used: Control diet (10 kcal% fat, D12450B, Research Diet Inc.); High Fat Diet (45 kcal% fat, D12451, and 32.5 kcal% fat, D12266, Research Diet Inc.). All mice were housed on a 12 h light/12 h dark cycle with free access to water and diet. Body weights were measured every week. Body fat and lean mass were measured in conscious animals using a whole-body composition analyzer (EchoMRI). For Compound C treatment, 3 mg/kg of compound C was intraperitoneally injected into the experimental group mice every day for two weeks. For AAV injection, pAAV-EF1a-Flex-DN-AMPK-K45R-T2A-mCherry plasmid56 was amplified in recombination-deficient bacteria Stbl3, and Serotype 8 AAVs were packaged by the University of North Carolina Vector Core and the following titer was achieved (vg/ ml): 1.9 × 1012.
## Cell lines and cell culture
293 T cells (ATCC® CRL-3216™) and HT1080 cells (ATCC® CCL-121™) were purchased from American Type Culture Collection (ATCC). SAPS3 KO and WT MEFs were generated from SAPS3 KO and WT mice. All cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (10-017-CV, Corning) supplemented with $10\%$ fetal bovine serum (FBS) (100−106, Gemini Bio-Products), 100 units/mL of penicillin, and 100 μg/mL of streptomycin (516106, Sigma). For glucose deprivation and add-back treatment, HT1080 cells were cultured in DMEM without glucose (11966025, Gibco), supplemented with $10\%$ dialyzed FBS (FB-07, Omega) for 1 h, then the medium was replaced with the complete medium for 20 min. 293 T cells were cultured in DMEM without glucose, supplemented with $10\%$ dialyzed FBS for 6 h, then the medium was replaced with the complete medium for 1 h or 3 h. For siRNA transfection, siSAPS1 (L-020420-01-0005, Horizon), siSAPS2 (L-021331-01-0005, Horizon) and siSAPS3 (L-014646-01-0005, Horizon) were transfected by Lipofectamine RNAiMAX transfection reagent (13778150, Invitrogen). *To* generate lentiviral particles, 293 T cells were cultured in a 6 cm dish and co-transfected with 1 μg control or shRNA vector, 0.5 μg pCMV-VSV-G, and 1 μg pCMV-dR8.2 dvpr by Lipofectamine 2000 (11668−019, Invitrogen) transfection reagent. The viral supernatants were collected and filtered. Cells were infected with the virus with 10 μg/ml polybrene.
## Protein mass spectrometric peptide sequencing
A total of 293 T cells were transfected with plasmids encoding Flag-tagged AMPKα2 protein. Cells were deprived of glucose for 6 h and glucose was added back for 1 h, followed by lysis using RIPA buffer (150 mM KCl, $0.2\%$ (v/v) NP-40, $10\%$ (v/v) glycerol, 20 mM Tris at pH 7.5, 0.5 mM DTT) containing protease inhibitor complex (4693159001, Roche) and phosphatase inhibitor (1862495, Thermo fisher scientific). 20 μl anti-Flag beads (A2220, Sigma) were added to the cell lysates and rotated overnight. Beads were washed four times with cold lysis buffer and eluted with Flag peptide (F3290, Sigma) for 4 h. The elutes were separated by SDS-PAGE and visualized by Coomassie Blue staining (LC6025, Invitrogen). The binding proteins were identified by mass spectrometry analysis, performed by the Taplin Mass Spectrometry Facility at Harvard Medical School.
## Immunofluorescence (IF)
Cells were washed twice with PBS and fixed with $4\%$ formaldehyde (Thermo Scientific, 28908) in PBS for 15 min at room temperature. Cells were washed again three times with PBS and permeabilized with $0.1\%$ TritonX-100 (Fisher, BP151-500) in PBS at room temperature for 10 min. Then cells were washed three times with PBS and blocked with $3\%$ BSA in PBS for 1 h. Thereafter, cells were incubated with anti-AMPKα (Thermo Scientific, MA537501, 1:50) and anti-SAPS3 (Thermo Scientific, 16944-1-AP, 1:100) antibodies overnight at 4 °C. Cells were washed three times with PBS and incubated with goat anti-rabbit 594 (Invitrogen, A11037, 1:500) and goat anti-mouse 488 (Invitrogen, A11029, 1:500) antibodies for 1 h at room temperature. After that, cells were washed three times and mounted with the Prolong Gold antifade reagent with DAPI (Invitrogen, P36931). The slides were visualized with Zeiss LSM 900 Airyscan.
## Immunoprecipitation (IP)
IP was performed as previously described57. Cells were cultured in 10 cm dishes in DMEM supplemented with $10\%$ FBS. $80\%$ confluent cells were washed twice with ice-cold PBS and lysed on ice using a cell scraper with lysis buffer (150 mM KCl, $0.2\%$ (v/v) NP-40, $10\%$ (v/v) glycerol, 20 mM Tris at pH 7.5, 0.5 mM DTT) containing protease inhibitor complex (4693159001, Roche). The protein concentrations of lysates were measured using the BCA Assay kit (23225, Thermo fisher scientific). 1 mg lysates were immunoprecipitated when rotating at 4֯C with 4 μg of antibodies or control IgG. A total of 20 μl Protein G agarose beads (15920010, Thermo fisher scientific), anti-Flag beads (A2220, Sigma), or anti-Myc beads (20169, Thermo fisher scientific) were added to each tube and rotated overnight. Beads were washed four times with cold lysis buffer, resuspended in SDS loading dye, and boiled for 5 min.
## Immunoblotting
Tissues were homogenized, or cells grown in 6 cm dish were lysed on ice in lysis buffer (50 mM Tris-HCL [pH 7.4], 5 mM Sodium Fluoride, 5 mM Sodium Pyrophosphate, 1 mM EDTA, 1 mM EGTA, 250 mM Mannitol, $1\%$ [v/v] Triton X-100) containing protease inhibitor complex (04693159001, Roche) and phosphatase inhibitor (1862495, Thermo fisher scientific). The protein concentrations were measured by using the BCA Assay kit (23225, Thermo fisher scientific). The lysates were boiled with NuPAGE LDS-PAGE sample buffer (1771559, Invitrogen) supplemented with $5\%$ β-mercaptoethanol (M3148, Sigma) for 5 min. Equal amounts of protein were loaded on precast NuPAGE Bis-Tris Gels (NP0321BOX, Life Technologies) followed by transfer onto nitrocellulose membrane (1620115, Bio-Rad). The immunoblotting was performed with the following antibodies: anti-AMPKα (ab80039, Abcam, 1:1000), anti-pAMPKα (ab133448, Abcam, 1:1000), anti-pAMPK (4188, Cell signaling, 1:1000), anti-ACC (3662, Cell signaling, 1:1000), anti-pACC (3661, Cell signaling, 1:1000), anti-β-ACTIN (A1978, Sigma,1:5000), anti-GAPDH (2218, Cell signaling, 1:1000), anti-SAPS3 (A300-971A, Bethyl, 1:1000), anti-PP6C (A300-844A, Bethyl, 1:1000), anti-MYC (ab9106, abcam, 1:1000), anti-FLAG (F3165, Sigma, 1:1000), anti-SAPS3 (A300-972A, Bethyl, 1:1000), anti-SAPS1 (A300-968A, Bethyl, 1:1000), and anti-SAPS2 (A300-969A, Bethyl, 1:1000). All the uncropped images of blot results were shown in Supplementary Fig.7.
## PCR and DNA gel electrophoresis
PCR was performed using GoTaq® Master Mixes (M7122, Promega). PCR products were detected using agarose gel electrophoresis ($1.5\%$). PCR primers are ppp6r3-F: 5’-TTCACACATACCCAGGAATCAGA-3’; R: 5’-GACTGCTGAGACACAAGGGC-3’. The PCR cycle parameters were as follows: 95 ˚C for 3 min; 40 cycles with denaturation at 95 ˚C for 10 sec, annealing at 50 ˚C for 30 sec.
## Glucose tolerance and insulin tolerance
Blood glucose levels were determined using an automated blood glucose reader (Accu-Check; Roche). Glucose tolerance tests were performed on mice that were fasted overnight. Blood was collected immediately before as well as 15, 30, 60, 90, and 120 min after intraperitoneal injection of glucose (1 g/kg body weight). For insulin tolerance tests, mice were fasted for 3 h and then injected with 0.5 U/kg body weight of insulin (91077 C, Sigma). Blood glucose was measured at 0, 15, 30, 60, 90, and 120 min.
## Haematoxylin and eosin (H&E) staining
After mice were euthanized, tissues were collected and fixed in $10\%$ formalin. Formalin-fixed, paraffin-embedded blocks of tissues were used for haematoxylin and eosin (H&E) staining57. 5 µm paraffin-embedded formalin-fixed slides were deparaffinized with xylene, and rehydrated by different concentrations of ethanol. Rinse the slides in distilled water and stain them in hematoxylin/Eosin. After dehydrating the slides in different concentrations of ethanol, slides were cleared in Xylene, and mounted with Cytoseal (Thermo fisher scientific). Image analysis was performed under a microscope (SeBa Laxco).
## Serum biochemistry
Mice were fasted for 4 h before blood was collected via cardiac puncture in BD vacutainer tubes (Fisher Scientific). The serum was obtained by centrifugation at 2000 x g for 15 min. Serum triglyceride (ab65336, Abcam), C-peptide (90050, Crystal Chem), alanine transaminase (ALT) (EALT-100, EnzyChrom), and aspartate transaminase (AST) (EASTR-100, BioAssay System) were measured according to manufacturers’ instructions.
For the triglyceride test, 50 µl standard dilutions or samples (5 µl sample was adjusted volume to 50 µl with triglyceride assay buffer) were loaded into plates. Then, 50 µl of the reaction mixture was added to each standard, sample, and background control wells. The plate was mixed and incubated at room temperature for 60 min protected from light. After that, the absorbance was measured using a plate reader.
For the C-peptide test, 95 µl of sample diluent and 5 µl of sample or working standards were added to each well. The microplate was covered with the plate sealer and mixed for 10 sec. Then, the plate was incubated for 1 h at room temperature. The contents were aspirated, and the wells were washed with 300 µl of wash buffer six times followed by adding 100 µl anti-C peptide enzyme conjugate. The microplate was covered with the plate sealer and incubated for 1 h at room temperature. After that, contents were aspirated, and wells were washed with 300 µl of wash buffer six times. Immediately, 100 µl per well of the enzyme-substrate solution was dispensed. The plate was covered to let the mix react for 30 min at room temperature. In the end, the reaction was stopped by adding 100 µl of enzyme reaction stop solution, and the absorbance was measured using a plate reader.
For the ALT test, 20 µl of each sample was transferred to a 96 well plate. 200 µl working reagent (200 µl assay buffer, 5 µl cosubstrate, 1 µl LDH, and 4 µl reconstituted NADH) were added to the sample and standard wells. The plate was taped to mix and incubated at 37 ֯C. The plate was read at 5 min and 10 min. For the AST test, 20 µl of each sample was transferred to a 96 well plate. 200 µl working reagent (200 µl assay buffer, 1 µl cofactor, 1 µl enzyme mix, and 4 µl NADH) were added to the sample and standard wells. The plate was taped to mix and incubated at 37 ֯C. The plate was read at 5 min and 10 min.
## Liquid chromatography-mass spectrometry (LC-MS)
A total of 3−10 mg of tissue samples were cut on dry ice and soaked in pre-cooled $80\%$ methanol in HPLC-grade water. Samples were homogenized by Precellys 24 homogenizer using Precellys ceramic kit. After centrifuging at 17,000 x g at 4 ֯C for 10 min, the supernatant was transferred to a new tube and dried by speed vacuum. Liquid chromatography-mass spectrometry (LC-MS) was carried out at Duke University. The HPLC (Ultimate 3000 UHPLC) with an Xbridge amide column (Waters) is coupled to Q exactive plus hybrid quadrupole-orbitrap mass spectrometer (QE-MS) (Thermo Scientific) for compound separation and detection58. 301 metabolites were detected from all of the samples Mass isotopomer distributions were determined by integrating metabolite ion fragments and corrected for natural abundance as described59. MetaboAnalyst was used to analyze significantly changed metabolites and generate the heatmap and KEGG pathway analysis (www.metaboanalyst.ca/).
## Purified protein pull-down assay
The pull-down was performed as previously described60. 3 μg of recombinant Myc-PPP6R3 protein (TP328606, OriGene), GST-PPP6C protein (H00005537-P01, Novus), His-pAMPK (P48-10H-10, SignalChem) or His-AMPK (P48-14H-20, SignalChem) were mixed. The mixture was incubated at 37 ֯C for 1 h in 200 μl of assay buffer containing 25 mM Tris (pH8.0), 150 mM NaCl, 2 mM dithiothreitol, and 1 mg/ml BSA to block non-specific binding. Then, 20 μl His select Nickel affinity beads (P6611, Sigma) or 20 μl Protein G agarose beads with 4 μl anti-PP6C antibody were added to the assay buffer and rotated for 1 h at room temperature. After three washes with the assay buffer, the beads were resuspended in SDS loading dye, and examined by western blotting.
## Glucose uptake measurement
A total of 2 × 105 cells WT or SAPS3 KO MEFs were plated on each well of a 6-well plate and cultured overnight. Then, the medium was replaced with 1 mL 10 mM glucose DMEM supplemented with $10\%$ FBS. After culturing for 24 h, the medium was collected, and the cell number was counted by an automated cell counter (Bio-Rad). After cell debris was spun down, 0.8 mL medium was used to measure glucose by the Nova Biomedical BioProfile 100 with fresh 10 mM glucose DMEM as control. This analysis was as followed: glucose uptake = glucose in fresh medium (mM)-glucose in cultured medium (mM). All metabolite measurements were normalized based on cell number.
## 13C6-Glucose and 13C16-Palmitate tracing by GC-MS
2×105 cells were seeded in 6 cm plates containing DMEM supplemented with $10\%$ FBS and cultured overnight. Cells were washed with PBS twice and cultured in glucose-free DMEM supplemented with $10\%$ dialyzed FBS containing 13C6-glucose (10 mM; Cambridge Isotope Laboratory). After culturing for 6 h which led to $85\%$ labeling of the total glucose pool, the medium was aspirated at room temperature. Cells were washed by cold saline and put on dry ice. U-13C16-palmitate was complexed to fatty-acid-free BSA (A8806, Sigma) in 6:1 molar ratio in PBS by rolling overnight at room temperature. When cells were at $80\%$ confluence, the medium was replaced by DMEM (A1443001, Gibco) with 10 mM glucose, 2 mM glutamine, $10\%$ dialyzed FBS and 0.1 mM [U-13C16] palmitate. Cells were cultured in the labeled medium for 48 h which led to approximately $40\%$ labeling of the total cellular palmitate pool.
For polar metabolites, 1 ml $80\%$ methanol/water (HPLC grade) with norvaline as internal standard was added to cells. The plate was transferred to a −80 ˚C freezer and left for 15 min to further inactivate enzymes. Cells were then harvested by a silicone scraper and the whole-cell extract was transferred to a tube and centrifuged at 17000 x g for 10 min at 4 ˚C. The supernatant was transferred into tubes and dried by speed vacuum. Fifty microlitres of MOX (10 mg/ml in pyridine, 226904 Sigma) was added and the mixture was incubated at 42 ˚C for 1 h. After the samples were cooled down, 100 μl TBDMS (394882, Sigma) was added, and samples were incubated at 70 ˚C for 1 h. Then samples were transferred to GC vials and analyzed by Agilent 7820 A chromatography and Agilent 5977B mass spectrometer57.
For nonpolar metabolites, 500 μl methanol, 500 μl chloroform, 200 μl H2O and 10 μl 10 mM d31-C16:0 as internal standard was added to cells. The samples were vortexed and centrifuged at 14,000 xg for 5 min. The chloroform phase was dried and then derivatized to form fatty acid methyl esters (FAMEs) via addition of 500 μl $2\%$ H2SO4 in methanol at 50 °C for 2 h followed by adding 100 μl saline and 500 μl hexane. After extraction, the hexane layer was dried and dissolved in 100 μl hexane. These samples were analyzed using a select FAME column.
## Seahorse assay for fatty acid oxidation
5×104 cells were seeded into seahorse XF24 microplates (100850, Agilent) and cultured for 6 h. Before incubation, the culture medium was removed and cells were washed with substrate limited medium (DMEM supplemented with 0.5 mM Glucose, 1 mM GlutaMAX (35050061, Thermo fisher scientific), 0.5 mM Carnitine (C0283, Sigma), and $1\%$ FBS). Then, 300 μl substrate limited medium was added to each well and cultured overnight. On the next day, the substrate limited medium was replaced by 375 µl FAO buffer (111 mM NaCl, 4.7 mM KCl, 1.25 mM CaCl2, 2 mM MgSO4, and 1.2 mM NaH2PO4) supplemented with 2.5 mM glucose, 0.5 mM carnitine, and 5 mM HEPES. After 30 min, 37.5 µl etomoxir (40 μM final) (236020, Sigma) was added to the FAO buffer. 15 min after that, 87.5 µl palmitate: BSA or BSA control was added into the buffer, and XF cell mito stress assay (103015-100, Agilent) was initiated. 56 µl oligomycin (30 µM), 62 µl FCCP (20 µM), and 69 µl Rotnone/Antimycin A (20 µM) were added to the cartridge wells. OCR level was determined using Seahorse bioscience XF24 extracellular flux analyzer (Agilent) and each cycle of measurement involved mixing (3 min), waiting (2 min), and measuring (3 min) cycles. The data were analyzed in Seahorse Wave Desktop Software 2.4.
## Crispr-cas9 knockout
Two oligos were designed to knockout AMPKα1 and α2 using the software at http://crispor.tefor.net/crispor.py. F-5’-CACCGGAAGCAGAAGCACGACGGGC-3’; R-5’-AAACGCCCGTCGTGCTTCTGCTTC. LentiCRISPRv2 encoding Cas9 (52961, Addgene) was digested with BsmBI (R0580, Biolabs) and purified by DNA gel. The two oligos were ligated to the digested plasmid. SAPS3 KO and WT MEFs were infected with lentivirus containing lentiCRISPRv2-AMPKα1/α2-specific oligos and then selected by puromycin.
## Quantitative real-time PCR
Total RNA was extracted using Trizol reagent. Reverse transcription reaction was performed using qScript cDNA Synthesis Kit (95047-100, Quanta Biosciences). qRT-PCR was performed in a CFX Connect Real-Time PCR Detection System (Bio-Rad) by using a reaction mixture with SYBR Green PCR Master Mix (95072, Quanta Biosciences). The PCR cycle parameters: 95 ˚C for 3 min; 40 cycles with denaturation at 95 ˚C for 10 sec, annealing at 55 ˚C for 30 sec. All the PCR amplification was performed in triplicate and repeated in three independent experiments. The relative quantities of genes were normalized to mouse 18 S RNA. The primers were as follows: ppp6r1 F: 5’- TGATCGCTTCCATCAGCTCC −3’; ppp6r1 R: 5’- GACCACGTGTAACCTCGTGT −3’; ppp6r2 F: 5’- ATCCATCCCCACCAGGATGA −3’; ppp6r2 R: 5’- CAGTCCTGCGACTCCAATGT −3’; ppp6r3 F: 5’- CTCCACAACCCAGGCAAGAT −3’; ppp6r3 R: 5’- TGCCGATCTTCTTCTTGCGA −3’; ampk α1 F: 5’- CGCAGACTCAGTTCCTGGAG −3’; ampk α1 R: 5’- CTTCACTTTGCCGAAGGTGC −3’; ampk α2 F: 5’- TCCTGAACACCTCAGCGTTC −3’; ampk α2 R: 5’- CTTCCGGTCAAAGAGCCAGT −3’; acc2 F: 5’- GAACCGGCTTCCTGGTTGTA −3’; acc2 R: 5’- TCCTCCCCTATGCCGAAAGA −3’; fas F: 5’- CAAGTGTCCACCAACAAGCG −3’; fas R: 5’- GGAGCGCAGGATAGACTCAC −3’.
## Statistics
All experiments were repeated independently at least three times with similar results. Results are shown as means; error bars represent standard deviation (s.d.). The unpaired two-tailed Student’s t-test or one-way ANOVA was used to determine the statistical significance of differences between means (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$) and were calculated in Microsoft Excel or GraphPad Prism (v 9) software unless indicated separately.
## 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/s41467-023-36809-1.
## Source data
Source Data
## Peer review information
Nature Communications thanks Chung-Ho Lau and the other, anonymous, reviewers for their contribution to the peer review of this work.
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|
---
title: A new monitoring system for nutritional status assessment in children at home
authors:
- Annamaria Zsakai
- Dorina Annar
- Beatrix Koronczai
- Kinga Molnar
- Petra Varro
- Erika Toth
- Szilvia Szarvas
- Tamas Tauber
- Zsolt Karkus
- Dora Varnai
- Agota Muzsnai
journal: Scientific Reports
year: 2023
pmcid: PMC10011558
doi: 10.1038/s41598-023-30998-x
license: CC BY 4.0
---
# A new monitoring system for nutritional status assessment in children at home
## Abstract
Regular monitoring of children’s nutritional status is essential to prevent micronutrient deficiencies, nutritional status abnormalities as stunting, wasting, overweight and obesity. Nutritional status assessment is usually performed by paediatricians by using anthropometry (body mass index, weight to height indices) and/or by body fat-mass measurement (bioimpedance analysis, dual-energy x-ray absorptiometry, computer tomography, etc.). Parents are also interested in but usually fail to evaluate their child’s nutritional status. To help the sufficient collaboration between the physician and parents a new nutritional status monitoring method is developed for families. The new monitoring system was developed under a paediatrician’s supervision by considering national and international recommendations, references as well as the anthropometric measurement possibilities at home. The model requires age, sex, body mass, height, waist circumference and hand circumference as predictor (input) variables of nutritional status, while [1] the centile values of the measured body dimensions, [2] body fat percentage and the centile of body fat percentage, [3] the nutritional status category (undernutrition, normal nutritional status, overfat/obese) can be predicted (outcome variables) by the new method. The predictive accuracy of the model for nutritional status category was $94.88\%$ in boys and $98.66\%$ in girls. The new model was developed for nutritional status assessment in school-aged children and will be incorporated in the healthy lifestyle module of ‘Teenage Survival Guide’ educational package to be developed by the Health Promotion and Education Research Team, Hungarian Academy of Sciences, Hungary. The new monitoring system could help the families to identify the early signs of malnutrition in children. Nutritional status assessment in children at home is suggested twice a year, and in case of suspicious nutritional status abnormality it is recommended to visit the general practitioner.
## Introduction
The obesity epidemic has become one of the most important health issues in this lifestyle transition leading to noncommunicable disease burdens. Obesity appears to be an unavoidable consequence of modern living1. Our perspective on obesity and unhealthy lifestyle should be reframed: “Being healthy—avoiding obesity is a life choice” should be a main message of health behaviour change programmes in our societies2. Moreover, obesity is such a serious medical condition that affects nowadays not only adults but also children and adolescents.
Beside obesity, underweight status also makes children much more vulnerable to somatic and mental illnesses. This nutritional status abnormality is still under-recognized and undertreated in spite of its irreversible and long-lasting health implications.
The identification of early signs of these two types of abnormal nutritional status is crucial. Usually, nutritional status assessment is done once a year in school-aged children in the public health system. However, to screen once a year for nutritional status abnormalities is not enough, since nutritional status can alter very intensively in children and adolescents due to the natural growth and developmental processes, and significant changes of lifestyle factors or health status can also result in nutritional status alterations.
Obesity and underweight are usually defined as body mass index (BMI) being over or under the age-dependent cut-off (critical) value (WHO)7. BMI is an inexpensive and easy screening method of nutritional status abnormalities; however, BMI is not an adequate indicator of percent of excess body fat8, BMI has serious limitations in nutritional status assessment. Body fat percentage (BF%) estimation is also a possible tool for estimating nutritional status, however it does also have some limits: [1] the body fat percentage estimation requires special equipment (e.g. dual-energy x-ray absorptiometry or bioelectrical impedance analyser), or [2] very few equations exist to calculate body fat BF% based on anthropometric measurements (usually circumferences, height and weight)9,10. [ 3] Moreover, the critical cut-offs of BF% has been determined only for obesity and only for adults11.
The parental involvement in children’s nutritional status assessment could sufficiently help the family-based lifestyle intervention activities. Since parents usually fail to evaluate their child’s nutritional status, an easy-to-use and sufficiently accurate method of children’s nutritional status assessment could help their evaluation3.
Therefore, a new monitoring system is suggested and introduced in details in the present paper for the monitoring of overfat/obesity and undernutrition in children aged between 6 and 18 years (and adults aged 18+) by using only very few, easy-to-perform body measurements. The new method does not require either children to remove clothing or special equipment.
## Sampling method
Subject recruitment was done by using multilevel multistage sampling so as to obtain subjects that represented the diverse types of settlements and the age and sex distribution of Hungarian children aged between 7 and 18 years. The cross-sectional study was carried out in 2014–2015 in the Hungarian counties and in the capital. The main aim of the study was to collect information on children’s bone development and to analyze the accuracy of an ultrasonic method for skeletal maturity estimation compared to the standard radiographic methods used in the clinical practice. Body composition analysis and nutritional status assessment of children were also carried out in the auxological study.
The recruitment of participants was done by gaining access to children from the school authorities and by the official social media surfaces of Eotvos Lorand University. Sample size calculation was done by considering the margin of error, the population size of healthy children aged between 7 and 18 years (data were published by the Hungarian Central Statistical Office4, https://www.ksh.hu/stadat_files/okt/hu/okt0008.html), requested population proportion.
## Informed consent
The printed subject information sheet (that provided information to the potential participants about the objectives and procedures of the study and sources of information) was delivered to the families via the school authorities. Written informed consent was obtained from the parents of children, and assent was obtained from the children as well.
## Ethical approval
All experimental protocols were reviewed and approved by the institutional committee of the Research Ethics Committee of the Hungarian Scientific Research Fund (approval reference number: K-47073).
## Data collection
Data on the level of habitual physical activity, the level of health status were collected by questionnaires (validated for the Hungarian population) through personal interviews. Anthropometric examinations were done in the schools.
## Body structural analysis
Body structural data of children (aged 7–18 ys, n: 1745, Table 1) were collected in a cross-sectional study in Hungary in 2014–20155. The anthropometric measurements of children were performed using standardized techniques and standard equipment6. Body composition (body fat mass was expressed in the percentage of body weight, %) was estimated by body impedance analysis (by an InBody 720 analyser).Table 1Case numbers by age-groups and sex. Age-group (years)BoysGirls7564581189899278109577119089125271137889145169157071167681174860183754Together863882
## Statistical analysis
By following the WHO [1995] recommendation, the age-dependent body fat percentage cut-off values were used to define obesity on the basis of body fatness and constructed by using the smoothed centiles passing through the values of $25\%$ BF% in boys and $35\%$ BF% in girls at the age of 18 (Table 2). The centile pattern of body fat percentage was estimated by Cole’s LMS method12,13. Not only the cut-off values of centiles crossing $25\%$ BF% in boys and $35\%$ BF% in girls at the age of 18 were determined for screening obesity, but also the cut-off values of the 90th centiles for the screening of overfat nutritional status. In this case there was not any international or national recommendation or former method on how to construct critical values of overfat by using body fat percentage either in adults or in children. The 90th centile of BF% was used as the indicator of overfat (Table 2), since the 90th centile is usually used in epidemiological surveys in the case anthropometric dimensions or indices as cut-off criteria for abnormalities. Table 2The new body fat percentage cut-off values (%) for overfat and obesity in children and adolescents aged 7–18 years (P90: 90th centile, P96.95: 96.95th centile, P96.40: 96.40th centile values).Age (years)BoysGirlsOverfat (P90)Obese (P96.95)Overfat (P90)Obese (P94.60)7.021.327.524.227.47.522.429.125.328.78.023.430.626.430.08.524.532.127.531.19.025.533.628.432.29.526.234.729.132.910.026.835.529.533.410.527.035.929.733.511.026.935.929.833.511.526.735.529.933.512.026.235.030.133.512.525.734.230.333.713.024.933.230.734.013.524.132.031.234.314.023.130.631.734.714.522.129.332.135.015.021.228.032.435.115.520.227.032.635.216.020.026.232.835.316.519.625.632.935.317.019.525.333.035.317.519.525.133.035.118.019.625.033.035.0 Children were divided randomly into the estimation subgroup and the cross-validation subgroup in both sexes (~ 50–$50\%$ of children were assigned to the two subgroups). Children’s data in the estimation subgroup were used to generate the multinomial logistic models for estimating nutritional status categories based on body fat percentage with anthropometric measurements and indices as predictors. Data of the cross-validation subgroup were used to check the accuracy of the multinomial logistic regressions in both sexes. More than $95\%$ of children’s estimated (by the logistic regression equations constructed in the estimation subgroup) and observed nutritional status categories matched in the cross-validation subgroup.
Using the same group of predictor variables included in the estimation model, logistic analysis was also performed in the cross-validation subgroup in both sexes. Since the equations of estimation and cross-validation subgroups did not differ significantly (khi2 test was performed to compare the predicted nutritional status categories estimated by using the equations of estimation and cross-validation subgroups), the two subgroups were combined in both sexes, and the final models were estimated by using these combined samples in both sexes.
All statistic calculations and analyses were performed using SPSS (version 24, IBM Corporation, 2016). Significance was set at $$p \leq 0.05$$ level in the analyses.
In the beginning, regression analysis was performed to identify the possible predictors of body fat percentage among the body and extremity circumferences and weight to height ratios. However, none of the regression models predicted responses for new observations with higher R2 than 0.67. Therefore, as a next phase of the analysis, multinomial logistic regression was chosen to identify the strongest predictors of nutritional status category based on body fat percentage (not the exact BF% value, only its categorization into normal, overfat/obese nutritional status categories). Pearson correlation analysis was performed to determine the strength of association between body fat percentage and the easy-to-measure body dimensions (circumference) and easy-to-calculate indices (Table 3). In this preselection step the circumferences of waist, upper arm, lower arm, hand and the weight/height3 index were chosen for the multinomial logistic regression as possible predictors of nutritional status category in both sexes. Table 3Pearson correlation coefficients (r) of trunk and extremity circumferences and weight to height indices ($p \leq 0.05$ in each correlation) with body fat percentage. Anthropometric dimensions, indicesBoysGirlsChest circumference (cm)0.2790.679Waist circumference (cm)0.4290.751Hip circumference (cm)0.3370.705Upper arm circumference (relaxed, cm)0.3780.748Lower arm circumference (cm)0.2360.641Wrist circumference (cm)0.2470.593Calf circumference (cm)0.3100.662Ankle circumference (cm)0.2950.575Hand circumference (cm)−0.3130.420Weight/height ratio (kg/m)0.3380.726Weight/height2 ratio (kg/m2)0.5740.796Weight/height3 ratio (kg/m3)0.7820.713 Body fat percentage (estimated by InBody 720 equipment) was divided into normal and overfat/obese nutritional status categories by considering the new, age-dependent cut-off values of BF% (introduced by the present paper).
## Results
The description statistics of the logistic models that best approximate the relationship of the predictors to the BF% categories are presented in Table 4. The final model of multinomial logistic regression (best measures of goodness of fit, R2, kappa coefficient) revealed waist/height3 index, waist circumference and hand circumference (which circumference represents skeleto-muscular development and fatness at the same time, this might explain the negative correlation between hand circumference and body fat percentage in boys) as strong predictors of nutritional status in children (Table 4). By using the logistic model in boys $94.9\%$, in girls $98.7\%$ of children’s estimated nutritional status (by using the logistic model prediction) matched the observed nutritional status category (undernutrition, normal nutritional status or overfat/obese status) based on BF% cut-off criteria. Table 4Descriptive statistics of multinomial logistic model to identify strongest predictor of nutritional status in children aged between 7–18 years. BSEpBoys Intercept−22.7052.723 < 0.001 Waist circumference (cm)0.1780.041 < 0.001 Hand circumference (cm)−0.6640.180 < 0.001 Weight/height3 ratio (kg/m3)1.5140.187 < 0.001 R2 = 0.751, $k = 0.765$ (goodness of fit: $p \leq 0.001$)Girls Intercept−14.3542.255 < 0.001 Waist circumference (cm)0.2060.038 < 0.001 Hand circumference (cm)−0.8690.179 < 0.001 Weight/height3 ratio (kg/m3)0.960.136 < 0.001 R2 = 0.671, $k = 0.677$(goodness of fit: $p \leq 0.001$)B: estimated regression coefficient, SE: standard error of B, p: significance level of the regression; goodness of fit test: R2: the proportion of variance in nutritional status explained by the predictors, k: kappa coefficient, p: significance level of goodness of fit test.
The new method of nutritional status assessment—introduced in this paper—was tested in another sample of children (studied between 2016 and 2020 in the somatometric laboratory of the Department of Biological Anthropology, Eotvos Lorand University, Hungary; n: 299 boys and girls together, aged between 6–18 years), in which sample we also estimated body fat percentage by InBody 720 equipment and collected data on the anthropometric dimensions. The consistency between the nutritional status categorisation by using [1] BF% categories (the new body fat percentage cut-off values for overfat status, Table 2) and [2] the new method was $94.0\%$ in the girls and $93.1\%$ in the boys. The discrepancies were caused both the not identified overfat/obese status and the overfat/obese-labelled normal nutritional status cases in both sexes.
The new method is a suggestion for families to estimate children’s nutritional status in a very easy way. The nutritional status estimation of school-aged children should be done twice yearly at home. If the nutritional status is undernutrition or overfat/obese, and/or the nutritional status category changes between two examinations, a consultation with the general practitioners or a paediatrician is strongly recommended.
The healthy lifestyle module of the ‘Teenage Survival Guide’ educational package will include the new method of children’s nutritional status estimation. The instruction for the families on how to measure the required body dimensions will also be presented in the module. Furthermore, [1] the major medical comorbidities associated with nutritional status abnormalities (undernutrition, overfat, obesity) in childhood; and [2] the possibilities to intervene via diet or activity-related behaviours to prevent or treat childhood nutritional status abnormalities will be discussed in detail in the module.
## How to use the new method—instructions for parents to measure their children to get the required measurements for nutritional status assessment
Height and circumference measurements should be performed by an inelastic tape, while a weight scale is needed in the case of weight measurement.
## Waist circumference
Measure the waist circumference of the child in the horizontal plane at the narrowest region between the chest and hips in standing position.
## Hand circumference
Measure the circumference around the child’s left hand at the fullest part, where the fingers meet the palm (outstretched and closed fingers; exclude thumb!).
## Height
Measure the child when she/he is standing with her/his back against a wall (legs are straight, arms are at sides, she/he stands with head, shoulders, buttocks and heels touching the wall), position the child facing forward, gently place a book on her/his head, mark with a pencil where the lower side of the book meets the wall. Measure the distance from the floor to the mark on the wall.
## Weight
Measure the weight of the child (wearing normal indoor clothes) taken to the nearest tenth of a kilogram (or the nearest half kilogram).
Having these measurements, the examiner only has to [1] use them as input variables in the attached excel file and the excel file estimates the nutritional status of the studied child, or [2] use these measurements in the following equations (Table 4):boys: −22.705 + 0.178 × WC − 0.664 × HC + 1.514 × WH3;girls: −14.354 + 0,206 × WC—0,869 × HC + 0.96 × WH3,where WC: waist circumference (cm), HC: hand circumference (cm), WH3: weight/height3 ratio (kg/m3). If the result equals or is smaller than 0, the estimated nutritional status is normal, if it is bigger than 0, the nutritional status is overfat/obese.
## Discussion
A new monitoring system for nutritional status assessment in children was introduced in the present paper. The regression analysis revealed that weight, height, waist/height3 index, waist circumference and hand circumference were the strongest predictor of nutritional status of children. Children were divided into nutritional status categories by considering the traditional anthropometric method, e.g. the international cut-off values of BMI, as well as by the cut-off values of body fat percentage constructed in the presented analyses. The accuracy of the new method for nutritional status category assessment was very high, namely $94.88\%$ in boys and $98.66\%$ in girls. Since the predictive equations were constructed by using body dimensions and mass components of Hungarian (representing Europid geographical variation/race) children, the body fat% estimation by using the kit is recommended in Europid children. The validation of the new method for other groups of children with non-Europid geographical variations will be done in 2023.
By entering the measurement data in the calculator, it estimates [1] the centile values of the measured body dimensions (by using the national reference centile distributions of height, weight, waist and hand circumferences14, [2] body fat% and the centile of body fat%, [3] the nutritional status category (undernutrition, normal nutritional status, overfat/obese status).
Nutritional status calculators are very rare all over the world, based only BMI calculation and categorisation, and mostly constructed to assess nutritional status in adults15–18. BMI-based assessment of nutritional status is not precise enough to distinguish between overweight caused by fat, muscular or their common excess. The absence of an accurate nutritional status assessment tool that uses only easy-to-measure body dimensions, which method can be used for nutritional status assessment children, was solved by the new monitoring system.
The new monitoring system will be incorporated into the healthy lifestyle module of a new online educational package to be constructed for health behaviour education in Hungarian children, but will be also available for the families interested in estimating their children’s nutritional status. Comorbidities of nutritional status abnormalities in childhood, the basic criteria for healthy living, advice on how to promote positive changes in diet and physical activity behaviours in children, the reliable online sources in Hungary on lifestyle factors, the registry of medical centres and public health systems will be also presented in the healthy lifestyle module of ‘Teenage Survival Guide’ educational package.
The nutritional status of children usually assessed once a year in the public health system. The nutritional status risk screening and assessment should be done more frequently in children, since body structure, body composition, nutritional status alter very intensively in children and adolescents during the body developmental processes. Moreover, the lifestyle and health status factors may also impact their nutritional status in a shorter interval.
The Health Promotion and Education Research Team (Eotvos Lorand University and the Hungarian Academy of Sciences, Budapest, Hungary, egyk.elte.hu) aims to provide an online platform to access educational resources for supporting school-aged children’s health behaviour development in connection with healthy lifestyle beside the topics of sex education, substance use, hygiene education, online activity and environment awareness. The healthy lifestyle module will be open from December 2023. The new monitoring system for the nutritional status assessment in children at home—introduced in the present paper—will be also incorporated in the web-based platform. Definitions of nutritional status categories, the morbidity and mortality risk of nutritional status abnormalities and the new tool for nutritional status assessment will be available in the healthy lifestyle module, too.
As final conclusions we could state that the main objectives have been met, (i) a new method for nutritional status assessment in children at home has been constructed, (ii) the predictive accuracy of the model for nutritional status category is high enough to encourage the families to use it; (iii) the nutritional status calculator has been available for the families on the website of the Health Promotion and Education Research Team. However, the Research *Team is* just in the beginning of the planned project, the full ‘Teenage Survival Guide’ must be available in 2 years. Our mission is to serve a useful educational package to the families and schools in Hungary to increase the level of knowledge of children in health behaviour, social media use and environmental awareness.
## Declarations
The Authors declare that [1] all methods were carried out in accordance with relevant guidelines and regulations, [2] all experimental protocols were reviewed and approved by the institutional committee of the Research Ethics Committee of the Hungarian Scientific Research Fund (approval reference number: K-47073), [3] informed consent was obtained from the legal guardians of all subjects, the participation was voluntary and data were anonymised and analysed for scientific purposes only.
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|
---
title: Consequences of reprogramming acetyl-CoA metabolism by 2,3,7,8-tetrachlorodibenzo-p-dioxin
in the mouse liver
authors:
- Giovan N. Cholico
- Karina Orlowska
- Russell R. Fling
- Warren J. Sink
- Nicholas A. Zacharewski
- Kelly A. Fader
- Rance Nault
- Tim Zacharewski
journal: Scientific Reports
year: 2023
pmcid: PMC10011583
doi: 10.1038/s41598-023-31087-9
license: CC BY 4.0
---
# Consequences of reprogramming acetyl-CoA metabolism by 2,3,7,8-tetrachlorodibenzo-p-dioxin in the mouse liver
## Abstract
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is a persistent environmental contaminant that induces the progression of steatosis to steatohepatitis with fibrosis in mice. Furthermore, TCDD reprograms hepatic metabolism by redirecting glycolytic intermediates while inhibiting lipid metabolism. Here, we examined the effect of TCDD on hepatic acetyl-coenzyme A (acetyl-CoA) and β-hydroxybutyrate levels as well as protein acetylation and β-hydroxybutyrylation. Acetyl-CoA is not only a central metabolite in multiple anabolic and catabolic pathways, but also a substrate used for posttranslational modification of proteins and a surrogate indicator of cellular energy status. Targeted metabolomic analysis revealed a dose-dependent decrease in hepatic acetyl-CoA levels coincident with the phosphorylation of pyruvate dehydrogenase (E1), and the induction of pyruvate dehydrogenase kinase 4 and pyruvate dehydrogenase phosphatase, while repressing ATP citrate lyase and short-chain acyl-CoA synthetase gene expression. In addition, TCDD dose-dependently reduced the levels of hepatic β-hydroxybutyrate and repressed ketone body biosynthesis gene expression. Moreover, levels of total hepatic protein acetylation and β-hydroxybutyrylation were reduced. AMPK phosphorylation was induced consistent with acetyl-CoA serving as a cellular energy status surrogate, yet subsequent targets associated with re-establishing energy homeostasis were not activated. Collectively, TCDD reduced hepatic acetyl-CoA and β-hydroxybutyrate levels eliciting starvation-like conditions despite normal levels of food intake.
## Introduction
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) is the prototypical member of a class of persistent environmental contaminants termed polyhalogenated aromatic hydrocarbons, which include polychlorinated dibenzodioxins (PCDDs), dibenzofurans (PCDFs) and biphenyls (PCBs)1. A subset of these contaminants possesses lateral chlorines that induce a diverse spectrum of aryl hydrocarbon receptor (AHR)-mediated species-, sex-, tissue-, cell- and promoter-specific responses including the dose-dependent progression of hepatic steatosis to steatohepatitis with fibrosis. Moreover, TCDD and dioxin-like PCBs are classified as an International Agency for Research on Cancer (IARC) group 1 human carcinogen while the carcinogenicity of other toxic PCDDs and PCDFs in humans is equivocal2–4. TCDD and related compounds are non-genotoxic with most, if not all of their effects mediated by the AHR, a ligand-activated basic helix-loop-helix PER-ARNT-SIM transcription factor that is conserved in all vertebrate species5. It is activated by several structurally diverse chemicals, endogenous metabolites, microbial products, and natural compounds, although the physiological ligand is unknown. Following ligand binding and the dissociation of chaperone proteins, the AHR translocates to the nucleus and dimerizes with the AHR nuclear translocator (ARNT)6–8. In the proposed canonical mechanism, the heterodimer binds dioxin response elements (DREs; 5’-GCGTG-3’) throughout the genome and recruits multiple coactivators to elicit differential gene expression. However, several studies also report differential gene expression independent of DREs as well as alternate AHR binding partners suggesting alternative mechanisms of gene regulation7,9–12.
The emergence of transcriptomics and metabolomics provides the opportunity to comprehensively assess the effects of exogenous agents on gene expression and endogenous metabolite levels. Numerous studies have examined the consequences of PCDD, PCDF, or PCB exposure on gene expression and/or metabolite levels in diverse in vivo and in vitro models13–20. However, few have integrated transcriptomic and metabolomic datasets with complementary histopathology to distinguish adaptive events from key responses to elucidate causative mechanisms associated with adverse outcomes21–25. Despite decades of research establishing the central role of the AHR in mediating the effects of TCDD and related compounds, the mechanisms leading to toxicity remain poorly understood.
Acetyl-coenzyme A (acetyl-CoA) occupies a central position in multiple metabolic pathways. As a metabolite, it straddles carbohydrate, lipid, and amino acid catabolism, and can be used as a substrate for the synthesis of fatty acids, cholesterol, and ketone bodies. Acetyl-CoA is also used as a substrate for the posttranslational modification of proteins to regulate enzyme activity, protein stability, cellular location, and to remodel chromatin via histone acetylation to control gene expression, thus linking intermediate metabolism to cellular homeostasis26–29. Acetyl-CoA is not membrane permeable, therefore resulting in specific cellular pools each of which support distinct activities that are independently generated within mitochondrial, peroxisomal, endoplasmic reticulum, and nucleo-cytosolic compartments. For example, the mitochondrial pool is produced by the pyruvate dehydrogenase complex (PDC), fatty acid β-oxidation, and amino acid metabolism, while the nucleo-cytosolic pool is sourced from ATP-citrate lyase (ACLY), acyl-CoA synthetase short-chain family member 2 (ACSS2), and nuclear PDC30. Levels of protein acetylation are directly linked to acetyl-CoA levels that fluctuate depending on intra- and extra-cellular cues that also undergo circadian regulation. This dynamic regulation of acetyl-CoA metabolism not only affects global histone modifications but also synchronizes intermediate metabolism with feeding and active/rest cycles. Consequently, acetyl-CoA is not only a metabolic intermediate but also a surrogate indicator of nutritional status that coordinates metabolic reprogramming through epigenetic regulation and posttranslational modification to sustain survival, growth and proliferation during periods of starvation, nutrient availability and metabolic stress30,31. For instance, nutrient starvation causing the rapid depletion of acetyl-CoA triggers autophagy due to the activation of AMPK32. However, the dose-dependent effects of environmental contaminants, drugs, chemicals, or natural products on intermediate metabolism, and more specifically, acetyl-CoA levels, have not been examined and warrant further investigation.
Previous studies have reported decreased ATP levels in the liver and the induction of steatosis to steatohepatitis with fibrosis following treatment with TCDD and related compounds33–38. Moreover, TCDD dose-dependently reprogramed glucose metabolism by switching from PKM1 to PKM2 expression resulting in reduced glycolytic flux and the redirection of accumulating upstream intermediates to other pathways to support proliferation and/or reactive oxygen species (ROS) defenses39. In addition, the integration of transcriptomic and metabolomic data with chromatin immunoprecipitation analyses showed TCDD dose-dependently repressed fatty acid β-oxidation40. In this study, we further examined the effects of TCDD on pathways associated with acetyl-CoA metabolism to test the hypothesis that acetyl-CoA levels reduced by TCDD also affected ketone body synthesis, protein acetylation and β-hydroxybutyrylation, and AMPK activation. Our analysis found TCDD lowered hepatic acetyl-CoA and β-hydroxybutyrate levels. Accordingly, total hepatic protein acetylation and hydroxybutyrylation levels were reduced with increased levels of activated AMPK, suggesting the induction of a starvation-like phenotype in the liver despite unaffected levels of food intake. These results indicate that in addition to differential gene expression mediated by the AHR, TCDD can also elicit secondary effects by disrupting acetyl-CoA homeostasis.
## Gross morphology and histopathology
In agreement with previously reported findings, 30 μg/kg TCDD decreased terminal body weight by ~ $14\%$ (Supplementary Fig. 1A), despite no significant change in daily food intake23. Absolute liver weights increased 12–$30\%$ between 0.3 and 30 μg/kg TCDD (Supplementary Fig. 1B), while relative liver weight dose-dependently increased 7–$44\%$ between 0.3 and 30 μg/kg TCDD (Supplementary Fig. 1C). Absolute and relative gonadal white adipose tissue weights have previously been reported to decrease $46\%$ and $28\%$, respectively, at 30 μg/kg TCDD with no change in terminal brown adipose tissue weights41. Although a modest increase in serum ALT levels was observed following oral gavage with 30 µg/kg TCDD every 4 days for 28 days for a total of 7 treatments (Supplementary Fig. 1D), previous studies eliciting comparable effects exhibited no evidence of overt toxicity or body weight loss > $15\%$41,42.
Hepatic steatosis, immune cell infiltration, fibrosis, and bile duct proliferation have previously been reported to be dose-dependently induced following oral gavage with TCDD every 4 days for 28 days33. Specifically, there was evidence of hepatocyte vacuolization (fatty change) with minimal to slight hepatocyte necrosis at 3 μg/kg TCDD and immune cell infiltration after doses of ≥ 3 μg/kg TCDD after 28 days of exposure, with F$\frac{4}{80}$ staining confirming the presence of macrophages. At 30 μg/kg, bile duct proliferation was observed along with picro-Sirius red staining for collagen and inflammation surrounding the bile ducts (pericholangitis). Collectively, the gross morphology, histopathology, and ALT results suggest the effects on gene expression, protein levels and metabolite levels do not induce overt toxicity following oral gavage with TCDD every 4 days for 28 days.
## MS analysis of acyl-CoA and CoA levels
Previously reported untargeted metabolomics identified a dose-dependent decrease in hepatic acetyl-CoA levels40. These results were confirmed by targeted analysis with internal standards (Fig. 1A,B). Due to circadian regulation of carbohydrate, lipid, and protein metabolism, targeted analysis assessed samples collected in the morning (zeitgeber time [ZT] 0–3.5) and afternoon (ZT5.5–8.5) samples. Acetyl-CoA levels in controls (74.3 ± 6.4 nmol/g wet tissue; or 2.4 ± 0.21 nmol/mg total protein) did not change between morning and afternoon cohorts, and were comparable to previously reported levels43,44. TCDD lowered morning and afternoon acetyl-CoA levels 3.8- and 3.4-fold, respectively. In contrast, CoA levels dose-dependently increased 2.2-fold at lower TCDD doses but decreased 2.9-fold at 30 μg/kg TCDD in morning samples. Although TCDD did not affect afternoon CoA levels, the acetyl-CoA/CoA ratio exhibited a decreasing trend due to lowering acetyl-CoA levels (Fig. 1B).Figure 1Hepatic acetyl-CoA and coenzyme A (CoA) levels assessed in mice using targeted liquid chromatography tandem mass spectrometry. Mice were gavaged with TCDD (or sesame oil vehicle) between zeitgeber time (ZT) 0–1. Samples were collected at (A) ZT 0–3.5 h, or (B) ZT 5.5–8.5 h. Asterisk (*) denotes a significant change (p ≤ 0.05) as determined using a one-way ANOVA and Dunnett’s post-hoc analysis.
## Glycolysis and PDH as a source of Acetyl-CoA
We next examined changes associated with carbohydrate catabolism, a primary source of acetyl-CoA (Fig. 2A). Figure 2 summarizes the TCDD-induced changes in the expression of glycolytic genes following oral gavage every 4 days for 28 days. The presence of a computationally identified putative DRE (pDRE) and AHR genomic binding (ChIPseq) data, as well as time course, diurnal regulated dose–response, and diurnal regulated RNAseq gene expression data are included in all heatmaps (Fig. 2B–E). Gene expression changes reported in the text represent the maximum fold-change determined in the diurnal regulated gene expression dataset with the corresponding ZT.Figure 2Glucose metabolism as a source of acetyl-CoA. *Differential* gene expression was assessed using RNA-seq. ( A) *The glycolysis* pathway with regulated steps denoted with a double dagger (‡). ( B) Computational identification of putative dioxin response elements (pDREs), and the detection of hepatic AhR genomic binding in ChIPseq analysis 2 h after oral gavage of 30 μg/kg TCDD. Genes are listed by the official symbol as designated in the mouse genome informatics (MGI) database. ( C) Time-dependent hepatic expression of glycolysis-related genes ($$n = 3$$) following a single bolus gavage of 30 μg/kg TCDD. ( D) Hepatic dose-dependent gene expression ($$n = 5$$) following oral gavage with TCDD every 4 days for 28 days. ( E) Diurnal regulated gene expression denoted with a “Y”. An orange ‘X’ indicates oscillating gene expression was abolished following oral gavage with 30 μg/kg TCDD every 4 days for 28 days. ZT indicates the time of maximum induction/repression (P1(t) > 0.8). Counts represents the maximum number of raw reads for any treatment group. Low transcript counts (< 500 reads) are denoted in yellow with high transcript counts (> 10,000) denoted in pink. *Differential* gene expression with a posterior probability (P1(t)) > 0.80 are depicted with a black triangle in the upper right corner of the tile. Capillary electrophoresis was used to assess (F) phosphorylated pyruvate dehydrogenase (Ser300), (G) total pyruvate dehydrogenase, (H) ATP-citrate lyase, and (I) acyl-CoA synthetase short chain family member 2 protein levels in total lysate prepared from liver samples harvested between ZT0-3 ($$n = 3$$). ( J) Hepatic levels of acetate, a precursor for acetyl-CoA, were assessed. Bar graphs denote the mean ± SEM. Significance (*p ≤ 0.05) was determined using a one-way ANOVA followed by Dunnett’s post-hoc analysis. The heatmap was created using R (v4.0.4). Plots were created using GraphPad Prism (v8.4.3). The biochemical reaction was created using Adobe Illustrator (v25.2).
Glucokinase (Gck), aldolase B (Aldob), liver/red blood cell pyruvate kinase (Pklr), and pyruvate dehydrogenase phosphatase catalytic subunit 2 (Pdp2) showed time and dose-dependent repression, while hexokinase 1, 2, and 3 (Hk1, 2, and 3), muscle pyruvate kinase (Pkm), and pyruvate dehydrogenase kinase 4 (Pdk4) were induced. Hk2 has recently been reported to be positively regulated by the AHR in human U2OS and 143B osteosarcoma cells, as well as human HCT116 colon cancer cell lines45. Glucose transporters GLUT2 (Slc2a2) and GLUT9 (Slc2a9), which are responsible for transporting glucose primarily in the liver46, were also dose-dependently repressed. Only Slc2a2, Slc2a9, Gck, Aldob, Pkm and Pdk4 exhibited AHR genomic enrichment 2 h after treatment with 30 μg/kg TCDD. TCDD and related polychlorinated biphenyls (PCB126 and 118) have been reported to specifically induce nuclear and cytosolic pyruvate kinase muscle isoform 2 (Pkm2)39,47,48. Nuclear PKM2 has been implicated in gene regulation while the cytosolic dimer is associated with the Warburg effect with lower catalytic activity compared to PKM1 and PKLR49,50. Following TCDD-induced PKM isoform switching, accumulating upstream glycolytic intermediates are redirected to the pentose phosphate pathway and serine/folate biosynthesis for biomass and NADPH production to support proliferation and the biosynthesis and recycling of glutathione, depending on the microenvironment51. Consequently, the reduced glycolytic flux following PKM2 induction and PKLR repression is consistent with decreased acetyl-CoA levels.
Acetyl-CoA levels are also affected by the activity of the pyruvate dehydrogenase complex (PDC). PDC is posttranslationally regulated by phosphorylation, acetylation, and succinylation52. The 131.1-fold induction of Pdk4 and the 4.8-fold repression of pyruvate dehydrogenase phosphatase 2 (Pdp2) suggest phosphorylation of the E1α subunit of pyruvate dehydrogenase (PDH) at Ser300. PDK4 is reported to primarily phosphorylate Ser293 and Ser300 sites of the PDH53, and was confirmed by capillary electrophoresis with the increase in phosphorylated PDH at 30 μg/kg TCDD that could not be attributed to overall higher total hepatic PDH levels (Fig. 2F,G). Moreover, estrogen-related receptor gamma (Esrrg), which induces PDK4 activity under hypoxic conditions54, was induced 3.9-fold. ACLY and ACSS2, which produce cytosolic pools of acetyl-CoA from citrate and acetate, were also repressed 4.3- and 8.8-fold, repression, respectively (Fig. 2H,I). Total hepatic acetate levels also decrease, but only at 30 μg/kg TCDD (Fig. 2J).
## Coenzyme A biosynthesis
Acetyl-CoA biosynthesis is dependent on the availability of coenzyme A (CoA), an ubiquitous cofactor synthesized from the essential vitamin pantothenate55 (Fig. 3A). CoA is crucial to many metabolic pathways including the tricarboxylic acid and β-oxidation cycles. Biosynthesis begins with intestinal absorption of either dietary or microbial-derived pantothenate absorbed from the gut, after which it is transported to peripheral tissues and imported into cells via the sodium-dependent multivitamin transporter (Slc5a6; repressed 3.3-fold), or by passive diffusion55,56 (Fig. 3B–E). Pantothenate kinases (Pank1-4; repressed 2.8-, 1.5-, 2.9-, and 1.8-fold, respectively) phosphorylate pantothenic acid to yield 4′-phosphopantothenate which is then converted to 4′-phosphopantothenoylcysteine by 4′-phosphopantothenoylcysteine synthetase (Ppcs; repressed 1.6-fold), followed by decarboxylation mediated by 4′-phosphopantothenoylcysteine decarboxylase (Ppcdc; induced 2.1-fold) to yield 4′-phosphopantetheine. Bifunctional coenzyme A synthase (Coasy; repressed 1.5-fold) catalyzes the last two reactions to first yield dephospho-coenzyme A, and finally CoA. TCDD elicited a non-monotonic decrease in hepatic pantothenic acid levels that was only significant at 3 μg/kg TCDD (Fig. 3F). This increase in CoA (Fig. 1) cannot be attributed to CoA biosynthesis gene induction or increased hepatic levels of pantothenic acid (Fig. 3).Figure 3Biosynthesis of coenzyme A (CoA). *Differential* gene expression pertaining to CoA biosynthesis was assessed using RNA-seq. ( A) The CoA biosynthesis pathway is shown with regulated steps denoted with a double dagger (‡). ( B) Computational identification of putative dioxin response elements (pDREs), and the detection of hepatic AhR genomic binding in ChIPseq analysis 2 h after oral gavage of 30 μg/kg TCDD. Genes are listed by the official symbol as designated in the mouse genome informatics (MGI) database. ( C) Time-dependent hepatic expression of glycolysis-related genes ($$n = 3$$) following a single bolus gavage of 30 μg/kg TCDD. ( D) Hepatic dose-dependent gene expression ($$n = 5$$) following oral gavage with TCDD every 4 days for 28 days. ( E) Diurnal regulated gene expression denoted with a “Y”. An orange ‘X’ indicates oscillating gene expression was abolished following oral gavage with 30 μg/kg TCDD every 4 days for 28 days. ZT indicates the time of maximum induction/repression (P1(t) > 0.8). Counts represent the maximum number of raw reads for any treatment group. Low transcript counts (< 500 reads) are denoted in yellow with high transcript counts (> 10,000) denoted in pink. *Differential* gene expression with a posterior probability (P1(t)) > 0.80 are depicted with a black triangle in the upper right corner of the tile. ( F) Hepatic levels of pantothenic acid were assessed using target LC–MS/MS. Bar graphs denote the mean ± SEM. Significance (*p ≤ 0.05) was determined using a one-way ANOVA followed by Dunnett’s post-hoc analysis. The heatmap was created using R (v4.0.4). Plots were created using GraphPad Prism (v8.4.3). The biochemical reaction was created using Adobe Illustrator (v25.2).
## Ketone body formation
In the fed state, cells can utilize acetyl-CoA for fatty acid biosynthesis, protein acetylation, cholesterol biosynthesis, and energy production via the TCA cycle30. Under fasting conditions, however, metabolic reprogramming shifts acetyl-CoA into mitochondrial oxidative catabolism to support the synthesis of either ATP or the easily transported ketone bodies, acetoacetate, β-hydroxybutyrate, and acetone30,57. Biosynthesis consumes two acetyl-CoAs for each ketone body (Fig. 4A), beginning with the formation of acetoacetyl-CoA via acyl-CoA:cholesterol acyltransferase (Acat1 and 2, repressed 3.1- and 4.3-fold, respectively)58,59. An additional acetyl-CoA undergoes condensation with acetoacetyl-CoA to form hydroxymethylglutaryl-CoA catalyzed by the rate-limiting hydroxymethylglutaryl (HMG)-CoA synthase (HMGCS). Hmgcs1 and 2 were repressed 10.4- and 1.9-fold, respectively, by TCDD (Fig. 4B–E). The HMG-CoA intermediate can then be metabolized into ketone bodies or shunted to the mevalonate pathway for cholesterol metabolism. However, TCDD repressed gene expression associated with de novo cholesterol biosynthesis14,60. Although HMG-CoA can be lysed to acetoacetate, HMG-CoA lyase (Hmgcl) was repressed 2.1-fold. In addition, TCDD repressed β-hydroxybutyrate dehydrogenase 1 (Bdh1) 3.2-fold thus limiting the oxidation of acetoacetate to β-hydroxybutyrate. β-Hydroxybutyrate dehydrogenase 2 (Bdh2) which catalyzes the reverse reaction and the formation of acetoacetate, was also repressed 106.4-fold. Although ketone bodies can be exported61, the induction of Slc16a6, the ketone body transporter, was negligible. Furthermore, following 30 μg/kg TCDD, serum β-hydroxybutyrate were reduced 2.5-fold, while hepatic β-hydroxybutyryl-CoA levels were undetectable (Fig. 4F,G). Overall, gene expression involved in ketone body biosynthesis was repressed, and for the most part, exhibited AHR genomic binding in the presence of a pDRE. TCDD also elicited a dramatic dose-dependent decrease in β-hydroxybutyrate levels in serum and hepatic extracts suggesting that decreased hepatic acetyl-CoA levels were not due to cholesterol nor ketone body biosynthesis. Figure 4Effects of TCDD on ketone body gene expression and levels. *Differential* gene expression pertaining to ketone body biosynthesis was assessed using RNA-seq. ( A) The ketone body biosynthesis pathway with regulated steps denoted with a double dagger (‡). ( B) Computational identification of putative dioxin response elements (pDREs), and the detection of hepatic AhR genomic binding in ChIPseq analysis 2 h after oral gavage of 30 μg/kg TCDD. Genes are listed by the official symbol as designated in the mouse genome informatics (MGI) database. ( C) Time-dependent hepatic expression of glycolysis-related genes ($$n = 3$$) following a single bolus gavage of 30 μg/kg TCDD. ( D) Hepatic dose-dependent gene expression ($$n = 5$$) following oral gavage with TCDD every 4 days for 28 days. ( E) Diurnal regulated gene expression denoted with a “Y”. An orange ‘X’ indicates oscillating gene expression was abolished following oral gavage with 30 μg/kg TCDD every 4 days for 28 days. ZT indicates the time of maximum induction/repression (P1(t) > 0.8). Counts represent the maximum number of raw reads for any treatment group. Low transcript counts (< 500 reads) are denoted in yellow with high transcript counts (> 10,000) denoted in pink. *Differential* gene expression with a posterior probability (P1(t)) > 0.80 are depicted with a black triangle in the upper right corner of the tile. ( F) Serum β-hydroxybutyrate and (G) hepatic β-hydroxybutyryl-CoA levels were assessed using a commercially available kit and targeted LC–MS/MS, respectively. Bar graphs denote the mean ± SEM. Significance (*p ≤ 0.05) was determined using a one-way ANOVA followed by Dunnett’s post-hoc analysis. The heatmap was created using R (v4.0.4). The biochemical reaction was created using Adobe Illustrator (v25.2).
## Protein acetyl and β-Hydroxybutyryl posttranslational modifications
In addition to being metabolic intermediates, acetyl-CoA and β-hydroxybutyrate are also used as substrates for posttranslational modifications (PTMs). Approximately $90\%$ of eukaryotic proteins undergo different types of reversible PTM, with moieties typically added onto lysine residues to regulate enzymatic activity, alter protein stability, and change protein localization and interactions with other proteins62,63. The level for protein acetylation and β-hydroxybutyrylation are dependent on the levels acetyl-CoA and β-hydroxybutyryl-CoA, respectively, that serve as source of the donor group64. β-*Hydroxybutyrate is* first activated to β-hydroxybutyryl-CoA by acyl-CoA short-chain synthetases such as ACSS263, which was repressed 9.1-fold in the present study. Consequently, capillary electrophoresis and Western blotting were used to investigate the effect of TCDD on the levels of total hepatic lysine-specific protein acetylation and β-hydroxybutyrylation (Fig. 5). TCDD dose-dependently decreased the level of total acetylated (Fig. 5A) and β-hydroxybutyrylated proteins in hepatic extracts prepared from treated mice. 135, 53, and 46 kDa protein(s) were dose-dependently decreased in acetylation (Fig. 5B–D), while β-hydroxybutyrylation was decreased in 28 to 155 kDa proteins (Fig. 5E–G). Reductions in the levels of acetylated and β-hydroxybutyrylated hepatic proteins are consistent with lower levels of acetyl-CoA and β-hydroxybutyrate. Figure 5(A) Total lysine-specific acetylated proteins were assessed in livers of mice ($$n = 3$$–5) by capillary electrophoresis. Mice were orally gavaged every 4 days for 28 days with 0.01, 0.03, 0.1, 0.3, 1, 3, 10 or 30 μg/kg TCDD prior to tissue collection. ( B–D) Area under the three most prominent peaks for total proteins with acetylated lysine was quantified. ( E,F) Total β-hydroxybutyrylated proteins were assessed by traditional Western blot ($$n = 3$$). Depicted is a representative Western blot of total β-hydroxybutyrylated proteins, as well as β-actin from total protein extracts. The full Western blots can be found in Supplementary Fig. 2. ( G) The signal intensity of total β-hydroxybutyrylated proteins at various molecular weights for vehicle and 30 μg/kg treatment groups quantified using ImageJ as outlined in materials and methods. Bar graphs denote the mean ± SEM. Significance (*p ≤ 0.05) was determined using a one-way ANOVA followed by Dunnett’s post-hoc analysis. Plots were created using GraphPad Prism (v8.4.3).
## AMPK activation
Cellular levels of acetyl-CoA provide a surrogate measure of the current metabolic state65. AMP-activated protein kinase (AMPK) senses energy status by monitoring changes in the AMP:ATP and ADP:ATP ratios66. Phosphorylation of the catalytic subunit of the trimeric AMPK protein is required for activation66. In the present study, TCDD dose-dependently decreased total hepatic AMPK, with a precipitous decrease at 30 μg/kg TCDD that corresponded with an increase in phosphorylated AMPK (P-AMPK) and the phosphorylated AMPK:total AMPK ratio (Fig. 6A–C). In response to low energy status, P-AMPK activates catabolic pathways to increase energy production. For example, in response to low energy levels, P-AMPK induces autophagy in an attempt to compensate for the lack of nutrients and provide substrates for catabolism. Autophagy can be independently regulated by P-AMPK following phosphorylation of the ULK complex that can then bind and phosphorylate autophagy related 14 (ATG14), a marker of cellular autophagy activity. Total and phosphorylated ATG14 levels (Fig. 6D,E) in hepatic extracts show that both ATG14 and phosphorylated ATG14 decreased following treatment with 30 μg/kg TCDD. Further investigation of P-AMPK activity on other known targets including acetyl-CoA carboxylase and RAPTOR were also equivocal suggesting P-AMPK was not activated despite phosphorylation. Figure 6Markers of AMPK activation. Mice ($$n = 3$$–5) were orally gavaged every 4 days for 28 days with TCDD prior to tissue collection. Protein levels were assessed in hepatic extracts using capillary immunoassay analysis. ( A) Total AMPK and (B) phosphorylated AMPK (P-AMPK) and (C) the P-AMPK/AMP ratio two were assessed. Capillary immunoassay analysis of (D) total ATG14 and (E) phosphorylated ATG14 at serine 29 (P-ATG14 Ser29), a marker of P-AMPK activation. Bar graphs denote the mean ± SEM. Significance (*p ≤ 0.05) was determined using a one-way ANOVA followed by Dunnett’s post-hoc analysis. Plots were created using GraphPad Prism (v8.4.3).
## Discussion
This is one of the first studies to examine acetyl-CoA levels in a model of TCDD-induced steatohepatitis with fibrosis. Acetyl-CoA is a central metabolite associated with several catabolic pathways including glycolysis, the tricarboxylic acid cycle, β-oxidation, and amino acid metabolism (e.g. lysine, valine, leucine and isoleucine), as well as a substrate used for the synthesis of fatty acids, cholesterol, and ketone bodies30,67. Previous studies have shown that TCDD dose-dependently caused metabolic reprograming affecting both glycolysis and fatty acid β-oxidation22,40. The present study further investigated TCDD-elicited metabolic reprogramming associated with acetyl-CoA metabolism. Our results indicate TCDD dose-dependently decreased the ratio of acetyl-CoA/CoA, an overall indicator of cellular energy status, and therefore additional enzymatic reactions and pathways associated with acetyl-CoA homeostasis were examined.
TCDD elicits a dose-dependent induction of pyruvate kinase isoform 2 (PKM2) which is reported to decrease glycolytic flux and shunt accumulating glycolytic intermediates to other pathways resulting in lower glucose-6-phosphate and fructose-6-phosphate levels22,39. Pyruvate, the end product of glycolysis, can then be oxidized to acetyl-CoA by the PDC composed of the (i) pyruvate dehydrogenase (E1), (ii) acetyl transferase (E2), and (iii) dihydrolipoyl dehydrogenase (E3) subunits. PDC is regulated by phosphorylation and dephosphorylation of the E1 subunit by pyruvate dehydrogenase kinases (PDKs) and pyruvate dehydrogenase phosphatases (PDPs), respectively. Specifically, E1 phosphorylation at Ser232, Ser293, and Ser300, inhibits PDC activity and reduces acetyl-CoA production68. TCDD dose-dependently induced Pdk4 and repressed Pdp2, while estrogen-related receptor γ (ERRγ) was induced which is associated with the induction of PDK4 in hepatoma cell lines under hypoxic conditions54. In addition, TCDD repressed expression of the highly expressed Acss2 and Acly, two important enzymes that contribute to the cytosolic acetyl-CoA pool. In previous studies, TCDD treatment increased hepatic levels of the TCA cycle intermediate oxaloacetate39. Increased oxalacetate levels may be due to depleted acetyl-CoA levels since both are required for the synthesis of the TCA cycle intermediate citrate. Collectively, metabolic reprogramming due to PKM2 induction, the inhibition of PDC following E1 phosphorylation, and the repression of Acss2 and Acly, two sources of acetyl-CoA produced from free acetate and citrate, respectively, are consistent with the overall reduction in acetyl-CoA. Missing in this analysis is an examination of the effects of TCDD on amino acid metabolism, another minor source of acetyl-CoA that is currently being investigated in a companion study.
Lower acetyl-CoA levels are indicators of insulin resistance, obesity, and cancer69,70. For example, squamous cell carcinomas and adenocarcinomas express high levels of PDK1 with evidence of PDC inactivation during metastasis71,72. Coincidentally, the International Agency for Research on Cancer (IARC) classifies TCDD as a human carcinogen3. Long-term treatment of rodents with TCDD leads to the development of tumors in multiple tissues including the liver, although the carcinogenic mechanism has not been resolved73. In humans, TCDD exposure has been associated with obesity, insulin resistance, and diabetes74–76. The doses used here take into consideration the relatively short duration of this study compared to the lifelong exposure of humans to diverse AhR ligands, the bioaccumulative nature of halogenated AhR ligands, and the differences in the half-life of TCDD between humans (1–11 years77,78) and mice (8–12 days79). The same dose levels and treatment regimen have also been used in previous studies and recently shown to approach steady-state levels14,22,41,80–82. Specifically, orally gavaging mice with 0.01 to 30 μg/kg TCDD every 4 days for 28 days resulted in mouse hepatic tissue levels that span human background serum concentrations reported in the United States, Germany, Spain, and the United Kingdom as well as serum levels in Viktor Yushchenko 4–39 months following intentional poisoning22. Collectively, this suggests that metabolic reprogramming resulting in reduced acetyl-CoA levels following persistent AhR activation by TCDD may have a role in the etiology of cancer and metabolic diseases such as insulin resistance and diabetes.
Few studies have assessed how liver disease progression affects acetyl-CoA and acetyl-CoA-derived intermediates. Studies examining fatty liver in mice on high-fat diets (HFD) have reported reduced pyruvate dehydrogenase complex activity, as well as Pdk2 and Pdk4 induction83. Lower β-hydroxybutyric acid in the serum for NAFLD patients also suggests fatty liver may impair ketogenesis84. Studies also report conflicting results regarding hepatic acetyl-CoA levels in HFD-induced NAFLD but this can likely be attributed to significant differences in models, HFDs and study duration85,86. Nevertheless, the possibility that TCDD does not act directly, and that hepatic fat accumulation reduces acetyl-CoA levels cannot be excluded given the results.
Under conditions of lower acetyl-CoA levels, intracellular ATP levels would also decrease and trigger the activation of AMPK, a ubiquitous sensor of cellular energy and nutrient status. AMPK monitors the AMP/ATP ratio, and is activated following phosphorylation to restore energy homeostasis by turning on catabolic pathways that provide substrates for ATP production while switching off biosynthetic pathways and other nonessential processes that consume energy87. Accordingly, TCDD dose-dependently increased the phosphorylated AMPK (P-AMPK, active form)/unphosphorylated AMPK (inactive form) ratio. However, known targets of P-AMPK such as acetyl-CoA carboxylase, a regulated enzyme in fatty acid biosynthesis, and downstream targets such as ATG14, a marker of autophagy, did not exhibit phosphorylation but were instead transcriptionally repressed by TCDD. Likewise, there was no evidence of RAPTOR phosphorylation that would induce the dissociation of mTOR from lysosomes to reduce biomass production in support of cell growth and proliferation. ATG14, a key regulator of autophagy, was also not phosphorylated and consequently, macromolecules were not available for catabolism to generate ATP. Overall, the lower acetyl-CoA/CoA ratio following TCDD treatment is consistent with the inability of P-AMPK to restore energy homeostasis. Further studies are needed to investigate why P-AMPK did not activate autophagy under conditions of low acetyl-CoA levels.
In addition to being a substrate for fatty acid and cholesterol synthesis which are repressed by TCDD14,88, acetyl-CoA can be used as a substrate for hepatic ketone body production during starvation or low circulating glucose levels30. The present study showed that after 6 h of fasting, hepatic and serum levels of β-hydroxybutyrate were dose-dependently decreased by TCDD, consistent with lower levels of acetyl-CoA production. Furthermore, acetyl-CoA and β-hydroxybutyrate-CoA can be used as substrates for the reversible posttranslational modification of proteins that can affect protein structure, enzymatic activity, cellular location, and protein–protein interactions89,90. Protein acetylation and β-hydroxybutyrylation PTMs are particularly important in gluconeogenesis, glycolysis, the TCA and urea cycles, glycogen metabolism, and fatty acid metabolism. Consequently, these intermediates can exert a signaling function that links metabolism and metabolite levels to gene expression63,90,91. In the present study, TCDD dose-dependently decreased total hepatic protein acetylation and β-hydroxybutyrylation PTMs which has been shown to correlate with acetyl-CoA and β-hydroxybutyrate levels92. This extends the potential effects of TCDD beyond direct AHR-mediated effects on gene expression to later indirect consequences due the disruption of protein PTM.
In summary, TCDD elicited dose-dependent hepatic metabolic reprogramming by direct AHR-mediated action on gene expression, while also causing later indirect effects by altering protein acetylation and β-hydroxybutyrylation. Specifically, the present study presents further evidence of TCDD-mediated metabolic reprograming events that contribute to an energy crisis as demonstrated by the reduced levels of intracellular acetyl-CoA and the induction of P-AMPK. We have previously shown that exposure to TCDD in mice results in the metabolic inhibition of glycolysis and fatty acid β-oxidation, pathways that replenish acetyl-CoA levels when cell energy stores are low. The present study provides evidence that TCDD impeded glycolysis not only due to PKM isoform switching, but also through the inactivation of the PDC, the gateway between glycolysis and the TCA cycle. Further investigation is necessary to elucidate the paradoxical energy dysregulation induced by TCDD.
## Animal treatment
Mice were housed and treated as previously described40. Briefly, postnatal day (PND) 25 male C57BL/6 mice, obtained from Charles River Laboratories (Kingston, NY), were house in Innovive Innocages (San Diego, CA) containing ALPHA-dri bedding (Shepherd Specialty Papers, Chicago, IL). Cages were housed in a 12 h/12 h light/dark cycle and at 23 °C environment with 30–$40\%$ humidity. Harlan Teklad $\frac{22}{5}$ Rodent Diet 8940 (Madison, WI) and Aquavive water (Innovive) were provided ad libitum. A TCDD stock was prepared as previously described40. On PND28, mice were orally gavaged at the start of the light cycle (zeitgeber [ZT] 0) with 0.1 ml sesame oil vehicle (Sigma-Aldrich, St. Louis, MO) or 0.03, 0.1, 0.3, 1, 3, 10, and 30 μg/kg body weight TCDD every 4 days for 28 days for a total of 7 treatments. This dosing regimen was selected to approach steady state levels given the 8–12 day half-life of TCDD in mice93. Comparable treatment has been used in previous studies14,22,23,25,40,81,94. Following 28 days, mice were weighed and euthanized. Serum and liver tissues were collected and immediately flash-frozen in liquid nitrogen and stored at -80 °C. This study was conducted in accordance with relevant guidelines and regulations. All animal procedures were approved by the Michigan State University (MSU) Institutional Animal Care and Use Committee (IACUC; PROTO201800043) and meet the ARRIVE guidelines.
## Liquid chromatography tandem mass spectrometry
Previously published untargeted liquid chromatography tandem mass spectrometry data was used to assess hepatic β-hydroxybutyryl-CoA levels40. Dataset was accessed through the NIH Metabolomics Workbench (ST001379). Targeted acetyl-CoA and coenzyme A samples were measured on a Xevo G2-XS QTof attached to a Waters UPLC (Waters, Cambridge, Massachusetts, United States). The liquid chromatography mobile phases, gradient flow rates, and columns were used as previously published23,40. Mass spectra were acquired using negative-mode electrospray ionization run in MSE continuum mode. The metabolite raw signals were quantified by retention time and accurate mass using MassLynx Version 4.2 (Waters). Acetyl-CoA levels were determined by measuring the response (unlabeled acetyl-CoA signal: 13C2-acetyl-CoA signal) in each sample. Concentration was determined by a 5-point calibration curve containing unlabeled acetyl-CoA (0.005–5 µM) and 13C2-acetyl-CoA at a constant 2 µM. Since isotopic labeled standards of CoA were unavailable, the standard addition method was used to measure CoA concentrations and correct for matrix effects. Briefly, CoA raw signal was measured in the samples used for acetyl-CoA analysis and in the sample with 1 µM unlabeled CoA standard added. To account for signal carry-over, CoA raw signal was corrected with the average % carry-over. The average % carry-over was determined by averaging % carry-over signal of CoA in the blanks and the sample run on the instrument prior to the blank. For all samples, the raw CoA signal was corrected for carry-over by the following equation: (corrected raw CoA signal) = (raw CoA signal)—(the average % carry-over) X (raw CoA signal in sample run prior on the instrument).
## Clinical chemistry and hepatic acetate quantitation
β-Hydroxybutyrate levels in undiluted serum samples were assessed using a commercially available kit (Sigma-Aldrich) according to manufacturer’s protocol. Similarly, acetate levels were assessed liver samples (~ 40 mg) using a commercially available kit (Sigma-Aldrich) according to manufacturer’s protocol. An Infinite M200 plate reader (Tecan, Durham, North Carolina) was used to assay all replicates.
## Protein extraction and quantification
Frozen liver tissues (~ 50 mg) were homogenized in RIPA buffer with protease inhibitors (Sigma-Aldrich) using a Polytron PT2100 homogenizer (Kinematica, Lucerne, Switzerland) followed by sonication on ice. Samples were centrifuged, after which the supernatant was collected, and protein concentration measured using a bovine serum albumin standard curve and a bicinchoninic acid (BCA) assay (Sigma-Aldrich).
## Capillary electrophoresis protein analysis
The WES capillary electrophoresis system (ProteinSimple, San Jose, CA) was used following standard manufacturer protocols to assess protein levels on total liver lysates. Compass for SW (v4.0.0; ProteinSimple) was used to assess the area under each peak using the Gaussian fit method. The following antibodies and dilutions were used from the respective manufacturers: PDH (1:50; #2784; Cell Signaling, Danvers, MA); PDH p-Ser300 (1:50; AP1064; Sigma-Aldrich); ACLY (1:65; #4332; Cell Signaling); Total Acetylated Lysine (1:65; #9441; Cell Signaling); AMPK (1:50; #2603; Cell Signaling); AMPK p-Thr172 (1:50; #2535; Cell Signaling); ATG14 (1:50; PD026MS; MBL International, Woburn, MA); ATG14 p-Ser29 (1:50; #92340; Cell Signaling).
## Western blotting
Protein samples (20 μg) from total liver lysates were resolved via $10\%$ SDS-PAGE gels (Bio-Rad, San Diego, CA, USA) and transferred to nitrocellulose membranes (GE Healthcare, Chicago, IL) using the Mini Trans-Blot Cell Unit (BioRad) by wet electroblotting (100 V, 45 min). The membranes were then blocked with $5\%$ nonfat milk (in Tris-buffered saline [TBS] + $0.01\%$ Tween) for 1 h and incubated with primary antibodies: anti-β-hydroxybutyryllysine (1:1000; PTM-1201; PTM Biolabs, China) or anti-β-actin (1:1000; #4970; Cell Signaling) overnight at 4 °C. Blots were visualized using horseradish peroxidase (HRP)-linked secondary antibodies (1:3,000; Cell Signaling Technology) and an ECL kit (Millipore Corporation, Billerica, MA). Membranes were scanned on a Sapphire Biomolecular Imager (Azure Biosystem, Dublin, CA). Protein density values were assessed and calculated using ImageJ software (version 1.47; National Institutes of Health, Bethesda, MD). The expression for the protein of interest was standardized to β-actin levels.
## Protein localization data
Protein subcellular localization data were acquired using COMPARTMENTS95 as previously described40. Subcellular localizations are listed using the following abbreviations unless otherwise noted: cytosol (C), endoplasmic reticulum (ER), extracellular space (ES), *Golgi apparatus* (GA), lipid droplet (LD), lysosome (L), mitochondrion (M), mitochondrial outer membrane (OMM), mitochondrial inner membrane (IMM), nucleus (N), peroxisome (P), and plasma membrane (PM).
## Supplementary Information
Supplementary Figures. The online version contains supplementary material available at 10.1038/s41598-023-31087-9.
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|
---
title: Predictive and prognostic value of leptin status in asthma
authors:
- Juan Wang
- Ruochen Zhu
- Wenjing Shi
- Song Mao
journal: NPJ Primary Care Respiratory Medicine
year: 2023
pmcid: PMC10011586
doi: 10.1038/s41533-023-00332-z
license: CC BY 4.0
---
# Predictive and prognostic value of leptin status in asthma
## Abstract
Asthma is closely associated with inflammation. We evaluated the predictive and prognostic value of leptin status in asthma. We searched the electronic databases for articles that determined the leptin level in asthma cases through May 2020. We compared the differences of leptin level between asthma and non-asthma controls, as well as between severe and mild asthma cases. We also investigated the impact of age and gender on these differences by using meta-regression analysis. 59 studies were included in our pooled analysis. Asthma cases demonstrated significantly higher leptin level than that in non-asthma controls among overall populations (SMD:1.061, $95\%$ CI: 0.784–1.338, $p \leq 10$−4), Caucasians (SMD:0.287, $95\%$ CI: 0.125–0.448, $$p \leq 0.001$$), Asians (SMD:1.500, $95\%$ CI: 1.064–1.936, $p \leq 10$−4) and Africans (SMD: 8.386, $95\%$ CI: 6.519–10.253, $p \leq 10$−4). Severe asthma cases showed markedly higher leptin level than that in mild asthma cases among overall populations (SMD:1.638, $95\%$ CI: 0.952–2.323, $p \leq 10$–4) and Asians (SMD:2.600, $95\%$ CI: 1.854–3.345, $p \leq 10$–4). No significant difference of leptin level between severe and mild asthma was observed in Caucasians (SMD:−0.819, $95\%$ CI: −1.998–0.360, $$p \leq 0.173$$). Cumulative analyses yielded similar results regarding the difference of leptin status between asthma and non-asthma controls, as well as between severe and mild asthma cases among overall populations. Age and male/ female ratio were not associated with the difference of leptin status between asthma and non-asthma controls (coefficient:−0.031, $95\%$ CI: −0.123–0.061, $$p \leq 0.495$$; coefficient:0.172, $95\%$ CI: −2.445–2.789, $$p \leq 0.895$$), as well as between severe and mild asthma cases among overall populations (coefficient:−0.072, $95\%$ CI: −0.208–0.063, $$p \leq 0.279$$; coefficient: 2.373, $95\%$ CI: −0.414–5.161, $$p \leq 0.090$$). Asthma demonstrated significantly higher level of leptin than that in non-asthma controls among overall populations, Caucasians, Asians and Africans. Severe asthma cases showed markedly higher leptin level than that in mild cases among overall populations and Asians. Leptin may be a risk predictor and prognostic marker of asthma. Early monitoring and intervention of leptin may be needed for asthma.
## Introduction
Asthma, a common respiratory tract disease, is likely to occur in both children and adults1. Frequent attacks of asthma may lead to irreversible airway obstruction, cardiac events, and even death2. In terms of the morbidity and mortality of asthma, early prevention and monitoring of asthma seems imperative. The past decades witnessed an increasing trend of asthma prevalence across the world due to many factors, such as environmental and lifestyle changes3. Allergy and inflammation are well-documented inducers of asthma, whereas the occurrence and progression of certain asthma cases remained unexplained4. Hence, an in-depth investigation of the potential risk factors for asthma susceptibility and progression is necessary.
Leptin, a hormone secreted by adipocyte, plays a main role in controlling body weight through influencing appetite and energy expenditure5. Obesity cases demonstrated higher level of leptin than that in normal controls, indicating that obesity may be a leptin resistance condition6. Meanwhile, obesity is closely associated with asthma susceptibility7. On the other hand, leptin plays a role in the pro-inflammatory activities, which is closely associated with asthma risk and progression8. Leptin secretion is associated with bronchial hyperresponsiveness and insulin resistance9. Leptin receptor is also expressed in the lung10. In this sense, we speculated that leptin may also be associated with asthma risk and progression.
In the past decades, many studies were performed to determine the leptin levels in asthma cases11–69. The results were not consistent among the studies. Some investigations yielded that leptin status was significantly higher in asthma cases than that in non-asthma controls, whereas some studies showed a null difference of leptin levels between asthma and controls. An improved understanding of this issue has important significance that early monitoring or intervention may lower the risk or progression of asthma. A previous pooled analysis showed that higher level of leptin was associated with asthma70. However, the association between leptin status and asthma progression was not studied.
With the accumulating evidence, we conducted this updated pooled analysis to investigate the predictive and prognostic value of leptin status in asthma, we also studied the influence of age, gender and ethnicities on the differences of leptin status among different groups with the aim of yielding a more robust finding on this issue.
## Search strategy
We searched the papers that tested the leptin levels in asthma cases through May 2020 by using PubMed, Embase, Cochrane and Chinese WanFang databases. No restriction was imposed on the searched language. The used terms were as follows: [1] leptin, adipocyte, adiponectin; and [2] asthma, bronchial asthma, respiratory tract disease. We searched the associated papers by combining these terms. We also reviewed the references of extracted papers. If the same participants were recruited in more than one study, we chose the study with the complete analysis. The participants data were extracted from the public publications, hence the consent was waived. Ethics approval: This study was approved by the institutional review board of Shanghai Sixth People’s Hospital (No: 2018–106).
## Inclusion and exclusion criteria
Inclusion criteria: [1] case-control, cohort, prospective or observational study; and [2] asthma as the cases; and [3] leptin status (mean and standard deviation or data to calculate them) available.
Exclusion criteria: [1] case reports, reviews and editorials; [2] levels of other factors in asthma; and [3] detailed leptin level was not available and multiple publications of the same data.
## Data extraction and synthesis
We extracted the characteristics from each recruited study. The data were recorded as the following: first author’s family name, publication year, ethnicity of participants, study design, gender, number of asthma cases and controls, leptin levels, and adjustment for covariates. The criteria for the definition of severe and mild asthma was not totally same among the recruited studies. Severe asthma was defined as the continuous use of inhaled steroids and bronchodilators, and mild asthma as the intermittent use of inhaled steroids or bronchodilators in the majority of enrolled studies. On the other hand, controlled and uncontrolled asthma were defined as severe and mild asthma, respectively. In a word, the severity of asthma depends on the treatment response and dependence across the included studies. We also evaluated the quality of each recruited study using Newcastle-Ottawa Quality Assessment Scale, which included the assessment for participants selection, exposure and comparability. A study can be awarded a maximum of one score for each numbered item within the selection and exposure categories. A maximum of two scores can be given for comparability71. Two authors conducted the literature search independently, study selection, quality assessment and data extraction with any disagreements resolved by discussion.
## Statistical analysis
Standard mean difference (SMD) was used to measure the differences of leptin levels between asthma and non-asthma controls, as well as severe and mild asthma cases across the recruited studies. Heterogeneity of SMDs across the studies was tested by using the Q statistic (significance level at $p \leq 0.05$). The I2 statistic, a quantitative measure of inconsistency across studies, was also calculated. The combined SMDs were calculated using a fixed-effects model, or, in the presence of heterogeneity, random-effects model. In addition, $95\%$ confidence intervals (CIs) were also calculated. We evaluated the influence of a single study on the pooled SMDs by excluding one study in each turn. Subgroup analyses were conducted according to the ethnicity. Meta-regression analyses were performed to investigate the influence of age and gender on the SMDs between asthma and controls, and as well as between severe and mild asthma. Potential publication bias was assessed by Egger’s test and Begg rank correlation test at the $p \leq 0.05$ level of significance. All analyses were performed using STATA version 12.0 (Stata Corp, College Station, TX). $P \leq 0.05$ was considered statistically significant, except where otherwise specified.
## Literature search
We initially extracted 417 relevant publications from the PubMed, Embase, Cochrane and Chinese WanFang databases. Of these, 358 studies were excluded according to the inclusion and exclusion criteria, 59 articles11–69 were included in our final meta- analysis (Fig. 1). The retrieved data were recorded as follows: first author’s surname, publication year, ethnicity, study design, gender (male/female ratio), age, the number of severe asthma, mild asthma, and non-asthma controls. A flow chart showing the study selection is presented in Fig. 1.Fig. 1Flow chart of study selection.
## Characteristics for included studies
51 studies were identified for the analysis of the differences of leptin levels between asthma and non-asthma controls. 25 studies were performed for the analysis of the differences of leptin levels between severe and mild asthma. These studies were published between 2004 and 2019. Twenty-one studies were conducted in Caucasians, thirty-seven in Asians, and one in Africans. Forty-nine studies were case-control design, six for cross-sectional design, and four for cohort. A total of 1044 severe asthma, 2536 mild asthma and 7176 non-asthma controls. The number of awarded scores of included studies ranged from 4 to 6. Thirty-one studies were awarded for four scores, twenty-five for five scores and three for six scores. As shown in Table 1.Table 1Characteristics of studies included in our analysis. StudyStudy designEthnicityCase1/Case2/ControlAdjustment for confounding factorsMethod Quality of testing scoreAge(Y)nmale/femaleLeptinDoniec et al. [ 2004]45CCCaucasians−−/$\frac{27}{16}$−−/2.84 ± $\frac{2.1}{3.49}$ ± 1.65 ng/mLAgeRIA4Gurkan et al. [ 2004]67CCAsians−/6.4 ± $\frac{3.1}{7.0}$ ± 2.7−/$\frac{23}{20}$−/$\frac{16}{7}$/$\frac{13}{7}$−/19.3 ± $\frac{5.1}{9.8}$ ± 1.6 ng/mlAge, GenderEIA5Guler et al. [ 2004]64CCAsians−5.99 ± $\frac{3.46}{6.12}$ ± 3.49−/$\frac{102}{33}$−/$\frac{65}{37}$/$\frac{19}{14}$−/3.53(2.06–7.24)/2.26(1.26–4.71)ng/mLmedian(IQR)Age, BMIELISA5Sood et al. [ 2006]33CSCaucasians−/43.6 ± $\frac{1.2}{44.4}$ ± 0.7−/$\frac{290}{5586}$−/$\frac{116}{174}$/$\frac{2709}{2877}$−/13.7 ± $\frac{0.9}{11.1}$ ± 11.2 ug/L−RIA4Erel et al. [ 2007]63PCAsians−−/$\frac{10}{33}$−−/10.45 ± $\frac{11.613}{7.90}$ ± 10.609 ng/mL−ELISA4Kim et al. [ 2008]68CCAsians−/10.1(8.8–$\frac{11.5}{9.1}$(8.0–11.1)Median(IQR)−/$\frac{149}{54}$−/$\frac{98}{51}$/$\frac{28}{26}$−/2.27(0.65–5.03)/2.10(0.71–4.49)ng/mlmedian(IQR)Age, Gender,BMIELISA5Canoz et al. [ 2008]66CCAsians−/34.92 ± $\frac{10.28}{33.25}$ ± 9.50−/$\frac{24}{20}$Female−/24.38 ± $\frac{5.63}{9.75}$ ± 1.59 pg/ml−IM4Chen et al. [ 2009]61CCAsians−−/$\frac{18}{10}$−−/6.82 ± $\frac{1.16}{5.38}$ ± 1.20 ng/mL−RIA4Bruno et al. [ 2009]62CCCaucasians53(44–61)46(30–51)/29.5(25–34)Median (IQR)$\frac{15}{8}$/$\frac{159}{6}$ /$\frac{3}{5}$/$\frac{9}{62372}$(867–3714)/5722(3547–6761)/5300(4031–7514)cells/mm2median(IQR)−microscope4Jang et al. [ 2009]55CCAsians−46.4(18–71)/46.4(19–70)−/$\frac{60}{30}$− /$\frac{16}{44}$/$\frac{8}{22}$−/2.31 ± $\frac{0.04}{2.22}$ ± 0.06 ng/mLAge, Gender, BMIELISA5Xiao et al. [ 2009]20CCAsians7.2 ± $\frac{2.2}{6.9}$ ± $\frac{2.3}{7.5}$ ± $\frac{3.120}{18}$/$\frac{2011}{9}$/$\frac{10}{8}$/$\frac{8}{123.62}$ ± $\frac{0.17}{3.04}$ ± $\frac{0.11}{2.26}$ ± 0.12 ug/L−ELISA4Arshi et al. [ 2010]60CCCaucasians−11.6 ± $\frac{3.1}{11.8}$ ± 3.3−/$\frac{21}{10}$−−/9.7 ± $\frac{12.4}{7.1}$ ± 6.0 ng/mLAge, Gender, BMIELISA5Quek et al. [ 2010]58CCAsians−8.74 ± $\frac{2.73}{8.16}$ ± 1.86−/$\frac{68}{46}$−/$\frac{38}{30}$/$\frac{29}{17}$−/12.59 ± $\frac{12.22}{8.73}$ ± 8.04 ng/mLAgeELISA5Pan et al. [ 2011]23CCAsians18–$\frac{68}{18}$–$\frac{68}{25}$–$\frac{6670}{70}$/$\frac{6036}{34}$/$\frac{36}{34}$/$\frac{32}{288.64}$ ± $\frac{0.75}{2.77}$ ± $\frac{0.02}{2.32}$ ± 0.01 ng/mLAge, Gender, Height, WeightRIA4Baek et al. [ 2011]28CCAsians−/8.0(6.9–9.3)/9.0(8.1–10.0)−/$\frac{23}{20}$−/$\frac{16}{7}$/$\frac{11}{9}$−/4.51 ± $\frac{2.61}{4.81}$ ± 3.64ng/mLAge, GenderELISA5Dajani et al. [ 2011]54CCAsians−−/$\frac{10}{12}$Female−/831.21 ± $\frac{118.71}{592.54}$ ± 64.22 signal intensity−ELISA4Leivo-Korpela et al. [ 2011]56CCCaucasians−33.9 ± $\frac{2.1}{33.8}$ ± 2.1−/$\frac{35}{32}$−−/0.5(0.5–1.1)/0.6(0.4–0.8)ng/Lmedian (IQR)Age, GenderELISA5Holguin et al. [ 2011]57CCCaucasians−28(18–60)/30(22–39)median (range)−/$\frac{5}{7}$− /$\frac{2}{3}$/$\frac{4}{3}$−/2(0.6–11)/11(4–17) ng/Lmedian (IQR)−ELISA4Giouleka et al. [ 2011]59CCCaucasians−52 ± $\frac{14}{50}$ ± 16−/$\frac{100}{60}$− /$\frac{40}{60}$/$\frac{25}{35}$−/9.6(7.6, 16.25)/7.2(4.6, 10.3)ng/mLmedian(IQR)Age, BMIELISA5Tanju et al. [ 2011]65CCAsians6.13 ± $\frac{3.01}{5.93}$ ± 3/−$\frac{16}{20}$/−$\frac{8}{8}$/$\frac{11}{9}$/−7.75 ± $\frac{1.55}{1.70}$ ± 1.10/-Age, Gender, BMIELISA5Zhang et al. [ 2012]13CCAsians5.58 ± $\frac{2.34}{5.58}$ ± $\frac{2.34}{5.49}$ ± $\frac{2.1452}{52}$/$\frac{4332}{20}$/$\frac{32}{20}$/$\frac{28}{1513.33}$ ± $\frac{2.53}{7.92}$ ± $\frac{1.12}{3.96}$ ± 2.02 ng/ml−RIA4He et al. [ 2012]27CCAsians51.9 ± $\frac{13.68}{41.35}$ ± $\frac{13.70}{46.30}$ ± $\frac{11.4220}{17}$/$\frac{207}{13}$/$\frac{7}{10}$/$\frac{10}{1033.8}$ ± $\frac{24.02}{18.93}$ ± $\frac{17.68}{10.16}$ ± 6.08 ng/mL−ELISA4Berthon et al. [ 2012]50CSCaucasians−/−/−$\frac{56}{41}$/52−/−/−5050[2689, 8088]/3539[2246, 8088]/1025[419, 1817]pg/mLmedian (IQR)Age, GenderIA5Sideleva et al. 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[ 2013]49CCCaucasians55.3 ± $\frac{9.9}{59.6}$ ± $\frac{7.8}{57.6}$ ± $\frac{10.915}{17}$/22Female31.1 ± $\frac{15.5}{19.2}$ ± $\frac{12.1}{13.7}$ ± 10.0 ng/mLAge, BMIRIA5Abdul Wahab et al. [ 2013]41PCAsians12.5 ± $\frac{1.4}{10.75}$ ± 1.9/−$\frac{4}{32}$/−$\frac{2}{2}$/$\frac{22}{10}$/−22.25 ± $\frac{12.4}{17.01}$ ± 14.0/−ng/mLAge, Gender, BMIELISA6Mohammed Youssef et al. [ 2013]42CCAfricans−/10.4 ± $\frac{1.3}{5.5}$ ± 1.8−/$\frac{25}{20}$−/$\frac{14}{11}$/$\frac{9}{11}$−/31.3 ± $\frac{2.8}{12.1}$ ± 1.4 ng/mL−ELISA4El-Kader et al. [ 2013]43CCAsians13.16 ± $\frac{3.54}{13.16}$ ± 3.54/−$\frac{40}{40}$/−−/−/−31.43 ± $\frac{5.47}{26.98}$ ± 4.50/−ng/mL−ELISA4Cobanoglu et al. [ 2013]44CSAsians−/8.2 ± $\frac{1.2}{8.8}$ ± 1.4−/$\frac{23}{51}$−/$\frac{14}{9}$/$\frac{20}{31}$−/5.3(0.4, 27.4)/8.8(0.3,31.3)ng/mLmedian(min, max)Age, Gender, BMIEIA5Baek et al. [ 2013]9CCAsians−/8.3 ± $\frac{1.6}{7.8}$ ± 1.8−/$\frac{25}{21}$−/$\frac{17}{8}$/$\frac{9}{12}$−/3.3(2.3, 6.3)/4.0(1.9,5.7)ng/mLmedian(IQR)−ELISA4Liu et al. [ 2013]46CCAsians−−/ M$\frac{53}{56}$−/ F$\frac{47}{52}$−−/4.51 ± $\frac{1.75}{4.29}$ ± 1.76−/14.61 ± $\frac{2.95}{13.26}$ ± 3.66 ug/L−ELISA4Peng et al. [ 2014]14CCAsians10.46 ± $\frac{1.93}{10.46}$ ± $\frac{1.93}{9.75}$ ± $\frac{2.2829}{29}$/$\frac{2821}{8}$/$\frac{21}{8}$/$\frac{18}{625.37}$ ± $\frac{3.72}{10.16}$ ± $\frac{2.73}{9.29}$ ± 1.71 ng/mlAge, Gender, BMIELISA5Li et al. [ 2014]15CCAsians−/45.76 ± $\frac{9.41}{48.79}$ ± 11.95−/$\frac{57}{24}$−/−/$\frac{25}{32}$/$\frac{6}{18}$−/1.68 ± $\frac{0.58}{1.04}$ ± 0.12 mmol/LAge, GenderRT-PCR4Zhao et al. [ 2014]18CCAsians5.2 ± $\frac{1.9}{5.2}$ ± $\frac{1.9}{6.3}$ ± $\frac{2.216}{18}$/30−/−/$\frac{16}{1411.32}$ ± $\frac{1.02}{6.26}$ ± $\frac{0.97}{4.36}$ ± 0.81 ng/mLAge, GenderELISA4Xu et al. [ 2014]19CCAsians8.5 ± $\frac{1.5}{8.5}$ ± $\frac{1.5}{9.2}$ ± $\frac{1.827}{27}$/$\frac{2514}{13}$/$\frac{14}{13}$/$\frac{13}{1216.64}$ ± $\frac{3.53}{14.91}$ ± $\frac{3.24}{13.72}$ ± 5.79 ng/mLAge, Gender, BMIELISA5Li et al. [ 2014]21CCAsians57.8 ± $\frac{16.8}{54.5}$ ± $\frac{15.3}{50.7}$ ± $\frac{16.766}{64}$/$\frac{6027}{39}$/$\frac{27}{37}$/$\frac{34}{265048}$[2687, 8086]/3537[2242, 8086]/1023[417, 1819]pg/mLmedain(min max)−RIA4Zhang et al. [ 2014]24CCAsians8.6 ± $\frac{2.6}{8.0}$ ± $\frac{2.6}{8.9}$ ± $\frac{3.025}{20}$/$\frac{2012}{13}$/$\frac{9}{11}$/$\frac{10}{109.9}$ ± $\frac{2.5}{8.2}$ ± $\frac{1.6}{6.2}$ ± 1.2ug/LAge, Gender, BMIRIA4Yang et al. [ 2014]25CCAsians6.03 ± $\frac{3.02}{5.23}$ ± $\frac{2.86}{5.85}$ ± $\frac{3.1215}{31}$/$\frac{197}{8}$/$\frac{14}{17}$/$\frac{8}{116.51}$ ± $\frac{1.37}{2.86}$ ± $\frac{1.27}{1.88}$ ± 0.46uAge, Gender, BMIELISA4Rastogi MBBS et al. [ 2015]39CCCaucasians−/15.9 ± $\frac{1.7}{16.3}$ ± 1.7−/$\frac{42}{44}$−/$\frac{21}{21}$/$\frac{16}{28}$−/10.2 ± $\frac{9.5}{10.9}$ ± 9.3ng/mL−RIA4Haidari et al. [ 2014]40CCAsians−/31.28 ± $\frac{7.33}{35.08}$ ± 4.87−/$\frac{47}{47}$−/$\frac{26}{21}$/$\frac{24}{23}$−/1.41 ± $\frac{0.50}{0.59}$ ± 0.19ng/mLAge, Gender, BMIELISA6Muc et al. [ 2014]47CCCaucasians−−/$\frac{28}{25}$−/$\frac{11}{17}$/$\frac{14}{11}$−/78.12 ± $\frac{44.65}{78.06}$ ± 54.65ng/mL−ELISA4Coffey et al. [ 2015]36CCCaucasians−/32.7 ± $\frac{12.3}{37}$ ± 12.1−/$\frac{42}{40}$−/$\frac{15}{27}$/$\frac{15}{25}$−/24.9 ± $\frac{22.3}{17.4}$ ± 15.3ng/mLAgeRIA5Morishita et al. [ 2015]37CSCaucasians6.9(2.9,15.4)/9.9(3.4,16.5)/−$\frac{16}{76}$/−$\frac{12}{4}$/$\frac{39}{37}$/−3.5(0.4, 15.3)/2.97(0.21, 44.1)/−pg/mLmedian(min, max)Age, GenderIA5Van Huisstede et al. [ 2015]38CCCaucasians−/36[19,48]/39[18,50]−/$\frac{27}{39}$−/$\frac{7}{20}$/$\frac{7}{32}$−/69[18, 100]/55[11,100]ng/mLmedain(min max)Age, Gender−4Bian et al. [ 2016]12CCAsians13.4 ± $\frac{3.2}{13.2}$ ± $\frac{3.1}{13.5}$ ± $\frac{3.442}{36}$/$\frac{4027}{15}$/$\frac{23}{13}$/$\frac{26}{1410.33}$ ± $\frac{1.88}{7.48}$ ± $\frac{0.86}{4.36}$ ± 0.77ng/mlAge, Gender, BMIELISA5Liang et al. [ 2016]26CCAsians−/39 ± $\frac{12}{40.4}$ ± 11.6−/$\frac{78}{29}$−/$\frac{24}{54}$/$\frac{9}{20}$−/15.0 ± $\frac{10.4}{15.2}$ ± 11.7ug/LAge, GenderELISA5Huang et al. [ 2016]35CCCaucasians−/12.4 ± $\frac{1.4}{12.2}$ ± 1.5−/$\frac{58}{63}$−/$\frac{29}{29}$/$\frac{36}{27}$−/20.0 ± $\frac{18.9}{19.0}$ ± 20.4ng/mLAge, GenderELISA5Li et al. [ 2016]48CCAsians8.5 ± $\frac{2.56}{9.1}$ ± $\frac{2.70}{8.8}$ ± $\frac{2.4628}{26}$/$\frac{2515}{13}$/$\frac{14}{12}$/$\frac{13}{1219.98}$ ± $\frac{5.40}{13.73}$ ± $\frac{2.28}{12.17}$ ± 3.95ng/mLAge, Gender, BMIELISA5Gao et al. [ 2016]16CCAsians54.26 ± $\frac{11.73}{52.64}$ ± 10.25/−$\frac{34}{11}$/−$\frac{19}{15}$/$\frac{4}{7}$/−5.98 ± $\frac{2.99}{3.81}$ ± 2.29/−ng/mLAge, Gender, BMIELISA5Bodini et al. [ 2017]30CSCaucasians−/10.53 ± $\frac{1.96}{10.6}$ ± 2.69−/$\frac{15}{15}$−/$\frac{10}{5}$/$\frac{4}{11}$−/12.7 ± $\frac{13.2}{11.1}$ ± 11.2ng/mL−ELISA4Nasiri Kalmarzi et al. [ 2017]31CSAsians−/−/−$\frac{25}{35}$/−−/−/−50.6 ± $\frac{19.2}{8.2}$ ± 6.9/−u−ELISA4Li et al. [ 2018]17CCAsians−/45.69 ± $\frac{16.70}{47.86}$ ± 13.96−/$\frac{50}{25}$−/$\frac{25}{25}$/$\frac{12}{13}$−/5.98 ± $\frac{3.03}{4.55}$ ± 2.33ng/mLAge, Gender, WeightELISA5Szczepankiewicz et al. [ 2018]34CCCaucasians9.77 ± $\frac{3.73}{9.77}$ ± $\frac{3.73}{12.6}$ ± $\frac{3.0225}{25}$/$\frac{1013}{12}$/$\frac{13}{12}$/$\frac{5}{513.81}$ ± $\frac{10.56}{10.46}$ ± $\frac{11.55}{6.32}$ ± 5.20ng/mLGender,BMIELISA4Li et al. [ 2019]29CCCaucasians39 ± $\frac{17}{34}$ ± 13/−$\frac{305}{26}$/−$\frac{153}{152}$/$\frac{11}{15}$/−4.4 (2.5–4.7))/3.0(1.4–3.0)/−ng/mLgeometric means(IQR)Age, GenderLuminexxMAG5CC Case-control, PC Prospective cohort, CS Cross sectional, Case1 Severe asthma, Case2 Mild asthma, IQR Interquartile range, BMI Body mass index, ELISA Enzyme linked immunosorbent assay, RIA Radioimmunoassay, IA Immunoassay, EIA Enzyme immunoassay, IM Immunometric method, min Minimum, max Maximum.
## Differences of leptin levels between asthma and controls
Asthma cases demonstrated significantly higher leptin level than that in non-asthma controls among overall populations (SMD: 1.061, $95\%$ CI: 0.784–1.338, $p \leq 10$–4), Caucasians (SMD: 0.287, $95\%$ CI: 0.125–0.448, $$p \leq 0.001$$), Asians (SMD: 1.500, $95\%$ CI: 1.064–1.936, $p \leq 10$−4) and Africans (SMD: 8.386, $95\%$ CI: 6.519–10.253, $p \leq 10$−4) (Table 2, Fig. 2). Significant heterogeneity was observed using Q and I2 statistic for overall populations ($p \leq 10$−4, I2 = $94.1\%$), Caucasians ($$p \leq 0.005$$, I2 = $54.7\%$) and Asians ($p \leq 10$−4, I2 = $95.2\%$). Exclusion of any single study did not change the overall SMDs for overall populations ($95\%$ CI: 0.727–1.455), Caucasians ($95\%$ CI: 0.085–0.502) and Asians ($95\%$ CI: 0.829–2.014) (Table 2). Cumulative analysis indicated that leptin status was significantly higher in asthma cases than that in non-asthma controls among overall populations (Fig. 3).Table 2Meta-analysis of the relationship between leptin status and asthma risk/progression. IndexStudiesQ testModel selectedSMD ($95\%$ CI)P-valueP-valueRisk Overall51< 10−4Random1.061 (0.784–1.338)< 10−4 Caucasians180.005Random0.287 (0.125–0.448)0.001 Asians32< 10−4Random1.500 (1.064–1.936)< 10−4 Africans1−Fixed8.386 (6.519–10.253)< 10−4Progression Overall25< 10–4Random1.638 (0.952–2.323)< 10−4 Caucasians7< 10–4Random−0.819 (−1.998–0.360)0.173 Asians18< 10–4Random2.600 (1.854–3.345)< 10−4Sensitivity analysesSMD (range)risk Overall510.727–1.455 Caucasians180.085–0.502 Asians320.829–2.014Progression Overall250.682–2.568 Caucasians7−2.572–0.692 Asians181.548–3.528SMD Standard mean difference. Fig. 2Differences of leptin status between asthma and controls. Fig. 3Cumulative analysis of the differences of leptin status between asthma and controls.
## Differences of leptin levels between severe asthma and mild asthma
Severe asthma cases showed markedly high leptin level than that in mild asthma cases among overall populations (SMD: 1.638, $95\%$ CI: 0.952–2.323, $p \leq 10$−4) and Asians (SMD: 2.600, $95\%$ CI: 1.854–3.345, $p \leq 10$−4) (Table 2, Fig. 4). No significant difference of leptin level between severe and mild asthma was observed in Caucasians (SMD: −0.819, $95\%$ CI: −1.998–0.360, $$p \leq 0.173$$) (Table 2, Fig. 4). Significant heterogeneity was observed using Q and I2 statistic for overall populations ($p \leq 10$−4, I2 = $96.7\%$), Caucasians ($p \leq 10$−4, I2 = $96.5\%$) and Asians ($p \leq 10$−4, I2 = $95.7\%$). Exclusion of any single study did not change the overall SMDs for overall populations ($95\%$ CI: 0.682–2.568), Caucasians ($95\%$ CI: −2.572–0.692) and Asians ($95\%$ CI: 1.548–3.528) (Table 2). Cumulative analysis indicated that leptin status was significantly higher in severe asthma cases than that in mild asthma cases among overall populations (Fig. 5).Fig. 4Differences of leptin status between severe and mild asthma. Fig. 5Cumulative analysis of the differences of leptin status between severe and mild asthma.
## Meta-regression analysis of the age/gender in the association between leptin status and asthma risk/progression
Age and male/female ratio were not associated with the differences of leptin status between asthma and non-asthma controls among overall populations (coefficient: −0.031, $95\%$ CI: −0.123 to 0.061, $$p \leq 0.495$$; coefficient: 0.172, $95\%$ CI: −2.445 to 2.789, $$p \leq 0.895$$) (Table 3). Age and male/female ratio were not associated with the differences of leptin status between severe and mild asthma cases among overall populations (coefficient: −0.072, $95\%$ CI: −0.208 to 0.063, $$p \leq 0.279$$; coefficient: 2.373, $95\%$ CI: −0.414 to 5.161, $$p \leq 0.090$$) (Table 3).Table 3Meta-regression analysis of the variables in the association between leptin status and asthma risk/progression. VariableCoefficient$95\%$CIPRisk Age−0.031−0.123–0.0610.495 Male/female ratio0.172−2.445–2.7890.895Progression Age−0.072−0.208–0.0630.279 Male/female ratio2.373−0.414–5.1610.090CI Confidence interval.
## Publication bias
The Begg rank correlation test and Egger linear regression test indicated no significant publication bias among Caucasians in the difference of leptin status between asthma and non-asthma controls (Begg, $$p \leq 0.65$$; Egger, $$p \leq 0.994$$). The Begg rank correlation test and Egger linear regression test showed marked publication bias among Asians in the difference of leptin status between asthma and non-asthma controls (Begg, $p \leq 10$−4; Egger, $p \leq 10$−4). The Begg rank correlation test and Egger linear regression test indicated no marked publication bias among Caucasians in the difference of leptin status between severe and mild asthma cases (Begg, $$p \leq 0.230$$; Egger, $$p \leq 0.054$$). The Begg rank correlation test and Egger linear regression test showed marked publication bias among Asians in the difference of leptin status between severe and mild asthma cases (Begg, $$p \leq 0.002$$; Egger, $$p \leq 0.003$$).
## Discussion
Increasing attention has been paid to the potential role of leptin in the development and progression of asthma. Our pooled analysis showed that asthma cases had markedly higher leptin level than that in non-asthma controls among overall populations, Caucasians, Asians and Africans, and severe asthma cases had significantly higher leptin level than that in mild asthma cases among overall populations and Asians. Age and gender did not influence the association between leptin level and asthma risk/progression. Our results indicated that leptin dysregulation may be associated with asthma risk/progression, frequent monitoring and early intervention of leptin status may be helpful for asthma prevention and therapy.
Several mechanisms may explain the association between leptin status and asthma risk/progression. First, asthma was essentially the breathing problems induced by airway narrowing and obstruction, which was exacerbated by the inflammation72. Inflammation was positively associated with the severity of asthma. Systemic inflammation acted as a mechanism linking insulin resistance with asthma73. Leptin showed pro-inflammatory actions, stimulating the production of inflammatory cytokines in bronchial and alveolar cells74. Persistent stimulation of inflammation may induce the injury and fibrosis of airway, increasing the susceptibility and progress of asthma. Meanwhile, leptin played a role in the regulation of T cell proliferation and activation, monocytes/macrophages recruitment, exerting effects in airway inflammation, respiratory diseases and immune system75. In this sense, leptin increased the inflammatory response through various ways, leptin may increase the risk and severity of asthma through activating the inflammation. Second, obesity was a risk factor for asthma susceptibility, and some immune changes present in asthma cases were augmented in obese asthmatics76. Meanwhile, obesity was closely associated with an obstructive pattern induced by disproportionated growth between lung parenchyma size and airway caliber, which led to a reduced lung function. Weight loss may lead to an improvement in lung function, airway reactivity and asthma control. Leptin, an adipocyte-derived hormone produced by white fat tissue in the conditions of excessive caloric intake, played a role in controlling body weight by influencing appetite and energy expenditure77. Leptin level was higher in obese than that in the normal weight cases, which means that obesity may be a leptin resistance condition. In terms of the close relationship between leptin and obesity, it was reasonable to predict that high level of leptin may increase the risk and severity of asthma through its interaction with obesity. Regrettably, the lack of detailed data of obesity and BMI made it unfeasible to study the influence of obesity/BMI on the association between leptin status and asthma. Further studies should be performed on this issue. Finally, leptin is also expressed in the lung and produced by the lung fibroblasts during alveolar differentiation, promoting the synthesis of surfactant protein78. Leptin plays a direct role in the lung development and remodeling, indicating that leptin disorder may affect the lung pulmonary homeostasis79. Lepin may influence the lung function, Which was consistent with our findings that leptin staus was higher in the asthma cases compared with non-asthma controls, as well as in severe asthma compared with mild asthma cases. In this sense, it is reasonable to predict that the pulmonary function may be influenced by leptin dysregulation.
Our findings for the association between leptin levels and asthma risk/progression were consistent with the above-mentioned evidence. It indicated that leptin may be a risk predictor and prognostic marker of asthma independent of age and gender. Asthma showed significantly higher leptin level than that in non-asthma controls, which might be due to the effects of leptin in the inflammation, obesity and lung development. Notably, we found that no marked difference of leptin level was observed between severe and mild asthma among Caucasians, indicating that leptin was not associated with asthma progression among Caucasians. We speculated that it may be due to the facts that Caucasians were more prone to be obese than other populations, and obesity may be associated with high level of leptin. It may lead to the comparatively similar leptin level between severe and mild asthma. On the other hand, only seven studies were recruited for the analysis of the difference of leptin level between severe and mild asthma among Caucasians, which may reduce the statistical power. Further larger number of participants should be involved in the future studies to verify our findings. Nevertheless, no marked publication bias was observed in the studies regarding the difference of leptin level between severe and mild asthma among Caucasians, which indicated that our finding was comparatively robust. Interestingly, we found that age and gender did not affect the differences of leptin levels between asthma and non-asthma, as well as severe and mild asthma, which indicated that leptin status was associated with asthma risk/progression independent of age and gender. Early monitoring and intervention of leptin level may be of great clinical implications.
Our study has obvious strengths. For example, the enrolled subjects were from different regions and the quality of the included studies was comparatively high, which increased the statistical power and promoted the generalization of our conclusions, which made the risk prediction for asthma susceptibility and progression possible. On the other hand, the analysis of the potential role of age and gender in the association between leptin status and asthma also provided a comparatively robust conclusion. Meanwhile, several limitations merited attention in our pooled analysis. First, the heterogeneities among included studies might affect the results of our investigation, although a random-effects model had been performed. Publication bias was also observed. Nevertheless, the sensitivity analyses did not change the overall results, cumulative analyses also showed a similar trend to our results and meta- regression also excluded the possibility of the influence of age and gender in our results, which proved that our conclusions were comparatively solid. Second, the study design of recruited paper were mainly case-control, which may lead to the recall bias, the disease course and medications may also affect the results. Due to the limit of available data, the in-depth analysis was not performed. Hence, further larger number, prospective studies with controlling confounding factors should be performed in the future. Third, obesity and BMI may influence the leptin level, higher leptin level was usually observed in obesity and high-BMI cases. Many asthma cases were obese than non-asthma controls, and obesity was also a risk factor for asthma susceptibility and progress. We also found that asthma cases had higher level of BMI in some of the included studies, while there were no differences of obesity ratio and BMI between asthma and controls in some of enrolled participants. The unavailable detailed data of BMI and obesity made it not possible to perform the in-depth influence of obesity and BMI on the association between leptin level and asthma. Nevertheless, our findings still had important implications that leptin level may be an auxiliary indicator for asthma susceptibility and progress due to the facts the some severe asthma cases were not obese and comprehensive analysis of multiple factors may be a better choice. Meanwhile, further multiple regression analysis involving multiple risk factors for asthma susceptibility and progress may needed in the future.
Finally, although a total of 59 studies were included in our studies, the number of studies regarding the difference of leptin level between severe and mild asthma among Caucasians was relatively small, which may decrease the statistical power. Larger number of participants with different ethnicities should be involved in the further studies to verify our findings.
In terms of our findings, further investigations may be performed to focus on the following issues: [1] elucidation of the detailed mechanism behind leptin and asthma risk/progression, [2] in-depth analysis of the association of disease course and medications with leptin status, [3] long-term, continuous observation of the changes of leptin status in asthma with a favorable study design.
## Conclusion
Our study indicated that asthma had significantly higher level of leptin than that in non-asthma controls among overall populations, Caucasians, Asians and Africans. Severe asthma cases showed markedly higher leptin level than that in mild cases among overall populations and Asians. Our findings were of great implications that leptin may be a risk predictor and prognostic marker of asthma. Early monitoring and intervention of leptin may be needed for asthma.
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|
---
title: 'Diet and risk of Barrett’s oesophagus: Melbourne collaborative cohort study'
authors:
- Sabrina E. Wang
- Allison Hodge
- S Ghazaleh Dashti
- Suzanne C. Dixon-Suen
- Natalia Castaño-Rodríguez
- Robert Thomas
- Graham Giles
- Alex Boussioutas
- Bradley Kendall
- Dallas R. English
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10011587
doi: 10.1017/S0007114522002112
license: CC BY 4.0
---
# Diet and risk of Barrett’s oesophagus: Melbourne collaborative cohort study
## Body
Barrett’s oesophagus (BE) is a premalignant metaplastic condition of the distal oesophagus and the precursor to oesophageal adenocarcinoma[1]. Incidence of both BE and oesophageal adenocarcinoma has been rising in Western populations(2–4). Major risk factors for BE and oesophageal adenocarcinoma include gastroesophageal reflux disease (GERD) and adiposity[1,5], and diet is a modifiable risk factor for both conditions. We previously identified that while some dietary compositions might affect risk of developing GERD, adherence to a diet that has been associated with lower cancer risk and mortality, as reflected by the Mediterranean Diet Score[6,7] or the Alternative Healthy Eating Index-2010[8], did not appear to reduce the risk of GERD[9].
A systematic review and meta-analysis based on three cohort studies by the World Cancer Research Fund/American Institute for Cancer Research in 2017 reported that consumption of vegetables (risk ratio [RR] = 0·89; CI: 0·80, 0·99 per 100 g/d) and green leafy vegetables (RR = 0·85; CI: 0·74, 0·96 per 50 g/d) reduced risk of oesophageal adenocarcinoma[10]. No conclusive evidence was found for other dietary factors[10]. A recent meta-analysis based on three case–control studies reported that dietary fibre intake was associated with lower risk of BE (highest v. lowest category: OR = 0·42; CI: 0·29, 0·61)[11].
Results from case–control studies might be affected by recall bias because cases and controls are likely to report their diet differently. A prospective study design where diet is measured before onset of BE could overcome this bias. Only one large cohort study has examined diet and risk of BE. The Netherlands Cohort Study reported that vegetable intake was associated with reduced BE risk for males but not for females[12], and no association was observed for meat intakes[13]. There a paucity of evidence on diet and risk of BE in other populations, which might differ due to difference in dietary patterns and prevalence of GERD and BE. We thus conducted a comprehensive analysis to investigate the potential effect of diet, including macronutrients, carotenoids, food groups, specific food items, beverages and dietary scores, on risk of BE in a culturally diverse cohort.
## Abstract
Barrett’s oesophagus (BE) is the precursor of oesophageal adenocarcinoma, which has become the most common type of oesophageal cancer in many Western populations. Existing evidence on diet and risk of BE predominantly comes from case–control studies, which are subject to recall bias in measurement of diet. We aimed to investigate the potential effect of diet, including macronutrients, carotenoids, food groups, specific food items, beverages and dietary scores, on risk of BE in over 20 000 participants of the Melbourne Collaborative Cohort Study. Diet at baseline (1990–1994) was measured using a food frequency questionnaire. The outcome was BE diagnosed between baseline and follow-up (2007–2010). Logistic regression models were used to estimate OR and 95 % CI for diet in relation to risk of BE. Intakes of leafy vegetables and fruit were inversely associated with risk of BE (highest v. lowest quartile: OR = 0·59; CI: 0·38, 0·94; P-trend = 0·02 and OR = 0·58; CI: 0·37, 0·93; P-trend = 0·02 respectively), as were dietary fibre and carotenoids. Stronger associations were observed for food than the nutrients found in them. Positive associations were observed for discretionary food (OR = 1·54; CI: 0·97, 2·44; P-trend = 0·04) and total fat intake (OR per 10 g/$d = 1$·11; CI: 1·00, 1·23), the association for fat was less robust in sensitivity analyses. No association was observed for meat, protein, dairy products or diet scores. Diet is a potential modifiable risk factor for BE. Public health and clinical guidelines that incorporate dietary recommendations could contribute to reduction in risk of BE and, thereby, oesophageal adenocarcinoma.
## Study participants
The Melbourne Collaborative Cohort Study (MCCS) is a cohort of 41 513 participants. In addition to participants born in Australian, the cohort intentionally targeted recruitment of people born in Italy and Greece to broaden the range of observations of measured lifestyle factors including diet[14]. Participants aged 40–69 years were recruited through the electoral roll between 1990 and 1994. The study protocol was approved by the Human Research Ethics Committee at Cancer Council Victoria (CCV IEC 9001). Written consent to participate was obtained on recruitment. For this study, participants older than 63 years at baseline were excluded as they were not followed-up for BE outcomes. We further excluded participants with history of cancer (except for keratinocyte skin cancers), diabetes mellitus or CVD at baseline as they likely had changed their diet following diagnosis; those whose total energy intake was deemed implausible (in the top or bottom 1 % of total energy intake); and those who had missing data for diet or any identified confounders (detailed below) at baseline. A total of 28 504 participants were eligible. A post hoc exclusion was applied to participants who were diagnosed with BE before baseline (n 3), based on diagnosis date collected at follow-up.
## Measurement of diet at baseline
Information on diet at baseline was collected using a self-administered 121-item FFQ developed specifically for the MCCS[15]. Food items were selected for inclusion in the questionnaire based on results of weighed food records from 810 volunteers of similar demographic background to the MCCS participants. Items were included if they contributed to the first 80 % of any nutrient for at least one of the sex-specific country of birth stratum (Australia, Italy or Greece). Intake for each food item was reported as one of nine frequencies, from never or less than once per month to six or more times per day. The estimated frequencies for food groups were calculated by converting each of the nine food frequencies to a daily value and summing across items[15]. The ‘discretionary food’ group included foods containing ‘high saturated fat’ or ‘added sugars’ as suggested by the Australian Dietary Guidelines[16], which included the following items from the questionnaire: ice cream, sweet biscuits, cakes or sweet pastries, puddings and chocolate confectionary. To calculate nutrient intakes, sex-specific portion sizes were allocated to each item based on the weighed food record data. Evaluation studies within the MCCS suggest diet was reasonably measured with moderate correlation between several nutrient intakes and their plasma concentrations[17,18].
We selected dietary factors that have been associated with risk of gastroesophageal reflux symptoms[9,19], BE[12] or oesophageal adenocarcinoma[5,10]. We also investigated two diet scores, the Mediterranean Diet Score and the Alternative Healthy Eating Index-2010. We used a modified version of the Mediterranean Diet Score from Trichopoulou et al. [ 7] Briefly, one point each was assigned for intake above the sex-specific medians for vegetables, fruit, cereal, legumes and fish; one point each was assigned for intake below the medians for dairy and red meat; one point was assigned for daily alcohol intake between 10–50 g/d for men and 5–25 g/d for women; and the ninth point was assigned based on olive oil intake[6]. A score of 9 indicates the highest degree of adherence. The Alternative Healthy Eating Index-2010 scores diet based on consumption frequency of foods and nutrients that are predictive of chronic diseases risk, including: vegetables, fruit, whole grains, sugar-sweetened beverages and fruit juice, nuts and legumes, red or processed meat, trans fat, long-chain fats, polyunsaturated to saturated fat ratio, sodium and alcohol[8]. A higher score predicts lower risk of chronic diseases, with 110 being the highest score.
Information on demographic and other lifestyle factors was also collected at baseline via structured interviews[14], and anthropometric measures, including height and weight, were measured.
## Ascertainment of Barrett’s oesophagus
Information on gastroesophageal reflux and BE was collected via telephone interviews between 2007–2010. Participants were asked if they had ever been diagnosed with BE by a doctor. If so, information was collected on when the diagnosis was made, and details of the treating doctor. For all participants who said they had been diagnosed or did not know if they had, attempts were made to obtain copies of relevant endoscopy and pathology reports and correspondence from gastroenterologists and endoscopists to the participants’ usual medical practitioners.
BE cases were defined an endoscopic diagnosis of columnar-lined oesophagus. If an endoscopy report was not available, an oesophageal biopsy showing columnar epithelium or correspondence from a gastroenterologist stating endoscopically diagnosed BE was used. We used the definition endoscopically confirmed columnar epithelium for BE to prevent misclassifying intestinal metaplasia identified from biopsy taken from a regular or irregular Z line as BE. Diagnoses of BE were reviewed by a gastroenterologist (BJK). The primary BE definition is consistent with the British Society of Gastroenterology guidelines[20]. We additionally examined BE cases restricted to those with specialised intestinal metaplasia diagnosed from an oesophageal biopsy as a secondary definition, which is consistent with the American College of Gastroenterology[21] and Australian guidelines[22].
## Statistical analyses
Logistic regression was used to estimate OR and CI for dietary variables in relation to risk of BE. As the risk of BE was rare (0·9 % in our eligible cohort), the OR is a good approximation of the risk ratio[23]. Macronutrients and carotenoids were analysed as continuous variables. Macronutrients were energy-adjusted using the residual method[24]. For example, the energy-adjusted fat intake is the residuals from a regression of fat intake on total energy intake. Increments reported are based on approximately one sd of intake. Food groups, food items and beverages were analysed as approximate quartiles of frequency (times/d) using the lowest quartile as the reference group. The Mediterranean Diet Score was analysed using predefined categories (score 1–3, 4–6, 7–9) and the Alternative Healthy Eating Index-2010 was analysed as quartiles, both using the least adherent category as the reference. Tests for linear trend for food intake and diet score were performed using the median in each category. Tests for linearity assumption were performed using likelihood ratio tests comparing models with each dietary variable fitted as a categorical v. a pseudo-continuous variable.
All analyses included potential confounders identified from a causal diagram based on the literature (online Supplementary 1). The potential confounders included: age, sex, country of birth (Australia/New Zealand/Northern Europe, Italy or Greece), an area-based measure of socio-economic position (the Index of Relative Socioeconomic Disadvantage from the Socio-economic Indexes for Areas[25]), educational attainment (primary school or less, high/technical school or tertiary), smoking status (never, former or current), physical activity score (four categories from least to most active) and average lifetime alcohol intake (g/d).
Our primary analysis assumed that the association between diet and BE was the same for males and females. We performed a secondary analysis stratified by sex as existing literature suggests sex may be a potential effect modifier[9,12]. Test for interaction between diet and sex was performed by including an interaction term in analysis models and using likelihood ratio tests.
With a sample of 20 793 participants, an average BE risk of 0·9 % and a reference group made up with one-quarter of the sample, a minimum OR of 1·5 or 0·67 can be detected with 80 % power and two-sided significance level of 0·05.
## Further adjustment for dietary confounders
The observed effect of one dietary factor on risk of BE could be due to low intake of another inversely correlated dietary factor. For example, an apparent effect of low vegetable intake on BE risk could either truly be attributable to vegetable deficiency in diet, or it could be due to high fat intake, which is often inversely correlated with vegetable intake. We thus performed a sensitivity analysis further adjusting for dietary confounders that were inversely correlated with each dietary exposure. To avoid collinearity in the regression model, we examined the Pearson correlation for dietary factors (online Supplementary 2). The strongest inverse correlation included in analysis models was between total carbohydrate and total meat intake (r = −0·57). For the analysis of fat and protein, the models additionally included total vegetable and total fruit; for the analysis of carbohydrate and fibre, the models additionally included total meat intake; for the analysis of meat intake, the model additionally included total carbohydrate and total fibre; for the analysis of carotenoids, vegetable and fruit, the model additionally included total fat intake as a dietary confounder. The analysis of dairy, discretionary food, chocolate, carbonated beverages, tea and coffee were not further adjusted for dietary confounders, as their intake was not strongly correlated with other dietary factors (all correlation coefficients < 0·26).
## Further adjustment for adiposity
The primary analysis assumed adiposity to be a mediator (i.e., on the causal pathway from diet to risk of BE; online Supplementary 1). *In* general, it is more likely that diet influences adiposity risk. However, it is also plausible given the age of our cohort participants that adiposity affected diet at baseline. In this case, adiposity would be a confounder (i.e., a common cause of exposure and outcome). We thus performed a sensitivity analysis further adjusting for adiposity, measured as BMI.
## Further adjustment for H. pylori infection
H. pylori infection could affect appetite-regulating hormones[26], which, in turn, could have reduced dietary intake at baseline. H. pylori infection has also been associated with lower risk of BE[27]. We were unable to include H. pylori infection status as a confounder in our primary analysis as it was not measured for all participants. To assess the potential confounding impact of H. pylori infection on our findings, we repeated the primary analyses and further adjusted for H. pylori infection in a random subset of participants with H. pylori data from a previous case–control study nested in the MCCS (n 1311). H. pylori antibodies were measured in baseline plasma using an immunoblotting kit (Helicoblot 2·1; Genelabs Diagnostics, Singapore).
## Assessing potential bias from gastroesophageal reflux symptoms at baseline
The analysis included some participants who reported gastroesophageal reflux symptoms before baseline. This was to ensure that the distribution of reflux symptoms among BE cases was representative of the distribution in the target population (around two-thirds of BE patients reported reflux symptoms before their diagnosis[28]). However, participants with symptoms before baseline might have changed their diet as a mean to alleviate symptoms, which means diet measured at baseline might not accurately reflect their average diet. To investigate how this measurement error in diet might have affected our results, we performed a sensitivity analysis restricted to participants without reflux symptoms at baseline. As information on reflux symptoms was collected at follow-up, there were missing data on symptom status (n 218) and time of symptom onset (n 732) for some eligible participants. Missing data on reflux symptoms were multiply imputed using chained equations, methods are described in Supplementary 3[29]. We excluded participants born in Italy or Greece from this sensitivity analysis because few BE cases from this group (n 2) reported symptoms before baseline.
In addition, for those who reported ever having reflux symptoms ≥ 1 d/week, we compared dietary intakes at baseline for those who had symptom onset before v. after baseline as a proxy to examine how participants with symptoms might have changed their diet.
## Assessing potential selection bias from loss to follow-up
There is risk for selection bias when characteristics of participants lost to follow-up are different from those who completed follow-up (online Supplementary 4). One way to minimise this selection bias is by including participant characteristics related to lost to follow-up in the analysis models (online Supplementary 4A). We included most of the demographic and lifestyle factors related to lost to follow-up in the main analysis models, as they were also identified as potential confounders for the association between diet and BE. We did not however include adiposity in the analysis models, as it was identified as a potential mediator (i.e., on the causal path) between diet and risk of BE. As BMI was slightly higher in those who did not complete follow-up in our study, there was risk for selection bias. To examine the potential impact of this selection bias on our findings, we compared the predicted probability of completing follow-up for BMI 20 kg/m2, 25 kg/m2 and 30 kg/m2 at baseline using logistic regression models that did and did not include other demographic and lifestyle factors as covariates. Similar predicted probability of completing follow-up across BMI values would suggest the impact of selection bias on our study results is small.
All analyses were performed using Stata version 16.
## Results
Of the 28 504 eligible participants, 20 796 (73 %) attended follow-up and provided complete data (Fig. 1). Participants who did not complete follow-up were more likely to be older at baseline, born in Italy or Greece, more socioeconomically disadvantaged, with lower educational attainment, current smokers at baseline, less physically active, have had lower alcohol intake or higher BMI at baseline (online Supplementary 4). Distributions of dietary intake at baseline for those who did and did not complete follow-up were similar (online Supplementary 4). Three BE cases were excluded post hoc due to diagnosis before baseline, leaving 20 793 for analysis.
Fig. 1.Participants flow diagram. Footnotes: 1Total energy intake in the 1st or 99th percentile. 2Version of the questionnaire did not contain questions on Barrett’s oesophagus.
During a median follow-up of 16 years (range: 13–20 years), 193 participants (0·9 %) were diagnosed with BE, of whom 131 had confirmed specialised intestinal metaplasia. BE cases were more likely to be men, older, born in Australia/New Zealand/Northern Europe, less socioeconomically disadvantaged, former smokers or had higher BMI at baseline compared with the eligible cohort (Table 1). Those diagnosed with BE consumed less fruit at baseline compared with the eligible cohort (Table 2).
Table 1.Baseline characteristics of Barrett’s oesophagus cases and total eligible participants in the Melbourne Collaborative Cohort Study(Numbers and percentages)BE cases (n 193)Total eligible (n 20793) n % n %Sex Male10353·4785337·8 Female9046·61294062·2Age 44–443920·2475522·9 45–494121·2464922·4 50–544322·3441621·2 55–593719·2407519·6 60–633317·1289813·9 Median, years51·951·1 IQR46·6, 57·945·4, 57·0Country of birth AU/NZ/Northern Europe18093·31696381·6 Italy84·1219710·6 Greece52·616337·9Socio-economic position* Q1 (most disadvantaged)2311·9311015·0 Q22613·5382118·4 Q32915·0337216·2 Q44623·8407319·6 Q56935·8641730·9Education Some high/technical school8041·51032749·7 High/technical school6031·1444421·4 Tertiary5327·5602229·0Cigarette smoking Never10051·81264060·8 Former7739·9606729·2 Current168·3208610·0Physical activity level† Q1 (least active)2714·0446821·5 Q24724·4423120·3 Q36634·2674832·5 Q45327·5534625·7Alcohol intake‡ Never4020·7513424·7 Former189·3231811·1 Low11358·51089252·4 High2211·4244911·8BMI <25 kg/m2 6433·2855241·1 25–< 30 kg/m2 8845·6862641·5 ≥ 30 kg/m2 4121·2361517·4 Median, kg/m2 27·025·9 IQR24·4, 29·223·4, 28·7AU, Australia; BE, Barrett’s oesophagus; IQR, interquartile range; NZ, New Zealand.*In quintiles of socio-economic position.†In quartiles of physical activity level.‡Low intake for male < 40 g/d and female < 20 g/d; high intake for male ≥ 40 g/d and female ≥ 20 g/d.
Table 2.Baseline diet for Barrett’s oesophagus cases and total eligible participants in the Melbourne collaborative cohort study(Mean values and standard deviations)BE casesTotal eligible n 193 n 20 793Mean sd Mean sd Mean nutrient intake (sd), g/dTotal fat84138214 Saturated fat347337 Monounsaturated fat295296 Polyunsaturated fat144134Total protein98159815Total carbohydrate2443224837 Starch1232712127 Sugar1203612540 Total fibre308318 Vegetable/fruit fibre137158 Cereal fibre125125Alpha-carotene2·11·21·91·3Beta-carotene5·82·85·73·1Beta-cryptoxanthin0·330·290·380·32Lutein and zeaxanthin3·31·73·72·2Lycopene74·87·95·0Mean food group intake (sd), times/dTotal meat1·70·81·70·8 Red meat1·10·61·10·7 Fish0·30·20·30·2 Chicken0·30·20·30·3 Processed meat0·40·30·40·4Total vegetable52·55·53·1 Leafy vegetable0·90·60·80·7 Cruciferous vegetable0·50·40·70·6Total fruit3·52·74·23·1Total dairy products5·43·052·8Discretionary food1·51·51·31·3Mean food item intake (sd), times/dCitrus0·80·810·9Tomato0·30·30·40·5Chocolate0·20·40·20·4Carbonated beverage0·30·60·40·8Tea2·21·91·81·8Coffee1·81·721·7Mean dietary score (sd)Mediterranean Diet Score4242Alternate Healthy Eating Index-201064116511BE, Barrett’s oesophagus; sd, standard deviation.
OR for nutrient intakes in relation to risk of BE are presented in Fig. 2. Fat intake (OR = 1·11 per 10 g/d; CI: 1·00, 1·23; P-trend = 0·05) was positively associated with BE risk with the strongest association observed for polyunsaturated fat (OR = 1·20 per 5 g/d; CI: 1·02, 1·41; P-trend = 0·03) compared with other types of fat. Vegetable and fruit fibre (OR = 0·90 per 5 g/d; CI: 0·80–1·01; P-trend = 0·06), beta-cryptoxanthin (OR = 0·94 per 100 mcg/d; CI: 0·89, 1·00; P-trend = 0·04), lutein and zeaxanthin (OR = 0·92 per g/d; CI: 0·85, 1·01; P-trend = 0·08) and lycopene (OR = 0·97 per g/d; CI: 0·93, 1·00; P-trend = 0·08) were weakly associated with lower risk of BE. There was also a weak positive association for alpha-carotene. No association was observed for protein or beta-carotene.
Fig. 2.OR for nutrient intakes in relation to risk of Barrett’s oesophagus. Footnotes: OR estimated from analysis models including age, sex, country of birth, socio-economic position, educational attainment, smoking status, physical activity score and average lifetime alcohol intake as covariates.
OR for food intake and adherence to diet scores in relation to risk of BE are presented in Table 3. For those in the highest quartile of intake for leafy vegetables (OR = 0·59; CI: 0·38–0·94; P-trend = 0·02), total fruit (OR = 0·58; CI: 0·37, 0·93; P-trend = 0·02), citrus (OR = 0·56; CI: 0·36, 0·87; P-trend = 0·01) and tomato (OR = 0·57; CI: 0·37, 0·87; P-trend = 0·02), the risk of BE was almost halved compared with those in the lowest quartile. Discretionary food (Q4 v. Q1 OR = 1·54; CI: 0·97, 2·44; P-trend = 0·04) and tea (Q4 v. Q1 OR = 1·51; CI: 1·00, 2·29; P-trend = 0·04) were positively associated with BE risk. No association was observed for meat, dairy, chocolate, carbonated beverage, coffee intake or the dietary scores.
Table 3.OR for food intakes and adherence to diet scores in relation to Barrett’s oesophagus(OR and 95 % CI)Quartiles of intakeQuartile 2Quartile 3Quartile 4Increase of one time/dOR95 % CI* OR95 % CI* OR95 % CI* OR95 % CI* Test for trend, P-valueTest for linearity, P-value† Food group, Quartile 1 (ref)Total meat0·900·60, 1·351·010·69, 1·480·890·56, 1·410·950·73, 1·250·7230·799 Red meat1·300·87, 1·931·100·73, 1·671·110·71, 1·741·030·75, 1·410·8590·428 Fish0·980·66, 1·441·130·78, 1·640·910·59, 1·410·850·31, 2·280·7410·560 Chicken1·110·77, 1·591·030·62, 1·701·130·75, 1·691·150·55, 2·400·7060·848 Processed meat0·960·63, 1·480·880·60, 1·280·920·61, 1·380·880·48, 1·640·6940·581Total vegetable0·840·58, 1·200·750·49, 1·140·830·52, 1·320·970·90, 1·050·4370·511 Leafy vegetable0·670·48, 0·950·710·46, 1·090·590·38, 0·940·620·41, 0·930·0210·367 Cruciferous vegetable1·030·66, 1·591·110·73, 1·691·320·86, 2·031·250·92, 1·690·1560·980Total fruit0·940·64, 1·380·750·51, 1·100·580·37, 0·930·890·81, 0·980·0180·873Total dairy products1·160·76, 1·760·770·49, 1·221·290·84, 1·981·020·96, 1·090·5050·045Discretionary food1·120·71, 1·741·150·73, 1·811·540·97, 2·441·181·00, 1·390·0440·942Food item, Quartile 1 (ref)Citrus0·900·61, 1·320·930·63, 1·370·560·36, 0·870·750·60, 0·940·0120·468Tomato0·760·50, 1·140·640·44, 0·920·570·37, 0·870·570·36, 0·900·0170·290Chocolate1·240·85, 1·810·860·56, 1·320·900·59, 1·370·630·25, 1·620·3420·195Carbonated beverage1·120·75, 1·671·210·83, 1·761·090·72, 1·641·040·70, 1·530·8540·358Tea0·960·63, 1·460·870·57, 1·341·511·00, 2·291·101·01, 1·200·0360·115Coffee0·990·67, 1·470·790·55, 1·130·780·51, 1·210·930·85, 1·030·1520·764Mediterranean Diet ScoreScore 4–6Score 7–9Increase of one unit Score 0–3 (ref)0·820·59, 1·151·030·61, 1·720·830·43, 1·620·5900·194Alternative Health Eating Index-2010Quartile 2Quartile 3Quartile 4Increase of one unit Quartile 1 (ref)1·040·70, 1·560·970·64, 1·461·010·66, 1·551·000·89, 1·120·9640·939*OR estimated from logistic regression models including age, sex, country of birth, socio-economic position, educational attainment, smoking status, physical activity score and average lifetime alcohol intake.† P-value from likelihood ratio test for departure from linearity.
When the outcome was BE defined as specialised intestinal metaplasia (n 131) (online Supplementary 5), the inverse associations for lutein and zeaxanthin, lycopene and leafy vegetables, and the positive association for discretionary food were stronger. The positive association for tea was no longer observed. A positive association was observed for cruciferous vegetables.
When the analysis was stratified by sex, there was no evidence for effect modification by sex (results not shown). The only exception was Mediterranean Diet Score (P–value for interaction = 0·03) but the CI for OR were wide for both men (OR = 0·44; CI: 0·18, 1·10) and women (OR = 2·28; CI: 0·62, 8·39).
## Sensitivity analyses
To examine the potential impact of confounding, we performed sensitivity analyses further adjusted for dietary confounders, BMI and H. pylori infection (in a subset of participants with H. pylori data) respectively, in addition to confounders already included. When further adjusted for dietary confounders (online Supplementary 6), the positive association for fat intake was attenuated; an inverse association was observed for total carbohydrate; the results for food groups, food items and diet scores were minimally changed. Further adjustment for BMI did not change the results markedly (online Supplementary 7). In a subset of participants with H. pylori data (n 1311), further adjusting for H. pylori infection did not change the results markedly (results not shown).
For sensitivity analysis that examined potential differential measurement error in diet due to reflux symptoms before baseline, there was no evidence for interaction between dietary factors and symptoms at baseline (results not shown). Results from analysis restricted to participants without reflux symptoms before baseline were similar to the primary analysis (Supplementary 8). For those who reported ever having symptoms ≥ 1 d/week, those who had onset before baseline had slightly higher intake of lycopene and lower intake of citrus at baseline compared with those who had onset after baseline (online Supplementary 9).
For sensitivity analysis examining the impact of selection bias from loss to follow-up, the predicted probability of providing complete BE data were more similar across BMI values after accounting for demographic and lifestyle factors (BMI 20 kg/m2 = 75 %; BMI 25 kg/m2 = 74 %; BMI 30 kg/m2 = 72 %) compared with without accounting for them (BMI 20 kg/m2 = 80 %; BMI 25 kg/m2 = 75 %; BMI 30 kg/m2 = 69 %).
## Discussion
Overall, vegetable and fruit fibre, beta-cryptoxanthin, lutein and zeaxanthin, lycopene, leafy vegetable, fruit, citrus and tomato intake reduced risk of BE, the inverse associations remained robust in most sensitivity analyses. Stronger associations were observed for fruits and leafy vegetables than for the nutrients found in them. The positive association for discretionary food remained robust in most sensitivity analyses, whereas the positive associations for fat, alpha-carotene and tea intake were less robust against sensitivity analyses.
Existing literature on diet and BE is mostly based on case–control studies, where diet is measured after diagnosis. It is possible that BE cases reported their diet differently from non-cases, and thus results may be affected by recall bias. A prospective study design ensures diet is measured in a disease-free cohort, thereby minimising the risk differential measurement error in diet. We were also able to account for key confounding factors, as well as investigate the robustness of our results under different assumptions on the underlying causal structure between diet and risk of BE. BE diagnosis was confirmed by a gastroenterologist (BJK) reviewing endoscopy and pathology reports, minimizing misclassification.
There are potential measurement errors, both random and systematic, in intake measured by the FFQ. Random errors may arise from inaccuracy in participant’s recall of their diet or difference in interpretation of the questionnaire items. Systematic errors may arise from the design of the FFQ. For example, the number of items included under each food group was different, with more items included for vegetables and fruits resulting in apparent higher intake. This limitation of FFQ has been pointed out in the nutritional epidemiology literature – the absolute intake measured is directly related to the number of questions[30,31]. However, the primary aim for the use of FFQ in our study was to rank people into quantiles of intake of foods and nutrients, rather than to accurately measure absolute intakes. Listing more items thus allowed more detailed analysis of nutrient composition than if items were further combined[31]. The systematic measurement error in absolute intake is unlikely to affect OR estimated based on categorised dietary variables (e.g., quartiles of intake), as the ranking is preserved[24]. However, OR based on increments of nutrient intake (e.g., g/d) may be underestimated due to systematic overestimation of intake caused by having more items under certain food groups (e.g., vegetables)[24]. This systematic error would be unequally distributed among participants[24]. Both systematic and random errors in measurement of diet would be non-differential as it is not affected by participant’s outcome for BE.
Some participants included in the analysis had reflux symptoms before baseline. This was to ensure that the study sample was representative of the target population in relation to distribution of reflux symptoms in BE cases[28]. However, this may have introduced differential error in measurement of diet, as participants with symptoms might have changed their diet for symptom alleviation. Our sensitivity analysis suggests the impact of this bias may be minimal, as there was no evidence for effect modification by symptom at baseline for the estimated OR for any dietary factor in relation to risk of BE.
Selection bias is possible as 27 % of the eligible participants were lost to follow-up and the distributions of demographic and lifestyle factors for those who did and did not provide complete BE data were different. However, given we have already accounted for most of the factors associated with completeness of follow-up in the analysis models, the impact of selection bias is minimised. From our sensitivity analysis, the impact from BMI, which is associated with completeness of follow-up but not included in the analysis models, is likely to be small after accounting for other pre-exposure demographic and lifestyle factors.
We performed a comprehensive analysis of the potential effect of diet on risk of BE, including dietary factors that have not been investigated in previous cohort studies on BE[12,13]. Consistent with Keszei et al. ’s Netherlands Cohort Study[12,13], we observed no association between meat intake and risk of BE. We also observed an inverse association for leafy vegetable intake but unlike Keszei et al. we did not observe an association for total, raw or Brassica vegetable intakes. Of all forms of vegetables studied, Keszei et al. observed the strongest association for raw leafy vegetable intake in males (HR = 0·55; CI: 0·36, 0·86)[12]. We did not observe effect modification by sex for the potential effect of vegetable intakes on risk of BE. In addition to leafy vegetable and total fruit, we observed inverse associations for vegetable and fruit fibre, beta-cryptoxanthin, lutein and zeaxanthin, lycopene, citrus and tomato intake. Cohort studies on oesophageal adenocarcinoma have reported inverse associations for green leafy vegetables[32], raw vegetables[33] and citrus fruits[33], but not for total vegetable or fruit intake(32–34). A recent meta-analysis of the three aforementioned studies(32–34) reported a weak inverse association between vegetable intake and risk of oesophageal adenocarcinoma (RR = 0·89 per 100 g/d; CI: 0·80, 0·99)[10]. Another meta-analysis of four case–control studies reported beta-carotene was associated with reduced oesophageal adenocarcinoma risk[35], but we did not observe an association for beta-carotene. We observed a positive association for ‘discretionary food’ as defined by the Australian Dietary Guidelines[16]. Dietary added sugar has been associated with increased risk of oesophageal adenocarcinoma in a cohort study (highest v. lowest quintiles: HR = 1·62; CI: 1·07, 2·45)[36].
With results from our previous study on diet and risk of GERD in the MCCS[9], we showed that GERD and BE may share some, but not all, dietary risk factors. For nutrients in relation to GERD, we observed sex-specific positive associations for fat and an inverse association for carbohydrate in men, but no associations with nutrients was observed for women. We also observed inverse associations for fruit, citrus and tomato and risk of GERD. We did not observe an inverse association between leafy vegetable and GERD. In contrast, we observed a positive association between cruciferous vegetables and GERD, which might be due to overlapping symptoms between irritable bowel syndrome and GERD[9]. In addition, carbonated beverages were associated with increased risk of GERD but not with BE. Diet scores were not associated with GERD or BE.
It is possible that any effect of diet on GERD is predominantly mechanistic. Both fat and carbonated beverage intake have been associated with increased transient relaxation of the lower oesophageal sphincter that leads to gastroesophageal reflux[37,38]. Conversely, the potential effect of diet on BE might be predominantly systemic. Dietary fibre has been associated with lower concentrations of inflammation biomarkers that promote carcinogenesis, such as interleukin-6 and tumour necrosis factor-α receptor-2[39]. An endoscopic study found that dietary fibre, but not fat intake, was associated with increased abundance of Firmicutes, the gram-positive bacteria that predominate in normal oesophagus, and with decreased abundance of gram-negative bacteria that predominate reflux oesophagitis and BE[40]. These gram-negative bacteria could trigger innate immune responses and subsequently induce chronic inflammation of the oesophageal lining[41]. In addition, in vitro and in vivo studies have suggested that carotenoids have antioxidant, antiapoptotic and anti-inflammatory properties that reduce risk of developing cancer[42]. It has been demonstrated ex vivo on forty-five BE tissues that oxidative stress and DNA damage can be induced by short exposure to low pH and bile acids[43].
High dietary fibre intake could also reduce BE risk by reducing risk of gastroesophageal reflux and adiposity; both could mediate the effect of diet on risk of BE. Diets high in fibre may promote satiation, decrease macronutrients absorption and delay gastric emptying[44]. In our previous study on diet and GERD, there was a weak inverse association between fibre and risk of GERD in men[9]. In contrast, further adjusting for BMI did not remove the association between vegetable and fruit fibre and risk of BE in this present study.
We observed stronger associations for vegetables and fruits than for the nutrients found in them. This might be due to approximation in the calculation of nutrient intakes from the FFQ. It might also suggest fibre and carotenoids have synergistic effect on reducing risk of BE. The stronger association observed for food than nutrients could also be attributed to other phytonutrients found in vegetables and fruits. For instance, phytic acid found in high fibre food has been demonstrated to reduce cellular proliferation of BE cell lines in vitro [45].
Compared with case–control studies, our cohort study provided less biased estimates for the potential effect of diet on risk of BE. Dietary recommendations, particularly on increasing leafy vegetable and fruit intake, could be considered as a point of intervention in public health and clinical practice. Guidelines that incorporate dietary modifications could contribute to reduction in risk of BE and, thereby, oesophageal adenocarcinoma.
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|
---
title: 'Associations of dietary intakes of vitamins B1 and B3 with risk of mortality
from CVD among Japanese men and women: the Japan Collaborative Cohort study'
authors:
- Chengyao Tang
- Ehab Salah Eshak
- Kokoro Shirai
- Akiko Tamakoshi
- Hiroyasu Iso
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10011590
doi: 10.1017/S0007114522001209
license: CC BY 4.0
---
# Associations of dietary intakes of vitamins B1 and B3 with risk of mortality from CVD among Japanese men and women: the Japan Collaborative Cohort study
## Body
Vitamin B complex exerts its function in energy metabolism, immune function and DNA synthesis, methylation and repair[1]. Vitamin B deficiency has been associated with cardiovascular disorders, particularly in ageing population[1].
Among the B complex group, vitamin B1 and vitamin B3 come mainly from cereals, beef and pork, seeds and nuts, and yeast[2]. Vitamin B1 deficiency results in beriberi, a neurological and cardiovascular disorder[3], while deficient vitamin B3 can cause pellagra-induced dilated cardiomyopathy[4]. The potential positive impacts of vitamin B1 and B3 on cardiovascular health were suggested in several animal studies and supplemental clinical trials. In a Langendorff perfused rat hearts, vitamin B1 excreted protective effects against myocardial ischaemic injury via maintaining mitochondrial size and ATP levels[5]. A randomised controlled trial on chronic heart failure patients who used diuretics reported that 300 mg/d of vitamin B1 supplementation for 28 d increased the left ventricular ejection fraction by 3·9 %[6]. On the other hand, 1500–2000 mg/d vitamin B3 supplementation was shown to decrease LDL-cholesterol, TAG and lipoprotein(a) levels, while increasing HDL-cholesterol level[7,8]. A meta-analysis of clinical trials suggested that vitamin B3 supplements significantly reduced major coronary events, stroke and other cardiovascular events[9].
Despite the abundant evidence on cardiovascular beneficial effects of vitamins B1 and B3 from animal studies[3,10,11] and supplemental clinical trials[12,13], no human observational studies so far have investigated the associations of dietary vitamins B1 and B3 intakes with risk of CVD. Previous studies indicated that food sources rich in vitamins B1 and B3 such as fish/seafood and vegetables were associated with a reduced risk of mortality from CVD[14,15]. Yet, the evidence on dietary vitamin B complex/CVD association was mainly directed to vitamins B2, B6, B12 and folate, while the effects of dietary vitamins B1 and B3 intakes were not studied. Another issue is that most of the clinical trials on cardiovascular risk used high dosages of vitamin B1 (200–300 mg/d) and B3 supplement (1500–2000 mg/d), while the RDA of vitamin B1 for Japanese men and women aged 50–69 years were 1·3 and 1·1 mg/d, respectively, and RDA of vitamin B3 were 14 and 11 mg/d, respectively[16]. Among Japanese men aged 30–49 years, the estimated average requirement and RDA were 1·2 and 1·4 mg/d, respectively, for vitamin B1, and those for vitamin B3 were 13 and 15 mg/d, respectively. Among Japanese women aged 30–49 years, the corresponding estimated average requirement was 0·9 and 1·1 mg/d, and RDA was 10 and 12 mg/d. The estimated average requirement and RDA of vitamins B1 and B3 in Japanese men and women aged over 70 years were even less than any other age groups[16]. Not all individuals prefer or can afford vitamin supplements for their health; thus, improving the dietary vitamins intakes is more achievable and acceptable by the general population. Therefore, after established effects of supplementary intakes of vitamins B1 and B3 have been determined, studying the cardiovascular impacts of dietary intakes of vitamins B1 and B3 is now warranted. Owing to the research gap in the field of epidemiology and similar food sources of vitamins B1 and B3, their associations with CVD mortality were hypothesised in the present study. Therefore, we aimed to investigate the associations of dietary vitamins B1 and B3 intakes with risk of CVD mortality, which was considered as a proxy of CVD incidence risk, among Japanese men and women using the Japan Collaborative Cohort (JACC) study, a nationwide, community-based prospective cohort study.
## Abstract
The evidence on the association between B vitamins and the risk of CVD is inconclusive. We aimed to examine the association of dietary vitamins B1 and B3 intakes with risk of CVD mortality among 58 302 Japanese men and women aged 40-79 years participated in the Japan Collaborative Cohort (JACC) study. The Cox proportional hazard model estimated the hazard ratios (HR) and $95\%$ CI of CVD mortality across increasing energy-adjusted quintiles of dietary vitamins B1 and B3 intakes. During 960 225 person-years of follow-up, we documented a total of 3371 CVD deaths. After adjustment for age, sex, and other CVD risk factors, HR of mortality from ischemic heart disease, myocardial infarction, and heart failure in the highest v. lowest vitamin B1 intake quintiles were 0.57 (95 % CI 0·40, 0·80; Pfor trend < 0·01), 0.56 (95 % CI 0·37, 0·82; Pfor trend < 0·01), and 0.65 (95 % CI 0·45, 0·96; Pfor trend = 0·13). The multivariable HR of myocardial infarction mortality in the highest v. lowest vitamin B3 intake quintiles was 0.66 (95 % CI 0·48, 0·90; Pfor trend = 0·02). Atendency towards a reduced risk of haemorrhagic stroke mortality was observed with a higher dietary intake of vitamin B3 (HR: 0·74 (95 % CI 0·55, 1·01)) but not vitamin B1. In conclusion, higher dietary intakes of vitamins B1 and B3 were inversely associated with mortality from ischemic heart disease and a higher dietary intake of vitamin B1 was inversely associated with a reduced risk of mortality from heart failure among Japanese men and women.
## Study population and baseline data
Under the sponsorship of the Ministry of Education, Sports, and Science, the JACC study had the baseline survey (1988–1990) of 110 585 Japanese men (n 46 395) and women (n 64 190) aged 40–79 years from forty-five areas all over Japan. A detailed cohort profile of the JACC study was published previously[17]. Data on the baseline lifestyle and participants’ characteristics, including demographic data, medical history of chronic diseases, diabetes mellitus, hypertension, smoking, alcohol consumption, exercise, diet and other items, were compiled via a self-administered questionnaire (online Supplementary Methods). The questionnaire included a validated forty-food item/FFQ which was distributed in thirty-two areas; therefore, we started with 86 401 subjects from those thirty-two areas. After the exclusion of non-respondents to FFQ (n 24 614), we further excluded those who reported a medical history of CVD or cancer (n 3142) and those who had implausible energy intakes defined as outliers of mean ± three standard deviations (n 343). Finally, a total of 58 302 individuals were eligible for the present study (22 989 men and 35 313 women) (online Supplementary Fig. S1). Written informed consent was acquired from community leaders or the individuals. The protocol of JACC study was approved by the Medical Ethical Committees of Nagoya University School of Medicine.
## Dietary intake assessment
The participants were required to choose one from five frequency responses to describe the usual consumption frequency of forty food and beverage items over the past 12 months without specification of the portion size. The five responses were rarely, 1–2 times/month, 1–2 times/week, 3–4 times/week and almost every day. These frequencies were transformed into weekly consumption scores of 0, 0·38, 1·5, 3·5 and 7·0 per week, respectively[17,18]. A validation study among eighty-five individuals using four 3-d weighed dietary records over a 1-year period as a reference standard determined the portion size for each food and validated the FFQ intakes. The amount of nutrients in each food was calculated by multiplying the weekly consumption scores by the estimated portion size. The values of vitamins B1 and B3 and other nutrients from each food category were calculated according to the Standardised Tables of Food Composition, 5th revised version[19] which listed the nutrients content in 100 mg of different foods. Thus, the total vitamins B1 and B3 intakes were calculated by summing their intakes from all over the foods in the FFQ. The details of computation of nutrient intakes from FFQ[18] and the accuracy of food composition tables in Japan[20,21] were published previously. The Spearman rank correlation coefficients for vitamins B1 and B3 intakes between the FFQ and the four 3-d dietary records were 0·36 and 0·32, respectively, after energy adjustment[22]. The energy-adjusted mean ± standard deviation intakes in mg/d from weighed dietary record and FFQ were 1·08 (sd 0·20) and 0·71 (sd 0·20) for vitamin B1, but the respective values for vitamin B3 were not reported[18].
## Mortality surveillance
The investigators annually or biannually confirmed the dates and causes of death in each area[17]. The International Classification of Diseases, 10th revision (ICD10) codes were applied to determine the underlying causes of death. In this study, our primary outcome was the total CVD mortality (ICD I01-I99). Cause-specific outcomes included mortalities from total stroke (ICD I60-I69), haemorrhagic stroke (ICD I60-I61), ischaemic stroke (ICD I63.0-I63.9), ischaemic heart disease (ICD I20-I25), myocardial infarction (ICD I20) and heart failure (ICD I50). This death certificate ascertainment was applied to all deaths within our cohort except for deaths that occurred outside of the original resident areas, which were treated as censored cases.
## Statistical analysis
Energy-adjusted dietary intakes of vitamins B1 and B3 were categorised into five categorical groups (quintiles). The significance of differences in means or proportions of participants’ characteristics and known risk factors of CVD in each quintile was tested by the ANCOVA and χ 2 test.
Person-years of follow-up were calculated from the baseline in 1988–1990 to their first endpoint in this follow-up as follows: death, moving out or the end of follow-up, whichever came first. The follow-up for mortality from CVD was conducted until 31 December 2009 in general; however, in four areas the follow-up was stopped until 31 December 1999, in another four areas until 31 December 2003 and in two areas until 31 December 2008[17]. The Cox proportional hazard model was applied to calculate crude and multivariable-adjusted hazard ratios and 95 % CI for risk of mortality from CVD during the follow-up period (1988–2009) across quintiles of dietary vitamins B1 and B3 intakes. We confirmed no violation of the Cox proportional hazard assumption because there were no significant interactions between the categorical rank variables of dietary vitamins B1 and B3 intakes and a function of survival time for all the tested outcomes. Multiplicative interactions of vitamins B1 and B3 with sex were tested to decide on presenting the data sex specifically or for combined men and women. The hypothesised confounders included age, sex, medical history of hypertension and diabetes, smoking status, ethanol intake, hours of sports, hours of walking, quintiles of BMI, years of education, perceived mental stress, daily utilisation of multivitamin supplementation, energy-adjusted quintiles of Na and SFA intakes and quintiles of total energy intake. Details of these factors are given in online Supplementary Methods.
We assigned the median values to each quintile of vitamins B1 and B3 and tested their significance to calculate the trends across quintiles of vitamins B1 and B3 intakes. We further conducted a sensitivity analysis by excluding those who died within first 3 years of follow-up to avoid potential as-yet-undiagnosed diseases at baseline. All probability values for statistical test were two-tailed, and $P \leq 0$·05 was regarded as statistically significant. We applied the SAS statistical package (Version 9.4; SAS Institute Inc.) for statistical analysis.
## Results
As shown in Table 1, participants in the highest quintile of both vitamins B1 and B3 intake were older, less educated, under less mental stress, had more walking time, had higher BMI and were less likely to be current smoker and to have a history of hypertension or diabetes. They also used multivitamin supplementation less frequently and consumed less alcohol but consumed more Na, SFA and total energy when compared with those in the lowest quintile. In this study, sources of vitamin B1 were 31 % from pork, 17 % from vegetables, 10 % from fish and 7 % from potatoes, while sources of vitamin B3 were 43 % from fish, 13 % from vegetables, 8 % from pork, 7 % from coffee and 6 % from green tea (data not shown in tables).
Table 1.Participants’ characteristics and dietary variables according to quintiles of dietary vitamins B1 and B3 intakes at baseline in a cohort of 22 989 men and 35 313 women with a total of 3371 CVD mortality casesQuintile1 (low)2345 (high) P for differenceNo. at risk11 66011 66111 66011 66111 660Vitamin B1 Vitamin B1 intake, mg/d0·80·91·01·01·2< 0·001Female, %21·157·269·976·378·3< 0·001Age, years54·856·356·356·656·8< 0·001BMI, kg/m2 22·722·822·823·022·9< 0·001Ethanol intake, g/d42·821·517·314·112·8< 0·001Current smoker, %52·426·818·114·112·7< 0·001History of hypertension, %20·820·720·219·818·4< 0·001History of diabetes, %5·64·94·54·23·5< 0·001Sports ≥ 5 h/week, %5·65·35·15·25·70·14Walking ≥ 1 h/day, %47·748·449·252·156·4< 0·001> 18 years education, %16·713·913·612·911·8< 0·001High mental stress, %26·223·321.020·920·2< 0·001Multivitamin supplementation, %3·93·43·03·23·0< 0·001Na intake, mg/d1498·41779·62005·32221·42515·4< 0·001SFA intake, mg/d8·09·29·810·411·9< 0·001Total energy intake, kcal/d1665·61446·31457·51512·21680·2< 0·001Vitamin B3 intake, mg/d15·217·318·118·920·4< 0·001Vitamin B3 Vitamin B3 intake, mg/d14·416·717·919·221·5< 0·001Female, %30·159·667·471·374·4< 0·001Age, years55·756·256·156·256·7< 0·001BMI, kg/m2 22·722·822·822·923·0< 0·001Ethanol intake, g/d42·323·819·716·913·5< 0·001Current smoker, %42·924·520·718·817·6< 0·001History of hypertension, %22·020·819·619·518·0< 0·001History of diabetes, %5·84·64·14·14·1< 0·001Sports ≥ 5 h/week, %5·84·75·25·55·70·88Walking ≥ 1 h/day, %47·547·750·552·056·0< 0·001> 18 years education, %17·514·213·112·611·5< 0·001High mental stress, %24·823·122·221·020·5< 0·001Multivitamin supplementation, %4·23·62·93·03·0< 0·001Na intake, mg/d1678·51894·92016·82113·82316·1< 0·001SFA intake, mg/d8·89·69·810·110·9< 0·001Total energy intake, kcal/d1639·11461·61478·01536·61646·6< 0·001Vitamin B1 intake, mg/d0·80·91·01·01·1< 0·001 Since no interaction with sex was observed for the association of vitamins B1 and B3 with CVD and specific endpoints, we combined the results of men and women in the main analyses. During 960 225 person-years of follow-up for 58 302 participants, we documented a total of 3371 deaths due to CVD, among whom there were 1504 deaths due to stroke (549 of which were due to haemorrhagic stroke and 816 of which were due to ischaemic stroke), 699 deaths were due to ischaemic heart disease (including 524 deaths due to myocardial infarction) and 564 deaths were due to heart failure.
As shown in Table 2, the dietary intake of vitamin B1 was not associated with mortality from total stroke or its subtypes. On the other hand, a higher dietary vitamin B1 intake was associated with the reduced risk of ischaemic heart disease, myocardial infarction and total CVD; hazard ratios were 0·57 (95 % CI 0·40, 0·80; P for trend < 0·01), 0·56 (95 % CI 0·37, 0·82; P for trend < 0·01) and 0·85 (95 % CI 0·73, 0·99; P for trend = 0·03), respectively, in the highest v. lowest intake quintile. Moreover, the multivariable-adjusted hazard ratio of heart failure mortality in the highest v. lowest intake quintiles was 0·65 (95 % CI 0·45, 0·96; P for trend = 0·13).
Table 2.CVD mortality according to quintiles of vitamin B1 intake (Hazard ratios and 95 % confidence intervals)Q2Q3Q4Q5 (high)Q1 (low)HR95 % CIHR95 % CIHR95 % CIHR95 % CI P for trend Total n 11 66011 66111 66011 66111 660Person-years182 559185 652192 066197 635202 313CVDNo. of case675701651674670Age- and sex-adjusted HR ($95\%$ CI)1·000·920·83, 1·030·860·77, 0·960·830·75, 0·930·800·71, 0·89< 0·001Multivariable HR ($95\%$ CI)1 1·000·920·83, 1·030·860·77, 0·960·840·75, 0·930·800·71, 0·89< 0·001Multivariable HR ($95\%$ CI)2 1·001·010·90, 1·140·930·82, 1·050·900·79, 1·020·860·75, 0·980·01Multivariable HR ($95\%$ CI)3 1·000·980·87, 1·110·880·76, 1·000·850·74, 0·980·850·73, 0·990·03StrokeNo. of case284300290313317Age- and sex-adjusted HR ($95\%$ CI)1·000·940·80, 1·110·910·77, 1·080·930·78, 1·100·910·76, 1·070·27Multivariable HR ($95\%$ CI)1 1·000·940·79, 1·110·920·77, 1·090·930·78, 1·100·900·76, 1·070·28Multivariable HR ($95\%$ CI)2 1·001·110·92, 1·331·080·89, 1·311·100·90, 1·341·070·87, 1·300·68Multivariable HR ($95\%$ CI)3 1·001·060·87, 1·281·000·81, 1·231·020·82, 1·271·050·83, 1·330·77Haemorrhagic strokeNo. of case119105101104120Age- and sex-adjusted HR ($95\%$ CI)1·000·790·60, 1·040·740·56, 0·970·710·54, 0·950·790·60, 1·040·09Multivariable HR ($95\%$ CI)1 1·000·790·60, 1·040·740·56, 0·980·730·55, 0·960·800·61, 1·050·15Multivariable HR ($95\%$ CI)2 1·000·940·70, 1·270·890·65, 1·230·880·64, 1·220·970·70, 1·330·93Multivariable HR ($95\%$ CI)3 1·000·910·66, 1·240·850·61, 1·190·840·59, 1·211·020·70, 1·500·74Ischaemic strokeNo. of case141169161175170Age- and sex-adjusted HR ($95\%$ CI)1·001·050·84, 1·321·030·82, 1·301·040·83, 1·310·990·79, 1·250·89Multivariable HR ($95\%$ CI)1 1·001·040·83, 1·311·030·81, 1·301·030·82, 1·300·980·77, 1·230·83Multivariable HR ($95\%$ CI)2 1·001·220·95, 1·571·180·90, 1·541·180·90, 1·541·120·85, 1·480·62Multivariable HR ($95\%$ CI)3 1·001·130·87, 1·471·060·80, 1·411·080·80, 1·451·060·77, 1·470·99Ischaemic heart diseaseNo. of case174163134122106Age- and sex-adjusted HR ($95\%$ CI)1·000·900·72, 1·110·750·60, 0·950·650·51, 0·830·550·43, 0·71< 0·001Multivariable HR ($95\%$ CI)1 1·000·890·72, 1·110·750·60, 0·950·650·51, 0·830·550·42, 0·71< 0·001Multivariable HR ($95\%$ CI)2 1·000·900·71, 1·150·740·57, 0·960·640·48, 0·840·540·40, 0·71< 0·001Multivariable HR ($95\%$ CI)3 1·000·890·69, 1·140·730·55, 0·960·640·47, 0·870·570·40, 0·80< 0·001Myocardial infarctionNo. of case1331221019078Age- and sex-adjusted HR ($95\%$ CI)1·000·900·70, 1·150·770·58, 1·000·650·49, 0·870·550·41, 0·74< 0·001Multivariable HR ($95\%$ CI)1 1·000·890·69, 1·150·760·58, 1·000·650·49, 0·860·540·40, 0·73< 0·001Multivariable HR ($95\%$ CI)2 1·000·900·68, 1·190·740·55, 1·010·630·46, 0·870·530·38, 0·74< 0·001Multivariable HR ($95\%$ CI)3 1·000·880·66, 1·170·730·53, 1·000·630·44, 0·900·560·37, 0·820·001Heart failureNo. of case103118107117119Age- and sex-adjusted HR ($95\%$ CI)1·000·910·69, 1·190·800·60, 1·060·800·60, 1·050·780·59, 1·030·06Multivariable HR ($95\%$ CI)1 1·000·900·69, 1·180·800·60, 1·060·810·61, 1·070·790·60, 1·040·34Multivariable HR ($95\%$ CI)2 1·000·910·67, 1·230·770·56, 1·060·780·57, 1·070·750·55, 1·040·27Multivariable HR ($95\%$ CI)3 1·000·870·64, 1·180·690·49, 0·970·670·47, 0·960·650·45, 0·960·13 1Adjusted for age, sex, and socio-economic status (educational status). 2Adjusted for age, sex, socio-economic status (educational status), and health behaviours (hours of walking, hours of sports, ethanol intake and smoking status). 3A full mode with adjustment for age, sex, educational status, hours of walking, hours of sports, ethanol intake, smoking status, history of hypertension and diabetes, BMI, perceived mental stress, multivitamin supplementation, quintiles of energy-adjusted Na and SFA intakes and total energy intakes.
For vitamin B3, as shown in Table 3, there was no association with the mortality from stroke or heart failure. Statistically significant inverse trends in risks of mortality from total CVD, haemorrhagic stroke, ischaemic heart disease and myocardial infarction were observed in the age- and sex-adjusted model. However, after the multivariate adjustment, these associations were weakened; the multivariable-adjusted hazard ratios in the highest v. lowest quintiles of dietary vitamin B3 were 0·90 (95 % CI 0·80, 1·03; P for trend = 0·13) for total CVD mortality, 0·74 (95 % CI 0·55, 1·01; P for trend = 0·16) for haemorrhagic stroke, 0·79 (95 % CI 0·60, 1·04; P for trend = 0·05) for ischaemic heart disease and 0·66 (95 % CI 0·48, 0·90; P for trend = 0·02) for myocardial infarction.
Table 3.CVD mortality according to quintiles of vitamin B3 intake (Hazard ratios and 95 % confidence intervals)Q2Q3Q4Q5 (high)Q1 (low)HR95 % CIHR95 % CIHR95 % CIHR95 % CI P for trend Total n 11 66011 66111 66011 66111 660Person-years180 265187 575194 533197 250200 601CVDNo. of case715658663649686Age- and sex-adjusted HR ($95\%$ CI)1·000·890·80, 0·990·880·79, 0·980·850·76, 0·940·860·77, 0·950·004Multivariable HR ($95\%$ CI)1 1·000·890·80, 0·990·880·79, 0·980·840·75, 0·940·850·77, 0·950·004Multivariable HR ($95\%$ CI)2 1·000·930·83, 1·050·920·82, 1·030·870·78, 0·990·880·78, 1·000·03Multivariable HR ($95\%$ CI)3 1·000·920·82, 1·040·910·81, 1·030·880·78, 0·990·900·80, 1·030·13StrokeNo. of case316268309311300Age- and sex-adjusted HR ($95\%$ CI)1·000·820·69, 0·970·930·79, 1·090·920·78, 1·080·850·72, 1·000·17Multivariable HR ($95\%$ CI)1 1·000·810·69, 0·960·920·78, 1·080·910·77, 1·070·840·71, 0·990·08Multivariable HR ($95\%$ CI)2 1·000·900·75, 1·071·020·85, 1·211·000·84, 1·190·930·77, 1·110·46Multivariable HR ($95\%$ CI)3 1·000·870·73, 1·040·980·82, 1·170·970·80, 1·160·910·75, 1·100·38Haemorrhagic strokeNo. of case13894104101112Age- and sex-adjusted HR ($95\%$ CI)1·000·630·49, 0·830·680·52, 0·880·640·49, 0·830·680·52, 0·880·009Multivariable HR ($95\%$ CI)1 1·000·630·49, 0·830·670·52, 0·880·640·49, 0·830·670·52, 0·880·01Multivariable HR ($95\%$ CI)2 1·000·690·52, 0·910·740·55, 0·980·690·52, 0·930·730·55, 0·980·13Multivariable HR ($95\%$ CI)3 1·000·670·50, 0·890·710·53, 0·950·680·50, 0·920·740·55, 1·010·16Ischaemic strokeNo. of case155139179182162Age- and sex-adjusted HR ($95\%$ CI)1·000·870·69, 1·101·130·91, 1·411·120·90, 1·400·960·77, 1·210·67Multivariable HR ($95\%$ CI)1 1·000·860·68, 1·091·110·89, 1·381·110·89, 1·380·950·76, 1·200·98Multivariable HR ($95\%$ CI)2 1·000·950·75, 1·221·230·97, 1·561·210·95, 1·551·050·81, 1·350·69Multivariable HR ($95\%$ CI)3 1·000·920·72, 1·181·170·92, 1·501·170·91, 1·501·010·77, 1·320·96Ischaemic heart diseaseNo. of case174149136115125Age- and sex-adjusted HR ($95\%$ CI)1·000·890·71, 1·110·810·64, 1·020·680·53, 0·860·710·56, 0·90< 0·001Multivariable HR ($95\%$ CI)1 1·000·880·71, 1·100·800·64, 1·010·670·53, 0·860·710·56, 0·900·001Multivariable HR ($95\%$ CI)2 1·000·880·70, 1·120·800·62, 1·020·660·51, 0·860·690·53, 0·900·002Multivariable HR ($95\%$ CI)3 1·000·900·71, 1·150·840·65, 1·080·720·55, 0·950·790·60, 1·040·05Myocardial infarctionNo. of case134119958690Age- and sex-adjusted HR ($95\%$ CI)1·000·930·72, 1·200·750·57, 0·980·670·51, 0·890·680·51, 0·89< 0·001Multivariable HR ($95\%$ CI)1 1·000·930·72, 1·190·740·57, 0·970·670·50, 0·880·680·51, 0·89< 0·001Multivariable HR ($95\%$ CI)2 1·000·920·70, 1·200·720·54, 0·960·640·47, 0·870·640·48, 0·870·001Multivariable HR ($95\%$ CI)3 1·000·910·70, 1·200·720·54, 0·970·650·48, 0·880·660·48, 0·900·02Heart failureNo. of case10311911499129Age- and sex-adjusted HR ($95\%$ CI)1·001·030·79, 1·350·960·73, 1·270·810·61, 1·071·010·77, 1·310·60Multivariable HR ($95\%$ CI)1 1·001·020·78, 1·340·950·73, 1·250·800·60, 1·061·000·76, 1·310·81Multivariable HR ($95\%$ CI)2 1·001·040·78, 1·390·950·71, 1·280·790·58, 1·070·970·72, 1·310·97Multivariable HR ($95\%$ CI)3 1·001·030·77, 1·380·950·70, 1·280·790·57, 1·080·970·71, 1·330·88 1Adjusted for age, sex and socio-economic status (educational status). 2Adjusted for age, sex, socio-economic status (educational status) and health behaviours (hours of walking, hours of sports, ethanol intake and smoking status). 3A full mode with adjustment for age, sex, educational status, hours of walking, hours of sports, ethanol intake, smoking status, history of hypertension and diabetes, smoking status, BMI, hours of walking, hours of sports, educational status, perceived mental stress, ethanol intake, multivitamin supplementation, quintiles of energy-adjusted Na and SFA intakes and total energy intakes.
There were 456 participants who died within the first 3 years of follow-up, and excluding those subjects yielded no substantial changes in the associations of vitamins B1 and B3 with mortality from ischaemic heart disease and myocardial infarction (online Supplementary Table S1).
## Discussion
In this large community-based prospective cohort study of Japanese men and women, higher dietary intakes of vitamins B1 and B3 were associated with reduced risks of mortality from total CVD, ischaemic heart disease and myocardial infarction. Neither dietary vitamin B1 nor vitamin B3 intake was associated with the mortality risk of stroke, except for a tendency towards a reduced risk of haemorrhagic stroke with a higher dietary vitamin B3 intake. Moreover, a higher dietary intake of vitamin B1 was associated with a reduced risk of heart failure.
As far to our knowledge, the present study is the first to investigate associations of dietary vitamins B1 and B3 intakes with risk of CVD mortality despite the abundant evidence from animal studies and clinical trials on vitamins B1 and B3 supplements. Vitamins B1 and B3 in animal studies and human clinical trials showed protective effects against myocardial ischaemia.
One study on dogs showed that administration of vitamin B1 decreased the metabolic needs of the heart, which was manifested as reduced myocardial oxygen consumption, mean peripheral pressure and left ventricular pressure up to 45, 25 and 10 % respectively[11]. A clinical trial on ten healthy adults and ten type 2 diabetes patients reported improvements in the brachial artery vasoactivity and the endothelium-dependent vasodilatation in both groups after a week of daily intravenous administration of 100 mg of vitamin B1 [23]. Another randomised, cross-over and investigator-blinded trial on twenty adult healthy volunteers indicated the flow-mediated dilatation of the brachial artery was reduced by 50 % of its baseline diameter after smoking one cigarette, and the reduction in the flow-mediated vasodilatation with smoking one cigarette was only 25 % when 1050 mg/d oral benfotiamine was administered for 3 d before the experiment[24].
On the other hand, vitamin B3 is a candidate to lower the risk of CVD as it is known to decrease LDL-cholesterol, TAG and lipoprotein(a) levels, while increasing HDL-cholesterol level[7]. Among 8341 American men aged 30–64 years from Coronary Drug Project with previous myocardial infarction, 3000 mg/d vitamin B3 v. placebo for a follow-up of 15 years reduced 14 % of the mortality from total CVD and 26 % of the mortality from ischaemic attack after a mean follow-up of 15 years[12]. Additionally, a meta-analysis of twenty-three randomised controlled trials including 39 195 participants reported a pooled risk ratio (CI) of mortality from fatal or non-fatal myocardial infarction (OR: 0·93; 95 % CI 0·87, 1·00) for vitamin B3 (median dose: 2 g/d; median duration: 11·5 months) v. control[13]. Another meta-analysis of eleven randomised controlled trials including 6616 participants showed vitamin B3 (250–3000 mg/d) significantly decreased major coronary events (OR: 0·75; 95 % CI 0·65, 0·96), stroke (OR: 0·74; 95 % CI 0·59, 0·92) and any cardiovascular events (OR: 0·73; 95 % CI 0·63, 0·85)[9]. In a recent meta-analysis of seventeen clinical trials including 35 760 participants, vitamin B3 therapy (100–4000 mg/d) was shown to be associated with reduction of acute coronary syndrome (relative risk: 0·74; 95 % CI 0·58, 0·96) and stroke (relative risk: 0·74; 95 % CI 0·59, 0·94)[25]. A meta-analysis including 9959 subjects reported similar results for total CVD events (OR: 0·66; 95 % CI 0·49, 0·89) and major coronary events (OR: 0·75; 95 % CI 0·59, 0·96) but not for stroke (OR: 0·88; 95 % CI 0·50, 1·54)[26]. Another meta-analysis of thirteen trials of vitamin B3 treatment demonstrated a significant reduced risk of non-fatal myocardial infarction (risk ratio: 0·85; 95 % CI 0·73, 1·00), a weak association with CVD mortality (risk ratio: 0·91; 95 % CI 0·81, 1·02) and no association with stroke (risk ratio: 0·89; 95 % CI: 0·72, 1·10)[27].
The mechanisms by which vitamins B1 and B3 might be protective against CVD mortality, especially those from ischaemic heart disease could be summarised here. Vitamin B1 deficiency was highly prevalent in patients with type 2 diabetes[28], which is considered as one of the risk factors for ischaemic heart disease. In addition, vitamin B1 inhibits human infragenicular accelerated proliferation of arterial smooth muscle cells and mitigates atherosclerosis and endothelial dysfunction[29]. Another potential mechanism might be the protective effects of vitamin B1 against ischaemic injury via reducing the metabolic needs of heart[5]. For vitamin B3, the reduced CVD risk may be involved in the favourable effects of vitamin B3 on lipid metabolism[7,8]. Vitamin B3 also has anti-inflammatory properties demonstrated by lowering C-reactive protein lipoprotein-associated phospholipase A2, inhibiting pro-atherogenic chemokines and enhancing serum levels of adiponectin[26]. Moreover, an antihypertensive effect of vitamin B3 was also suggested[30].
We observed that a higher dietary vitamin B1 intake was associated with reduced risk of mortality from heart failure. Vitamin B1 deficiency was commonly considered to be correlated with a failing heart. In a meta-analysis of nine observational studies, the prevalence of vitamin B1 deficiency was higher with an OR of 2·5 (95 % CI 1·7, 3·9) in heart failure group than in control[31]. Also known as wet beriberi or cardiac beriberi, vitamin B1 deficiency was characterised by peripheral neuropathy and muscle weakness resulting in heart failure[3]. The vitamin B1 deficiency-related heart failure was attributed to the vitamin B1 role in energy metabolism[3]. Some studies reported that vitamin B1 supplementation had beneficial effects on cardiac function[6], but the evidence is still inconclusive[3].
The dietary intake of vitamin B3 in our study tended to associate with a lower risk of mortality from haemorrhagic stroke. In a case–control study including sixty-nine stroke cases and sixty-nine controls, vitamin B3 was found to be inversely correlated with risk of stroke (OR: 0·17; 95 % CI 0·04, 0·82)[32]. However, some meta-analyses concluded that vitamin B3 had similar protective effects on both CHD and stroke outcomes[9,25], while others failed to find protective effects against stroke[26,27]. The available studies and meta-analyses did not comment on the effect of vitamin B3 on stroke subtypes. Vitamin B3 was shown to reduce the blood pressure, an important risk factor for haemorrhagic stroke[30]. On the other hand, vitamin B3 may promote vascular plasticity after an acute attack of stroke. In an animal experiment, Niaspan treatment of stroke in rats with diabetes promoted vascular remodelling and improved functional outcome[33]. Therefore, an impact of vitamin B3 on risk of haemorrhagic stroke mortality could be plausible.
To the best of our knowledge, this is the first study to investigate the association of dietary intakes of vitamins B1 and B3 with the risk of mortality from CVD among Japanese population. The JACC study is a large, nationwide, community-based, prospective Japanese cohort study. The large sample size allowed us to investigate the associations of quintile categories of dietary vitamins B1 and B3 intakes with the risks of type-specific cardiovascular mortality as well as total CVD in Japanese population. Other strengths of this study included the prospective study design, the utilisation of a validated FFQ, the consistent endpoint determination and the exclusion of participants with CVD and cancer before the starting point of follow-up.
Limitations of this study mainly originate from the dietary assessment. The one-time measurement of dietary intakes cannot completely represent the consumption of nutrients during a long-term follow-up. The exclusion of 18 428 non-respondents to FFQ might result in a selection bias. Compared with 24 614 non-respondents to FFQ, the 61 787 respondents were more likely to be young and more educated (online Supplementary Table S2). Several research of the JACC study reported the underestimation of nutrients intakes, which could be attributed to the limited number of food items in the used FFQ. Second, we had no data about the exact amounts or types of vitamin supplementation. To our knowledge, in the past century, vitamin supplementation was not common among Japanese population; thus, we believe that it would not affect the result substantially. In this study, approximately 88 % of participants did not use any vitamin supplementation and only 3 % used it on daily basis. The exclusion of daily supplementation uses did not change the result. Third, in Japan, the accuracy of heart failure death certificate diagnosis is a questionable issue. It is generally believed that heart failure death was overestimated before 1994, because most deaths of unknown origin such as cardiac arrest or arrhythmic death were more likely diagnosed as unspecific heart failure[34]. Therefore, approximately 27–50 % heart failure deaths were accounted for this misclassification[34]. Lastly, we did not have data on biomarkers of atherosclerosis and endothelial dysfunction, lipid metabolism and systematic inflammation such as C-reactive protein, lipoprotein-associated phospholipase A2, pro-atherogenic chemokines or serum levels of adiponectin and cannot determine all confounding effects from some other nutrients, lifestyles and socio-economic factors.
## Conclusions
In this prospective cohort study, higher dietary intakes of vitamins B1 and B3 were inversely associated with a reduced risk of mortality from CVD among Japanese men and women. Dietary intakes of these vitamins from their food sources are available, accessible, affordable, safe and more acceptable by the general population than supplementary intakes. Therefore, dietary intakes of food rich in these vitamins could be encouraged for decreasing the risk of CVD mortality. However, our findings warrant further studies in different populations.
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|
---
title: 'Adherence to a traditional Mexican diet and non-communicable disease-related
outcomes: secondary data analysis of the cross-sectional Mexican National Health
and Nutrition Survey'
authors:
- Selene Valerino-Perea
- Miranda E. G. Armstrong
- Angeliki Papadaki
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10011591
doi: 10.1017/S0007114522002331
license: CC BY 4.0
---
# Adherence to a traditional Mexican diet and non-communicable disease-related outcomes: secondary data analysis of the cross-sectional Mexican National Health and Nutrition Survey
## Body
Traditional diets refer to long-established food patterns that represent a region’s food culture[1]. Given that these diets generally contain large amounts of plant-based and non-industrialised foods[1,2], consuming certain traditional diets (e.g. the Mediterranean diet) has been recommended for preventing non-communicable diseases (NCD)(3–6), also known as chronic diseases, such as diabetes and CVD[7]. In addition, traditional diets have been recognised as environmentally friendly and culturally appropriate nutrition strategies, which are public health priorities set by global health institutions[3,4,8]. However, not all traditional diets follow all nutrient recommendations in current food guidelines and therefore must be evaluated in relation to health before their promotion. For instance, the traditional Mexican diet (TMexD) may contribute to better health outcomes through high intakes of plant-based foods (e.g. maize, legumes, vegetables, grains, fruits and seeds)(9–11). These foods, rich in dietary fibre, diverse micronutrients and antioxidants[12], have been associated with reduced body weight(13–15), glucose[16], insulin[16], blood pressure[17] and some types of blood cholesterol levels[17,18]. However, the TMexD is also abundant in items incompatible with current food guidelines, like energetic beverages and energy-dense dishes (e.g. tamales)[11].
In Mexico, the TMexD must be evaluated in relation to obesity, diabetes and CVD, which are outcomes of major public health interest, before it is promoted in any nutrition strategies. Studying the link between traditional diets and the high burden of disease in *Mexico is* particularly important to study, as prevalence rates of obesity and diabetes are amongst the highest worldwide[19] (36·1 % and 13·7 %, respectively)[20,21], while CVD remain the leading cause of death in the country (22·7 %)[22]. This high burden of disease has been attributed to the population’s poor adherence to dietary guidelines, with few Mexican adults meeting recommended intakes of protective foods (e.g. fruits and vegetables) and most exceeding the recommendations for foods high in energy, fat and added sugars (e.g. sugar-sweetened beverages (SSB))[23].
To date, no studies have explored the association between adherence to a TMexD diet and NCD outcomes in Mexico[11]. Previous studies have evaluated the associations between health outcomes and the TMexD using a posteriori analyses of the diet(24–28), which provide an evaluation of the population’s current dietary intakes[29] but do not necessarily reflect a traditional diet. Similarly, other studies have used Mexican diet indices by measuring the consumption of a range of foods[30,31]; however, these indices have generally omitted potentially relevant foods (i.e. beverages, herbs and condiments, or nuts and seeds), which are typical of the Mexican food culture[11]. Using a comprehensive, a priori and evidence-based TMexD index to evaluate its association with health outcomes could therefore provide essential evidence on the importance of this traditional dietary pattern, before implementing public health efforts to promote it to the wider Mexican population.
A comprehensive index to measure adherence to the TMexD was recently created, using systematic reviewing[11] and subsequently Delphi methodologies[10]. The latter employed expert opinion to select the foods and food-related habits that reflect the TMexD[32,33]. The current study aimed to utilise this recently developed TMexD index[10] to investigate the association between TMexD adherence and anthropometric characteristics, and with diabetes and CVD biomarkers and prevalence, in a representative sample of Mexican adults. It was hypothesised that higher TMexD adherence would be associated with more favourable outcomes for obesity, diabetes and CVD, compared with low adherence.
## Abstract
This study evaluated the association between adherence to a traditional Mexican diet (TMexD) and obesity, diabetes and CVD-related outcomes in secondary data analysis of the cross-sectional Mexican National Health and Nutrition Survey 2018–2019. Data from 10 180 Mexican adults were included, collected via visits to randomly selected households by trained personnel. Adherence to the TMexD (characterised by mostly plant-based foods like maize, legumes and vegetables) was measured through an adapted version of a recently developed TMexD index, using FFQ data. Outcomes included obesity (anthropometric measurements), diabetes (biomarkers and diagnosis) and CVD (lipid biomarkers, blood pressure, hypertension diagnosis and CVD event diagnosis) variables. Percentage differences and OR for presenting non-communicable disease (NCD)-related outcomes (with 95 % CI) were measured using multiple linear and logistic regression, respectively, adjusted for relevant covariates. Sensitivity analyses were conducted according to sex, excluding people with an NCD diagnosis and using multiple imputation. In fully adjusted models, high, compared with low, TMexD adherence was associated with lower insulin (−9·8 %; 95 % CI (−16·0, −3·3)), LDL-cholesterol (−4·3 %; 95 % CI (−6·9, −1·5)), non-HDL-cholesterol (−3·9 %; 95 % CI (−6·1, −1·7)) and total cholesterol (−3·5 %; 95 % CI (−5·2, −1·8)) concentrations. Men and those with no NCD diagnosis had overall stronger associations. Effect sizes were smaller, and associations weakened in multiple imputation models. No other associations were observed. While results may have been limited due to the adaptation of a previously developed index, the results highlight the potential association between the TMexD and lower insulin and cholesterol concentrations in Mexican adults.
## Study design
This study consisted of secondary data analyses of the National Health and Nutrition Survey (ENSANUT) 2018–2019 in Mexico. ENSANUT is a cross-sectional survey with a probabilistic, multi-stage, stratified cluster sampling design, representative at a national level[34,35]. Data are publicly available on ENSANUT’s website[36]. The data in the present study are reported using the ‘Strengthening the Reporting of Observational studies in Epidemiology’ (STROBE)[37] and the ‘STROBE-Nutritional Epidemiology’ (STROBE-nut)[38] statements (online Supplementary materials I, Table S1).
## Ethical approval
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Mexican National Institute for Public Health Institutional Review Board. The School for Policy Studies Research Ethics Committee (SPSREC/18-$\frac{19}{053}$) at the University of Bristol approved the current study. Written informed consent was obtained from all subjects[35].
## Data sources
Data from ENSANUT 2018–2019 were collected from June 2018 to July 2019 through visits to 50 654 randomly selected households (with an 87 % response rate). Specific information about the sample size calculation is reported elsewhere[34]. At least one adult in each household was interviewed; no dietary or physiological characteristics were considered when selecting participants. Data were obtained face to face by trained personnel using standardised procedures[35]. Sociodemographic and health data were obtained from 84 490 individuals; anthropometric measurements, blood biomarkers, physical activity and dietary data were obtained from random subsamples[34,39].
The current study included analyses in adults aged 20 to 69 years who completed all questionnaires (older adults (>69 years) were excluded as different measures for some outcomes are reported for this population). Pregnant and lactating women (including women <50 years with missing data for this variable), individuals with implausible health outcomes, and participants with low or extreme dietary intakes were excluded as follows. Participants with implausible health outcomes were those with unlikely values for height (<1·30 m or >2·0 m), BMI (<10 kg/m2 or >58 kg/m2), waist circumference (<50 cm or >200 cm), systolic blood pressure (<80 mmHg) and diastolic blood pressure (<50 mmHg)[40]. Individuals with low dietary intakes were those with an energy intake/BMR ratio below 0·5, according to the Mifflin-St Jeor equation[41] (individuals with missing anthropometric data were assigned the mean BMR by sex group[42]). Those with high total energy intakes (TEI) were defined as those with TEI greater than 3 sd from the mean by sex group. Given the use of blood biomarker analyses, individuals with less than 8 h of fasting at the time of the interview were also excluded[21,43,44].
## Dietary variables
Dietary data were collected using a validated[45], interviewer-administered, semi-quantitative FFQ. This FFQ included the 140 most consumed foods in Mexico and those of particular public health interest (relevant to NCD development, such as processed foods, dressings, SSB and full-fat dairy products)[40,42]. Participants reported the times per week, times per d, number of portions and portion size of each food consumed during the 7 d before the interview[40]. The portion sizes were calculated using the FFQ standard and alternative portions sizes, expressed using home measurements (e.g. pieces of fruit, spoons and cups)[42].
Daily grams and TEI were calculated from weekly intakes by calculating the grams per portion and energy densities reported in the ENSANUT 2012 database. This database, alongside this particular FFQ, was generated by the Mexican National Institute for Public Health[42]. For the purposes of the current study, implausible daily intakes (i.e. those greater than 4 sd from the mean by sex, area and region groups) were calculated for each food item in the questionnaire. Participants with seven or more implausible intakes were excluded from the analysis[40]. Additional cleaning procedures were applied for measuring tortilla intake, given the large missing values on this variable (as tortilla consumption is reported by reporting the weight of tortillas consumed). For individuals with missing tortilla weight data, each state’s mean weight was calculated and imputed[46] (6·8 % values imputed). Additionally, only intervals of 10–500 g were considered valid tortilla weights. Tortillas weighing <10 g or >500 g were considered implausible and imputed with minimal (10 g) or maximum (500 g) values[46] (0·5 % values imputed).
Adherence to the TMexD was assessed using an adapted version of the TMexD index, recently developed using systematic reviewing and Delphi methodologies[10,11]. Briefly, this index was created in a three-round Delphi study, where experts in the TMexD reached a consensus on the items representing a diet traditional of Mexico. The resulting index mainly reflects, according to the participating experts, foods highly consumed in Mexico and past dietary habits[10]. This index (score ranging from 0 to 21 points) measures the consumption of fifteen food groups (maize products, legumes, vegetables, fruits, beverages, herbs and condiments, nuts and seeds, vegetable fats and oils, grains, plain water, tubers, meats, dairy products, eggs, and maize-based dishes) and three food-related habits (consuming home-made meals, socialising at meals and buying foods locally) that represent a TMexD (online Supplementary materials II, Table S1). This index was adapted to assess TMexD adherence in the present study; the three components reflecting food-related habits were omitted, as these are not measured in ENSANUT. Foods omitted included items like amaranth, tostadas, cacao drinks and vegetable oil, as these are not measured in ENSANUT (online Supplementary materials II, Table S1). The complete list of foods omitted can be found in Supplementary materials II (Table S2). The TMexD index used in the present study consisted of fifteen food groups; scores ranged from 0 to 18, with higher scores representing higher adherence to this traditional diet.
## Outcome variables
Obesity outcomes were assessed using anthropometric measurement variables of weight (kg), height (m) and waist circumference (cm). Data were measured according to international standardised protocols and using calibrated stadiometers and electronic scales[40]. All measures were recorded twice; the average was calculated and used for analyses. BMI was calculated using a standard equation (kg/m2) and classified as underweight (≤18·4 kg/m2), normal weight (18·5–24·9 kg/m2), overweight (≥25 kg/m2) and obesity (≥30 kg/m2)[47].
Diabetes outcomes were measured using glucose, glycated Hb (HbA1c) and insulin concentration values. Diabetes was defined as having either a high fasting plasma glucose (≥126 mg/dl)[48], high HbA1c levels (≥6·5 %)[48] or a previous medical diagnosis of diabetes[21] (not including women diagnosed during pregnancy).
CVD biomarkers measured were blood lipids and blood pressure. The blood lipids used to evaluate CVD risk included LDL-cholesterol, HDL-cholesterol, non-HDL-cholesterol, total cholesterol and TAG. Systolic and diastolic blood pressure measures were recorded twice, and the average was calculated and used for analyses. Hypertension was established in participants with high systolic (>130 mmHg) or diastolic (>80 mmHg) pressure values (according to the updated hypertension guidelines)[49], as well as those with a previous medical diagnosis of hypertension[50] (not including women diagnosed during pregnancy). The occurrence of previously diagnosed CVD events was also assessed via self-report of a previous medical diagnosis of different CVD (i.e. “*Has a* doctor ever diagnosed you with heart attack, angina or heart failure?”).
## Sociodemographic and other health data
Sociodemographic and health-related data were self-reported and collected using interviewer-administered questionnaires. Sociodemographic data included sex, age, education, area of residence, region of the country and socio-economic status (SES; using the quartiles developed by ENSANUT, which consider household head education, income, access to services and household assets[51]). Health data included medication use, family history of disease, smoking status and physical activity (reported in metabolic equivalent task (MET) minutes and assessed using the short form of the International Physical Activity Questionnaire[52]).
## Statistical analyses
Analyses were performed in Stata version 16.0[53] and using the survey prefix command (SVY) to adjust for the complex survey design[54]. The sample characteristics and intakes of food groups in the TMexD were reported using means and proportions. Reporting medians, which is more appropriate for data not normally distributed (e.g. anthropometric and biomarker data)[55], was not possible using the SVY module. The sample characteristics and food intakes were calculated across the categories of the TMexD index using simple linear regression (ANOVA analyses are not available for SVY data) for continuous variables and Pearson’s χ 2 tests for categorical ones.
The associations between adherence to the TMexD (low, medium and high; classified using tertiles) and continuous outcomes for obesity, diabetes, and CVD risk markers were evaluated using multiple linear regression in complete-case analyses. The assumptions for homoskedasticity, normality and model specification were tested visually and using statistical tests (link test and omitted variable test for model misspecification)[56]; all assumptions tested were met. Other regression assumptions (i.e. multicollinearity, constant variance and influential points) were not tested as these are unavailable in the SVY module[57]. In all models, the outcome variable was log-transformed to meet these assumptions. Log-transformed results were translated into percentage differences between the highest v. the lowest level of TMexD adherence to facilitate interpretation. The association between TMexD adherence (low, medium and high) and the odds for having NCD-related outcomes (yes/no) was tested using multiple logistic regression (in complete-case analyses). The assumption of model specification, the only one available for complex survey-designed data, was tested using statistical tests (link test for model misspecification)[58]; all assumptions tested were met. The P-value was adjusted ($P \leq 0$·004) for multiple comparisons using the Bonferroni correction[59].
All analyses were adjusted for confounders identified from a broad literature search. These confounders included age (years; continuous or in categories (20–29, 30–39, 40–49, 50–59, 60–69) based on the model that met regression assumptions), sex (female/male), education (primary or less, secondary, high school, or higher education), SES (quartiles), region (North, Centre-Mexico City and South)(60–62), area of residence (urban/rural), physical activity (MET minutes and continuous) and smoking status (current smoker, previous smoker and never smoker). While there was no difference in TMexD scores by sex, the analyses were adjusted for this variable[63] as earlier studies have found differences in both dietary outcomes and the outcomes evaluated according to sex(20,64–69). For diabetes- and CVD-related outcomes, family history of disease (yes/no) and medication use (yes/no) were also included as confounders; these were added for each condition specifically (e.g. family history of diabetes, diabetes medication use were only used in diabetes-related analyses). While food security has also been associated with dietary intake[70] and health outcomes (e.g. obesity[20], diabetes[71] and hypertension[71]) in Mexico, it is not commonly adjusted for and was available in few participants only (55·6 % of the sample), so it was discarded. Since TEI (continuous) or the presence of overweight and obesity (<25 kg/m2/ ≥25 kg/m2) might affect the association between the outcomes evaluated and the TMexD, the analyses also adjusted for these variables in separate models.
## Sensitivity analyses
Further sensitivity analyses were conducted. Since studies in Mexican adults have reported sex and education, sex and age, and sex and SES interactions(72–74), all analyses were additionally performed separately by sex. To reduce potential reverse causation bias, individuals previously diagnosed with a chronic disease (i.e. diabetes, hypertension and CVD) or individuals who reported changing their diet after a chronic disease diagnosis (i.e. following a specific diet after diabetes or hyperlipidaemia diagnosis) were excluded in separate sensitivity analyses. Multiple imputation was also conducted to include individuals with incomplete data. Data were imputed using chained equations and twenty imputed datasets and using sociodemographic (i.e. age, sex, education, SES, geographical region and area of residence) and health data (i.e. previous diagnosis of an NCD) as auxiliary variables. The imputed data ranged from 0·9 % to 43·9 %.
When performing separate analyses by sex, men with a higher TMexD adherence had greater differences in insulin (−14·0 %; 95 % CI (−23·1, −3·7)), LDL-cholesterol (−7·3 %; 95 % CI (−11·3, −3·0)), non-HDL-cholesterol (−5·1 %; 95 % CI (−8·6, −1·6)) and total cholesterol (−4·7 %; 95 % CI (−7·5, −2·0)) than in the main analyses. Women with high TMexD adherence had a tendency towards lower non-HDL-cholesterol (−2·8 %; 95 % CI (−5·4, −0·1)) and total cholesterol (−2·4 %; 95 % CI (−4·5, −0·2)) only, but these associations were weak (online Supplementary materials II, Table S3). Except for insulin, slightly stronger associations were observed when excluding individuals with an NCD diagnosis (LDL-cholesterol: −5·3 %; non-HDL-cholesterol: −4·4 %; total cholesterol −3·9 %) or individuals dieting after an NCD diagnosis (LDL-cholesterol: −4·8 %; non-HDL-cholesterol: −4·6 %; total cholesterol −4·1 %) (online Supplementary materials II, Table S4). Similar, albeit slightly weaker, associations were observed between a high TMexD adherence and insulin, LDL-cholesterol, non-HDL-cholesterol and total cholesterol when performing multiple imputation (online Supplementary materials II, Table S5). The association between adherence to the TMexD and diagnosis of diabetes, hypertension, or CVD did not differ in any sensitivity analyses (online Supplementary materials II, Table S6–S8).
## Participant characteristics
Data from 10 180 participants (mean age, 42·8 years; mean BMI, 28·8 kg/m2) were analysed (Fig. 1). The mean TMexD index score was 7·0 for the whole sample (range 0–16) and 5·0, 7·5 and 10·0 for the low, medium and high adherence tertiles, respectively. Older individuals, people living in rural areas or in Central and Southern Mexico, those with lower SES or education, and higher physical activity levels and TEI had higher TMexD adherence (Table 1).
Fig. 1.Flow diagram of participants included in a secondary data analysis to examine the association between adherence to the traditional Mexican diet and health outcomes.
Table 1.Sociodemographic and health characteristics of 10 180 Mexican adults by tertiles of the traditional Mexican diet index(Mean values with their standard errors)Traditional Mexican diet scores‖ (n 10 087)Total sample§ (n 10 180)Low 0–6 (n 4199)Medium 7–8 (n 3411)High 9–18 (n 2472) P ¶ Mean se Mean se Mean se Mean se Age (years)42·80·341·70·543·10·444·40·50·001Sex (%) Female57·80·960·01·356·11·556·51·70·09 Male42·20·940·01·343·91·543·51·7Area (%) Urban77·80·683·70·774·21·269·81·6<0·001 Rural22·20·616·30·725·81·230·21·6Region North17·10·521·80·913·90·812·01·0<0·001 Centre51·70·850·01·350·81·554·81·8 South31·20·728·21·035·31·433·21·6Socio-economic status (%) 1st quartile19·10·713·70·722·31·226·41·7<0·001 2nd quartile50·40·950·21·352·31·650·01·9 3rd quartile21·00·725·61·217·11·215·91·4 4th quartile9·50·410·51·08·41·27·71·5Education level (%) Primary or less29·20·824·21·131·91·435·71·6<0·001 Secondary school29·40·826·71·232·61·630·41·5 High school22·20·825·71·419·91·318·71·6 Higher education19·20·823·41·115·71·415·21·4Obesity outcomes BMI (kg/m2)28·80·128·60·129·00·228·90·20·18 Waist circumference (cm)95·40·395·20·495·90·495·20·50·44Diabetes biomarkers Glucose (mg/dl)104·70·9104·41·4104·21·3106·91·80·43 Glycated Hb (%)5·70·05·60·05·70·05·80·10·08 Insulin (μm/ml)14·90·416·20·714·40·513·30·40·003 Blood lipids107·50·7109·21·0106·21·0105·51·50·03 LDL-cholesterol (mg/dl)44·10·244·80·343·20·343·90·40·001HDL-cholesterol(mg/dl)142·70·7144·11·0141·81·0141·71·50·19 Non-HDL-cholesterol (mg/dl)186·80·7188·91·1185·01·1185·61·60·02 Total cholesterol (mg/dl)206·62·8204·04·8209·73·9210·75·70·57 TAG (mg/dl)107·50·7109·21·0106·21·0105·51·50·03Blood pressure Systolic (mmHg)123·40·4122·20·6124·20·6124·60·70·01 Diastolic (mmHg)76·20·275·70·376·80·376·70·50·05Diabetes diagnosis* (%) Yes18·20·717·21·118·61·319·31·30·45 No81·80·782·81·181·41·380·71·3Hypertension diagnosis† (%) Yes50·51·048·41·553·51·550·71·90·05 No49·51·051·61·546·51·549·31·9Previous CVD diagnosis‡ (%) Yes3·10·32·80·43·40·52·90·50·51 No96·90·397·20·496·60·597·10·5 Physical activity (MET-minutes)3780·063·33541·597·03796·8101·54178·1128·2<0·001 Total energy intake (kcal)2183·418·22079·527·82155·827·82422·837·8<0·001Smoking status (%) Current17·30·619·31·116·11·014·51·30·03 Former19·90·718·81·020·41·221·71·4 Never62·80·861·91·363·51·363·81·8MET, metabolic equivalent of task.*Previous diabetes medical diagnosis or presence of high fasting plasma glucose (≥126 mg/dl or 6.99 mmol/l) or glycated Hb (≥6.5 %) levels.†Previous hypertension medical diagnosis or presence of high systolic (>130 mmHg) or diastolic (> 80 mmHg) pressure values.‡Heart attack, angina and heart failure.§Sample sizes: n 10 180 except for BMI (n 8737), WC (n 8716), HbA1c (n 9968), insulin (n 10 179), LDL-cholesterol (n 7826), systolic and diastolic blood pressure (n 9077), physical activity (n 10 151), total energy intake (n 10 154), and smoking (n 10 148).‖The score ranges refer to the range in the original traditional Mexican diet index, scores in this population ranged from 0 to 16. The scores were calculated as the sum of points across all dietary components in the traditional Mexican diet index, with a higher score indicating a higher adherence.¶Calculated using linear regression for continuous variables and Pearson’s χ 2 tests for categorical variables.
## Intakes of food groups measured in the traditional Mexican diet index
The intake of all fifteen components of the TMexD index differed significantly across the TMexD score tertiles, mainly in an expected direction (Table 2). The percentage of participants following the recommended intakes for each component of the TMexD index was highest for the tortillas (70·3 %), herbs and condiments (70·7 %), beverages (72·3 %), and eggs (65·1 %) groups, while lowest for legumes (14·3 %), vegetables (16·8 %), and nuts and seeds (2·9 %).
Table 2.Recommended and current intakes of the food groups of 10 180 Mexican adults, according to the traditional Mexican diet index(Mean values with their standard errors)Food group recommendation in TMexD indexDietary intakes (g, ml or portion)Adhering to food group recommendationTraditional Mexican diet scores* Total sample (n 10 180)Traditional Mexican diet scores* Total sample (n 10 180)Low 0–6 (n 4199)Medium 7–8 (n 3411)High 9–18 (n 2472) P † Low 0–6 (n 4199)Medium 7–8 (n 3411)High 9–18 (n 2472) P ‡ Mean se Mean se Mean se Mean se %%%Maize tortilla≥4 portion/d6·90·15·40·17·80·28·50·2<0·00170·349·882·692·6<0·001Legumes≥100 g/d48·01·030·40·946·41·485·22·7<0·00114·32·812·839·4<0·001Vegetables≥240 g/d138·72·894·02·5121·43·6242·98·0<0·00116·84·213·644·0<0·001Fruits≥160 g/d215·14·4162·55·3208·46·5319·411·3<0·00145·432·146·167·9<0·001Herbs and condiments≥1 portion/d2·10·01·60·02·30·12·70·1<0·00170·756·776·687·7<0·001Nuts and seeds≥30 g/d3·40·32·80·52·50·25·30·6<0·0012·91·51·77·0<0·001Avocado≥66 g/d23·51·117·51·123·92·133·22·3<0·00120·413·821·430·2<0·001Plain water≥1440 ml/d1286·219·51056·824·71329·733·61682·641·1<0·00139·626·042·562·1<0·001Beverages≤240 ml/d370·111·5440·719·5324·118·1278·819·4<0·00172·365·077·580·4<0·001Other grains≥100 g/week144·44·3110·24·8146·37·6203·010·8<0·00130·019·932·944·8<0·001Tubers≥120 g/week57·71·939·32·257·62·790·14·7<0·00118·510·918·532·6<0·001Meats≤240 g/week600·610·3616·814·0559·213·6627·028·90·00524·217·127·732·7<0·001Dairy products≤90 g/week114·33·0129·44·394·14·7103·16·4<0·00163·955·871·271·1<0·001Eggs≤4 portion/week4·10·14·50·23·70·23·90·20·00171·465·175·078·1<0·001Maize-based meals≤1 portion/week5·00·15·70·24·60·23·90·2<0·00133·221·336·351·9<0·001*The score ranges refer to the range in the original traditional Mexican diet index, and scores in this population ranged from 0 to 16. The scores were calculated as the sum of points across all dietary components in the traditional Mexican diet index, with a higher score indicating a higher adherence.†Calculated using linear regression.‡Calculated using Pearson’s χ 2 tests.
## Association between the traditional Mexican diet and health outcomes
Results for the association between the TMexD and continuous outcomes are presented in Table 3. In fully adjusted models, high, compared to low, TMexD adherence was associated with lower insulin (−9·8 %; 95 % CI (−16·0, −3·3)), LDL-cholesterol (−4·3 %; 95 % CI (−6·9, −1·5)), non-HDL-cholesterol (−3·9 %; 95 % CI (−6·1, −1·7)) and total cholesterol (−3·5 %; 95 % CI (−5·2, −1·8)) concentrations. Adults with a higher TMexD adherence also had a tendency towards lower HDL-cholesterol (−2·3 %; 95 % CI (−4·2, −0·3)) and higher systolic blood pressure values (1·5 %; 95 % CI (0·2, 2·7)), but these associations were weak. No associations were found between TMexD adherence and any measures of obesity, or with glucose, HbA1c, TAG concentrations or diastolic blood pressure (Table 3). Adherence to the TMexD was not associated with a diagnosis of diabetes, hypertension or CVD (Table 4).
Table 3.Percentage differences in non-communicable disease-related outcomes* in adults in the highest tertile v. the lowest tertile of adherence† to the traditional Mexican diet(Differences and 95 % confidence intervals)Model 1‡ Model 2§ Model 3‖ Model 4¶ % Difference95 % CI P ** % Difference95 % CI P ** % difference95 % CI P ** % Difference95 % CI P ** Obesity measures BMI1·0−0·6, 2·70·220·8−0·9, 2·50·360·5−1·1, 2·20·54NANA n 865386068576 R 2 †† 0·18·28·1 Waist circumference0·2−1·0, 1·40·79−0·4−1·6, 0·70·44−0·6−1·7, 0·60·33NANA n 863185808551 R 2 †† 0·110·910·8Diabetes outcomes Glucose0·9−1·9, 3·70·54−1·2−3·6, 1·20·31−1·4−3·8, 1·00·24−1·3−3·8, 1·30·31 n 10 087944594138116 R 2 †† 0·029·029·228·0 Glycated Hb2·10·2, 4·00·020·3−1·1, 1·70·650·1−1·3, 1·60·870·2−1·2, 1·60·76 n 9879925392217952 R 2 †† 20·042·542·840·7 Insulin−10·9−16·6, −4·90·001−7·5−13·7, −0·90·02−7·9−14·1, −1·20·02−9·8−16·0, −3·30·004 n 10 086944494128115 R 2 †† 0·35·15·215·0Blood lipids LDL-cholesterol−3·8−6·8, −0·80·01−3·6−6·6, −0·50·02−3·5−6·4, −0·50·02−4·3−6·9, −1·50·002 n 7746652064965699 R 2 †† 0·38·68·99·7 HDL-cholesterol−2·2−4·0, −0·20·02−1·6−3·6, 0·30·10−2·1−4·0, −0·20·03−2·3−4·2, −0·30·02 n 10 087849284627423 R 2 †† 0·55·75·910·1 Non-HDL-cholesterol−1·7−4·0, 0·60·15−3·3−5·5, −0·90·007−3·2−5·5, −0·90·006−3·9−6·1, −1·70·001 n 10 087849284627423 R 2 †† 0·111·010·914·7 Total cholesterol−1·9−3·7, 0·00·05−2·9−4·7, −1·00·003−3·0−4·8, −1·20·001−3·5−5·2, −1·8<0·001 n 10 087849284627423 R 2 †† 0·39·79·711·1 TAG4·4−0·8, 9·80·09−1·6−6·7, 3·80·55−2·2−7·3, 3·20·42−3·9−9·2, 1·70·17 n 10 087849284627423 R 2 †† 0·112·612·518·5Blood pressure Systolic2·20·8, 3·60·0020·9−0·3, 2·10·120·7−0·5, 1·80·271·50·2, 2·70·01 R 2 †† 8985829082607155 n 0·425·525·423·2 Diastolic1·3−0·2, 2·90·081·3−0·2, 2·80·081·0−0·5, 2·50·211·3−0·4, 2·90·12 n 8985829082607155 R 2 †† 0·29·49·415·1NA, non-applicable.*All analyses were conducted through multiple linear regression.†High adherence reflects individuals with higher scores in the traditional Mexican diet index.‡Model 1: unadjusted model.§Model 2: adjusted for age, sex, socio-economic status, education level, region of the country, area of residence, physical activity, smoking. Diabetes, blood lipid and blood pressure outcomes were additionally adjusted for family history of disease and use of medication.‖Model 3: model 2 plus total energy intake.¶Model 4: model 3 plus overweight/obesity status (≥25 kg/m2).**Significance assessed at $P \leq 0$·004 using the Bonferroni correction.††Percent of variance explained by the model.
Table 4.OR for having non-communicable disease-related outcomes* in adults in the highest tertile v. the lowest tertile of adherence† to the traditional Mexican diet(Odd ratio and 95 % confidence intervals)Model 1¶ Model 2** Model 3†† Model 4‡‡ OR95 % CI P §§ OR95 % CI P §§ OR95 % CI P §§ OR95 % CI P §§ Presence of diabetes‡ 1·150·92, 1·440·210·860·63, 1·170·330·890·65, 1·210·460·870·62, 1·220·40 n 9902842283947427Presence of hypertension§ 1·090·92, 1·310·320·970·80, 1·190·800·970·79, 1·180·750·960·79, 1·180·71 n 9159845472687268Presence of CVD‖ 1·020·66, 1·580·910·960·60, 1·550·870·930·58, 1·500·7610·58, 1·700·99 n 10 087922491927948*All analyses were conducted through multiple logistic regression.†High adherence reflects individuals with higher scores in the traditional Mexican diet index.‡Defined as having high fasting glucose (≥126 mg/dl), high glycated Hb levels (≥6·5 %) or a previous diabetes medical diagnosis; total number of cases: 1700.§Defined as having either high blood systolic (>130 mmHg) or diastolic (> 80 mmHg) pressure values, or a previous hypertension medical diagnosis; total number of cases: 4751.‖Defined as having a previous medical diagnosis of heart attack, angina or heart failure; total number of cases: 332.¶Model 1: unadjusted model.**Model 2: adjusted for age, sex, socio-economic status, education level, region of the country, area of residence, physical activity and smoking. Diabetes, blood lipid and blood pressure outcomes were additionally adjusted for family history of disease and use of medication.††Model 3: model 2 plus total energy intake.‡‡Model 4: model 3 plus overweight/obesity status (≥25 kg/m2).§§Significance assessed at $P \leq 0$·004 using the Bonferroni correction.
## Discussion
This study evaluated the associations of adherence to a TMexD with obesity, diabetes and CVD outcomes or risk biomarkers, which are main outcomes of public health concern in Mexico[19,22]. To our knowledge, this is the first study to apply a comprehensive, evidence-based index of adherence to the TMexD[10,11] to a nationally representative survey in order to assess associations with health outcomes in Mexican adults.
## Current food intakes according to the traditional Mexican diet index
According to the TMexD index[10], Mexican adults had overall medium adherence to the TMexD, with the mean score being seven out of 18 points, and no individuals reaching the highest score. Higher adherence was reported for tortillas, herbs and condiments, beverages, and eggs. In line with previous research[23,62,65,66,75,76], few participants achieved recommendations for legumes, vegetables, and nuts and seeds. However, intakes of some foods might have been slightly underestimated as not all food items present in the TMexD index were assessed in the FFQ used. For instance, foods like amaranth, various herbs and condiments, tubers, and vegetable oils, present in the original index, were not evaluated in ENSANUT. Lastly, three items assessing traditional Mexican food-related habits, present in the original index[10], were omitted as these are not assessed in ENSANUT. This might have led to an underestimation of TMexD adherence in the current sample.
Intakes of foods recommended to be limited were also noteworthy. Few adults (33 %) met the recommendation of maize-based meals (e.g. tamales), which are generally energy-dense[76,77]. In contrast, many participants (72 %) met the recommended beverage intake, which contrasts with previous research[62,66,75,76,78]. Since the index measures traditional drinks only (i.e. atole, coffee and aguas frescas) but not industrialised beverages (e.g. soda), the percentage of participants meeting the recommendation of energetic beverages is very likely underestimated. Finally, assessing the dairy products group intake was challenging when using this specific index as reference, as a TMexD, as measured by the current index, recommends limiting the consumption of animal-based foods[10] like cheese, but does not establish a limit for yogurt and milk.
## Associations between the traditional Mexican diet and health outcomes
Participants with high TMexD adherence had better outcomes for some diabetes-related biomarkers only. Compared with those with the lowest adherence, they had approximately 10 % lower insulin levels, a biomarker relevant to glucose homoeostasis[79]. However, glucose and HbA1c levels did not differ across TMexD tertiles. In previous prospective studies, a Mexican-style diet led to a 14–15 % reduction in insulin values, but not in glucose levels[30,31]. This could indicate that these diets improve insulin sensitivity[80], but that further diet or lifestyle factors might need to be tackled to improve glucose levels. The Mexican diet definition in these previous studies, like the TMexD index used in this study, is described as high in beans, maize tortillas, fruits and vegetables but also high in animal fats and full-fat dairy and does not consider items like nuts and seeds, or herbs and condiments. While another cross-sectional study did find that a traditional-style diet was associated with a 51 % reduced odds of having pre-diabetes, a glucose-dependent outcome, the study[25] evaluated a diet with a high fish and low-fat cereal content (which the TMexD index used in the current study does not measure) and was carried out in Comcáac Indians only (as opposed to a nationally representative sample).
Similar results were observed for some blood lipids. Participants in the highest TMexD adherence tertile had about 4 % lower LDL-cholesterol, non-HDL-cholesterol and total cholesterol levels, but no difference in TAG concentrations. Previous prospective studies (defining a Mexican diet as high in beans, maize tortillas, fruits, vegetables, Mexican dishes, animal fats and full-fat dairy products) have observed no changes in TAG in individuals following a Mexican-style diet[30,31]. The high fibre content in the TMexD (via fruits, vegetables and legumes[77,81]) could explain these results, as they have been suggested to reduce LDL-cholesterol only(17,82–84). However, further studies would need to confirm these claims, as we did not explore the particular macro- or micronutrients that the TMexD is abundant, or low, in. Alternatively, other factors might be relevant for improving TAG concentrations. For instance, obesity can modify the association between diet and triglyceride concentrations[30,85]. While this study did adjust for overweight/obesity, no separate analyses were performed by BMI status, which could provide further insights; however, such analyses were beyond the scope of the current study. Intakes of SSB have also been proposed to increase TAG concentrations[85], which is a food group potentially underestimated in the current TMexD index.
It is noteworthy that, apart from total cholesterol, most associations were evident only after adjusting for both TEI and overweight/obesity status, so these differences might be highly dependent on not only TMexD adherence but also on adequate TEI and normal weight.
The associations between the TMexD and lower insulin, LDL-cholesterol, non-HDL-cholesterol and total cholesterol were greater in men, which could be attributed to the higher physical activity levels usually reported among men in the literature[74,86]. When excluding participants with no NCD diagnosis, the cholesterol-related associations became stronger, which might indicate that participants modified their diets to one similar to the TMexD after having an NCD diagnosis. Instead, for insulin, diet might only be an important factor in individuals with a disease already in course, like diabetes[87]. All associations weakened after multiple imputation, so individuals who self-perceived as having healthy diets or outcomes were potentially more likely to provide complete data[88].
In this study, no benefits of following the TMexD were observed for any obesity, hypertension or CVD (i.e. heart attack, angina or heart failure) outcomes. Previous cross-sectional studies analysing Mexican-style diets (described simply as high in maize foods or as high in tortillas, tacos, cakes and cookies, SSB, and legumes) have reported equivocal findings for obesity[24,26]. As for other indices evaluated in Mexico, a sustainable diet index was inversely associated with obesity in men[65], while the Mexican Alternate Healthy Eating Index was inversely associated with hyperlipidaemia in women with lower educational attainment[86] and lower BMI and waist circumference in men with lower educational attainment[74]. Like the TMexD index, these indices promote high intakes of plant-based foods and low intakes of animal source foods. However, the TMexD index, unlike these earlier indices, does not discriminate by the type of meat (e.g. poultry), fat (e.g. polyunsaturated) or grain (e.g. whole grains) consumed. Future studies could evaluate if considering the type of meat, fat or grain modifies the results observed, especially since a high TMexD adherence was associated, albeit weakly, with lower HDL-cholesterol and higher systolic blood pressure values. Future studies could also conduct analyses in adults with lower educational attainment, which seem to have stronger diet–health associations[74,86], possibly given their higher physical activity level or their higher cereal and legumes intake[74,86,89].
## Strengths and limitations
This research studied the associations between the TMexD and an extensive range of NCD risk factors and outcomes in a large and nationally representative sample of Mexican adults. These outcomes were all measured by trained personnel using standardised procedures and clinically relevant parameters[40]. The TMexD index used, while still in need of validation, was developed using a systematic review of the evidence[11] and expert consultation[10] to represent a dietary pattern that is objectively traditionally Mexican, including food groups ignored in previous research and not incorporated in earlier indices, like herbs and condiments and nuts and seeds. Findings are relevant to adults residing in Mexico and contribute to the study of traditional diets and indices to measure adherence to traditional diets.
The results presented need to be interpreted considering the study’s limitations. While FFQ are highly valuable for studying habitual diets in epidemiological studies at relatively low costs[42,90], they do not measure all foods consumed, and they can introduce memory recall and social desirability bias[91,92]. The FFQ used, while previously validated[93], has been shown to underestimate maize-based meals, potatoes, meat, and legumes, and overestimate tortillas, fruits and vegetables[45], all relevant for calculating TMexD adherence. Likewise, some items present in the TMexD were not evaluated in the FFQ, and thus an adapted version of the originally developed index[10] was used. This issue could have introduced measurement error. For example, vegetable oil, which is included in the TMexD index but not measured in the ENSANUT FFQ, contributes to 4·9 % of the TEI of Mexicans[60]. Other non-measured items, such as amaranth, cacao, or native fruits and vegetables like zapote and squash blossoms, could also contribute to current diets, although, to our knowledge, no nationally representative studies explore these intakes. Future studies should ideally examine, preferably in prospective studies, the associations with health outcomes of adherence to the full TMexD, as opposed to the adapted version used in the current study. Future research should also evaluate the validity and reliability of the index[94], as this process has not been carried out. Likewise, since few individuals achieved the highest TMexD score range (i.e. ≥12 points out of 18 points), the highest tertile of adherence was constituted by participants with relatively medium scores (i.e. ≥9 points out of 18 points), which might have attenuated the observed associations[91]. Moreover, only some assumptions for regression analyses were available for survey data, so models were not tested for issues like influential points, which can affect estimations[56]. Lastly, given the cross-sectional nature of this study, it is not possible to claim causality[95] or discard residual confounders or reverse causality bias[91].
Some limitations regarding the index used were also observed. Given that industrialised products (i.e. SSB, salty snacks, desserts, sugars, and cereals with added fats and sugar) considerably contribute to contemporary Mexican diets[66,75,76,78], these might need to be incorporated into the TMexD index as foods whose consumption needs to be limited in order to adhere to a TMexD. The study where the TMexD index was developed theorised that high intakes of healthy plant-based foods would displace non-healthy energy-dense foods (like industrialised products)[10]. Nevertheless, this theory could not be tested in the present study, and it might not apply in our sample, as people with higher scores also had higher TEI. Likewise, although current Mexican food guidelines recommend to limit the intake of alcoholic beverages[77], the latter are not measured in the TMexD index, which is similar to other traditional diet indices (e.g. the Nordic or Japanese diet)[96,97]. These aspects hinder the ability to classify the TMexD index used as one representing a healthy diet and should be considered in future research. Future studies could explore the relevance of including industrialised products and alcoholic beverages in the TMexD index, as items to be limited, or adjust for their intake in statistical analyses.
Another limitation is that the index contains thresholds for some food group quantities (i.e. herbs and condiments, plain water, nuts and seeds, grains, tubers, dairy products) that did not reach a high consensus amongst the participants who contributed to its development. As such, it is unclear if these thresholds might need some revisions. Similarly, the food groups suggested do not have both lower and upper thresholds of recommended intakes. For instance, the index recommends consuming at least four tortillas per d. However, participants in the highest TMexD tertile consumed an average of eight maize tortillas per d. While maize tortillas are considered a healthy and staple food in Mexico[77], their consumption in exceedingly high amounts might not be optimal, especially since these are not the only grain usually consumed in Mexico[66]. In addition, while the geographical region was included as a confounder and although the TMexD index specifically aimed to include foods characteristic of all geographical regions of Mexico[10,11], the differences in food availability and culture across areas could have influenced the level of adherence reached across regions. Future studies should also aim for consistency regarding the geographical area classification in Mexico. For example, previous studies considered central states and Mexico City as separate geographical areas[76,98], whereas others treat them as the same area(60–62). Since the studies used to inform the development of the TMexD index used the North/Centre-Mexico City/South grouping classification (considering Mexico City as part of the central area), we used this latter grouping in the current work. Finally, while multiple imputation was performed, results should be interpreted with caution, particularly for variables where a high percentage of participants had missing data, such as LDL-cholesterol.
## Conclusion
This study evaluated the association of the TMexD with NCD outcomes, which is essential before embarking on promoting this traditional diet or developing interventions to endorse it. Only a small proportion of Mexican adults achieved high TMexD adherence scores, and few met the recommendations for legumes, vegetables, and nuts and seeds. High, compared to low, TMexD adherence was associated with lower concentrations of insulin and some blood lipids (LDL-cholesterol, non-HDL-cholesterol and total cholesterol), but not obesity, diabetes, hypertension or other CVD-related outcomes. Adequate TEI and normal BMI might be required to observe these results, as associations were mostly only evident in models adjusting for these factors. The associations were similar to previous studies evaluating Mexican-style diets, particularly those diets described as high in beans, maize tortillas, fruits and vegetables, even if these were also considered high in animal fats and full-fat dairy products. However, the observed associations in the current work differed from studies describing a Mexican-style diet as high in fish and low-fat cereals. Results must be interpreted with caution due to the study’s limitations, primarily due to the incompatibilities between the TMexD index and the FFQ used, the limited ability of the index to measure industrialised products, and the cross-sectional nature of the study. Moreover, the TMexD index could be modified to improve its compatibility with current health concerns. Specific recommendations to improve the index include dissecting food groups according to public health recommendations (e.g. meat, fat or grain type), adding industrialised products and incorporating an upper limit for tortilla intake.
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|
---
title: 'Selenium intakes and plasma selenium of New Zealand toddlers: secondary analysis
of a randomised controlled trial'
authors:
- Lisa Daniels
- Jillian J. Haszard
- Rosalind S. Gibson
- Rachael W. Taylor
- Elizabeth A. Fleming
- Jody C. Miller
- Christine D. Thomson
- Anne-Louise M. Heath
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10011592
doi: 10.1017/S0007114522002379
license: CC BY 4.0
---
# Selenium intakes and plasma selenium of New Zealand toddlers: secondary analysis of a randomised controlled trial
## Body
Se is a trace element that has been reported to play an important role in many areas of the human body including thyroid metabolism, cognitive function, immune function and growth(1–3). While there are many functions of Se in the human body, there appears to be a U-shaped curve of adverse effects, where deficits and excessive Se concentrations can impact on the risk and onset of certain diseases[4,5].
It is well known that groups of the New Zealand population, particularly in the South Island, are not meeting the recommended intakes of Se and have lower status than what has been reported in some other countries[6,7]. This is in most part due to low Se concentrations in the soil[6,8]. However, only one study has reported information on Se intakes and status of South Island (New Zealand) infants and toddlers, in 1998–1999[7]. Also, it is not known whether the increasingly popular alternative approach to introducing solid foods to infants known as Baby-Led Weaning (in which infants feed themselves rather than being spoon-fed) influences Se intakes. A baby-led approach to infant feeding has been shown to influence food and nutrient intake in infancy(9–11) so may influence Se intake and therefore status.
The objectives of this study were to determine: [1] whether Se intakes and plasma Se concentrations differ between 12-month-old New Zealand toddlers who followed a baby-led approach to complementary feeding and those who followed traditional spoon-feeding, [2] what food sources contribute to Se intakes of 12-month-old toddlers and [3] what factors are associated with plasma Se concentrations in New Zealand 12-month-old toddlers.
## Abstract
Little is known about Se intakes and status in very young New Zealand children. However, Se intakes below recommendations and lower *Se status* compared with international studies have been reported in New Zealand (particularly South Island) adults. The Baby-Led Introduction to SolidS (BLISS) randomised controlled trial compared a modified version of baby-led weaning (infants feed themselves rather than being spoon-fed), with traditional spoon-feeding (Control). Weighed 3-d diet records were collected and plasma Se concentration measured using inductively coupled plasma mass spectrometry (ICP-MS). In total, 101 (BLISS n 50, Control n 51) 12-month-old toddlers provided complete data. The OR of Se intakes below the estimated average requirement (EAR) was no different between BLISS and Control (OR: 0·89; 95 % CI 0·39, 2·03), and there was no difference in mean plasma Se concentration between groups (0·04 μmol/l; 95 % CI −0·03, 0·11). In an adjusted model, consuming breast milk was associated with lower plasma Se concentrations (–0·12 μmol/l; 95 % CI −0·19, −0·04). Of the food groups other than infant milk (breast milk or infant formula), ‘breads and cereals’ contributed the most to Se intakes (12 % of intake). In conclusion, Se intakes and plasma Se concentrations of 12-month-old New Zealand toddlers were no different between those who had followed a baby-led approach to complementary feeding and those who followed traditional spoon-feeding. However, more than half of toddlers had Se intakes below the EAR.
## Experimental methods
This is a secondary analysis of the Baby-Led Introduction to SolidS (BLISS) study which has been described in full elsewhere[12]. The information provided here is information of relevance to the current analysis. The BLISS randomised controlled trial investigated the impact of a modified version of Baby-Led Weaning on infant outcomes including growth[13], choking[14], Fe[15] and Zn[16]. Women (n 206) were recruited (between November 2012 and March 2014) during the third trimester of their pregnancy through an opt-out enrolment process (i.e. all potentially eligible women were approached and offered the opportunity to participate) at the only birthing facility in the city of Dunedin, New Zealand (southern region of New Zealand). Participants were eligible if they: spoke English or Te Reo Māori (indigenous language of New Zealand); planned to live in the area of Dunedin, New Zealand, until their child was at least 2 years of age and were 16 years of age or older. Exclusion criteria were if the infant was born before 37 weeks gestation or had a congenital abnormality, physical condition or intellectual disability that was likely to affect their feeding or growth. The study was conducted in accordance with the Declaration of Helsinki for research involving human subjects and was approved by the New Zealand Lower South Regional Ethics Committee (LRS/$\frac{11}{09}$/037). Adult participants gave written informed consent.
After obtaining consent, participants were randomised into the Control or BLISS intervention group (Fig. 1). Both groups received standard ‘Well Child’ care available to all New Zealand children up to 5 years of age[17], and BLISS participants received further education and support regarding the BLISS approach (i.e. infant self-feeding, with no spoon-feeding, from 6 months of age with modifications to address concerns about the possible risks of Fe deficiency, choking and growth faltering[18]).
Fig. 1.Flow diagram of participants through the BLISS study, with an emphasis on the participants analysed for this secondary analysis. BLISS, Baby-Led Introduction to SolidS.
Demographic data were collected by questionnaire, including the participant’s address which was used to determine the level of household deprivation with the New Zealand Index of Deprivation (NZDep) score[19]. Infant sex, birth weight and gestational age at birth were obtained from hospital records. When the child was 12 months of age, parents were asked about the mother’s smoking status during pregnancy (daily, occasional, quit during pregnancy, non-smoker).
Parent participants completed a weighed diet record on three randomly assigned non-consecutive days (two week days and one weekend day), using dietary scales (Salter Electronic, Salter Housewares Ltd.) accurate to ±1 g, when the child was aged 12 months. Parents recorded all information on what their child ate and drank (time of day the food was consumed, type and brand of food, cooking method), the total food weight before offering and at the end of the eating occasion (i.e. that was leftover), any dietary supplements taken and any recipes used. Data from the 3-d weighed diet record were analysed using the database Kai-culator (Version 1.13s, University of Otago) which contains data from the New Zealand Food Composition Database[20], recipes from the $\frac{2008}{2009}$ New Zealand Adult Nutrition Survey[21], and commercial infant foods collated by the research team[22]. During the dietary data entry, food items were selected carefully to ensure that they contained appropriate Se concentrations, notably for flour and bread products because of the known regional variations in their Se content[6]. For toddlers consuming breast milk (no infant formula), intakes were estimated to be 448 g/d[23]. If the toddler was mixed fed (breast milk and infant formula), the amount of breast milk was estimated to be the amount left after subtracting their weighed consumption of infant formula (g per day) from 448 g/d. The Se content of breast milk was assumed to be 2 μg/100 g[20]. The data from the 3-d weighed diet record were used to determine daily intakes of: energy, protein, Se and Se intakes from breast milk, infant formula and nine food groups (‘seafood’, ‘breads and cereals’, ‘meat’, ‘dairy’, ‘fruits and vegetables’, ‘eggs’, ‘nuts and seeds’, ‘legumes’ and ‘miscellaneous’). Intakes of energy, protein and Se were then entered into the Multiple Source Method programme[24] to calculate ‘usual’ daily intakes, which accounts for intra-individual variation in intake.
A non-fasting venous blood sample was obtained at 12 months of age to determine plasma Se, C-reactive protein and α1-acid glycoprotein concentrations, as well as for determination of biomarkers of Fe and Zn status, as has been reported earlier[16,17]. For the purposes of Zn analysis, parents were instructed to give their child a milk feed 90 min prior to blood collection and then no other food or drink until after the blood test, and a rigorous trace-element free protocol was used during both blood collection and analysis (including use of trace-element free lithium heparin anticoagulated S-Monovette tubes). After sample collection, plasma was separated within 2 h (3500 rpm for 5 min) and aliquots were stored at –80°C until analysis. Plasma Se was analysed using inductively coupled plasma mass spectrometry (ICP-MS) at the Centre for Trace Element Analysis, Department of Chemistry, University of Otago, Dunedin, New Zealand. C-reactive protein and α1-acid glycoprotein were analysed using a Cobas C311 automatic electronic analyser (Roche), in the Department of Human Nutrition Laboratories (University of Otago).
The accuracy and precision of the analyses were checked using certified controls and in-house pooled samples (after every 15 samples), respectively. The analysed mean ± sd (CV) value for the Se control (UTAK Laboratories, Inc.) was 1·32 ± 0·02 μmol/l (1·3 %), compared with the manufacturer’s concentration of 1·37 μmol/l. The mean ± sd (CV) for the C-reactive protein control (Roche Diagnostics) was 9·5 ± 0·4 mg/l (4·6 %), compared with the manufacturer’s concentration of 9·1 mg/l. The multilevel controls for α1-acid glycoprotein (Roche Diagnostics) were 0·5 ± 0·01 g/l (1·1 %) and 0·8 ± 0·01 g/l (1·4 %), compared with the manufacturer’s concentrations of 0·7 and 1·2 g/l, respectively.
## Statistical analysis
Plasma Se concentrations were adjusted for inflammation using the Biomarkers Reflecting Inflammation and Nutrition Determinants of Anaemia (BRINDA) regression approach[25,26] calculated for each participant as: Se adjusted = exp (ln Se – (β1 × ln C-reactive protein) – (β2 × ln α1-acid glycoprotein)). Differences between groups were estimated using a linear regression model adjusted for parity (1 child v. > 1 child) and maternal education (non-tertiary v. tertiary).
Usual Se intakes were used to calculate the number of participants with an intake below the estimated average requirement (EAR) of 20 μg/d for 1–3 year olds[27,28]. Logistic regression was used to determine whether the odds of having Se intakes below the EAR were different between groups after adjustment for parity and maternal education.
An analysis of the Control group was used to determine the contribution of breast milk, infant formula and food groups towards daily Se intakes. The percentage contribution of each food group to Se intakes for consumers, and for the total sample, was calculated as medians and 25th and 75th percentiles (inter-quartile range). Only the Control group was included in this analysis because the BLISS intervention changed eating behaviour[13].
Univariate unadjusted linear regression analyses were used to describe associations between plasma Se (adjusted for inflammation) and potential predictor variables. These variables were decided a priori based on previous associations described in the literature(7,29–32) or were considered to be potentially associated with plasma Se concentrations for mechanistic reasons. Maternal predictor variables were household deprivation, maternal education, maternal age, smoking status during pregnancy; child predictor variables were ethnicity, sex, weight gain and linear growth between 6 and 12 months, plasma Zn, Fe deficiency anaemia, body Fe, intakes of energy, protein, Se, ‘breads and cereals’, ‘meat’, ‘dairy’, and ‘fruits and vegetables’ and consumers of breast milk, infant formula and ‘seafood’. Prior to the regression analysis, smoking status during pregnancy was collapsed into two categories: none (n 89) and any (n 8; which included daily smokers (n 2), occasional smokers (n 2) and those who quit during pregnancy (n 4)). Plasma Zn concentration was adjusted for time of blood sampling (variable adjusted for reference time of 08.00 hours) and time since last meal (variable adjusted for reference period of 90 min)[33] using the regression equation: Zn time adjusted = exp (ln plasma Zn – (β1 × adj_08.00 hours) – (β2 × adj_90 min)). Following this, an adjustment was also made to plasma Zn concentrations for inflammation using the same approach as for Se explained above. Body Fe was calculated in mg/kg using the equation: –(log10 (soluble transferrin receptor × 1000/plasma ferritin)–2·8229)/0·1207[34] and a body Fe concentration < 0 mg/kg and a Hb concentration < 110 g/l was used to define Fe deficiency anaemia[15]. All continuous variables were standardised to allow for comparison of the regression coefficients; the dietary variables were calculated as daily intakes in grams. In the adjusted multivariate regression analysis, variables were chosen to be included in the model if the regression coefficient was ≥ 0·06 μmol/l per unit (this number is approximately 0·3 sd, commonly referred to as a ‘small effect’[35]) or with a $P \leq 0$·1, and were adjusted for infant sex.
All analyses were conducted using Stata, version 15.1 (StataCorp LP). P values < 0·05 were considered to be statistically significant.
## Results
A total of 101 participants (n 51 Control and n 50 BLISS) contributed both dietary intake data by 3-d weighed diet record and a plasma Se sample (Fig. 1) at 12 months of age (50 % of initial sample). Although only twenty-two participants (10·6 %) had formally withdrawn from the study at this stage, a number of participants in both groups did not provide dietary (n 11), biochemical (n 41) or both (n 53) types of data making them ineligible for these analyses. Maternal, household and infant characteristics of those included in this analysis are shown in Table 1. There was no evidence of differences between those included and excluded from this analysis (all $P \leq 0$·05) except for maternal education (those included were more likely to have a university education: 57 % compared with 40 %, $$P \leq 0$$·044) and maternal age (those included were 2·4 years older on average, $$P \leq 0$$·002).
Table 1.Characteristics of participants who provided dietary intake and plasma *Se data* at 12 months of age(Numbers and percentages; mean values and standard deviations)VariablesControl (n 51)BLISS (n 50) n % n % Maternal and household Maternal age at birth (years) Mean32·732·4 sd 5·64·5Maternal parity First child18352244 Two children22432040 Three or more children1122816Maternal ethnicity New Zealand European45883978 Māori612510 Other00612Maternal education School only14271224 Post-secondary7141020 University30592856Household deprivation* 1–3 (low)14271428 4–725492652 8–10 (high)12241020 Infant Sex Female24473264 Male27531836Ethnicity New Zealand European39773570 Māori1223918 Other00612Infant birth weight (g)† Mean35463525 sd 459449Infant gestational age at birth (weeks) Mean39·840·0 sd 1·51·2*Household deprivation categorised into: 1–3 (low), 4–7 and 8–10 (high) using the New Zealand Index of Deprivation 2013. The index combines different dimensions of deprivation from New *Zealand census* data. A deprivation score is assigned to each meshblock (geographical area defined by Statistics New Zealand)[19].†*Available data* for Control n 50 and BLISS n 48.
## Selenium intake and status
Usual Se intakes are shown in Table 2. The OR of Se intakes below the EAR of 20 μg/d[27,28] was no different between the two groups (OR: 0·89; 95 % CI 0·39, 2·03) (Table 2). The highest usual Se intake was 37 μg/d. No dietary supplements containing Se were taken. Plasma Se concentrations (adjusted for inflammation) are also shown in Table 2. Plasma Se concentrations without adjustment for inflammation are shown in online Supplementary Table 1.
Table 2.Usual daily Se intake and status of 12-month-old toddlers by complementary feeding approach(Mean values and standard deviations; mean differences and 95 % confidence intervals; odds ratios and 95 % confidence intervals)Control (n 51)BLISS (n 50)Mean difference* 95 % CIOR95 % CIMean sd Mean sd Dietary intake† Energy (kJ/d)36486583510619–139–394, 116 Protein (g/d)31·37·629·77·4–1·7–4·6, 1·3 Se (μg/d)18·26·319·25·80·9–1·5, 3·2 Below EAR‡, n (%)326330600·890·39, 2·03Biochemical status Plasma Se (μmol/l)§ 0·810·170·850·180·04–0·03, 0·11EAR, estimated average requirement; AGP, α1-acid glycoprotein; CRP, C-reactive protein.*Mean differences and 95 % confidence intervals for BLISS compared with Controls (adjusted for maternal education and parity).†Intake reported in the 3-d weighed diet records collected at 12 months of age, and usual dietary intakes calculated using the Multiple Source Method[24].‡Se intake below the EAR of 20 μg/d for 1–3 year olds[27,28].§Adjusted for inflammation using the BRINDA[25] approach: exp (unadjusted ln plasma Se−(regression coefficient for CRP) × (CRP−(maximum of lowest decile for CRP))−(regression coefficient for AGP) × (AGP−(maximum of lowest decile for AGP))).
## Food groups contributing to selenium intakes
The food group contributing the most to total Se intakes of 12-month-old toddlers appeared to be breast milk, contributing 20 % in the whole sample and 39 % when restricted to those who consumed breast milk (Table 3). In total, ‘breads and cereals’ contributed the most Se (12 %), followed by ‘meat’ (11 %) and ‘dairy’ (10 %). When data for ‘consumers’ only were analysed both ‘breads and cereals’ (12 %) and ‘meat’ (12 %) equally contributed to Se intakes. Of those consuming infant formula (the infant formulas consumed in this study contained between 0 and 17·3 μg Se per 100 g), formula intakes contributed 9 % of daily Se intakes (Table 3).
Table 3.Contribution of food groups to the Se intakes of toddlers at 12 months of age*,† (Numbers and percentages; median values and interquartile ranges)Number of consumersContribution of food group to Se intake of consumers‡, %Contribution of food group to Se intake of total sample (n 51), % n %MedianIQR§ MedianIQR§ Food groupBreast milk28553935–49200–40Breads and cereals51100127–20127–20Meat|| 4792127–22115–21Dairy products¶ 51100105–15105–15Infant formula244790–4000–2Seafood** 81692–2100–0Fruits and vegetables5110064–1164–11Eggs316142–2810–9Miscellaneous†† 509820–420–4Nuts and seeds244710·6–200–1Legumes21410·30·1–300–0·3IQR, inter-quartile range.*Only Control group included (n 51).†Intake reported in the weighed 3-d diet records collected at 12 months of age.‡Ordered by the food group contributing the most to Se intakes by consumers (‘Consumers’: the contribution of Se intakes of the toddlers consuming this food group (e.g. breast milk contributes a median of 39 % of Se intakes for the twenty-eight toddlers consuming breast milk)).§Data expressed as median percentages (NB: mean percentages added to 100 % of total Se intakes from food groups).||Comprises all meat: red meat, poultry, pork, processed meat, etc.¶Includes cows’ milk as a drink.**Comprises fish and shellfish.††Miscellaneous comprises: fats, sugar, sweet foods, herbs and spices, sauces, spreads, beverages, etc.
## Factors associated with plasma selenium concentrations
Univariate and multivariate associations between factors decided a priori and plasma Se concentrations (adjusted for inflammation) are shown in Table 4. In the unadjusted analysis, toddlers of mothers who reported any smoking (daily, occasional, quit during) during pregnancy had on average 0·16 μmol/l lower plasma Se concentrations compared with toddlers of mothers who did not smoke during pregnancy, although this association was attenuated in the adjusted model (0·13 μmol/l; 95 % CI −0·25, −0·003). ‘ Seafood’ intake in toddlers was associated with higher plasma Se concentrations in the unadjusted analysis (0·12 μmol/l; 95 % CI 0·04, 0·20), but this association was also attenuated in the adjusted analysis. Consuming breast milk was associated with 0·12 μmol/l (95 % CI −0·19, −0·04) lower plasma Se concentrations in toddlers in the adjusted model. The R 2 for the final multivariate model was 0·30 indicating that 30 % of the variance in plasma Se concentration was explained by the factors included in this model.
Table 4.Factors associated with plasma Se concentrations (μmol/l, adjusted for inflammation) in toddlers at 12 months of age(Regression coefficients and 95 % confidence intervals)Unadjusted (n 101)Adjusted (n 96)* Regression coefficient95 % CIRegression coefficient95 % CIHousehold deprivation† 1–3 (low)0·00–0·08, 0·090·01–0·06, 0·09 4–7ReferenceReference 8–10 (high)–0·08–0·17, 0·00–0·03–0·11, 0·06Ethnicity (child) New Zealand EuropeanReference Other0·02–0·06, 0·10Sex (child), female‡ 0·03–0·04, 0·100·04–0·02, 0·11Maternal education School onlyReferenceReference Post-secondary0·07–0·04, 0·180·01–0·09, 0·12 University0·02–0·06, 0·110·01–0·07, 0·09Maternal age at infant birth (years)0·00–0·01, 0·01Smoking status during pregnancy§ –0·16–0·28, −0·03–0·13–0·25, −0·003Plasma Zn||,¶ 0·02–0·01, 0·06Fe deficient anaemia** –0·09–0·17, −0·003–0·08–0·17, 0·02Body Fe¶,†† 0·02–0·02, 0·05Energy intake¶,‡‡ –0·01–0·05, 0·02Protein intake¶,‡‡ 0·02–0·01, 0·06Se intake¶,‡‡ 0·040·002, 0·070·03–0·01, 0·07Consumes any breast milk§§ –0·12–0·19, −0·05–0·12–0·19, −0·04Consumes any infant formula|||| 0·03–0·04, 0·10Consumes any ‘seafood’¶¶ 0·120·04, 0·200·08–0·002, 0·16‘Bread and cereal’ intake¶ –0·01–0·04, 0·03‘Meat’ intake¶ –0·01–0·05, 0·02‘Dairy’ intake¶ 0·02–0·02, 0·05‘Fruit and vegetable’ intake¶ –0·01–0·05, 0·02Weight gain between 6 and 12 months¶,*** 0·02–0·02, 0·06Linear growth between 6–12 months¶,*** 0·00–0·04, 0·04*Variables chosen to be included in the final model were those with regression coefficient ≥ 0·06 μmol/l or $P \leq 0$·1, adjusted for child sex.†Household deprivation categorised into: 1–3 (low), 4–7 and 8–10 (high) using the New Zealand Index of Deprivation 2013. The index combines different dimensions of deprivation from New *Zealand census* data. A deprivation score is assigned to each meshblock (geographical area defined by Statistics New Zealand)[19].‡Female (n 56) compared with male (n 45).§Data available for n 97; any smoking (n 8) compared with no smoking (n 89).||Plasma Zn adjusted for time of blood sampling and time since last meal[33] and inflammation[25].¶*Standardised continuous* variable; dietary intake variables as daily intakes in grams.**Data available for n 100; yes (n 19) compared with no (n 81), Fe deficiency anaemia defined as Hb < 110 g/l and body Fe < 0 mg/kg.††Body Fe calculation (mg/kg): –(log10(sTfR × 1000/ferritin)−2·8229)/0·1207 from Cogswell et al. [ 34] ‡‡Usual intakes: calculated using the Multiple Source Method[24] from 3-d weighed diet records.§§Yes (n 56) compared with no (n 45).||||Yes (n 42) compared with no (n 59).¶¶Yes (n 24) compared with no (n 77).***Data available for n 97.
## Discussion
The current results demonstrate that amongst New Zealand toddlers (12 months of age) many have Se intakes that are below the EAR, this is regardless of how complementary foods are introduced to them as infants, and there was no difference in plasma Se concentrations between toddlers who had followed a baby-led approach to complementary feeding and those who followed traditional spoon-feeding. Of the infant milk sources (breast milk and infant formula), breast milk appeared to contribute the most to dietary Se intakes of consumers in this age group. For those who consumed them, ‘breads and cereals’ and ‘meat’ contributed the most Se of all the food groups, followed by ‘dairy’. In the adjusted analysis, consumption of breast milk was negatively associated with plasma Se concentrations in toddlers. Maternal smoking during pregnancy had a weak negative association, and consumption of ‘seafood’ had a weak positive association, with plasma Se concentrations.
While very few studies have assessed Se intakes in young New Zealand children, our mean Se intakes of 18·2 μg/d for Control and 19·2 μg/d for BLISS toddlers were both higher than previously reported intakes of 13·7 μg/d for New Zealand toddlers (12–24 months) collected two decades earlier[7]. However, the exclusion of breastfed toddlers from the study by McLachlan et al. [ 7] may contribute to some of the discrepancy here, as well as the fact that the earlier New Zealand Food Composition Database may not have sufficiently estimated regional variations in the Se content of foods. Our results suggest that more than half (63 % Control, 60 % BLISS) of the toddlers in this study had Se intakes below the EAR; however, it has been recently suggested that there is a great need for change to the dietary reference values for Se (for all age groups), given that the recommended intake values were set when there was scarce evidence on the health effects of Se[36].
Se is considered to be highly toxic at the upper level of intake, or UL (usually as a result of high intakes of Se supplements), with reported symptoms of hair, skin and gastrointestinal abnormalities, and fatigue[37]. While no toddlers in this study consumed Se supplements, and none had total intakes above the upper level of intake (90 μg/d[28]), evidence in adults suggests that long-term exposure at intakes lower than the current adult UL may increase the risk of type-2-diabetes[38] and this has led to debate and a call for an up to date risk assessment on the adverse effects levels for all ages[39,40].
The main (non-infant milk) food group contributing to Se intakes of the total study sample was ‘breads and cereals’, consistent with previous findings in young New Zealand children[7,31]. While offal is a good source of Se[41,42], only one participant consumed any (chicken liver pâté) at 12 months of age. The infant formulas consumed had a wide range of Se concentrations (between 0 and 17·3 μg/100 g), but interestingly, none of the ‘toddler milks’ (marketed for toddlers > 12 months of age) contained added Se. The contribution of Se intakes from different infant formulas (with varying Se concentrations) was not assessed in this study.
The finding that breast milk consumption appeared to be the largest contributor to Se intake was surprising given that consuming breast milk was associated with poorer Se status. There are a number of possible explanations for this and this finding should be treated with caution. First, it was not possible to measure breast milk intake, and the single value used for breast milk intake[23], while used in other studies, was not generated using New *Zealand data* so may not accurately reflect actual breast milk intake in these toddlers. Second, the Se content of breast milk is dependent on the *Se status* of the mother[43], but we were not able to directly measure the Se concentration of breast milk consumed by toddlers in this study. Instead, a Se value of 2 μg/100 g taken from the New Zealand Food Composition Database[20] was used, which may be too high, especially for South Island mothers and their infants. Clearly, analysis of infant breast milk intake and of the Se concentrations of breast milk from mothers in the South Island of New *Zealand is* urgently required.
The mean plasma Se concentrations reported in this study were 0·81 μmol/l for Control and 0·85 μmol/l for BLISS infants, which were higher than has been previously reported in infants (0·69 μmol/l), toddlers (0·61 μmol/l) and young children (0·79 μmol/l) in the South Island of New Zealand[7,31]. The results are also at the upper end of the range for infants and young children reported in studies internationally: 0·67–0·83 μmol/l[29,30,44], which is surprising given the lower Se concentrations reported in this region. There are currently no universal (age appropriate) criteria for low plasma Se due to the wide variability in the soil Se concentrations of different geographical locations[42], and the paucity of data on possible health effects. Earlier research suggested a cut-off of ≤ 0·82 μmol/l as a level considered to ensure optimal activity of enzymes (iodothyronine 5’ deiodinases) associated with thyroid function[45]; however, there is little evidence that concentrations above this cut-off are beneficial to all areas of human health[36]. The current study did not assess any health effects related to the plasma Se concentrations reported, and long-term implications of low *Se status* in this age group remain relatively unclear, as does the specific level/cut-off for defining low plasma concentrations.
For all toddlers, maternal smoking status during pregnancy and breast milk consumption were negatively associated with plasma Se concentrations after adjustment for child sex and other potential predictor variables in the model. Lower *Se status* of young children has previously been shown to be associated with parental[29] and household smoking[7]. Se intakes below recommendations have been reported in the New Zealand population[6,7] and low maternal *Se status* reduces breast milk Se concentrations[43] which could be contributing to the association between breast milk consumption and toddlers’ plasma Se concentrations.
This is the first study to assess whether the baby-led or spoon-feeding approach to complementary feeding has an impact on *Se status* in toddlerhood. Rigorous methods were used for the dietary analysis to ensure regional variations in food Se concentrations were taken into account, an approach not possible previously[31], and to adjust the distribution of observed intakes to estimate usual Se intakes. However, our study also has some limitations. This was a secondary analysis of the BLISS study which was not specifically designed or powered to assess Se intake and status. Se intake from breast milk was estimated, because the volume of breast milk and concentration of Se in breast milk were not measured. There were a large number of participants excluded from this analysis because of incomplete data. Lastly, the study was not designed to determine the specific health effects related to the Se intakes and plasma concentrations reported and further studies are needed to determine what, or whether any negative health consequences at these plasma Se concentrations occur.
In conclusion, Se intakes and plasma Se concentrations of 12-month-old New Zealand toddlers were no different between those who had followed a baby-led approach to complementary feeding and those who followed traditional spoon-feeding. However, more than half of toddlers had Se intakes below the EAR. Further research is required to determine whether any negative health consequences at these intakes and status occur.
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|
---
title: 'The impact of increasing the availability of lower energy foods for home delivery
and socio-economic position: a randomised control trial examining effects on meal
energy intake and later energy intake'
authors:
- Tess Langfield
- Andrew Jones
- Eric Robinson
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10011593
doi: 10.1017/S0007114522002197
license: CC BY 4.0
---
# The impact of increasing the availability of lower energy foods for home delivery and socio-economic position: a randomised control trial examining effects on meal energy intake and later energy intake
## Body
The high prevalence of obesity in most developed countries is likely to have been impacted by changes to the food environment[1,2] and, in particular, the widespread availability of energy-dense food and drink products served in large portion sizes[3,4]. Therefore, it is now widely recognised that changes to the structure of the food environment are needed to reduce population-level energy intake and obesity[1]. However, because diet and obesity are socio-economically patterned, whereby lower socio-economic position (SEP) is associated with an increased risk of higher BMI and obesity[5,6], it is imperative that interventions designed to address the food environment do not further widen SEP inequalities in obesity.
One intervention approach that targets the structure of the food environment is to increase the relative availability of lower energy food options (i.e. by increasing the proportion of food items available that are lower in kcals). Increasing availability of lower energy options may have an equitable effect on diet because unlike other types of intervention (e.g. information provision interventions), it is less reliant on consumers being motivated or able to consciously change their behaviour[7,8]. However, the extent to which increasing availability of lower energy options has an equitable effect on the diet of lower and higher SEP individuals has received some but limited empirical testing[9]. While studies to date have found that the effects of increasing availability of healthier foods on food selection are not statistically moderated by participant SEP(10–14), testing has been limited to a small number of studies that have predominantly used hypothetical food choice which does not require participants to select and consume actual meals.
A further limitation of studies examining availability interventions is that none we are aware of have examined the impact of increasing availability of lower energy options at a meal on energy intake beyond that meal. Because controlled nutrition experiments indicate that consuming less energy at a meal will sometimes result in later ‘compensation’ (i.e. consuming more energy later in the day)[15,16] and the selection of a lower energy meal may make some consumers feel that they are licensed to ‘overeat’ later in the day[17], the overall effect that increasing the availability of lower energy options has on diet is currently unclear. Furthermore, recent research indicates that SEP may affect the likelihood of compensatory eating. In particular, it has been hypothesised that because lower SEP is associated with experiencing food scarcity and insecurity, this may result in a greater drive to avoid periods of lower energy intake[18]. In line with this, studies have found that lower perceived SEP (observed or experimentally manipulated) is associated with an increased sensitivity to the energy content of foods[19] and an increased likelihood of eating beyond energy needs after consuming a lower energy meal[20]. Therefore, there is a need to understand the effect that increasing the availability of lower energy meal options has on acute (i.e. at that meal) and subsequent (i.e. after that meal) energy intake in both lower and higher SEP groups.
In the present study, we examined the effect of increasing the availability of lower energy meal options on meal energy intake and subsequent 24-h energy intake in participants of lower v. higher SEP. We primarily based SEP on highest achieved education level to be consistent with existing literature[13,14,21] and because education level is reliably related to diet quality and obesity[22,23]. In line with an increasing trend in use of online food delivery services in the UK[24], participants made supermarket ready meal choices using an online food ordering platform and meals were home delivered for consumption.
## Abstract
Increasing the availability of lower energy food options is a promising public health approach. However, it is unclear the extent to which availability interventions may result in consumers later ‘compensating’ for reductions in energy intake caused by selecting lower energy food options and to what extent these effects may differ based on socio-economic position (SEP). Our objective was to examine the impact of increasing availability of lower energy meal options on immediate meal energy intake and subsequent energy intake in participants of higher v. lower SEP. In a within-subjects design, seventy-seven UK adults ordered meals from a supermarket ready meal menu with standard (30 %) and increased (70 %) availability of lower energy options. The meals were delivered to be consumed at home, with meal intake measured using the Digital Photography of Foods Method. Post-meal compensation was measured using food diaries to determine self-reported energy intake after the meal and the next day. Participants consumed significantly less energy (196 kcal (820 kJ), 95 % CI 138, 252) from the menu with increased availability of lower energy options v. the standard availability menu ($P \leq 0$·001). There was no statistically significant evidence that this reduction in energy intake was substantially compensated for (33 % compensated, $$P \leq 0$$·57). The effects of increasing availability of lower energy food items were similar in participants from lower and higher SEP. Increasing the availability of lower energy food options is likely to be an effective and equitable approach to reducing energy intake which may contribute to improving diet and population health.
## Participants
Participants were recruited between April and July 2021. Eligibility criteria were as follows: UK residents aged 18 years or over, fluent in English, access to Internet, a camera (e.g. camera phone) and a microwave/oven to prepare ready meals at home, no current or history of eating disorders, not on medication affecting appetite, no dietary restrictions (e.g. vegetarian), no history of food allergy or anaphylaxis. Potential participants were recruited using social media posts (Facebook, Instagram, Twitter), participant mailing lists and from staff/students at the University of Liverpool. The study was described as investigating food choices, personality and mood (cover story). To be broadly representative of the UK adult, population recruitment was stratified by sex (50 % men, women) and student status (3·5 % yes). As education level was our primary measure of SEP, we recruited 50 % of the sample to be lower (up to A level or equivalent) and 50 % higher (above A level – which equates typically to University/College level and above in the USA) SEP.
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human participants were approved by the Central University Ethics Committee at the University of Liverpool (reference 8710). Written informed consent was obtained from all participants.
## Design
The study used a 2 (within-subjects: control menu v. increased availability menu) × 2 (between-subjects: lower SEP v. higher SEP) mixed design. Participants were randomised to one of two menu order conditions with randomisation stratified by sex and SEP, using the Microsoft Excel (rand()) function. Participants selected a main (from ten options) and side (from six options) from two menus which consisted of popular items from a UK supermarket (Tesco). Lower and higher energy mains and sides were defined as ≤400 kcal (1674 kJ) v. >400 kcal (1674 kJ) and ≤300 kcal (1255 kJ) v. >300 kcal (1255 kJ), respectively, based on the energy content of the range of products available and Public Health England guidance on main meal energy intake[25]. The distribution of lower v. higher energy options in the control menu condition was representative of the supermarket’s online stock when the study was conducted; $\frac{3}{10}$ mains and $\frac{2}{6}$ sides were lower energy options. Proportions were reversed in the increased availability menu ($\frac{7}{10}$ mains and $\frac{4}{6}$ sides were lower energy), with the lowest and highest energy meal items retained across both menus. See online Supplementary Materials for details about menu design, menu items and nutritional information.
## Socio-economic position
Highest educational qualification was the primary measure of SEP. Consistent with other research[26], participants with A levels or less were categorised as lower in SEP, and those with above A levels (e.g. degree level) were higher in SEP. Participants also reported their number of years in higher education, household income and subjective social status as additional measures of SEP for use in sensitivity analyses. See online Supplementary Materials for questionnaire items in full.
## Demographic and individual difference measures
Participants’ self-reported demographic (sex, age, ethnicity, employment status) and personal (physical activity days in last week, frequency of ready meal consumption, weight, height) characteristics were collected. Participants also completed eating behaviour individual difference measures that we reasoned could potentially moderate the effect of increased availability on meal energy intake. The food choice questionnaire[27] subscales relating to ‘Health’ (six items, e.g. ‘Keeps me healthy’) and ‘Weight control’ (three items, e.g. ‘Is low in calories’) motives were included. Satiety responsiveness was measured using the four-item satiety responsiveness subscale of the Adult Eating Behaviour Questionnaire[28]. To assess tendencies to plate clear, we used the five-item plate-clearing tendencies scale[29,30], and food waste concerns were assessed using the five-item food waste concerns scale[31].
## Study outcome measures
The primary outcome measures were total meal energy selected (i.e. kcal content of selected main plus side) and total meal energy consumed (in kcals). The latter was calculated by coding photographs taken of plate and packaging before and after consumption of each meal, using the Digital Photography of Foods Method[32]. Two independent coders assessed images to estimate the percentage of each food item consumed using reference images at 10 % intervals (0–100 %). Percentage consumed was then transformed based on the energy content of food items. Between the two coders, 80·4 % of coding was identical or within 10 %. For coding that was inconsistent, a third coder checked the images to resolve the difference (i.e. agreement with either coder or average between two when unclear). The secondary outcome was total later energy intake. Participants completed a dietary recall for food and drink consumption after the study meal and up to midnight the following day: Myfood24 – a validated online assessment tool for measuring self-reported 24-h energy intake[33].
## Procedure
Prior to consenting to the study, participants were made aware that the study would involve selecting food items that would be delivered to their home to be eaten as evening meals. Participants did not pay for the meals or meal delivery. After providing consent, participants accessed an online survey portal and answered demographic questions and filler mood questions. Participants then selected a main and side from each menu consecutively (order of control v. increased availability menus randomised), with the meal options presented as images and short descriptions, and the opportunity to view additional nutritional information on request (‘Yes, I’d like to see more nutritional information’). See online Supplementary Fig. S2 for how menu options were presented to participants. Participants then rated expected liking of all menu items on a separate page. This information was collected so that a substitute item that had a similar energy content and liking could be ordered if any menu items were unavailable to be delivered. Next participants provided details to enable home delivery and completed the individual difference measures. After completing these online tasks, participants were contacted by the research team to arrange a delivery date. The meals (main and side) chosen from the control and increased availability menus were delivered together, and participants were instructed to eat the two meals on separate days (meal order determined by randomisation – same as order of menu presentation) with a 48-h washout period between meals. On the morning of delivery (herein referred to as ‘Study Day 1’), participants received a text and email reminder. On Study Day 1, participants were asked to heat meals for dinner – at the usual time they ate their evening meal – as per the instructions indicated on the packaging, to take photos of their meals (i.e. plate and packaging) before and after finishing eating, and to not share food with others. On Study Day 2, participants were sent instructions to complete the dietary recall. On Study Days 3 and 4, the same process was repeated for the participants’ second meal. Once participants had completed their study days, they were emailed a debriefing questionnaire which probed what they thought the aim of the study was (later coded by two independent researchers to identify any participants guessing the study aims), before being debriefed and compensated for their time. During the study, participant questionnaires included attention checks (e.g. ‘In the past week, how many times have you been to the moon?’) as well as consistency checks (e.g. probing highest educational qualification multiple times) to identify inconsistent/inaccurate responses.
## Sample size and statistical analysis
To detect small- to moderate-sized effects of availability menu type and moderation of the effect of availability on outcome measures by SEP, after accounting for potential attrition (about 25 %), we required eighty-eight participants (forty-four lower and forty-four higher SEP). See online Supplementary Materials for detailed power calculation information. The primary analysis was a mixed ANOVA, testing the effects of menu type (within-subjects, categorical: control v. increased availability), SEP (between-subjects, categorical: lower v. higher educational qualification) and the interaction between menu type*SEP on total meal kcal selected and consumed. In sensitivity analyses, we reran primary analyses to determine whether results remained the same after the following adjustments: removing participants who guessed the study aims, substituting the primary SEP measure with alternative measures (years in higher education, subjective social status, equivalised household income), retaining all participants who were excluded from primary analyses, controlling for menu order effects (unplanned) and excluding individuals who did not receive their preferred menu items (unplanned). The primary analysis approach was repeated with total later energy intake (secondary outcome). To account for bias in self-reported daily energy intake reporting, this secondary analysis was also repeated (unplanned) after using a conservative cut-off (defined as total daily energy intake reported as being outside of the following ranges: 500–3500 kcal (2092–14644 kJ) for females and 800–4200 kcal (3347–17573 kJ) for males[34]) and a more stringent self-devised cut-off (self-reported daily energy intake <50 % of daily recommended intake; 1000 kcals (4184 kJ) for females and 1250 kcal (5230 kJ) for males) to remove participants with improbable total later energy intake. If we found evidence that the effect of availability on primary outcomes was moderated by SEP, we planned to explore whether any of the individual difference measures differed between SEP group and if any moderated the effect of availability on meal energy selection and intake (secondary analyses). We computed Bayes factors for the main effects and interactions in primary analyses for total meal energy selected, consumed and total energy intake. We used default priors (r scale fixed effects = 0·5, r scale random effects = 1, r scale covariates = 0·353). We report BF10s which indicate relative support for H1/H0, with conventional cut-offs of 1–3 as anecdotal evidence for H1, 3–10 moderate evidence for H1, 10–30 strong evidence for H1, 30–100 very strong evidence for H1 and > 100 extreme evidence for H1[35] with inverse values indicative of the same degree of evidence for H0. Frequentist analyses were conducted using SPSS version 26, and Bayes factors computed in JASP version 0.16 were used for Bayesian analyses. Level of significance for statistical tests was set at $P \leq 0$·05 for primary and $P \leq 0$·01 for secondary analyses.
## Results
A total of eighty-eight participants (50 % female) completed the study. Eleven participants (12·5 %) were excluded as follows; on the basis of not following study instructions on when to eat their meals or when to complete dietary recall (n 4), not sending meal photos (n 3), inconsistent responding on highest education level at screening and in the study leading to inconsistent categorisation of higher v. lower SEP (n 1) or failing questionnaire attention checks (n 3), leaving a total of seventy-seven for the main analysis (see Fig. 1 for CONSORT diagram). For participant characteristics, see Table 1, and for ratings of menu items see online Supplementary Materials (higher v. lower SEP participants did not differ in liking of the two menu types).
Fig. 1.CONSORT flow chart for participant enrolment and study completion.
Table 1.Summary participant characteristics by SEP group (socioeconomic position)(Numbers and percentages; mean values and standard deviations)Lower SEP (n 37)Higher SEP (n 40)Overall (n 77) n % n % n %Sex Male1823·382025·973843·95 Female1924·682025·973950·65Age Mean45·3538·3041·69 sd 10·6911·3211·51Ethnic group White3646·83950·67597·4 Mixed or multiple11·311·322·6 Asian or Asian British–––––– Black, African, Caribbean or Black British–––––– Other ethnic group––––––Student or employment status Current student––45·245·2 Full or part time2633·830395672·8 Looking after home/family45·245·2810·4 Retired45·222·667·8 Temporary or permanently sick or disabled11·3––11·3 Unemployed/other22·6––22·6Highest educational qualification No formal qualifications22·6––22·6 1–3 GCSE or equivalent56·5––56·5 4 + GCSE or equivalent1620·8––1620·8 A level or equivalent1418·2––1418·2 Certificate of higher education (CertHE) or equivalent––56·556·5 Diploma of higher education (DipHE) or equivalent––45·245·2 Bachelor or equivalent––2024·72024·7 Master’s degree or equivalent––810·4810·4 Doctorate or equivalent––33·933·9Years in higher education Mean1·225·453·42 sd 1·372·142·79Equivalised household income (£) Mean21 783·0927 997·8025 011·51 sd 16 209·1216 167·7116 381·65Subjective socio-economic status (1–10) Mean5·005·785·40 sd 1·451·251·40BMI (kg/m2) Mean33·1228·4930·71 sd 9·045·967·90Dieting status Yes67·856·51114·3 No3140·33545·56685·8 Physical activity level (no. of days in the last week)2·761·893·682·283·232·14Ready meal consumption frequency Never or not in the last year11·322·633·9 Less than once per month1316·91316·92633·8 1–3 times per month1924·71722·13646·8 1–2 times per week33·979·11013·0 3 times per week or more11·311·322·6SEP, socio-economic position. GCSE, General Certificate of Secondary Education. Values are mean values and standard deviations unless otherwise stated.
## Primary analyses
As analyses examining total meal energy selected and consumed produced the same pattern of results, analyses for energy of meal selected are reported in full in the online Supplementary Materials. There were missing data (n 1) on energy consumed due to unclear photos taken. ANOVA revealed a main effect of menu type on total kcal consumed, with 196 fewer kcal (820 kJ) consumed from the meal chosen from the increased availability menu v. the control menu, F[1,74] = 46·45, $P \leq 0$·001, η 2 $$p \leq 0$$·386. There was no main effect of SEP on kcal consumed, F[1,74] = 0·179, $$P \leq 0$$·673, η 2 $$p \leq 0$$·002, and no interaction between menu type and SEP, F[1,74] = 1·580, $$P \leq 0$$·213, η 2 $$p \leq 0$$·021, see Fig. 2. The Bayes factor for the main effect of menu type was BF10 > 100, indicative of extreme evidence for the alternative hypothesis (i.e. increased availability of lower energy foods decreases energy intake). The Bayes factor for the main effect of SEP was BF10 = 0·25, indicative of moderate support for the null hypothesis. Finally, the Bayes factor for the menu*SEP interaction was BF10 = 0·30, indicative of moderate support for the null hypothesis.
Fig. 2.Meal energy intake (kcal) by menu condition and SEP., higher;, lower. SEP, socio-economic position.
## Primary analyses (sensitivity)
No participants guessed the primary aim of the study, although a minority (n $\frac{10}{77}$) believed the study was measuring healthiness of food selected or energy intake. The pattern of results remained the same with these participants excluded. All other sensitivity analyses produced the same pattern of results as in the primary analysis, including when highest education qualification was substituted for other measures of SEP and when order of availability menus was controlled for. See online Supplementary Materials.
## Secondary analyses (total later energy intake)
In our pre-registered analyses, in error we did not specify removal of participants self-reporting implausible or very low later energy intake. Analyses produced the same results with no participants removed, when using the conservative cut-off (n 6 (8 %) removed) and using the more stringent cut-off (n 25 (32 %) removed). Given that approximately 30–35 % of 24-h energy intake recalls are estimated to be implausible[36], analyses using the more stringent cut-off are reported and the alternative analyses are reported in full in the online Supplementary Materials, see Table 2. Although total later energy intake was somewhat higher after the meal from the increased availability menu (64 kcals/268 kJ), ANOVA revealed no main effect of menu type on later kcal consumed, F[1,50] = 0·33, $$P \leq 0$$·57, η 2 $$p \leq 0$$·007. There was also no main effect of SEP, F[1,50] = 3·51, $$P \leq 0$$·067, η 2 $$p \leq 0$$·066, and no interaction between menu and SEP, F[1,50] = 0·003, $$P \leq 0$$·95, η 2 $p \leq 0$·001. The Bayes factor for the main effect of menu was BF10 = 0·23, indicative of moderate support for the null hypothesis. The Bayes factor for SEP was BF10 = 1·22, indicative of anecdotal support for the alternative hypothesis. Finally, the Bayes factor for the Menu*SEP interaction was BF10 = 0·29, indicative of moderate support for the null hypothesis.
Table 2.Energy selection and intake (in kcal) by menu type and SEP(Mean values and standard deviations)Lower SEPHigher SEPControl menuIncreased availability menuControl menuIncreased availability menuMean sd Mean sd Mean sd Mean sd Meal energy Total energy of meal selected956·76177·03746·95230·97969·45220·61694·78169·77 Total energy intake from meal809·91206·65651·08270·48828·40214·82597·73172·60Later energy intake Total energy intake after meal: dataset with stringent cut-offs for implausible energy intake* 1975·155922045·276252328·969442386·27895 Total energy intake after meal: dataset with conservative cut-offs for implausible energy intake† 1820·51598·9818576441893·67768·92066·00909·56 Total energy intake after meal: full dataset with no removal‡ 1784·576151892·147072008·639582257·631283SEP, socio-economic position.*Values derived from stringent cut-off analysis resulted in an analytic (n 52; lower SEP = 26 and higher SEP = 26) after excluding twenty-five participants whose next day energy intake was less than 1000 kcal (female) or 1250 kcal (male).†Values derived from conservative analysis cut-off analysis resulted in an analytic (n 71; lower SEP = 35 and higher SEP = 36) after excluding participants whose next day energy intake was outside of the following ranges: 500–3500 kcal (female) or 800–4200 kcal (male).‡Values derived from full dataset with no removal are from an analytic (n 77; lower SEP = 37 and higher SEP = 40).Values are mean and standard deviations for meal energy and later energy intake. Meal energy refers to energy content of meal items selected and total energy consumed from the meal. Values derived from total energy of meal selected are from an analytic (n 77; lower SEP = 37 and higher SEP = 40). Values derived from total energy intake from meal are from an analytic (n 76; lower SEP = 37 and higher SEP = 39). Later energy intake refers to self-reported energy intake after the study meal and during the next day.
## Secondary analyses (moderation by individual differences)
There was no evidence that the lower v. higher SEP groups differed on any of the individual difference measures and no evidence that individual difference measures moderated the effect of increased availability of lower energy menu options on total meal energy selected or consumed. See online Supplementary Materials for analyses in full.
## Discussion
Changing the availability of lower energy ready meal food options (main meal and side dish) from 30 % (standard availability) to 70 % resulted in participants consuming 196 fewer kcal (820 kJ) during an evening meal. Subsequent energy intake that evening and the next day was somewhat higher in the increased v. standard availability condition (+64 kcal/268 kJ), but this difference was small and not statistically significant. There was no evidence that the effects of increasing lower energy food options were moderated by SEP, indicating that this intervention approach – on immediate and later energy intake – is likely to have equitable effects for those with lower and higher SEP. These findings are in line with suggestions that increasing the availability of lower energy food options is a potentially powerful and equitable approach to improving diet[7].
No studies we are aware of have examined whether interventions designed to increase the availability of lower energy meals alter subsequent eating behaviour. It is well established from laboratory appetite experiments that reductions to energy intake are in part compensated for later in the day by eating more. For example, recent meta-analyses examining the impact that serving food with a lower energy content at a meal estimate that between 11 and 42 % of reduced energy intake at that meal is compensated for[15,16]. In the present study, this figure equated to 33 %. Critically, we found no evidence that this degree of compensation differed between participants of higher and lower SEP. Furthermore, the effects of increased availability on energy intake were not dependent on a range of participant characteristics (e.g. results were similar in participants who reported being more v. less motivated by health or weight control when making day-to-day food choices).
A better understanding of why increasing the availability of lower energy options decreases energy intake may inform attempts to identify when and for whom availability interventions will be of most benefit. In line with Pechey and colleagues[37], we presume that effects are largely explained by the observation that under conditions of lower availability, the probability of a lower energy option being highly preferred is markedly reduced than under conditions of increased availability. More direct testing of this proposed mechanism would now be informative as it suggests that if lower energy options selected to replace higher energy options are similarly liked across population subgroups, increasing the availability of lower energy food options would be a socially equitable dietary intervention.
A strength of the present study is that it is the first we are aware of to examine longer-term effects on energy intake of increasing availability of healthier foods in real-world settings, as opposed to hypothetical experiments[12] or real-world studies unable to quantify participant-level energy intake[38]. We examined energy intake for 24 h, and it is plausible that greater compensation would occur over longer time frames. However, findings on energy intake compensation over time suggest that any increase in compensation over time would likely be small[15,16]. The experimental design allowed us to isolate the independent effect of increasing availability of lower energy options, but in doing so, price was held constant across availability conditions. Cost motivates food choices, and therefore it would be informative to examine whether the present findings would be replicated when participants have to pay for their own meals. Further, while cost was held constant (price information was not provided and participants did not pay for their meals), the average retail price of higher energy options was slightly more expensive than the lower energy options offered. It is possible that obtaining the more expensive items for free would be perceived as a greater gain than obtaining the less expensive items. However, this would be contingent on participants being aware of the true price of the food items on offer, and we presume this would be unlikely. In a survey of over 3000 individuals, half responded that they eat ready meals at least weekly; in general, the prevalence of ready meal consumption in the UK is high with an estimated 79 million ready meals consumed in the UK each week[39]. The experimental paradigm meant participants ordered single-serve ready meals, so it is unclear whether these findings would apply to ready meals which serve groups of individuals (e.g. families eating together). To be able to examine food consumption in real-world settings, we relied on participants photographing their meals and reporting on their energy intake. Self-reported dietary data are prone to bias and may be larger in participants of lower v. higher SEP[40]. However, compliance with study instructions was high, and sensitivity analyses accounted for implausible/improbable dietary reporting. Because SEP is multi-faceted, a further strength of the present study is that results were consistent when using a range of SEP indices (i.e. education level, household income, subjective social status). As the sample was predominantly White, future research of more ethnically diverse samples and implementing availability interventions in real-life settings to examine longer-term changes to energy intake and body weight would now be informative. Finally, the present study was not powered to detect very small effects (such as the interaction between availability and SEP). However, Bayesian analyses of the availability manipulation and SEP interaction effect indicated support for the null (evidence of absence), as opposed to a meaningful interaction effect that was not detectable due to a lack of statistical power (absence of evidence)[41].
## Conclusions
Increasing the availability of lower energy evening meal food options in online food ordering decreases energy intake, and the impact that availability had on meal and subsequent 24-h energy intake was similar in participants of lower and higher SEP. Increasing the availability of lower energy food options is likely to be an effective and equitable public health approach to improving diet.
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|
---
title: A standardized extract of Coleus forskohlii root protects rats from ovariectomy-induced
loss of bone mass and strength, and impaired bone material by osteogenic and anti-resorptive
mechanisms
authors:
- Chirag Kulkarni
- Shivani Sharma
- Konica Porwal
- Swati Rajput
- Sreyanko Sadhukhan
- Vaishnavi Singh
- Akanksha Singh
- Sanjana Baranwal
- Saroj Kumar
- Aboli Girme
- Alka Raj Pandey
- Suriya Pratap Singh
- Koneni V. Sashidhara
- Navin Kumar
- Lal Hingorani
- Naibedya Chattopadhyay
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011618
doi: 10.3389/fendo.2023.1130003
license: CC BY 4.0
---
# A standardized extract of Coleus forskohlii root protects rats from ovariectomy-induced loss of bone mass and strength, and impaired bone material by osteogenic and anti-resorptive mechanisms
## Abstract
### Introduction
In obese humans, *Coleus forskohlii* root extract (CF) protects against weight gain owing to the presence of forskolin, an adenylate cyclase (AC) activator. As AC increases intracellular cyclic adenosine monophosphate (cAMP) levels in osteoblasts that has an osteogenic effect, we thus tested the skeletal effects of a standardized CF (CFE) in rats.
### Methods
Concentrations of forskolin and isoforskolin were measured in CFE by HPLC. CFE and forskolin (the most abundant compound present in CFE) were studied for their osteogenic efficacy in vitro by alkaline phosphatase (ALP), cAMP and cyclic guanosine monophosphate (cGMP) assays. Femur osteotomy model was used to determine the osteogenic dose of CFE. In growing rats, CFE was tested for its osteogenic effect in intact bone. In adult ovariectomized (OVX) rats, we assessed the effect of CFE on bone mass, strength and material. The effect of forskolin was assessed in vivo by measuring the expression of osteogenic genes in the calvarium of rat pups.
### Results
Forskolin content in CFE was $20.969\%$. CFE increased osteoblast differentiation and intracellular cAMP and cGMP levels in rat calvarial osteoblasts. At 25 mg/kg (half of human equivalent dose), CFE significantly enhanced calcein deposition at the osteotomy site. In growing rats, CFE promoted modeling-directed bone formation. In OVX rats, CFE maintained bone mass and microarchitecture to the level of sham-operated rats. Moreover, surface-referent bone formation in CFE treated rats was significantly increased over the OVX group and was comparable with the sham group. CFE also increased the pro-collagen type-I N-terminal propeptide: cross-linked C-telopeptide of type-I collagen (PINP: CTX-1) ratio over the OVX rats, and maintained it to the sham level. CFE treatment decreased the OVX-induced increases in the carbonate-to-phosphate, and carbonate-to-amide-I ratios. CFE also prevented the OVX-mediated decrease in mineral crystallinity. Nanoindentation parameters, including modulus and hardness, were decreased by OVX but CFE maintained these to the sham levels. Forskolin stimulated ALP, cAMP and cGMP in vitro and upregulated osteogenic genes in vivo.
### Conclusion
CFE, likely due to the presence of forskolin displayed a bone-conserving effect via osteogenic and anti-resorptive mechanisms resulting in the maintenance of bone mass, microarchitecture, material, and strength.
## Introduction
Coleus forskohlii (CF), commonly known as Coleus in English, is a medicinal herb with rich ethnopharmacological applications. CF is also used in Ayurvedic medicine to treat a variety of ailments, including inflammatory diseases, hypertension, respiratory disorders, aging, and weight management [1, 2]. CF is a rich source of secondary metabolites, including terpenoids, flavonoids, and alkaloids [3]. The major bioactive compound of Coleus root is forskolin, a labdane diterpene which is of clinical interest because of its weight-loss property. CF is the only species known to contain significant amount of forskolin [4]. Forskolin acts by increasing the accumulation of cyclic adenosine monophosphate (cAMP) without hormonal stimulation of adenylate cyclase (AC) [2, 5]. cAMP binds to, and activates protein kinase A (PKA), which then activates lipases by phosphorylating them, resulting in lipolysis. Given this mechanism of action of forskolin, CF extract is thought to have an anti-obesity effect that has been demonstrated in a few preclinical studies (5–8). Human studies, albeit scanty present inconsistent outcomes in reducing the weight of obese men and women [5, 9, 10] although it could attenuate weight gain [10]. Despite lack of strong evidence base from human studies concerning its weight-reduction effect, CF standardized to contain 10-$20\%$ forskolin is widely available as a dietary supplement.
Activation of AC by forskolin resulting in the rise in intracellular cAMP appears to underlie the osteogenic effect of the compound. The osteogenic drugs teriparatide (PTH 1-34) and abaloparatide (PTHrP 1-36) act by type 1 PTH receptor to activate AC to increase intracellular cAMP in osteoblasts [11]. However, cAMP can either stimulate or inhibit osteogenic differentiation in human mesenchymal stem cells, depending on the duration of the rise in its intracellular levels [12]. Rises in the intracellular cAMP stimulate mesenchymal stem cells (MSC) proliferation and differentiation to osteoblasts, as well as osteoblastic differentiation from pre-osteoblasts [13]. db-cAMP, a synthetic cAMP analog, stimulated osteogenic differentiation in vitro and new bone formation in vivo [14]. Sustained stimulation of cAMP signaling, on the other hand, decreases osteoblast differentiation and mineralization [15] while inducing adipocyte differentiation [13]. We previously observed that the profile of cAMP activation kinetics of forskolin matches with PTH [15], which led us to surmise that CF rich in forskolin may have an osteogenic property.
Here, we used a standardized preparation of CF root extract (CFE, rich in forskolin) and studied the osteogenic and anti-osteoporotic effects in rats. Three models were used for these purposes, i) femur osteotomy model (for rapid assessment of bone regeneration) for determining the osteogenic dose of CFE, ii) growing rats for determining the modeling-directed bone formation in intact rat bones, and iii) OVX rats (a model for postmenopausal osteopenia) to assess the effect of CFE on maintaining bone mass, microarchitecture, bone formation, bone turnover, bone strength, and bone quality. Ex vivo cultures of bone marrow cells were used to evaluate the effect of CFE on osteoblast differentiation. Finally, we determined the amount of forskolin in CFE and assessed its in vivo osteogenic effect.
## Plant material, chemicals, and reagents
CFE used in this study was procured from Pharmanza Herbal Pvt. Ltd (Anand, Gujrat, India). Forskolin was procured from Phytocompounds (Bangalore, India). Acetonitrile and methanol MS grade were procured from JT Baker and Rankem. Cell culture medium and all chemicals were procured from Sigma-Aldrich (St. Louis, MO, USA). FBS, collagenase and diaspase were purchased from Invitrogen (Carlsbad, CA, USA). Gum acacia was purchased from Santa Cruz Biotechnology, Inc. (Dallas, TX, USA).
## Preparation of analytical solutions for high-performance liquid chromatographic-based study
The solution of forskolin standard 1.0 mg/mL (1000 µg/ml) was prepared by dissolving 10.0 mg forskolin in 5.00 ml acetonitrile and making up the volume up to 10.0 ml in acetonitrile. The sample for CFE (1 mg/ml) was prepared by dissolving 25.0 mg sample in 15.0 ml of acetonitrile and making up the volume 25.0 ml. The mixture was then centrifuged, filtered, and used for further analysis using HPLC according to our previously described method [16].
## Chromatographic conditions
The analysis was performed on a Phenomenex® Luna C18 column (250 ×4.6 mm, 5 μm). With gradient elution of water and acetonitrile at a flow rate of 1.8 ml/min was carried out as follows: 0.01 – 25 min, $45\%$ B; 25 – 28 min, 45-$95\%$ B; 28 – 35 min, $95\%$ B; 35 –36 min, 95-$45\%$ B; 36 - 45 min, $45\%$ B at 220 nm wavelength for UV. The column temperature was kept at 30°C, with an injection volume was 20.0 μL [16].
## Ethics statement and husbandry
Animal husbandry and all animal experimental procedures were prior approved by the Institutional Animal Ethics Committee (Registration no.: 34/GO/ReBiBt-S/Re-L/99 CPCSEA) (IAEC/$\frac{2021}{16}$/Renew-0/Dated-$\frac{04}{01}$/2021). Female Sprague Dawley (SD) rats were obtained from the National Laboratory Animal Centre, CSIR-CDRI, Lucknow, and kept under controlled conditions: temperature (22-25°C), humidity (50-$60\%$), and a 12-hour light/dark cycle. The rats were acclimated for 8 days prior to surgery. The rats were maintained on standard rodent chow diet and purified water ad libitum during the experimental period. Rats received intramuscular ketamine (40 mg/kg) and xylazine (10 mg/kg) anaesthesia prior to all surgeries.
## Femur osteotomy model
Adult rats with a drill-hole (0.8mm) osteotomy in the femur diaphysis provide a rapid and reliable model for evaluating bone regeneration that is proportional to bone formation [17, 18]. Furthermore, this model is useful for the determination of effective osteogenic dose of a drug.
Twenty four female SD rats (220 ± 20 g) were used for femur osteotomy following a previously described protocol [19]. Post-surgery, rats were randomly divided into four groups ($$n = 6$$ rats/group); vehicle (water, orally), CFE (25-, 50- and 100 mg/kg, orally). All the treatments were given daily for 12 days. 24 h before sacrifice, all the animals were given subcutaneous (s.c.) injections of calcein (20 mg/kg). After sacrifice, bones were collected and processed for calcein labeling studies according to our previously published protocol. 60 μm sections were made through the osteotomy site using Isomet-Slow Speed Bone Cutter (Buehler, Lake Bluff, IL, USA) [19, 20]. Sections were photographed using a confocal microscope (Leica TCS SP-8, Wetzlar, Germany) and analyzed using LAS-X software.
## Modeling-directed bone formation model
Modeling-directed bone formation is the dominant event in growing rats, which enables evaluation of the osteogenic response in intact skeleton [21, 22]. Accordingly, 1-month-old 12 SD female rats (65 ± 5 g) were randomly divided into two groups ($$n = 6$$/group): vehicle (water), CFE (25 mg/kg; oral). Treatments were given for 1 month. For dynamic histomorphometry study (to measure surface-referent bone formation parameters), each animal was given two s.c. injections of calcein (20 mg/kg) at 10 days interval before sacrifice. At the end of the experiment, bones were collected for μCT, bone strength, and histomorphometry analyses, for which the details are given below. Femur length was measured with a Vernier caliper.
## Post-menopausal osteoporosis model
Adult rats with bilateral OVX is a widely used model for post-menopausal osteoporosis, which, we used to evaluate CFE’s prophylactic anti-osteoporosis impact. To this aim, 24 SD rats (220 ± 20 g, 3 months old) underwent either bilateral OVX or a sham operation (ovary intact) [19, 23]. Following surgery, the OVX rats were randomized into two equal groups ($$n = 8$$ rats/group) in the presence of two researchers: OVX + vehicle (water); OVX + CFE (25 mg/kg, oral); and the sham-operated animals received vehicle. All the therapies were given daily for 3 months. For dynamic histomorphometry, all animals received 2 doses of calcein (20 mg/kg, s.c.) before sacrifice at the interval of 10 days [19, 20].
After the treatment, serum samples, and bones (femur, tibia, and L5 vertebrae) were dissected and stored at -80°C for further studies.
## Body composition analysis
Throughout the experimental period, the body weights of each study group were measured once a week. The body composition of live rats was evaluated by the EchoMRI™-500 body composition analyzer (EchoMRI Corporation Pvt. Ltd. Singapore) 24 h before the end of the experiment [23]. Total body mass and lean mass were plotted, while fat mass was plotted by normalizing with the total body mass.
## μCT analysis of bones
Bone samples were scanned using a SkyScan 1276 computed tomography (μCT) scanner (SkyScan, Ltd., Kartuizersweg, Kontich, Belgium), in accordance with the instructions in our previously published technique [19, 23]. CTAn software was used manually to quantify various bone parameters as described previously [24]. Reconstructed μCT images underwent a blind evaluation by a third person to determine the extent of bone loss. The degree of bone loss was assessed using reconstructed μCT images that had undergone a blind examination by a third person.
## L5 compression
Biomechanical strength was measured by L5 compression test using a bone strength tester, TK 252C (Muromachi Kikai Co. Ltd. Tokyo, Japan) according to our previously published method [25].
## Nanoindentation
The rat femur bone was cut from mid-diaphysis with a low-speed diamond blade saw (IsoMet; Buehler, Lake Bluff, IL, USA), and after that, the samples were kept in epoxy for nearly 2 h for proper cured and, further samples were polished under the ground (Buehler Eco 250 grinder and polisher) with the abrasive papers of 1200, 2000, and 4000 grit size under the cooling condition and polished with a diamond solution of particle sizes of 1, 0.5, and 0.25 µm. After completion of polishing, samples were sonicated for 10 min. The experiment was performed on the T1-950 Tribo Indenter (Hysitron Inc., MN, USA) with Berkovich pyramidal tip in the moist state. Eight indents with a peak load of 3000 µN were applied to cross-section of the bone. The load sequence consists a loading time of 10 s, an unloading segment, and a hold for 10 s. The resultant load-displacement curve was used to calculate the reduced modulus (Er) and hardness (H) by the method of Oliver and Pharr [26, 27].
## Assessment of bone material
The mineral and collagen properties were analyzed by a Bruker IFS 66v/S Fourier Transformed Infrared (FTIR) spectrophotometer in the attenuated total reflectance mode, under the constant pressure, in the range of 4000 to 400 cm-1. From the obtained data, we calculated the following parameters: carbonate-to-phosphate ratio (area ratio of the carbonate peak [852-890 cm-1] to phosphate peak [916-1180 cm-1]), carbonate-to-amide I ratio (area ratio of the carbonate peak [852-890 cm-1] to the amide-1 peak [1596-1712 cm-1]) and mineral crystallinity ratio (intensity ratio of [1030 to 1020 cm-1]), which is related to crystal size and stoichiometric perfection. The amide I band peak contains several sub-peaks that provide information about the collagen matrix and the location of cross-linkage and non-cross linkage. The sub-band of the amide I peak were fited with Gaussian curves at 1610, 1630, 1645, 1660, 1675, and 1690 cm-1 using peak analyzing tools OriginPro 8.5 software [26, 28].
## Bone histomorphometry
Surface-referent bone formation was measured by bone histomorphometry by double calcein labeling in accordance with our previously published protocols to determine the mineralizing surface per bone surface (MS/BS), mineral apposition rate (MAR), and bone formation rate per bone surface (BFR/BS) [19, 20, 29].
## Measurement of serum bone turnover markers
Rat cross-linked C-telopeptide of type I collagen (CTX-1) kit (Cat. No. E-EL-R1456) and pro-collagen type I N-terminal propeptide (PINP) kit (Cat. No. E-EL-R1414) were purchased from Elabscience, USA, and measured in accordance with manufacturer’s instructions.
## Measurement of osteogenic gene expression
Rat pups (1 to 2-day-old) were treated with vehicle or forskolin (1- and 2.5 mg/kg) for 5 days. After treatment, calvaria were removed and processed for RNA isolation by trizol method [30]. qPCR was performed by SYBR green chemistry (Thermo Fisher Scientific, Ealtham, MA, USA) for the quantitative determination of bone morphogenetic protein 2 (BMP2), type 1 collagen (Col I), receptor activator of nuclear factor kappa-B ligand (RANKL), and osteoprotegerin (OPG) as described previously [31]. cDNA was synthesized by using 2μg RNA (Cat no. 4368814, High Capacity cDNA Reverse Transcription Kit, Applied Biosystems by Thermo Fisher Scientific). *All* genes were analyzed using a real-time PCR machine (QuantStudio© 3 Real-time PCR Instrument, A28132), keeping GAPDH as control. Primer sequences are listed below:
## Ex vivo mineralization assay
Bone marrow was collected from rat femur by flushing out using PBS and cells were measured by a hemocytometer. In a 6-well plate, bone marrow cells were seeded at a density of 2 × 106 per well in a differentiating medium (α-MEM with 10 mM β-glycerophosphate, 50 μg/ml ascorbic acid, and 100 nM dexamethasone). After every 48 h media was changed for 21 days. After 21 days, cultures were fixed using $10\%$ formalin and 40 mM Alizarin red-S stain was used to visualize mineralized nodules. $10\%$ cetylpyridinium chloride (CPC) was used to extract the stain and the mineralization was calorimetrically measured at OD 595 nm [32].
## Osteoblast culture and ALP assay
Rat pups (1 to 2-day-old) were used to culture calvarial osteoblasts (RCO) as described previously [20]. For ALP assay, cells were trypsinized at $90\%$ confluency and seeded in 96-well plate. The adherent cells were treated with CFE (7.8-, 15.63-, 31.25-, 62.5-, 125- and 250 µg/ml) or forskolin (100 pM, 1 nM, 10 nM, and 100 nM) for 48 h in a differentiation medium (α-MEM supplemented with 10 mM β-glycerophosphate and 50 μg/mL ascorbic acid). After 48 h, ALP activity was assessed by adding diethanol amine buffer (DAE) with 2 mg/ml para-nitrophenyl phosphate (pNPP) and measured colorimetrically at OD 405 nm.
## cAMP and cGMP assays
RCO were treated with CFE or forskolin for 0 min, 5 min, 15 min, 30 min, 60 min and 90 min. After treatments, cAMP and cGMP levels were determined by ELISA kits (Cayman Co., Ann Arbor, MI, USA) in accordance with the manufacturer’s protocol.
## Statistical analyses
Data are presented as the mean ± standard error of the mean (SEM). One-way ANOVA with a post hoc Tukey test using GraphPad Prism 5 and a significance level of $0.05\%$ ($95\%$ significance) was used to assess statistical differences between the various treatment groups. An unpaired t-test using GraphPad Prism 5 with a significance level of $0.05\%$ ($95\%$ significance) was used in the experiments with two groups to assess statistical differences.
## Qualitative and quantitative analysis of CFE
The HPLC method was applied for simultaneous quantification of analytes in CFE. The chromatograms for the standard mixture and samples are presented in Supplementary Figure 1. The chromatogram of the sample solution obtained in the test for the content of forskolin showed a major peak at a retention time corresponding to that of the forskolin reference standard. Other diterpene peaks in the sample chromatogram exhibited an additional peak corresponding to isoforskolin. The detection was based on approximate relative retention time/minute, which for isoforskolin and forskolin were respectively 0.51 and 1.00 [16]. Forskolin and isoforskolin content in the CFE were $20.969\%$ and $3.396\%$, respectively.
## In vitro effect of CFE in RCO
Osteoblasts are the principal cells that are involved in fracture healing, so we firstly studied the effect of CFE on the differentiation of RCO by ALP activity. At 7.8-, 15.63- and 31.25 μg/ml concentrations CFE significantly increased ALP activity in RCO over the vehicle-treated RCO (Figure 1A). As forskolin is an AC activator, we next studied the effect of CFE (15.63 μg/ml) on intracellular cAMP kinetics in RCO and observed a significant increase in the cAMP levels compared with vehicle-treated RCO (Figure 1B). Furthermore, at the same concentration, CFE (15.63 μg/ml) increased the cGMP levels compared with the vehicle-treated RCO (Figure 1C).
**Figure 1:** *CFE stimulated osteoblast differentiation, cAMP and cGMP in vitro and promoted bone regeneration at the femur osteotomy site. (A) RCO were treated with CFE, and differentiation was assessed by ALP assay. (B) RCO were treated with CFE at the indicated time points and cAMP and (C) cGMP production were measured. (D) Adult female rats were treated with vehicle and CFE at indicated doses after femur osteotomy for 12 days, and representative images (10X) of calcein deposition at the osteotomy site are shown. (E) Quantification of the calcein deposition data are presented (n = 6 bones/group). All data are expressed as mean ± SEM; *p < 0.05, **p < 0.01, ***p<0.001 vs. vehicle.*
## CFE increased bone regeneration at the fracture site
In obese men, 250mg CFE has been used to study its impact on body mass [5, 9]. When converted based on body surface area, rat dose comes to 50 mg/kg. We tested the bone regenerative effect of CFE at 25-, 50- and 100 mg/kg doses by calcein labeling at the femur osteotomy site. Compared with vehicle-treated rats, CFE at all doses significantly increased calcein intensity (Figures 1D, E). Since 25 mg/kg dose, which is a half of the human equivalent dose of CFE showed significant bone regenerative effect, we selected this as the minimum effective dose in subsequent studies.
## CFE promoted modeling-based bone growth in female rats
Daily supplementation of CFE (25 mg/kg) increased the femur length compared with the vehicle-treated (control) group (Figure 2A). Trabecular bones at metaphysis and cortical bone parameters were studied using μCT. Compared to the control group, CFE increased bone volume/tissue volume (BV/TV%), trabecular number (Tb. N) and trabecular thickness (Tb. Th) compared with control (Figure 2B). Cortical parameters including cortical thickness (Ct. Th) and bone area (B.Ar) were significantly increased by CFE over the control (Figure 2C).
**Figure 2:** *CFE promoted new bone formation in growing rats. (A) Femur length. (B) Representative μCT images (left panel) and quantitative μCT parameters of the tibia metaphysis (right panel). %BV/TV, percent bone volume per tissue volume; Tb.N, trabecular number; Tb.Th, trabecular thickness. (C) Representative μCT images (upper panel) and quantitative μCT parameters of the femur and tibia diaphysis (lower panel). Ct.Th, Cortical thickness and B.Ar, bone area. (D) Left panel showing the representative images of double calcein labeling (scale bar, 100 µm) and histomorphometry parameters (right panel) of the indicated groups. (E) 3-point bending strength of femur was determined by a bone-strength tester. All data are expressed as mean ± SEM (n = 6 bones/group); *p <0.05, **p <0.01, and ***p<0.001 vs. vehicle-treated group.*
The effect of CFE on bone accrual was measured by dynamic histology by time-spaced calcein labeling study in the periosteal (p) region of the femur diaphysis. Surface-referent bone formation parameters calculated from this study, including pMS/BS (percentage of bone surface undergoing active formation), pMAR (indicating an average rate of osteoblast activity) and pBFR (total bone formation rate during the study period) were significantly increased in the CFE group compared with the control (Figure 2D).
Increase in the surface referent bone formation parameters indicative of increased periosteal apposition complemented our observation of higher Ct. Th in the CFE group over the control and is likely to afford greater resistance to fracture [33]. Accordingly, we measured the bending strength of femur and observed that maximum power and energy to failure were significantly higher in the CFE group compared with control (Figure 2E).
The treatment of CFE had no effect on the body weight compared to the vehicle treated group (data not shown).
## CFE showed bone conserving and osteogenic effect in OVX rats
Because CFE promoted bone regeneration and stimulated modeling-based bone growth, we speculated that it would have bone conserving effect in OVX model of osteopenia. At the end of 3 months of treatment, body composition of all groups were assessed by Echo-MRI. Compared with the sham, OVX rats had increased total body mass, lean mass, and fat mass. CFE had no effect on OVX-induced increase in total body mass and lean mass but significantly decreased fat mass (Supplementary Figure 2).
We next studied the effects of CFE on appendicular (tibia) and axial (L5) skeletons of OVX rats (Figure 3A for representative images). Bone mineral density (BMD) and BV/TV were significantly decreased in OVX rats compared with sham, and CFE significantly increased these parameters over the OVX. Tb. N and Tb. Th were reduced in the OVX group and CFE significantly increased Tb. Th only. Consequently, trabecular spacing (Tb.sp) that was increased in the OVX group was significantly reduced by CFE treatment. Similar effects were observed in L5 as OVX-induced decrease in BV/TV, Tb. N and Tb. Th were significantly reversed by CFE with consequent recovery of Tb.sp. ( Figure 3B).
**Figure 3:** *CFE prevented bone loss in osteopenic rats. (A) Representative images of tibia metaphysis, and L5 vertebrae are shown. (B) Shown are the quantitative μCT parameters of the tibia metaphyses, and L5. BMD, bone mineral density; Tb.Sp, trabecular spacing; Conn.D., connectivity density; and SMI, structure model index. (C) The L5 compression strength was determined by a bone-strength tester. All data are expressed as mean ± SEM (n = 6 bones/group); *p <0.05, **p <0.01, and ***p<0.001 vs. sham.*
Although there was not a significant difference among groups in maximum power and failure energy of the L5 after the end of the treatment, stiffness was significantly lower in the OVX compared with the other groups (Figure 3C).
We next studied whether the preservation of bone mass and strength by CFE in OVX rats occur by an osteogenic mechanism. Dynamic histology of proximal tibia showed significant decreases in MS/BS, MAR and BFR/BS in the OVX rats compared with the sham, and the CFE group completely maintained these parameters to the level of the sham (Figure 4A). Complementing the osteogenic effect of CFE through increase in surface-referent bone formation, ex vivo mineralization of BM stromal cells in the CFE-treated OVX rats was significantly higher than the OVX group (Figure 4B).
**Figure 4:** *CFE has an osteoanabolic effect in osteopenic rats. (A) Upper panel showing representative images (scale bar, 100 µm) of single and double calcein labeled bone surfaces at tibia metaphysis; white arrows- single label and yellow arrows- double label surfaces; and the lower panel showing the histomorphometry parameters in the indicated groups. (B)
Ex vivo mineralization assay was performed in bone marrow stromal cells obtained from the indicated groups. (C) Serum procollagen type I N-propeptide (PINP), cross-linked C-telopeptide of type I collagen (CTX1) levels and their ratio were determined by ELISA from the serum of rats with indicated treatments. For ELISA, serum samples of n = 6 rats from each group were taken. For ex vivo mineralization femurs and for histomorphometry tibia sections of n = 3 rats from each group were used. All data are expressed as mean ± SEM; *p <0.05, and ***p<0.001 vs. sham.*
Consistent with the in vivo and ex vivo osteogenic effect of CFE, we observed that the serum bone formation marker, PINP, that was significantly decreased in the OVX group was maintained to the sham level by CFE. Conversely, CFE suppressed the OVX-induced increase in CTX-1, the serum resorption marker. Accordingly, PINP-to-CTX-1 ratio, an indicator of “anabolic window” that was decreased in the OVX group, was maintained to the sham level by CFE (Figure 4C).
## CFE improved mineral composition and material properties of bones in OVX rats
The mineral-based parameters including mineral crystallinity and carbonate:phosphate ratio were respectively decreased and increased in the OVX group, and CFE treatment maintained these parameters to the levels of sham. The carbonate:amide-I ratio was significantly increased in the OVX group, while CFE treatment maintain this parameter to the sham level (Table 1).
**Table 1**
| Parameters | SHAM | OVX+VEHICLE | OVX+CFE |
| --- | --- | --- | --- |
| FTIR | FTIR | FTIR | FTIR |
| Carbonate : Phosphate | 0.0115 ± 0.0007726 | 0.03041 ± 0.003989*** | 0.01401 ± 0.0003478 |
| Mineral crystallinity | 0.9993 ± 0.01559 | 0.9949 ± 0.001087* | 0.9961 ± 0.00054 |
| Carbonate : Amide I | 0.03869 ± 0.001656 | 0.08877 ± 0.01514** | 0.03525 ± 0.001264 |
| Nanoindentation | Nanoindentation | Nanoindentation | Nanoindentation |
| Modulus (Gpa) | 22.74 ± 1.422 | 17.23 ± 1.251* | 18.74 ± 1.044 |
| Hardness (Gpa) | 0.9991 ± 0.1281 | 0.5988 ± 0.07142* | 1.146 ± 0.08074 |
We next studied the material properties of bones by nanoindentation. Under a 3000 µN load, the OVX group had significantly lower modulus and hardness compared with sham and CFE groups (Table 1).
## Osteogenic effect of forskolin
Since CFE is rich in forskolin we surmised that it contributes to the osteogenic effect of the extract. In the osteoblast ALP assay for assessing differentiation, the EC50 of forskolin was 3.8 μM (Figure 5A). Prolonged increase in cAMP as caused by theophylline caused osteoblast apoptosis [15]. On the other hand, PTH through Gsα-coupled activation of PTH receptor-1 stimulates AC to increase osteoblastic cAMP which leads to osteoblast differentiation [34]. Therefore, we compared the intracellular cAMP kinetics of forskolin (at 10 nM) with PTH. Forskolin had a greater total cAMP level than PTH (Figure 5B). Furthermore, forskolin increased the intracellular cGMP levels in the RCO compared with vehicle treated RCO (Figure 5C).
**Figure 5:** *Forskolin has osteogenic effect in vitro and in vivo. (A) RCO were treated with forskolin at the indicated concentrations and differentiation was assessed by ALP assay. (B) RCO were treated with forskolin (10nM) for the indicated time points and intracellular cAMP and (C) cGMP production were measured. (D) Rat pups (1-day old) were injected with forskolin at the indicated doses for 5 consecutive days and the relative expression of osteogenic genes in the calvarial tissue were measured. All values are expressed as mean ± SEM; *p <0.05, **p<0.01 and ***p<0.001 vs. sham.*
## Forskolin stimulated osteogenic genes' expression in vivo
The in vivo osteogenic efficacy of forskolin (1- and 2.5 mg/kg) was assessed by injecting it to rat pups and at both the doses forskolin showed positive osteogenic effects. Real-time PCR (qPCR) data showed that forskolin increased the expression of BMP2 and Col I in the treatment groups compared with the vehicle treated pups. There was no change in RANKL/OPG ratio among the groups (Figure 5D).
## Discussion
We observed that CFE has osteogenic effect that resulted in a) enhanced bone accrual during growth and b) preservation of bone mass in estrogen deficiency (OVX model). The increase in bone mass by the osteogenic impact of CFE in OVX rats was accompanied by the significant inhibition of bone resorption resulting in increased bone strength and improved bone quality. Moreover, the osteogenic compound, forskolin present in high amount in CFE likely contributed to its observed positive skeletal effect.
We used femur osteotomy model as it is suitable for rapid quantitative assessment of bone regeneration due to osteoblastic action in vivo. Our dose determination study in this model found a 25 mg/kg oral dose, which is half the adult human equivalent dose to be effective in bone regeneration. Osteogenic efficacy of CFE at lower dose is advantageous as it reduces the possibility of adverse hepatic effects reported in some preclinical studies [8, 35, 36]. Postmenopausal osteoporosis is a chronic disease that requires life-long therapeutic intervention or preventive measures. Using 25 mg/kg CFE, we studied the skeletal effects of CFE in OVX rats for three months which is comparable to 9 human years [37].
In growing animals, modeling is the dominant event in bone formation particularly in the cortical shafts of long bones [38]. Evaluation of pMS/BS, pMAR and pBFR by dynamic histology at diaphysis in growing rats showed significant increase over the control suggesting enhanced osteoblast activity giving rise to the modeling-directed apposition of periosteal bone. Increased modeling-directed apposition may have contributed to increased bone width as evidenced from increased cortical thickness (Ct. Th) and larger cross-sectional bone area (B.Ar) in the CFE group. In addition, bones with greater cortical thickness will require more strength in bending, and accordingly we observed that femurs of CFE treated rats required greater energy in breaking in three-point bending test, suggesting functional bone accrual. Because peak bone mass achievement have direct consequence on the incidence of fracture risk in old age, we surmise that CFE supplementation by adolescent girls and women till the fourth decade of life before menopause would contribute to maximizing peak bone mass and thereby protect them from the development of osteoporosis and fragility fracture after menopause.
In an osteopenic model of rat induced by bilateral OVX, there is a simultaneous decrease in bone formation and increase in bone resorption [30]. In the current study, CFE treatment in OVX rats maintained trabecular bones of both axial and appendicular skeleton. Trabecular bones are readily lost under the estrogen deficient condition leading to compression fracture of spine. We observed that CFE by conserving the trabecular bones afforded resistance against compression collapse of L5 by increasing stiffness. Our ex vivo data showed that the OVX-induced loss of mineralizing ability by the stromal cells was maintained by CFE treatment likely by expanding the pool of osteoblast precursor cells that were subsequently recruited to the remodeling site. Expansion of the osteoblastic pool appeared to have increased MAR which is dependent on the number of functional osteoblasts within the basic multicellular unit (BMU) at the remodeling site. Increase in osteoblast precursors and their differentiation in the bone marrow of CFE-treated rats together appeared to enhance the surface-referent bone formation leading to an increase in stiffness.
One of the limitations of mechanical strength testing ex vivo is that it is impacted by the size and shape of the bones. We therefore next studied the lamellar-level bone mechanical properties by nanoindentation to assess the mechanical environment to which bone cells are exposed, and subsequently coordinate the adaptation to loads experienced at the whole bone level. The elastic modulus, representing elastic deformation was reduced in the OVX group but was comparable between the sham and CFE groups. Hardness, representing resistance to plastic deformation was also decreased in the OVX group and was comparable between the sham and the CFE groups. A significant increase in the hardness in the CFE over the OVX group indicated the greater formation of new mineralized bone and corroborated the bone formation-promoting effect of CFE. Indentation modulus, also known as indentation stiffness, indicates lamellar-level stiffness and correlates with calcium content [39]. A lower value of indentation modulus in the OVX group compared to the sham control suggests greater deformation, which was maintained to the levels of sham by CFE, and may explain higher stiffness in compression test at L5 of CFE group over the OVX.
The carbonate-to-phosphate ratio depicts carbonate substitution in the mineral lattice, and can alter the apatite crystallinity by substitution to phosphate. Our data showed that OVX rats have higher carbonate-to-phosphate and carbonate-to-amide I ratios, which were attributed to high levels of remodeling and variability in bone material composition. A higher carbonate-to-phosphate ratio in the OVX rats would limit crystal growth thereby reducing BMD. An increased carbonate-to-phosphate ratio may positively correlate with fracture risk and bone aging in both humans and animals. Restoration of carbonate-to-phosphate ratio by CFE in OVX rats suggests mitigation of osteoporotic changes that cause a reduction in bone strength.
We next studied the osteogenic effects of forskolin in vitro and in vivo. The differentiation promoting effect of forskolin in osteoblast is known for a long time [40] although its in vivo effects have not been studied. Forskolin stimulated ALP, cAMP and cGMP in calvarial osteoblasts. In vivo, it upregulated osteogenic genes in the calvarium of new born pups. Being an AC activator, forskolin induced cAMP like the osteoanabolic drug, PTH. However, unlike PTH, forskolin increased cGMP. One of the major limitations of PTH is the loss of osteogenic window with time due to activation of RANKL via the upregulation of PKA pathway [41]. Forskolin is known to activate PKA resulting in osteogenic response in osteoblast cultures [41]. However, there are equivocal reports regarding its effect on RANKL, one showed increase in RANKL/OPG ratio and the other showed inhibition [41, 42]. We observed that despite upregulation of osteogenic genes, RANKL/OPG ratio was unchanged by forskolin. Moreover, because cGMP levels is inversely related with osteoclast formation [43], and we observed that CFE and forskolin increased intracellular cGMP, which may be attributed to decrease in the osteoclastogenic serum marker CTX-1 in the CFE treated OVX rats compared with the OVX rats treated with vehicle. Increased intracellular cGMP levels by CFE and forskolin suggested their guanylate cyclase stimulatory effect beside AC activation, and requires further studies. The unchanged RANKL/OPG ratio further explained why forskolin being a PKA activator did not increase bone resorption marker, CTX-1, in the CFE treatment, unlike PTH. Because CFE treatment maintained the PINP: CTX-1 ratio at the sham level, which had reduced by half in the OVX group, the osteogenic effect of CFE is expected to continue unabated. Such type of mechanism leading to bone conservation has a distinct advantage over PTH which only stimulates bone formation while resorption continues resulting in the loss of its effect over time.
## Conclusions
Our study demonstrated that at a half of human equivalent dose, CFE acts as a dual agent by stimulating bone formation and inhibiting bone resorption in rats, and consequently improves bone mass, strength, and quality. These attributes could potentially afford significant protection from fragility fracture in postmenopausal women. High concentration of forskolin in CFE contributed to in vitro and in vivo osteogenic effects as well as anti-resorptive effect in vivo. Because CFE is already used by humans in the form of a nutraceutical for weight management, our findings in the preclinical models demonstrating significant salutary effects in bone may stimulate conducting clinical studies in postmenopausal osteoporosis.
## 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 Institutional Animal Ethics Committee (Registration no.:34/GO/ReBiBt-S/Re-L/99 CPCSEA) (IAEC/$\frac{2021}{16}$/Renew-0/Dated-$\frac{04}{01}$/2021).
## Author contributions
NC, LH, and CK conceptualized the idea of manuscript. CK performed major experiments. KP, SS, and SR helped in the osteotomy and OVX surgery. NC and CK wrote and evaluated the manuscript. µCT of bones and calcein labeling assessment in osteotomy study was conducted by CK and VS. CK and AS performed μCT, calcein labeling assessment, bone strength testing, and bone histomorphometry. OVX study was conducted by CK and SB, and the analysis related to trabecular bone parameters, bone strength test experiment, and body composition analysis were conducted by CK. Bone dynamic histomorphometry, ex-vivo mineralization, and ELISA were performed and analyzed by CK. qPCR was performed by CK and SrS. AG and LH did the extract preparation and forskolin analysis in the extract. SK and NK performed nanoindentation and FTIR experiments. AP, SPS, and KS performed the relevant phytochemical analyses. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Authors AG and LH were employed by Pharmanza Herbal Pvt. 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/fendo.2023.1130003/full#supplementary-material
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title: Silver nanoparticle interactions with glycated and non-glycated human serum
albumin mediate toxicity
authors:
- Hee-Yon Park
- Christopher Chung
- Madeline K. Eiken
- Karl V. Baumgartner
- Kira M. Fahy
- Kaitlyn Q. Leung
- Evangelia Bouzos
- Prashanth Asuri
- Korin E. Wheeler
- Kathryn R. Riley
journal: Frontiers in Toxicology
year: 2023
pmcid: PMC10011623
doi: 10.3389/ftox.2023.1081753
license: CC BY 4.0
---
# Silver nanoparticle interactions with glycated and non-glycated human serum albumin mediate toxicity
## Abstract
Introduction: Biomolecules bind to and transform nanoparticles, mediating their fate in biological systems. Despite over a decade of research into the protein corona, the role of protein modifications in mediating their interaction with nanomaterials remains poorly understood. In this study, we evaluated how glycation of the most abundant blood protein, human serum albumin (HSA), influences the formation of the protein corona on 40 nm silver nanoparticles (AgNPs) and the toxicity of AgNPs to the HepG2 human liver cell line.
Methods: The effects of glycation on AgNP-HSA interactions were quantified using circular dichroism spectroscopy to monitor protein structural changes, dynamic light scattering to assess AgNP colloidal stability, zeta potential measurements to measure AgNP surface charge, and UV-vis spectroscopy and capillary electrophoresis (CE) to evaluate protein binding affinity and kinetics. The effect of the protein corona and HSA glycation on the toxicity of AgNPs to HepG2 cells was measured using the WST cell viability assay and AgNP dissolution was measured using linear sweep stripping voltammetry.
Results and Discussion: Results from UV-vis and CE analyses suggest that glycation of HSA had little impact on the formation of the AgNP protein corona with protein-AgNP association constants of ≈2x107 M-1 for both HSA and glycated HSA (gHSA). The formation of the protein corona itself (regardless of whether it was formed from HSA or glycated HSA) caused an approximate 2-fold decrease in cell viability compared to the no protein AgNP control. While the toxicity of AgNPs to cells is often attributed to dissolved Ag(I), dissolution studies showed that the protein coated AgNPs underwent less dissolution than the no protein control, suggesting that the protein corona facilitated a nanoparticle-specific mechanism of toxicity. Overall, this study highlights the importance of protein coronas in mediating AgNP interactions with HepG2 cells and the need for future work to discern how protein coronas and protein modifications (like glycation) may alter AgNP reactivity to cellular organisms.
## 1 Introduction
The medicinal use of nanoparticles has expanded in the last few decades, including diagnostic, imaging, and therapeutic applications. This growth is driven by the increasing control of the chemical and physical properties of nanoparticles (NPs). In parallel, a mechanistic understanding of cellular and organismal response to NPs has developed. At the molecular level, NPs entering a biological system adsorb a population of biomolecules, including proteins, lipids, sugars, or DNA. This biomolecular coating is dominated by an abundance of proteins, forming a protein corona (PC) (Lynch et al., 2007; Zhang et al., 2018; Griffith et al., 2020; Lima et al., 2020). The PC significantly alters NP physicochemical properties, including surface charge and agglomeration state, as well as biological fate, biodistribution, and toxicity (Walczyk et al., 2010; Fleischer and Payne, 2014; Caracciolo et al., 2017; Shannahan, 2017; Nierenberg et al., 2018; Lima et al., 2020). Although often cited as a barrier to development of nanomedicines, the PC can also be used to increase sensitivity and therapeutic efficacy of NPs (Caracciolo et al., 2017; Corbo et al., 2017; García Vence et al., 2020).
Formation of the PC is mediated by the properties of the NP, as well as the properties of the solution and population of biomolecules within the solution. Biochemical features of the latter may vary across populations and individuals, depending upon physiological conditions and disease states. There is also significant evidence of spatiotemporal changes to the composition of the corona as biomolecules undergo dynamic association and dissociation with the NP surface and with each other over time and as the NP is transported through a biological organism (Cai et al., 2022; Cox et al., 2018; Dell’Orco et al., 2010; Weiss et al., 2019). Thus, PCs formed in serum, around otherwise identical NPs, vary significantly depending upon whether a patient is pregnant, has a cold, or has diabetes, among other medical conditions (Hajipour et al., 2014; Corbo et al., 2017; García Vence et al., 2020; Ju et al., 2020). This personalized PC, in turn, not only dictates the abundance and populations of proteins in the corona, but also results in variations in the zeta-potential of the protein coated NP and its interaction with cells (Hajipour et al., 2014; Ju et al., 2020). Therefore, the personalized PC can be used to extrapolate and identify biomarkers for disease, while serving to improve the safety and efficacy of nanomedicines.
Among the biophysical features of proteins that change across individuals are post-translational modifications (PTMs). Despite the abundance and significance of PTMs, their role at the nano-bio interface remains relatively unexplored. Glycation and glycosylation are among the most abundant PTMs. Glycosylation, which refers to the enzyme-mediated addition of glycans, plays a variety of structural and functional roles—from molecular recognition and cellular signaling to protein trafficking and enzymatic regulation (Varki and Gagneux, 2015). Glycation, on the other hand, refers to the non-enzymatic addition of monosaccharides to proteins. Changes in glycosylation and glycation profiles are associated with a variety of diseases, including cancer and diabetes (Varki and Gagneux, 2015). For example, HbA1c, a common indicator test for diabetes, examines glucose bound to hemoglobin due to glycation and serves as an indicator of long-term blood glucose levels. In parallel, the glycation of another common blood protein, human serum albumin (HSA), can be used similarly. Interestingly, glycation contributes to changes in HSA structure and function, depending on the nature and amount of glycation (Qiu et al., 2021).
While impacts of protein glycation have not been specifically interrogated within PC studies, a few studies have assessed the role of glycosylation on PC formation and NP cellular interactions. Such studies are often motivated by the understanding that glycosylation can mediate cellular uptake of drugs (Cai et al., 2018; Ghazaryan et al., 2019). In the context of PC studies, glycosylation alters the population of proteins in the PC and alters adhesion of NPs to cell membranes (Wan et al., 2015; Ghazaryan et al., 2019; Clemente et al., 2022). More recently, the PC was used to identify enrichment of glycoproteins and identify disease biomarkers (Trinh et al., 2022). In a more focused study of just one commonly glycosylated blood protein, transferrin, significant variations in binding strength were observed with glycosylation, as well as protein structural changes upon interaction with gold NPs (Barbir et al., 2021). With an understanding of the impact of glycans on NP interactions and PC formation, the safety and efficacy of NP-enabled drugs and diagnostics will increase, enabling precision medicine and personalized medicines (Hajipour et al., 2014; Corbo et al., 2017; García Vence et al., 2020).
Due to their antimicrobial properties, silver nanoparticles (AgNPs) are used in a variety of medical conditions, including bandages for long-term wound healing for diabetic wounds (Choudhury et al., 2020). In disease states such as poorly managed diabetes, high blood sugar drives glycation of common blood proteins and may influence PC formation and AgNP behavior. Thus, we aimed to assess the role of glycation on the interaction of the most abundant blood protein, HSA, with AgNPs, and evaluate subsequent impacts on cellular toxicity. In our study, we chose 40 nm AgNPs, which is within the size range of AgNP sizes commonly used in wound dressings (Sussman et al., 2015; Paladini and Pollini, 2019; Krishnan et al., 2020). HSA is glycated under a variety of conditions (Varki and Gagneux, 2017) and has been consistently identified as a component of the AgNP-PC formed from human serum and plasma (del Pilar Chantada-Vázquez et al., 2019; Gorshkov et al., 2019; Lai et al., 2017; Shannahan, 2017). We hypothesized that glycation of HSA would alter interactions with AgNPs, due to changes in the accessible functional groups on HSA, as well as other changes in structure upon glycation. In turn, we also speculated that these structural changes would alter HSA-coated AgNP cellular impacts, as glycation of HSA been shown to alter absorption, distribution, efficacy and excretion of other drugs (Qiu et al., 2021). We expand upon previous studies of HSA and AgNP interactions to first evaluate the role of HSA glycation in AgNP and HSA structural stability and biophysical properties, including AgNP agglomeration, oxidation, and charge state. To evaluate the impacts of AgNPs on protein interactions and structure, binding affinities and protein structures are also compared. Second, we place these biophysical results in the context of downstream impacts by monitoring the toxicity of HSA and gHSA coated AgNPs in a live human liver cell line. Finally, we evaluate the effect of the HSA and gHSA PCs on AgNP dissolution as a first step towards understanding the mechanism by which PCs alter AgNP toxicity.
## 2.1 Materials
Sodium citrate monobasic, sodium chloride, Trizma base, glycine, hydrochloric acid, $70\%$ nitric acid, HSA, and glycated HSA (gHSA) were from Sigma-Aldrich (St. Louis, MO). The degree of glycation of gHSA was 3 mol hexose (as fructosamine) per mol albumin and the purity was $94\%$ according to the manufacturer’s certificate of analysis. BioPure citrate stabilized AgNPs were purchased from nanoComposix (San Diego, CA). AgNPs had a nominal diameter of 40 nm and were supplied at a concentration of 4.8 nM in 2 mM citrate solution.
Unless otherwise noted, all solutions were prepared in Millipore water (18.2 MΩ·cm at 25°C) and all samples were prepared in a buffer consisting of 5 mM citrate—5 mM NaCl buffer at pH 6.5 (herein, citrate buffer). The pH of the buffer was adjusted through dropwise addition of 0.1 M and 1.0 M NaOH. Stock solutions of HSA and gHSA were prepared to a concentration of 5 μM in Millipore water. Stocks were aliquoted and frozen at −20°C for later use.
Human hepatoma (HepG2) cells were obtained from ATCC (Manassas, VA); Dulbecco’s modified *Eagle medium* (DMEM) from Mediatech (Manassas, VA); fetal bovine serum and penicillin—streptomycin from Invitrogen (Carlsbad, CA); sodium pyruvate and MEM non-essential amino acids from Life Technologies (Carlsbad, CA); trypsin/EDTA from CellGro (Manassas, VA); and WST cell proliferation assay kit from Dojindo Molecular Technologies (Rockville, MD).
## 2.2 Preparation of AgNPs-HSA and AgNPs-gHSA
The various techniques used across this study have different sample concentration requirements, so the concentration of AgNPs, HSA, and gHSA were varied to improve the signal-to-noise for each experiment. Specifically, for dynamic light scattering (DLS), zeta potential, UV-vis spectroscopy, and dissolution studies the concentration ratio was maintained as approximately 7×104:1 protein:AgNPs, which was achieved by incubating 24 pM. AgNPs with 1.5 μM HSA or gHSA. For cell viability studies the concentration ratio was also approximately 7 × 104:1 protein:AgNPs, which was achieved by incubating 10 pM. AgNPs with 0.70 μM protein or by incubating 100 pM. AgNPs with 7.0 μM protein. Langmuir adsorption isotherms were performed using a concentration ratio of 2 × 104:1 protein:AgNPs, which was achieved by incubating 24 pM. AgNPs with varying concentrations of protein (25–500 nM). The decrease in the protein:AgNP ratio was necessary to achieve optimal binding conditions for quantitative analysis. For CE, the AgNP concentration was increased to improve detection sensitivity while the protein concentration was decreased to maintain the separation efficiency, resulting in a lower molar ratio than used in the other experiments. CE studies were performed using a concentration ratio of 6 × 102:1 protein:AgNPs, which was achieved by incubating 24 pM. AgNPs with varying concentrations of protein (50–150 nM). Finally, for circular dichroism (CD) studies, the protein concentration was increased to improve detection sensitivity while the AgNP concentration was decreased to minimize interference due to light scattering from the NPs, which also resulted in a lower molar ratio than used in the other experiments. CD studies were performed using a concentration ratio of 3 × 102:1 protein:AgNPs, which was achieved by incubating 3.0 nM AgNPs with 0.94 μM protein.
## 2.3 AgNP characterization
AgNPs were characterized using dynamic light scattering (DLS) to measure the hydrodynamic diameter, polydispersity index (PDI) and zeta potential. Measurements were recorded with a Malvern Zetasizer Nano-ZS instrument (Malvern, PA). All samples were prepared with a citrate buffer that was twice filtered with a 0.2 μm nylon syringe filter. Triplicate samples of AgNPs were prepared to a final concentration of 24 pM and contained 0 or 1.5 μM HSA or gHSA. Samples were incubated in the dark at room temperature for 24 or 72 h prior to analysis. DLS measurements were recorded using 173° backscatter mode after temperature equilibration for 2 min at 25°C. For each replicate sample, 11 sub-runs were recorded per measurement and 5 measurements were recorded and averaged. Reported values represent the average and standard deviation of three preparative replicates of AgNPs, AgNPs-HSA, and AgNPs-gHSA. Zeta potential measurements were recorded in the same way, but the number of sub-runs per measurement was set to automatic and constrained between 10–100. A Pd dip cell was used to measure zeta potential and values were determined using the Smoluchowski equation.
## 2.4 UV-vis spectroscopy
UV-vis experiments were performed using a Cary UV-vis spectrophotometer (Agilent Technologies, Inc.). AgNPs were prepared to a final concentration of 24 pM. in citrate buffer and were titrated with HSA or gHSA in the concentration range 0–500 nM. All samples were prepared in triplicate and incubated for 24 h in the dark at room temperature. All samples were analyzed in a semi-micro quartz cuvette with a 1 cm pathlength. Absorbance spectra were recorded from 400—450 nm at 60 nm·min−1. The shift in the localized surface plasmon resonance (LSPR) band was used to determine the association constant, K a, for the formation of the AgNP-protein complex according to (Boulos et al., 2013; Dennison et al., 2017; Boehmler et al., 2020): ∆λ∆λmax=KaCp1+KaCp [1] where Δλ is the shift in the LSPR band relative to the sample without protein, Δλ max is the maximum shift in the LSPR band, and C p is the protein concentration. Qualitative absorbance spectra of AgNPs were also recorded using the same concentration ratio of protein:AgNPs used in other studies. Specifically, 24 pM. AgNPs were incubated for 24 h in the dark at room temperature in buffer alone or in the presence of 1.5 μM HSA or gHSA. Absorbance spectra were recorded from 350 to 500 nm using a scan rate of 300 nm·min−1.
## 2.5 Capillary electrophoresis (CE)
CE was carried out on a P/ACE MDQ Plus capillary electrophoresis system from AB SCIEX. An uncoated fused silica capillary was used with an inner diameter of 50 μm. The total length of the capillary was 60.2 cm and the length to the detector was 50.0 cm. The separation buffer consisted of 5 mM Tris—500 mM glycine with a pH of 7.8. Each day the capillary was flushed successively for 10 min with Millipore water, 10 min with 0.1 M NaOH, 10 min with Millipore water, and 20 min with separation buffer. Between each run, the capillary was flushed for 1 min with Millipore water and 2 min with separation buffer. The separation buffer and the solutions used for rinsing the capillary were filtered with a 0.2 μm nylon syringe filter prior to use. Triplicate samples of AgNPs were prepared to a final concentration of 240 pM. in the separation buffer and contained 0, 50, 100, or 150 nM HSA or gHSA. All samples were incubated in the dark at room temperature for at least 30 min prior to analysis. Samples were injected hydrodynamically at 4 psi for 5 s (≈35 nL injection volume) and a 25 kV separation voltage was applied (applied field ≈ 415 V/cm). Each replicate sample was subjected to triplicate CE analysis to obtain reliable parameters for quantitative analysis, as described below.
Non-equilibrium capillary electrophoresis of equilibrium mixtures (NECEEM) was used to characterize AgNP-protein complex formation (Berezovski and Krylov, 2002; Berezovski et al., 2003; Krylov and Berezovski, 2003; Okhonin et al., 2004; Riley et al., 2018). In accordance with NECEEM theory, electropherograms were integrated to determine the areas under the unbound AgNP peak (A 1), the AgNP-protein complex peak (A 2), and in the region of dissociation (A 3; Supplementary Figure S3). These peak areas were used to calculate the ratio of unbound to bound AgNPs, according to: R=A1A2+A3 [2] Then, using the calculated value of R and the initial concentrations of protein and AgNPs, [P]0 and [AgNPs]0, respectively, the dissociation constant was calculated, according to: Kd=P01+R−AgNPs01+1R [3] To determine the rate constant for AgNP-protein complex dissociation, k off, CE electropherograms were fit in region A 3 according to: It=It0ekofftAgNP−PtAgNP−tAgNP−Pt−t0 [4] where I t is the absorbance intensity at some point in time t and I t0 is the intensity of the absorbance signal at time t 0. Time t 0 represents the beginning of the exponential decay between the unbound AgNP peak at time t AgNP and the AgNP-protein complex at time t AgNP-P (Supplementary Figure S3). Using the calculated values of K d and k off, the rate constant for AgNP-protein complex association, k on, was calculated according to: kon=koffKd [5]
## 2.6 Circular dichroism (CD) spectroscopy
Samples to be analyzed by CD were prepared by diluting HSA or gHSA to a concentration of 0.94 μM in 20 mM sodium phosphate buffer at pH 7.4. Samples were analyzed in a cylindrical quartz cuvette with a 1 mm pathlength and analyzed with an Olis Rapid-Scanning monochromator. A subsequent scan was taken after the addition of AgNPs to a final concentration of 3.0 nM and for a total sample volume of 280 μL. Measurements were recorded in the wavelength range 185–260 nm and the number of increments was set to 150. Consistent with previous studies of this kind (Fleischer and Payne, 2014), spectra were acquired in millidegrees (θobs) and converted to mean residue ellipticity (θ), according to: θ=MW θobs10 l C n [6] The mean residue ellipticity (units of degrees cm2 dmol-1) is a function of the observed signal in millidegrees, the average molecular weight of the protein (MW), path length (l in cm), protein concentration (C in g/L), and the total number of amino acids (n). The % α-helicity of the protein was determined according to (Adler et al., 1973): % α helicity=−θMRE−40003300−4000 [7] The percent α-helicity of a protein is a function of the mean residue ellipticity at 208 nm ([θMRE]) minus the contribution from the β-form and random coil conformations at 208 nm [4000]. The observed value is compared to the mean residue ellipticity of a pure α-helix protein [33000].
## 2.7 Cell culture and WST assay
HepG2 cells were maintained and grown in 100 mm tissue culture dishes (Greiner, Bio-One, Monroe, CA, United States) using DMEM supplemented with $10\%$ fetal bovine serum, sodium pyruvate, MEM non-essential amino acids, and $1\%$ penicillin-streptomycin, at 37°C in a $5\%$ CO2 humidified environment. The cells were grown to $70\%$–$80\%$ confluence (in approximately 7–10 days) and passaged using $0.25\%$ trypsin/EDTA. For cell viability assays, cells (15,000 cells per well) were seeded in 96-well flat bottom plates and allowed to proliferate for 48 h. Cells were then washed with culture media alone (no AgNP or protein control), with culture media containing 0.70 or 7.0 μM HSA or gHSA (no AgNP control), or with culture media containing 10 pM or 100 pM of uncoated or protein-coated AgNPs (200 μL total volume per well). All samples were prepared in triplicate. Protein-coated AgNPs for the cell viability assays were prepared by reacting 100 pM AgNPs with 7.0 μM HSA or gHSA or 10 pM AgNPs with 0.7 μM HSA or gHSA for 10 min in DMEM. Control cells and cells exposed to the AgNP preparations were incubated for either 24 h or 72 h at 37°C in a humidified atmosphere containing $5\%$ CO2. At collection, the cells were washed with media to remove the NPs and eliminate any interference due to the NPs.
Cell viability was then measured using the commercially available WST assay according to the manufacturer’s instructions. WST solution (20 μL) was added to cells at a 1:10 dilution in DMEM (200 μL total volume per well), followed by a 2-h incubation period at 37°C in a humidified atmosphere containing $5\%$ CO2. Absorbance was measured at 570 nm using a Tecan Infinite 200 PRO plate reader (Tecan, Switzerland). Background absorbance due to NPs was recorded using no cell controls for all the NP preparations and subtracted from the absorbance values of the experimental samples. Cell viabilities (percent) were thereafter calculated relative to controls not treated with AgNPs or proteins.
## 2.8 Linear sweep stripping voltammetry (LSSV)
LSSV was carried out as previously reported (Hui et al., 2019; Boehmler et al., 2020). Briefly, a three-electrode setup consisting of a glassy carbon working electrode, Ag/AgCl reference electrode, and Pt wire counter electrode was used to carry out the analysis. Measurements were recorded using a BASi Epislon Eclipse potentiostat and C-3 Cell Stand from BioAnalytical Systems, Inc., (West Lafayette, IN). All solutions were sparged with N2 (g) for 10 min and had a final dissolved oxygen concentration of approximate 8.0 mg·L−1. Each day, the working electrode was polished with 0.05 μm alumina polish and conditioned using 200 cycles of cyclic voltammetry from −0.5 to 0.35 V at 0.3 V·s−1. Between experiments, all electrodes were rinsed thoroughly with Millipore water, while the working electrode was placed in a $35\%$ nitric acid solution for 30 s, rinsed thoroughly with Millipore water, and sonicated in Millipore water for 30 s. Stripping voltammetry was carried out by deposition at −0.5 V for 30 s with stirring, followed by a 5 s quiet time, and a linear sweep from −0.5 to 0.35 V at 0.1 V·s−1. A same-day matrix-matched calibration curve was prepared with Ag(I) standards in the concentration range 0–240 μg·L−1.
The initial dissolution rate of AgNPs, AgNPs-HSA, and AgNPs-gHSA was measured by placing citrate buffer and the appropriate volume of protein (final concentration of 300 nM) into the electrochemical cell and sparging for 10 min. Then, the appropriate volume of AgNPs was added so that the final concentration was 4.7 pM (protein:AgNP ratio of ≈7 × 104:1) and LSSV was carried out every 5 min for 2 h. The experiment was repeated in triplicate and the resulting dissolution curves (plots of [Ag(I)]dissolved vs. time) were used to determine the dissolution rate constant, k dissolution, according to: ln1−AgItAgNP0=−kdissolutiont [8] The percentage of dissolved silver, %AgNPsdissolved, was determined by normalizing the [Ag(I)]dissolved measured at the end of the dissolution kinetics experiment ($t = 2$ h) to the initial silver concentration placed into solution [Ag]0, according to: % AgNPsdissolved=AgIdissolved,$t = 2$hAg0×100 [9] The dissolution of AgNPs was also monitored after 24 and 72 h incubation in buffer alone or buffer containing protein. Six samples of AgNPs, AgNPs-HSA, and AgNPs-gHSA were prepared in citrate buffer with final concentrations of 24 pM. AgNPs and 1.5 μM protein (protein:AgNP ratio of ≈7 × 104:1) and incubated in the dark at room temperature. After 24 h, three replicates of each sample were analyzed using LSSV to determine the percentage of dissolved Ag(I) using same-day matrix matched calibration curves. After 72 h from the initial exposure, the remaining three replicates of each sample were analyzed in the same manner.
## 3.1 Characterization of AgNP-protein complex formation
The formation of AgNP-protein complexes was confirmed qualitatively by measuring the hydrodynamic diameter, PDI, and zeta potential of AgNPs alone, in the presence of HSA, or in the presence of gHSA. Samples were analyzed at time points of 24 and 72 h following incubation to assess the stability of the AgNPs over time. After 24 h, an increase in the hydrodynamic diameter of AgNPs was observed in the presence of both HSA and gHSA relative to the no protein control (Figure 1A; Supplementary Figure S1; Supplementary Table S1). However, the magnitude of this increase differed between the two proteins, with the addition of HSA resulting in a larger increase in AgNP diameter (≈20 nm) and the addition of gHSA resulting in a more modest increase (≈7 nm). Differences in the thickness of the HSA and gHSA PCs formed on the AgNP surface could be a result of differences in the protein structure and/or orientation on the surface (vide infra). After 72 h, an increase in the diameter of AgNPs was observed suggesting slight aggregation of the particles over time. No significant differences were noted for the protein-coated AgNPs after 72 h, suggesting that the coronas was relatively stable and prevented significant aggregation of the AgNPs (Figure 1A; Supplementary Figure S1; Supplementary Table S1).
**FIGURE 1:** *(A) Hydrodynamic diameter (B) PDI, and (C) zeta potential of AgNPs after 24 and 72 h incubation in buffer alone or buffer containing HSA or gHSA. AgNPs were prepared to a final concentration of 24 pM and the proteins to a final concentration of 1.5 μM. All solutions were prepared in 5 mM citrate—5 mM NaCl buffer (pH 6.5).*
After 24 h incubation, the increase in AgNP diameter upon addition of HSA or gHSA was accompanied by either no change or a slight decrease in the PDI, providing further evidence that the formation of the AgNP-PCs provided some steric stabilization of the colloidal suspension (Figure 1B; Supplementary Table S1). Based on the decrease in PDI values, the degree of steric stabilization conferred by the proteins was more significant for gHSA than for HSA (Supplementary Table S1). After 72 h, the PDI value for all AgNPs increased, suggesting changes in the homogeneity of the samples over time and the potential for particle-particle or protein-protein aggregation. The formation of the AgNP-protein complex was further demonstrated by a decrease in the AgNP zeta potential (toward more positive potential) after 24 h incubation with HSA or gHSA (Figure 1C; Supplementary Table S1). After 72 h incubation, the zeta potential significantly decreased for the sample of AgNPs alone, further supporting the likelihood that the uncoated AgNPs had begun to aggregate. The zeta potentials for the protein-coated AgNPs showed only a subtle decrease in magnitude, further supporting the stability conferred by the protein coating. Overall, only subtle differences were observed between the AgNP-HSA and AgNP-gHSA coronas and more significant changes were observed between uncoated and protein-coated AgNPs.
The formation of AgNP-protein complexes was also confirmed quantitatively using UV-vis spectroscopy and CE to measure association and rate constants for complex formation. The 40 nm AgNPs exhibited a prominent LSPR band at approximately 420 nm that shifted to longer wavelength in the presence of HSA and gHSA (Supplementary Figure S2). Langmuir adsorption isotherms were constructed to determine the association constant, K a, for AgNP-protein complex formation (Figure 2). The K a values for HSA and gHSA were 2.3 (±0.2) × 107 M−1 and 2.4 (±0.1) × 107 M−1 ($$n = 3$$, R 2 > 0.97), respectively, suggesting that both proteins had similar affinity for the AgNP surface (Table 1).
**FIGURE 2:** *UV-vis Langmuir adsorption isotherms for complex formation between AgNPs and (A) HSA and (B) gHSA. The experimental data was fit using Eq. 1 (R
2 > 0.97). AgNPs were prepared to a concentration of 24 pM and the protein concentrations were as indicated. All solutions were prepared in 5 mM citrate—5 mM NaCl buffer (pH 6.5).* TABLE_PLACEHOLDER:TABLE 1 The formation of AgNP-protein complexes was further quantified using CE and NECEEM analysis. While NECEEM theory was initially developed to measure DNA-protein complexes, it has recently been demonstrated as an effective tool to measure the NP-PC (Riley et al., 2018). According to NECEEM theory, injection of the equilibrium mixture (containing unbound AgNPs, unbound protein, and AgNP-protein complexes) onto the capillary and application of the separation voltage will lead to a non-equilibrium condition, whereby each component of the mixture will migrate according to their unique electrophoretic mobilities. As a result, when the AgNP-protein complexes dissociate and the unbound AgNPs and protein migrate away from one another in the capillary, they are no longer proximate and cannot reestablish the AgNP-protein complex (i.e., a non-equilibrium condition) (Berezovski and Krylov, 2002; Berezovski et al., 2003; Krylov and Berezovski, 2003; Okhonin et al., 2004; Riley et al., 2018). By directly monitoring the dissociation of AgNP-protein complexes, both the dissociation constant (K d) and the on/off rate constants (k on/k off) can be determined in a single experimental run (Berezovski and Krylov, 2002).
In this work, only the AgNPs generated an absorbance signal in the electropherogram because the concentration and volume of protein injected onto the capillary was too low to be detected. In the absence of protein, a single peak at approximately 3.7 min was observed and attributed to the AgNPs (Figure 3, red trace). With the addition of HSA, a new peak was observed at approximately 3.3 min and was attributed to the AgNP-HSA complex due to an observed increase in absorbance intensity of the peak with increasing protein concentration (Figure 3). Electropherograms for NECEEM analysis of AgNPs-gHSA complexes exhibited similar features (Supplementary Figure S4). By applying quantitative NECEEM analysis (Eqs 2, 3; Supplementary Figure S3), the K d values of AgNP-HSA and AgNP-gHSA complexes were obtained and then converted to K a values for ease of comparison. The values obtained by NECEEM were consistent with those obtained using UV-vis spectroscopy within error, confirming that HSA and gHSA have similar affinity for the AgNP surface (Table 1). NECEEM analysis of the dissociation kinetics was also carried out (Eqs 4, 5; Supplementary Figure S3), and likewise, no significant differences were noted between the k on and k off values determined for the AgNP-HSA and AgNP-gHSA complexes. Generally, the values obtained suggest fast adsorption and slow desorption of the proteins from the AgNP surface (Table 1).
**FIGURE 3:** *Representative CE electropherograms of AgNPs in the presence of increasing concentrations of HSA. The boxed region highlights the AgNP-HSA complex peak which increases with increasing [HSA]. Electropherograms are vertically offset for clarity. AgNPs were prepared to a concentration of 240 pM and the protein concentrations were as indicated. All solutions were prepared in 5 mM Tris—500 mM Gly buffer (pH 7.8).*
Finally, changes to protein structure upon PC formation were probed using CD spectroscopy (Figure 4A; Table 2). A comparison of the two proteins in the absence of AgNPs revealed slight differences in the protein secondary structure, with gHSA exhibiting slightly greater α-helicity than HSA (Figure 4A; Table 2). Others have noted changes in HSA structure upon glycation, where the degree and exact nature of those changes depend upon the type of monosaccharide, concentration, and length of exposure (Nakajou et al., 2003; Qiu et al., 2021). Upon the addition of AgNPs to each protein solution, the α-helical character of the protein decreased, as measured by changes in the ellipticity at 208 and 220 nm and the calculated % α-helicity (Figure 4B; Table 2). While CD suffers from low sensitivity and an inability to distinguish free from surface-adsorbed protein, it can provide qualitative and semi-quantitative analysis of changes in protein secondary structure, which may be attributed to the formation of NP-protein complexes. Generally, changes in protein ellipticity can be taken as further evidence of the formation of the NP-PC. Our results demonstrate that the reduction of α-helicity upon interaction with AgNPs was slightly larger for HSA than for gHSA, which suggests possible differences in the surface interaction of HSA and gHSA due to protein glycation.
**FIGURE 4:** *CD spectra of HSA and gHSA (blue and green, respectively) alone (solid lines) and with AgNPs (dashed). Spectra, in units of mean residue ellipticity (MRE), are the average of 3 consecutive scans. (A) Raw CD spectra. (B) CD difference spectra calculated by subtracting the spectrum of HSA (blue) or gHSA (green) from that in the presence of AgNPs. The black vertical lines correspond to the spectral peaks at 195, 208, and 222 nm. The protein concentration was 0.94 μM and the AgNP concentration was 3.0 nM. All solutions were prepared in 5 mM sodium phosphate buffer (pH 7.4).* TABLE_PLACEHOLDER:TABLE 2
## 3.2 Cell viability studies
Studying the effect of protein coating on cell viability is important because NPs used in the body will inevitably be coated with a PC when they come in contact with biological fluids (e.g., blood) or culture medium. Such a protein coating has been shown to influence targeting abilities, cellular uptake, and immunotoxicity of NPs (Monteiro-Riviere et al., 2013; Nierenberg et al., 2018). Thus, we hypothesized that the HSA and gHSA PCs would each alter cell toxicity, but that there would be no significant differences between their impact since biophysical analyses suggested that the coronas had remarkably similar properties under the chosen experimental conditions. To evaluate this hypothesis, we investigated the role of protein coatings on the toxicity of AgNPs to human cells. We chose liver hepatocellular carcinoma (HepG2) cells as our cell model, as they are widely used in the literature to determine toxicity. The liver has also shown to be a target organ for several NPs, making liver cells biologically relevant for toxicity studies (Balasubramanian et al., 2010; Lankveld et al., 2010; Khosravi-Katuli et al., 2017; Wu and Tang, 2018).
HepG2 cells were incubated with varying concentrations of AgNPs under three conditions: AgNPs exposed to HSA, gHSA, and no protein (uncoated AgNPs). Control studies included incubation of HepG2 cells in culture media alone or with culture media containing different concentrations of HSA or gHSA. After 24 or 72 h incubation, a WST assay was performed to evaluate cell viability. As expected, relative to cells incubated in cell culture media alone, no toxicity was observed for cells exposed to just HSA or gHSA with no AgNPs (Supplementary Table S2). The toxicity due to the addition of AgNPs was concentration dependent with higher levels of toxicity observed at higher AgNP concentrations. Interestingly, the results revealed increased toxicity for both HSA-coated and gHSA-coated AgNPs relative to uncoated AgNPs (Figure 5; Supplementary Table S3).
**FIGURE 5:** *Percent cell viability of HepG2 cells, as determined by WST assay, after 24 and 72 h exposure to 10 pM and 100 pM AgNPs, AgNPs-HSA, or AgNPs-gHSA. Cell viability data was corrected by subtracting background absorbance due to AgNPs and normalized against HepG2 cells incubated in culture media alone (no protein and no AgNPs). Error bars represent the standard deviation of three samples and statistical significance was independently evaluated for each protein condition relative to the no protein control using a two-tailed t-test evaluated at the 95% (*) or 99% (**) confidence interval.*
## 3.3 Towards a mechanistic understanding of protein mediated AgNP toxicity
As a first step towards investigating the mechanism by which the HSA and gHSA PCs decrease HepG2 cell viability, LSSV was used to measure AgNP dissolution in the absence and presence of both proteins. For all LSSV analyses, same-day matrix-matched calibration curves were obtained, which was important to account for any impact of the proteins on the electrochemical signal. All calibration curves had sub-micromolar LODs (typically around 0.10–0.30 μM) under all experimental conditions evaluated (Supplementary Tables S4, S5) suggesting that the protein did not unduly effect LSSV analyses.
First, the initial rate of dissolution was measured for AgNPs alone or in the presence of HSA or gHSA over the first 2 h following incubation. The dissolution rate constant, k dissolution, of AgNPs alone was 1.6 × 10−4 min−1 indicating rapid dissolution of AgNPs after dilution in buffer (Supplementary Figure S5; Supplementary Table S4). In contrast, AgNPs added to buffer containing either HSA or gHSA exhibited slow dissolution and a nearly constant concentration of Ag(I) over the 2 h analysis period, so dissolution rate constants were unable to be measured (Supplementary Figure S5; Supplementary Table S4). At the conclusion of the 2 h AgNP dissolution experiment, the faster dissolution kinetics of AgNPs in the no protein condition led to a 2-fold increase in the percentage of dissolved Ag(I) relative to AgNPs in the presence of HSA and gHSA (Supplementary Figure S5; Supplementary Table S4).
Analysis of AgNP dissolution behaviors was extended by measuring the percentage of dissolved Ag(I) after 24 and 72 h incubation in buffer alone, or in buffer containing HSA and gHSA. These time points were chosen to exactly mimic the conditions used for the cell viability assay. Under all experimental conditions a slight increase in the percentage of dissolved Ag(I) was observed after 72 h incubation relative to 24 h (Figure 6; Supplementary Table S5). This increase was most significant for the AgNPs incubated in buffer only, consistent with kinetic dissolution experiments that showed the AgNPs alone undergo much faster dissolution. Relative to the AgNPs alone, AgNPs incubated with HSA or gHSA exhibited a statistically significant decrease in the percentage of dissolved Ag(I) at both 24 and 72 h following incubation (Figure 6; Supplementary Table S5). Specifically, the dissolution of AgNPs in the presence of protein was ≈5-fold lower than the no protein control after 24 h and ≈6-fold lower after 72 h.
**FIGURE 6:** *Percent dissolution of AgNPs after 24 and 72 h incubation in buffer alone or buffer containing HSA or gHSA. Dissolution was measured by LSSV and the percentage of dissolved Ag(I) was quantified using Eq. 9. AgNPs were prepared to a final concentration of 24 pM and the proteins to a final concentration of 1.5 μM. All solutions were prepared in 5 mM citrate—5 mM NaCl buffer (pH 6.5). Statistical significance was independently evaluated for each protein condition relative to the no protein control using a two-tailed t-test evaluated at the 99% (**) confidence interval.*
## 4 Discussion
Biophysical analyses demonstrated subtle differences in the HSA and gHSA PCs, including the thickness of the corona measured using DLS (Figure 1A) and changes in the protein secondary structure upon interaction with AgNPs measured using CD spectroscopy (Figure 4). Inherent differences in the chemical composition and secondary structure of HSA and gHSA due to glycation may alter the orientation of the proteins on the AgNP surface, which may contribute to the observed differences in the corona thickness in DLS analyses. In a previous study, the thickness of the PC formed on quantum dots varied between unmodified HSA and HSA modified either through succinylation or amination due to differences in protein orientation on the surface (Treuel et al., 2014). Further, the subtle increase in α-helicity upon glycation of HSA measured using CD spectroscopy has been previously observed and is presumed to be due to slight structural changes conferred by the glycation of specific amino acid residues (Coussons et al., 1997; Barzegar et al., 2007). Consistent with previous results, a decrease in α-helicity of HSA and gHSA upon interaction with AgNPs suggests slight unfolding of the protein as it adsorbs to the NP surface, presumably due to favorable interactions (e.g., hydrophobic) between specific regions of the protein and the NP surface (Shang et al., 2007; Bardhan et al., 2009; Pan et al., 2012; Fleischer and Payne, 2014). For example, lysine residues are commonly glycated in HSA (Anguizola et al., 2013), which can lead to changes in protein structure that cause further changes in NP-protein interactions. Our results demonstrate that the reduction of α-helicity upon interaction with AgNPs was slightly larger for HSA than for gHSA, which is consistent with previous studies of transferrin interactions with citrate-coated AgNPs (Barbir et al., 2021), and further suggests possible differences in the surface interaction of HSA and gHSA due to protein glycation.
Quantitative UV-vis and CE studies (Figures 2, 3) showed no distinguishable differences between the equilibrium and rate constants for the formation of AgNP-HSA and AgNP-gHSA complexes. AgNP-protein association constants (K a values) obtained in this work are also similar to those reported in the literature for BSA and 40 nm citrate-stabilized AgNPs (Dasgupta et al., 2016; Boehmler et al., 2020). Ultimately, under our experimental conditions, biophysical characterization indicates no significant differences in the formation of the AgNP-HSA and AgNP-gHSA complexes, but distinct changes in AgNP physical properties upon corona formation compared to protein-free conditions. Specifically, the formation of the HSA and gHSA PCs resulted in increased colloidal stability of the AgNPs as evidenced by less significant changes in AgNP diameter and zeta potential over time (Figures 1A, C). The stabilization of AgNPs due to the formation of the PC is consistent with a previous study that showed that the analogous protein bovine serum albumin (BSA) can stabilize AgNPs even in solution conditions that promote AgNP aggregation (Levak et al., 2017). Our overall observations are also consistent with a previous study conducted with SiO2 NPs, where only subtle changes in NP diameter, PDI, and zeta potential were observed between PCs with and without glycosylation, and more significant changes were observed between the NPs with and without protein (Wan et al., 2015).
The HSA and gHSA PCs also altered the reactivity of the AgNPs to HepG2 cells. Specifically, after 72 h exposure to protein-coated AgNPs, HepG2 cells showed an approximately 2-fold decrease in cell viability compared to uncoated AgNPs. These results indicate no observable differences in toxicity between AgNPs coated with HSA compared to gHSA, for the experimental conditions and endpoints evaluated herein, a conclusion consistent with the biophysical experiments. Notably, there are limited studies on the role of PC glycation in toxicity. For example, Wan et al. [ 2015] demonstrated increased cell membrane adhesion and uptake in macrophages upon removal of glycans from the human plasma PC on silica NPs. More recent studies with different NPs show that glycation can vary nanocarrier cellular uptake in different directions, depending on the protein (Ghazaryan et al., 2019). Given the differences across studies, including corona composition, NP types, and cell types, it is difficult to draw any meaningful connections. Further, both studies are limited to evaluation of the role of glycation in hard corona formation. Glycation of the PC may also influence soft corona formation and other toxicity related endpoints, which deserve further attention.
The observed increase in toxicity with a PC is consistent with a recent paper that shows an increase in toxicity for citrate-coated AgNPs when coated with serum proteins (Barbalinardo et al., 2018). This is not a consistently reported finding, as other studies have demonstrated that the PC may decrease cellular uptake of AgNPs and their toxicity (Monteiro-Riviere et al., 2013; Shannahan et al., 2015). The observed differences in toxic responses may be due to myriad of factors, including nanoparticle size and concentration, exposure times, different cell types, and choice of toxicity assays (Sohaebuddin et al., 2010; Kroll et al., 2011). Additional studies are necessary to further evaluate the source(s) of the differences, as well as the role of PC on the response of AgNPs to human cells. The oxidative dissolution of AgNPs to release bioactive Ag(I) is the primary contributor to AgNP antimicrobial properties. Ag(I) dissolved from AgNPs can interfere with transport proteins and enzymes in the respiratory chain reaction, compromise the proton-motive force, and interfere with phosphate uptake (Schreurs and Rosenberg, 1982; Dibrov et al., 2002; Holt and Bard, 2005; Lok et al., 2006). However, there is strong evidence that AgNPs also contribute to toxicity (Navarro et al., 2008; Li et al., 2017), which can proceed through direct disruption of the cell membrane and/or the generation of reactive oxygen species (ROS), which disrupt protein structures and interfere with DNA replication (Sondi and Salopek-Sondi, 2004; Choi et al., 2008; Smetana et al., 2008; Monteiro-Riviere et al., 2013; Barbalinardo et al., 2018; Nierenberg et al., 2018). Thus, to elucidate whether the decreased cell viability observed for protein-coated AgNPs was due to protein-induced enhancement in the dissolved Ag(I) concentration we measured the dissolution of AgNPs in the absence and presence of HSA or gHSA. After 2 h incubation, the AgNP only control underwent 2-fold greater dissolution than the protein-coated AgNPs, which increased to a factor of 5-fold greater dissolution after 24 h and 6-fold greater dissolution after 72 h (Supplementary Figure S5; Figure 6). Other reports in the literature highlight the concentration-dependence of protein-mediated AgNP dissolution. At low protein concentrations, where the AgNP surface is unsaturated or there is only monolayer surface coverage, proteins can enhance dissolution through a nucleophilic mechanism (Martin et al., 2014; Liu et al., 2018; Boehmler et al., 2020). At higher protein concentrations (like the ones used in this study), where the AgNP surface is fully saturated and a stable corona is formed, the surface-bound proteins form a protective coating that inhibits dissolution (Martin et al., 2014; Tai et al., 2014; Shannahan et al., 2015; Liu et al., 2018). Taken together, the protein-coated AgNPs were more toxic to HepG2 cells compared to the no protein control, even as the dissolution of protein-coated AgNPs was decreased relative to the no protein control. These results suggest that a NP- and PC-specific mechanism rather than a dissolution mechanism is likely responsible for the increased toxicity measured for protein-coated AgNPs. In fact, since the completion of this work, a recent study in the literature showed the colocalization of 20 nm and 100 nm citrate-stabilized AgNPs and the mitochondria of HepG2 cells, leading to greater mitochondrial damage and apoptosis relative to exposure to Ag(I) alone (Wang et al., 2022). This supports our hypothesis that a NP-specific mechanism is responsible for the observed toxicity of AgNPs to HepG2 cells. The present study extends this work by demonstrating that the HSA and gHSA coronas potentiate AgNP toxicity.
Previously reported biophysical studies using model lipid membranes can provide insights to the mechanism of NP- and PC-specific toxicity of NPs to cells. Previous studies demonstrated that very small (<5 nm) AgNPs can be trapped within liposomes and increase the fluidity of model lipid bilayers (Park et al., 2005; Bothun, 2008). Similarly, another study showed that 40 nm AgNPs coated with HSA can lead to increased bilayer fluidity (Chen et al., 2012), while a separate study showed that the interaction of HSA-coated AgNPs with model membranes is strongly dependent on solution pH (Wang et al., 2016). Few studies have examined protein-mediated interactions between AgNPs and model membranes with the level of detail needed to elucidate a full mechanism. However, more sophisticated experimental techniques have been developed to investigate NP-membrane interactions with AuNPs and can serve as a model for future work with AgNPs. Using model lipid membranes, researchers showed that AuNPs caused membrane disruption through lipid extraction. This process was strongly influenced by the particle capping agents, with more positively charged particles leading to greater lipid extraction from the membrane (Olenick et al., 2018; Zhang et al., 2018). Another study demonstrated the complexity of nano-bio interactions, whereby the AuNP surface properties influenced the composition of the PC, and the corona, in turn, impacted the interactions of the AuNPs with model membranes (Melby et al., 2017). Further adding to this complexity is the possibility that AuNPs can form PCs through extraction of peripheral membrane proteins (Melby et al., 2018), and the likelihood that a combination of all of these processes (and those yet to be studied) contribute to toxicity mechanisms in vivo.
## 5 Conclusion
This study serves as a first step to interrogating the role of protein modification, like glycation, in mediating PC formation and AgNP toxicity. We have provided robust, qualitative and quantitative characterization of the AgNP-HSA and AgNP-gHSA PCs. We have further demonstrated that, under our experimental conditions, glycation of HSA does not significantly alter the formation of the AgNP-PC or its impact on AgNP dissolution and toxicity to HepG2 cells. Instead, we have shown that both HSA- and gHSA-coated AgNPs are more toxic to HepG2 cells than uncoated AgNPs, and that the mechanism of toxicity cannot be simply explained by AgNP dissolution. Indeed, we observe decreased AgNP dissolution upon formation of the HSA and gHSA coronas at the same time that we observe increased cell toxicity, suggesting a NP- and PC-induced mechanism of toxicity. While glycation of HSA did not appear to have a significant impact in our study, the incorporation of protein modifications into the study of the PC is ultimately an important first step toward understanding the complete AgNP biocorona that includes proteins, metabolites, and lipids (Chetwynd and Lynch, 2020). Our study further highlights the need for continued experimentation and development of biophysical and analytical methods to elucidate the mechanism of protein-mediated AgNP toxicity.
## Data availability statement
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.
## Author contributions
H-YP—Writing and Experiments, including DLS, electrochemistry, and UV-vis; CC—Experiments, including UV-vis and CE; ME and KB—Writing and Experiments, including toxicity studies; KF and KL—Experiments, including UV-vis and CD; EB—Experiments, including toxicity studies; KW, PA, KR—Conceptualization, Methodology, Experiments, Writing, and editing.
## 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/ftox.2023.1081753/full#supplementary-material
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|
---
title: Effects of non-fasting molting on performance, oxidative stress, intestinal
morphology, and liver health of laying hens
authors:
- Meng Lei
- Lei Shi
- Chenxuan Huang
- Yawei Yang
- Bo Zhang
- Jianshe Zhang
- Yifan Chen
- Dehe Wang
- Erying Hao
- Fengling Xuan
- Hui Chen
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10011624
doi: 10.3389/fvets.2023.1100152
license: CC BY 4.0
---
# Effects of non-fasting molting on performance, oxidative stress, intestinal morphology, and liver health of laying hens
## Abstract
Animal welfare concerns in laying-hen production facilities have necessitated research on alternative strategies for improving egg production and hen health. At present, most laying-hen facilities in China use the fasting method, but with international emphasis on animal welfare, scholars have begun to find ways to improve production efficiency while ensuring animal welfare standards are adhered to. Therefore, this study investigated the effects of non-fasting molting on production performance, oxidative stress, intestinal morphology, and liver health of laying hens. A total of 180 healthy 90-week-old Dawu Jinfeng laying hens with similar body weights and laying rates (76 ± $2\%$) were randomly divided into three groups, with five replicates per group and 12 hens per replicate. The hens in the experimental group (NF) were molted using the non-fasting method, the negative control group (C) was not treated with centralized molting, and the positive control group (F) was molted using the fasting method. The results showed that: [1] During the molting period, the laying rate in the NF group ($10.58\%$) decreased and was significantly lower than that in the other two groups ($P \leq 0.05$). During the secondary laying peak period, the laying rate in the NF group was highest ($89.71\%$); significantly higher than that in the C group ($P \leq 0.05$). [ 2] During the molting period, compared to the C group, the NF group showed a significant decrease and increase in the total antioxidant capacity (T-AOC) and superoxide dismutase (T-SOD) activity, respectively ($P \leq 0.05$). During the secondary laying peak period, the T-SOD activity of the NF group was significantly lower than that of the C group ($P \leq 0.05$). [ 3] During the molting period, the villus height (VH) and the ratios of VH to crypt depth (V/C) of the duodenum, jejunum, and ileum in the NF group were significantly lower than those in the C group ($P \leq 0.05$). At the secondary laying peak period, the jejunum V/C was significantly higher than that in the C group ($P \leq 0.05$), whereas in the duodenum and ileum it increased but not significantly ($P \leq 0.05$). [ 4] During the molting period, serum glutathione transaminase (AST) and glutathione alanine transaminase (ALT) activities were significantly higher ($P \leq 0.05$), and very low-density lipoprotein (VLDL) content and liver weight were significantly lower ($P \leq 0.05$) in the non-fasted and fasted groups. However, there was a low degree of liver injury (cell boundary still visible) in the NF group. At the secondary laying peak period, there was no significant difference ($P \leq 0.05$) in the indices among the three groups and the liver returned to normal. In summary, non-fasting molting can improve the production performance of laying hens in the later stages, ensure the welfare and health of animals, and provide a theoretical basis for the efficient production of laying hens.
## 1. Introduction
Poultry molting is a natural physiological phenomenon. However, owing to the inconsistency of molting time and speed, the peak period in secondary egg production, which affects the production efficiency of laying hens, is challenging to identify. In order to shorten the molting time, extend the service life, and therefore maximize the benefits of laying hens to industry, artificial forced-molting techniques have received widespread attention. Artificial forced-molting techniques are used by humans to induce tissue metabolism disorder, prompting the synchronous weight loss and cessation of egg laying, and then restoring the process of egg laying [1]. At present, most laying-hen facilities in China use the fasting method, but with international emphasis on animal welfare, scholars have begun to find ways to improve production efficiency while ensuring animal welfare standards are adhered to. Zhang et al. [ 2] showed that artificial forced-molting techniques could delay the aging of laying hens and redevelop their brain, reproductive system, and other tissues and organs. Studies have shown that the production performance of laying hens, such as the egg production rate and feed conversion rate, improves after molting [3, 4]. Bozkurt et al. [ 5] showed that eggshell thickness, eggshell strength, and the Haugh unit significantly improved after molting. Furthermore, a large number of studies have shown that artificial forced-molting techniques can prolong the laying period of hens and effectively improve laying performance and egg quality (6–9). However, there are few studies on the functional changes in various tissues of laying hens during molting. Therefore, in this experiment, the non-fasting method was used to molt laying hens and these were compared with a negative control group (no centralized molting treatment) and a positive control group (fasting molting treatment) to observe and analyze its effects on production performance, oxidative stress, intestinal morphology, and liver health, in order to provide a theoretical basis for the application of the non-fasting molting technique.
## 2.1. Experimental animals
A total of 180 90-week-old Dawu Jinfeng laying hens with a good mental state, similar body weights, and a similar laying rate (76 ± $2\%$) were randomly divided into three groups with five replicates per group and 12 laying hens per replicate. The experiment was conducted in a two-level cage in an environmentally controlled chamber at Hebei Agricultural University. The experimental group (NF) was treated with the non-fasting method, the negative control group (C) was not treated with centralized molting, and the positive control group (F) was treated with the fasting method. The experiment lasted for 90 days: 7 pre-experimental and 83 experimental days.
## 2.2. Test scheme
The laying hens in the NF group were fed a replacement diet (50 g/hen per day) throughout the molting period and the water supply was normal. The weight loss rate of laying hens was observed and recorded. When the weight loss rate reached ~$25\%$, the basal diet was resumed (gradually increasing the feed amount to 120 g/hen per day and then changing to free feeding). The chamber was maintained under constant light conditions of 8 h/d (8:00–16:00) during the molting period, and the light was gradually increased to 16 h/d (8:00–22:00) after the basal diet was restored.
The laying hens in the F group were fasted throughout the molting period, water was stopped on day 8 and 9, and free drinking water was allowed at other times. When the body weight decreased to ~$25\%$, the basal diet was resumed (gradually increasing the feeding amount to 120 g/hen per day and then changing to free feeding). During the molting period, the henhouse was maintained under light conditions of 8 h/d (08:00–16:00), which was gradually increased to 16 h/d (08:00–22:00) after the basal diet was reintroduced.
The C group was fed a basal diet without any treatment, with normal drinking water and light maintenance for 16 h/d (8:00–22:00). The composition and nutritional levels of the basal and feather replacement diet are shown in Table 1.
**Table 1**
| Items | Content | Content.1 |
| --- | --- | --- |
| | Basal diet | Diet of molting period |
| Ingredients | Ingredients | Ingredients |
| Corn | 62.50 | 54.00 |
| Soybean meal | 25.20 | |
| Corn gluten meal | 0.50 | |
| Rice bran | | 16.00 |
| Wheat bran | | 24.00 |
| Limestone | 9.30 | 4.10 |
| CaHPO4 | 0.90 | 0.70 |
| DL-Met | 0.10 | |
| Premixa | 1.00 | 1.00 |
| NaCl | 0.50 | 0.20 |
| Total | 100.00 | 100.00 |
| Nutrient levels b | Nutrient levels b | Nutrient levels b |
| ME/(MJ/kg) | 10.91 | 10.75 |
| CP | 15.98 | 10.51 |
| CF | 2.10 | 3.78 |
| Ca | 3.63 | 1.77 |
| P | 0.27 | 0.87 |
| Lys | 0.80 | 0.36 |
| Met | 0.45 | 0.17 |
## 2.3. Sample collection
In order to observe the physiological changes of laying hens during the whole molting process, the whole experiment period was divided into three stages: stage I, the laying rate during molting was $0\%$ and the weight loss rate was ~$25\%$; stage II, the laying rate was ~$50\%$ after reintroducing the basal diet; and stage III, the egg production rate was stable at more than $85\%$ after reintroducing the basal diet; that is, the secondary laying peak was reached.
Two laying hens were randomly selected from each replicate at each of the three stages (I–III). Blood was collected from the wing vein, centrifuged at 3,000 r/min, and the supernatant was collected and stored at −20°C. At the same time, one laying hens was randomly selected for slaughter test.
## 2.3.1. Production performance
Under the same management, the number of eggs laid during the molting period (from the beginning of the experiment to the time when the egg production rate dropped to $0\%$ and the weight loss rate was ~$25\%$) and the secondary laying peak period (2 weeks after the egg production rate reached $85\%$) was recorded in units of repetition. The egg production rate was calculated, and the days required for the egg production rate to drop to $0\%$ were observed.
During the molting period, the body weights and number of days in the molting period (laying rate = $0\%$, weight loss rate ~$25\%$) of the laying hens in the NF and F groups were recorded.
## 2.3.2. Serum antioxidant indices
The levels of corticosterone (CROT), total antioxidant capacity (T-AOC), malondialdehyde (MDA), and superoxide dismutase (T-SOD) in the serum were measured using kits. The CROT, T-AOC, and MDA kits were purchased from Shanghai Jianglai Biotechnology Co. Ltd. The T-SOD kit was purchased from Nanjing Jiancheng Bioengineering Institute.
## 2.3.3. Intestinal morphology
One laying hen was randomly selected from each replicate at each of the three stages (I–III). The intestinal segments of ~3 cm were cut from the middle of the duodenum, jejunum, and ileum, and stored in $4\%$ paraformaldehyde solution to prepare intestinal tissue sections and hematoxylin and eosin (HE) staining. Villus height (VH) and crypt depth (CD) of the intestinal mucosa were measured, and the ratio of VH to CD (V/C) was calculated.
## 2.3.4. Serum biochemical indices
The levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and very low-density lipoprotein (VLDL) in the serum were measured using specific kits purchased from Nanjing Jiancheng Bioengineering Institute.
## 2.3.5. Liver tissue morphology
One laying hen was randomly selected from each replicate at each of the three stages (I–III) and was slaughtered after bloodletting. The liver was separated non-destructively, the gallbladder was removed, weighed, and ~1 cm2 of the left liver was cut and immersed in a pre-prepared $4\%$ paraformaldehyde solution. Liver tissue sections were prepared and HE stained to observe the degree of liver damage.
## 2.3.6. Serum immune index
Serum levels of immunoglobulin A (IgA), immunoglobulin M (IgM), and immunoglobulin G (IgG) were measured using an ELISA kit purchased from Shanghai Jianglai Biotechnology Co., Ltd.
## 2.4. Statistical analysis
The experimental data were initially sorted using Excel 2010, and then one-way analysis of variance (ANOVA) was performed using SPSS software (version 19.0). Duncan's method was used for multiple comparisons between the groups. $P \leq 0.05$ indicated that the difference was significant.
## 3.1. Effects of non-fasting molting on production performance
As shown in Table 2, the egg production rate of the NF group was the lowest during the molting period, which was significantly lower than that of the F and C groups ($P \leq 0.05$). During the secondary laying peak period, the egg production rate of the NF and F groups increased, and was significantly higher than that of the C group ($P \leq 0.05$). The NF group had the longest molting period of 40 days, whereas the F group molted faster and molting was completed within 10 days.
**Table 2**
| Indices | Groups | Groups.1 | Groups.2 | P-value |
| --- | --- | --- | --- | --- |
| | C | F | NF | |
| Egg production rate during molting period | 75.00 ± 1.64a | 14.74 ± 0.91b | 10.58 ± 1.17c | < 0.001 |
| Egg production rate during secondary laying peak period | 70.85 ± 1.98b | 89.31 ± 1.45a | 89.71 ± 1.64a | < 0.001 |
| Molting period days | - | 10 | 40 | - |
## 3.2. Effect of non-fasting molting on oxidative stress
As shown in Table 3, in stage I, compared with the C group, the T-SOD activity of the NF and F groups was significantly increased ($P \leq 0.05$), and the T-AOC was significantly decreased ($P \leq 0.05$). In stage II, with the reintroduction of the basal diet, the T-SOD activity of the NF and F groups decreased but was still higher than that of the C group ($P \leq 0.05$). The T-AOC index was significantly higher in the NF group than in the other two groups ($P \leq 0.05$). In stage III, the T-SOD activity in the NF and F groups was significantly lower than that in the C group ($P \leq 0.05$), however there was no significant difference in the other oxidative stress indicators among the three groups ($P \leq 0.05$).
**Table 3**
| Testing stages | Indices | Groups | Groups.1 | Groups.2 | P-value |
| --- | --- | --- | --- | --- | --- |
| | | C | F | NF | |
| Stage I | CORT (ng/mL) | 75.38 ± 18.62 | 87.21 ± 11.13 | 85.71 ± 17.32 | 0.469 |
| Stage I | T-AOC (U/mL) | 19.36 ± 1.28a | 16.94 ± 1.81b | 16.53 ± 1.59b | 0.031 |
| Stage I | MDA (nmol/mL) | 8.22 ± 1.12 | 9.76 ± 2.25 | 9.12 ± 1.77 | 0.412 |
| Stage I | T-SOD (U/mL) | 213.81 ± 21.74b | 332.06 ± 15.61a | 308.26 ± 37.35a | < 0.001 |
| Stage II | CORT (ng/mL) | 79.55 ± 5.03 | 82.03 ± 18.67 | 79.40 ± 16.65 | 0.955 |
| Stage II | T-AOC (U/mL) | 18.51 ± 0.76b | 18.39 ± 2.19b | 22.59 ± 3.22a | 0.021 |
| Stage II | MDA (nmol/mL) | 8.95 ± 0.67 | 9.04 ± 0.78 | 8.09 ± 1.08 | 0.166 |
| Stage II | T-SOD (U/mL) | 236.44 ± 15.75b | 309.46 ± 6.03a | 295.86 ± 31.82a | 0.010 |
| Stage III | CORT (ng/mL) | 77.33 ± 14.90 | 80.52 ± 19.19 | 77.39 ± 21.65 | 0.951 |
| Stage III | T-AOC (U/mL) | 18.30 ± 2.09 | 19.64 ± 1.77 | 19.82 ± 1.57 | 0.295 |
| Stage III | MDA (nmol/mL) | 8.48 ± 1.30 | 7.97 ± 0.75 | 8.04 ± 1.74 | 0.804 |
| Stage III | T-SOD (U/mL) | 218.94 ± 38.33a | 176.88 ± 2.27b | 162.39 ± 24.59b | 0.012 |
## 3.3. Effect of non-fasting molting on intestinal morphology
As shown in Table 4, in stage I, the VH and V/C of the duodenum, jejunum, and ileum in the NF and F groups were significantly decreased in both groups ($P \leq 0.05$). In stage II, the VH and V/C of the duodenum in the NF and F groups were significantly lower than those in the C group ($P \leq 0.05$). The VH and V/C of the jejunum in the NF group were significantly lower than those in the F group ($P \leq 0.05$). The V/C of the ileum in the NF group was significantly higher than that in the F and C groups ($P \leq 0.05$). In stage III, the VH of the duodenum in the NF and F groups was significantly lower than that in the C group ($P \leq 0.05$), and the V/C ratio in the F group was significantly lower than that in the other two groups ($P \leq 0.05$). The VH and V/C of the jejunum in the NF and F groups were significantly higher than those in the C group ($P \leq 0.05$). The V/C of the ileum in the NF group was significantly lower than that in the F group ($P \leq 0.05$).
**Table 4**
| Testing stages | Indices | Groups | Groups.1 | Groups.2 | P-value |
| --- | --- | --- | --- | --- | --- |
| | | C | F | NF | |
| Stage I | Duodenum | Duodenum | Duodenum | Duodenum | Duodenum |
| Stage I | VH/μm | 1,610.40 ± 286.12a | 1,260.98 ± 267.97b | 1,074.87 ± 125.06c | < 0.001 |
| Stage I | CD/μm | 237.82 ± 37.88c | 310.96 ± 34.66a | 287.51 ± 30.58b | < 0.001 |
| Stage I | V/C | 6.90 ± 1.59a | 4.08 ± 0.86b | 3.77 ± 0.54b | < 0.001 |
| Stage I | Jejunum | Jejunum | Jejunum | Jejunum | Jejunum |
| Stage I | VH/μm | 930.34 ± 206.32a | 648.57 ± 166.03b | 697.10 ± 176.09b | < 0.001 |
| Stage I | CD/μm | 176.33 ± 44.30b | 224.55 ± 40.67a | 186.75 ± 32.06b | < 0.001 |
| Stage I | V/C | 5.47 ± 1.35a | 2.93 ± 0.73c | 3.78 ± 0.86b | < 0.001 |
| Stage I | Ileum | Ileum | Ileum | Ileum | Ileum |
| Stage I | VH/μm | 857.07 ± 154.14a | 543.87 ± 108.96b | 431.58 ± 122.30c | < 0.001 |
| Stage I | CD/μm | 157.96 ± 24.77b | 197.36 ± 21.59a | 168.50 ± 32.74b | < 0.001 |
| Stage I | V/C | 5.53 ± 1.34a | 2.77 ± 0.55b | 2.62 ± 0.77b | < 0.001 |
| Stage II | Duodenum | Duodenum | Duodenum | Duodenum | Duodenum |
| Stage II | VH/μm | 1,596.67 ± 258.30a | 1,401.04 ± 378.62b | 1,044.07 ± 181.30c | < 0.001 |
| Stage II | CD/μm | 222.46 ± 25.59b | 317.38 ± 59.19a | 221.63 ± 29.01b | < 0.001 |
| Stage II | V/C | 7.27 ± 1.46a | 4.47 ± 1.19b | 4.78 ± 0.99b | < 0.001 |
| Stage II | Jejunum | Jejunum | Jejunum | Jejunum | Jejunum |
| Stage II | VH/μm | 918.60 ± 255.50b | 1,340.56 ± 253.71a | 790.85 ± 198.12b | < 0.001 |
| Stage II | CD/μm | 167.85 ± 28.21 | 157.32 ± 41.29 | 156.07 ± 64.37 | 0.605 |
| Stage II | V/C | 5.54 ± 1.54b | 9.09 ± 2.74a | 5.45 ± 1.35b | < 0.001 |
| Stage II | Ileum | Ileum | Ileum | Ileum | Ileum |
| Stage II | VH/μm | 852.98 ± 132.91 | 871.09 ± 171.47 | 772.04 ± 241.58 | 0.124 |
| Stage II | CD/μm | 145.77 ± 24.88a | 150.46 ± 28.74a | 94.25 ± 13.20b | < 0.001 |
| Stage II | V/C | 5.97 ± 1.11b | 5.88 ± 1.05b | 8.15 ± 2.12a | < 0.001 |
| Stage III | Duodenum | Duodenum | Duodenum | Duodenum | Duodenum |
| Stage III | VH/μm | 1,574.49 ± 290.33a | 1,334.80 ± 194.79b | 1,389.72 ± 191.92b | 0.001 |
| Stage III | CD/μm | 248.18 ± 29.60a | 264.20 ± 28.24a | 219.93 ± 39.63b | < 0.001 |
| Stage III | V/C | 6.34 ± 0.89a | 5.11 ± 0.96b | 6.40 ± 0.77a | < 0.001 |
| Stage III | Jejunum | Jejunum | Jejunum | Jejunum | Jejunum |
| Stage III | VH/μm | 964.51 ± 125.22b | 1,084.58 ± 111.90a | 1,123.99 ± 250.26a | 0.004 |
| Stage III | CD/μm | 193.28 ± 48.03a | 129.87 ± 21.46b | 127.43 ± 38.00b | < 0.001 |
| Stage III | V/C | 5.24 ± 1.26b | 8.60 ± 1.83a | 9.86 ± 4.04a | < 0.001 |
| Stage III | Ileum | Ileum | Ileum | Ileum | Ileum |
| Stage III | VH/μm | 897.38 ± 193.99a | 835.04 ± 70.90a | 731.52 ± 87.70b | < 0.001 |
| Stage III | CD/μm | 156.42 ± 38.59a | 114.17 ± 22.36b | 115.24 ± 20.69b | < 0.001 |
| Stage III | V/C | 6.04 ± 1.90b | 7.54 ± 1.42a | 6.57 ± 1.55b | 0.005 |
## 3.4. Effect of non-fasting molting on serum biochemical indices
Table 5 shows that in stage I, the VLDL content of the NF and F groups was significantly lower than that in the C group ($P \leq 0.05$). The AST and ALT levels were significantly higher in the F group than in the NF and C groups ($P \leq 0.05$). In stage II, the activity of AST in the NF group was significantly lower than that in the F and C groups ($P \leq 0.05$). In stage III, the liver function of laying hens gradually recovered, and there were no significant differences in AST, ALT, and VLDL levels among the three groups ($P \leq 0.05$).
**Table 5**
| Testing stages | Indices | Groups | Groups.1 | Groups.2 | P-value |
| --- | --- | --- | --- | --- | --- |
| | | C | F | NF | |
| Stage I | AST (U/L) | 27.32 ± 1.18c | 44.34 ± 2.39a | 41.16 ± 1.59b | < 0.001 |
| Stage I | ALT (U/L) | 0.74 ± 0.16b | 1.01 ± 0.10a | 0.86 ± 0.04b | 0.002 |
| Stage I | VLDL (mmol/L) | 11.02 ± 1.24a | 7.93 ± 1.21b | 7.91 ± 1.47b | 0.004 |
| Stage II | AST (U/L) | 27.49 ± 2.92a | 25.99 ± 3.73a | 21.49 ± 1.23b | 0.026 |
| Stage II | ALT (U/L) | 0.70 ± 0.13 | 0.64 ± 0.11 | 0.75 ± 0.03 | 0.100 |
| Stage II | VLDL (mmol/L) | 10.21 ± 0.48 | 10.74 ± 1.35 | 11.68 ± 1.71 | 0.285 |
| Stage III | AST (U/L) | 27.04 ± 1.17 | 22.71 ± 2.72 | 24.42 ± 3.59 | 0.108 |
| Stage III | ALT (U/L) | 0.71 ± 0.03 | 0.63 ± 0.10 | 0.68 ± 0.12 | 0.424 |
| Stage III | VLDL (mmol/L) | 10.44 ± 2.03 | 12.08 ± 1.98 | 12.06 ± 1.09 | 0.143 |
## 3.5. Effect of non-fasting molting on liver tissue morphology
As shown in Table 6, in stage I, the liver weights of hens in the NF and F groups were significantly lower than that in the C group ($P \leq 0.05$). After resuming the basal diet, the liver weights of the laying hens in the NF and F groups gradually increased, and there was no longer a significant difference in the liver weights of the three groups from stage II to stage III ($P \leq 0.05$).
**Table 6**
| Testing stages | Groups | Groups.1 | Groups.2 | P-value |
| --- | --- | --- | --- | --- |
| | C | F | NF | |
| Stage I | 42.54 ± 4.57a | 21.88 ± 3.90b | 16.76 ± 2.52b | < 0.001 |
| Stage II | 39.83 ± 2.87 | 35.94 ± 4.45 | 46.45 ± 9.67 | 0.104 |
| Stage III | 36.82 ± 3.09 | 41.28 ± 5.12 | 36.46 ± 2.38 | 0.115 |
Figures 1–3 show that in stage I, the livers of all hens were damaged to some extent, among which the damage in the F group was more serious, with obvious swelling of hepatocytes, unclear intercellular boundaries, inflammatory cell infiltration, dilated and congested liver sinusoids, and slight cytoplasmic infection. The hepatocytes in the NF group increased in size, but there were still intercellular boundaries, and the cytoplasm began to show signs of infection. The liver gradually recovered as the level of nutrition increased. In stage II, the hepatocytes in the NF and F groups of laying hens were still slightly swollen, and in stage III, the liver in the NF and F groups had almost completely recovered.
**Figure 1:** *The effect of non-fasting molting on liver tissue morphology (stage I). (A, a) Not treated with centralized molting; (B, b) fasting molting; (C, c) non-fasting molting. Upper-case letters are ×100, lower-case letters are ×400. Green arrows represent hepatocellular edema and blue arrows indicate the occurrence of steatosis.* **Figure 2:** *The effect of non-fasting molting on the morphology of liver tissue (stage II). (A, a) Not treated with centralized molting; (B, b) fasting molting; (C, c) non-fasting molting. Upper-case letters are ×100, lower-case letters are ×400. Green arrows represent hepatocellular edema and blue arrows indicate the occurrence of steatosis.* **Figure 3:** *The effect of non-fasting molting on liver tissue morphology (stage III). (A, a) Not treated with centralized molting; (B, b) fasting molting; (C, c) non-fasting molting. Upper-case letters are ×100, lower-case letters are ×400. Green arrows represent hepatocellular edema and blue arrows indicate the occurrence of steatosis.*
## 3.6. Effect of non-fasting molting on immune performance
As shown in Table 7, in stage I, the IgG content of laying hens in the NF and F groups decreased significantly ($P \leq 0.05$), whereas in stage II, IgG gradually increased, and there were no significant differences in IgG, IgA, and IgM among NF group, F group, and C group up to stage III ($P \leq 0.05$).
**Table 7**
| Testing stages | Indices | Groups | Groups.1 | Groups.2 | P-value |
| --- | --- | --- | --- | --- | --- |
| | | C | F | NF | |
| Stage I | IgA (μg/mL) | 206.47 ± 33.72 | 165.68 ± 21.64 | 173.95 ± 32.24 | 0.113 |
| Stage I | IgM (μg/mL) | 485.99 ± 50.74 | 449.26 ± 15.84 | 455.16 ± 14.88 | 0.19 |
| Stage I | IgG (μg/mL) | 1,415.25 ± 245.31a | 989.76 ± 119.78b | 1,005.53 ± 130.98b | 0.003 |
| Stage II | IgA (μg/mL) | 197.40 ± 7.25 | 213.20 ± 3.68 | 226.60 ± 20.92 | 0.246 |
| Stage II | IgM (μg/mL) | 453.13 ± 23.14 | 475.59 ± 74.38 | 490.43 ± 38.84 | 0.52 |
| Stage II | IgG (μg/mL) | 1,200.66 ± 115.12 | 1,320.23 ± 205.76 | 1,361.13 ± 158.83 | 0.373 |
| Stage III | IgA (μg/mL) | 215.25 ± 31.24 | 225.18 ± 29.63 | 229.20 ± 26.38 | 0.644 |
| Stage III | IgM (μg/mL) | 467.19 ± 36.40 | 469.26 ± 56.92 | 477.96 ± 34.91 | 0.86 |
| Stage III | IgG (μg/mL) | 1,321.78 ± 238.07 | 1,477.93 ± 238.07 | 1,497.47 ± 246.91 | 0.394 |
## 4. Discussion
Studies have shown that a weight loss rate of 25–$35\%$ is appropriate for hens during the molting period [10], and either too much or too little weight loss will affect later production performance. Berry and Brake [11] showed that a weight loss rate of 25–$30\%$ during the molting period can result in better egg production performance after molting. In this experiment, the $25\%$ weight loss rate and egg production cessation were used as markers of the molting period to obtain better post-molting production performance. Tiwary et al. [ 7] showed that the egg production rate of hens significantly increased after molting and this was supported in a study by Onbaşilar et al. [ 12]. Gongruttananun et al. [ 13] found that by comparing the effect of different molting methods on the egg production rate of late-laying hens, compared with the control group, different molting methods can improve the laying performance of hens in later periods. In this study, the results showed that the laying rate of hens in the NF group decreased significantly during the molting period and then gradually recovered as the basal diet was reintroduced. During the secondary laying peak period, the laying rate of hens in the NF group was significantly higher than that in the C group. Brake and Thaxton [14] showed that the improvement in egg production performance after molting was associated with a reduction in fat and an increase in tissue efficiency after the redevelopment of reproductive organs. Additionally, in the present study, we found that the period of non-fasting molting lasted 40 days, which was longer than the period of fasting molting, and this finding was attributed to the high energy of the diet provided during the molting period or the high feeding amount. However, this aspect will require further investigation.
Prolonged starvation disrupts the homeostasis of oxygen free radicals in organisms, leading to oxidative stress [15]. MAD is an end product of oxidative reactions and has toxic effects on cells, whereas high levels of CORT are considered to be an indicator of stress in chickens. Studies have shown that the levels of CORT and MAD are significantly increased in laying hens stimulated by starvation during the molting period [9, 16]. Furthermore, Andreatti Filho et al. [ 17] found that plasma CORT levels were increased in the group fed wheat bran during the molting period but these did not reach significant levels. The results of this experiment showed that CORT and MAD levels were elevated in the NF group during stage I but did not reach significant levels compared to the C group, presumably because of the high nutritional level of the diet during the molting period, and the low stress on the hens that had adapted to the experimental environment. At the same time, the content of CORT and MAD in the NF group was lower than that in the F group, indicating that the stress on the laying hens in the NF group was less during the replacement period. Morales et al. [ 18] showed that T-SOD activity increased significantly under starvation and gradually returned to normal levels after refeeding. The present study showed that in stage I, the T-AOC and T-SOD activity were significantly lower in the NF group, which was the result of eliminating excess free radicals generated in the body due to the stress of restricted feeding. In stage III, the T-AOC and T-SOD activity returned to normal levels.
Small intestine tissues are the main site of digestion and absorption in poultry. Under normal conditions, longer VH, shallower CD, and a larger V/C indicate better digestion and absorption of nutrients in the small intestine [19]. The gastrointestinal tract of chickens is very responsive to stressors (fasting, temperature changes, etc.), and once stressed, the intestinal epithelial integrity and microbiota are immediately altered (20–22). Numerous studies have found that fasting leads to a significant reduction in duodenal VH [23, 24]. Yamauchi et al. [ 25] also found that during fasting, the reduction in VH in laying hens was very rapid in the first 24 h, followed by a gradual and slow reduction, and a significant increase in VH after resumption of feeding. In the present study, it was shown that in stage I, both the VH and V/C of the small intestine tissues were significantly reduced, but were gradually restored with the resumption of basal feeding. In stage III, the CD of small intestine was significantly decreased, and V/C was increased, indicating that non-fasting molting could improve the intestinal function to some extent. Furthermore, it has been shown that dietary fiber can be fermented to some extent by microorganisms in the gut, and this fermentation can maintain normal digestion and absorption in the gastrointestinal tract and act as a defense against diseases and pathogenic bacteria [26, 27]. Therefore, the use of low-energy, low-protein, high-fiber diet for molting may be more beneficial to the intestinal health of laying hens.
ALT and AST are common indicators of animal liver health, and an increase in their activity in plasma usually indicates that the liver has been damaged to varying degrees (28–30). The results of this study showed that in stage I, the ALT and AST activities were significantly higher in the F group than in the NF and C groups. The activities of both enzymes were significantly higher in the NF group than in the C group, and then gradually recovered in stage III when there was no significant difference between the three groups. Studies have shown that fasting or restricted feeding leads to elevated plasma transaminase activity [31, 32]; therefore, during the molting period, ALT and AST activities may be associated with fasting or restricted feeding, causing stress in laying hens. At the same time, some studies have found that molting may indirectly affect the activity of plasma transaminases by affecting the target response of steroid hormones [33]. Combined with the previous studies of our research group, it can be seen that during the molting period, sex steroid hormones such as progesterone (Prog) and estrogen (E), are significantly reduced, cortical steroids are increased, and plasma ALT and AST activities are indirectly affected, therefore they are significantly increased. VLDL and vitellogenin (Vg) are primarily synthesized by liver cells, providing fatty acids and cholesterol to eggs [34]. Studies have shown that there may be a correlation between the egg production rate and plasma VLDL levels. When the egg production rate is low, the plasma VLDL content is correspondingly low; when the egg production rate is high, the plasma VLDL content is also high [35]. Walzem et al. [ 36] also reached the same conclusion in a molting experiment of laying hens. Feed restriction during the molting period resulted in the cessation of egg-laying by laying hens; therefore, the plasma VLDL content decreased accordingly. Our experiment showed that in stage I, as the hens stopped laying eggs, the plasma VLDL content in the NF and F groups also decreased significantly, and then gradually returned to normal levels with an increase in the laying rate.
Weight reduction is one of the necessary processes in all molting procedures, and ~$\frac{1}{4}$ of the weight reduction comes from the weight of the liver, ovaries, and oviducts (37–40). Grals et al. [ 41] experimented on 9-month- old rats and found that when feeding was controlled to $60\%$, the average body weight slowly decreased by $30\%$ and the weight of the liver was also reduced, but after the recovery of free feeding, the increase in feed intake also stimulated liver growth. Therefore, the average body weight and liver weight returned to normal in ~1 week. Landers et al. [ 42] showed that during the molting period, liver weight was significantly lower in both groups of molting hens than in the control group, but there was no significant difference in liver weight between the fasting and alfalfa-fed groups. The fat content in the liver gradually accumulates with age, and excessive fat can be a burden to the liver. Studies have shown that the fat content in the liver is significantly reduced after molting, which can reduce the burden on the liver and thus improve production performance [39]. The results of this experiment are consistent with the above-mentioned studies, in which the liver weight of laying hens in the NF and F groups significantly decreased in stage I, and significantly increased with the resumption of the basal diet; in stage III, there was no significant difference between NF group, F group and C group. Furthermore, it has been shown that a shortened light duration and ovarian degradation also decrease liver weight [11].
According to the pathological section of liver tissue, in stage I, the livers of the two groups of concentrated molting hens in this experiment were damaged to a certain extent; among them, the hepatocytes in the F group were more severely swollen, with unclear intercellular boundaries and slight cytoplasmic infection, whereas the hepatocytes in the NF group had intercellular boundaries, but cytoplasmic infection had started to appear. This result was also supported by the above-mentioned serum biochemical indices of AST and ALT activities, which indicated that the increase in both enzyme activities in the F group was significantly higher than that in the NF group during stage I. The liver recovered gradually with the resumption of the basal diet, and in stage III, the livers of both the NF and F groups had recovered completely, indicating that the molting technique does not cause irreversible damage to the liver of laying hens. Meng et al. [ 43] also concluded the same conclusion, which indicated that forced molting would not cause irreversible damage to the liver of laying hens. However, according to the serum biochemical indexes and liver sections during the molting period, the non-fasting method was used to molt the laying hens. The degree of damage to the liver is smaller and more conducive to the health of laying hens.
IgG is the most abundant immunoglobulin in the serum of laying hens, and mainly plays antibacterial, antiviral, and neutralizing roles against bacterial toxins, followed by IgA, which plays an important role in resistance to microbial invasion, and IgM, the least abundant initial immune antibody, which protects against pathogens [44]. Immunoglobulin levels in the serum of laying hens decreased during the molting period and gradually increased after the resumption of feeding [45]. Alondan and Mashaly [6] showed that the immune performance of laying hens during the molting period was suppressed, presumably because prolonged starvation caused stress in laying hens and produced large amounts of CORT, leading to the degeneration of lymphoid tissue, thereby suppressing cellular and humoral immunity [46]. Sandhu et al. [ 45] showed that molting increased immunoglobulin content and improved the immune performance of late-laying hens. The results of this experiment showed that, in stage I, the IgG content of the NF and F groups decreased significantly. With the recovery of the basal diet, IgG content gradually recovered. In stage III, compared with the C group, the IgG content of the NF and F groups increased to a certain extent, but did not reach a significant level. The IgA and IgM contents first decreased and then increased during the three periods.
## 5. Conclusion
In summary, non-fasting molting is less stressful to hens, causes less liver damage, can improve the production performance of hens in the second egg laying cycle, and can improve the intestinal digestion and absorption capacity of laying hens. Therefore, non-fasting molting can improve production efficiency by ensuring animal welfare, which is of great significance in laying hen production facilities.
## 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 Animal Use and Ethical Committee of Hebei Agricultural University.
## Author contributions
ML, LS, CH, and FX: designed and completed the experiment. ML, LS, HC, and BZ: statistics and contributions. YC, DW, EH, JZ, HC, and YY: provided experimental guidance. 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: Prediction of apoptosis protein subcellular location based on amphiphilic pseudo
amino acid composition
authors:
- Wenxia Su
- Shuyi Deng
- Zhifeng Gu
- Keli Yang
- Hui Ding
- Hui Chen
- Zhaoyue Zhang
journal: Frontiers in Genetics
year: 2023
pmcid: PMC10011625
doi: 10.3389/fgene.2023.1157021
license: CC BY 4.0
---
# Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition
## Abstract
Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied.
Methods: *In this* paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location.
Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached $90.5\%$, $93.9\%$ and $84.0\%$, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.
## 1 Introduction
Apoptosis is a type of programmed cell death mechanism that eliminates unnecessary or damaged cells from the body for cellular homeostasis regulation. The apoptotic program is executed by multiple pathways and controlled by the interactions between several molecules. Apoptosis proteins, such as the inhibitor of apoptosis protein (IAP) family, are proteins involved in the process of cell apoptosis for various stress responses. The different functions of apoptosis proteins are related to their subcellular location (Reed and Paternostro, 1999). The subcellular location of apoptosis proteins will not only help us understand the life process and mechanism of programmed cell death but also provide a very important method for understanding the structure and function of proteins (Chou, 2001). It can provide a new perspective for subsequent protein-related tasks such as protein structure prediction and drug-protein relationship prediction (Li et al., 2022a; Li et al., 2022b). However, it is expensive and time-consuming to carry out various experiments to obtain location information (Koroleva et al., 2005). With the explosive growth of protein sequences in the post-genomic era, it is both challenging and necessary to develop an automatic method for quick and accurate prediction of the apoptosis proteins’ subcellular location.
In recent years, serval methods have been proposed for the prediction of apoptosis proteins’ subcellular location. Yu et al. [ 2012] proposed a prediction method called CELLO, which used multiple SVM classifiers based on N-peptide features. The overall accuracies for their two datasets achieve $87.1\%$ and $90\%$, respectively. Zhou and Doctor, [2003] established a 98 apoptosis protein data set named ZD98 based on the SWISS-PROT database. They constructed the predictor based on the amino acid composition of the apoptosis protein sequences. The overall success rates of the self-consistent test and jackknife test were $90.8\%$ and $72.5\%$, respectively. Bulashevska and Eils [2006] used the ZD98 dataset and the jackknife test overall prediction accuracy of the single Bayesian classifier (BC) and hierarchical Bayesian classifier (HensBC) was $85.7\%$ and $89.8\%$ respectively. Chen et al. [ 2021]. proposed a new method to predict the subcellular location of apoptosis proteins by combining dipeptide composition and a discrete increment (ID) algorithm. They predicted the subcellular location of apoptosis proteins based on the main sequence of proteins and the measurement and increase of diversity. According to the latest SWISS-PROT database, they selected 317 apoptosis proteins to establish a data set CL317 and classified them into six subcellular locations (Chen and Li, 2007a). Subsequently, the self-consistent test and jackknife test were conducted, and the overall prediction success rates were $92.1\%$ and $82.7\%$, respectively. At the same time, they applied this method to ZD98. The overall prediction success rates of the self-consistent test and jackknife test were $94.9\%$ and $90.8\%$, respectively. Chen and Li, [2007] applied Discrete Incremental Fusion to the dataset. The overall prediction accuracy obtained by the Jackknife test reached $90.8\%$. For other classes with small samples, the sensitivity reached $91.7\%$. Later, they combined the ID with a support vector machine (SVM) to propose a new algorithm. For the database of 317 apoptosis proteins in six categories, the overall accuracy of the jackknife test was improved to $85.8\%$. Zhang et al. [ 2006] built a larger data set named ZW225. They adopted the feature extraction method based on grouping weight coding, and the overall prediction success rates of self-consistent and jackknife tests were $97.3\%$ and $75.1\%$ respectively. Then they combined the support vector machine with the encoding based on grouped weights feature extraction method, and the overall accuracy of the jackknife test rose to $83.1\%$.
In this article, we proposed a novel algorithm for apoptosis proteins’ subcellular location prediction. The amphiphilic pseudo amino acid components were used to extract the features from protein sequences. Then, the optimal features were inputted into a machine-learning method to train, test and build a model. The developed approach will be useful for studying apoptosis proteins’ localization and distribution.
## 2.1 Datasets
Reliable data is the basis of model construction (Su et al., 2021). Three datasets extracted from the Uniprot (https://www.uniprot.org/) were used to construct the benchmark dataset. The dataset CL317 provided by Chen and Li [2007] consists of 317 apoptosis proteins divided into six subcellular locations with 112 cytoplasmic proteins (Cyto), 55 plasma membrane-bound proteins (Memb), 52 nuclear proteins (Nucl), 47 endoplasmic reticulum proteins (Endo), 34 mitochondrial proteins (Mito) and 17 secreted proteins (Secr). All the accession numbers can be found in the literature (Zhou and Doctor, 2003; Chen and Li, 2007; Zhang et al., 2006). ZW225 is a larger dataset provided by Zhang et al. [ 2006]. It contains 225 apoptosis proteins divided into four subcellular locations of which 41 are Nucl, 70 Cyto, 25 Mito and 89 Memb. The dataset ZD98 was generated by Zhou and Doctor, 2003. The 98 apoptosis proteins were classified into four location categories, of which 43 are Cyto, 30 Memb, 13 Mito and 12 other proteins (Other). In this study, the jackknife test was applied to build the prediction model and examine the effectiveness of these three datasets.
## 2.2 Feature encoding
We need to convert sequences into vectors in mathematical representation (Amanatidou, and Dedoussis, 2021; Dao et al., 2022a; Jeon et al., 2022; Li H et al., 2022; Nidhi et al., 2022; Sun et al., 2022; Tran and Nguyen, 2022; Wang et al., 2022; Yang et al., 2022; Zhang H et al., 2022). The amino acid composition (ACC) of the protein has a great impact on its subcellular location (Chou and Elrod, 1999a; Awais et al., 2021; Chou and Elrod, 1999b; Rout et al., 2022; Naseer et al., 2021; Manavalan and Patra, 2022; Shoombuatong et al., 2022). By using the ACC to extract features of the protein sequences. a protein sequence can be represented as a 20-D (dimension) vector as follows: Pkξ=pk,1ξ,pk,2ξ,…,pk,iξ,…,pk,20ξT,$i = 1$,2,…,20; ξ=1,2,…,μ; $k = 1$,2,…,m [1] In Eq. 1, ξ represents the different subcellular locations of proteins, μ is the total number of subcellular location categories, k represents the sequence number in the subcellular position ξ, m is the total number of sequences contained in the subcellular position ξ, and T means that the feature vector is expressed in the form of a column vector. pk,iξ means the occurrence frequency of the amino acid i of the protein sequence k in the subcellular position ξ. The amphiphilic pseudo amino acid composition (APAAC) was originally proposed by Chou [2005] to reflect the sequence-order effects by using the hydrophobicity and hydrophilicity of the constituent amino acids in a protein (Hosen et al., 2022; Qian et al., 2022). By using APAAC, a protein sample can be represented as follows: P=p1,…,p20,p20+1,…,p20+λ,p20+λ+1,…,p20+2λT [2] where the first 20 numbers in Eq. 2 are the classic AAC features, and the next 2λ discrete numbers are sequence-correlation factors, which can be calculated according to the literature (Chou, 2005). For different problems, the optimal value of λ is variable. In this study, the optimal value of λ was selected as the one that yielded the highest overall accuracy through the jackknife test. The APAAC features were generated by the iLearnPlus (Chen, 2021) web server (https://ilearnplus.erc.monash.edu/).
## 2.3 Support vector machine
Support vector machine (SVM) is a powerful supervised machine learning method based on statistical learning theory (Manavalan et al., 2019a). It was originally designed for solving binary classification problems. The basic idea of the generalized linear classifier is as follows: 1) mapping input vector to feature space (possibly high-dimensional space); 2) In the mapped feature space, a separating hyperplane is constructed to separate the two categories (Vapnik, 2019). To sidestep the expensive calculations, the mapping function only involves the relatively low dimensional vector in the input space and the dot product in the feature space. SVM always seeks solutions for global optimization and avoids overfitting. SVM has been successfully applied to many bioinformatic problems (Wei et al., 2017; Wei et al., 2018; Manayalan et al., 2019a; Manayalan et al., 2019b; Ao et al., 2021; Basith et al., 2021; Zeng et al., 2021; Basith et al., 2022; Zhang Q et al., 2022), such as the disease development prediction (Zhang et al., 2020; Zhang et al., 2021a; Ren et al., 2022; Yu et al., 2022), protein prediction (Tang et al., 2018; Tao et al., 2020; Zou et al., 2021; Ao et al., 2022), etc. In this paper, a widely used software LIBSVM (http://www.csie.ntu.edu.tw/∼cjlin/libsvm) (Chang and Lin, 2011) was used to implement the support vector machine. The radial basis function which is defined as Kxi,xj=exp−γxi−xj2 was chosen as the kernel function. The regularization parameter C and the kernel width parameter γ were optimized on the training set using a grid search strategy.
## 2.4 Evaluation methods
At present, there are three main test methods to evaluate the prediction results: the re-substitution test, the Jackknife test and the k-fold cross-validation test (Zhang et al., 2020; Zhang et al., 2021b; Deng et al., 2021; Liu et al., 2021; Tabaie et al., 2021; Ao et al., 2022a; Dai et al., 2022; Dao et al., 2022; Jin et al., 2022; Wei et al., 2022; Xiao et al., 2022; Zhou et al., 2022). Chou and Zhang have discussed in depth the classification performance estimation in bioinformatics and found the Jackknife test and k-fold cross-validation test have extrapolation ability in statistics (Malik et al., 2021; Hasan et al., 2022). In this article, we used the Jackknife test to evaluate the prediction results. The sensitivity (S n), specificity (S p), overall prediction accuracy (OA) and Matthew’s correlation coefficient (MCC) were used to evaluate the prediction performance of the algorithm (Jiang et al., 2013; Guo et al., 2020; Lv et al., 2020; Xu et al., 2021; Yang et al., 2021; Yu et al., 2021; Han et al., 2022; Zhang Z Y et al., 2022), which are defined as follows: Sn=TPTP+FN [3] Sp=TNTN+FP [4] MCC=TP×TN−FP×FNTP+FNTP+FPTN+FPTN+FN [5] OA=TP+TNTP+TN+FN+FP [6] where TP represents the number of the positive sample correctly identified, FN represents the positive sample wrongly identified as a negative sample, FP represents the negative sample wrongly identified as a positive sample, and TN represents the negative sample correctly identified (Jia et al., 2020; Li et al., 2021).
## 3.1 Model performance
The proposed algorithm based on APACC and SVM was named APACC_SVM. APAAC was generated by the iLearnPlus, with two parameters to be determined, λ and ω namely. In order to obtain ideal results, the selected values of ω were 0.05, 0.1, 0.2, 0.3, 0.4 and 0.5. The selected values of λ were the integers from 2 to 9. The jackknife test was applied to examine APAAC_SVM model. The predictive results for the three apoptosis protein datasets were enumerated in Table 1. When ω = 0.1 and λ = 7, the overall prediction effect was the best for the CL317 dataset. For CL317, the predictive results showed that the overall accuracy was $90.5\%$ in the jackknife test. We noticed the prediction result on the Secr was far lower than the other which may be due to the small subset (17 proteins). To improve the accuracy of prediction, it is necessary to collect enough proteins in the dataset.
**TABLE 1**
| Dataset | Location | Sn | Sp | MCC | OA (%) |
| --- | --- | --- | --- | --- | --- |
| CL317 | Cyto | 0.94 | 0.91 | 0.88 | 90.5 |
| CL317 | Memb | 0.89 | 0.96 | 0.91 | 90.5 |
| CL317 | Mito | 0.88 | 0.81 | 0.83 | 90.5 |
| CL317 | Secr | 0.76 | 0.76 | 0.75 | 90.5 |
| CL317 | Endo | 0.89 | 0.98 | 0.92 | 90.5 |
| CL317 | Nucl | 0.92 | 0.91 | 0.9 | 90.5 |
| ZW225 | Cyto | 0.83 | 0.82 | 0.74 | 84.0 |
| ZW225 | Memb | 0.93 | 0.91 | 0.87 | 84.0 |
| ZW225 | Mito | 0.68 | 0.85 | 0.73 | 84.0 |
| ZW225 | Nucl | 0.76 | 0.72 | 0.68 | 84.0 |
| ZD98 | Cyto | 0.95 | 0.98 | 0.94 | 93.9 |
| ZD98 | Memb | 0.97 | 0.94 | 0.93 | 93.9 |
| ZD98 | Mito | 0.92 | 0.92 | 0.91 | 93.9 |
| ZD98 | Other | 0.83 | 0.91 | 0.85 | 93.9 |
When ω = 0.3 and λ = 7, the overall prediction effect was the best for the ZW225 dataset. For ZW225, the jackknife test showed the overall accuracy was $84.0\%$. According to the prediction results obtained from the training of the ZW225 dataset, although the prediction effect was not as good as CL317, the overall appearance was similar. In the subsets Mito and Nucl (25 and 41 proteins, respectively) with fewer sequences, the prediction accuracies were significantly lower than the others. It showed that expanding the data scale was important for prediction improvement.
When ω = 0.2 and λ = 7, the overall prediction effect was the best for the ZD98 dataset. The predictive results for ZD98 apoptosis protein sets showed that the overall accuracy was $93.9\%$ in the jackknife test.
## 3.2 Model comparison
To prove the prediction ability of our APAAC_SVM algorithm, we compared our model with previous algorithms. For the CL317 dataset, Chen and Li proposed the ID method and ID-SVM method, Zhang Li et al. used the DF-SVM method for the apoptosis proteins’ subcellular location prediction, respectively. The comparison results were shown in Table 2. It can be seen from the table that our APAAC-SVM method significantly improved the prediction results in both the overall prediction accuracy and in each subcellular location, especially in Cyto, Mito and Endo.
**TABLE 2**
| Localization | ID | ID-SVM | DF-SVM | APAAC-SVM |
| --- | --- | --- | --- | --- |
| | Sn (%) | Sn (%) | Sn (%) | Sn (%) |
| Cyto | 81.3 | 91.1 | 92.9 | 93.8 |
| Memb | 81.8 | 89.1 | 85.5 | 89.1 |
| Mito | 85.3 | 79.4 | 76.5 | 88.2 |
| Secr | 88.2 | 58.8 | 76.5 | 76.5 |
| Nucl | 82.7 | 73.1 | 93.6 | 92.3 |
| Endo | 83.0 | 87.2 | 86.5 | 89.4 |
| OA (%) | 82.7 | 84.2 | 88.0 | 90.5 |
For the ZW225 dataset, Zhang and Wang used the EBGW-SVM and DF-SVM methods, and Chen and Li used the ID-SVM method for prediction. The prediction model performances were shown in Table 3. It can be seen from Table 3 that the overall prediction accuracy of each method was relatively close. However, the APAAC-SVM algorithm achieved good prediction accuracy in both the Memb and Nucl. It indicated that our algorithm was relatively ideal.
**TABLE 3**
| Localization | EBGW-SVM | DF-SVM | ID-SVM | APAAC-SVM |
| --- | --- | --- | --- | --- |
| | Sn (%) | Sn (%) | Sn (%) | Sn (%) |
| Cyto | 90.0 | 87.1 | 92.9 | 82.9 |
| Memb | 93.3 | 92.1 | 91.0 | 93.3 |
| Mito | 60.0 | 64.0 | 69.0 | 68.0 |
| Nucl | 63.4 | 73.2 | 73.2 | 75.6 |
| OA (%) | 83.1 | 84.0 | 85.8 | 84.0 |
For the ZD98 dataset, Zhou and Doctor, Huang Jing, Bulashevska, Eils, Chen and Li have all conducted research. They have respectively applied covariant discrimination algorithm, SVM algorithm, Bayesian discrimination algorithm and discrete incremental fusion algorithm. The predicted results were shown in Table 4. The overall prediction accuracy of the APAAC-SVM method was $93.9\%$ for the ZD98 dataset, which was higher than other methods. When the Jackknife test was used, the overall prediction accuracy was improved by $21.3\%$ compared with the covariant discriminant algorithm of Zhou and Doctor. Compared with the Bayesian discriminant method of Bulashevska Eils, the overall prediction accuracy was increased by about $8.1\%$. For a small sample of other apoptosis proteins in the data set, the sensitivity of these two methods was only $25\%$ and $50\%$, while the sensitivity of this method can reach $83.33\%$. Compared with Huang Jing’s SVM algorithm, this method had a higher overall prediction success rate, which was increased by about $3\%$; Moreover, the sensitivity of Cyto was higher, which reached $95.3\%$. Compared with the discrete incremental fusion method of Chen Yingli and Li Qianzhong, the overall prediction success rate of this method was also higher.
**TABLE 4**
| Unnamed: 0 | Covariant | SVM | BC | Unnamed: 4 | Hensbc | Unnamed: 6 | IDF | Unnamed: 8 | Unnamed: 9 | APAAC-SVM | APAAC-SVM.1 | APAAC-SVM.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Sn(%) | Sn(%) | Sn(%) | MCC | Sn(%) | MCC | Sn(%) | Sp(%) | MCC | Sn(%) | Sp(%) | MCC |
| Cyto | 97.7 | 86.0 | 90.7 | 0.81 | 95.3 | 0.89 | 90.7 | 95.1 | 0.87 | 95.3 | 97.6 | 0.94 |
| Memb | 73.3 | 90.0 | 90.0 | 0.83 | 90.0 | 0.83 | 90.0 | 93.1 | 0.88 | 96.7 | 93.5 | 0.93 |
| Mito | 30.8 | 100 | 92.3 | 0.83 | 92.3 | 0.83 | 92.3 | 70.6 | 0.77 | 92.3 | 92.3 | 0.91 |
| Other | 25.0 | 100 | 50.0 | 0.57 | 66.7 | 0.80 | 91.7 | 100 | 0.95 | 83.3 | 90.9 | 0.85 |
| OA (%) | 72.5 | 90.8 | 85.7 | — | 89.8 | — | 90.8 | — | — | 93.9 | — | — |
By compared with previous studies, it can be found that the APAAC-SVM method was better for category prediction with more sequence data. It showed that this method was more suitable for the prediction of apoptosis protein subcellular locations in the case of increasing sequence data, and it also had an optimistic application prospect in future research.
## 4 Conclusion
Previous apoptosis proteins’ subcellular location analysis demonstrated that information in protein sequence has a great influence on its subcellular localization. However, the performance of the proposed algorithms for apoptosis proteins’ subcellular location prediction is inadequate. This study selected three apoptosis protein sequence datasets CL317, ZD98 and ZW225 to develop a new prediction algorithm. The APAAC feature extraction method and SVM were combined to predict the subcellular location of apoptosis proteins. Through the reasonable selection of parameters, our algorithm APAAC_SVM achieved jackknife test prediction accuracy of $90.5\%$, $93.9\%$ and $84.0\%$ on CL317, ZD98 and ZW225, respectively. Compared with other methods, APAAC-SVM improved the prediction performance.
## 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
Project design and oversight: ZZ, HC, and HD; Sample collection and curation: WS and KY; Experiment conduction and data analysis: WS, SD, and ZG; Table preparation: WS and SD; Result interpretation and discussion: WS, HC, and ZG; Manuscript writing and revision: WS and ZZ; Funding acquisition: WS and ZZ. All authors have read and approved the final version of this 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: Increased fat mass negatively influences femoral neck bone mineral density
in men but not women
authors:
- Nipith Charoenngam
- Caroline M. Apovian
- Chatlert Pongchaiyakul
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011632
doi: 10.3389/fendo.2023.1035588
license: CC BY 4.0
---
# Increased fat mass negatively influences femoral neck bone mineral density in men but not women
## Abstract
### Background
Obesity is known to be a protective factor against osteoporosis. However, recent studies have shown that excessive adiposity may be detrimental for bone health.
### Objective
To determine the association of lean mass (LM) and fat mass (FM) with bone mineral density (BMD) in Thais.
### Methods
Bone density studies of consecutive patients of Srinagarind Hospital, Khon Kaen, Thailand between 2010 and 2015 were reviewed. LM, FM, lumbar spine (LS) and femoral neck (FN) BMD were measured. Lean mass index (LMI) and fat mass index (FMI) were calculated [LMI=LM (kg)/height (m)2, FMI=FM (kg)/height (m)2] and analyzed to determine the association with LS and FN BMD using multiple regression analysis. This study was approved by the institutional ethical committee (HE42116).
### Results
A total of 831 participants were included. The mean ± SD age was 50.0 ± 16.3 years. In men, LMI (per 1 kg/m2 increase) was positively correlated with FN BMD (g/cm2, β 0.033) and LS BMD (g/cm2, β 0.031), after adjusting for age, height and FMI. Whereas FMI (per 1 kg/m2 increase) was negatively correlated with FN BMD (g/cm2, β -0.015) but not with LS BMD (g/cm2, β 0.005) after adjusting for age, height and LMI. In women, both LMI and FMI were positively correlated with LS BMD (g/cm2, LMI: β 0.012; FMI: β 0.016) and FN BMD (g/cm2, LMI: β 0.034; FMI: β 0.007) with age, height, LMI and FMI included in the model.
### Conclusion
Our findings indicate that FM has a sex-specific influence on BMD in Thais.
## Introduction
Osteoporosis, a condition characterized by bone fragility secondary to low bone mass and loss of connectivity and structural integrity of bone tissue, is the most common metabolic bone disease that affects over 200 million people worldwide [1, 2]. It is estimated that one in every three women over the age of 50 years and one in every five men will suffer from fragility fractures as a result of osteoporosis during their lifetime [3]. Traditional risk factors for osteoporosis include advanced age, female sex, family history, low calcium intake, malabsorption, vitamin D deficiency, lack of physical activity, weight loss, smoking, excessive alcohol use, and the presence of chronic inflammatory diseases [4]. On the other hand, increased body weight and obesity have long been thought to be a protective factor against osteoporosis [4, 5].
Interestingly, recent evidence suggests that excess fat mass (FM) may be detrimental for bone health, as recent studies have found an inverse relationship between FM and bone mineral density (BMD), whereas previous studies found the opposite (6–9). Given the inconsistencies of the data, it is assumed that the relationship between FM and BMD is complex and different across sex and sites of BMD measurements [5, 6, 10]. Therefore, we aimed to investigate the association of lean mass (LM) and fat mass (FM) with lumbar spine (LS) and femoral neck (FN) BMD in Thai men and women.
## Study population
Bone density studies of male and female consecutive community-dwelling patients aged 20 – 90 years were retrospectively reviewed from the medical record database of Srinagarind Hospital, Khon Kaen, Thailand between 2010 and 2015. Participants aged 20 to 90 years who underwent BMD testing at both the lumbar spine and the hip were included in this study. Patients with one of the following exclusion criteria were excluded: history of fragility fractures at any sites; history of traumatic fractures of the spine or femur; medications that may affect bone metabolism except calcium and vitamin D; history of any spinal surgery; lumbar scoliosis greater than 20 degrees; two or more non-assessable lumbar vertebrae; early or surgical menopause; and Z-score outside the range of ± 2.0 at either the lumbar spine, total proximal femur, or the femoral neck. This study was reviewed and approved by the Khon Kaen University Human Research Ethics Committee in accordance with the Helsinki Declaration and the Good Clinical Practice Guidelines (Reference No. HE42116).
## Study measurements
Demographic data were collected including age, body weight, height, and body mass index (BMI) was calculated. Lumbar spine (LS), femoral neck (FN) BMD, lean mass (LM) and fat mass (FM) were measured using dual energy x-ray absorptiometry on a Lunar Prodigy bone densitometer (GE Healthcare, Madison, WI). Lean mass index (LMI) and fat mass index (FMI) were calculated [LMI=LM (kg)/height (m)2, FMI=FM (kg)/height (m)2] and were analysed to determine the association with LS and FN BMD using multiple regression analysis.
## Statistical analysis
Comparisons of participants’ characteristics between males and females were performed using independent sample t-test for continuous parametric data, Mann Whitney U-test for continuous non-parametric data and Chi-square test for categorical data. Comparisons of participants’ characteristics among groups with different LMI and FMI were performed using one-way ANOVA followed by post-hoc LSD and Bonferroni tests for continuous parametric data. Pearson correlation analysis was used to determine univariate association of age with LM, FM, LMI, FMI and FN and LS BMD. Linear regression analysis was performed to determine univariate and multivariate association of LMI and FMI with FN and LS BMD. Logistic regression analysis was used to determine unadjusted and adjusted odds ratios (OR) and $95\%$ confidence interval (CI) that represent the association of LMI and FMI with osteoporosis at FN and LS. Statistical significance was defined as p-value <0.05. SPSS version 27 (SPSS Inc., Chicago, IL) was used to perform statistical analysis. Data illustrations were generated using the GraphPad Prism software 9.4.0 (GraphPad, La Jolla, CA, USA).
## Characteristics of participants
A total of 831 participants were included in the study. As demonstrated in Table 1, the mean ± SD age was 50.0 ± 16.3 years and 498 ($59.9\%$) were female. The mean ± SD FN and LS BMD were 0.866 ± 0.177 g/cm2 and 1.060 ± 0.191 g/cm2, respectively. There were 333 ($40.1\%$) and 55 ($6.6\%$) participants with osteopenia (T-score FN BMD -1 to -2.5) and osteoporosis (T-score FN BMD ≤-2.5) of the FN, respectively. There were 229 ($27.6\%$) and 120 ($4.4\%$) participants with osteopenia and osteoporosis of the LS, respectively. The mean ± SD BMI, FM, LM, FMI and LMI were 23.3 ± 3.7 kg/m2, 15.5 ± 7.7 kg, 38.9 ± 8.0 kg, 6.4 ± 3.3 kg/m2, 15.6 ± 2.3 kg/m2, respectively. As shown in Table 1, female participants had statistically significantly lower BMI, FN and LS BMD, LM and LMI; higher FM and FMI; and higher proportion of osteoporosis compared with male participants (all $p \leq 0.001$). Age was positively correlated with FM ($R = 0.143$, $p \leq 0.001$) and FMI ($R = 0.168$, $p \leq 0.001$) and was negatively correlated with LM (R = -0.191, $p \leq 0.001$), LMI (R = -0.107, $$p \leq 0.002$$), FN BMD (R = -0.576, $p \leq 0.001$) and LS BMD (R = -0.421, $p \leq 0.001$).
**Table 1**
| Unnamed: 0 | All participants | Males | Females | p-value |
| --- | --- | --- | --- | --- |
| | N = 831 | N = 333 (40.1%) | 498 (59.9%) | p-value |
| Age (years) | 50.0 ± 16.3 | 49.3 ± 17.3 | 50.5 ± 15.5 | 0.337 |
| Body Weight (kg) | 57.8 ± 10.3 | 60.9 ± 10.3 | 55.7 ± 9.8 | <0.001 |
| Height (cm) | 157.3 ± 7.5 | 163.1 ± 6.4 | 153.4 ± 5.4 | <0.001 |
| Body mass index (kg/m2) | 23.3 ± 3.7 | 22.8 ± 3.3 | 23.6 ± 3.9 | <0.001 |
| Body mass index <23 kg/m2 | 420 (50.5%) | 192 (57.7%) | 228 (45.8%) | 0.001 |
| Body mass index 23 - <25 kg/m2 | 183 (22.0%) | 70 (21.0%) | 113 (22.7%) | |
| Body mass index ≥25 kg/m2 | 228 (27.4%) | 71 (21.3%) | 157 (31.5%) | |
| FN BMD (g/cm2) | 0.866 ± 0.177 | 0.920 ± 0.176 | 0.829 ± 0.168 | <0.001 |
| FN T-score | -0.8 ± 1.2 | -0.8 ± 1.1 | -0.9 ± 1.2 | 0.170 |
| FN normal BMD (T-score >-1) | 443 (53.3%) | 181 (54.4%) | 262 (52.6%) | 0.134 |
| FN osteopenia (T-score -1 – -2.5) | 333 (40.1%) | 137 (41.4%) | 196 (39.4%) | |
| FN osteoporosis (T-score <-2.5) | 55 (6.6%) | 15 (4.5%) | 40 (8.0%) | |
| LS BMD (g/cm2) | 1.060 ± 0.191 | 1.111 ± 0.167 | 1.026 ± 0.200 | <0.001 |
| LS T-score | -0.8 ± 1.7 | -0.4 ± 1.3 | -1.1 ± 1.8 | <0.001 |
| LS normal BMD (T-score >-1) | 482 (58.0%) | 236 (70.9%) | 246 (49.4%) | <0.001 |
| LS osteopenia (T-score -1 – -2.5) | 229 (27.6%) | 81 (24.3%) | 148 (29.7%) | |
| LS osteoporosis (T-score <-2.5) | 120 (14.4%) | 16 (4.8%) | 104 (20.9%) | |
| Osteoporosis LS or FN (LS or FN T-score <-2.5) | 132 (15.9%) | 22 (6.6%) | 110 (22.1%) | <0.001 |
| Fat mass (kg) | 15.5 ± 7.7 | 10.8 ± 6.1 | 18.6 ± 7.0 | <0.001 |
| Fat mass index (kg/m2) | 6.4 ± 3.3 | 4.0 ± 2.2 | 7.9 ± 3.0 | <0.001 |
| % Body fat | 27.9 ± 11.4 | 18.0 ± 7.9 | 34.5 ± 8.1 | <0.001 |
| Lean mass (kg) | 38.9 ± 8.0 | 46.6 ± 6.0 | 33.8 ± 4.1 | <0.001 |
| Lean mass index (kg/m2) | 15.6 ± 2.3 | 17.5 ± 1.8 | 14.4 ± 1.6 | <0.001 |
| % Lean mass | 72.1 ± 11.4 | 82.0 ± 7.9 | 65.5 ± 8.1 | <0.001 |
## Femoral neck and lumbar spine T-score stratified by quartiles of lean mass index and fat mass index
Figure 1 demonstrated mean FN and LS BMD T-score stratified by quartiles of LMI and FMI in male and female participants. The analysis of variance revealed significant differences in FN and LS T-score across the groups with different LMI among both male and female participants (all ANOVA $p \leq 0.01$). FN T-scores were different across the groups with different FMI in male participants (ANOVA $p \leq 0.001$), with post-hoc analysis revealing the Q1 FMI group having lower FN T-score than the Q4 FMI group (Bonferroni $p \leq 0.001$), while the difference was not observed among the female participants (ANOVA $$p \leq 0.834$$). On the other hand, LS T-scores were not significantly different across the groups with different FMI in both male and female participants.
**Figure 1:** *Femoral neck and lumbar spine bone mineral density T-score stratified by quartiles of lean mass index and fat mass index in male and female participants. BMD, Bone Mineral Density; FMI, Fat Mass Index; FN, Femoral Neck; LMI, Lean Mass Index; LS, Lumbar Spine.*
Subgroup analysis among women in the Q1 and Q2 LMI groups revealed a significant difference in LS T-score, but not FN T-score, across the groups with different FMI (ANOVA $p \leq 0.01$ for LS T-score, ANOVA $$p \leq 0.384$$ for FN T-score), with post-hoc analysis showing a trend of lower LS T-score in the Q1 FMI group compared with the Q4 FMI group (LSD $$p \leq 0.012$$, Bonferroni $$p \leq 0.070$$). In the subgroup of women in the Q3 and Q4 LMI groups, no difference in LS or FN T-score across the groups with different FMI was observed.
## Association of lean mass index and fat mass index with femoral neck and lumbar spine bone mineral density
Regression coefficients of LMI and FMI on FN and LS BMD were demonstrated in Table 2. In male participants, FN BMD (g/cm2) was positively correlated with LMI (per 1 kg/m2 increase; β 0.033, $95\%$CI 0.024 – 0.041) and inversely correlated with FMI (per 1 kg/m2 increase; β -0.015, $95\%$CI -0.022 – -0.007), after adjusting for age and height with LMI and FMI included in the same model (Model 3, Table 2). Whereas LS BMD was associated with only increased LMI (per 1 kg/m2 increase; β 0.031, $95\%$CI 0.021 – 0.040) but not FMI (per 1 kg/m2 increase; β 0.005, $95\%$CI -0.003 – 0.014), with adjustment for the same variables (Model 3, Table 2).
**Table 2**
| Unnamed: 0 | Unadjusted | Unadjusted.1 | Unadjusted.2 | Model 1 | Model 1.1 | Model 1.2 | Model 2 | Model 2.1 | Model 2.2 | Model 3 | Model 3.1 | Model 3.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | β | 95%CI | p-value | β | 95%CI | p-value | β | 95%CI | p-value | β | 95%CI | p-value |
| Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) | Femoral neck BMD (g/cm2) |
| All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants |
| LMI (per 1 kg/m2 increase) | 0.034 | 0.029 – 0.039 | <0.001 | 0.032 | 0.027 – 0.037 | <0.001 | 0.029 | 0.022 – 0.037 | <0.001 | 0.032 | 0.026 – 0.037 | <0.001 |
| FMI (per 1 kg/m2 increase) | -0.007 | -0.010 – -0.03 | <0.001 | 0.009 | 0.005 – 0.012 | <0.001 | -0.022 | -0.029 – -0.015 | <0.001 | 0.002 | -0.001 – 0.006 | 0.221 |
| Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants |
| LMI (per 1 kg/m2 increase) | 0.036 | 0.027 – 0.045 | <0.001 | 0.027 | 0.019 – 0.036 | <0.001 | 0.046 | 0.033 – 0.059 | <0.001 | 0.033 | 0.024 – 0.041 | <0.001 |
| FMI(per 1 kg/m2 increase) | -0.017 | -0.026 – -0.009 | <0.001 | -0.006 | -0.014 – -0.001 | 0.108 | -0.048 | -0.061 – -0.035 | <0.001 | -0.015 | -0.022 - -0.007 | <0.001 |
| Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants |
| LMI(per 1 kg/m2 increase) | 0.041 | 0.032 – 0.050 | <0.001 | 0.038 | 0.031 – 0.045 | <0.001 | 0.028 | 0.018 – 0.037 | <0.001 | 0.034 | 0.027 – 0.041 | <0.001 |
| FMI(per 1 kg/m2 increase) | 0.009 | 0.004 – 0.014 | <0.001 | 0.014 | 0.010 – 0.018 | <0.001 | -0.014 | -0.022 - -0.006 | <0.001 | 0.007 | 0.003 – 0.010 | <0.001 |
| Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) | Lumbar spine BMD (g/cm2) |
| All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants |
| LMI (per 1 kg/m2 increase) | 0.027 | 0.022 – 0.033 | <0.001 | 0.023 | 0.016 – 0.029 | <0.001 | 0.004 | -0.005 – 0.013 | 0.386 | 0.018 | 0.011 – 0.025 | <0.001 |
| FMI (per 1 kg/m2 increase) | 0.001 | -0.003 – 0.005 | 0.640 | 0.018 | 0.014 – 0.022 | <0.001 | -0.003 | -0.011 – 0.006 | 0.495 | 0.014 | 0.009 – 0.018 | <0.001 |
| Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants |
| LMI (per 1 kg/m2 increase) | 0.032 | 0.023 – 0.041 | <0.001 | 0.030 | 0.021 – 0.040 | <0.001 | 0.024 | 0.010 – 0.038 | 0.001 | 0.031 | 0.021 – 0.040 | <0.001 |
| FMI(per 1 kg/m2 increase) | 0.011 | 0.003 – 0.020 | 0.005 | 0.016 | 0.007 – 0.024 | <0.001 | -0.027 | -0.042 – -0.013 | <0.001 | 0.005 | -0.003 – 0.014 | 0.209 |
| Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants |
| LMI(per 1 kg/m2 increase) | 0.024 | 0.013 – 0.035 | <0.001 | 0.020 | 0.012 – 0.029 | <0.001 | -0.002 | -0.014 – 0.009 | 0.699 | 0.012 | 0.003 – 0.021 | 0.010 |
| FMI(per 1 kg/m2 increase) | 0.012 | 0.007 – 0.018 | <0.001 | 0.019 | 0.014 – 0.023 | <0.001 | 0.006 | -0.004 – 0.015 | 0.263 | 0.016 | 0.011 – 0.021 | 0.001 |
In female participants, both LS and FN BMD (g/cm2) were positively correlated with LMI (per 1 kg/m2 increase; LS BMD: β 0.012, $95\%$CI 0.003 – 0.021; FN BMD: β 0.034, $95\%$CI 0.027 – 0.041) and FMI (per 1 kg/m2 increase; LS BMD: β 0.016, $95\%$CI: 0.011 – 0.021; FN BMD: β 0.007, $95\%$CI: 0.003 – 0.010), after adjusting for age, height and LMI and FMI included in the same model (Model 3, Table 2).
## Association of lean mass index and fat mass index with osteoporosis at femoral neck and lumbar spine
Multivariate logistic regression analysis of the association of LMI and FMI with presence of osteoporosis at FN and LS (defined by T-score BMD ≤-2.5) were demonstrated in Table 3. In male participants, LMI (per 1 kg/m2 increase) was statistically significantly associated with decreased odds of FN osteoporosis (OR 0.466, $95\%$CI 0.305 – 0.711) but not LS osteoporosis, after adjusting for age, height and FMI (Model 3, Table 3). FMI (per 1 kg/m2 increase) was statistically significantly associated with increased odds of FN osteoporosis (OR 2.037, $95\%$CI 1.132 – 3.666) after adjusting for age, height and BMI (Model 2, Table 3), but the association became insignificant in the model adjusting for age, height and LMI (Model 3, Table 3).
**Table 3**
| Unnamed: 0 | Unadjusted | Unadjusted.1 | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | OR | 95%CI | OR | 95%CI | OR | 95%CI | β | 95%CI |
| All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants | All participants |
| FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis |
| LMI (per 1 kg/m2 increase) | 0.650 | 0.557 – 0.759 | 0.616 | 0.492 – 0.770 | 0.739 | 0.557 – 0.980 | 0.627 | 0.493 – 0.796 |
| FMI (per 1 kg/m2 increase) | 0.902 | 0.823 – 0.988 | 0.787 | 0.696 – 0.891 | 1.330 | 1.019 – 1.736 | 0.844 | 0.744 – 0.958 |
| LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis |
| LMI (per 1 kg/m2 increase) | 0.695 | 0.625 – 0.772 | 0.842 | 0.722 – 0.981 | 1.234 | 0.991 – 1.536 | 0.926 | 0.776 – 1.105 |
| FMI (per 1 kg/m2 increase) | 0.982 | 0.962 – 1.042 | 0.733 | 0.662 – 0.812 | 0.834 | 0.666 – 1.044 | 0.741 | 0.666 – 0.825 |
| Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants | Male participants |
| FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis |
| LMI (per 1 kg/m2 increase) | 0.410 | 0.279 – 0.603 | 0.465 | 0.311 – 0.695 | 0.504 | 0.287 – 0.884 | 0.466 | 0.305 – 0.711 |
| FMI (per 1 kg/m2 increase) | 0.886 | 0.666 – 1.125 | 0.775 | 0.587 – 1.023 | 2.037 | 1.132 – 3.666 | 0.926 | 0.675 – 1.271 |
| LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis |
| LMI (per 1 kg/m2 increase) | 0.541 | 0.393 – 0.743 | 0.619 | 0.444 – 0.862 | 0.686 | 0.415 – 1.133 | 0.589 | 0.647 – 1.180 |
| FMI (per 1 kg/m2 increase) | 0.849 | 0.656 – 1.100 | 0.762 | 0.580 – 1.001 | 1.569 | 0.924 – 2.661 | 0.874 | 0.404 – 0.860 |
| Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants | Female participants |
| FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis | FN osteoporosis |
| LMI (per 1 kg/m2 increase) | 0.660 | 0.525 – 0.830 | 0.702 | 0.531 – 0.928 | 0.851 | 0.598 – 1.210 | 0.716 | 0.528 – 0.972 |
| FMI (per 1 kg/m2 increase) | 0.766 | 0.670 – 0.876 | 0.798 | 0.694 – 0.917 | 1.171 | 0.828 – 1.656 | 0.837 | 0.727 – 0.964 |
| LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis | LS osteoporosis |
| LMI (per 1 kg/m2 increase) | 0.846 | 0.734 – 0.977 | 0.912 | 0.763 – 1.089 | 1.426 | 1.104 – 1.843 | 1.052 | 0.850 – 1.302 |
| FMI (per 1 kg/m2 increase) | 0.807 | 0.740 – 0.880 | 0.723 | 0.645 – 0.809 | 0.714 | 0.553 – 0.922 | 0.715 | 0.635 – 0.805 |
In female participants, LMI (per 1 kg/m2 increase) was statistically significantly associated with decreased odds of osteoporosis at FN (OR 0.716, $95\%$CI 0.528 – 0.972) but not LS, after adjusting for age, height and FMI. Whereas FMI (per 1 kg/m2 increase) was statistically significantly associated with decreased odds of osteoporosis at both FN (OR 0.837, $95\%$CI 0.727 – 0.964) and LS (OR 0.715, $95\%$CI 0.635 – 0.805), after adjusting for age, height and LMI (Model 3, Table 3).
## Discussion
In this cross-sectional study of 333 men and 498 women, we found that increased LM had a positive effect on LS and FN BMD in both men and women. On the other hand, we revealed a sex-specific association between FM and BMD as increased FM had a negative effect on FN BMD and no significant effect on LS BMD in men but a positive effect on FN and LS BMD in women. Furthermore, the subgroup analysis revealed that FM was positively associated with LS BMD only among women with low LM.
The results of our study confirm the previously reported positive impact of LM on BMD in multiple studies (7, 11–13). More importantly, our results support the recent observation from the NHANES 2011 – 2018 database that increased FM was negatively associated with total body BMD, particularly in men (0.13 lower T-score per 1 kg/m2 increase in FMI), which is contrary to the result from a prior meta-analysis of 44 studies demonstrating a positive association between FM and BMD [6]. Notably, our findings underscore that increased FM in men may selectively affect FN BMD, rather than LS BMD, which suggests that high body fat may selectively affect cortical bone rather than trabecular bone.
Although the exact underlying mechanism of the negative impact of FM on BMD in men, but not in women, is still unclarified, it is thought to involve the effects of obesity-related hormonal changes on the skeleton [14, 15]. First, obesity and increased fat mass are known to cause decreased testosterone level, an anabolic hormone that stimulates bone formation, in men due to suppression of the hypothalamic‐pituitary‐testicular axis and insulin resistance−associated reductions in sex hormone binding globulin [16, 17]. Therefore, men with increased fat mass could have lower testosterone levels, which may explain the observed sex-specific association between fat mass and lower FN BMD. It is however unclear why this would selectively affect FN BMD but not LS BMD. Additionally, it should be noted that obesity is associated with increased estrogen concentrations among males and that estrogen is protective against osteoporosis in both sexes [18, 19]. Data on sex hormones concentrations would have been valuable to identify the potential explanations for our observations.
Another explanation could be the difference in visceral and subcutaneous fat proportions between men and women, as previous studies have suggested that increased visceral fat may have a detrimental effect on BMD compared to subcutaneous fat due to its associated low-grade chronic systemic inflammation (increased interleukin-6 and tumor necrosis factor-α) [5]. Data on body fat distribution and inflammatory markers would have been valuable to explain the difference in the results between men and women. Unfortunately, such data were not available in our study.
Other possible explanations for the inverse association between FM and BMD involve leptin, insulin resistance, vitamin D status and lifestyle factor. It has been shown that leptin-deficient and leptin-receptor deficient mice were shown to have increased bone formation, suggesting the negative effect of increased leptin in obesity on bone formation [20]. Insulin resistance may also play a role in triggering bone loss, although previous studies have shown mixed results [21, 22]. Furthermore, vitamin D deficiency is well-known to be associated with increased FM and obesity and therefore could mediate this association [23, 24]. Finally, increased FM may represent sedentary lifestyle and lack of physical activity, which can be associated with decreased mechanical load to the skeleton and low cortical BMD [25, 26]. This could particularly explain our observation of the inverse association between FMI and FN BMD in men.
Interestingly, we found that FM was positively correlated with both LS and FN BMD in women with low LM, but not in those with high LM. This suggests that LM and sex could be effect modifiers of the association between FM and BMD, which may explain the discrepancy in the results among the prior studies (6–9). The positive effect of FM on BMD could be due not only to increased mechanical load to the skeleton, but also increased estrogen produced by the adipose tissue, especially in postmenopausal women [15].
This study has certain limitations that should be acknowledged. First, data were collected retrospectively and thus factors on how DXA examinations were acquired may not have been adequately controlled, despite the established standard practice protocols in our institution. Examinations were done by several technologists, which could have some effects on the precision of the data [27], but this would, on the other hand, permit better generalizability of our finding (e.g. our results are generalizable regardless of the experience level or other characteristics of the technologist). In addition, the causal association cannot be concluded with certainty as this study is cross-sectional by design. Data on potential confounders and mediators, such as medical comorbidities, functional status, physical activity, vitamin D status, fat distribution, sex hormones and inflammatory markers were also not available in this study. Further prospective cohort studies with more robust adjustments are needed to confirm our observations.
## Conclusion
Our results indicate sex-specific influence of fat mass on BMD in Thais. Increased lean mass had a positive association with LS and FN BMD in both men and women. On the other hand, increased fat mass had a negative association with FN BMD and no significant association with LS BMD in men but a positive association with FN and LS BMD in women. Further prospective cohort studies are needed to draw causality of these associations.
## 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 Khon Kaen University Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
All authors contributed to the article and approved the submitted version.
## Conflict of interest
CA has participated on advisory boards for Abbott Nutrition, Allergan, Inc., Altimmune, Inc., Bariatrix Nutrition, Cowen and Company, LLC, Curavit Clinical Research, EnteroMedics, Gelesis, Srl., Janssen, Jazz Pharmaceuticals, Inc., L-Nutra, Inc., NeuroBo Pharmaceuticals, Inc., Novo Nordisk, Inc., Nutrisystem, Pain Script Corporation, Real Appeal, Riverview School, Rhythm Pharmaceuticals, Roman Health Ventures, Inc., SetPoint Health, Scientific Intake Ltd. Co., Tivity Health, Inc., Xeno Biosciences and Zafgen Inc.Dr. Apovian has received research funding from NIH, PCORI and Novo Nordisk.
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: Vertical sleeve gastrectomy associates with airway hyperresponsiveness in a
murine model of allergic airway disease and obesity
authors:
- Jack T. Womble
- Mark D. Ihrie
- Victoria L. McQuade
- Akhil Hegde
- Matthew S. McCravy
- Sanat Phatak
- Robert M. Tighe
- Loretta G. Que
- David D’Alessio
- Julia K. L. Walker
- Jennifer L. Ingram
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011633
doi: 10.3389/fendo.2023.1092277
license: CC BY 4.0
---
# Vertical sleeve gastrectomy associates with airway hyperresponsiveness in a murine model of allergic airway disease and obesity
## Abstract
### Introduction
Asthma is a chronic airway inflammatory disease marked by airway inflammation, remodeling and hyperresponsiveness to allergens. Allergic asthma is normally well controlled through the use of beta-2-adrenergic agonists and inhaled corticosteroids; however, a subset of patients with comorbid obesity experience resistance to currently available therapeutics. Patients with asthma and comorbid obesity are also at a greater risk for severe disease, contributing to increased risk of hospitalization. Bariatric surgery improves asthma control and airway hyperresponsiveness in patients with asthma and comorbid obesity, however, the underlying mechanisms for these improvements remain to be elucidated. We hypothesized that vertical sleeve gastrectomy (VSG), a model of metabolic surgery in mice, would improve glucose tolerance and airway inflammation, resistance, and fibrosis induced by chronic allergen challenge and obesity.
### Methods
Male C57BL/6J mice were fed a high fat diet (HFD) for 13 weeks with intermittent house dust mite (HDM) allergen administration to induce allergic asthma, or saline as control. At week 11, a subset of mice underwent VSG or Sham surgery with one week recovery. A separate group of mice did not undergo surgery. Mice were then challenged with HDM or saline along with concurrent HFD feeding for 1-1.5 weeks before measurement of lung mechanics and harvesting of tissues, both of which occurred 24 hours after the final HDM challenge. Systemic and pulmonary cytokine profiles, lung histology and gene expression were analyzed.
### Results
High fat diet contributed to increased body weight, serum leptin levels and development of glucose intolerance for both HDM and saline treatment groups. When compared to saline-treated mice, HDM-challenged mice exhibited greater weight gain. VSG improved glucose tolerance in both saline and HDM-challenged mice. HDM-challenged VSG mice exhibited an increase in airway hyperresponsiveness to methacholine when compared to the non-surgery group.
### Discussion
The data presented here indicate increased airway hyperresponsiveness in allergic mice undergoing bariatric surgery.
## Introduction
Asthma is a heterogenous chronic airway inflammatory disease impacting roughly 300 million people worldwide [1] and over 25 million people in the United States [2]. Each year around 250,000 adults in the US are diagnosed with asthma and comorbid obesity [3, 4], a condition known to be poorly controlled by currently available pharmacologic therapies, contributing to increased hospitalization, poor asthma management, and decreased quality of life (5–9). Asthma in adults with comorbid obesity may be characterized by distinct endotypes involving different immune mechanisms [10]. Generally, asthma which is diagnosed early in life is associated with high type 2 immune responses and an allergic clinical phenotype [5]. Obesity contributes to the severity of allergic asthma [11] and corticosteroid insensitivity [12], complicating effective treatment of patients. Asthma onset following the development of obesity in adulthood is more prevalent in females and associated with a non-allergic phenotype and low type 2 immune responses [5, 13]. Current projections estimate that $48.9\%$ of the US population will experience obesity by 2030 [14]. The rising prevalence of both asthma and obesity will necessitate a better understanding of disease development and care.
Allergic asthma is classically defined by allergen-induced airway inflammation and is represented by elevated serum immunoglobin E (IgE), type 2 cytokine (interleukins-4, -5, and -13 [IL-4, IL-5, IL-13]) production, and sputum eosinophilia (>$1\%$-$3\%$) [15, 16]. The type 2 cytokine response drives airway inflammation, hyperresponsiveness (AHR), and remodeling which manifests as increased sub-epithelial fibrosis, mucus hypersecretion, and hyper-reactivity to environmental allergens [17]. Although allergic asthma is commonly well controlled with beta-2-adrenergic agonists and inhaled corticosteroids [18], obesity significantly impairs therapeutic efficacy (7–9); however, the underlying mechanism(s) of this therapeutic impairment is poorly understood.
Despite the lack of effective standard and biologic therapies for asthma patients with comorbid obesity, weight loss through bariatric surgery is associated with improved asthma control and maintenance [19]. A study conducted by Santos et al. found individuals who underwent bariatric surgery experience improved lung capacity, dynamic lung volumes and total respiratory resistance, with greater improvement occurring in patients with asthma and comorbid obesity [20]. Additionally, a review of weight loss through surgical and non-surgical intervention identified bariatric surgery patients to have greater decreases in medication use, hospitalization, AHR, and improved lung function [21]; benefits which occur as soon as 30 days post-operative and are sustained for at least 3 years [22].
Bariatric surgery may be modeled in obese rodents using vertical sleeve gastrectomy (VSG) procedures, as reviewed in [23, 24]. These models have low mortality and may closely resemble the human surgical procedures in that post-operative outcomes may be observed that allow for evaluation of the physiological effects of bariatric surgery in obese mice and rats. Obesity in rodents may be induced using high fat, high carbohydrate or Western “cafeteria” diets [25], which stimulates significantly altered immune responses [26], gut microbiota changes [27] and inherent airway hyperresponsiveness [28]. When combined with acute or chronic allergic challenge, models of obesity demonstrate that marked changes to pulmonary inflammation, remodeling and responsiveness occur that are distinct from changes induced by allergen exposures alone [26].
While studies have identified improvements in asthma exacerbation and control with bariatric surgery, the underlying physiological changes are poorly understood. This study reports the effects of VSG in a mouse model of obese allergic asthma. We hypothesized that VSG would improve glucose tolerance and airway inflammation, resistance, and fibrosis induced by chronic allergen challenge in obese mice. Surprisingly, and contrary to our hypothesis, our data show that in this model, VSG augmented allergen-induced airway resistance. A better understanding of how modeling of bariatric surgery in rodent modulates experimental asthma responses may provide insights regarding the impact of bariatric surgery in asthma and comorbid obesity.
## Animals
Five-week-old male C57BL/6J mice were purchased from Jackson Laboratory. Animal care and experimental protocols were approved by the Duke University Institutional Animal Care and Use Committee and carried out in accordance with the American Association for the Accreditation of Laboratory Animal Care guidelines. Male C57BL/6J mice were used for experiments as they are more susceptible to diet-induced obesity than female C57BL/6J mice [29]. All mice were housed in pathogen free facilities at Duke University. At week 0, 10, and 12 of the protocol, mice were tested for oral glucose tolerance and weighed.
## Oral glucose tolerance test
Mice were fasted for 5 hours prior to the glucose tolerance test. A bolus of $10\%$ glucose solution (200 μL) was administered by oral gavage. The mice were restrained, and blood was collected from the tail vein. Blood was collected prior to gavage and at 10-, 30-, and 90-minutes post-gavage. Glucose levels were determined by an Accu-Chek Performa (Roche) blood glucose meter.
## Diet and treatments
Mice were fed a high-fat diet (HFD – $60\%$ kcal fat, Research Diets #D12492i) for 10 weeks to induce obesity. A separate group of mice were fed a standard chow diet (normal chow – $13\%$ kcal fat) for the same time frame. For the first 2 weeks, HFD-fed mice received intranasal phosphate-buffered saline (saline) or house dust mite (HDM) allergen (Greer Laboratories, XPB70D3A2.5, lots #360924 (Endotoxin 872.5 EU/vial) and #369446 (Endotoxin 1542.5 EU/vial), 50 µg of protein delivered in 40 µl PBS [30]) 3 days per week under light isoflurane anesthesia for allergen sensitization. After a 4-week break, in which mice continued to consume HFD, intranasal administration of PBS or HDM continued 3 days per week for 4 weeks. On week 11, mice underwent vertical sleeve gastrectomy [31] (VSG; $$n = 8$$ per group) or sham surgery [31] ($$n = 9$$ - 10 per group) followed by a 1-week recovery and were pair fed a liquified standard chow diet (powdered chow mixed with water). A third group of mice ($$n = 11$$) did not undergo surgery (Non-surgery, NS) and were rested for 2 weeks and fed standard chow to match the surgery group. After recovery, all mice were then placed back on HFD and administered intranasal saline or HDM 3 days per week for 1-1.5 weeks (Figure 1).
**Figure 1:** *Schematic depiction of experimental time course. 5-week-old mice underwent oral glucose tolerance testing (OGTT) on weeks (Wk) 0, 10 and 12. 50 μg of house dust mite extract or saline (vehicle control) was delivered via intranasal administration in 40 μLs saline 3 times per week (doses shown as arrows) for the durations shown. High fat diet (HFD – 60% kcal fat) or standard chow diet (13% kcal fat) was given ad libitum for the durations shown. Vertical sleeve gastrectomy (VSG) or Sham surgeries were performed at week 11. Mice were pair fed after surgery. Lung mechanics measurements as well as bronchoalveolar lavage (BAL) and tissue collection in mice occurred at week 13.*
## Surgery
Surgeries were performed as described in Douros et al. [ 32] Prior to VSG or Sham surgery, mice were fasted overnight. Mice were anesthetized under isoflourane and a roughly 1.5 cm midline incision was made below the xyphoid process. The suspensory ligament was incised, and the spleen was separated from the stomach. A Ligaclip (LS400, Ethicon) was placed on the stomach at the angle of His, forming a tube between the esophagus and pylorus, separating the majority of the stomach which was then excised. The Ligaclip was attached with three sutures through the stomach walls, and then the excision was closed with a continuous suture. Sham surgeries were identical to VSG except the stomach was not clipped and excised: a midline incision was made, the suspensory ligament was incised, and the stomach was separated from the spleen. The stomach was removed temporarily and then placed back in the abdomen and the incision was closed.
## Lung mechanics measurements
Airway responsiveness to intravenous methacholine was measured 24 hours after the final HDM exposure using a computer-controlled small animal ventilator (FlexiVent, Scireq) as previously described [33]. The resulting impedance signal was used to calculate Newtonian resistance (Rn), total respiratory system resistance (Rtot), elastance (E), tissue damping (G), and tissue elastance (H). Central airway and total respiratory sensitivity (provocative concentration of methacholine resulting in a doubling of baseline airway resistance [PC100] and reactivity [K]) were calculated using non-linear regression analysis with exponential growth curve fit of the methacholine dose-response curve for each animal [34]. Briefly, the data points in the dose response to methacholine underwent non-linear regression analysis with exponential growth curve fit. In doing so, dose response data were linearized. Thus, the linear curve fits the equation Y = mX + b. The slope (m) of the linear curve represents “K” (reactivity). PC100 is the bronchoconstrictor provocative concentration that causes a $100\%$ increase (doubling) in baseline resistance.
## Bronchoalveolar lavage (BAL)
Immediately following lung mechanics measurements, lungs were lavaged with 1 mL saline 3 times. BAL fluid cells were separated by centrifugation, and cells were attached to slides using a Cytospin 3 Cytocentrifuge (ThermoFisher), fixed and stained with Easy III (Azer Scientific). Differential cell counts were obtained by counting 200 total cells under 200x magnification.
## Lung histology
Tissues were harvested immediately after BAL. Lungs were inflated to 25 cmH2O, fixed in $4\%$ paraformaldehyde, and embedded in paraffin. Sections were stained with hematoxylin and eosin (H&E), Masson’s trichrome, and periodic acid-Schiff (PAS). Histological scoring was performed as described in Ihrie et al. [ 35] H&E sections were scored 0-4 for peribronchial inflammation, including depth and circumference of inflammatory cells in 10 circular airways per mouse lung section. PAS-stained sections were scored 0-4 for positive staining of airway epithelial layer mucus in 10 airways per mouse lung section [35]. For both H&E and PAS, a score of 0 indicates little-to-no inflammation or mucus, respectively, and a score of 4 indicates severe inflammation or mucus, respectively. Scores for H&E staining were recorded across the 10 airways and the circumference and depth parameters were averaged together to determine the mean H&E score per mouse. For PAS staining, scores were recorded across the 10 airways to determine the mean PAS score per mouse. Peribronchial Masson’s trichrome staining was quantified by color thresholding in ImageJ in 9-10 airways per mouse lung section [35].
Histological assessments of peribronchial inflammation, mucus production and fibrosis as well as airspace inflammatory cell counts were determined at the end of the protocol. H&E- and PAS-stains of mouse lung tissue demonstrated elevated peribronchial inflammatory cell infiltrates and airway epithelium mucus production, respectively, in HDM-challenged mice when compared to saline-challenged control mice (Figures 4A–D), consistent with models of allergic airway disease. No difference was observed between surgery groups in either H&E- or PAS-stained airways (Figures 4A–D). In HDM-challenged mice, peribronchial collagen deposition had a non-significant increase following VSG ($$p \leq 0.06$$) compared to NS mice (Figures 4E, F); however, no difference is observed between HDM- or PBS-challenged mice.
**Figure 4:** *Peribronchial inflammation, mucus and fibrosis. (A) Representative images of H&E-stained mouse lung sections taken at x20 magnification, scale bar = 50μm. (B) Mean peribronchial inflammation score of H&E-stained lung sections, n=5-6 mice per group, 9-10 airways per mouse. (C) Representative images of PAS-stained mouse lung sections taken at x20 magnification, scale bar = 50μm. Arrows indicated positively stained cells in airway epithelium. (D) Mean airway mucus score of PAS-stained lung sections, n=5-6 mice per group, 9-10 airways per mouse. (E) Representative images of Masson’s trichrome-stained mouse lung sections taken at x20 magnification, scale bar = 50μm. (F) Mean percentage peribronchial Masson’s trichrome staining, n=5-6 mice per group, 9-10 airways per mouse. Grey bars and ▲ = saline-challenged mice; pink bars and ▼ = HDM-challenged mice. (B, D, F) were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. ****p<0.0001.*
Differential cell counts in BAL fluid are consistent for allergic airway disease, with HDM-challenged mice displaying increased eosinophilia (Figure 5A) and reduced macrophages (Figure 5D), when compared to saline-challenged mice. HDM-challenged surgery mice also exhibited increased lymphocytes compared to saline-challenged surgery mice, which was not apparent in NS mice (Figure 5B). No differences in neutrophil counts were observed in HDM-challenged mice compared to saline-challenged mice (Figure 5C). Additionally, no differences were observed for any cell type as a result of Sham or VSG when compared to NS mice in either the saline or HDM challenged groups (Figures 5A–D). These histological data demonstrate that our model of chronic allergic airways disease resulted in marked airway eosinophilic inflammation and mucus production, but that no differences were observed between surgical groups, either at baseline or following HDM exposure.
**Figure 5:** *BAL fluid cellularity. Relative percentages of (A) eosinophils, (B) lymphocytes, (C) neutrophils, and (D) macrophages in BAL fluid, n=7-11 mice per group. Grey bars and ▲ = saline-challenged mice; pink bars and ▼ = HDM-challenged mice. (A–D) were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. **p<0.01, ***p<0.001, ****p<0.0001.*
## mRNA quantification in mouse lung tissue
During harvest, the right middle lung lobe was immediately placed into TRI Reagent (MilliporeSigma). Lungs were then homogenized, and total RNA was isolated via standard procedure according to manufacturer’s instructions. RNA concentration was measured on a NanoDrop ND-1000 (ThermoFisher) and cDNA was prepared using the Applied Biosystems High-Capacity Reverse Transcription Kit. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed using Applied Biosystems TaqMan Gene Expression Master Mix and TaqMan primers (glyceraldehyde-3 phosphate dehydrogenase [Gapdh] Mm99999915_g1, elastin [Eln] Mm00514670_m1, collagen type 1 alpha 1 chain [Col1a1] Mm00801666_g1, collagen type 1 alpha 2 chain [Col1a2] Mm00483888_m1, glucagon-like peptide-1 receptor [Glp1r] Mm00445292_m1, interleukin-13 receptor alpha 1 [Il13ra1] Mm01302068_m1, interleukin-13 receptor alpha 2 [Il13ra2] Mm00515166_m1, interleukin-4 receptor alpha [Il4ra] Mm01275139_m1, transforming growth factor beta 1 [Tgfb1] Mm01178820_m1, mucin 5AC [Muc5ac] Mm01276718_m1, and mucin 5B [Muc5b] Mm00466391_m1). Fold change was calculated with the delta Ct method using the saline/NS as the control treatment, and Gapdh as the endogenous control.
## Enzyme-linked immunosorbent assay (ELISA)
Leptin (DY498), IL-13Rα2 (DY539), total (EMIGHE) and HDM-specific [3037] IgE, IL-13 (DY413), IL-5 (DY405), and total and active TGF-β1 (DY1679), were measured in serum, BAL fluid or homogenized lung tissue by ELISA using kits purchased from R&D systems or ThermoFisher Scientific. For lung tissue homogenates, total protein was determined using bicinchoninic acid (BCA) assay (Pierce) and 15-20 ng protein/well were used in ELISAs. All ELISAs were quantified using a microplate reader (FLUOstar Omega). Total (K1503PD-1) and active (K1503OD-1) glucagon-like peptide-1 (GLP-1) in serum was measured with V-PLEX Kits by Meso Scale Diagnostics, and plates were read using the MESO QuickPlex SQ 120 (MSD). All protein concentrations were analyzed as recommended by the manufacturer.
## Statistical analysis
Statistical analyses were performed in GraphPad Prism 9 or JMP (SAS, Cary, NC). Outliers were tested with the Robust Regression and Outlier Removal (ROUT) method [36] and were removed where appropriate. We compared the mouse allergen challenge and surgery groups using parametric or non-parametric tests accordingly (one or two-way ANOVA, Kruskal-Wallis, or t-test), with appropriate post-test, to evaluate significance. PC100 and Reactivity (K) values were determined using non-linear regression with exponential growth curve fit and analyzed with one-tailed t-test with Welch’s correction factor when variances were significantly different as described previously [34].
## Diet and glucose tolerance
To assess the effects of HFD feeding on body weight and glucose tolerance, mice were weighed and administered an oral glucose tolerance test at the beginning of the protocol, 3 days prior to surgery and again 1-1.5 weeks after surgery. Mice gained weight after 8 weeks of HFD feeding, with increased body weight observed in mice fed the HFD compared to a standard diet for the same time frame (Figure 2A). In addition, serum leptin levels were markedly elevated in HFD-fed mice compared to standard chow fed mice after 8 weeks (Figure 2B). Greater weight gain was observed in HDM-challenged mice when compared to saline-challenged control mice (Figure 2C). Saline/VSG mice had less weight gain over the course of the study when compared to saline/NS mice (Figure 2D), with a similar trend seen in the HDM mice. Sham mice did not experience notable weight change when compared to NS mice, and no differences in post-surgical weight change were observed between Sham and VSG mice regardless of challenge group (Figure 2E). HFD contributed to glucose intolerance at week 10 for both HDM and saline treated mice (Figures 3A, B). Glucose tolerance improved across both procedural groups at week 12 when compared to week 10 measures (Figures 3C–F, Supplementary Figures 1A, B), with greatest improvement in the VSG mice (Figures 3F, G). The mortality rate for Sham and VSG procedures mice was $13.6\%$ and $19.0\%$, respectively. Taken together, these data show that our model of diet-induced obesity effectively induced weight gain, increased circulating leptin and glucose intolerance in mice, and that VSG improved glucose tolerance in both HDM- and saline-challenged obese mice.
**Figure 2:** *Mouse body weight change. (A) Body weight after 8 weeks of HFD or standard chow (SC) diet feeding. (B) Serum leptin levels after 8 weeks of HFD or standard chow diet feeding. (C) Change in mouse body weight from week (wk) 0 to week 8, n=18-20 mice per group. (D) Body weight change in mice from wk 0 to wk 12, n=8-11 mice per group. (E) Body weight change from pre-surgery (wk 10) to post-surgery (wk 12), n=8-10 mice per group. (A, B were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. (C) was analyzed using a non-parametric t-test. (D, E) were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. *p<0.05, **p<0.01, ****p<0.0001.* **Figure 3:** *Glucose tolerance. (A) Glucose tolerance curves at week (wk) 0 and wk 10, n= 28-29 mice per group. (B) Blood glucose area under the curve (AUC) at wk 0 and wk 10. (C, D) Glucose tolerance curves at wk 10 and wk 12 for (C) Sham (*p<0.05 for HDM/Sham wk 10 vs wk 12 and PBS/Sham wk 10 vs wk 12) and (D) VSG mice (*p<0.05 for HDM/VSG wk 10 vs wk 12), n=8-10 mice per group. (E) Glucose tolerance curves at wk 10 for HDM-challenged mice. (F) Glucose tolerance curves at wk 12 for HDM-challenged mice. (G) Blood glucose AUC at wk 10 and wk 12 for Sham and VSG mice. (A, C–F) were analyzed using paired t-test and One-way ANOVA and (B, G) were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. *p<0.05, ****p<0.0001.*
## Lung mechanics
Airway hyperresponsiveness (AHR) is a key feature of allergic asthma pathobiology [37]. Obese mice exhibit inherent AHR to bronchocontrictors (38–40). In order to determine the effect of VSG on lung mechanics, mice were assessed via Flexivent at 1-1.5 weeks following surgery. We observed that airway responsiveness across all groups of HFD-fed mice increased with increasing doses of methacholine (Figures 6A, C). Although HDM challenge did not further increase airway resistance in obese NS mice compared to saline control, the percent change from baseline in Rn was higher in HDM/VSG mice when compared to saline/VSG mice at 400 μg/kg of methacholine (Figures 6B, D). In mice challenged with HDM, we observed reduced Rtot sensitivity (PC100) and increased Rtot reactivity (K) with VSG (Figures 6E, F), compared to Sham and NS groups, again indicating that VSG enhances airway resistance. Central airway resistance was also augmented in HDM-challenged VSG mice compared to saline-challenged VSG mice, as demonstrated by reduced Rn sensitivity and elevated Rn reactivity (Figures 6F, G). No differences were observed between groups with regards to elastance (E), tissue damping (G), tissue elastance (H) (Supplementary Figure 2). Taken together, our data provide evidence that at early time points following surgery, VSG stimulates increased total respiratory and central airways resistance in allergic mice.
**Figure 6:** *Effects of HDM challenge and VSG on lung mechanics. (A) Newtonian resistance [Rn], (B) percent change in Newtonian resistance (p<0.05 for HDM/VSG vs Saline/VSG), (C) total respiratory resistance [Rtot], and (D) percent change in total respiratory resistance with intravenous methacholine challenge, n=8-11 mice per group. (E) PC100 for Rtot, n=8-10 mice per group. (F) Reactivity [K] for Rtot, n=8-10 mice per group. (G) PC100 for Rn, n=8-10 mice per group. (H) Reactivity [K] for Rn, n=7-10 mice per group. (A–D) were analyzed using repeated measures ANOVA and (E–H) were analyzed using one-tailed t-test with Welch’s correction factor. *p<0.05; **p<0.01.*
## Lung tissue mRNA expression
Quantitative RT-PCR was used to determine mRNA expression changes for genes involved in mediating allergen-induced airway inflammatory and fibrotic responses in lungs of mice in each treatment and surgery group. NS mice challenged with HDM exhibited expected increased expression of Muc5ac, Muc5b, Tgfb1, and Il13ra2 in lung tissue compared to NS saline-treated mice (Figures 7A, C–E). Interestingly, both Sham and VSG HDM-challenged mice display decreased lung expression of Il13ra2 when compared to HDM-challenged NS mice (Figure 7A). When compared to NS in saline control mice, VSG exhibited a non-significant increase in expression of lung Glp1r ($$p \leq 0.07$$) while expression of lung Glp1r in HDM-challenged mice is consistent across all surgery groups (Figure 7B). Lung mRNA expression of Il4ra, Il13ra1, Col1a1, Col1a2, and Eln was not different between groups (Supplementary Figure 3). Collectively, these data show that interleukin-13 signaling regulation and airway mucin expression may be disrupted in allergic mice with surgery.
**Figure 7:** *mRNA expression in lung tissue. Lung mRNA expression as measured by quantitative RT-PCR of (A)
Il13ra2, (B)
Glp1r, (C)
Tgfb1, (D)
Muc5ac, (E)
Muc5b, n=6-11 mice per group. Grey bars and ▲ = saline-challenged mice; pink bars and ▼ = HDM-challenged mice. (A–E) were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. *p<0.05, **p<0.01.*
## Circulating and pulmonary markers
As allergen and high fat diet feeding as well as VSG can alter circulating and tissue-specific levels of metabolic factors and immune signaling molecules [26, 31], we sought to measure these factors in serum, BAL fluid or lung tissue of mice in our model. Active GLP-1 levels were elevated in serum of HDM-challenged mice that underwent VSG, when compared to the HDM/NS group (Figure 8A). Serum leptin levels appeared to be elevated in HDM-challenged mice when compared to saline control across all surgery groups although this effect did not reach significance (Figure 8B). However, VSG appeared to reduce serum leptin levels in HDM-challenged mice relative to Sham and NS mice, with a non-significant reduction in VSG mice challenged with HDM compared to HDM/NS mice ($$p \leq 0.07$$). In saline treated mice, serum IL-13Rα2 levels were elevated in the VSG group when compared to the NS and Sham groups (Figure 8C), while HDM-challenged mice displayed similar IL-13Rα2 concentrations across all surgery groups. As expected, total IgE levels were elevated in HDM-challenged mice relative to saline control mice, characteristic of allergic airway disease (Figure 8D). Total IgE was elevated in both groups of VSG mice relative to Sham and NS mice and was further increased in HDM/VSG mice when compared to saline/VSG mice. HDM-specific IgE levels were similar in HDM/VSG mice compared to HDM/NS mice (Figure 8E), and undetectable in all groups of saline-challenged mice (data not shown). GLP-1 and IL-13Rα2 protein production was undetectable in all mouse BAL fluid samples (data not shown). Pulmonary levels of total or active TGF-β1, as measured in BAL fluid or lung tissue, were unremarkable between the saline and HDM-challenged mice, irrespective of surgery groups (Supplementary Figure 4). Additionally, IL-5, IL-13 and IL-13Rα2 levels, measured in lung tissue, were consistent across all surgical and intranasal treatment groups (Supplementary Figure 4). Thus, measurement of serum and pulmonary biomarkers show that allergic responses were induced in all three surgical groups, but that VSG may be involved in modulation of IL-13Rα2 production.
**Figure 8:** *Measurement of cytokines and total and HDM-specific IgE in mouse serum. (A–E) Serum concentrations of (A) Active GLP-1, (B) Leptin, (C) IL-13Rα2, (D) Total IgE, and (E) HDM Specific IgE, n=6-11 mice per group. Grey bars and ▲ = saline-challenged mice; pink bars and ▼ = HDM-challenged mice. (A–D) were analyzed using a Two-way ANOVA with a Tukey and Sidak post-hoc test. (E) were analyzed using a non-parametric t-test *p<0.05, **p<0.01, ***p<0.001.*
## Discussion
In this study, we investigated the impact of VSG on airway and metabolic physiology, inflammation and fibrosis in a murine model of allergic asthma following chronic HDM challenge. To our knowledge, this study is the first to use a combined chronic allergic airway disease model with obesity and metabolic surgery in experimental mice to assess respiratory disease parameters. Previous studies have investigated the association of bariatric surgery in human patients with allergic asthma [41, 42], or in models of inherent AHR in obese mice, but to date, no studies have been performed in mouse models of chronic allergic airway disease, which allow for analyses of metabolic and inflammatory mechanisms associated with allergen-driven airway pathology. Furthermore, prior VSG studies in mice and rats have shown the influence of this surgery on metabolic physiology, adipose tissue inflammation, gut hormone and adipokine regulation, but these studies did not report features of lung physiology (43–46). Our work provides a foundation for the biological impact of VSG on lung function and airway and systemic inflammatory biomarkers in obese allergic mice.
Obesity was effectively induced in mice after 8 weeks of HFD feeding as demonstrated by elevated body weight and serum leptin levels in HFD-fed mice compared to mice on a normal chow diet for the same time frame, and these observations were valid for both HDM-exposed mice and those administered saline. Additionally, we observed increased body weight gain in HDM-challenged C57BL/6 male mice compared to saline challenged mice after 8 weeks of HFD feeding. Liang et al. made a similar observation in experiments involving chronic administration of ovalbumin to female C57BL/6 mice fed a $60\%$ kcal fat diet for 12 weeks in which the authors reported increased body weight in HFD-fed mice administered ovalbumin compared to mice on HFD alone. Both studies commenced the HFD feeding in 4-5-week-old mice. This intriguing finding that suggests an interaction between allergic exposures and weight gain that may be attributable to alterations of gut microbiota or immune signaling in mice with combined obesity and allergic exposures [47, 48]. Future investigations will explore possible mechanisms of increased house dust mite-induced weight gain in obese mice.
The design of our model was intended to reflect acute airway sensitization with a clinically relevant aeroallergen (HDM), followed by chronic (4 week) exposure to the same allergen in the context of obesity. We show that our model of chronic respiratory HDM challenge resulted in the expected robust induction of murine peribronchial inflammation, airway mucus production and eosinophilia, increased serum IgE and lung expression of mucins, Tgfb1, and Il13ra2, regardless of surgery status. Furthermore, We expected that at the end of this 10-week protocol, these obese mice would exhibit allergen-induced airway inflammation, and indeed our model was effective in eliciting these responses.
The addition of VSG or Sham surgery to the model allowed us to test the hypothesis that VSG would improve features of allergic airway disease in obese mice. At baseline, we observed that VSG mice had increased circulating levels of IL-13Rα2, a negative regulator of type 2 cytokine signaling and allergic asthma responses [49, 50]. However, contrary to our hypothesis, we found that AHR to methacholine increased in these mice in the setting of reduced lung tissue expression of Il13ra2. A summary of our findings is shown in Table 1.
**Table 1**
| Measurement | Tissue | VSG vs NS | VSG vs Sham |
| --- | --- | --- | --- |
| IL-13Rα2 | Serum | ↑ | ↑ |
| Il13ra2 mRNA | Lung tissue | ↓ | No change |
| Total IgE | Serum | ↑ | ↑ |
| Total respiratory resistance (Rtot) | Lung mechanics | ↑ | ↑ |
| Central airway resistance (Rn) | Lung mechanics | ↑ | No change |
Ather et al. showed that sleeve gastrectomy in HFD-fed mice significantly reduced airway responsiveness to methacholine compared to the inherent AHR exhibited by non-surgery HFD-fed mice, driven primarily by changes in distal airway responses [51]. Unlike our study, mice were not challenged with allergen. On the other hand, similar to our study, they also found that surgery led to increased airway and systemic biomarkers of inflammation but reduced levels of leptin compared to HFD-fed non-surgery mice. Serum levels of the gut hormone, peptide YY (PYY), were also elevated after the sleeve gastrectomy mice; however, the authors were unable to detect GLP-1 in either BAL fluid or serum. Sham surgery mice were not included in their study. In keeping with our study, their results highlight the impact obesity on airway inflammation and hyperresponsiveness. However, in the present study, we demonstrate that in the presence of hypersensitivity and exposure to inhaled allergens, metabolic surgery may not reduce airway inflammation and AHR in individuals with obesity.
In a study conducted by Dixon et al., asthma patients with obesity who underwent bariatric surgery experienced improved AHR at 12 months post-surgery [41]. This improvement was only evident in patients with normal baseline serum IgE levels, and not in patients with atopic asthma and elevated baseline IgE levels, suggesting that the interaction of atopy and obesity impacts the response to surgery in patients with asthma. Our mouse model attempts to recapitulate a phenotype of chronic allergic asthma specific in patients with comorbid obesity; thus, our findings that VSG fails to improve AHR in this model is consistent with the reports that bariatric surgery has differing effects in patients with asthma and comorbid obesity, depending on the underlying asthma phenotype. Indeed, similar to the findings reported by Dixon et al., we observed no improvement in airway resistance following VSG in mice with elevated IgE. An important post-operative complication of vertical sleeve gastrectomy in humans is development of de novo gastroesophageal reflux [52], which is a known risk factor for exacerbations associated with airways hyperresponsiveness in asthma [53]. It is possible that in our model, VSG contributed to increased gastroesophageal reflux, leading to the observed increased in airways resistance in the mice that underwent VSG. Allen et al. demonstrated that acid aspiration in mice triggered acute AHR, possibly related to increased airway epithelium permeability [54]. Although we did not specifically investigate gastroesophageal reflux or airway epithelium permeability in our model, it is plausible that in our model, VSG stimulated acute post-operative acid aspiration that led to increased airway resistance to methacholine.
VSG in rodent models has been used extensively by researchers to study the effects of the procedure on metabolic physiology, including body weight [55], insulin resistance [56], glucose tolerance [57], gastric emptying [46] and central control of satiety [58]. VSG also improves hypercapnic ventilatory responses in mice, in a mechanism requiring leptin signaling [59]. Our model corroborated that VSG reduced body weight gain and improved glucose tolerance, an effect that was not observed in Sham surgery mice. The lack of specific VSG effects on body weight gain and glucose tolerance may be attributed to the short time of follow-up after surgery at 3 weeks, designed to capture the maximal effects of HDM challenge and better assess the specific effects of VSG while avoiding resolution of airway inflammation that may occur over longer times of follow-up after surgery. Other rodent models [31, 44] that have employed VSG/Sham surgery to investigate glucose tolerance and body weight have reported these measurements at 5-12 weeks after surgery and resumption of HFD feeding, allowing time for the HFD to stimulate weight gain and impaired glucose tolerance in Sham surgery mice. Thus, we speculate that in our model, more time on a HFD following surgery would be needed to demonstrate significant effects of VSG on these features of metabolic disease.
Unique to our study is the investigation of Il13ra2 expression and production. IL-13Rα2 was originally believed to be a non-signaling decoy receptor which bound IL-13 with high affinity, competing with the IL-4Rα and IL-13Rα1 subunits for IL-13 binding and negatively regulating IL-13 signaling [50]. Recent studies suggest its role in development of inflammatory and fibrotic features of disease which may include a capacity to mediate IL-13 signaling [60, 61]. Consistent with findings by Piyadasa et al. [ 62], our study demonstrated higher Il13ra2 expression in lung tissue in the HDM/NS group when compared to PBS/NS mice. However, expression decreased in HDM-challenged mice that underwent Sham or VSG surgeries, suggesting a surgery effect. On the other hand, baseline levels of circulating soluble IL-13Rα2 were increased in HDM-challenged VSG mice when compared to Sham and NS group, suggesting that VSG elicits changes in Il13ra2 transcript processing that increase soluble IL-13Rα2 [63] or that changes in Il13ra2 expression occur in response to the development of airway inflammation. Further studies are warranted to investigate the biological importance of VSG- and allergen-induced changes in IL-13Rα2 expression in obesity.
Our study has several limitations that impact interpretation of the data. As this study aimed to serve as a model of bariatric surgery in human asthma patients, induction of obesity in mice using a HFD was required; thus, no comparisons with lean mice were included in the study. Also, only male mice were used for this study as male C57BL/6J mice are more susceptible to weight gain on a HFD than female mice [29]. Sex differences may be important in this setting as asthma, in general, as well as obesity-associated asthma is more prevalent in adult females than males [2, 64]. More studies using female mice in a mixed sex study to compare responses to male mice are needed to define sex differences using our model. In addition, collection of tissues and lung mechanics measurements was done at a short follow-up time after surgery when some metabolic effects were not as yet evident. Future work including both sexes, as well as in other strains of mice (e.g. Balb/c [65]) would help validate our counter-intuitive findings and provide a better approximation of how bariatric surgery impacts features of asthma in patients with obesity. Additionally, conducting the experiment across longer timepoints following surgery, with increased chronic exposure to allergen challenge and HFD, will give a better understanding of specific changes in physiology (e.g. glucose tolerance) and how these impact allergic airway disease.
In conclusion, the current study demonstrates that VSG in a murine model of chronic allergic airway disease increases airway resistance in the short term following surgery and alters IL-13Rα2 expression. This study offers insight as to the mechanisms governing effects on airway pathobiology following bariatric surgery in patients with allergic asthma and comorbid obesity. Further studies of allergic airway disease in experimental mice employing VSG that investigate the long-term impacts of surgery in the context of obesity are warranted.
## 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 Duke University Institutional Animal Care and Use Committee.
## Author contributions
JW, methodology, investigation, validation, formal analysis, and writing - original draft. MI, methodology, formal analysis, investigation, writing- review, and editing. VM and AH, methodology, investigation, writing- review, and editing. MM, RT, and LQ, methodology, writing- review, and editing. SP, writing- review, and editing. JW, methodology, formal analysis, writing- review, and editing. JI, conceptualization, methodology, investigation, resources, writing - review and editing, and funding acquisition. 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.2023.1092277/full#supplementary-material
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|
---
title: Effects of thiram exposure on liver metabolism of chickens
authors:
- Meng Wang
- Lei Wang
- Sana Shabbir
- Dongliang Zhou
- Muhammad Akbar Shahid
- Houqiang Luo
- Huixia Li
- Ziwei Li
- Xingya Sun
- Chunqin Wu
- Yan Zhao
journal: Frontiers in Veterinary Science
year: 2023
pmcid: PMC10011634
doi: 10.3389/fvets.2023.1139815
license: CC BY 4.0
---
# Effects of thiram exposure on liver metabolism of chickens
## Abstract
Pesticides are widely used to control crop diseases, which have made an important contribution to the increase of global crop production. However, a considerable part of pesticides may remain in plants, posing a huge threat to animal safety. Thiram is a common pesticide and has been proven that its residues in the feed can affect the growth performance, bone formation, and intestinal health of chickens. However, there are few studies on the liver metabolism of chickens exposed to thiram. Here, the present study was conducted to investigate the effect of thiram exposure on liver metabolism of chickens. Metabolomics analysis shows that 62 metabolites were down-regulated (ginsenoside F5, arbekacin, coproporphyrinogen III, 3-keto Fusidic acid, marmesin, isofumonisin B1, 3-Hydroxyquinine, melleolide B, naphazoline, marmesin, dibenzyl ether, etc.) and 35 metabolites were up-regulated (tetrabromodiphenyl ethers, deoxycholic acid glycine conjugate, L-Palmitoylcarnitine, austalide K, hericene B, pentadecanoylcarnitine, glyceryl palmitostearate, quinestrol, 7-Ketocholesterol, tetrabromodiphenyl ethers, etc.) in thiram-induced chickens, mainly involved in the metabolic pathways including glycosylphosphatidylinositol (GPI)-anchor biosynthesis, porphyrin and chlorophyll metabolism, glycerophospholipid metabolism, primary bile acid biosynthesis and steroid hormone biosynthesis. Taken together, this research showed that thiram exposure significantly altered hepatic metabolism in chickens. Moreover, this study also provided a basis for regulating the use and disposal of thiram to ensure environmental quality and poultry health.
## Introduction
Increasing evidence indicated that pesticides play a vital role in agricultural production. Statistical analysis indicated that *China is* the main consumer of pesticides, using 1.8 million tons per year, followed by the America (1–3). At present, pesticides have been listed as priority pollutants by the United Nations Environment Protection Agency (UNEP) [4, 5]. Although, the use of pesticides has effectively increased crop yield and reduced disease. However, the extensive use of pesticides will also cause serious environmental pollution, posing a serious threat to food security and animal health [6]. In addition, some pesticides may remain in plants and be introduced into nearby waters after rainfall, endangering the health of aquatic animals and causing drinking water safety problems (7–16). Moreover, aerial spray of pesticides may cause the pollution of nearby or distant areas through transboundary movement [17]. It is worth noting that humans and animals may also ingest plants containing pesticides through the food chain, seriously endangering public health and human security (18–21).
Poultry including chickens, ducks and geese are the largest livestock species. These species developed rapidly in the past few decades, effectively solving the problem of protein shortage. Among the above-mentioned poultry, chickens are widely farmed because of their fast growth and low price [22, 23]. Consequently, any factors that endanger chickens should be given enough attention. However, chickens are likely to be exposed to feed containing pesticide residues (24–27). Previous studies have indicated that most pesticides could accumulate in multiple tissues and inhabit the exposed organisms from few months to several years, thus even very low concentration is also harmful to health [5, 28, 29]. Liver is the vital metabolic and alexipharmic organ in the animal and humans, which is considered as one of the primary target organs for various hazardous substances such as pesticides and heavy metal (30–32). Therefore, the intake of feed containing pesticide residues will inevitably affect the liver of broilers.
Thiram is one of the common pesticides, mainly used to increase crop yield and reduce disease [33, 34]. However, the abuse of thiram not only cause pesticide residues, but also pose a serious threat to the safety of humans and animals (35–37). Previous studies have shown that thiram exposure causes abnormal bone development and reduced growth performance in chickens (38–40). In addition, thiram exposure has been demonstrated to cause intestinal flora imbalance and liver histopathology injuries in chickens [18, 26]. However, studies regarding the influences of thiram exposure on liver metabolism in chickens remain scarce. Taking advantage of this gap, we explored the effect of thiram exposure on liver metabolism in chickens.
## Animal experiments and sample acquisition
A group of 60 one-day-old healthy Arbor Acres chickens were purchased from a commercial hatchery and maintained under the standard ambient temperature, sanitary condition and illumination as previously described. Prior to the experiment, all the subjects were performed physical examinations to avoid deformity and other congenital diseases. After acclimatization for 3 days, an equal number of chickens ($$n = 30$$) regardless of sex were divided into control and thiram-treated groups. Throughout the trial, the control chickens were provided sufficient feed and water. Moreover, the chickens in thiram-treated group received same diet as controls but supplemented with thiram (50 mg/kg) purchased from Macklin Biochemical Co., Ltd. (Shanghai, China) in feed as suggested by previous research from days 3–7 [39]. All chickens were euthanized and liver tissue was collected on days 18 of the experimental study. The achieved samples were snap-frozen utilizing liquid nitrogen and stored at −80°C for further study.
## Sample preparation
The metabolomic procedure was conducted based on the previous protocols with minor improvements [41, 42]. Briefly, the acquired liver samples (~100 mg) were triturated in methanol and then centrifuged for 15 min at 14,000×g. The supernatant of mixture was collected and stored in Eppendorf tubes for 10 min. Subsequently, the deionized water (400 μl) was added to the obtained supernatant and kept at −80°C for further study. The extract (100 μl) of each sample was mixed for preparing quality control (QC) sample and QC samples were performed testing between every five samples. The 0.22 μm membranes were applied to filter the supernatant and then the filtered supernatant was performed UPLC-QTOF/MS (Waters, USA) analysis. The condition of UPLC was determined as described previously [42]. Moreover, the reagents used in this study were HPLC grade.
## Differential metabolite analysis
The original mass spectrometry was subjected to process using Marker View 1.1 (AB SCIEX, USA). Subsequently, PCA and PLS-DA were performed by importing metabolomics data into SIMCA (version 14.1, Umetrics, Sweden). The determination of differential metabolites was based on the variable weight value (VIP) and p-value obtained from the OPLS-DA model. To obtain pathways involved in differential metabolites, MetaboAnalyst and KEGG database (https://www.kegg.jp/kegg/pathway.html) was used to perform cluster analysis and metabolic pathway annotation of differential metabolites.
## Thiram exposure disrupts liver metabolism
The plots of PCA analysis showed that the samples in the thiram-exposed group were clustered closely and separated from the control group, indicating that thiram exposure significant changes in liver metabolome (Figures 1A, B). To further reveal the alterations of liver metabolome during thiram exposure, OPLS-DA score plots was applied for pattern discriminant analysis. Results indicated that there was a clear separation between both groups and no fitting occur (Figures 1C–F).
**Figure 1:** *Thiram exposure altered liver metabolism. (A, B) PCA score plots based on positive-ion mode and negative-ion mode, respectively. (C, D) OPLS-DA plot based on positive-ion mode and negative-ion mode, respectively. (E, F) Permutation tests based on positive-ion mode and negative-ion mode, respectively.*
## Identification of metabolites associated with thiram exposure
The differential metabolites were recognized based on the criterion of VIP > 1, $P \leq 0.05.$ Results indicated that a total of 97 differential metabolites were detected between both groups (Table 1). Among significantly different metabolites, 62 metabolites (ginsenoside F5, arbekacin, coproporphyrinogen III, 3-keto Fusidic acid, marmesin, etc.) were down-regulated, whereas 35 metabolites (L-Palmitoylcarnitine, quinestrol, 7-Ketocholesterol, tetrabromodiphenyl ethers, etc.) were up-regulated in thiram-induced chickens. Moreover, the alternations of metabolites also could be observed in the heatmap (Figure 2).
## Metabolic pathway analysis
The differential metabolites were subjected to pathway analysis by utilizing MetaboAnalyst 4.0 and results indicated that 13 metabolic pathways (linoleic acid metabolism, glycerophospholipid metabolism, taurine and hypotaurine metabolism, vitamin B6 metabolism, alpha-Linolenic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, sphingolipid metabolism, porphyrin and chlorophyll metabolism, arachidonic acid metabolism, fatty acid degradation, primary bile acid biosynthesis, purine metabolism and steroid hormone biosynthesis) involved in hepatotoxicity induced by thiram (Figure 3). Among above-mentioned differential pathways, 5 pathways with highest pathway impact value were the glycosylphosphatidylinositol (GPI)-anchor biosynthesis, porphyrin and chlorophyll metabolism, glycerophospholipid metabolism, primary bile acid biosynthesis and steroid hormone biosynthesis. The metabolic diagram in the intestine is shown in Figure 4.
**Figure 3:** *Differential metabolic pathway analysis based on the positive-ion mode (A) and the negative-ion mode (B). Each circle represents a metabolic pathway.* **Figure 4:** *The representative schematic diagram of liver metabolic exposed to thiram. (A) Glycerophospholipid metabolism. (B) Primary bile acid biosynthesis. (C) Glycosylphosphatidylinositol (GPI)-anchor biosynthesis. (D) Porphyrin and chlorophyll metabolism. (E) Steroid hormone biosynthesis. The red boxes represent the differential metabolites associated with thiram exposure.*
## Discussion
Thiram is widely used in agricultural production and is likely to accumulate in plants (43–45). Some plant-sourced feeds that accumulate pesticides are likely to enter poultry farming through the food chain, posing a serious threat to the health of poultry [18, 38, 39]. At present, the harm of thiram exposure to various species such as mice, chickens and fish has been widely confirmed. For instance, thiram has been shown to dramatically affect the respiratory tract, central nervous system, stimulate skin and restrain the formation of white blood cells [34, 46, 47]. Furthermore, some studies have also demonstrated the role of thiram exposure in the induction of lipid metabolism [18]. The liver is an important metabolic and detoxifying organ in animals and humans, which is regarded as one of the main target organs for multiple stimulations including pesticides, heavy metals and various environmental pollutants (48–50). Therefore, pesticide residues in feed are likely to affect liver health, which will cause great damage to poultry production. However, study on thiram toxicities to liver of chicken is still lacking. In this study, we explored the effect of thiram exposure on liver metabolism in chickens.
In this study, 97 differential metabolites were totally recognized, which was closely related to multiple metabolic pathways including glycerophospholipid metabolism, porphyrin and chlorophyll metabolism, primary bile acid biosynthesis, steroid hormone biosynthesis and glycosylphosphatidylinositol (GPI)-anchor biosynthesis. These metabolic pathways may play an important role in the hepatotoxicity induced by thiram. Remarkably, some of the decreased metabolites including ginsenosides, arbekacin, coproporphyrinogen III, Fusidic acid, marmesin and fluorouracil play important roles in antioxidant capacity, anti-cancer and oxygen transport. Ginsenosides were widely recognized because of multiple beneficial effects, such as inhibiting the growth of cancer cells, inducing tumor cell apoptosis, reversing the abnormal differentiation of tumor cells, and anti-tumor metastasis [51]. Moreover, ginsenoside has been demonstrated to improve immunity and antioxidant capacity of host [52]. Zhang et al. revealed that the concentrations of aspartate aminotransferase and alanine aminotransferase in thiram-induced chickens significantly increased, but antioxidant enzyme dramatically decreased, suggesting liver injury and antioxidant dysfunction [53]. Therefore, we speculated that the decreased ginsenoside may be one of the important pathways for thiram exerts its toxic effects and cause antioxidant dysfunction. Previous research indicated that arbekacin have an inhibitory effect on multiple pathogens such as Pseudomonas aeruginosa, *Klebsiella pneumonia* and *Acinetobacter baumannii* [54]. Moreover, arbekacin can be used for treating multiple drug resistant pneumonia and septicemia as well as infections caused by resistant Staphylococcus Aureus [55, 56]. Coproporphyrinogen III play a key role in the production of heme [57, 58]. Heme is also an important component of hemoglobin, which plays a key role in the transport of oxygen. Oxygen has been demonstrated to play key roles in blood vessel development and bone formation [59]. Previous studies indicated that the chickens exposed to thiram showed weight loss, accompanied by angiogenesis disorder and tibial dyschondroplasia [60, 61]. Therefore, decreased coproporphyrinogen III may be one of the causes of angiogenesis disorder and abnormal bone development of chickens.
Fusidic acid can treat infections induced by methicillin-susceptible and methicillin-resistant *Staphylococcus aureus* [62]. Marmesinpossess multiple pharmacological functions including anti-inflammatory, antihepatotoxic and antitumor activities [63, 64]. Fluorouracil has anti-cancer effects [65]. Moreover, we observed increased levels of L-palmitoylcarnitine, quinestrol, 7-ketocholesterol, and tetrabromodiphenyl ether during thiram exposure. L-palmitoylcarnitine is an ester derivative of carnitine, which participated in fatty acids metabolism and its abundance increased during hepatic lipid accumulation [66, 67]. Consistent with this study, Sheng et al. indicated that the abundance of L-palmitoylcarnitine increased significantly in zebrafish exposed to organic pollutants [68]. Moreover, increased L-palmitoylcarnitine was closely related to poorer prognosis in patients with chronic heart failure [69]. Quinestrol can disrupt internal secretion and cause fertility disorders by inducing testicular damage [70]. Moreover, quinestrol can increase the levels of serum MDA and aggravate the oxidative damage of cells [71]. As a pro-oxidant and pro-inflammatory molecule, 7-ketocholesterol not only induces inflammation and nerve cell damage, but also affects membrane permeability and causes oxidative stress [72]. Tetrabromodiphenyl ether is known to possess reproductive toxicity, which weaken sperm activity and increase the quantity of abnormal sperm [73]. Moreover, tetrabromodiphenyl ether has also been demonstrated to induce liver inflammation and promote the expression of inflammatory genes including IL-6, TNF-α and IL-l β [74]. Increasing evidence demonstrated that long-term pesticide exposure can result in cancer and reproductive disorders. In this study, we observed significant changes in metabolites associated with anti-cancer, oxidative stress and reproductive function, indicating that thiram may also be a potential cancer-inducing factor. Previous study indicated that thiram exposure could induce liver autophagy and apoptosis. Notably, some studies also showed that oxidative stress could cause the initiation and development of apoptosis and autophagy. Therefore, thiram induced liver apoptosis and autophagy may be mediated by differential metabolites related to oxidative stress.
In conclusion, this study investigated the effect of thiram exposure on liver metabolism in chickens. Results showed that thiram exposure can significantly alter liver metabolism, characterized by significant changes in some metabolites and metabolic pathways. These results filled in the blank of thiram exposure on liver metabolism characteristics of chickens, and conveyed an important message that hepatic metabolic disorder may be one of the important ways thiram affects broiler liver metabolism. Moreover, this study will help prevent and control the effects of thiram on liver metabolism in chickens from the perspective of liver metabolism.
## 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 study was performed under the instructions and approval of Ethics Committee of the Wenzhou Vocational College of Science and Technology.
## Author contributions
MW and LW conceived, designed the experiments, and wrote the manuscript. DZ, HLu, HLi, ZL, XS, CW, and YZ contributed sample collection and reagents preparation. MW analyzed the data. SS and MS revised the manuscript. All authors reviewed 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: Maternal high-fat diet decreases milk endocannabinoids with sex-specific changes
in the cannabinoid and dopamine signaling and food preference in rat offspring
authors:
- Camilla P. Dias-Rocha
- Julia C. B. Costa
- Yamara S. Oliveira
- Larissa B. Fassarella
- Juliana Woyames
- Georgia C. Atella
- Gustavo R. C. Santos
- Henrique M. G. Pereira
- Carmen C. Pazos-Moura
- Mariana M. Almeida
- Isis H. Trevenzoli
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011635
doi: 10.3389/fendo.2023.1087999
license: CC BY 4.0
---
# Maternal high-fat diet decreases milk endocannabinoids with sex-specific changes in the cannabinoid and dopamine signaling and food preference in rat offspring
## Abstract
### Introduction
Maternal high-fat (HF) diet during gestation and lactation programs obesity in rat offspring associated with sex-dependent and tissue-specific changes of the endocannabinoid system (ECS). The ECS activation induces food intake and preference for fat as well as lipogenesis. We hypothesized that maternal HF diet would increase the lipid endocannabinoid levels in breast milk programming cannabinoid and dopamine signaling and food preference in rat offspring.
### Methods
Female Wistar rats were assigned into two experimental groups: control group (C), which received a standard diet ($10\%$ fat), or HF group, which received a high-fat diet ($29\%$ fat) for 8 weeks before mating and during gestation and lactation. Milk samples were collected to measure endocannabinoids and fatty acids by mass spectrometry. Cannabinoid and dopamine signaling were evaluated in the nucleus accumbens (NAc) of male and female weanling offspring. C and HF offspring received C diet after weaning and food preference was assessed in adolescence.
### Results
Maternal HF diet reduced the milk content of anandamide (AEA) ($p \leq 0.05$) and 2-arachidonoylglycerol (2-AG) ($p \leq 0.05$). In parallel, maternal HF diet increased adiposity in male ($p \leq 0.05$) and female offspring ($p \leq 0.05$) at weaning. Maternal HF diet increased cannabinoid and dopamine signaling in the NAc only in male offspring ($p \leq 0.05$), which was associated with higher preference for fat in adolescence ($p \leq 0.05$).
### Conclusion
Contrary to our hypothesis, maternal HF diet reduced AEA and 2-AG in breast milk. We speculate that decreased endocannabinoid exposure during lactation may induce sex-dependent adaptive changes of the cannabinoid-dopamine crosstalk signaling in the developing NAc, contributing to alterations in neurodevelopment and programming of preference for fat in adolescent male offspring.
## Introduction
Nutritional, hormonal, or environmental adversities during critical periods of development, such as gestation and lactation, increase the risk for chronic diseases across the lifespan [1, 2]. This phenomenon is known as metabolic programming and has been implicated in the developmental origins of obesity, diabetes, and hypertension in humans and experimental models [3, 4].
Obesity is associated with an overactivation of the endocannabinoid system (ECS), which is also a hallmark of neurodevelopment (5–7). The major lipid endocannabinoids are anandamide (AEA) and 2-arachidonoylglycerol (2-AG) that bind to cannabinoid receptor type 1 (CB1) and type 2 (CB2). AEA and 2-AG are synthesized “on demand” by the enzymes N-acylphosphatidylethanolamine (NAPE)-phospholipase D hydrolase (NAPE-PLD) and diacylglycerol lipase (DAGL), respectively, from membrane phospholipids containing arachidonic acid. AEA is preferentially degraded by the fatty acid amide hydrolase (FAAH) while 2-AG is mostly metabolized by monoacylglycerol lipase (MAGL). Along with these major components, others bioactive lipids as oleoylethanolamide (OEA) and palmitoylethanolamide (PEA), and a complex network of receptors comprise the “paracannabinoid system” or the “endocannabinoidome” [6, 8].
In the developing brain, the ECS regulates neurogenesis, cell lineage commitment, neuronal migration, axonal elongation, synaptogenesis, glial formation, and postnatal myelination [7, 9]. The ECS is expressed in the human brain as early as gestational week nine [10] and in rodent brain from gestational day twelve [11], evidencing the importance of the ECS regulation during gestation and lactation. Early adversities in nutrition (over or undernutrition) or exposure to environmental toxicants (tobacco, cannabis or alcohol) can modulate the ECS components in the brain and peripheral tissues throughout life, in a sex-specific manner, programming energy metabolism and behavior as compiled in a recent review [7].
The ECS stimulates food intake, the appetite for fat [12, 13] and increases adiposity [14, 15]. Food intake regulation also has a major contribution of several peripheral hormones such as the adipose-derived factor leptin, insulin, and gut hormones as peptide YY (PYY) and glucagon-like peptide 1 (GLP-1), all presenting anorexigenic effect [13, 16]. The hypothalamus is the principal brain region involved in the homeostatic regulation of food intake while mesolimbic regions (nucleus accumbens, NAc; ventral tegmental area, VTA) are important modulators of the hedonic eating and motivational behaviors, with participation of the cannabinoid and dopamine signaling crosstalk [13, 17, 18].
In rats, there is an important maturation of the feeding circuitry during the early postnatal period and leptin (serum level peaks around postnatal day 10) has a marked role in the developing hypothalamus with sex-divergent responses (19–21). Leptin is present in the breast milk of humans [22] and rodents [23, 24]. Despite the fluctuations in the serum levels of rodent neonates, the breast milk leptin levels remain relatively constant across lactation [24], suggesting that serum leptin peak is more influenced by the offspring adipose production rather than milk ingestion.
It has been suggested that other milk components (nutrients and bioactive molecules) during neonatal period could modulate brain maturation (25–27). The lipid endocannabinoids AEA and 2-AG and other endocannabinoid-like lipids have also been identified in human milk (28–30) but their physiological role remains to be elucidated. On the other hand, in rodents, it has been demonstrated that cannabinoid signaling is important for suckling initiation [31] but the presence of these lipids in rat breast milk had not been demonstrated.
We have previously shown that maternal high-fat (HF) diet programs early obesity in rat offspring [23], with sex-dependent changes of the ECS in the hypothalamus [32, 33], white and brown adipose tissue [32, 34, 35], and liver [36]. In the present study, we hypothesized that maternal HF diet would increase the lipid endocannabinoid levels in breast milk programming cannabinoid and dopamine signaling in the NAc of weanling rats and food preference in adolescence, which is a critical window for both development and ECS modulation [9]. To test our hypothesis, we used a well characterized rat model of metabolic programming induced by maternal intake of HF diet from preconception to lactation.
## Animal model and diets
Twenty 60-day-old female Wistar rats weighing 180-220 g (female progenitors) and ten 100-day-old male Wistar rats weighing 250-300 g (male progenitors) were obtained from the Center of Reproduction Biology of the Federal University of Rio de Janeiro, Rio de Janeiro, Brazil. All animal procedures met National Institutes of Health guide for the care and use of Laboratory animals (NIH Publications No. 8023, revised 1978) and were approved by the Animal Care and Use Committee of the Health Science Center of the Federal University of Rio de Janeiro (process number $\frac{059}{19}$). For all animal procedures, rats were kept in a controlled temperature environment (23 ± 2 °C) with a photoperiod of 12 hours (7 a.m. to 7 p.m. – light, and 7 p.m. to 7 a.m. - dark). Water and the experimental diets were offered ad libitum throughout the study.
Progenitor female rats were randomly assigned to two dietary treatments ($$n = 10$$/group): Control group (C), which received a standard diet for rodents ($10.9\%$ of the calories as fat), and a high-fat group (HF), which received a high-fat diet ($28.7\%$ of the calories as fat) (Table 1). In the HF diet, lard was used as fat source, and we also added soy oil to provide the minimal amount of polyunsaturated fatty acids for adequate development of rats. We have recently published that the HF diet contains higher saturated fatty acid than control diet (+2.7-fold, $p \leq 0.05$), including more C12:0 (+15.3-fold, $p \leq 0.05$), C14:0 (+13.9-fold, $p \leq 0.05$), C16:0 (+$86.7\%$, $p \leq 0.05$) and C18:0 (+5.7-fold, $p \leq 0.05$) [37]. C and HF diets contain 3.34 kcal/g and 3.65 kcal/g, respectively (Table 1). Both diets followed the AIN-93G recommendations (23, 32–34, 38, 39). Female rats were fed these diets during 8 weeks before mating (2 females to 1 male), and throughout gestation and lactation. This experimental design aimed to isolate the effect of maternal dietary insult (HF diet) during early development (gestation and lactation) on the offspring metabolic outcomes and ECS regulation in the NAc, an important brain area for food intake and preference.
**Table 1**
| Unnamed: 0 | C diet - Nuvilab® | C diet - Nuvilab®.1 | C diet - Nuvilab®.2 |
| --- | --- | --- | --- |
| | (g) | Kcal | % Kcal |
| Protein | 220 | 880 | 26.31 |
| Lipid | 40 | 364 | 10.89 |
| Carbohydrate | 525 | 2100 | 62.80 |
| Mineral | 90 | 0 | 0 |
| Humidity | 125 | 0 | 0 |
| Energy | - | 3.34 kcal/g | - |
| | HF diet | HF diet | HF diet |
| | (g) | Kcal | % Kcal |
| C diet - Nuvilab | 150 | 501 | - |
| Condensed milk | 395 | 1267.99 | - |
| Skim powdered milk | 280 | 952 | - |
| Maize starch | 150 | 571.5 | - |
| Soy oil | 9.3 | 83 | - |
| Lard | 80 | 720 | - |
| AIN-93G Mix Mineral1 | 29 | 0 | - |
| AIN-93G Mix Vitamin1 | 8 | 0 | - |
| Choline2 | 0.82 | 0 | - |
| L-Cystine2 | 2.625 | 0 | - |
| BHT2 | 0.1 | 0 | - |
| Total | 1121.5 | 4096.79 | - |
| | (g) | Kcal | % Kcal |
| Protein | 155.6 | 622.4 | 15.19 |
| Lipid | 130.8 | 1177.2 | 28.73 |
| Carbohydrate | 582.1 | 2328.4 | 56.83 |
| Energy | - | 3.65 kcal/g | - |
Pregnant rats were housed in individual standard rat cages. At birth, all the litters were adjusted to three males and three females per dam to standardize and adequate the milk supply/demand among the litters [40, 41]. The remaining neonate pups from each litter were used for serum hormonal analysis. At weaning, a subset of male and female offspring was euthanized for adiposity evaluation by weighting the retroperitoneal fat pad as representative of visceral adipose tissue (VIS) and the inguinal fat pad as representative of subcutaneous adipose tissue (SUB). Serum hormonal profile and the ECS and dopamine signaling in the Nac of weanling male and female offspring were analyzed.
Dams were weighted before mating and during pregnancy and lactation weekly. Milk samples were collected at the postnatal day 11 (mid-lactation) and 21 (late lactation) under oxytocin (5 IU/mL) and anesthesia (55 mg/Kg of ketamine and 100 mg/Kg of xylazine) (Aché, Brazil). For milk extraction, lactating rats were separated from their offspring for 2 hours to maximize milk volume. The milk samples were extracted by gently squeezing the thoracic and abdominal teats and were stored at -80°C, as we previously described [23]. Milk samples were used for quantification of fatty acids and lipid endocannabinoids by mass spectrometry. At weaning, lactating rats were also euthanized, and serum and mammary gland samples were collected.
From weaning (postnatal day 21) until adolescence (postnatal day 45), both C and HF offspring received control diet to isolate the effect of maternal dietary insult (HF diet) during early development (gestation and lactation) on the offspring metabolic outcomes and ECS regulation. Offspring were weighted every three days until weaning and weekly after weaning. In the postnatal day 45, male and female offspring were tested for food preference as previously described [32]. Briefly, rats were allowed to access control, HF or high-sugar (HS; $30\%$ sucrose) diets simultaneously for 24h. Food intake was recorded to evaluate preference for dietary fat or sugar. The timeline of the animal procedures is described in Figure 1.
**Figure 1:** *Experimental model of maternal high-fat diet and study design. Female Wistar rats (60 day-old) were divided into control group (C, control diet) or High-fat group (HF, high-fat diet). Dams received C or HF diet for 8 weeks prior mating and throughout gestation (21 days) and lactation (21 days). At birth (postnatal day 1, PND 1), male and female pups underwent euthanasia for hormone and glycemia measurement. During lactation, milk samples were collected for endocannabinoid and fatty acid quantification. At weaning (PND 21), a subset of male and female offspring was killed for hormone, glycemia and adiposity evaluation, and analysis of the endocannabinoid system (ECS) and dopamine signaling in the Nucleus Accumbens (NAc). In the adolescence (PND 45), another subset of the offspring was submitted to a food preference test.*
Euthanasia of all animals occurred between 9 a.m. and 12 p.m. in a fed state by decapitation and serum obtained from trunk blood. The glycemia was measured using a glucometer (Accu-Chek, Roche, Switzerland). Serum samples were stored at −80°C. For each experimental procedure, rats from at least five different litters per group were used to avoid “litter effects” on the statistical analysis.
## Serum lipid and hormonal profile
Blood samples were collected and centrifuged (1233×g for 15 min, 4°C) for serum separation. Total triglycerides and cholesterol were measured by an enzymatic colorimetric kit (Bioclin, Quibasa Química, MG, Brazil). Leptin, insulin, PYY and GLP-1 levels were determined using a specific rat milliplex Kit from Merck Millipore (cat# RMHMAG-84K, MA, USA), with assay sensitivity of 8 pg/mL for leptin, 14 pg/mL for insulin, 1 pg/mL for PYY and 28 pg/mL for GLP-1. Intra-assay coefficient of variation was < $10\%$ for all analytes. We evaluated 5-7 samples per group.
## Milk endocannabinoid and fatty acid quantification
AEA and 2-AG milk concentration was assessed by liquid chromatography–mass spectrometry based on the method previously described for human milk [28] with slight adaptations. The assay was conducted using 150μL of whole rat milk or 150μL of each standard of the calibration curve. The standard curve was obtained by serial dilution of the AEA and 2-AG standards (Cayman Chemical, USA) in a $10\%$ milk powder solution in the range of 0.03-5.0 ng/mL for AEA and 23-3,000 ng/mL for 2-AG. The deuterated standards AEAd4 (1 ng) and 2-AGd5 (10 ng) (Cayman Chemical, USA) were added to each sample or standards as internal calibrators (20μL). An ice-cold mixture of acetonitrile and PBS (1:1) was added to the samples and standards (1,000 μL) for protein precipitation and lipid extraction. The proteins were separated by centrifugation (14,000 x g, 4°C, 5 minutes). The supernatant was acidified with 5 volumes of $5\%$ phosphoric acid. A solid phase extraction was performed using reversed phase chromatography cartridges OASIS HLB (Waters Corp., Mildford, MA, USA) following manufacturer’s instructions. The lipid fraction was eluted from the columns with 1 mL of acetonitrile at room temperature. The eluate was dried under nitrogen steam at 32°C for 20 min and resuspended in 100μL of methanol for the endocannabinoid detection (in triplicate) by LC-HRMS.
The chromatographic separation was performed in a reversed-phase column ACE CORE-25A-0502U (2.5μm, 50mm x 2.1mm) at 40°C. The mobile phases were composed of (A) H2O with 5 mM ammonium formate and $0.1\%$ formic acid, (B) MeOH with $0.1\%$ formic acid. The flow rate was set at 500 µL.min-1. The elution profile was 0-0.5min, $75\%$ B; 0.5-2 min, 75-$100\%$ B; 2-4 min, $100\%$ B; 4-4.1min, $75\%$ B; 4.1-5.0, $75\%$ B to equilibration to the initial conditions. The overall run time was 5 minutes, and the injection volume was 20.0 µL. The LC effluent was pumped to a Q-Exactive mass (Q Exactive Plus - Ultimate 3000 HPLC system, Thermo spectrometer Scientific Germany) operating in positive ionization mode. The spray voltage was set at 3.9 kV and 2.9 kV in positive ionization mode. The capillary temperature was 380°C, and the S-lens radio frequency (RF) level was set at 80 (arbitrary units). The nitrogen sheath and auxiliary gas flow rates were set at 60 and 20 (arbitrary units), respectively. To ensure mass accuracies below 6 ppm, the instrument was calibrated in positive and negative mode using the manufacture’s calibration solutions (Thermo Fisher Scientific, Bremen, Germany). The mass spectrometer acquired FullMS and T-SIM at resolution of 70,000 full width at half maximum (FWHM) and with an automatic gain control (AGC) of 106, maximum IT 75ms. The target mass was 348.2890 m/z to AEA, 379.2837 m/z to 2-AG, 352.3143 m/z to D4-AEA and 384.3153 m/z to D5-2AG. For this analysis, we used 5-8 samples per group.
Fatty acid profile in milk samples was assessed by gas chromatography-mass spectrometry based on the method previously described [33, 42]. The lipid samples were homogenized in a toluene and $1\%$ sulfuric acid in methanol solution. GC-MS analysis was carried out on a Shimadzu GCMS-QP2010 Plus system, using an Agilent column (25 m x 0.20 mm x 0.33 μm), HP Ultra 2 ($5\%$ Phenyl-methylpolysiloxane). Injector was set at 250°C and column temperature was programmed from 40–160°C at 30°C/min, 160-233°C at 1°C/min, 233-300°C at 30°C/min and held at 300°C for 10 min. Electro ionization (EI-70 eV) and a quadruple mass analyzer, operated in scans from 40 to 440 amu. Interface was set at 240°C and the ion source at 240°C. The components were identified by comparing their mass spectra with those of the library NIST05 contained in the computer’s mass spectrometer. Retention indices were also used to confirm the identity of the peaks in the chromatogram by Supelco 37 Component FAME Mix (Sigma-Aldrich). Fatty acids were quantified by determining peak-area ratios with the 9:0 and 19:0 internal standards. We used 3-5 samples per group.
## Microdissection of the nucleus accumbens
The whole brain of weanling rats was carefully removed and conditioned to -20°C for subsequent dissection in cryostat by the punch technique as we previously described for hypothalamic nuclei [23, 43], with adaptations for the NAc. The NAc punch samples were obtained from thick coronal brain sections using the bregma as reference [44] and the rat brain atlas [45]. Briefly, four subsequent sections of 500 μM were made: from Bregma +2.76 mm to Bregma +0.72 mm for microdissection of two subregions of the NAc, i.e., core (cNAc) and shell (sNAc). The NAc was dissected bilaterally from all sections in one punch with a 1.5 mm-diameter round needle using the lateral ventricles and 1.5 mm from the base of the brain as references. Immediately afterwards, the tissue samples were kept at −80°C until Western blotting assays.
## Western blotting
Western blotting was used to investigate the protein content of the ECS components in the mammary gland from dams and in the NAc of offspring (32–34). In addition, the dopamine signaling was also evaluated in the NAc of weanling offspring. The protein content of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) or cyclophilin was used as loading control.
The mammary gland samples were homogenized in pH 7.4 lysis buffer (20mM TRIS-HCl, 10mM NaF, $1\%$ NP40, 150mM NaCl, 5mM EDTA, $0.1\%$ SDS) and the NAc samples were homogenized in pH 7.4 lysis buffer (50 mM Tris, 150 mM NaCl, $1\%$ Triton 100×, $0.1\%$ SDS, 5 mM EDTA, 50 mM NaF, 30 mM sodium pyrophosphate, 1 mM sodium orthovanadate) both containing protease inhibitor cocktail (Thermo Scientific, catalog number A32959). After centrifugation, the total protein content of supernatant was quantified using the PierceTM BCA Protein Assay Kit (Thermo Scientific, Rockford, USA).
The samples were denatured in sample buffer (50mM Tris-HCl, pH 6.8, $1\%$ SDS, $5\%$ 2-mercaptoethanol, $10\%$ glycerol, $0.001\%$ bromophenol blue) and heated at 95°C for 5 min. Total proteins were analyzed by SDS-PAGE, with a $12\%$ polyacrylamide gel, and transferred onto polyvinylidene difluoride membranes (Hybond-P 0.45 μm PVDF; Amersham Biosciences BKM, ENG). The membranes were incubated with T-TBS containing $5\%$ Bovine Serum Albumin (Sigma Life Science MO, USA) for 90 minutes to block non-specific binding sites. Then, the membranes were incubated overnight at 4°C with specific primary antibodies. Membranes were washed and incubated for 2 hours at room temperature with peroxidase labeled specific secondary antibodies. All blots were washed and incubated with a luminogen detection reagent (Amersham ECL Prime Western Blotting Detection reagent; Amersham Bioscience, Inc). Information about primary and secondary antibodies is described in Table 2. Chemiluminescent signal was detected by ImageQuant LAS 4000 equipment followed by densitometric analyses (GE Healthcare Life Sciences). Data are expressed as percentage of control male group (set at $100\%$). For each protein analyzed by Western blotting, we evaluated 6 or 7 samples per group.
**Table 2**
| Primary Antibodies | Primary Antibodies.1 | Primary Antibodies.2 | Secondary Antibodies | Secondary Antibodies.1 | Secondary Antibodies.2 |
| --- | --- | --- | --- | --- | --- |
| | Company | Dilution | Company | Dilution | Specificity |
| CB1 Cat #101500 | CaymanMI, USA | 1:500 | Amersham Bioscience Cat #NA934 | 1:5000 | Anti-rabbit |
| CB2 WH0001269M1 | SigmaMO, USA | 1:1000 | Cell Signaling MA, USA Cat #7076 | 1:10000 | Anti-mouse |
| FAAH Cat #101600 | CaymanMI, USA | 1:200 | Amersham Bioscience Cat # NA934 | 1:5000 | Anti-rabbit |
| MAGL Cat #sc-398942 | Santa Cruz CA, USA | 1:1000 | Amersham Bioscience Cat # NA934 | 1:10000 | Anti-rabbit |
| NAPE-PLD Cat #10305 | CaymanMI, USA | 1:200 | Invitrogen CA, USA Cat #31460 | 1:5000 | Anti-rabbit |
| DAGLα Cat #sc-390409 | Santa Cruz CA, USA | 1:100 | Cell Signaling MA, USA Cat #7076 | 1:10000 | Anti-mouse |
| D1 Cat #NBP2-162113 | Novus BiologicalsCO, USA | 1:500 | Invitrogen CA, USA Cat #31460 | 1:5000 | Anti-rabbit |
| D2 Cat #NB600-1261 | Novus BiologicalsCO, USA | 1:500 | Invitrogen CA, USA Cat #31460 | 1:5000 | Anti-rabbit |
| DAT Cat #ab184451 | AbcamCambridge, UK | 1:1000 | Invitrogen CA, USA Cat #31460 | 1:5000 | Anti-rabbit |
| TH Cat #AB152 | Merck MilliporeMA, USA | 1:1000 | Amersham Bioscience Cat # NA934 | 1:10000 | Anti-rabbit |
| DARPP-32 Cat #AB10518 | Merck MilliporeMA, USA | 1:1000 | Amersham Bioscience Cat # NA934 | 1:5000 | Anti-rabbit |
| β-Actin Cat #sc-1615 | Santa Cruz CA, USA | 1:5000 | Invitrogen CA, USA Cat # 31402 | 1:10000 | Anti-goat |
| GAPDH Cat #2118 | Cell Signaling MA, USA | 1:5000 | Amersham Bioscience Cat #NA934 | 1:10000 | Anti-rabbit |
| Cyclophilin Cat #PA1-027A | ThermoFisherMA, USA | 1:5000 | Amersham Bioscience Cat #NA934 | 1:10000 | Anti-rabbit |
## Statistical analysis
The statistical analysis was performed using the software GraphPad Prism 8 (GraphPad Software Inc., CA, USA). For all analyses, normality was assessed by the Kolmogorov-Smirnov test and Grubb’s test was used to detect outliers.
The unpaired Student’s t test was used for comparisons between C and HF dams and to analyze the food preference in adolescent offspring. Two-way ANOVA with Bonferroni’s post hoc test was used to analyze offspring data, considering maternal diet or offspring sex as main factors. Statistically significant differences were considered when $p \leq 0.05.$ Results are shown as mean ± standard deviation. For the western blotting assays data are expressed as percentage of change compared to control dam or male group set as $100\%$.
## Maternal metabolic phenotype
Before gestational period, C and HF dams received dietary treatments for 8 weeks and the HF diet increased the body weight gain compared to C diet (+$41\%$, $p \leq 0.05$). However, there were no differences in the maternal body weight gain during pregnancy and lactation (Figure 2A). At weaning, HF diet did not alter maternal serum levels of glucose, triglycerides, or cholesterol (Figure 2B).
**Figure 2:** *Effect of high-fat (HF) diet on the body weight and serum metabolites of rat female progenitors. (A) Maternal body weight gain for 8 weeks prior conception, and during pregnancy and lactation; and (B) Serum glucose, triglycerides, and cholesterol levels of control (C) and HF dams at weaning. Data are presented as mean ± standard deviation and statistically significant differences were determined by unpaired Student’s t test. *p < 0.05.*
## Offspring metabolic phenotype
Maternal HF diet slightly decreased birth weight in female offspring (-$8\%$, $p \leq 0.05$) but did not alter birth weight of male pups (Figure 3A). During lactation, maternal HF diet increased body weight of male offspring at postnatal day 15 (+$19\%$, $p \leq 0.05$), 18 (+$15\%$, $p \leq 0.05$) and 21 (+$15\%$, $p \leq 0.05$), compared to C offspring (Figure 3A). In the female offspring, maternal HF diet induced a “catch up” of the body weight during lactation, with increased body weight of female HF offspring at postnatal day 12 (+$14\%$, $p \leq 0.05$), 15 (+$20\%$, $p \leq 0.05$), 18 (+$16\%$, $p \leq 0.05$) and 21 (+$14\%$, $p \leq 0.05$), compared to C offspring (Figure 3A).
**Figure 3:** *Effect of maternal high-fat (HF) diet on offspring body weight and adiposity in the early life. (A) Body weight and (B) visceral (VIS) and subcutaneous (SUB) adiposity of control (C) and HF male and female offspring at weaning (postnatal day 21). Data are presented as mean ± standard deviation and statistically significant differences were determined by two-way ANOVA followed by Bonferroni test. *p < 0.05.*
Maternal HF diet did not change body weight of male offspring from weaning to adolescence, but female HF offspring remained with increased body weight compared to their sex-matched controls at postnatal day 35 (+$10\%$, $p \leq 0.05$) and 42 (+$11\%$, $p \leq 0.05$) (Figure 3A).
Maternal HF diet increased VIS WAT mass (+ 2.1-fold, $p \leq 0.05$) and SUB WAT mass (+ $95\%$, $p \leq 0.05$) of male offspring at weaning compared to C offspring (Figure 3B). A similar profile was observed in female HF offspring, with increased VIS WAT mass (+ 2.7-fold, $p \leq 0.05$) and SUB WAT mass (+ 2.1-fold, $p \leq 0.05$), compared to C offspring (Figure 3B).
Maternal HF diet did not change the glycemia or hormonal profile of newborn male or female offspring (Table 3). However, maternal HF diet increased the glycemia and insulinemia of weanling offspring (maternal diet effect $p \leq 0.05$). The post hoc analysis of glycemia showed a statistically increase in male (+ $24\%$, $p \leq 0.05$) and female (+ $14\%$, $p \leq 0.05$) offspring compared to their sex-matched controls at weaning. Maternal HF increased the serum levels of insulin in the offspring (maternal diet effect $p \leq 0.05$), with statistically difference in the post hoc test for males (+ 3.6-fold, $p \leq 0.05$) compared to sex-matched controls, but maternal HF diet did not change the serum levels of leptin, PYY or GLP-1 (Table 3).
**Table 3**
| Unnamed: 0 | Unnamed: 1 | Serum metabolites (Mean ± SD) | Serum metabolites (Mean ± SD).1 | Serum metabolites (Mean ± SD).2 | Serum metabolites (Mean ± SD).3 | Source of variation | Source of variation.1 | Source of variation.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | C male | HF male | C female | HF female | MD | S | MD x S |
| Offspring at birth | Glycemia (mg/dL) | 59.0 ± 12.3 | 60.2 ± 11.2 | 57.6 ± 12.7 | 62.6 ± 11.8 | 0.34 | 0.87 | 0.55 |
| Offspring at birth | Leptin (ng/mL) | 1.27 ± 0.73 | 0.76 ± 0.50 | 0.92 ± 0.61 | 0.99 ± 0.84 | 0.48 | 0.83 | 0.34 |
| Offspring at birth | Insulin (ng/mL) | 0.75 ± 0.39 | 1.05 ± 0.53 | 1.09 ± 0.44 | 1.27 ± 0.71 | 0.32 | 0.25 | 0.79 |
| Offspring at birth | PYY (ng/mL) | 4.75 ± 4.36 | 3.33 ± 2.46 | 4.10 ± 4.51 | 4.19 ± 4.17 | 0.70 | 0.95 | 0.66 |
| Offspring at birth | GLP-1 (ng/mL) | 0.88 ± 0.70 | 1.43 ± 1.36 | 1.38 ± 0.94 | 1.79 ± 1.78 | 0.39 | 0.44 | 0.89 |
| Offspring at weaning | Glycemia (mg/dL) | 114 ± 15.0 | 141 ± 16.4* | 119 ± 12.5 | 136 ± 15.9* | <0.05 | 0.94 | 0.14 |
| Offspring at weaning | Leptin (ng/mL) | 2.83 ± 0.57 | 3.10 ± 1.00 | 2.46 ± 0.49 | 2.70 ± 0.70 | 0.37 | 0.19 | 0.97 |
| Offspring at weaning | Insulin (ng/mL) | 0.81 ± 0.43 | 2.90 ± 1.06* | 1.30 ± 0.73 | 2.20 ± 0.92 | <0.05 | 0.74 | 0.08 |
| Offspring at weaning | PYY (pg/mL) | 94.0 ± 14.0 | 153 ± 93.2 | 84.4 ± 9.65 | 90.0 ± 36.9 | 0.15 | 0.11 | 0.23 |
| Offspring at weaning | GLP-1 (ng/mL) | 0.15 ± 0.03 | 0.14 ± 0.03 | 0.13 ± 0.04 | 0.13 ± 0.03 | 0.66 | 0.31 | 0.50 |
## Milk composition and the ECS in the mammary gland
Maternal HF diet decreased AEA content in the milk at postnatal day 11 (- $71\%$, $p \leq 0.05$) and 21 (- $72\%$, $p \leq 0.05$) compared to maternal C diet (Figure 4A). Maternal HF diet also decreased milk 2-AG content at postnatal day 11 (- $87\%$, $$p \leq 0.10$$) and 21 (- $62\%$, $$p \leq 0.054$$) (Figure 4B).
**Figure 4:** *Effect of high-fat (HF) diet on the content of endocannabinoids in the milk of rat progenitors. (A) Milk anandamide (AEA) and (B) 2-arachidonoylglycerol (2-AG) in the postnatal days 11 and 21 of control (C) and HF progenitors. Data are presented as mean ± standard deviation and statistically significant differences were determined by unpaired Student’s t test. *p < 0.05.*
Maternal HF diet did not change the content of CB1 but increased the content of CB2 in the mammary tissue (+ 1.6-fold %, $p \leq 0.05$). Maternal HF diet did not change the endocannabinoid degrading enzymes FAAH or MAGL neither the synthesizing enzymes NAPE-PLD or DAGL (Figure 5).
**Figure 5:** *Effect of high-fat (HF) diet on the endocannabinoid system in the mammary gland of rat progenitors. (A) Type-1 cannabinoid receptor (CB1) and type-2 cannabinoid receptor (CB2) protein content; (B) fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL) protein content; (C) N-acyl phosphatidylethanolamine phospholipase D (NAPE-PLD) and diacylglycerol lipase (DAGL) protein content of mammary gland of control (C) and HF progenitors. (D) representative blots are shown. Data are expressed as percentage of control group (set at 100%) ± standard deviation. Statistically significant differences were determined by unpaired Student’s t test. *p < 0.05.*
The analysis of fatty acid profile in the milk showed that maternal HF diet increased the content of saturated fatty acid in late lactation (postnatal day 21) (2-3-fold increase, $p \leq 0.05$) (Table 4). In addition, maternal HF diet decreased the milk content of the polyunsaturated fatty acids (PUFA) eicosapentanoic acid (n3, -$30\%$, $p \leq 0.05$), linoleic acid (n6, -$51\%$, $p \leq 0.05$), arachidonic acid (n6, -$40\%$, $p \leq 0.05$), docosatetraenoic acid (n6, -$55\%$, $p \leq 0.05$) in mid-lactation (postnatal day 11) (Table 4).
**Table 4**
| Unnamed: 0 | Unnamed: 1 | 11st day | 11st day.1 | 11st day.2 | 21st day | 21st day.1 | 21st day.2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | C | HF | p | C | HF | p |
| Fatty acid profile (μg/μL) | C12:0 | 13.70 ± 1.02 | 8.643 ± 1.32* | 0.04 | 10.28 ± 2.72 | 8.703 ± 1.45 | 0.65 |
| Fatty acid profile (μg/μL) | C15:0 | 0.487 ± 0.03 | 1.061 ± 0.26 | 0.10 | 0.490 ± 0.13 | 1.398 ± 0.36* | 0.04 |
| Fatty acid profile (μg/μL) | C16:0 | 27.57 ± 3.0 | 24.19 ± 3.03 | 0.47 | 14.64 ± 2.75 | 27.40 ± 3.48* | 0.02 |
| Fatty acid profile (μg/μL) | C17:0 | 0.323 ± 0.02 | 0.557 ± 0.11 | 0.13 | 0.292 ± 0.07 | 0.653 ± 0.10* | 0.02 |
| Fatty acid profile (μg/μL) | C18:0 | 4.657 ± 0.52 | 6.784 ± 0.89 | 0.11 | 3.164 ± 0.52 | 8.017 ± 0.88* | 0.01 |
| Fatty acid profile (μg/μL) | C16:1 | 2.071 ± 0.51 | 3.615 ± 0.7 | 0.15 | 2.093 ± 0.60 | 5.413 ± 1.39 | 0.06 |
| Fatty acid profile (μg/μL) | C18:1 | 19.64 ± 2.85 | 22.50 ± 3.32 | 0.55 | 13.33 ± 2.50 | 29.46 ± 3.40* | 0.01 |
| Fatty acid profile (μg/μL) | C20:5 n3 | 0.279 ± 0.02 | 0.153 ± 0.02* | 0.02 | 0.108 ± 0.01 | 0.138 ± 0.01 | 0.16 |
| Fatty acid profile (μg/μL) | C22:5 n3 | 0.301 ± 0.03 | 0.149 ± 0.03 | 0.04 | 0.132 ± 0.02 | 0.143 ± 0.02 | 0.78 |
| Fatty acid profile (μg/μL) | C22:6 n3 | 0.212 ± 0.02 | 0.136 ± 0.03 | 0.16 | 0.163 ± 0.04 | 0.125 ± 0.02 | 0.18 |
| Fatty acid profile (μg/μL) | C18:2 n6 | 27.65 ± 1.69 | 13.54 ± 1.99* | 0.01 | 21.10 ± 3.99 | 18.17 ± 0.02 | 0.56 |
| Fatty acid profile (μg/μL) | C20:4 n6 | 1.951 ± 0.07 | 1.171 ± 0.13* | 0.01 | 1.466 ± 0.29 | 1.156 ± 0.14 | 0.42 |
| Fatty acid profile (μg/μL) | C22:4 n6 | 0.551 ± 0.07 | 0.250 ± 0.06* | 0.04 | 0.277 ± 0.06 | 0.241 ± 0.04 | 0.69 |
| | ARA/EPA+DHA | 4.038 ± 0.56 | 4.077 ± 0.40 | 0.93 | 5.438 ± 1.27 | 4.414 ± 0.578 | 0.18 |
## ECS and dopamine signaling in the NAc of weanling offspring
Maternal HF diet differently altered the ECS in the NAc of male and female weanling offspring, represented for interaction effect observed in all parameters analyzed ($p \leq 0.05$). In addition, there is a marked sex effect on the CB1 content ($p \leq 0.05$). In male offspring, maternal HF diet increased CB1 (+ $56\%$, $$p \leq 0.06$$) and CB2 (+ $67.6\%$, $p \leq 0.05$), while maternal HF diet did not change cannabinoid receptors in female offspring (Figures 6A, B). Maternal HF diet decreased FAAH content (- $56.1\%$, $p \leq 0.05$) (Figure 6C) in the NAc of males with no changes in MAGL (Figure 6D), compared to sex-matched controls. In female HF offspring, we observed an different profile, with decreased MAGL (- $50\%$, $p \leq 0.05$) (Figure 6D) with no changes in FAAH content (Figure 6C), compared to sex-matched controls.
**Figure 6:** *Effect of maternal high-fat (HF) diet on the endocannabinoid signaling in the Nucleus Accumbens (NAc) of weanling offspring. (A) Type-1 cannabinoid receptor (CB1); (B) type-2 cannabinoid receptor (CB2); (C) fatty acid amide hydrolase (FAAH); and (D) monoacylglycerol lipase (MAGL) protein content in the NAc of control (C) and HF male and female offspring at weaning. (E, F) representative blots are shown. Data are expressed as percentage of control male group (set at 100%) ± standard deviation. Statistically significant differences were determined by two-way ANOVA followed by Bonferroni’s test. *p < 0.05.*
Maternal HF diet differently altered the dopamine signaling in the NAc depending on offspring sex, since there was an interaction effect ($p \leq 0.05$) in the content of dopamine receptors (D1R and D2R), dopamine transporter (DAT) and cAMP-regulated neuronal phosphoprotein-32 kDa (DARPP-32) (Figures 7B-E, respectively). In these parameters, the male HF offspring presented a profile of increase while female HF offspring presented profile of decrease. There was a sex effect on the tyrosine hydroxylase (TH) content, a limiting enzyme of the dopamine synthesis, with female offspring presenting increased content (Sex effect, $p \leq 0.01$), regardless of maternal diet (Figure 7A). Similarly, female HF offspring have increased DAT content (+ $82.5\%$, $p \leq 0.05$) compared to male offspring, regardless of maternal diet (Figure 7D). Maternal HF diet decreased DARPP32 content in male offspring (- $46.3\%$, $p \leq 0.05$) while increased in female offspring (+ $71.2\%$, $p \leq 0.05$) (Figure 7E).
**Figure 7:** *Effect of maternal high-fat (HF) diet on the dopamine signaling in the Nucleus Accumbens (NAc) of weanling offspring. (A) Tyrosine hydroxylase (TH); (B) type-1 dopamine receptor (D1R); (C) type-2 dopamine receptor (D2R); (D) dopamine transporter (DAT); and (E) cAMP-regulated neuronal phosphoprotein-32 kDa (DARPP-32) protein content in the NAc of control (C) and HF male and female offspring at weaning. (F) and (G) show the representative blots. Data are expressed as percentage of control male group (set at 100%) ± standard deviation. Statistically significant differences were determined by two-way ANOVA followed by Bonferroni’s test. *p < 0.05.*
## Food preference test
Maternal HF diet induced higher preference for HF diet (+ 2.4-fold, $p \leq 0.05$) in adolescent male offspring, while did not change the preference for C or HS diets (Figure 8). In the female offspring, maternal HF diet did not alter food preference in the adolescence (Figure 8) Representative blots are show in Figure 6E, F.
**Figure 8:** *Effect of maternal high-fat (HF) diet on the food preference of rat offspring at adolescence. Intake of control (C), high sucrose (HS) and HF diets of male and female offspring born from progenitors receiving control (C) or HF diet. Statistically significant differences were determined by unpaired Student’s t test. *p < 0.05.*
## Discussion
In the present study, we characterized the presence of lipid endocannabinoids in breast milk of lactating rats and how maternal HF diet changes the levels of milk AEA and 2-AG in different time points of lactation. We also showed that maternal HF diet modulates the cannabinoid and dopamine signaling in the NAc of weanling rat offspring in a sex-dependent manner, which may be associated with the increased preference for dietary fat observed in male but not in female adolescent rats Representative blots are show in Figure 7F and Figure 6G.
The HF diet increased body weight gain of female progenitors prior mating but did not change weight gain during gestation or lactation. HF diet also did not change serum metabolic parameters (glycemia, triglycerides and cholesterol) in the dams at weaning. These data corroborate our previous results showing increased total body fat but not body weight of the dams before mating [23]. In contrast, the literature mainly shows that HF diet increases body weight, adiposity, glycemia, dyslipidemia, insulinemia and leptinemia in female rats prior pregnancy, and several serum alterations last until weaning (46–49). An important characteristic of the present study is that the HF diet used is isocaloric in comparison to the control diet (~3.9 kcal/g), while most of the studies published in the literature are performed using a high-fat and hypercaloric diet (over 5 kcal/g). Additionally, the diets used in several other studies are also often enriched in simple sugar (fructose or sucrose). This study was performed using a mild HF diet ($29\%$ fat), which did not induce a severe metabolic phenotype in the dams, but it was able to induce a marked phenotype in the offspring. On the other hand, we have demonstrated that maternal HF diet increases the content of macronutrients (lipids, protein, lactose) in the breast milk across lactation, which seems to be an important imprinting factor for the offspring [23].
During lactation, male and female offspring developed early obesity characterized by increase of body weight and adiposity at weaning in parallel to increased glycemia and insulin levels. This phenotype has also been shown by our previous studies [23, 34] and by several other research groups (47, 50–52). In the present study, maternal HF diet decreased birth weight only in female offspring. When evaluating the growth trajectories of both sexes, it is evident that female offspring present a more accelerated weight gain during lactation. It is possible that maternal HF diet differentially alters placenta function according to the offspring sex, resulting in different programming models. We speculate that male HF offspring is mainly exposed to changes in milk, while female HF offspring already present significant alterations before milk changes exposure. This difference in growth rates may contribute to the higher magnitude of adipose tissue accumulation observed in female HF offspring at weaning, compared to HF males. In addition, female HF offspring remain heavier compared with the control offspring during the adolescence, but this phenotype was not observed in male offspring. Interestingly, in a previous paper, we have demonstrated that maternal HF diet has a more pronounced effect on adipose tissue of adult female offspring compared with male offspring. In parallel, adult HF female have increased expression/content of CB1 in visceral and subcutaneous adipose tissue at postnatal day 180, associated with increased estrogen receptor binding to the *Cnr1* gene [35]. Despite presenting greater adiposity, adult female HF offspring do not have higher levels of triglycerides, glycemia or liver steatosis [36], suggesting that this energy accumulation (mainly in subcutaneous adipose tissue) is relatively controlled in terms of systemic metabolic complications. Differently, adult HF males present such alterations [36].
Surprisingly, in the animal set used for this study, we did not observe increased leptin levels as expected by the higher adiposity of the HF offspring and our previous results [23, 34]. We speculate that this variance among the experimental sets might be related to the magnitude of increase in adiposity. In the present experiment, HF offspring presented an increment of 2-3-fold in adiposity, while in the previous sets it reached 4-fold increase.
Lactation is a critical window for metabolic programming in rodents, and alterations in the milk macronutrients and hormones contribute to detrimental phenotypes [50, 51, 53]. In rodents, the completion of hepatocyte differentiation and bile duct formation as well as nephrogenesis take place during lactation [54]. The peak of adipose tissue development also occurs during lactation, when the number and the size of adipocytes increase gradually. After puberty, the adipocyte number and size remain relatively stable [55]. Similarly, during lactation occurs the peak of brain development, which reaches $90\%$ of adult weight around weaning, when there is a peak in synaptic density and myelination rate [54].
In rodents, maternal HF diet only during lactation recapitulates the offspring metabolic phenotype programmed by HF diet throughout gestation and lactation [47, 56], highlighting the key contribution of lactation. In humans, the exclusive breastfeeding for the first six months of life prevents morbidity and mortality as well as promotes the physical and mental health of infants [57]. Maternal obesity alters the milk fatty acid profile (increased saturated fatty acids and decreased n3 PUFA) associated with infant cognitive impairment [25].
In the present study, maternal HF diet decreased the milk content of n3 and n6 PUFA at mid-lactation (postnatal day 11). Castillo et al. also demonstrated that maternal HF ($40\%$ fat) decreases n3 PUFA but does not change the AA (n6 PUFA) levels in rat breast milk at mid-lactation (postnatal day 10) [58]. In a similar rat model, maternal obesity ($25\%$ fat) decreases n3 PUFA (EPA and DHA) milk content while increases milk AA at late lactation [51]. Breast milk PUFA are crucial for postnatal growth and neurodevelopment, and about half of the dry weight of the brain is made up by lipids, of which 20–$25\%$ are PUFA [59, 60]. The milk composition of fatty acids is a combination of dietary lipids and the de novo fatty acid synthesis in the mammary gland or in other maternal tissues, such as liver (51, 61–63). It was demonstrated that the mammary gland deletion of fatty acid synthase (FAS) induces the premature involution of lactating mammary gland and decreases medium- and long-chain fatty acids and total fatty acid content in breast milk of mice [62]. In the present model, we did not observe changes in lipogenic enzymes in the mammary gland (data not shown), suggesting a major contribution of the diet to the milk fatty acid profile. However, Bautista et al. showed that maternal obesity decreases the content of total n3 and n6 PUFA in the mammary gland associated with decreased expression of the enzyme delta 5 desaturase at weaning [51], and Castillo et al. observed that maternal HF diet increases the mRNA expression of FAS and lipoprotein lipase (lipogenic factors) in the mammary gland [58].
The imbalance between n6 and n3 PUFA can also affect the levels of endocannabinoids, since AEA and 2-AG are derived from membrane phospholipids containing AA [7]. We observed that maternal HF diet parallelly reduced AA and the endocannabinoid levels in the breast milk, and this association was more robust in mid-lactation. To investigate possible causes of the decreased milk endocannabinoid levels, we analyzed the ECS expression in the mammary gland. However, maternal HF did not affect the content of the synthesizing enzymes (NAPE-PLD and DAGLa) or the degrading enzymes (FAAH and MAGL). Thus, other metabolic factors in the dams, including the uptake and transport of endocannabinoids in the mammary gland, may be involved in this profile. Maternal HF diet increased the CB2 content in the mammary gland, possibly as an adaptation to counteract local inflammation [64] induced by HF diet.
There are very few studies in the literature showing the presence of endocannabinoids in human milk (28–30, 65–67), and to the best of our knowledge, this is the first study evaluating these bioactive lipids in rat milk. However, the physiological significance of the presence of lipid endocannabinoids in breast milk remains to be elucidated. In a cohort from Guatemala, the levels of PEA in mature milk increases across lactation and there is a positive correlation between milk AA and arachidonoyl glycerol (AG) [67], corroborating our data. On the other hand, in a small human cohort, it was demonstrated that maternal obesity or overweight do not alter the 2-AG levels in mature milk, and higher milk levels of 2-AG are observed during daylight compared to night levels [29]. In addition, milk levels of OEA and PEA are lower in the mothers of four-month babies with higher body weight compared to the ones presenting lower body weight [66]. This data suggests a negative correlation between milk endocannabinoids and body weight in lactation, like the profile observed in the present study.
We found that concentration of AEA and 2-AG in rat milk was comparable to that observed in human milk [28, 29] but slightly higher, possibly because rat milk has higher fat content compared to human milk (51, 68–70). Notedly, the relative amount of 2-AG in rat milk was found 1,000-fold more concentrated than AEA. This profile has been demonstrated in other biological samples, such as brain [71, 72]. We speculate that decreased levels of endocannabinoids in breast milk may result in decreased exposure to these lipids in the developing brain impairing maturation like what is known for other milk components such as leptin [73, 74]. Additionally, maternal exposure to Cannabis during pregnancy and lactation is related to the presence of phytocannabinoids in umbilical cord blood and breast milk and are associated with impaired offspring social, behavioral, and cognitive development [75].
Maternal HF diet increased the content of saturated fatty acids in the milk at late lactation, which can be a direct effect of the maternal diet rich in lard [76]. The high exposure to saturated fatty acids has been related to insulin resistance [77], and we observed hyperglycemia and hyperinsulinemia in the HF offspring at weaning, suggesting the contribution of the breast milk. We have also demonstrated that maternal HF diet induces hypothalamic leptin resistance in the offspring at weaning [23, 33] and adulthood [39]. High-fat diets rich in long-chain saturated fatty acids, mainly C16:0, contribute to hypothalamic inflammation, which has been shown to be causative of central leptin resistance, contributing to obesity development [78, 79]. Leptin signaling impairment is associated with increased central cannabinoid signaling [80], and we have also demonstrated that maternal HF diet increases CB1 content in the hypothalamus of male newborn rats [32].
Besides the evident importance of the hypothalamus on homeostatic feeding and energy expenditure regulation, the LH-VTA-NAc pathway regulates dopaminergic signaling and the hedonic aspects of food intake and reward [81]. The LH-VTA-NAc pathway is highly regulated by GABA and glutamate neurons expressing CB1 [80, 82]. Dopaminergic neurons in the VTA project to the NAc, where they release dopamine to bind to D1R and D2R and promote reward. The activity of dopamine neurons is regulated by GABAergic inputs projecting from the NAc and orexin-A neurons from the LH. Dopamine neurons synthesize 2-AG, which binds to CB1 expressed presynaptically in GABAergic and glutamatergic neurons, fine tuning dopamine neuron activity. In addition, the activity of GABAergic neurons projecting from the NAc to VAT is directly regulated by glutamatergic neurons expressing CB1. Therefore, increased CB1 signaling in the LHA-VTA-NAc circuitry is associated with increased dopamine release and reward [7].
In the present study, maternal HF diet increased cannabinoid receptors in the NAc of male weanling rats but not in females. In addition, it was observed a parallel decrease of the degrading enzymes FAAH and MAGL in males and females, respectively, suggesting an increase of tissue levels of endocannabinoids. Increased cannabinoid signaling in the NAc can result in inhibition of GABA inputs to the dopamine neurons in the VAT, thus increasing dopamine release as well as palatable food-seeking behavior [17]. We also analyzed the dopamine signaling in the NAc of the weanling offspring. Maternal HF diet induced a sex-specific regulation of this signaling, characterized by the “interaction” observed in the two-way ANOVA analysis, with increased content of dopamine receptors and DAT in males but not in females. However, maternal HF diet decreased the content of the DARPP-32 in males and increased in females. Because DARPP-32 signals the downstream pathway of both D1R and D2R, this result suggests that the intracellular signaling is impaired in male HF offspring. In chronic models of diet-induced obesity, it has been demonstrated a hyporesponsiveness of the dopaminergic mesolimbic circuitry [83, 84] associated with higher vulnerability to preference for highly palatable diets and compulsive behavior [84, 85]. We speculate that decreased DARPP-32 may be an initial response to maternal HF diet that could contribute to increased appetite for palatable food, which would probably be followed by a downregulation of the dopamine receptors in the long-term.
Here, changes in the cross-talk between cannabinoid and dopamine signaling were associated with increased preference for dietary fat in the HF male offspring at adolescence, a critical window for the developmental origins of health and disease (DOHaD) [86], neurodevelopment and addiction susceptibility [9, 87]. Previously, we have demonstrated that adult male and female HF offspring present higher preference for fat [32], and the present data showed that this phenotype is already evident during adolescence in males. Interestingly, male HF offspring develop a more pronounced metabolic phenotype compared to females, presenting liver steatosis, insulin resistance and dyslipidemia at adulthood [36]. In this model, nutritional intervention (fish oil) during adolescence represents an important approach to prevent part of the metabolic dysfunctions [76], highlighting this critical window.
Other studies have demonstrated that maternal HF diet alters the dopaminergic system in limbic areas of offspring contributing to preference to highly palatable foods (83, 88–94). In contrast, adolescent and adult male offspring from dams exposed to hypercaloric-hypoproteic diet present decreased chocolate preference associated with decreased endocannabinoid levels in the hypothalamus [95]. In a programming model of early weaning (early undernutrition), it was demonstrated that adult male offspring present increased “voracity” for high-fat diet (30 minutes of free choice diet) but increased preference for sugar (12h of free choice diet) while no effect was observed in females [96]. In parallel, early weaning induced decreased D2R in the NAc of adult male without marked changes in the cannabinoid signaling in the hypothalamus, but the ECS in the NAc was not evaluated [96].
In the present study, differently of adolescent males, female HF offspring had no alterations in the food preference, however, they showed decreased MAGL, D2R and DAT, and increased DARPP-32 content in the NAc at weaning, suggesting higher endocannabinoids and dopamine signaling. These data demonstrate sex-specific effects of maternal HF diet on the offspring. In addition, the two-way ANOVA analysis showed a “sex effect” on the CB1, TH and DAT content along with interaction between “maternal diet” and “offspring sex” in almost all ECS and dopamine signaling markers. There are several mechanisms that could potentially contribute to the sex differences observed in the ECS and dopamine signaling. Previously, it was demonstrated that sex hormones participate of brain development during the intrauterine life, and testosterone and estrogen surges are observed in neonate boys and girls, respectively, representing a “mini-puberty” [97]. In our animal model, regardless of changes in the sex hormone circulating levels, we have demonstrated that maternal HF diet increases the acetylation levels of the cannabinoid receptor 1 gene (Cnr1) promoter associated with increased binding of the androgen receptor (AR) and Cnr1 mRNA levels only in the hypothalamus of male offspring [33]. A similar mechanism may be involved in the sex-specific changes observed for the NAc in the present study. Considering the dopamine signaling, the estradiol receptor β (ERβ) is co-localized with TH in VAT neurons of males and females, which could contribute to higher TH expression in female [98]. Estradiol receptors (ERα/β) are also present in neuronal membranes, where they interact with metabotropic glutamate receptor to decrease the inhibitory GABAergic input from NAc to VTA with consequent increase of dopamine release in females [99]. However, more studies are needed to explore the direct regulation of sex hormones on ECS and dopamine signaling, possibly using in vitro models.
This study is not without limitations. It was not possible to identify the cause of decreased levels of AEA and 2-AG in rat milk induced by HF diet, and further experiments are needed (for example testing ECS enzyme activity). Also, it would be important to quantify the endocannabinoid levels in that NAc, which was not possible at this experimental design due to the limited amount of tissue obtained with the “punch” technique. Lastly, as mentioned, the investigation of molecular mechanisms to explain the sex differences in deeper is desired. On the other hand, this study brings novelty to the field of the cannabinoid research and DOHaD since it characterized the presence of endocannabinoids in breast milk of rats and its possible causative role on the development of food preference.
## Conclusion
Contrary to our hypothesis, maternal HF diet reduced AEA and 2-AG in rat breast milk. We speculate that decreased endocannabinoid levels in early development may alter neuronal maturation in the offspring. Maternal HF diet induced adaptive sex-specific changes in the NAc of the weanling rats, with males presenting increased cannabinoid receptors, possibly in response to the decreased milk endocannabinoid levels, and females presenting increased dopamine intracellular signaling. However, these molecular changes were only associated with increased preference for fat in the adolescent male offspring, but it is probably involved in the adult preference for fat in the females previously shown. Because the ECS signaling stimulates appetite for palatable foods and reward as well as adiposity and lipogenesis, the increase of this pathway at weaning may contribute to the earlier metabolic phenotype in males, which is known to present a more severe dysmetabolism in the long-term.
## 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 Animal Care and Use Committee of the Carlos Chagas Filho Biophysics Institute (process number $\frac{059}{19}$) of the Federal University of Rio de Janeiro.
## Author contributions
CD-R, JC, YO and LF: Conceptualization, Methodology, Formal analysis, Investigation, and Writing Original Draft. GA: Methodology – Fatty Acid Profile. JW: Methodology – Experimental model design and handling. GS and HP: Methodology – Endocannabinoid quantification. CP-M: Conceptualization, Resources, Writing - Review and Editing. MA: Conceptualization, Supervision, and Writing - Review and Editing. IT: Conceptualization, Resources, Supervision, Project administration, Funding acquisition, and Writing - Review and Editing. All authors contributed to the article and approved the submitted version.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Fibroblast growth factor 21 and prognosis of patients with cardiovascular
disease: A meta-analysis'
authors:
- Bing Yan
- Sicong Ma
- Chenghui Yan
- Yaling Han
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011636
doi: 10.3389/fendo.2023.1108234
license: CC BY 4.0
---
# Fibroblast growth factor 21 and prognosis of patients with cardiovascular disease: A meta-analysis
## Abstract
### Background
The role of fibroblast growth factor 21 (FGF21) in predicting the long-term prognosis of patients with cardiovascular disease (CVD) remains unknown.
### Methods
A comprehensive search in PubMed, Embase, and the Cochrane Library was performed to identify studies reporting the association between FGF21 and prognosis among patients with CVD. A meta-analysis was performed, with patients stratified by coronary artery disease (CAD) or heart failure (HF). The endpoint of CAD or HF was major adverse cardiovascular events defined by each study and a composite of death or HF readmission, respectively. The I2 method and linear regression test of funnel plot asymmetry were used to test heterogeneity (I2 > $50\%$ indicates substantial heterogeneity) and publication bias (asymmetry $P \leq 0.05$, indicating publication bias).
### Results
A total of 807 records were retrieved, and nine studies were finally included. Higher FGF21 levels were significantly associated with the risk of major adverse cardiovascular events in patients with CAD (multivariate hazard ratio [HR]: 1.77, $95\%$ confidence interval [CI]: 1.40–2.23, $P \leq 0.05$, I2 = $0\%$, fixed-effect model). Increased FGF21 levels were also associated with the risk of all-cause death among patients with CAD (multivariate HR: 2.67, $95\%$ CI: 1.25–5.72, $P \leq 0.05$, I2 = $64\%$, random-effect model). No association was found between FGF21 and the endpoint among patients with HF (HR: 1.57, $95\%$ CI: 0.99–2.48, $P \leq 0.05$, random-effect model), but a large heterogeneity (I2 = $95\%$) and potential publication bias (Asymmetry $P \leq 0.05$) existed in the analysis.
### Conclusion
Increased FGF21 levels were independently associated with poor prognosis of CAD, whereas the role of FGF21 in predicting clinical outcomes of HF requires further investigation.
## Introduction
Fibroblast growth factor 21 (FGF21), belonging to the FGF19 subclass of the FGF family, is a pleiotropic endocrine hormone [1] that acts in an autocrine/paracrine manner in multiple tissues [2, 3]. FGF21 is induced in white adipose tissue (WAT) by fasting and refeeding and can stimulate glucose entry and increase lipolysis and mitochondrial oxidative capacity [2]. FGF21 is a key regulator of energy homeostasis [4], which initiates fat mobilization and increases insulin sensitivity (5–7). Many studies have demonstrated that FGF21 protects against pancreatic damage and β-cell dysfunction and increases glucose transport via glucose transporter protein 1 [8]. A series of studies have shown that FGF21 has beneficial effects on body weight and glucose and lipid metabolism under physiological conditions [9].
Considering the close association between metabolic syndrome and cardiovascular disease [10], an increasing number of studies have investigated the beneficial effects of FGF21 on the cardiovascular system. Bench et al. reported that FGF21 prevents atherosclerosis [11] and protects against cardiac hypertrophy [12]. FGF21 protects against atherosclerosis via two independent mechanisms: regulation of adipocyte adiponectin production and suppression of hepatic expression of the transcription factor sterol regulatory element-binding protein-2 [10, 11]. Endogenous FGF21 protects against cardiac hypertrophy via the sirtuin 1 (SIRT1)–peroxisome proliferator-activated receptor α (PPAR-α) pathway [13]. Although a protective role of FGF21 in cardiac function and metabolism has been found, the link between FGF21 and cardiovascular disease is controversial. A series of clinical trials and meta-analyses reported that elevated serum FGF21 levels were associated with an increased incidence of cardiovascular diseases (CVD) [14] as well as cardiovascular mortality among patients with diabetes [15]. Collectively, these results from bench research and clinical trials created a paradox in determining the predictive value of FGF21 in CVD. Moreover, most studies have focused on the association between FGF21 levels and the primary prevention of CVD, whereas clinical evidence evaluating the role of FGF21 in the prognosis of patients with CVD is limited. Therefore, we conducted a meta-analysis to explore the association between FGF21 and long-term prognosis of patients with established CVD to provide new evidence unveiling the prognostic role of FGF21 in CVD.
## Study eligibility and outcomes
The present meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA) [16]. A comprehensive search was conducted in PubMed, Embase, and the Cochrane Library on June 9, 2022 to identify studies in English that reported the association between FGF21 and the clinical outcomes of patients with CVD. Studies meeting the following inclusion criteria were considered eligible: 1) study population comprising patients with established CVD; 2) studies reporting the relationship between FGF21 levels and CVD prognosis; 3) study endpoints with hard cardiovascular outcomes, such as all-cause or cardiac death, myocardial infarction (MI), and readmission for heart failure (HF); and 4) the follow-up period of studies was at least 6 months from discharge. Using these inclusion criteria and the PICOS (patient, intervention, comparison, outcomes, and study type) principle, we designed the following search terms: “cardiometabolic disease,” “cardiovascular disease,” “coronary artery disease,” “heart failure,” “cardiomyopathy,” “fibroblast growth factor 21,” and “FGF21.” We did not retrieve terms regarding study outcome or type to ensure complete and comprehensive search results (refer to the search strategy in PubMed in the Supplemental Materials).
First, the titles and abstracts of the records were reviewed. If relevant, the full texts and references of each record were manually searched and reviewed to evaluate eligibility. No limitations of study type (cohort or case-control study) were included. Conference abstracts were excluded owing to insufficient information. For patients with coronary artery disease (CAD), the primary endpoint was major adverse cardiovascular events (MACE), defined as the composite of ischemic events from each included study, and secondary endpoints included all-cause death and cardiovascular death. For patients with HF, the endpoint was the composite of all-cause death and readmission for HF.
## Data extraction and quality evaluation
The following items were extracted from each eligible study: first author, study type, year of publication, patient diagnosis and characteristics, sample size, cutoff value of FGF21 levels, endpoints, follow-up duration, and effect size (event and total numbers, univariate or multivariate hazard ratio [HR]). The authors of the included studies were contacted if key data were unavailable. Observational studies stratified by cohort and case-control studies were evaluated using two versions of the modified Newcastle–Ottawa Scale (NOS) [17, 18]. Studies were regarded as high-, medium-, or low-quality if the NOS score was ≥ 7, 5–6, or ≤ 4 points, respectively. All processes of study selection, data extraction, and quality evaluation were performed by two independent reviewers (B. Yan and S. Ma), and discrepancies were finally judged by a third reviewer (C. Yan).
## Statistical analyses
The event and total numbers were first calculated as unadjusted risk ratios (RR). Pooled RRs or HRs and $95\%$ confidence intervals (CIs) were synthesized to estimate the impact of FGF21 levels on the observed endpoints using a fixed- or random-effect model if significant heterogeneity existed. Heterogeneity was determined using the Q statistic and I2 method and considered significant for $P \leq 0.10$ for Q statistic or I2 > $50\%$. Publication bias was detected using funnel plots and a linear regression test for funnel plot asymmetry. Subgroup analysis for the primary endpoint of CAD was conducted among MI sub-populations, and sensitivity analyses were stratified by the effect size (RR derived from event and total numbers or HR) or by omitting each study. A two-sided $P \leq 0.05$ was considered statistically significant, except for the heterogeneity test ($P \leq 0.10$). All analyses were performed using RStudio (Version 1.2.1335) meta-packages.
## Results
A total of 807 records were identified through a comprehensive retrieval. After removing 27 duplicate studies, titles and abstracts of 781 records were screened. A total of 157 records remained for full-text review, and one record was identified from the reference lists. Finally, nine observational studies that fully met the pre-specified reporting clinical outcomes were included in this meta-analysis (see selection flow diagram in Figure 1). During the full-test review, we found that a series of studies reported both FGF21 and cardiovascular prognosis but were finally excluded from our meta-analysis because they were not in accordance with at least one of the inclusion criteria. Representative excluded studies and their respective reasons for exclusion are presented in Supplemental Materials, Table S1.
**Figure 1:** *Flowchart of screening eligible studies.*
The nine included studies contained five cohort (19–23) and four case-control studies (24–27). Seven studies reported the effect sizes of patients with CAD (19–25), and three studies specifically focused on patients with HF [23, 26, 27]. A total of 2674 patients from four studies were included in the analysis of the primary endpoint MACE for CAD [20, 22, 24, 25]. A total of 771 patients with HF from three studies were included in the analysis of a composite endpoint of death or readmission for HF [23, 26, 27]. Details of the studies included in this meta-analysis are presented in Table 1. All included studies were considered medium or high quality with scores ≥ 5 points in the NOS, except for one case-control study [26] that scored 4 points owing to the potential bias of population selection (Table 2).
## Association between FGF21 and MACE in CAD
As mentioned previously, four studies including 2674 patients explored the association between FGF21 levels and long-term MACE among patients with CAD [20, 22, 24, 25]. All four studies performed Cox regression analysis and reported multivariate HR as the effect size, and the median follow-up length was at least 24 months (Table 1). After effect size synthesis, higher FGF21 levels were independently and significantly associated with the long-term risk of MACE among patients with CAD (multivariate HR: 1.77, $95\%$ CI: 1.40–2.23, $P \leq 0.05$, I2 = $0\%$, fixed-effect model; Figure 2A). For the subgroup analysis of patients with MI, two studies were included in the analysis [22, 25], and the results showed that higher FGF21 levels were also independently associated with an increased risk of MACE in patients with MI (multivariate HR: 1.82, $95\%$ CI: 1.22–2.71, $P \leq 0.05$, I2 = $0\%$, fixed-effect model; Figure 2B). To test the stability of the results, sensitivity analysis was performed by omitting each study from the main analysis. The results demonstrated that higher FGF21 levels were consistently and significantly associated with the risk of MACE, irrespective of the removal of any single study ($P \leq 0.05$, Supplemental Materials, Figure S1). Funnel plots and asymmetry tests of the two analyses showed that no publication bias existed (Supplemental Materials, Figures S2A, B).
**Figure 2:** *Forest plots of the association of FGF21 with endpoints in patients with CAD or HF.
Figure 2
shows the synthesized effect sizes of FGF21 on predicting endpoints among either CAD or HF patients. The endpoint for CAD and HF was MACE and a composite of all-cause death or HF readmission, respectively. (A) FGF21 and MACE in CAD; (B) FGF21 and MACE in MI; (C) FGF21 and a composite of all-cause death or HF readmission in HF; (D) sensitivity analysis of endpoint in HF. FGF21, fibroblast growth factor 21; CAD, coronary artery disease; HF, heart failure; MACE, major adverse cardiovascular event; MI, myocardial infarction.*
## Association between FGF21 and death in CAD
Three studies including 2235 patients with CAD identified the relationship between FGF21 levels and all-cause death: two cohort studies reporting multivariate HR [19, 23] and one case-control study reporting the event and total numbers [25]. We first calculated the RR in this case-control study and then synthesized the RR using multivariate HRs. The results showed that higher FGF21 levels were not associated with the risk of all-cause death among patients with CAD (HR: 1.86, $95\%$ CI: 0.89–3.87, $P \leq 0.05$, I2 = $90\%$, random-effect model; Figure 3A). However, there was significant heterogeneity (I2 = $90\%$), which may have been because of the mixture of HRs and RR. In addition, funnel plots and asymmetry tests revealed that publication bias may exist (asymmetry $$P \leq 0.02$$; Supplemental Materials, Figure S3A). Therefore, we performed a sensitivity analysis by excluding the case-control study without multivariate HR [25], and an independent and significant association between higher FGF21 levels and the risk of all-cause death was found in patients with CAD (HR: 2.67, $95\%$ CI: 1.25–5.72, $P \leq 0.05$, I2 = $64\%$, random-effect model; Figure 3B).
**Figure 3:** *Forest plots of the association of FGF21 with all-cause death or CV death in patients with CAD.
Figure 3
shows the synthesized effect sizes of FGF21 on predicting all-cause death or CV death among CAD patients. (A) FGF21 and all-cause death in CAD; (B) sensitivity analysis of all-cause death in CAD; (C) FGF21 and CV death in CAD; (D) sensitivity analysis of CV death in CAD. FGF21, fibroblast growth factor 21; CV death, cardiovascular death.*
In terms of FGF21 and CV death, three studies that enrolled patients with CAD were included in the analysis. Two of the three studies reported the event and total numbers [21, 25], and only one cohort study reported the multivariate HR [19]. No association was found between FGF21 levels and the risk of CV death among patients with CAD after the effect size synthesis with substantial heterogeneity (RR: 1.04, $95\%$ CI: 0.93–1.17, $P \leq 0.05$, I2 = $80\%$, random-effect model; Figure 3C). To eliminate the heterogeneity from the mixture of RR and HRs, a sensitivity analysis including the two studies [21, 25] reporting event and total numbers was performed, which also found no significant association between FGF21 and the rate of CV death among patients with CAD (RR: 1.01, $95\%$ CI: 0.99–1.02, $P \leq 0.05$, I2 = $0\%$, fixed-effect model; Figure 3D). Funnel plots and asymmetry tests found no potential publication bias in the two meta-analyses of CV death (Supplemental Materials, Figures S3C, D).
## Association between FGF21 and prognosis of HF
Three studies recruiting 771 patients with HF reported the composite endpoint of death or readmission for HF and were included in the meta-analysis [23, 26, 27]. The effect sizes of these three studies were event and total numbers [23], univariate HR [27], and multivariate HR [26]. The results showed that there was no association between higher FGF21 levels and a composite of death or HF readmission among patients with HF, although there was a statistically significant trend (HR: 1.57, $95\%$ CI: 0.99–2.48, $P \leq 0.05$, I2 = $95\%$, random-effect model; Figure 2C). We also found a large heterogeneity (I2 = $95\%$) and potential publication bias in this effect size synthesis (asymmetry $P \leq 0.05$, funnel plots in Supplemental Materials, Figure S2C). A sensitivity analysis, omitting each study, did not significantly change the negative findings (Figure 2D).
## Discussion
In this meta-analysis, we explored the association between FGF21 levels and long-term clinical outcomes in patients with CVD stratified by CAD and HF. The results show that higher FGF21 levels were independently associated with the incidence of MACE among patients with CAD. Although the main analysis found no association between FGF21 levels and the rate of all-cause death in CAD, sub-analysis including high-quality studies reporting multivariate HRs showed a significant association between higher FGF21 levels and the risk of all-cause death. In patients with HF, FGF21 was not associated with the rate of a composite of all-cause death or HF readmission, although this outcome should be considered with caution due to the substantial study heterogeneity and variability of effect sizes, including RR, univariate, and multivariate HR. To our knowledge, this is the first meta-analysis to evaluate the association between FGF21 and prognosis of patients with CVD.
FGF21 is a well-known key endocrine hormone that regulates lipolysis in WAT and increases fatty acid oxidation in the liver (28–30). FGF21 increases insulin-independent glucose uptake, improves glucose tolerance, and reduces serum triglyceride levels [31]. It has also been recognized that FGF21 has a direct effect on the heart in an endocrine and autocrine manner, which is mediated by the FGFR and co-receptor β-Klotho [32]. FGFR1 and FGFR3 are the main FGF21 receptors in the heart [33, 34]. FGF21 binds to FGFR and the co-receptor β-Klotho in cardiomyocytes and activates downstream signaling pathways, including ERK, AMPK, and the SIRT1-PPAR-α pathway [35, 36]. Among these receptors, FGF21 exerts cardioprotective effects, mainly through FGFR1 [37]. A recent study also showed that sodium/glucose cotransporter-2 inhibitors (SGLT2i) can increase serum FGF21 levels, which is one of the mechanisms underlying the cardioprotective effects of SGLT2i [38]. Several preclinical trials have also demonstrated that mimics and long-acting derivatives of FGF21 have beneficial effects on body weight, lipoprotein profiles, and metabolic homeostasis [39, 40]. However, previous clinical trials have reported that elevated FGF21 levels are associated with increased cardiovascular risk and mortality (41–45). Obviously, a paradox between basic research and clinical studies exists regarding the definite role of FGF21 and CVD; therefore, further comprehensive studies are needed to resolve this issue.
One of the main findings of this meta-analysis was that high FGF21 levels were independently and significantly associated with an increased long-term risk of MACE in patients with CAD (multivariate HR: 1.77, $95\%$ CI: 1.40–2.23, $P \leq 0.05$, I2 = $0\%$, fixed-effect model). Even when focusing on the MI subgroup, the result was consistent (multivariate HR: 1.82, $95\%$ CI: 1.22–2.71, $P \leq 0.05$, I2 = $0\%$, fixed-effect model). No study heterogeneity or publication bias was found in the statistical analyses, indicating that these results were stable and reliable. In terms of all-cause death and FGF21 among patients with CAD, the meta-analysis did not find a significant association (HR: 1.86, $95\%$ CI: 0.89–3.87, $P \leq 0.05$, I2 = $90\%$, random-effect model). However, high study heterogeneity from a mixture of multivariate HRs and RR may discount credibility. Therefore, a sensitivity analysis including studies reporting multivariate HRs was conducted, and an independent and significant association was found between higher FGF21 levels and the risk of all-cause death in CAD. Similar meta-analyses were also performed to determine the relationship between FGF21 and CV death in patients with CAD, but no significant associations were found, irrespective of the main outcome, including three studies (RR: 1.04, $95\%$ CI: 0.93–1.17, $P \leq 0.05$, I2 = $80\%$, random-effect model) or the sensitivity analysis including two studies reporting RR (RR: 1.01, $95\%$ CI: 0.99–1.02, $P \leq 0.05$, I2 = $0\%$, fixed-effect model). Nevertheless, we should note that the study sample size involved in CV death was small; more importantly, two of the three studies only reported event and total numbers without adjusting for other multiple factors, which is inferior to the multivariate HR for authentically reflecting the effect size. Therefore, elevated FGF21 levels are independently associated with poor long-term prognosis in patients with CAD, although more high-level evidence is warranted.
For patients with HF, we pre-specified a composite of all-cause death and HF readmission as endpoints. After statistical analysis, there was no significant association between FGF21 and the long-term endpoint of patients with HF (HR: 1.57, $95\%$ CI: 0.99–2.48, $P \leq 0.05$, I2 = $95\%$, random-effect model). The sensitivity analysis showed that the result was positive only when a case-control study that reported a univariate HR was excluded. The study heterogeneity was also high owing to the effect size variability reported by the included studies (including RR, univariate HR, and multivariate HR). Hence, although a negative relationship was found between FGF21 and clinical outcomes in patients with HF, this result may be insufficient to determine the definite relationship between FGF21 and the prognosis of patients with HF and needs to be reconfirmed by more clinical trials. Overall, our meta-analysis and previous findings [14, 15, 45] collectively identify that increased FGF21 levels may be an independent predictor of poor prognosis among patients with CVD, rather than a protective factor of the heart, which was found in mechanistic studies. The FGF21 paradox not only exists in primary prevention but also in the long-term prognosis of CVD. This paradox may be because of a compensatory response to metabolic stress in patients with CVD. FGF21 resistance may be another underlying mechanism, according to recent findings reporting that stress conditions can decrease FGF21 co-receptor β-Klotho expression in the heart, impair FGF21 signaling, and weaken the protective effect of FGF21 on cardiomyocytes [46]. Both underlying mechanism studies and high-level clinical trials are needed to determine this uncertainty to provide potential drug targets.
The present meta-analysis had several limitations. First, although comprehensive retrieval was performed and nine studies were finally included, the study sample size was also relatively small. Second, the effect sizes reported by the included studies were varied and uneven, including event/total number, odds ratio, and univariate and multivariate HR. This diversity increases the study heterogeneity, which may influence data synthesis. Moreover, as shown in Table 1, the cutoff values of FGF21 used in the included studies varied without a uniform criterion. Therefore, a definite FGF21 cutoff value for predicting cardiovascular risk still needs to be explored. Finally, the study endpoints were not abundant because of limited data obtained from the included studies. These deficiencies require further clinical trials to fill the gap.
This meta-analysis demonstrated that increased FGF21 levels were independently associated with the long-term prognosis of patients with CAD. In patients with HF, no association was found between FGF21 levels and prognosis, and the role of FGF21 in predicting clinical outcomes remains unclear. The FGF21 paradox exists in the long-term prognosis of CVD.
## 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
BY designed this study. SM wrote the main manuscript and prepared figures. CY conducted the manuscript reviewing and editing. YH supervised all these procedures of this 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/fendo.2023.1108234/full#supplementary-material
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|
---
title: 'Assessment of the association between genetic factors regulating thyroid function
and microvascular complications in diabetes: A two-sample Mendelian randomization
study in the European population'
authors:
- Hongdian Li
- Mingxuan Li
- Shaoning Dong
- Sai Zhang
- Ao Dong
- Mianzhi Zhang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011638
doi: 10.3389/fendo.2023.1126339
license: CC BY 4.0
---
# Assessment of the association between genetic factors regulating thyroid function and microvascular complications in diabetes: A two-sample Mendelian randomization study in the European population
## Abstract
### Background
Observational studies have identified a possible link between thyroid function and diabetic microangiopathy, specifically in diabetic kidney disease (DKD) and diabetic retinopathy (DR). However, it is unclear whether this association reflects a causal relationship.
### Objective
To assess the potential direct effect of thyroid characteristics on DKD and DR based on Mendelian randomization (MR).
### Methods
We conducted an MR study using genetic variants as an instrument associated with thyroid function to examine the causal effects on DKD and DR. The study included the analysis of 4 exposure factors associated with thyroid hormone regulation and 5 outcomes. Genomewide significant variants were used as instruments for standardized freethyroxine (FT4) and thyroid-stimulating hormone (TSH) levels within the reference range, standardized free triiodothyronine (FT3):FT4 ratio, and standardized thyroid peroxidase antibody (TPOAB) levels. The primary outcomes were DKD and DR events, and secondary outcomes were estimated glomerular filtration rate (eGFR), urinary albumin-to-creatinine ratio (ACR) in diabetes, and proliferative diabetic retinopathy (PDR). Satisfying the 3 MR core assumptions, the inverse-variance weighted technique was used as the primary analysis, and sensitivity analysis was performed using MR-Egger, weighted median, and MR pleiotropy residual sum and outlier techniques.
### Results
All outcome and exposure instruments were selected from publicly available GWAS data conducted in European populations. In inverse-variance weighted random-effects MR, gene-based TSH with in the reference range was associated with DKD (OR 1.44; $95\%$CI 1.04, 2.41; $$P \leq 0.033$$) and eGFR (β: -0.031; $95\%$CI: -0.063, -0.001; $$P \leq 0.047$$). Gene-based increased FT3:FT4 ratio, decreased FT4 with in the reference range were associated with increased ACR with inverse-variance weighted random-effects β of 0.178 ($95\%$CI: 0.004, 0.353; $$P \leq 0.046$$) and -0.078 ($95\%$CI: -0.142, -0.014; $$P \leq 0.017$$), respectively, and robust to tests of horizontal pleiotropy. However, all thyroid hormone instruments were not associated with DR and PDR at the genetic level.
### Conclusion
In diabetic patients, an elevated TSH within the reference range was linked to a greater risk of DKD and decreased eGFR. Similarly, decreased FT4 and an increased FT3:FT4 ratio within the reference range were associated with increased ACR in diabetic patients. However, gene-based thyroid hormones were not associated with DR, indicating a possible pathway involving the thyroid-islet-renal axis. However, larger population studies are needed to further validate this conclusion.
## Introduction
Diabetic Kidney Disease (DKD) and Diabetic Retinopathy (DR) are among the most crucial microvascular lesions in diabetes, and frequently occur concomitantly [1]. The growing global prevalence of diabetes is also leading to an increase in the population affected by DKD and DR. DKD is a major contributor to End-stage Renal Disease (ESRD), with an estimated $91\%$ of all new cases of diabetes-related ESRD attributed to Type 2 Diabetes Mellitus (T2DM) according to the US Renal Data System [2]. In developed countries, nearly $40\%$ of DKD patients eventually require dialysis [3]. DR is a leading cause of blindness among adults, and it is projected that over 200 million people worldwide will develop DR by 2040 [4]. The complex chain reaction initiated by diabetes may be attributed to the buildup of Advanced Glycation End Products (AGEs) and increased erythrocyte adhesion to endothelial cells, which is a key pathogenic mechanism of the vascular complications associated with T2DM [5, 6]. There may also be potential associations with other factors, including thyroid function.
Thyroid-related diseases and diabetes mellitus are two of the most significant metabolic disorders that have a well-documented association (7–9). The presence of thyroid hormone receptors in the vascular endothelial tissue means that alterations in circulating thyroid hormone levels can contribute to the development and progression of vascular disease [10]. Hence, the influence of thyroid function on diabetic microvascular complications is attracting increased attention. The regulation of thyroid function is intricate and involves multiple components, including the pituitary and hypothalamus, feedback mechanisms, and the thyroid’s own characteristics and functions. The pituitary gland produces and releases thyroid-stimulating hormone (TSH), which stimulates the release of thyroxine from the thyroid gland. Thyroxine circulates in the body in a balanced state between its isolated protein-bound form and the bioavailable free form, referred to as free thyroxine (FT4). In both the thyroid and peripheral tissues, FT4 is converted to the active form of triiodothyronine (FT3), which can be assessed by the FT3:FT4 ratio in the circulation [11]. Thyroid peroxidase antibodies (TPOAB), a biomarker of autoimmune thyroid disease, is a sensitive indicator of thyroid function and diverse physiological responses [12]. Observational cross-sectional studies have revealed an independent association between the presence of subclinical hypothyroidism (SCH) and DKD [13]. The results showed that DKD was negatively correlated with levels of free triiodothyronine (FT3) and free thyroxine (FT4), and positively correlated with thyroid-stimulating hormone (TSH) levels. In addition, low to normal levels of thyroid hormones were associated with the presence of massive albuminuria, and TSH and FT3 were found to be potential predictors of DKD (14–16). The hypothalamic-pituitary-thyroid axis plays a critical role in retinal development and increases retinal vascular density [17, 18]. A recent study conducted in China explored the relationship between FT3 levels and DR in patients with T2DM who have normal thyroid function. Results showed a negative association between FT3 levels and DR in these patients [19]. This observation was further supported by the finding that treatment of T2DM patients with thyroid hormones was associated with improvement in retinopathy [20]. DKD and DR are interdependent risk factors for one another, with evidence suggesting a shared relationship with thyroid function (21–23). However, despite being influenced by common factors, current population-based clinical studies lack sufficient evidence to establish a direct causal link between thyroid function and the development of DKD and DR. Further research is needed to fully understand the underlying mechanisms and establish a clear relationship between these conditions.
Mendelian randomization (MR) is an analytical approach that leverages genetic variation to assess the causal relationship between independent and outcome variables in observational studies. This method is considered to be less susceptible to confounding or reverse causality compared to traditional observational analyses [24]. The premise of MR is that if 7thyroid hormone levels have a direct impact on the development of DKD and DR, then genetic variants affecting thyroid function should also be associated with DKD and DR, with the magnitude of this association being consistent with the observed relationships.
The aim of this study was to investigate the potential causal relationship between thyroid function and DKD and DR using two-sample MR analysis. To complement the findings of MR, we conducted a further analysis using estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (ACR) in patients with DM as secondary outcomes. The purpose of this complementary analysis was to provide additional evidence on the causal effect of thyroid function on DKD. Additionally, our study aimed to explore the causal effect of thyroid hormones on proliferative diabetic retinopathy (PDR), with the intention of identifying the need for increased protection of thyroid function in patients with severe DR lesions.
## Data sources
In a genome-wide association study (GWAS) of thyroid traits among individuals of European ancestry, instrument-exposure associations were identified for FT4, FT3:FT4 ratio, TSH, and TPOAB based on single nucleotide polymorphisms (SNPs). Only SNPs that reached genome-wide significance levels ($p \leq 5$ × 10-8) were considered in this European population. To avoid linkage disequilibrium reactions and the potential double counting of similar genes within a certain range, screening conditions were set at R2 < 0.001 and kb = 10,000. A GWAS of reference range FT4 levels was conducted using data from 72,167 European subjects, as published by Teumer et al. The study identified 31 genetic loci with significant associations with reference range FT4 levels, implicating a role for these loci in thyroid development, physiological function and transport of thyroid hormones, as well as metabolism of these hormones [25]. In a study conducted by Panicker et al., GWAS data for the FT3:FT4 ratio was obtained. The results revealed that a SNP, rs2235544, located in the DIO1 gene was significantly associated with the FT3:FT4 ratio at a genome-wide level. The researchers found that the presence of the C allele at this SNP locus was associated with an increase in the activity of deiodinase 1, leading to an elevated FT3:FT4 ratio. This elevated ratio was found to affect the physiological function of the thyroid gland [26]. Genetic susceptibility loci associated with TSH levels in the reference range were derived from two studies that identified a total of 61 susceptibility loci associated with TSH levels in the reference range, and these loci were strongly associated with the development of thyroid cancer and goitre [27, 28]. Medici et al. and Schultheiss et al. conducted two GWAS studies that identified a total of five susceptibility loci associated with TPOAB that predicted which TPOAB positivity predisposed to the development of clinical thyroid dysfunction [29, 30]. The studies were granted ethical clearance by the institutional review board. Table 1 displays the characteristics of the results from the Genome-Wide Association Study (GWAS).
**Table 1**
| Outcomes | Consortium | Sample size | Ethnicity | Web source |
| --- | --- | --- | --- | --- |
| DKD | Finngen | 3,676 cases and 283,456 controls | European | https://www.finngen.fi/en/access_results |
| DR | Finngen | 8,942 cases and 283,545 controls | European | https://www.finngen.fi/en/access_results |
| PDR | Finngen | 8,383 cases and 329,756 controls | European | https://www.finngen.fi/en/access_results |
| eGFR in diabetes | – | 55,114 individuals | European | PMID: 26831199 |
| ACR in diabetes | CKDgen | 5,825 cases and 46061 controls | European | http://ckdgen.imbi.uni-freiburg.de/ PMID: 26631737 |
The outcome measures were drawn from publicly available genetic association studies conducted in European populations. The primary outcomes were DKD and DR, and the raw data was obtained from the Finngen database (r8) [31], which included patients with all types of diabetes. *The* genetic association study cohort for DKD consisted of 3,676 cases and 283,456 controls, while the cohort for DR consisted of 8,942 cases and 283,545 controls. Secondary outcomes included eGFR and ACR in individuals with diabetes and PDR. *The* genetic association study data for eGFR in individuals with diabetes was obtained from the study by Pattaro et al. published in 2016, which consisted of 39 studies and a total of 55,114 individuals with diabetes. eGFR was calculated using the four-variable Modification of Diet in Renal Disease Study Equation [32]. *The* genetic association study data for ACR in 5,825 individuals with diabetes and 46061 controls was obtained from the study by Teumer et al. published in 2016, and ACR was calculated as the ratio of urinary albumin to urinary creatinine to account for variations in urine concentration [33]. *The* genetic association study cohort for PDR was also obtained from the Finngen database, consisting of 8,383 cases and 329,756 controls [31].
## Mendelian randomization analysis
In this study, the MR analysis tool was utilized to determine the causal relationship between thyroid function and various outcome indicators. SNPs were used as instrumental variables to estimate the causal effect. To ensure the validity of the results, three core assumptions were made: first, the genetic variations were associated with exposure factors; second, the genetic variations were independent of confounding factors; and third, the genetic variations only had an effect on the outcome through the exposure and not through any other pathways (Figure 1). To obtain the primary overall instrumental estimate, an Inverse-Variance Weighted Fixed-Effects MR method was employed, which considered all genetic variants as valid instruments without any pleiotropy. The individual instrumental estimates and their standard errors were then combined using the Inverse-Variance Weighted (IVW) method to produce the final MR results [34]. In order to mitigate the impact of horizontal pleiotropy, where genetic variation has a significant effect on results via pathways other than exposure, the current study utilized three statistical methods: IVW random-effects (IVW-RE), weighted median (WM), and MR-Egger methods [35, 36]. To ensure robust results, a range of sensitivity analyses were conducted, including heterogeneity tests, horizontal pleiotropy tests, funnel plot analysis, and a leave-1-variant-out analysis using the IVW-RE method, where one variable was excluded from the analysis in each iteration. The individual instrumental variables were analyzed using the Instrumental Variable Ratio (Wald) estimator.
**Figure 1:** *Assumptions of a Mendelian randomization analysis for thyroid function and risk of DKD and DR. Broken lines represent potential pleiotropic or direct causal effects between variables that would violate Mendelian randomization assumptions. FT4, free thyroxine; FT3, free triiodothyronine; TSH, thyroid-stimulating hormone; TPOAB, thyroid peroxidase antibodies; DKD, diabetic kidney disease; DR, diabetic retinopathy; PDR, proliferative diabetic retinopathy; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio.*
In this study, the strength of all instruments was evaluated using the F statistic (calculated as F = β2 exposure/SE2 exposure). This approach was taken to ensure that weak instrumental variables do not influence the results of the causal estimation. The results showed that there were no weak instrumental variables in the study, as the F-statistic ranges for FT4, TSH, TPOAB and FT3:FT4ratio tools were 29-394, 29-576, 10-19, and 21, respectively. To check for the presence of pleiotropy, several sensitivity analyses were performed. The Q statistics (Cochran’s Q for IVW and Rücker’s Q for MR-Egger) were calculated to assess heterogeneity in individual causal effects, with p-values less than 0.05 indicating the presence of heterogeneity [37]. The MR-Egger intercept term was used to evaluate horizontal pleiotropy, and a deviation from zero indicates directional pleiotropy. The slope of the MR-Egger regression was used to provide a valid MR estimate in the presence of horizontal multiplicity [38, 39]. A complementary weighted median method was also employed, which assumes that at least $50\%$ of the inverse-variance is valid and ranks the inverse of the weighted variance of MR estimates for each inverse-variance [34]. The MR pleiotropy residual sum and outlier (MR-PRESSO) outlier test was performed to correct for horizontal pleiotropy via outlier removal [40]. The effect values are expressed as β when the ending variable is a continuous variable and as odds ratio (OR) when it is a dichotomous variable.
## MR estimates of causal effects of thyroid function on DKD
Figure 2 demonstrates the MR estimation between thyroid function and DKD. After conditional screening and MR-PRESSO test, we identified 16 of 31 SNPs for FT4 within the reference range, 1 of 1 SNP for FT3:FT4 ratio (the DIO1, rs2235544), 39 of 61SNPs for TSH within the reference range, 4 of 5 SNPs for TPOAB concentration. Genetically predicted TSH was associated with DKD with an IVW-RE OR of 1.44 ($95\%$ CI 1.04-2.41; $$P \leq 0.033$$) (Figure 2, eTable 2, eFigure 2A, B in the Supplement) with similar and a more significant result in IVW-FE (OR=1.44, $95\%$ CI, 1.10-1.89; $$P \leq 0.009$$), and the results were similar with the MR-Egger and WM analyses. We did not find a significant risk association between FT4 (OR=0.83, $95\%$ CI 0.67-1.03; $$P \leq 0.093$$) and TPOAB (OR=1.17, $95\%$ CI 0.57-2.38; $$P \leq 0.672$$) in the reference range and DKD (Figure 2, eTable 1, 3, eFigure 1A, B, 3A, B in the Supplement). A genetically predicted 1 SD–increase in FT3:FT4 ratio by the C allele was not associated with increased DKD with an OR of 0.73 ($95\%$ CI 0.36-1.46; $$P \leq 0.371$$) (Figure 2, eTable 1 in the Supplement). There was some evidence of heterogeneity based on Q-statistic (Q-value IVW = 83.91, P-value = 0.000; Q-value MR-Egger = 57.39, P-value = 0.00) for the TPOAB analysis. Consequently, weights were penalized for the IVW method. The FT4 and TSH variants were distributed symmetrically about the combined effect size in the funnel plot (eFigure 1C, 2C in the Supplement), and MR-Egger did not show evidence of horizontal pleiotropy (FT4: P for MR-Egger = 0.446; TSH: P for MR-Egger = 0.544) (Figure 2). Because TPOAB has fewer relevant instrumental variables, the causal effects of its funnel plot are not symmetric (eFigure 3C in the Supplement). However, the MR-Egger intercept that we performed did not show evidence of horizontal pleiotropy (P for MR-Egger = 0.438) (Figure 2). These results show that no directional pleiotropic effects are present in our study. The leave-one-out test did not identify any thyroid function-related variants that had a strong effect on the overall results (eFigure 1D, 2D, 3 in the Supplement).
**Figure 2:** *Odds ratio for association of genetically predicted thyroid function with DKD. FT4, free thyroxine; FT4, free thyroxine; FT3, free triiodothyronine; TSH, thyroid-stimulating hormone; TPOAB, thyroid peroxidase antibodies; DKD, diabetic kidney disease; CI, confidence internal; OR, odds ratio; IVW-FE, inverse-variance weighted fixed-effects MR; IVW-RE, inverse-variance weighted random-effects MR; MR, mendelian randomization; WM, weighted median; IVR, instrumental variable ratio (Wald) estimator; SNP, single-nucleotide polymorphism. P value for heterogeneity based on Cochran’s Q statistic for IVW, and Rücker’s Q for MR-Egger.*
## MR estimates of causal effects of thyroid function on DR
Figure 3 demonstrates the MR estimation between thyroid function and DR. After conditional screening and MR-PRESSO test, we identified 16 of 31 SNPs for FT4within the reference range, 1of1SNP forFT3:FT4 ratio (the DIO1, rs2235544), 37 of 61SNPs for TSH within the reference range, 4 of 5 SNPs for TPOAB concentration. We did not find any statistically significant genetic risk association between thyroid function-related instruments and DR by IVW-RE (FT4: OR=0.95, $95\%$ CI 0.83-1.09; $$P \leq 0.483$$; TPOAB: OR=1.11, $95\%$ CI 0.68-1.82; $$P \leq 0.664$$; TSH: OR=1.00, $95\%$ CI 0.91-1.07; $$P \leq 0.828$$; Figure 3, eTable 4-6, eFigure 4-6, A, B in the Supplement), which is similar to the results of IVW-FE, MR-Egger and WM analyses. A genetically predicted 1 SD–decrease in FT3:FT4 ratio by the C allele was not associated with increased DR with an OR of 1.05 ($95\%$ CI, 0.74-1.48; $$P \leq 0.800$$) (Figure 2, eTable 4 in the Supplement). There was some evidence of heterogeneity in the analysis regarding FT4 (Q-value IVW = 27.23, P-value = 0.026; Q-value MR-Egger = 26.51, P-value = 0.022) and TPOAB (Q-value IVW = 64.73, P-value = 0.000; Q-value MR-Egger = 45.80, P-value = 0.000) according to Q-statistics. The FT4 and TSH variants were distributed symmetrically about the combined effect size in the funnel plot (eFigure 4C, e5C in the Supplement), and MR-Egger did not show evidence of horizontal pleiotropy (FT4: P for MR-Egger = 0.547; TSH: P for MR-Egger = 0.459) (Figure 3). Because TPOAB has fewer relevant instrumental variables, the causal effects of its funnel plot are not symmetric (eFigure 6C in the Supplement). However, the MR-Egger intercept that we performed did not show evidence of horizontal pleiotropy (P for MR-Egger = 0.903) (Figure 3). These results show that no directional pleiotropic effects are present in DR study. The leave-one-out test did not identify any thyroid function-related variants that had a strong effect on the overall results (eFigure 4D, 5D, 6D in the Supplement).
**Figure 3:** *Odds ratio for association of genetically predicted thyroid function with DR. FT4, free thyroxine; FT4, free thyroxine; FT3, free triiodothyronine; TSH, thyroid-stimulating hormone; TPOAB, thyroid peroxidase antibodies; DR, diabetic retinopathy; CI, confidence internal; OR, odds ratio; IVW-FE, inverse-variance weighted fixed-effects MR; IVW-RE, inverse-variance weighted random-effects MR; MR, mendelian randomization; WM, weighted median; IVR, instrumental variable ratio (Wald) estimator; SNP, single-nucleotide polymorphism. P value for heterogeneity based on Cochran’s Q statistic for IVW, and Rücker’s Q for MR-Egger.*
## MR estimates of causal effects of thyroid function on eGFR and ACR in diabetes
Figure 4 shows the MR estimation between thyroid function and renal impairment indicators eGFR and ACR in diabetic patients to further reflect the genetic association with DKD. After linkage disequilibrium screening and MR-PRESSO assay, we identified a total of 10 out of 31 SNPs for FT4 within the reference range, 1 SNP for FT3:FT4 ratio (DIO1, rs2235544), 19 out of 61 SNPs for TSH in the reference range, and 4 SNPs for TPOAB concentration. We found that IVW-RE genetically predicted TSH was negatively correlated with eGFR, i.e., for 1-sd increase in TSH in the reference range, eGFR decreased by 0.031 ($95\%$ CI -0.063, -0.001; $$P \leq 0.047$$), a result that was more significant in IVW-FE (Effect: -0.031, $95\%$ CI -0.057, - 0.005; $$P \leq 0.018$$) was more significant and similar to the results of WM analysis (Figure 4, eTable 8, eFigure 8A, B in the Supplement). No significant association between other thyroid function predictors and eGFR was found (eTable 7, 9, 7 A-B, 9 A-B in the Supplement). The IVW-RE OR for ACR per SD of FT4 within the reference range was -0.078 ($95\%$ CI -0.142, -0.014; $$P \leq 0.017$$) (Figure 4, eFigure 10 A-B in the Supplement). Results were similar for IVW-FE ($$P \leq 0.015$$), MR-Egger ($$P \leq 0.073$$) and WM ($$P \leq 0.043$$) analysis. Notably, we found that a 1 SD increase in the FT3:FT4 ratio of the C allele was associated with an increase in ACR with an effect of 0.178 ($95\%$ CI, 0.004-0.353; $$P \leq 0.046$$), a result consistent with the trend in FT4 results (eTable 10 in the Supplement). There was no evidence of heterogeneity based on Q-statistic for analyses of all thyroid function indicators (P for het >0.05). In our study, we found no evidence of pleiotropy, i.e., the P values for the pleiotropy of FT4, TSH and TPOAB were all greater than 0.05(eFigure 7-12C in the Supplement). The leave-one-out test did not identify any thyroid function-related variants that had a strong effect on the overall results (eFigure 7-12D in the Supplement).
**Figure 4:** *Odds ratio for association of genetically predicted thyroid function with eGFR and ACR in diabetes. FT4, free thyroxine; FT4, free thyroxine; FT3, free triiodothyronine; TSH, thyroid-stimulating hormone; TPOAB, thyroid peroxidase antibodies; eGFR, estimated glomerular filtration rate; ACR, urinary albumin-to-creatinine ratio; CI, confidence internal; OR, odds ratio; IVW-FE, inverse-variance weighted fixed-effects MR; IVW-RE, inverse-variance weighted random-effects MR; MR, mendelian randomization; WM, weighted median; IVR, instrumental variable ratio (Wald) estimator; SNP, single-nucleotide polymorphism. P value for heterogeneity based on Cochran’s Q statistic for IVW, and Rücker’s Q for MR-Egger.*
## MR estimates of causal effects of thyroid function on PDR
Figure 5 demonstrates the MR estimation between thyroid function and PDR. We did not find any statistically significant genetic risk association between thyroid function-related instruments and DR (eTable13-15, eFigure 13-15A, B in the Supplement). The MR-Egger intercept that we performed did not show evidence of horizontal pleiotropy (C of eFigure 13-15 in the Supplement), and the leave-one-out test did not identify any thyroid function-related variants that had a strong effect on the overall results (D of eFigure 13-15 in the Supplement). These results are similar to the MR analysis of DR.
**Figure 5:** *Odds ratio for association of genetically predicted thyroid function with PDR. FT4, free thyroxine; FT4, free thyroxine; FT3, free triiodothyronine; TSH, thyroid-stimulating hormone; TPOAB, thyroid peroxidase antibodies; PDR, proliferative diabetic retinopathy; CI, confidence internal; OR, odds ratio; IVW-FE, inverse-variance weighted fixed-effects MR; IVW-RE, inverse-variance weighted random-effects MR; MR, mendelian randomization; WM, weighted median; IVR, instrumental variable ratio (Wald) estimator; SNP, single-nucleotide polymorphism. P value for heterogeneity based on Cochran’s Q statistic for IVW, and Rücker’s Q for MR-Egger.*
## Discussion
The current study, to the best of our knowledge, is the first to assess the relationship between thyroid function and diabetic microvascular complications, including DKD and DR, using MR analysis. Using publicly available GWAS data from European populations, we have made a novel finding that there is a potential genetic influence on TSH levels in the normal range that is associated with DKD, a hypothesis that was supported by the evidence in the eGFR. Furthermore, we examined the risk association between genetically determined thyroid function and ACR, and found that both FT4 and FT3:FT4 ratio may be genetic factors that are implicated in ACR. However, the combined genetic effect of all thyroid function indices did not provide strong evidence for a direct association with DR and PDR.
The thyroid-islet-renal axis represents a range of phenotypes that are dependent on phenotypes upstream of the axis and also on negative feedback mechanisms downstream. Both insulin and thyroid hormones are affected by autoimmune pathology, are part of the metabolic syndrome, and affect cellular metabolism. The pathophysiological association between diabetes and thyroid dysfunction is thought to be the result of the interaction of various biochemical, genetic and hormonal dysfunctions [41]. As the most important microvascular complication of diabetes, the association between DKD and thyroid function is increasingly being demonstrated. We derived the presence of a genetic-based effect of thyroxine on DKD through multiple MR stratification analyses, based on unique and shared genetic tools, which is qualitatively different from extant epidemiological studies. Our study found that TSH levels in the reference range were positively associated with DKD risk and negatively associated with eGFR, i.e., elevated TSH may increase the risk of developing DKD as well as decrease eGFR. This result was confirmed in several observational studies. Renal function is directly correlated with thyroid function, as reflected by the positive correlation between TSH and serum creatinine and the negative correlation with eGFR [42, 43]. Even when thyroid function is within the normal range, patients with DKD have higher TSH levels than diabetic patients without DKD [44, 45]. A recent study reconfirmed that levothyroxine treatment reduced urinary albumin excretion in patients with early DKD with mildly elevated TSH levels and positive serum TPOAB [46] In addition, our study identified a risk association between FT3/FT4 and FT4 and ACR, and although this result was not statistically significant in DKD events and eGFR, the effect values showed a reliable and consistent trend. FT4 SNPs were derived from associations between individuals with FT4 levels in the reference range and no evidence of thyroid disease. In contrast, TSH instrumentation within the reference range is associated with hypothyroidism and hyperthyroidism. These differences may emphasize that normal variation in TSH and thyroid function drives the association of instrumentation with DKD. Another possible explanation is that although ACR and eGFR are the most commonly used clinical tools to assess chronic kidney disease, one study found no correlation between serum creatinine and ACR in patients with T2DM [47], so the association between thyroid function and them is informative but not determinative for the risk of DKD events. Clinical evidence found reduced FT4 and elevated TSH in the DKD population compared to non-DKD patients, and hypothyroidism was associated with increased ACR or reduced eGFR in patients with T2DM, and hypothyroid patients with T2DM exhibited higher ACR and urinary transferrin excretion [48], which is consistent with our results. The relationship between FT3:FT4 and DKD is unclear, but studies are currently being conducted in other areas related to diabetes. For example, a recent report from southern China found that low FT3/FT4 was associated with a poor prognosis of acute myocardial infarction in T2DM patients with normal thyroid function [49]. Another MR study showed that genetically based high FT3/FT4 was associated with an increased risk of atrial fibrillation [12]. In addition, a Belgian report showed that FT3:FT4 in late pregnancy was positively associated with gestational diabetes, adverse pregnancy outcomes and poor metabolic profile in the early postpartum period [7].
In our present study no correlation between thyroid hormones and DR was found, despite our stratification of DR. There is conflicting evidence regarding the association between DR and thyroid function (50–52), with some studies suggesting no significant association while others indicating a possible link. A decrease in thyroid hormone or SCH may increase the probability of DR, PDR, and diabetic macular edema (53–55). Lin et al. first retrospectively found that high TSH serum levels were associated with an increased prevalence of DR in diabetic patients, and then found in vitro that high glucose stimulated apoptosis and mitochondrial dysfunction in human peripapillary cells, which could be attributed to co-stimulation of glucose and high TSH [56]. These studies supporting the association of thyroid function with DR are based on Asian populations, whereas our study was conducted in a European population. Clinical observational studies are difficult to control for confounders and multiple biases making it difficult to derive a direct causal association between exposure and outcome, therefore the relationship between thyroid function and DR needs to be further demonstrated in larger well-designed trials.
The pathogenesis of abnormal thyroid function is associated with endothelial dysfunction, hyperlipidemia and atherosclerosis (57–61), which can increase the risk of diabetes and its complications [62, 63]. Diabetes often leads to hyperlipidemia, which increases the risk of atherosclerotic vascular disease, characterized by arterial lesions affected from the intima, usually preceded by accumulation of lipids and complex sugars, hemorrhage and thrombosis, followed by fibrous tissue proliferation and calcium deposition, as well as progressive chemosis and calcification of the arterial middle layer, leading to thickening and sclerosis of the arterial wall and narrowing of the lumen [64, 65]. DKD and DR are common result of hyperglycemia-induced accumulation of AGEs, which is inextricably linked to microangiopathy [66, 67]. Abnormal thyroid hormone secretion not only directly disrupts endothelial function, but also exacerbates the damage to endothelial cells by the hyperglycemic state, thus contributing to the development of DKD and DR. In addition, abnormal thyroid hormone secretion decreases endothelial nitric oxide availability, which further promotes microvascular damage in diabetic patients [68, 69]. Thus, thyroid hormones protect the endothelium of diabetic microvessels from degeneration, which may be reliable evidence to support our main results.
This study exhibits several strengths that warrant investigation. Firstly, the research employed MR methods to evaluate gene-based causality of FT4, TSH, TPOAB, and FT3:FT4 in DKD and DR. MR analysis provides a robust estimate of causality by minimizing reverse causal effects or confounding factors. Secondly, the study used eGFR and ACR as indicators for DKD evaluation, which strengthens the causal relationship between thyroid function and renal function in diabetic patients. Thirdly, the two-sample MR approach was utilized to assess the genetic association between thyroid function and diabetic microvascular complications from various perspectives. Fourthly, the study used multiple sensitivity analyses, such as the simple median, weighted median, and MR-Egger methods, to ensure consistent and robust causal estimation. Finally, the aggregated statistics of the GWAS were collected from European populations, providing a larger sample size than epidemiological studies, and suggesting a more reliable cause-and-effect relationship. Despite meeting the 3 core assumptions, there are still limitations to our MR study. Unobserved pleiotropy, beyond vertical pleiotropy, may exist. Additionally, the lack of an available FT3 instrument limits causal evidence for a genetic association with DKD and DR. Finally, race-based findings may limit generalizability to other populations. Further clinical studies with larger samples are needed to validate these issues.
In conclusion, our study provides direct evidence supporting that genetically based high TSH levels are associated with low eGFR and high DKD risk in diabetic patients, and that ACR in diabetic patients is negatively correlated with FT4 in the reference range and positively correlated with FT3:FT4. We found no genetic evidence of thyroid function associated with DR. These findings suggest that maintaining normal thyroid function and regulation of thyroid hormone secretion may be effective in preventing microvascular complications in diabetes, particularly DKD. Further larger population-based studies are necessary to investigate the causal relationship between thyroid function and diabetic microangiopathy.
## 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
HL and ML conceived and designed the study. SD and AD performed the initial data source acquisition. HL examined the data and performed the data analysis. SZ made methodological recommendations for the article. HL wrote the initial manuscript and MZ checked the manuscript and approved the final manuscript. All listed authors made substantial contributions to 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/fendo.2023.1126339/full#supplementary-material
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|
---
title: Metformin treatment and risk of diabetic peripheral neuropathy in patients
with type 2 diabetes mellitus in Beijing, China
authors:
- Ruotong Yang
- Huan Yu
- Junhui Wu
- Hongbo Chen
- Mengying Wang
- Siyue Wang
- Xueying Qin
- Tao Wu
- Yiqun Wu
- Yonghua Hu
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011647
doi: 10.3389/fendo.2023.1082720
license: CC BY 4.0
---
# Metformin treatment and risk of diabetic peripheral neuropathy in patients with type 2 diabetes mellitus in Beijing, China
## Abstract
### Background
Metformin treatment is associated with vitamin B12 deficiency, which is a risk factor for neuropathy. However, few studies have examined the relationship between metformin treatment and diabetic peripheral neuropathy (DPN), and the available findings are contradictory. We aimed to assess whether metformin treatment is associated with DPN in patients with type 2 diabetes mellitus (T2DM) in Beijing, China.
### Methods
All patients with newly diagnosed T2DM between January 2010 and September 2012 in the Medical Claim Data for Employees database were included. Metformin treatment was defined as any record of metformin prescription. The average daily dose of metformin during follow-up was calculated. DPN was defined as DPN admissions occurring after a diagnosis of T2DM in the database. Hazard ratios (HRs) and $95\%$ confidence intervals (CIs) were calculated using Cox proportional hazards models.
### Results
Among 49,705 T2DM patients, 1,933 DPN events were recorded during a median follow-up of 6.36 years. The crude incidence rates were 7.12 and 3.91 per 1000 person-years for patients treated with metformin ($$n = 37$$,052) versus those not treated ($$n = 12$$,653). Patients treated with metformin had an $84\%$ increased risk of DPN compared with patients not using metformin (HR, 1.84; $95\%$ CI, 1.62, 2.10). The daily dose was positively associated with DPN risk (HR, 1.48; $95\%$ CI, 1.46, 1.51; P for trend <0.001). The risk of DPN was 1.53-fold (1.30, 1.81) and 4.31-fold (3.76, 4.94) higher in patients with daily doses of 1.0-2.0 g and >2.0 g, respectively, than in patients who did not receive treatment. Patients aged less than 60 years had a higher risk of DPN ($P \leq 0.05$ for interaction test). Among patients taking vitamin B12 at baseline, there was no increased risk of DPN in the metformin group (1.92: 0.79, 4.69).
### Conclusions
In Chinese patients with T2DM, metformin treatment was associated with an increased risk of DPN admission and this risk responds positively to the daily dose of metformin. In particular, metformin use was a major risk factor for DPN in younger patients. Concomitant use of vitamin B12 may avoid the increased risk of DPN associated with metformin use.
## Introduction
Diabetic peripheral neuropathy (DPN) is the most prevalent chronic complication of diabetes, affecting 30–$50\%$ of patients with diabetes [1]. DPN is a leading cause of diabetic foot, lower-limb amputations and disabling neuralgia, with devastating effects on quality of life and a significant reduction in life expectancy. Although approximately half of patients with DPN may be asymptomatic, once DPN develops, it is extremely difficult to treat and incurs a range of additional medical costs [2].
Metformin is one of the most widely used oral glucose-lowering drugs for the treatment of type 2 diabetes and has been prescribed to more than 100 million people worldwide [3]. However, metformin treatment has been reported to be associated with vitamin B12 deficiency, potentially leading to irreversible nervous system damage and an increased risk of DPN [4, 5]. To date, direct evidence of the association between metformin treatment and DPN risk is scarce and conflicting [6]. Several cross-sectional studies with small sample sizes (<500 patients) found no significant association between metformin use and the prevalence of DPN or neuropathy scores (7–10). A retrospective cohort study involving 210,004 elderly veterans found that long-term metformin use was associated with an elevated risk of DPN [11], and another case−control study involving 150 patients found that metformin use for the previous 6 months was associated with an increased risk of moderate-to-severe DPN, particularly in the high-dose group [12].
Therefore, the aim of this study was to assess the association between metformin treatment and the risk of DPN in Chinese patients with type 2 diabetes mellitus (T2DM) to complement the evidence from Asian populations.
## Data source
This study was based on the Beijing Medical Claim Data for Employees (BMCDE) database, which has been previously described elsewhere [13, 14]. Briefly, the database contains anonymized medical claims data (demographic characteristics, clinical diagnosis, medications and reimbursement information) for all active or retired employees enrolled in basic medical insurance in Beijing. By the end of 2017, nearly $90\%$ of Beijing’s resident population had been included in the database. Clinical diagnosis information was presented in the International Classification of Diseases, 10th Revision (ICD-10) codes as well as descriptive texts. Drug information includes brand and generic drug names, formulations, costs, and dispensing dates. Our research was exempt from ethics committee review because of the use of encrypted retrospective information for administrative purposes.
## Study population
The study population was selected from patients ≥18 years of age who were newly diagnosed with T2DM between January 1, 2010, and September 30, 2012 ($$n = 60$$,327). Patients with T2DM were identified by ICD-10 coding (E11-E14) and text diagnosis. “ Newly diagnosed” was defined by applying a fixed 24-month look-back period in which the patient had continuous data coverage but no ICD-10 records (E11-E14) or textual diagnosis of diabetes. If a patient had multiple records of dispensation episodes, only the first one was used. The date of first diagnosis of T2DM was determined as the baseline index date. Subjects were excluded if they (Figure 1) [1] had a previous history of primary diagnosis of nervous system lesions (ICD-10: G00-G99 and text diagnosis), malignant tumors (ICD-10: C00-C75, C76-C80 and text diagnosis) or severe kidney disease (ICD-10: I12; I13; N00-N05; N07; N11; N14; N17-N19; Q61 and text diagnosis) before the date of diagnosis of T2DM [15] [2], developed DPN within 6 months of follow-up, or [3] lacked data on the dosage or brand of antidiabetic drugs.
**Figure 1:** *Subject Selection Criteria.*
## Metformin exposure
Medication use information included the trade name, dosage, and prescription date of all prescribed drugs for all patients. Metformin treatment in this study was defined as any metformin prescription recorded after diagnosis of T2DM from 2010 through 2017 ($$n = 37$$,052). Non-metformin treatment was considered as no metformin prescription recorded during follow-up ($$n = 12$$,653). The average daily dose of metformin was equal to the total dose divided by the total number of days of follow-up. According to the daily dose of metformin, patients were divided into four groups: non-metformin, metformin <1.0 g/d, 1.0-2.0 g/d and >2.0 g/d.
## Ascertainment of DPN
DPN was defined as receiving any of the ICD-10 diagnosis codes (G63.2) or text diagnosis during the study period. As previously described, we excluded those who developed DPN within 6 months to avoid baseline disease risk. Patients were followed from the date of diagnosis of T2DM (baseline) to the earliest outcome occurrence, death, withdrawal from the database, or study termination on December 31, 2017, whichever came first.
## Covariates
Baseline demographic characteristics included sex and age at baseline. Comorbidity history was determined by ICD-10 codes and text diagnosis from inpatient and outpatient case records from January 1, 2008, to baseline date. The Charlson comorbidity index was established from comorbidity history to reflect the baseline health status of the study population, as described in a previous study [15]. Medical resource utilization was measured by the number of hospital visits in the 12 months prior to baseline. In addition, baseline concomitant prescription medication records were included in this study, including antihypertensive, antihyperlipidemic, non-steroidal anti-inflammatory drugs (NSAIDs), neurotrophic drugs (vitamins B1 and B12) and other types of hypoglycemic drugs.
## Statistical analysis
Baseline characteristics, Charlson comorbidity, medical resource utilization, and concomitant medication use were expressed as the means (and standard deviations) of continuous variables and the numbers (and percentages) of categorical measures for patients in the metformin-treated and non-treated groups. Unadjusted Kaplan−Meier incidence curves and log-rank tests were used to assess the incidence of DPN in the treatment and non-treatment groups. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and $95\%$ confidence intervals (CIs) for the association between metformin treatment and DPN occurrence, adjusted for sex, age at baseline, comorbidity index, number of visits and concomitant prescription medications. The proportionality risk hypothesis was tested using Schoenfeld residuals (no deviations from proportionality were observed). The risk of DPN was calculated for different daily dose groups, and a dose−response relationship between daily dose and DPN risk was calculated. Because of the varying timing of the first use of metformin, we also examined the DPN risk among people who started metformin at different times after the diagnosis of T2DM compared with those who did not use metformin. Furthermore, we performed stratified analyses by sex, age, comorbidity index, number of visits, and concomitant medications to assess the association of metformin use with the risk of DPN in several subgroups and the interaction of stratification factors with metformin use.
Several additional sensitivity analyses were constructed for robustness of the results by [1] excluding patients without any type of prescription record for glucose-lowering drugs within 6 months to avoid including participants with mild diabetes or inaccurate diagnosis in the non-metformin group at baseline [2]; excluding patients who had used insulin within 6 months to avoid including patients with poor glycemic control at the beginning of follow-up [3]; excluding patients who had used two or more types of hypoglycemic agents to avoid inclusion of patients with poor glycemic control and to further calculate the relative risk of different hypoglycemic drug treatments (glycosidase inhibitors, metformin, thiazolidinediones, glinides, sulfonylureas or insulin); and [4] matching metformin-treated and non-metformin-treated populations 1:1 within a caliper of 0.01 times the standard error of the logit propensity score (for sex, age, date of diagnosis of T2DM, comorbidity index, number of visits, concomitant medication, and other hypoglycemic agents), and standardized mean difference (SMD) of acceptable matching was less than 0.1 [16]. Therefore, the demographic characteristics of the exposed and non-exposed groups were consistent as much as possible [5]. Patients who developed DPN within 12 months were excluded to observe the long-term effects of metformin.
All statistical analyses were performed by using Stata (version 14.0, StataCorp). A 2-sided P value of <0.05 was considered to be statistically significant.
## Baseline characteristics
A total of 37,052 patients with T2DM were treated with metformin, and 12,653 patients were not treated with metformin (Figure 1). The distribution of basic characteristics of the two groups was shown in Table 1. Specifically, patients in the metformin-treated group were younger, more female, had fewer comorbidities, had lower utilization of health care resources, used anti-hyperlipidemic drugs more frequently, and used vitamin B1, antihypertensive drugs, and NSAIDs less frequently ($P \leq 0.05$). The high-dose metformin-treated group (>2.0 g/d) had younger patients, a higher proportion of women, more comorbidities, higher health care resource utilization, and more combined drug use ($P \leq 0.05$).
**Table 1**
| Variable | Non-metformin(N=12,653) | Metformin(N=37,052) | P-value | Daily dose, g | Daily dose, g.1 | Daily dose, g.2 | P trend |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Variable | Non-metformin(N=12,653) | Metformin(N=37,052) | P-value | <1.0(N=19,651) | 1.0-2.0(N=8,188) | >2.0(N=9,213) | P trend |
| Age, y | 64.02 (13.57) | 58.17 (12.59) | <0.001 | 58.38 (13.14) | 57.20 (11.92) | 58.60 (11.02) | <0.001 |
| Female, % | 4,968 (39.26) | 16,090 (43.43) | <0.001 | 7,953 (40.47) | 3,603 (44.00) | 4,534 (49.21) | <0.001 |
| Comorbidity index | 0.95 (0.94) | 0.90 (0.85) | <0.001 | 0.88 (0.85) | 0.89 (0.85) | 0.93 (0.87) | <0.001 |
| Number of visits/y | 15.03 (25.73) | 13.96 (25.28) | 0.015 | 13.08 (24.75) | 13.64 (24.95) | 16.13 (26.51) | <0.001 |
| Medication use, % | | | | | | | |
| Vitamin B12 | 349 (2.76) | 905 (2.44) | 0.051 | 461 (2.35) | 176 (2.15) | 268 (2.91) | 0.001 |
| Vitamin B1 | 4,480 (35.41) | 12,436 (33.56) | <0.001 | 6,467 (32.91) | 2,576 (31.46) | 3,393 (36.83) | <0.001 |
| Antihypertensive | 9,733 (76.92) | 26,747 (72.19) | <0.001 | 13,609 (69.25) | 5,817 (71.04) | 7,321 (79.46) | <0.001 |
| Antihyperlipidemic | 7,757 (61.31) | 25,271 (68.20) | <0.001 | 12,742 (64.84) | 5,678 (69.35) | 6,851 (74.36) | <0.001 |
| NSAIDs | 5,924 (46.82) | 16,562 (44.70) | <0.001 | 8,315 (42.31) | 3,563 (43.51) | 4,684 (50.84) | <0.001 |
## Metformin therapy and the risk of diabetic peripheral neuropathy
Of the remaining 49,705 patients with T2DM, 1,933 incident cases of DPN were recorded during a median follow-up of 6.36 years. The crude incidence rates were 7.12 and 3.91 per 1000 person-years for patients treated with metformin versus those not treated. *In* general, patients receiving metformin treatment had a higher risk of DPN than those not receiving metformin treatment, with an adjusted HR of 1.84 ($95\%$ CI, 1.62, 2.10). The average daily dose was positively associated with DPN risk (HR, 1.48; $95\%$ CI, 1.46, 1.51; P for trend <0.001). There was no difference in DPN risk in the low-dose (<1.0 g/d) metformin group compared with non-metformin group (0.97: 0.84, 1.13). Patients with daily doses of 1.0-2.0 g and >2.0 g had a 1.53-fold (1.30, 1.81) and 4.31-fold (3.76, 4.94) higher risk of DPN compared with patients not receiving metformin, respectively (Table 2). In addition, Kaplan−Meier incidence curves showed that metformin-treated patients had a higher long-term (>2 years of follow-up) risk of DPN than non-metformin-treated patients, with the highest risk in the high-dose group (>2.0 g/d) ($P \leq 0.001$ for all log-rank tests: Figure 2). Throughout the follow-up period from the diagnosis of type 2 diabetes, patients treated had higher risk of DPN compared with those not treated, regardless of when metformin therapy was initiated (Figure 3).
In subgroup analyses, we found that patients who were younger than 60 years old (2.29: 1.85, 2.84) or had not seen a doctor a year ago (2.39: 1.92, 2.98) were at higher DPN risk ($P \leq 0.05$ for all interaction tests: Table 3). Metformin treatment did not increase the risk of DPN in patients with vitamin B12 prescription records at baseline (1.92: 0.79, 4.69). In sensitivity analyses, further excluding patients who did not use any hypoglycemic drugs within 6 months, or used insulin within 6 months did not substantially alter the risk estimates (Supplementary Tables 1, 2). A higher hazard ratio (2.00: 1.74, 2.29) was obtained by excluding people who developed DPN within 12 months of diabetes diagnosis (Supplementary Tables 3). Among patients using only one type of hypoglycemic drug, metformin treatment was associated with a 1.62-fold (1.10, 2.39) higher risk than non-metformin treatment. Compared to treatment with glycosidase inhibitors alone, none of the other types of hypoglycemic agents (thiazolidinediones, glinides, sulfonylureas or insulin) except metformin (2.27: 1.30, 3.94) increased the risk of DPN (Supplementary Table 4). After 1:1 matching with T2DM diagnosis date, sex, age, comorbidity index, utilization of medical resources, drug combination and other hypoglycemic agents (patient characteristics before and after matching are shown in Supplementary Table 5), the results were still consistent with the main results (Supplementary Table 6).
**Table 3**
| Subgroups | Unnamed: 1 | Case | Total | Non-metformin(Ref) | Metformin | P-int* |
| --- | --- | --- | --- | --- | --- | --- |
| Age, y | <60 | 1065 | 26367 | 1.0 | 2.29 (1.85, 2.84) | 0.005 |
| Age, y | ≥60 | 868 | 23338 | 1.0 | 1.57 (1.33, 1.84) | 0.005 |
| Sex | Male | 1107 | 28647 | 1.0 | 1.84 (1.56, 2.17) | 0.996 |
| Sex | Female | 826 | 21058 | 1.0 | 1.85 (1.51, 2.28) | 0.996 |
| CI-index | 0 | 665 | 17340 | 1.0 | 1.94 (1.56, 2.42) | 0.815 |
| CI-index | 1 | 944 | 23012 | 1.0 | 1.80 (1.49, 2.18) | 0.815 |
| CI-index | ≥2 | 324 | 9353 | 1.0 | 1.74 (1.30, 2.33) | 0.815 |
| Number of visits/y | 0 | 820 | 20827 | 1.0 | 2.39 (1.92, 2.98) | 0.013 |
| Number of visits/y | 1-2 | 392 | 9342 | 1.0 | 1.48 (1.13, 1.93) | 0.013 |
| Number of visits/y | >2 | 721 | 19536 | 1.0 | 1.62 (1.32, 1.97) | 0.013 |
| Vitamin B12 | Not use | 1892 | 48451 | 1.0 | 1.84 (1.62, 2.10) | 0.82 |
| Vitamin B12 | Use | 41 | 1254 | 1.0 | 1.92 (0.79, 4.69) | 0.82 |
| Vitamin B1 | Not use | 1202 | 32789 | 1.0 | 2.02 (1.70, 2.39) | 0.068 |
| Vitamin B1 | Use | 731 | 16916 | 1.0 | 1.62 (1.33, 1.97) | 0.068 |
| Antihypertensive | Not use | 552 | 13225 | 1.0 | 2.11 (1.62, 2.75) | 0.232 |
| Antihypertensive | Use | 1381 | 36480 | 1.0 | 1.75 (1.51, 2.02) | 0.232 |
| Antihyperlipidemic | Not use | 619 | 16677 | 1.0 | 2.17 (1.74, 2.71) | 0.09 |
| Antihyperlipidemic | Use | 1314 | 33028 | 1.0 | 1.68 (1.44, 1.97) | 0.09 |
| NSAIDs | Not use | 999 | 27219 | 1.0 | 2.11 (1.75, 2.55) | 0.058 |
| NSAIDs | Use | 934 | 22486 | 1.0 | 1.60 (1.34, 1.91) | 0.058 |
## Discussion
The current study found that metformin therapy increased the risk of hospital admission for peripheral neuropathy after diagnosis of T2DM. This increased risk was more likely to be observed after a long period of time and was not easily detected in a short period of time. A dose−response relationship was revealed between the daily dose of metformin and the increased risk of DPN, with high-dose metformin treatment (>2.0 g/d) associated with the highest risk of peripheral neuropathy.
Numerous studies have reported an association between metformin use and vitamin B12 deficiency and have raised the hypothesis of a potential peripheral neuropathy threat [5]. A latest meta-analysis that included 31 studies showed that metformin use led to significantly lowered vitamin B12 concentrations and significantly higher risk of vitamin B12 deficiency in diabetic patients [6]. Although plasma vitamin B12 levels have been widely found to be inversely associated with the risk of neuropathy [17, 18], several previous studies that explored the direct association between metformin and neuropathy did not find a significant association (6–10). However, these studies are few in number and poor in quality, such as small sample size, short follow-up, and lack of dose subgroups, so more high-quality studies are still needed to provide evidence. Our study is consistent with two previous cohort studies that have found an increased risk of DPN with long-term or high-dose metformin treatment. A retrospective cohort study using national Veterans *Affairs data* found that veterans over 50 years of age treated with metformin for at least 18 months were approximately 2-3 times more likely to develop DPN compared with those treated for at least 6 months but <18 months [11]. Another clinical prospective study also found that metformin use within the previous 6 months of T2DM was associated with more severe DPN, especially in the high-dose group [12].
In the early phase after metformin administration, the good hypoglycemic effect of metformin may inhibit the progression of diabetic complications to a certain extent. Overall, however, our findings confirm the speculation of other previous studies that long-term metformin use can result in serum vitamin B12 deficiency, leading to exacerbating evidence of peripheral nerve damage. Furthermore, we found a positive association between metformin dose and DPN risk, possibly due to the fact that higher doses of metformin lead to more severe vitamin B12 deficiency. Previous studies have found that a 1 mg increase in daily metformin dose was associated with 0.042 ($95\%$ CI -0.060, -0.023) decrease in vitamin B12 concentrations [19]. The mechanism by which metformin elevates the risk of DPN remains unclear to date [4, 20]. One possible explanation is that metformin blocks the absorption of vitamin B12 from the gastrointestinal tract by interfering with the calcium-dependent binding of the intrinsic factor vitamin B12 complex to the cuboid receptor at the end of the ileum [21]. Vitamin B12 is involved in the conversion of methylmalonyl-CoA and homocysteine, and higher concentrations of methylmalonyl-CoA and homocysteine have deleterious effects on the macrovascular system, including hematologic and neurological manifestations [11, 22].
In addition, we found that the adverse effect of metformin on DPN risk was more severe in T2DM patients with good glycemic control at baseline. That is, metformin treatment was associated with a higher risk of DPN in patients younger than 60 years of age, with fewer comorbidities, with lower utilization of medical resources, and not taking antihypertensive or lipid-lowering medications at baseline. In patients with poor baseline conditions (older age, more severe comorbidities, and more concomitant medications), the increased risk of DPN may be mainly due to lower glycosylated hemoglobin (hBA1c) levels as a result of more difficult glycemic control [23], and metformin treatment may contribute to some extent to glycemic control. These findings underscore the importance of adjusting medication prescriptions early in the course of diabetes to prevent peripheral neuropathy in patients with mild symptoms and low risk. We also found that metformin treatment did not increase the risk of DPN in patients with vitamin B12 prescription records at baseline, further suggesting the possible positive effect of early vitamin B12 supplementation in preventing DPN progression. Previous interventional studies have also demonstrated that B-vitamins may improve the symptoms of DPN [18]. However, this finding needs to be interpreted with caution due to the small sample size of patients documented to be treated with vitamin B12 in this study.
This is the first study to examine the direct association between metformin treatment and the risk of peripheral neuropathy in Asian patients with T2DM. Our findings highlight the great benefits of early surveillance and adjustment of hypoglycemic agents for the prevention of peripheral neuropathy in patients with T2DM, especially in young patients with few comorbidities. In this study, all employees with T2DM in Beijing were followed for a long period of time to explore medication use and subsequent complication through a prospective cohort study. The subjects included in this study were all newly diagnosed T2DM patients, and the impact of the duration of T2DM on outcomes was controlled for as much as possible. Furthermore, we accounted for metformin use and average daily dose throughout the follow-up period, further reducing the possibility of misclassification due to changes in metformin prescription during follow-up. We also found that long-term and high-dose use of metformin caused more severe damage to peripheral nerves and provided recommendations for clinical treatment. In addition, the association between metformin treatment and DPN was assessed by controlling for multiple confounders and multiple sensitivity analyses. Finally, subgroup analyses were conducted to provide evidence to support precise prevention and treatment of patients with different characteristics.
This study has some limitations. First, the DPN cases identified in this study were all hospitalized cases, while most of the DPN cases were asymptomatic and undetected in the early stage. Therefore, our study cannot speculate on the effect of metformin treatment on all DPN cases. We can explain those with symptoms requiring hospitalization, who are more likely to have adverse outcomes also more in need of protection. Second, although the BMCDE database includes information on all the drugs prescribed by patients, inconsistencies between the drugs prescribed and the drugs actually taken by patients cannot be avoided. Patients with metformin prescriptions may not take metformin at all or in lower amounts, but those without a prescription almost certainly did not take metformin, skewing the results toward the null hypothesis and proving that our results are relatively conservative. Third, the timing of metformin initiation varied among patients, but our results showed that $69.6\%$ patients started metformin within 6 months after diagnosis of T2DM. We excluded patients who developed DPN within 6 months and found that the risk was at least $60\%$ higher in the treated group than in the nontreated group, regardless of when metformin therapy was initiated. Fourth, the metformin treatment group was likely to have more serious conditions at baseline than the non-metformin group. Therefore, to align disease severity at baseline between the exposed and non-exposed groups, patients with good adherence to insulin (considered to have poor glycemic control) or who had not taken any hypoglycemic agents within 6 months (considered to have mild symptoms) were further excluded from the sensitivity analyses. In addition, propensity scores were used to match baseline characteristics and comorbidities between the treated and nontreated groups. Fifth, concomitant use of other types of hypoglycemic drugs may have affected the interpretation of the results attributed to metformin. Thus, we also controlled for the use of other hypoglycemic drugs by enrolling patients using only one type of hypoglycemic drug and by matching them with the propensity score. Sixth, serum vitamin B12 levels, hBA1c and other biochemical markers that affect DPN were not available within the medical claims database, so we cannot accurately explain the association between metformin and DPN. Seventh, although we adjusted for confounding factors in as much detail as possible, risk factors [23] such as financial status and lifestyle, which were again unavailable within the database, still cannot be included in the model. Last, since the study was based on a homogenous Asian population, these results may be limited in extrapolation to other racial groups.
In conclusion, our results demonstrate that metformin treatment was associated with an increased long-term risk of hospitalization for peripheral neuropathy in Chinese patients with T2DM, and the risk was positively dose-responsive to the daily dose of metformin. In particular, metformin use was a major risk factor for DPN in younger patients. Concomitant vitamin B12 use may avoid the increased DPN risk associated with metformin use.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: Restrictions apply to the availability of these data. Data were obtained from the administrative department of China’s health and medical system and are available with the permission of the administrative department. Requests to access these datasets should be directed to http://ybj.beijing.gov.cn/#.
## Ethics statement
The data were collected for an administrative purpose without any personal identifiers; therefore, 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
RY, YH, and YW conceived and designed the paper. YH and YW designed and supervised the conduct of the whole study, obtained funding, and acquired the data. RY analyzed the data and drafted the manuscript. HY, JW, HC, MW, SW, XQ and TW contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content. All authors 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.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2023.1082720/full#supplementary-material
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|
---
title: Construction of microneedle of Atractylodes macrocephala Rhizoma aqueous extract
and effect on mammary gland hyperplasia based on intestinal flora
authors:
- Yang Ping
- Qi Gao
- Changxu Li
- Yan Wang
- Yuliang Wang
- Shuo Li
- Mingjing Qiu
- Linqian Zhang
- Ailing Tu
- Yu Tian
- Hong Zhao
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011648
doi: 10.3389/fendo.2023.1158318
license: CC BY 4.0
---
# Construction of microneedle of Atractylodes macrocephala Rhizoma aqueous extract and effect on mammary gland hyperplasia based on intestinal flora
## Abstract
### Background
A microneedle patch loaded with *Atractylodes macrocephala* Rhizoma water extract was prepared for the treatment of mammary gland hyperplasia. To explore the relationship between Mammary gland hyperplasia and intestinal flora.
### Materials and methods
Preparation of the microneedle patch by micromolding method, the prescription of the microneedle was optimized by the Box-Behnken Design response surface test, and the micro-morphology, penetration, toughness, and brittleness were investigated. In vitro release of drug-loaded microneedles was measured by diffusion cell method. The rat model of mammary gland hyperplasia was prepared by the combination of estradiol benzoate-progesterone, and the microneedle patch of *Atractylodes macrocephala* *Rhizoma aqueous* extract was used for intervention treatment. The change of levels in E2, P, and PRL in rat serum was determined. The intestinal contents of rats were collected and the changes in intestinal flora in MGH rats were analyzed by 16s rRNA high-throughput sequencing.
### Results
The optimized microneedle formula is a PVA concentration of $6.0\%$, HA concentration of $15.5\%$, and PVPK30 concentration of $16.0\%$. The prepared microneedle tip loaded with *Atractylodes macrocephala* *Rhizoma aqueous* extract has complete, sharp, and no bubbles and the needle rate of the microneedle array is in the range of $95\%$~$100\%$. The bending rate of the microneedle is about $12.7\%$, and it has good flexibility, and the microneedle can puncture 4 layers of ParafilmⓇ membrane smoothly, and the puncture rate is more than $96\%$. The in vitro release of the microneedle was characterized by rapid release. The results of animal experiments showed that *Atractylodes macrocephala* *Rhizoma aqueous* extract microneedle patch could significantly reduce the E2 level, significantly reduce the PRL level, and significantly increase the P level. At the same time, it can regulate the abundance and diversity of intestinal flora in MGH rats, improve the intestinal flora disorder caused by mammary gland hyperplasia, and balance the community structure.
### Conclusion
The prepared microneedle containing *Atractylodes macrocephala* *Rhizoma aqueous* extract has good toughness and brittle strength, can penetrate the skin and enter the dermis, and effectively deliver drugs to play a role in the treatment of mammary gland hyperplasia.
## Introduction
Mammary gland hyperplasia (MGH) is a non-inflammatory and non-tumor chronic proliferative disease, mainly characterized by breast lumps and breast pain. At present, hormone inhibition and local resection are commonly used in western medicine. Hormone inhibition can only relieve symptoms and has a large side effect. Local resection is not easy for patients to accept. In recent years, traditional Chinese medicine (TCM) has had the advantages of good compliance and fewer adverse reactions in treating MGH, which can effectively improve the symptoms of patients. The patients were satisfied with the external treatment of TCM. External treatment of percutaneous drug delivery can avoid the “first-pass effect” of the liver and damage to the gastrointestinal tract so that the drug can enter the blood circulation through the skin and play a systemic role. At the same time, it can effectively improve bioavailability, maintain stable and lasting blood drug concentrations, and have other advantages.
Microneedle is a kind of transdermal drug delivery. The extremely delicate micro needle cluster made by micro-manufacturing technology can penetrate the cuticle of the skin, forming temporary aqueous micropores on the skin surface, so that the drug can diffuse into the skin through the micropores, thus exerting the efficacy to achieve microcirculation. Soluble microneedles, also known as dissolving microneedles (DMN) [1, 2], are polymer microneedles that encapsulate drugs in biodegradable polymer materials. When DMN is inserted into the skin, the polymer degrades spontaneously to release drugs. The drug release rate is mainly related to the properties of drugs and dosage forms. Therefore, DMN can achieve local or systemic therapeutic effects in the short or long term. At the same time, the tiny pores left by DMN on the skin surface can heal themselves in a short time, reducing the risk of infection caused by the fracture of the solid tip in the skin, and realizing a safe and painless drug delivery method in a real sense.
Modern medical investigation shows that MGH is caused by endocrine disorders. The mammary gland is a target organ of sexual hormones, it regulates hyperplasia of breast tissue and the cycle through by regulating serum hormones, and hypothalamic-pituitary-gonads [3]. As early as 1883, it was proposed that the MGH may be associated with sexual hormones. It was first proposed in 1947 that the disorder of estrogen and progestogen was the cause of the MGH [4]. However, the endocrine imbalance can affect the intestinal flora composition and directly or indirectly change bacterial physiology and independent gene expression [5]. Estrogen imbalance, as an important sign of endocrine disorder, has also been proven to significantly affect the changes in intestinal microbiota [6]. A large number of studies have proved that TCM can regulate the imbalance of intestinal microbiota significantly, Cuiru Li et al. showed that QWBZP crude polysaccharide helped to restore the diversity, relative abundance, and community structure of intestinal mucosal bacteria to a certain extent [7]. Xiaoya Li et al. explored the role of intestinal contents microbiota in the regulation of adverse effects caused by high-fat diet by DO from the perspective of intestinal microecology. And demonstrated that the mechanism of DO against a high-fat diet diseases might be attributed to the inhibition of Ruminococcus and Oscillospira, leading to a promotion in the state of host health [8, 9]. Moreover, intestinal flora coincides with the “spleen and stomach” theory of TCM. TCM can promote the growth of beneficial bacteria, inhibit the excessive production of harmful bacteria, balance the number of beneficial bacteria and pathogenic bacteria, and maintain a healthy intestinal environment [10]. The composition and quantity of intestinal flora are in balance under normal conditions. Once the balance is broken by factors such as intestinal pH value, mental pressure, eating habits, and antibiotic use, it will cause metabolic obstacles, metabolic disorders, and even other related diseases to some extent (11–16). The dried rhizome of the plant *Atractylodes macrocephala* *Koidz is* useful for drying and moistening water, nourishing the stomach, and strengthening the spleen. Atractylodes macrocephala Rhizoma has been reported to include amino acids, polysaccharides, and a range of volatile components. It also has been proven to have pharmacological effects, such as controlling intestinal flora and enhancing digestive function [17]. In order to provide theoretical support for the treatment of MGH from the perspective of intestinal microecology and provide an experimental basis for the full exploitation of the medicinal value of *Atractylodes macrocephala* *Rhizoma aqueous* extract (AM-ae), this study will prepare a microneedle patch for the treatment of MGH rats using AM-ae. It will also analyze the effect of AM-ae on the intestinal microbiota of MGH rats by 16S RNA high-throughput sequencing. Figure 1 depicts the concept for the research.
**Figure 1:** *Experimental flow graph.*
## Drugs and materials
Polyvinyl alcohol 224 (purchased from Shanghai McLean Biochemical Technology Co., Ltd., CAS No. 9002-89-5; alcoholysis degree: 87.0-89.0 mol%; viscosity: 40.0-48.0 mPa. s); Hyaluronic acid (purchased from Shanghai McLean Biochemical Technology Co., Ltd., batch number: C12698745; molecular weight: 200000-400000); Polyvinylpyrrolidone K30 (purchased from Shanghai Aladdin Biochemical Technology Co., Ltd., batch number: H2112016; molecular weight: 40000, high purity); Water extract of *Atractylodes macrocephala* Rhizoma (self-made in the laboratory); Absolute ethanol (purchased from Tianjin Kaitong Chemical Reagent Co., Ltd.); Parafilm M ® (Bemis Company, Inc).
Eighteen SPF-grade female non-pregnant SD rats (200 ± 20 g) were purchased from Changchun Lewis Laboratory Animal Technology Co., Ltd (Licence No.: SCXK(Ji)-2018-0007) and housed in SPF-grade animal rooms. The temperature and humidity in the rearing room were controlled, temperature: 24 ± 2°C, humidity: 60 ± $5\%$. The light and dark cycle was $\frac{12}{12}$ h. All experimental procedures involving animals were approved by the Animal Ethics Committee of the Animal Experimentation Centre of Jiamusi University.
Purchases were made from Shanghai Quanyu Biotechnology (Zhumadian) Animal Pharmaceutical Co., Ltd. for estradiol benzoate and progesterone injection. Estradiol (E2), progesterone (P), and prolactin (PRL) kit was purchased from Jiangsu enzyme immunity Industrial Co., Ltd.
## Instruments and equipment
FA2004N Electronic Analytical Balance (Shanghai Hengping Scientific Instrument Co., Ltd.); PDMS mold (Micropoint Technologies PTE LTD, Singapore); Xiangyi TDZ5-WS desktop low-speed centrifuge (Hunan Xiangyi Laboratory Instrument Development Co., Ltd.); Air blast dryer (Shanghai Hengping Scientific Instrument Co., Ltd.); Electron Microscope (Shanghai Hengping Scientific Instrument Co., Ltd.).
## Preparation method of microneedle
Weigh the appropriate amount of polyvinyl alcohol 224 (PVA), hyaluronic acid (HA), polyvinylpyrrolidone K30 (PVPK30), and add an appropriate amount of double steaming water to dissolve them, and put them in the refrigerator to swell for 30~40 min. The three kinds of drug solutions are proportional to 1:1:1 ~ 1:1: 5 Mix well (solution 1), take the extract of *Atractylodes macrocephala* Rhizoma water with the drug loading capacity of $0.3\%$~$2.5\%$, dissolve in a certain amount of anhydrous ethanol (solution 2), mix the solutions 1 and 2 evenly, drop them into the PDMS mold, and centrifuge at 2700~3300 r/min for 8~12min. After removal, it was dried for 50~70min in a drying box at 30°, then the backing layer solution was drip-added to the PDMS mold, centrifuged in a centrifuge at 3000r/min for 8~12min, and dried in a drying box at 55~65° for 50~70min. The drug-loaded microneedle array is obtained after demoulding.
## Response surface prescription optimization
Based on single factor test, three factors, namely PVA concentration (A), HA concentration (B), and PVPK30 concentration (C), were selected as independent variables with the microneedle penetration rate of 800μm was taken as the response value, and the response surface test is designed according to the principle of Box Behnken Design (BBD). The optimization analysis was carried out according to the test results, and the formulation composition response surface method test design is shown in Table 1.
**Table 1**
| Levels | Factors | Factors.1 | Factors.2 |
| --- | --- | --- | --- |
| Levels | A:PVA c% | B:HA c% | C:PVPK30 c% |
| -1 | 3 | 10 | 10 |
| 0 | 5 | 15 | 15 |
| 1 | 7 | 20 | 20 |
## Characterization of AM-DMN
The morphology of microneedle arrays was observed by electron microscope. The toughness of the microneedles was evaluated by the compression performance test: the microneedle tip was placed down on a smooth plane, a plane was added on the back of the microneedle to make it stressed evenly, and then place a certain weight of weight, and gradually increase the weight of the weight. Remove the weight after every 1-2 minutes of action, and the changes in the microneedle tip and height were observed.
The brittleness of the microneedles is characterized by the mechanical strength of the microneedles. The mechanical strength of the prepared microneedles can be evaluated by the ParafilmⓇ membrane insertion experiment. Stack 4 pieces of Parafilm Ⓡ film together, with a thickness of about 0.5~1 mm was placed on the foam. The microneedles were inserted into the ParafilmⓇ membrane and pressed for about 30~60 s. After the microneedles were removed, the number of layers of the membrane broken by the microneedles and the number of holes in each membrane was recorded.
The microneedle patch was placed on the dialysis membrane by diffusion cell method, and the receiving cell contained 10 mL PBS. The samples were taken at 37° for 1, 2, 5, 30, 60, and 300 min. The content of the aqueous extract of *Atractylodes macrocephala* Rhizoma in a microneedle patch was determined by the UPLC method, and the drug release performance of the microneedle patch was evaluated.
The morphology of the whole micro-needle array is a pyramid; The needle tip is complete and sharp, without bending, broken needle and bubble; The needle output rate of the microneedle array is in the range of $95\%$~$100\%$; The micro-needle tip and the backing layer have good compatibility, the tip and the backing layer are not separated, and the backing layer is flat without bubbles. As shown in Figure 3A.
**Figure 3:** *Microneedle characterization results (A). Microneedle, (B) Appearance of microneedle under microscope (Left: 800 μ m. Right: 696 ± 2.5 μ m), (C) Micropuncture into 4 layers ParafilmⓇ membrane (Left: front, right: reverse), (D) Microneedle release in vitro (n=3)).*
With the weight of 100g, 200g, 300g, 400g, and 500g increasing in turn, the tip of the microneedle will slightly bend. When the weight of the weight is 500g, the height of the microneedle will change from 800 μ M reduced to 696 ± 2.5 μ m. The bending rate of the microneedle is about $12.7\%$ (Figure 3B), which shows that the prepared microneedle has good flexibility.
As shown in Table 4 and Figure 3C, the prepared height is 800μm, the microneedle can puncture 4 layers of ParafilmⓇmembrane smoothly, and the puncture rate is more than $96\%$. The thickness of the cuticle of skin and active epidermis is about 100 μm. The thickness of the dermis is about 3-5 mm. Therefore, the prepared microneedle has good mechanical strength (brittleness) and can successfully penetrate the dermis to form a microchannel on the skin surface.
**Table 4**
| Number of layers of punctured film | Number of holes in each layer | Puncture rate (%) |
| --- | --- | --- |
| 1 | 100 | 100 |
| 2 | 100 | 100 |
| 3 | 100 | 100 |
| 4 | 96 | 96 |
The in vitro release results of drug-loaded microneedles are shown in Figure 3D. Because the microneedle carrier material is a soluble polymer material, the cumulative release percentage reaches $64.96\%$ within 30 minutes of the release, which is characterized by rapid release.
## Experimental approach
A total of 18 female SD rats were chosen, acclimated, and fed for a week. Six rats from each group were randomly assigned to one of three groups: an AM-DMN group, a model control group (Mod), and a blank control group (Con). The rats in each group were injected with estradiol benzoate 0.5 mg-kg-1-D-1 intramuscularly for 25 days, followed by progesterone 5 mg-kg-1-D-1 intramuscularly for 5 days, except for the Con group, which received an identical dose of saline. The AM-DMN group received treatment with a medication microneedle patch after successful modeling, whereas the Con and Mod groups received treatment with a blank microneedle patch. These treatments were alternated every two days for a total of 21 days [18].
## Collection and analysis of serum samples
Rats in each group fasted for 12 hours after the last dose. The rats were weighed and anesthetized intraperitoneally and injected with $2\%$ sodium pentobarbital. The execution was performed using the cervical dislocation method. To isolate the serum, blood was taken from the abdominal aorta and stored in a procoagulant tube for 20 min. ELISA was used to determine serum E2, P, and PRL levels [18].
## Collection of samples of intestinal cecum contents
After the death of the rats, the contents of the cecum were extracted using sterile forceps, placed in 5mL sterile EP tubes, numbered and weighed, then immediately immersed in liquid nitrogen under sterile conditions. After collection, the samples were transferred to -80°C for storage [18].
## 16S rRNA gene high-throughput sequencing
The cecum’s contents were taken. Following the instructions on the DNA extraction kit exactly, the total DNA content was extracted. DNA was measured with a nanodrop spectrophotometer (DNA quantitative analysis), and electrophoresis was used to determine the purity and concentration of the extracted genomic DNA. The 16S rDNA V3–V4 variable region was used for amplification, and the Quantit PicoGreen dsDNA analysis kit was used for fluorescence quantification. Samples were blended in the proper ratios for further fluorescence measurement. The contents of the rat cecum were then double-end sequenced using Illumina’s NovaSeq 6000 sequencer [18].
## Bioinformatics and statistical analysis
Using the QIME2 DADA2 program, non-repeat sequence OTU clustering was carried out at a $97\%$ similarity. The Greenenes database (version 13.8) was used to annotate the classification of species. To determine species richness and diversity, alpha diversity indices (Chao1, ACE, Simpson, and Shannon indices) were computed. Additionally, the beta diversity of the gut microbiota in various samples was examined, and this can determine the similarities and variations in the community composition of the various samples (or subgroups).
Data were analyzed using SPSS 26.0 statistical software. All data in the experiment are expressed as mean ± standard deviation (X ± s). One-way ANOVA was used for comparison between groups. $p \leq 0.05$ was considered statistically significant. Drawings were expected to be completed using R 4.1.3 and GraphPad Prism 8 software.
## Prescription optimization of microneedles
With the penetration rate of 800μm microneedle as the index, the drug-carrying material test results in Table 2 were analyzed by using Design Expert 8.0.6 software, and the regression equation of the penetration rate of 800μm microneedle was obtained as follows: 800μm=97.80+5.13A+2.00 B+3.13 C-0.75 AB+1.00 AC+0.75 BC-4.90 A2-4.65 B2-2.90 C2.
**Table 2**
| Serial Number | A:PVA/% | B:HA/% | C:PVPK30/% | Penetrability/% |
| --- | --- | --- | --- | --- |
| 1 | 3 | 10 | 15 | 80 |
| 2 | 7 | 10 | 15 | 92 |
| 3 | 3 | 20 | 15 | 86 |
| 4 | 7 | 20 | 15 | 95 |
| 5 | 3 | 15 | 10 | 82 |
| 6 | 7 | 15 | 10 | 90 |
| 7 | 3 | 15 | 20 | 88 |
| 8 | 7 | 15 | 20 | 100 |
| 9 | 5 | 10 | 10 | 87 |
| 10 | 5 | 20 | 10 | 89 |
| 11 | 5 | 10 | 20 | 90 |
| 12 | 5 | 20 | 20 | 95 |
| 13 | 5 | 15 | 15 | 96 |
| 14 | 5 | 15 | 15 | 100 |
| 15 | 5 | 15 | 15 | 100 |
| 16 | 5 | 15 | 15 | 96 |
| 17 | 5 | 15 | 15 | 97 |
The software Design Expert 8.0.6 was used to establish the model, and the multiple quadratic regression response surface model of 800μm microneedle penetration rate was obtained. The multiple linear regression and binomial fitting analysis were conducted on the test results of microneedle prescription materials to verify the significance of the regression model and factors. The results of ANOVA are shown in Table 3.
**Table 3**
| Source of variance | Sum of squares | freedom | mean square | F value | p value | Significance |
| --- | --- | --- | --- | --- | --- | --- |
| Model | 581.39 | 9.0 | 64.6 | 19.2 | 0.0004 | ** |
| A-PVA | 210.13 | 1.0 | 210.13 | 62.46 | <0.0001 | ** |
| B-HA | 32.0 | 1.0 | 32.0 | 9.51 | 0.0177 | * |
| C-PVPK30 | 78.13 | 1.0 | 78.13 | 23.22 | 0.0019 | ** |
| AB | 2.25 | 1.0 | 2.25 | 0.67 | 0.4404 | |
| AC | 4.0 | 1.0 | 4.0 | 1.19 | 0.3116 | |
| BC | 2.25 | 1.0 | 2.25 | 0.67 | 0.4404 | |
| A2 | 101.09 | 1.0 | 101.09 | 30.05 | 0.0009 | ** |
| B2 | 91.04 | 1.0 | 91.04 | 27.06 | 0.0013 | ** |
| C2 | 35.41 | 1.0 | 35.41 | 10.53 | 0.0142 | * |
| Residual | 23.55 | 7.0 | 3.36 | | | |
| Disfitting term | 6.75 | 3.0 | 2.25 | 0.54 | 0.6823 | |
| Pure error | 16.8 | 4.0 | 4.2 | | | |
| Total difference | 604.94 | 16.0 | | | | |
| R2 | | | | | 0.9611 | |
| R2 Adj | | | | | 0.9110 | |
| R2 pred | | | | | 0.7781 | |
It can be seen from Table 3 that $F = 19.20$, $$p \leq 0.0004$$ in the established regression model, indicating that the difference in the regression model is extremely significant; The p of the misfitting term is 0.6823>0.05, and the model difference is not significant, indicating that the equation is reliable; The regression coefficient R 2 = $96.11\%$>$85\%$, indicating that the equation is well fitted. The regression equation can be used to replace the real point of the test to describe the relationship between each variable and the response value. The correction coefficient RAdj2 = 0.9110 indicates that the model can explain the change in the penetration rate of microneedles of $91.10\%$ 800μm. The data in Table 3 show that the test design is reliable with small errors, which is suitable for the actual situation and can be used to analyze and predict the results of the microneedle preparation test.
It can be seen from the p-value in Table 3 that the PVA concentration (A) and PVPK30 concentration (C) in the primary item have a very significant impact on the preparation of microneedles, and the HA concentration (B) has a significant impact; In the quadratic term, A2 and B2 have extremely significant effects on the preparation of microneedles, and C2 has significant effects on the preparation of microneedles. Among the interaction terms, AB, AC, and BC had no significant influence on the preparation of microneedles. According to the value of F in Table 3, it can be concluded that the influence of the three factors on the preparation of microneedles is PVA concentration (A)>PVPK30 concentration (C)>HA concentration (B).
As shown in Figure 2 (A1, B1, C1) for the response surface and contour map of interaction effects of microneedle materials PVA, HA, and PVPK30 created by the response surface regression model.
**Figure 2:** *Response surface diagram and contour diagram of the interaction of various factors of microneedle prescription on the influence of microneedle preparation. (A) PVA and HA interactive response surface, (B) PVA and PVPK30 interactive response surface, (C) PVPK30 and HA interactive response surface)*
Validation experiment: Solve the regression fitting equation of the microneedle material, and obtain the best preparation conditions: $A = 6.08$, $B = 15.74$, $C = 16.27.$ *Under this* condition, the predicted penetration rate of the microneedle is $100.045\%$. According to the experimental and practical feasibility, the conditions for preparing microneedles by adjusting the modified drug loading material are as follows: PVA concentration was $6.0\%$, HA concentration was $15.5\%$, and PVPK30 concentration was $16.0\%$. Under these conditions, the penetration rate of microneedles was $97.5\%$, and the relative error of the predicted value of the model was only $2.54\%$ (< $5\%$), indicating that the response surface method optimized the conditions for the preparation of microneedles, the preparation scheme parameters obtained were accurate and reliable, and had certain application value.
## Effect of AM-ae on serum indexes of MGH rats
As shown in Figure 4, after modeling, compared with the Con group, the serum levels of E2 and PRL in the Mod group rats were highly significantly increased ($p \leq 0.01$), and P levels were highly significantly decreased ($p \leq 0.01$). After treatment with AM-DMN, compared with the Mod group, the E2 content in the serum of rats in the AM-DMN group was significantly decreased ($p \leq 0.01$), the PRL content was significantly decreased ($p \leq 0.05$), and the P content was significantly increased ($p \leq 0.01$).
**Figure 4:** *Effect of AM-ae on serum indexes of MGH rats (A). Effect of AM-ae on E2 of MGH rats, (B) Effect of AM-ae on P of MGH rats, (C) Effect of AM-ae on PRL of MGH rats).*
## Evaluation of the quality of sequencing data of intestinal contents microbiota
As shown in Figure 5A, according to the sequence length data derived from this sequencing, each sample’s sequence length was primarily 400–500 bp. Figure 5B illustrates as sample size rose, the number of OTUs climbed more slowly and then flattened out, showing that the total number of OTUs barely increased when more samples were added. This demonstrated that the samples used in this investigation were adequate to suit the needs of the study.
**Figure 5:** *Quality evaluations of sequencing data of rat intestinal contents microbiota (A). sequence length distribution diagram, (B) species accumulation curve diagram).*
## Effect of AM-ae on intestinal OTU, abundance grade, and alpha diversity in MGH rats
Figure 6A displays the OTU analysis. There was 1318, 492, and 982 OTUs total in the Con, Mod, and AM-DMN groups, respectively. There were 270 OTUs in the junction of the three groups. OTUs were considerably lower in the Mod group compared to the Con group, suggesting that MGH illness can cause rats’ gut microbiota to become unbalanced and to become less numerous. following AM-DMN therapy, the OTU count increased, but in the gut remained lower than in the Con group. Based on abundance log2 values, the rank-abundance distribution curve was drawn (Figure 6B). According to our findings, the Con group contained the most OTUs, which is in line with the Venn diagram previously mentioned. Alpha diversity refers to the diversity within a specific area or ecosystem and is a comprehensive indicator reflecting richness and evenness. Chao1 and Pielou indices are used to evaluate richness, and the larger its values, the more abundant the total number of species in the environment. As two other indicators for assessing diversity, the Shannon and Simpson index, the higher the value, the higher the diversity of species in the environment. As can be seen from Figures 6C-F, compared with the Con group, the Chao1, Pielou, Shannon, and Simpson in the intestinal flora of Mod rats were highly significantly decreased ($p \leq 0.01$), it was demonstrated that MGH decreased the rat gut microbiota’s diversity and abundance. The Chao 1 index ($p \leq 0.01$), Pielou index ($p \leq 0.05$), Shannon index ($p \leq 0.01$), and Simpson index ($p \leq 0.01$) in the rat microbiota were considerably greater in the AM-DMN group compared to the Mod group, demonstrating that AM-DMN can increase the quantity and diversity of the intestinal microbiota in MGH rats.
**Figure 6:** *Effect of AM-ae on the number of intestinal OUT and alpha diversity of MGH rats (A). number of intestinal OUT of rats, (B) Abundance grade curve, (C) Chao1 index, (D) Pielou index, (E) Shannon index, (F) Simpson index).*
## Effect of AM-ae on intestinal Beta diversity in MGH rats
Beta diversity is also known as inter-habitat diversity. It is often used to study the relationship of species diversity between communities or the differences between samples. Its research methods include PCA and NMDS. The PCA diagram (Figure 7A) and NMDS diagram (Figure 7B) show that the distance between the intestinal microflora structure of the Mod group and Con group is significantly different, indicating that MGH can change the intestinal microflora structure of rats.
**Figure 7:** *Effect of AM-ae on intestinal beta diversity of MGH rats (A). PCA, (B) NMDS).*
Compared with the Mod group, after AM-DMN treatment, the community structure similarity between the AM-DMN group and Con group is higher and relatively concentrated. β *Diversity analysis* showed that AM-ae could repair the bacterial structure of intestinal mucosa and restore it to normal.
## Effect of AM-ae on the relative abundance of intestinal microflora in MGH rats
The relative abundance of intestinal mucosal bacteria at the phylum level in rats was shown in Figure 8A, in which four taxa, Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria were the dominant phylum, accounting for a larger proportion of the total microbiota, about $95\%$. As shown in Figure 8B-D, compared with the Con group, in the Mod group the relative abundance of Firmicutes, Bacteroidetes, and Proteobacteria decreased highly significantly ($p \leq 0.01$). In the AM-DMN group compared with the Mod group, the relative abundance of Firmicutes and Bacteroidetes increased highly significantly ($p \leq 0.01$), and the Proteobacteres increased significantly ($p \leq 0.05$).
**Figure 8:** *Effect of AM-ae on the relative abundance of intestinal microbiota in MGH rats (A). Relative abundance of door level, (B) Firmicutes, (C) Bacteroidetes, (D) Proteobacteres, (E) Firmitutes/Bacteroides, (F) Horizontal relative abundance heat map, (G) Relative abundance at genus level).*
Figure 8E shows that the F/B ratio of Firmicutes and Bacteroides in the Mod group is significantly higher ($p \leq 0.01$), but the F/B ratio was significantly decreased after AM-DMN treatment ($p \leq 0.01$). The horizontal heat map of rat intestinal flora is shown in Figure 8F. The heat map can simultaneously reflect the information on species composition and abundance of the community, and visually reflect the differences and similarities of the composition of different samples or sub-groups of communities through color changes. At the same time, cluster analysis is carried out according to the similarity of species or samples. Among the tested genera, Con and AM-DMN can be well clustered into one group, while the Mod group is relatively scattered, which may be due to the differences in the changes of intestinal flora in rats caused by MGH. It can be seen from Figure 8G that Allobaculum, Clostridiales, S24-7, and Ruminococcaceae, etc. are the dominant genera at the genus level. Compared with the Con group, the relative abundance of Actinobacteria, Clostridium, S24-7, and Ruminoccaceae in intestinal flora of rats in the Mod group was significantly lower ($p \leq 0.01$); Compared with the Mod group, the relative abundances of Allobaculum, Clostriales, S24-7 and Ruminoccaceae in AM-DMN group were significantly higher ($p \leq 0.01$). It was confirmed that AM-ae could improve the disturbance of intestinal flora caused by MGH.
## Discussion
Skin is the main barrier to transdermal drug delivery, which is composed of the epidermis, dermis, subcutaneous tissue, sebaceous glands, and sweat glands. Among them, the cuticle is the largest drug administration barrier. It is a special lipid, which has a strong barrier effect on both hydrophilic and lipophilic compounds, resulting in low drug permeability and unsatisfactory therapeutic effect. Therefore, to improve the barrier effect of cuticles on hydrophilic and lipophilic drugs, chemical and physical methods are usually used to improve the drug’s skin permeability [19]. The chemical method is to add absorption enhancers into the prescription, which can improve the permeability of drugs on the skin surface. However, excellent absorption enhancers need to meet many conditions, such as non-toxicity, non-irritant and non-allergic and have good compatibility with drugs and other excipients, so it is difficult and limited to select the appropriate absorption enhancers. Microneedles are one of the physical transdermal drug delivery methods, which can puncture the cuticle and deliver drugs to the skin, improving the permeability and bioavailability, Besides, the drug delivery process is safe and non-irritating to the skin, which is a painless and minimal invasive drug delivery method [20, 21]. In this study, DMN was prepared from a biodegradable polymer material, which has the characteristics of biological stability, non-immunogenicity, non-toxicity, and no irritation to the human body. According to relevant literature reports, DMN is often used as a carrier for drug delivery such as insulin, 5-aminolevulinic acid, low molecular weight heparin, ovalbumin, adenovirus vector, a variety of vaccine antigens, and biomolecular molecules [22]. It has been used in the treatment of skin diseases (23–25), metabolic diseases [26, 27], immune diseases [2, 28], and medical cosmetology [29, 30]. The results of this study showed that the construction of soluble microneedles based on AM-ae has a good therapeutic effect on the treatment of breast hyperplasia.
MGH is a pathological hyperplasia of the breast lobule caused by an imbalance between estrogen and progesterone, when the E2 level is excessive or the P level is too low in vivo, may lead to incomplete epithelial differentiation of the proliferating glands and non-regeneration of proliferative tissues, leading to MGH [31]. In mammals, as a cytokine, PRL can regulate mammary gland development, promote milk secretion, and affect milk protein synthesis, while excessive PRL can cause structure disorder of the mammary gland [32]. Sex hormones affect the intestinal microbiota by regulating intestinal barrier permeability and integrity, sex hormone receptors, β-glucuronidase, bile acids, intestinal immunity, etc. At the same time, intestinal flora also affects the secretion of sex hormones. Yi Wu et al. demonstrated that sex hormones may be involved in sexual dimorphism in bile acid metabolism by regulating the abundance of these bacteria [33]. Atractylodis Macrocephalae Rhizoma, a dried rhizome of *Atractylodes macrocephala* Koidz., a plant of the composite family, has a long history of medicinal use in China. It is mainly used for deficiency of the spleen, lack of food, abdominal distension and diarrhea, phlegm, dizziness and palpitation, edema, spontaneous sweating, fetal movement, etc. The pharmacological effects of Atractylodis Macrocephalae Rhizoma are mainly in the gastrointestinal system, immune system, and urinary system. It has the functions of anti-aging, enhancing immunity, anti-tumor, anti-inflammatory, regulating gastrointestinal function, and regulating water and salt metabolism [34]. According to the results of pharmacological experiments, AM-DMN can significantly reduce the content of E2 and PRL, and increase the contents of P, which has the effect of treating MGH. Some studies have shown that intestinal flora plays an important role in estrogen metabolism because intestinal flora can affect the enterohepatic circulation and reabsorption of estrogen. Estrogen and its metabolites can be excreted from bile glycolaldehyde or sulfonation. According to radioactive element labeling, about $65\%$ of estradiol is excreted through bile, and the estradiol reabsorption process occurs when estrogen is excreted in the bile unclotted by β-glucuronidase in the gut, resulting in its reabsorption into circulation [35, 36]. Shimizu K et al. found that adding intestinal flora of normal mice could normalize the estrous cycle of sterile female mice with reproductive impairment [37]. The results of 16s rRNA showed that after administration of AM-DMN, the abundance, and diversity of intestinal flora in MGH mice increased, the relative abundance of dominant bacteria Firmicutes and Bacteroidetes increased, the F/B value decreased, and the resorption of estrogen decreased. Based on this, we speculated that the effect of AM-DMN on MGH mice may be related to the regulation of intestinal flora composition and abundance, but the specific mechanism still needs to be further explored. In conclusion, the prepared AM-DMN has a complete microneedle array, and the performance test results meet the requirements, which can improve intestinal flora and hormone disturbance induced by MGH through topical application.
## Data availability statement
The datasets presented in this study can be found here: doi: 10.5061/dryad.79cnp5j0h.
## Ethics statement
All experimental procedures involving animals were approved by the Animal Ethics Committee of the Animal Experimental Center of Jiamusi University.
## Author contributions
YP prepared the drafting of the manuscript and interpretation of data. QG and YLW analyzed in the analyzing of part of the data. CXL, YW, SL, MJQ, LQZ, ALT and YT performed the experiment. HZ designed the study and revised the manuscript. All authors contributed to the manuscript 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
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.2023.1158318/full#supplementary-material
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|
---
title: 'Four missense genetic variants in CUBN are associated with higher levels of
eGFR in non-diabetes but not in diabetes mellitus or its subtypes: A genetic association
study in Europeans'
authors:
- Nicoline Uglebjerg
- Fariba Ahmadizar
- Dina M. Aly
- Marisa Cañadas-Garre
- Claire Hill
- Annemieke Naber
- Asmundur Oddsson
- Sunny S. Singh
- Laura Smyth
- David-Alexandre Trégouët
- Layal Chaker
- Mohsen Ghanbari
- Valgerdur Steinthorsdottir
- Emma Ahlqvist
- Samy Hadjadj
- Mandy Van Hoek
- Maryam Kavousi
- Amy Jayne McKnight
- Eric J. Sijbrands
- Kari Stefansson
- Matias Simons
- Peter Rossing
- Tarunveer S. Ahluwalia
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011651
doi: 10.3389/fendo.2023.1081741
license: CC BY 4.0
---
# Four missense genetic variants in CUBN are associated with higher levels of eGFR in non-diabetes but not in diabetes mellitus or its subtypes: A genetic association study in Europeans
## Abstract
### Aim
*Rare* genetic variants in the CUBN gene encoding the main albumin-transporter in the proximal tubule of the kidneys have previously been associated with microalbuminuria and higher urine albumin levels, also in diabetes. Sequencing studies in isolated proteinuria suggest that these variants might not affect kidney function, despite proteinuria. However, the relation of these CUBN missense variants to the estimated glomerular filtration rate (eGFR) is largely unexplored. We hereby broadly examine the associations between four CUBN missense variants and eGFRcreatinine in Europeans with Type 1 (T1D) and Type 2 Diabetes (T2D). Furthermore, we sought to deepen our understanding of these variants in a range of single- and aggregate- variant analyses of other kidney-related traits in individuals with and without diabetes mellitus.
### Methods
We carried out a genetic association-based linear regression analysis between four CUBN missense variants (rs141640975, rs144360241, rs45551835, rs1801239) and eGFRcreatinine (ml/min/1.73 m2, CKD-EPIcreatinine[2012], natural log-transformed) in populations with T1D (n ~ 3,588) or T2D (n ~ 31,155) from multiple European studies and in individuals without diabetes from UK Biobank (UKBB, n ~ 370,061) with replication in deCODE ($$n = 127$$,090). Summary results of the diabetes-group were meta-analyzed using the fixed-effect inverse-variance method.
### Results
Albeit we did not observe associations between eGFRcreatinine and CUBN in the diabetes-group, we found significant positive associations between the minor alleles of all four variants and eGFRcreatinine in the UKBB individuals without diabetes with rs141640975 being the strongest (Effect=0.02, PeGFR_creatinine=2.2 × 10-9). We replicated the findings for rs141640975 in the Icelandic non-diabetes population (Effect=0.026, PeGFR_creatinine=7.7 × 10-4). For rs141640975, the eGFRcreatinine-association showed significant interaction with albuminuria levels (normo-, micro-, and macroalbuminuria; $$p \leq 0.03$$). An aggregated genetic risk score (GRS) was associated with higher urine albumin levels and eGFRcreatinine. The rs141640975 variant was also associated with higher levels of eGFRcreatinine-cystatin C (ml/min/1.73 m2, CKD-EPI2021, natural log-transformed) and lower circulating cystatin C levels.
### Conclusions
The positive associations between the four CUBN missense variants and eGFR in a large population without diabetes suggests a pleiotropic role of CUBN as a novel eGFR-locus in addition to it being a known albuminuria-locus. Additional associations with diverse renal function measures (lower cystatin C and higher eGFRcreatinine-cystatin C levels) and a CUBN-focused GRS further suggests an important role of CUBN in the future personalization of chronic kidney disease management in people without diabetes.
## Introduction
Urine albumin or albuminuria is one of the most important biomarkers of kidney damage in individuals with or without diabetes. In healthy individuals, the glomerular filter in the kidneys retains most of the albumin, although a small amount can usually pass through to the tubular system [1]. Reabsorption of albumin is facilitated by the kidney’s proximal tubular cells (PTCs), ensuring that almost no albumin is excreted in urine under normal conditions [2, 3]. Elevated excretion of albumin in the urine - initially coined as “microalbuminuria” - is one of the earliest signs of chronic kidney disease (CKD) and may be the kidney-related manifestation of general endothelial damage, where scarring of the glomerulus causes chronic leakiness through the filter of albumin and other proteins [4].
Over the past decades, the number of people with diabetes mellitus has more than doubled to a global prevalence of 537 million in 2021 [5], with serious consequences for the healthcare system and society. According to a recent European study [6], one in four hospitalized patients has diabetes. Up to $40\%$ of individuals with diabetes develop diabetic kidney disease (DKD), which is associated with elevated cardiovascular morbidity and mortality and progresses to dependency on kidney replacement therapies such as dialysis and transplantation and is a leading cause of CKD [7].
In the recent years, studies have begun to unravel genetic aspects of albuminuria. Recently, we and others identified that genetic variants (single nucleotide variants (SNVs)) in the gene encoding for cubilin (CUBN) – the main albumin-transporter in PTCs [1, 8] – are associated with microalbuminuria and higher urine albumin levels in populations with and without diabetes (8–14). Four variants in the C-terminal end of cubilin have been of particular interest (rs141640975 (c.5069C>T; p.Ala1690Val), rs144360241 (c.6469A>G; p.Asn2157Asp), rs45551835 (c.8741C>T; p.Ala2914Val), and rs1801239 (c.8950A>G, p.Ile2984Val)); these are functional (missense) variants that have been proposed to alter the function of cubilin, leading to a form of albuminuria that may reflect a lack of tubular reabsorption of albumin (i.e., tubular albuminuria) [8]. In silico structural and damage prediction analyses of the variants indicate their potential to change secondary or even tertiary structure(s) in the cubilin protein and to have different degrees of damaging effects on protein function, disease, or both [8]. Our recent study further suggests that the effect of some of these variants on urine albumin levels is 2-3 times higher in diabetes compared to non-diabetes [11].
However, the role of these CUBN variants in relation to estimated glomerular filtration rate (eGFR), a clinically used marker of kidney function, is largely unexplored, and most genetic studies have focused on the general population [8, 9, 11]. Recent efforts to uncover the role of these variants specifically in diabetes – and to clearly separate the effect seen here from the effect in the non-diabetes-proportion of the general population – have been performed as relatively small secondary analyses without including rs144360241 or diabetes subtypes [8]. Thus far, only rs45551835 has been connected to higher levels of eGFR in type 2 diabetes and rs141640975 in non-diabetes [8]. Therefore, we investigated the relationship between the four CUBN variants and eGFR in different contexts: First, we meta-analyzed studies of SNV-eGFRcreatinine regressions in Europeans with type 1 (T1D) or type 2 diabetes mellitus (T2D). We then examined single- and aggregate-variant associations separately in diabetes and non-diabetes populations of a large, nationally representative cohort facilitating application of identical phenotype definitions, including the dependency of albuminuria-stage in SNV-eGFRcreatinine associations, generation of a CUBN-specific genetic risk score (GRS), and identification of associations between individual SNVs and cystatin C-based measures of kidney function. Together, these analyses both seek to replicate previous associations in DM and NDM populations and to provide novel insights into the link between CUBN and eGFR.
## Study design and cohorts
For the genetic association meta-analysis in diabetes mellitus (DM), we included data collected via three approaches (Figure 1): First, we acquired summary statistics from up to 15,200 individuals of European origin with either type 1 diabetes (T1D) or type 2 diabetes (T2D) subsetted from six cohorts: AfterEU (T1D) (15–18), Rotterdam (T2D) [19], DiaGene (T2D) [20], UK-ROI (T1D) [21], Genesis (T1D) [22] and ANDIS (T2D) [23]. These studies (hereafter referred to as “DM cohorts”) were invited to the study and given a harmonized analysis plan provided that any subset of the requested genetic variants was available. A description of each cohort can be found in the Supplemental text.
**Figure 1:** *Flow chart of SNV-eGFRcreatinine meta-analyses in Diabetes. UKBB, UK Biobank; T2D, Type 2 diabetes; DM, diabetes mellitus; T1D, Type 1 diabetes; NDM, without diabetes mellitus; SNV, single nucleotide variant; eGFRcreatinine, Estimated glomerular filtration rate, natural log-transformed; PCs, Principal components of population structure; HbA1C, hemoglobin A1C; SBP, Systolic blood pressure; m1: model 1 (eGFRcreatinine ~ genotype + sex + age + 0-10 PCs); m2: model 2 (m1 + HbA1c + SBP + diabetes duration); * Sample sizes (n) reflect the maximal number of individuals (out of the total number of individuals in
Table 1
) available for rs45551835, model 1. ** See
Supplementary Figure 1
for a flow chart of additional analyses. Figure made with LucidChart (lucid.app).*
Second, we applied the same analysis plan to a subset of individuals with T2D (n ~ 14,860) from the UK Biobank [24] (henceforth referred to as “UKBB-T2D”). The approach we used to extract the T2D subset has been described previously [25, 26].
Third, we did a lookup in a subset of an exome-wide association study (henceforth referred to as “ExWas”) that included 3,990 individuals with T2D from three Danish studies (Inter99, Vejle biobank and Addition-DK) described previously [11].
We also searched the Type 2 Diabetes Knowledge Portal [at time of search: www.type2diabetesgenetics.org, now: https://t2d.hugeamp.org/ [27]] for large-scale studies with publicly available summary statistics fulfilling the following criteria: Summary statistics should a) be readily available through the knowledge portal or a direct link to a study website; b) be available for diabetes-stratified and European-only populations; c) include at least one target genotype; d) be based on natural log-transformed eGFR values rather than non-transformed eGFR values; and e) be based on regression models with covariate adjustments comparable to those in the other cohorts in this study. However, as of 10 July 2020, no studies in the portal fulfilled our criteria, and no additional studies were included.
For additional analyses, we used 1) a group of individuals without diabetes from UKBB (n ~ up to 370,000 individuals), henceforth referred to as “UKBB-NDM”) and 2) the UKBB-T2D group, which was also part of the meta-analysis (Supplementary Figure 1). 127,090 non-diabetes individuals from the Icelandic study deCODE participated as the replication cohort (Supplemental text).
This research work was conducted in accordance with the Helsinki Declaration. Ethical approval was previously obtained locally for individual studies. All participants gave written informed consent before participating.
## Phenotype details
For the DM cohorts and UKBB (both NDM and T2D groups), we calculated the creatinine-based estimated glomerular filtration rate (eGFRcreatinine) with the Chronic Kidney Disease Epidemiology Collaboration creatinine equation (CKD-EPIcreatinine[2012], ml/min/1.73 m2 [28], natural log-transformed). We included it here as a continuous variable. Other measures of kidney function were also calculated for UKBB; see section 2.4.2.4.
## Genotyping, imputation, quality control and variant selection
We obtained information on genotyping, imputation, and quality control of each cohort and summarized it in Supplementary Tables 1, 2.
Four variants were selected for further analysis: rs141640975 (Chromosome (chr) 10, position (pos) 16992011 (genome-build GRCh37.p13)) with minor allele frequency (MAF) 0.002-0.009; rs144360241 (chr 10, pos 16967417) with MAF 0.006-0.010; rs45551835 (chr 10, pos 16932384) with MAF 0.016-0.021; and rs1801239 (chr 10, pos 16919052) with MAF 0.097-0.114. For the deCODE study, the MAFs were in the same range except rs144360241 (MAF: 0.002). The minor alleles of these variants (A, C, A, and C, respectively) were used as effect alleles.
We used LDlink version 5.1 [29] with the European (CEU + GBR) reference panel to confirm the independent relationship (Linkage Disequilibrium (LD) r2< 0.1) between these SNVs.
The SNVs were first used in single-variant analyses and were then combined into a genetic risk score (GRS; see description below).
## Statistical methods
A flow chart of the meta-analyses is shown in Figure 1, and one of the additional analyses is shown in Supplementary Figure 1.
## Study-level SNV-eGFRcreatinine association analysis in diabetes and subsequent meta-analysis
In each DM cohort and UKBB-T2D, associations between eGFRcreatinine and genetic variants were assessed assuming an additive genetic model. We used natural log-transformed eGFRcreatinine in a linear regression model (model 1) adjusted for traditional clinical and genetic factors, i.e. age, gender, and study-specific covariates (i.e., 0-10 principal components of population structure to account for population stratification). To control for potential bias on kidney function in the diabetes population, another model was further adjusted for HbA1C, systolic blood pressure (a proxy for medication with Angiotensin receptor blockers (ARBs) or Angiotensin-converting enzyme inhibitor (ACEi) frequently used in diabetes treatment) and diabetes duration (model 2). Some of the cohorts used summary statistics calculated prior to our query, so we allowed minor deviations in the included covariates (Supplementary Table 3). A list of software used for association analysis can be found in Supplementary Table 1. Each study dealt with missing data separately. Once all summary results were collected, we performed study-level quality control. Summary results were meta-analyzed using a fixed-effect inverse-variance method in the “Metagen” package in R (version 3.6.3). We report results in any diabetes mellitus subtype (denoted “combined”) and in T1D and T2D subsets. Significant heterogeneity (Phet< 0.05) indicated variation across studies. Effect sizes (betas) are presented with $95\%$ confidence intervals. We evaluate statistical significance at an FDR-corrected level of $\frac{0.05}{4}$ = 0.0125 considering the number of tested SNVs.
## Additional analyses in UKBB populations with diabetes and non-diabetes
To explore the interplay between CUBN-variants and kidney-related traits in more detail, we did a range of additional linear regressions in the UKBB NDM and T2D groups. Further, we also applied a combined genetic risk score (GRS). We based the analyses on model 1 and model 3. The latter was very similar to model 2, in that it included adjustment for model 1 and SBP but not HbA1c and diabetes duration. The last two adjustments were absent from this model because they are less relevant in non-diabetes. We applied the same models in DM and NDM to provide consistency. Individuals were excluded if they had missing data for any variable.
## SNV-eGFRcreatinine association analysis in the UKBB population without diabetes and replication in the deCODE study
We examined SNV-eGFRcreatinine associations in the UKBB NDM and T2D populations. It was advantageous to use the UKBB dataset here as it is a well-powered, phenotypically homogenous dataset (n ~ up to 370,000 individuals without diabetes). Since effects are based on natural log-transformed eGFR (trait) values, we also calculated the percental difference in mean, non-transformed eGFR per added effect allele for significant effects as follows: % difference =(e beta−1)*$100\%$.. Again, we evaluated statistical significance at an FDR-corrected level of 0.0125.
SNV-eGFRcreatinine associations identified in the UKBB NDM group were also examined in the Icelandic deCODE study (nNDM=127,090) applying model 3.
## Interaction with albuminuria
In order to examine whether the SNVs associated with eGFRcreatinine in an albuminuria-dependent fashion, we assessed albuminuria-SNV interactions in SNV-eGFRcreatinine regression models in individuals with T2D (nT2D = 7,777) and without DM (nNDM = 107,276) for whom continuous urine albumin levels were available (derived from the UKBB “microalbumin” field). The interaction term in the regression models included albuminuria groups as a factor defined from these albumin levels as follows: i) normoalbuminuria: =< 30 mg/L (nDM = 5,566, nNDM = 93,728), ii) microalbuminuria: 30-300 mg/L (incl. lower but not upper threshold, nNDM = 1,954, nNDM = 12,690), and iii) macroalbuminuria: >300 mg/L (incl. lower threshold, nDM = 257, nNDM = 858). We used regression models based on model 1 and 3 (i.e., model 1: ln(eGFRcreatinine) ~ SNV + albuminuria group + age + sex + SNV*albuminuria group and model 3: model 1 + SBP). A significant p-value (< 0.05) for the SNV*albuminuria interaction term was considered evidence for interaction. Interaction analysis was done whenever primary SNV-eGFRcreatinine analyses were well-powered.
## Genetic risk score association with microalbuminuria and eGFRcreatinine
We estimated an albuminuria genetic risk score (GRS) using the four albuminuria-associated CUBN missense SNVs. The GRS was generated for each study participant using the sum of individual SNV effect alleles in the UKBB dataset. We then examined the associations between GRS CUBN and continuous urine microalbumin levels (mg/L) and eGFRcreatinine.
## SNV vs. other kidney function-related traits in UKBB
We examined the associations between the study SNVs and 1) circulating serum Cystatin C levels (mg/L) and 2) the more recent eGFRcreatinine-cystatin C equation [30] that uses both serum creatinine and cystatin C levels and applies to all ethnicities.
## Power calculations
We used Quanto (version 1.2.4) [31] to calculate post-hoc power for main SNV-eGFRcreatinine associations in DM and NDM groups.
For all power calculations in Quanto, we: a) chose a continuous design for independent individuals; b) assumed a gene-only hypothesis; c) assumed an additive inheritance mode; and d) set the two-sided type I error-rate to 0.05.
For the remaining options in Quanto, we typed in information specific to each variant and population (Supplementary Tables 13 - 14): For each variant, we used allele frequencies of the effect allele; for meta-analyses, this was done as a range of calculations spanning the frequencies reported by individual cohorts. We used effect sizes obtained through DM and NDM SNV-eGFRcreatinine association analyses (main effect). Means and standard deviations of ln(eGFRcreatinine) were derived from UKBB subsets. Unless otherwise specified, total DM sample sizes were used.
## Clinical characteristics
Up to 34,743 individuals with diabetes mellitus (type 1 diabetes (T1D), n ~ 3,588, or type 2 diabetes (T2D), n ~ 31,155) and up to 370,061 without diabetes participated in the current study (Figure 1 and Supplementary Figure 1). Clinical characteristics of participating studies can be found in Table 1 and Supplementary Tables 4 – 7.
**Table 1**
| Studyname | DMtype | Indi-viduals(N) | Males(N, %) | Age## [years] | BMI [kg/m2] | eGFRcreatinine [ml/min/1.73 m2] | SBP [mmHg] | Diabetes duration [years] | Urinary albumin | Urinary albumin.1 | Urinary albumin.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Studyname | DMtype | Indi-viduals(N) | Males(N, %) | Age## [years] | BMI [kg/m2] | eGFRcreatinine [ml/min/1.73 m2] | SBP [mmHg] | Diabetes duration [years] | AER[mg/24h] | UACR [mg/mmol] | ALB[mg/L] |
| AfterEU | T1D | 854 | 492(57.60) | 43.67 (11.15) | 24.23(3.21) | 89.48(26.61) | 139.22 (20.90) | 28.02(9.50) | 29.00(7.00 - 618.00) | NR | NR |
| UK-ROI | T1D | 1410 | 716(50.80) | 45.09 (11.35) | 26.30(4.40) | 54.30(30.00) | 135.02 (20.80) | 30.45(9.70) | | | |
| GENESIS | T1D | 1324 | 700(52.90) | 41.37 (12.21) | 22.21(8.15) | 80.87(28.49) | 129.41 (23.75) | 24.91 (10.45) | 9.00(4.16-37.25) | NR | NR |
| DiaGene | T2D | 1886 | 1,011(53.60) | 65.24 (10.57) | 30.47(5.43) | 78.33(20.55) | 141.83 (18.72) | 10.09(8.45) | NR | 5.85(30.45) | NR |
| Rotterdam | T2D | 1022 | 487(47.70) | 68.10 (9.70) | 29.40(4.80) | 78.30(16.40) | 147.10 (21.70) | | | | |
| ANDIS | T2D | 9367 | 5,548(59.22) | 66.29 (13.29) | 30.77(5.70) | 84.69(30.92) | | 8.07(4.40) | | | |
| ExWas** | T2D | 3990 | 2,370(59.30) | 61.00(8.50) | | 79.00(1.28) | | | | | |
| UKBB-T2D# | T2D | 14890 | 9,703(65.10) | 60.97(6.28) | 31.90(5.70) | 87.86(15.73) | 144.50 (18.20) | | NR | NR | 16.00(10.00-34.40) |
| UKBB-NDM# | NR* | 370061 | 166,976 (45.10) | 56.73(8.02) | 27.10(4.50) | 90.81(12.80) | 139.90 (19.60) | NR | NR | NR | 11.10(8.30-18.10) |
## CUBN variants are not associated with eGFRcreatinine in a diabetes meta-analysis
The effect of rs144360241 on eGFRcreatinine was studied in 32,904 individuals with diabetes. The variant was not available in UK-ROI (Supplementary Figures 2, 6). All eight studies contributed to the 34,050 individuals analyzed for rs45551835 (Supplementary Figures 3 and 7). The rs141640975 variant was available for 32,993 individuals and was unavailable in UK-ROI (Supplementary Figures 4, 8). The common variant, rs1801239, was available in all eight studies in 34,070 individuals (Supplementary Figures 2, 9).
After meta-analysis, none of the four CUBN variants were significantly positively associated with eGFRcreatinine in the DM group, neither in the T1D or T2D subgroup [Table 2 (Model 1) and Table 3 (Model 2)]. However, the positive directionality of the effect for the T2D group was consistent with the directionality of effect for the combined group for all variants with non-zero effects. The T2D group carried the largest weight in the combined meta-analyses and UKBB carried the largest weight within the T2D group (Supplementary Figures 2 – 5). There was no evidence of heterogeneity across studies, except in model 2 for rs45551835 and rs1801239 (Table 3).
## CUBN variants are associated with higher eGFRcreatinine in non-diabetes
In UKBB-NDM, we observed larger eGFRcreatinine-levels for minor alleles compared to major alleles for all four CUBN variants in both models, except for rs1801239 in NDM, model 3 (Table 4 and Supplementary Table 8): The effect and standard deviation of rs144360241 was, for model 1 (model 3), 0.008 ± 0.002 (0.007 ± 0.002), corresponding to a difference of +$0.8\%$ (+$0.7\%$) in mean eGFRcreatinine (ml/min/1.73 m2) for each additional copy of the affect allele, C. For rs45551835, the effect was 0.005 ± 0.001 (0.004 ± 0.001), corresponding to a difference of +$0.5\%$ (+$0.4\%$) in mean eGFRcreatinine per copy of the A-allele. rs141640975 had the largest effect size, 0.02 ± 0.003 (0.02 ± 0.003), corresponding to a +$2.02\%$ (+$2.02\%$) difference in mean eGFRcreatinine for each additional A-allele. The common variant, rs1801239, had the smallest effect size of 0.001 ± 0.0005, corresponding to a +$0.1\%$ difference in eGFRcreatinine for each C-allele. We replicated the finding that rs141640975 was significantly associated with higher eGFRcreatinine in non-diabetes in an Icelandic study (deCODE, $$n = 127$$,090, effect = 0.026, SE = 0.007, PeGFR_creatinine = 7.7 × 10-4, model 3, Supplementary Table 8). None of the other SNVs were replicated (data not shown). Meta-analysis for the rs141640975-eGFR-association in the NDM studies (UKBB and deCODE) is depicted in Supplementary Figure 10.
**Table 4**
| Genetic variant (EA) | EAF | Population ** | N | Effect (Beta [SE]) | P-value |
| --- | --- | --- | --- | --- | --- |
| rs144360241 (C) | 0.004 | NDM *** | 369832 | 0.008 (0.002) | 0.0008* |
| rs144360241 (C) | 0.004 | T2D **** | 14882 | 0.02 (0.02) | 0.23 |
| rs45551835 (A) | 0.014 | NDM *** | 369028 | 0.005 (0.001) | 0.0004* |
| rs45551835 (A) | 0.014 | T2D **** | 14860 | 0.01 (0.01) | 0.13 |
| rs141640975 (A) | 0.003 | NDM *** | 369987 | 0.02 (0.003) | 2.2 × 10-9* |
| rs141640975 (A) | 0.003 | T2D **** | 14885 | -0.01 (0.02) | 0.71 |
| rs1801239 (C) | 0.1 | NDM *** | 369849 | 0.001 (0.0005) | 0.006* |
| rs1801239 (C) | 0.1 | T2D **** | 14880 | 0.00 (0.00) | 0.42 |
In UKBB-T2D, none of the variants had statistically significant associations with eGFRcreatinine, although the effects of three of the variants (except rs141640975) were in the same direction as in NDM (Table 4 and Supplementary Table 8).
## Associations of rs141640975 with eGFRcreatinine depend on albuminuria-status in non-diabetes
To examine whether the SNVs are associated with eGFRcreatinine in an albuminuria-dependent fashion, we included albuminuria*SNV interactions in two regression models. For the first model, we observed significant interaction for rs141640975 in UKBB-NDM (Pinteraction = 0.03, Table 5). This was also observed in the other model (Pinteraction = 0.04, Supplementary Table 9). An interaction plot showed that for the eGFR-SNV-association, the effect on eGFR was even higher for more elevated albuminuria-levels (Supplementary Figure 11).
**Table 5**
| Genetic variant (EA) | Population | N | P-value of interaction term# |
| --- | --- | --- | --- |
| rs144360241 (C) | NDM ** | 107202 | 0.67 |
| rs45551835 (A) | NDM ** | 106964 | 0.88 |
| rs141640975 (A) | NDM ** | 107255 | 0.03* |
| rs1801239 (C) | NDM ** | 107216 | 0.49 |
## A CUBN-based GRS for albuminuria is associated with eGFRcreatinine in non-diabetes
We combined the four CUBN variants into a genetic risk score for albuminuria, verified its associations with continuous urine albumin levels and tested it against eGFRcreatinine in UKBB-T2D and UKBB-NDM. The GRS was associated with higher levels of both traits, except for eGFR in T2D (Tables 6, 7).
## rs141640975 is associated with additional markers of kidney function in non-diabetes
We examined the associations between the study SNVs and two additional markers of kidney function. The SNV rs141640975 was associated with higher levels of eGFRcreatine-cystatin C [a more recent ethnicity-independent GFR-estimator [28]] and lower levels of cystatin C, both observed in NDM (Supplementary Tables 10 – 12). The eGFRcreatinine-cystatin C association of rs144360241 was borderline significant in NDM.
## Meta-analysis (diabetes mellitus)
Given the ranges of EAFs obtained from individual studies participating in meta-analyses, we reached a power level of 35-$43\%$ for rs45551835, 16-$23\%$ for rs1444360241, and 9-$21\%$ for rs141640975 in the DM group (Supplementary Table 14). Effect sizes were assumed from the individual meta-analysis eGFRcreatinine-associations of each SNV. We did not calculate power for rs1801239 as the effect in the DM meta-analysis was 0.0.
## Association of SNVs with eGFR (UKBB population without diabetes)
In NDM, the power for main eGFRcreatinine analyses was between 70-$99\%$ for the four variants (Supplementary Table 15).
## Discussion
Recently, we demonstrated that individuals carrying the minor allele of the CUBN missense variant rs141640975 had higher albuminuria-levels than non-carriers. The effect of this variant was stronger in individuals with diabetes (DM) compared to those without diabetes (NDM) [11]. In continuation of these findings, Bedin et al. [ 8] performed secondary lookups for CUBN-variants in the CKDGen eGFR GWAS study population, reporting that missense variants in CUBN may also be associated with higher levels of eGFR in the general population. Our current large-scale study aimed to examine the effect of minor alleles of three rare CUBN missense variants (rs144360241 (c.6469A>G; p.Asn2157Asp), rs45551835 (c.8741C>T; p.Ala2914Val) and rs141640975 (c.5069C>T; p.Ala1690Val)) and one common variant (rs1801239 (c.8950A>G; p.Ile2984Val)) on eGFRcreatinine levels separately in people with and without diabetes (nDM ~ 34,000 individuals, nNDM ~ 370,000 individuals), including stratification for diabetes-type and supplemented by tests on circulating cystatin C levels, the recently updated eGFR-equation based on creatinine and cystatin C [30], and aggregate-variant tests. We were able to replicate the association between creatinine-based eGFR and rs141640975 in NDM and report new insightful connections with the alternative measures of kidney function for all four SNVs.
Previously, a borderline association between rs45551835 and higher eGFR-levels has been reported in a smaller type 2 diabetes (T2D) population from Denmark [8, 11], a finding which we could not replicate in our meta-analysis of up to 34,432 individuals with diabetes and its subtypes. Like the initial study [8], we could not establish a link between eGFR and the three other variants within the diabetes group. As for rs45551835, it was surprising to be unable to replicate the earlier findings as the current study has a larger sample size compared to earlier efforts. Our post-hoc power assessment indicated that insufficient power might be at play, even with a larger sample size for the diabetes group [8]. We also speculated whether the apparent lack of association between CUBN and eGFR in our diabetes meta-analysis could be due to use of Angiotensin receptor blockers (ARBs) or Angiotensin-converting enzyme inhibitor (ACEi) medication which is frequently used in diabetes treatment. As part of our sensitivity analyses, we included models adjusted for systolic blood pressure (a proxy for such medication) and did not find evidence that this could explain why no association was found in the diabetes group. Another reason could be the allele frequency of the variants may differ between Danish and UK populations. We need further validation in well-powered populations to confirm the relationship between the rs45551835 and eGFR in diabetes, especially in T2D. In case of a true lack of association, CUBN may be associated with higher levels of urine albumin [11] with no pleiotropic effect to eGFR in this population.
We proceeded to single- and aggregate-variant analyses in the UK Biobank (UKBB), shifting focus to non-diabetes populations. For all four CUBN variants, we report significantly higher eGFRcreatinine-levels in individuals without diabetes harboring more copies of the minor alleles compared to individuals with fewer or no copies of the minor alleles in the same group. For rs141640975, we observed the strongest association with eGFRcreatinine ($$P \leq 2.2$$ × 10-9) with replication in the Icelandic study (deCODE, $$P \leq 7.7$$ × 10-4), confirming what has previously been observed for this SNV in NDM [8] – but also a significant interaction between the SNV and albuminuria stages (PINT< 0.05). Taken together with the already known associations of the minor alleles with higher albuminuria [11], this not only demonstrates genetic pleiotropy of CUBN for albuminuria and eGFR in non-diabetes but also implies that these two associations are intertwined for this SNV, where the effect on eGFR is even higher for more elevated albuminuria-levels. Here, CUBN demonstrates a classic genetic pleiotropy phenomenon where a DNA variant influences multiple traits, usually in the same domain with concordant or sometimes discordant effects as observed earlier in complex disorders [32]. Further validation of independent biological or related causal effects might be required in additional follow up studies.
This finding is unusual as there is no obvious clinical or pathophysiological explanation for such an albuminuria-eGFR pattern in the context of non-diabetes. It has been suggested that the tubular albuminuria observed in presence of C-terminal variants in CUBN has a benign or even slightly protective effect on kidney function in chronic kidney disease if glomerular albuminuria is also present [8, 33, 34]. Another recent study on chronic isolated proteinuria suggests that different C-terminal CUBN variants uncouple proteinuria from glomerular filtration barrier through declined cubilin expression accompanied by aberrant amnionless (AMN) localization in renal tubules. AMN is part of the receptor complex (along with cubilin and megalin) necessary for tubular reabsorption of albumin. This is suggested to create a benign condition, not requiring any further proteinuria lowering treatment [35]. In non-diabetes, where the population can be assumed to consist mostly of healthy individuals, a concept of such protectiveness is less relevant. However, it is possible that an undetected subpopulation with relevant comorbidities exists in the non-diabetes group.
Our CUBN aggregate-variant method – which was defined as a genetic risk score (GRS) combining the four variants – showed that a higher number of C-terminal CUBN risk alleles is associated with higher urine albumin and eGFRcreatinine levels and confirms both the single-variant association with higher urine albumin levels reported previously in diabetes and non-diabetes [11, 14], and the consistency of the overall effects on urine albumin levels being greater in diabetes compared to non-diabetes [10, 11]. Through GRS CUBN, we also saw that a higher number of minor alleles across the four variants was associated with higher eGFRcreatinine-levels in the UKBB population without diabetes, which is in line with our single-variant findings and the previous findings for rs45551835 [8]. Using aggregate-variant methods is an optimal way to examine combined genetic effects and has been used extensively for polygenic traits [13, 36]. Using GRS is highly relevant here as three of the four variants are rare and mostly present as heterozygous variants in our populations. This might substantiate with some additional power to detect effects and adds further certainty to the presence of a CUBN-eGFR relationship in non-diabetes. Nevertheless, we still do not find an association with eGFR in T2D, even when the variants are combined in a GRS.
Finally, we examined the association between the study SNVs and two alternative markers of kidney function. In non-diabetes, the minor alleles of rs141640975 and rs144360241 were associated with higher levels of eGFRcreatinine-cystatin C. This measure was estimated using a recent update to the equation, CKD-EPI2021, which does not include ethnicity and is a more precise indicator of kidney function in comparison to the CKD-EPIcreatinine[2012] equation which is based only on creatinine. Our results using the conventional eGFRcreatinine equation are concordant with our results from the updated equation in terms of directionality of effect and with our finding that rs141640975 is associated with lower cystatin C levels, which is another indicator of kidney function. It should be noted, though, that considering Table 1 and Supplementary Tables 4 – 6, the $0.1\%$ – $2.02\%$ higher mean eGFR we report for each minor allele is modest and may reflect that individual harboring these genetic variants have normal kidney function rather than a better kidney function.
A strength of our study is the restriction to specifically diabetes- and non-diabetes-only subgroups so that effects from mixed diabetes-status are minimized. Heterogeneity is likely to be present in meta-analyses of a diverse set of cohorts originally used for different research purposes. Indeed, some of the cohorts included in our meta-analyses differ regarding available covariates and/or kidney disease status. However, we did not observe heterogeneity in our meta-analyses. In addition to this, we could minimize heterogeneity in the remainder of our analyses by using data from the UKBB, which is a nationally representative cohort facilitating application of identical phenotype definitions across subgroups. Another strength is the broad spectrum of additional analyses that we explored in the UKBB population to nuance our findings on the relationship between eGFR and CUBN. The judicious use of UKBB leveraging individual-level genotype information to investigate interaction-analyses based on albuminuria groupings is a great strength of the current study, especially for rare variants.
A major limitation is that we did not have sufficient statistical power for our meta-analyses in the diabetes group due to the limited availability of suitable datasets. Consequently, interpretations of T2D findings should not be overstated and we thus could not demonstrate, nor disprove, the presence of a CUBN-eGFR relationship in this population. Although we demonstrate that C-terminal missense variants in CUBN are associated with different measures of normal (or even higher) kidney function in non-diabetes, we emphasize that the current study is insufficient to establish causality. Finally, using multiple-testing-corrected significance thresholds might be too conservative when testing a very small number of variants from the same locus as it may remove true associations. In genome-wide studies, a conservative threshold of 5 × 10−8 is generally agreed upon for novel associations. There is less consensus on when and how to appropriately apply multiple testing correction in smaller-scale genetic studies dealing with a mixture of new and known associations. Nevertheless, we deemed that it would be fair to apply FDR-correction of the significance threshold to our primary analyses in DM and NDM.
In conclusion, the current study identifies the existence of pleiotropic genetic effects of CUBN on two facets of kidney function – albuminuria and eGFR – by reporting SNV-eGFR associations in a large study population without diabetes. The interaction between rs141640975 and albuminuria-status on eGFRcreatinine in this population and its associations with lower cystatin C and higher levels of eGFRcreatinine-cystatin C expands our knowledge of these variants in relation to measures of kidney function. The demonstration of a CUBN-focused GRS in relation to albuminuria and eGFRcreatinine further suggests an important role of CUBN-variants in the future personalization of chronic kidney disease management.
## 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 Ethical approval has previously been obtained locally for each individual study. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
NU, MS, PR, and TA contributed to conception and design of the study. NU wrote the first draft of the manuscript. NU, MC-G, CH, AN, AO, SS, D-AT, EA, MH, AM, EJS, MS, PR, and TA contributed to manuscript revision. D-AT, VS, KS, EA, MH, AM, PR and TA acquired data. NU, FA, MC-G, CH, AN, SS, LS, D-AT, and TA performed statistical analysis. NU, MC-G, LS, D-AT, AM, MS, PR, and TA contributed to interpretation of data. TA and MS acquired funding and TA administered this project. PR and TA supervised the project. LC and MG had other roles. All authors contributed to the article and approved the submitted version.
## Conflict of interest
Author MC-G was employed by Pfizer-University of Granada-Andalusian Regional Government. Authors AO, VS, and KS were employed by Amgen, Inc. PR reports personal fees from Bayer during the conduct of the study. He has received research support and personal fees from AstraZeneca and Novo Nordisk, and personal fees from Astellas Pharma, Boehringer Ingelheim, Eli Lilly, Gilead Sciences, Mundipharma, Sanofi, and Vifor Pharma. All fees are given to Steno Diabetes Center Copenhagen.
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/fendo.2023.1081741/full#supplementary-material
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|
---
title: 'Diabetes and intervertebral disc degeneration: A Mendelian randomization study'
authors:
- Peihao Jin
- Yonggang Xing
- Bin Xiao
- Yi Wei
- Kai Yan
- Jingwei Zhao
- Wei Tian
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC10011653
doi: 10.3389/fendo.2023.1100874
license: CC BY 4.0
---
# Diabetes and intervertebral disc degeneration: A Mendelian randomization study
## Abstract
### Introduction
Intervertebral disc degeneration (IVDD) is an important contributor of low back pain, which represents one of the most disabling symptoms within the adult population. Recently, increasing evidence suggests the potential association between Type 2 diabetes mellitus (T2DM) and IVDD. However, the causal relationship between these two common diseases remains unclear.
### Methods
We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal association between T2DM and IVDD. Sensitivity analysis was performed to test for heterogeneity and horizontal pleiotropy. Multivariable MR was also conducted to adjust for the effect of BMI on IVDD.
### Results
A total of 128 independent single-nucleotide polymorphisms (SNPs) that were significantly associated with T2DM were selected as instrumental variables in univariable MR analysis. Our results showed that patients with T2DM had a higher risk of developing IVDD (OR, 1.069; $95\%$ CI, 1.026–1.115; $$p \leq 0.002$$). The relationship remained stable in sensitive analysis including multivariable MR, which implicated the direct causal effect of T2DM on IVDD (OR, 1.080; $95\%$ CI, 1.041–1.121; $p \leq 0.001$) after adjusting for BMI.
### Conclusions
MR analysis indicated a causal effect of T2DM on IVDD, and the effect persisted even when we accounted for the impact of BMI.
## Introduction
Intervertebral disc degeneration (IVDD) is currently a common degraded condition in an aging society, referring to an age-dependent, cell-mediated molecular process [1, 2]. Degenerated discs are more prone to out-pouching and may press against the nerve roots, which eventually causes low back pain (LBP) or other clinical symptoms. As an increasingly prevalent health problem, IVDD significantly impacts patients’ quality of life and poses a substantial economic burden to countries with rapidly aging populations, such as China [3, 4]. To date, in spite of the high prevalence of IVDD, lines of evidence for the risk factors of IVDD have not been fully established yet. Traditionally, IVDD is considered to be a multifactorial disease affected by both genetic and environmental factors including diabetes, obesity, and smoking 5. Recently, increasing evidence has suggested that metabolic disturbances and inflammation might be involved in the development of IVDD, which shifts the focus of research to metabolic risk factors [5].
As the most common metabolic disorder, Type 2 diabetes mellitus (T2DM) threatened aging populations because of its various complications. Apart from being a strong risk factor for cardiovascular diseases and stroke, T2DM may also increase the risk of developing IVDD. To date, the potential relationship between diabetes and IVDD has been recognized in animal and clinical studies. In diabetic models, IVDD-related pathological changes in spine structure such as loss of disc height, decreased vertebral bone mass, and endplate sclerosis were well documented (6–9).However, in contrast to the consistently positive lines of evidence in laboratory studies, clinical studies have produced inconsistent results. Some researchers have inferred that T2DM is a significant risk factor for IVDD using cross-sectional and retrospective studies (10–13). Nevertheless, these cross-sectional or case–control studies failed to examine the independent association between DM and IVDD. It has been challenged that the correlation would disappear when controlling for body mass index (BMI) or other risk factors [14, 15]. In fact, these inconsistent results may be due to the limitation of observational studies with susceptibility to bias and an inability to make causal inference.
Recently, Mendelian randomization (MR) studies, which use an epidemiological approach that assesses the causal effect of a risk factor on an outcome, have been increasingly used to overcome the aforementioned limitations and explore causal relationships [16]. *Since* genetic variants are randomly assigned, the confounding factors are minimized by the MR method. Genetic variation significantly associated with exposure can therefore be used as instrumental variables (IVs). There are three assumptions that must be satisfied for instrumental variables: IV1, associated with the exposure; IV2, independent of the outcome given the exposure; and IV3, independent of all confounders known thus far 16. To date, limited evidence for causal factors of IVDD has been reported. In particular, the relationship between diabetes and IVDD has not been fully investigated by MR.
In this regard, we explored the causal effect of T2DM on IVDD using a two-sample MR analysis. Furthermore, as BMI and T2DM are strongly correlated, and because previous observational studies and MR analysis have suggested that causal association may exist between BMI and IVDD (10, 13, 17–19), we therefore conducted a multivariable MR to examine the direct effect of T2DM on IVDD after adjusting for BMI.
## Study design
The study design is shown in Figure 1. We first performed univariable MR to assess the causal relationship between T2DM and IVDD. Then, multivariable MR was conducted to adjust for BMI, which has been suggested to have a causal effect on IVDD (10, 13, 17–19), in order to assess the direct effect of T2DM. We used publicly available GWAS data with the informed consent and ethical approval previously obtained (20–22).
**Figure 1:** *The study design. IVDD, intervertebral disc degeneration; T2DM, Type 2 diabetes mellitus; GWAS, genome-wide association study; MR, Mendelian randomization; BMI, body mass index; IVW, inverse-variance weighted.*
## GWAS data source
Summary GWAS data for IVDD were available from the FinnGen consortium, including 29,508 cases and 227,388 controls [20]. IVDD was diagnosed according to ICD-10 M51, ICD-9 722, and ICD-8 275. Other detailed information of the outcome is presented in Supplementary Table 1.
The GWAS data of T2DM were from a meta-analysis with ~16 million genetic variants in 62,892 T2DM cases and 596,424 controls of European ancestry [21]. Analysis was adjusted for age, sex, and the first 20 PCs. Genetic instruments for BMI were identified using results from the largest available meta-analysis of GWAS in 681,275 individuals of European ancestry [22].
## Instrumental variable selection
For univariable MR analysis, we first identified independent (linkage disequilibrium, LD clumping r 2 threshold = 0.001 and window size = 1,000 kb), genome-wide single-nucleotide polymorphisms (SNPs) significantly associated with T2DM ($p \leq 5$ × 10−8). For the multivariable MR analysis, we pooled all genome-wide significant SNPs that were significantly associated with T2DM or BMI and then clumped these SNPs with respect to the lowest p-value corresponding to any of the two using a 1,000-kb window and pairwise LD r 2 < 0.001. We calculated the proportion of phenotypic variance explained by instrumental variable SNPs of T2DM and computed the F statistic to verify whether they were strong instruments [23].
## MR analysis
We used the inverse-variance weighted (IVW) method as the primary MR approach [16]. MR-Egger, weighted median, simple median tests, and MR-PRESSO were further conducted to control horizontal pleiotropy [16]. We also used the Cochran Q statistic and MR-Egger (intercept) to test for the heterogeneity and pleiotropy [16].
Next, as BMI and T2DM are strongly correlated and the causal association may exist between BMI and IVDD in previous studies (10, 13, 17–19), we conducted multivariable MR adjusting for BMI to show the casual effect of T2DM on IVDD. The methods we used to conduct multivariable MR included IVW, MR-Egger, and MR-Lasso [24]. Moreover, Cochran Q statistic and MR-Egger (intercept) were also conducted for the heterogeneity and pleiotropy of multivariable MR analysis. All statistical analyses were conducted using the “Two Sample MR” (version 0.5.6) and “Mendelian Randomization” (version 0.5.1) in the statistical program R (version 4.1.1). $p \leq 0.05$ was considered as statistically significant.
## Genetic instruments
We finally identified 128 independent SNPs that are significantly related to T2DM as instrumental variables (Supplementary Table 2). The phenotypic variances they accounted for was $13.9\%$, calculated by R 2. The F statistics of each SNP was greater than 10 (Supplementary Table 2). These findings suggested that there is no potential weak instrument bias, satisfying hypothesis IV1.
## Causal relationship between IVDD and T2DM
Cochran Q test showed that there was instrumental heterogeneity ($p \leq 0.05$) (Table 1). Therefore, we employed the random-effect IVW method. The result showed that patients with T2DM have a $6.9\%$ higher risk of IVDD than those without T2DM (OR, 1.069; $95\%$ CI, 1.026–1.115; $$p \leq 0.002$$) (Figure 2).
## Sensitivity analysis
The effect values obtained from simple median (OR, 1.081; $95\%$ CI, 1.022–1.143; $$p \leq 0.007$$), weighted median (OR, 1.082; $95\%$ CI, 1.018–1.151; $$p \leq 0.012$$), and MR-Egger (OR, 1.031; $95\%$ CI, 0.933–1.140; $$p \leq 0.550$$) methods were consistent with the IVW estimate (Figures 2 and 3). There was also no significant differences between MR-Egger intercept and 0 (Table 1), which suggested no interference of horizontal pleiotropy in our study.
**Figure 3:** *Scatter plot of the relationship between T2DM and IVDD using inverse-variance weighted, simple median, MR-Egger, and weighted median. SNP, single-nucleotide polymorphism; ebi-a-GCST006867, the GWAS ID of BMI; finn-b-M13-INTERVERTEB, the GWAS ID of IVDD.*
Furthermore, using MR-PRESSO, three outliers with horizontal pleiotropy were found. After removing these abnormal SNPs, we obtained corrected effect estimate showing similar results (OR, 1.059; $95\%$ CI, 1.017–1.102; $$p \leq 0.006$$). The leave-one-out plot also showed that removing any of the SNPs did not change the results significantly, suggesting the reliability of the results (Supplementary Figure 1).
The causal association between BMI and IVDD was suggested in previous studies. Therefore, we conducted a multivariable MR analysis including both BMI and T2DM as exposures to explore the direct effect of T2DM on IVDD. There were 829 independent SNPs selected as instrumental variables for T2DM and BMI (Supplementary Table 3). Although the relationship between BMI and IVDD was confirmed (OR, 1.189; $95\%$ CI, 1.091–1.288; $p \leq 0.001$), T2DM still showed a direct effect on IVDD (OR, 1.080; $95\%$ CI, 1.041–1.121; $p \leq 0.001$) conditioned on BMI (Figure 4) (Table 2). Moreover, multivariable MR-Egger suggested that there was no horizontal pleiotropy in MR analysis (Intercept $p \leq 0.05$). Moreover, although the Cochran Q test suggested that there may be heterogeneity ($p \leq 0.01$), the result of the MR-Egger test was the same as that of IVW (OR, 1.080; $95\%$ CI, 1.041–1.121; $p \leq 0.001$) (Table 2). The result of the MR-Lasso test also remained stable after removing heterogeneous SNPs (OR, 1.078; $95\%$ CI, 1.039–1.115; $p \leq 0.001$) (Table 2). Taken together, our results are proven to be reliable.
**Figure 4:** *Multivariable MR results. IVDD, intervertebral disc degeneration; T2DM, Type 2 diabetes mellitus; OR, odds ratio; CI, confidence interval.* TABLE_PLACEHOLDER:Table 2
## Discussion
To date, as the role of metabolic characteristics was evident in the development of IVDD, the influence of diabetes on IVDD has aroused widespread attention. However, strong clinical evidence for the direct relationship between diabetes and IVDD remains insufficient. In this study, we demonstrated that T2DM was an important risk factor causally associated with IVDD by using MR analysis. Furthermore, the multivariable MR suggested that the causal association between T2DM and IVDD was independent of BMI.
Our study was in line with five recent studies, which implicated the potential association between diabetes and IVDD. In 2016, Agius et al. first conducted a cross-sectional study on 100 patients with diabetes, investigating the changes in intervertebral disc of patients with T2DM. They found that diabetes might be a risk factor for IVDD since it is associated with significantly lower height of lumbar discs [11]. Furthermore, a retrospective single-center study in Chinese patients with diabetes suggested that longer duration and poorly controlled T2DM were risk factors for lumbar disc degeneration. In addition, long-standing diabetes may be a predictor for severe IVDD ($p \leq 0.05$) [12].
Considering that the above samples were sill not sufficient enough, larger populations are required for adequate power. Hence, Jakoi et al. performed a cross-sectional study using a large insurance industry database in USA and discovered that IVDD is correlated with diabetes [10]. Similarly, a case–control study that enrolled 160,911 patients with IVDD and 315,225 controls in a group of military members also suggested that diabetes was a risk factor for developing IVDD [13]. However, these two large studies still have some limitations such as coding bias. Additionally, since the use of cross-sectional study design cannot confirm the causal relationship, Teraguchi et al. conducted the Wakayama Spine Study in a longitudinal population-based cohort, demonstrating that diabetes was a significant contributor to IVDD in the upper lumbar spine (OR, 6.83; $95\%$ CI, 1.07-133.7) [25]. However, these results should also be interpreted with caution, as the sample size of patients with diabetes was small. Therefore, large-scale studies and highly persuasive lines of evidence are needed to further validate the causal relationship between diabetes and IVDD.
With respect to the underlying mechanism, crucial aspects of the linked pathogenesis of IVDD in T2DM are identified using animal models. *In* general, IVDD consists of three main components: the inner nucleus pulposus (NP), the outer annulus fibrosus (AF), and the cartilaginous endplates (CEPs), which anchor the disc to the adjacent vertebrae [26]. In T2DM, irreversible formation and accumulation of advanced glycation end products (AGEs) due to hyperglycemia may result in pathophysiological changes in CEPs and contribute to undermining the nutrient supply, cell viability, matrix homeostasis, and biomechanical properties of the intervertebral disc, leading to structural weakening and, ultimately, IVDD. Interestingly, preclinical evidence from a study of a rat model suggests that T2DM compromises IVDD composition, ECM homeostasis, and biomechanical behavior changes, rather than obesity [6]. In summary, diabetic models indicated that hyperglycemia could exert a direct effect on IVDD by multiple diabetic-related pathways [1, 5, 27, 28].
As mentioned above, observational studies could not provide insight into the causal relationship between diabetes and IVDD, even based on a larger sample scale. Furthermore, unmeasured confounding variables, reverse causality, and survival bias may fail to give strong evidence on the relationship of interest. Therefore, we conducted the first MR study of T2DM and IVDD to address this uncertainty. MR analysis used genetic variants as instrumental variables for causal inferences about the effect of modifiable exposures on health- and disease-related outcomes in the presence of unobserved confounding variables [29]. In consequence, differences in the outcome can be credited to the difference in the risk factor if the genetic variants are not related to confounders [30].
It should be noted that increasing evidence suggested that higher BMI, especially being overweight or obese, is associated with the risk of IVDD (10, 13, 17–19). Moreover, as higher BMI is interrelated with T2DM [31], the confounding effect of BMI should be paid attention to when discussing the relationship between T2DM and IVDD. As a result, we included both T2DM and BMI in our multivariable MR analysis to explore whether the effect of T2DM on IVDD is independent of BMI. In our study, although the causal association between BMI and IVDD was observed, T2DM was still associated with a higher risk of IVDD after adjusting for BMI in IVW analysis. Hence, we suggested that the causal effect of T2DM on IVDD persisted even when the impact of BMI was accounted for. In addition, the effect of BMI should also be considered when discussing other risk factors for IVDD.
The strengths of the study are as follows: First, a causal association was demonstrated using two large GWAS summary datasets for the first time, which is important for the prevention of IVDD and future clinical research. Second, we used multiple methods to test and account for heterogeneity and horizontal pleiotropy, in order to ensure the reliability of the results. Last, we used multivariable MR to examine the direct effect of T2DM on IVDD adjusting for BMI. However, some limitations should be noted: The GWAS data we used were from the European descent population, and the result cannot be generalized to other populations.
In summary, this is the first MR study to explore the causal effect of T2DM on IVDD, and the effect persisted even when we accounted for the impact of BMI. Moreover, further research is warranted to understand the biological mechanism of this causal effect.
## 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 in the article/ Supplementary Material.
## Author contributions
PJ and WT conceptualized and designed the study. PJ, YX, BX, YW, KY, and JZ performed data analysis. PJ 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/fendo.2023.1100874/full#supplementary-material
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|
---
title: Physical performance and muscle strength rather than muscle mass are predictor
of all-cause mortality in hemodialysis patients
authors:
- Xiaoyu Chen
- Peipei Han
- Kun Zhang
- Zhenwen Liang
- Chen Yu
- Ningtao Lu
- Zhouyue Shen
- Fengyan Chang
- Xin Fang
- Qi Guo
journal: Frontiers in Public Health
year: 2023
pmcid: PMC10011695
doi: 10.3389/fpubh.2023.1087248
license: CC BY 4.0
---
# Physical performance and muscle strength rather than muscle mass are predictor of all-cause mortality in hemodialysis patients
## Abstract
### Objectives
Patients undergoing maintenance hemodialysis usually suffer a high burden of poor functional status. The aim of this study was to investigate the association between muscle mass, muscle strength as well as physical performance with all-cause mortality in hemodialysis patients.
### Methods
923 hemodialysis patients (565 men, mean aged 61.3 ± 12.7 years) were included from eight facilities in Tianjin and Shanghai of China from 2019 to 2021. Muscle mass was evaluated by skeletal muscle index (SMI) and muscle strength was assessed by handgrip strength. Different measures of physical performance were measured via gait speed, Timed Up and Go Test (TUGT) and short physical performance battery (SPPB). Cox proportional hazards regression models were used to determine the adjusted hazard ratios (HRs) of mortality with $95\%$ confidence intervals ($95\%$ CIs) for baseline muscle mass, muscle strength and different measures of physical performance. Additionally, the area under the Receiver Operating Characteristic (ROC) curves were constructed to determine which index is a better predictor of mortality.
### Results
During a median follow-up of 14 (12–17 months), 79 ($8.6\%$) patients died. Using the Cox regression analysis, we founded that muscle strength and physical performance rather than muscle mass were significantly negatively associated with mortality. The C-index for different measures of physical performance in predicting mortality were 0.709 for SPPB, 0.7 for TUGT and 0.678 for gait speed, respectively. The C-index for muscle strength was 0.635, and the ability of prediction was significantly lower than the physical performance.
### Conclusions
Physical performance seems to a better indicator of mortality than muscle mass and strength in hemodialysis patients. Simple measures of physical performance may be appropriately used as a screening tool targeting high-risk hemodialysis patients for the prevention of mortality.
## Introduction
The prevalence of patients with end-stage renal disease (ESRD) is increasing rapidly, which has been a major public health problem in most countries [1, 2]. Despite considerable improvement in dialysis modalities and patient care, hemodialysis patients still have an exceedingly higher mortality rate compared to the general population [3]. The potential contributors to the poor survival status might be older age, comorbidities, malnutrition, underdialysis and decreased physical function [4, 5]. It is reported that poor functional status is strongly related to advanced risks of adverse events in hemodialysis patients (6–8). Therefore, there is growing interest in finding effective and practical tests that can be used as screening tools to identify early populations that may benefit from targeted interventions.
Sarcopenia is a clinical disorder defined as loss of skeletal muscle mass and low muscle strength and/or physical performance [9]. It's worth noting that muscle strength is not entirely dependent on muscle mass, and two elements may disassociate. With increasing age, muscle strength decreases at a rate greater than the rate of loss of muscle mass, even when muscle mass is maintained or increased [10, 11]. As a result, there is a great interest in correctly distinguishing between the loss of muscle mass with muscle strength. Indeed, this is particularly important because treatments to maintain or increase muscle mass or muscle strength are not necessarily the same [12]. Skeletal muscle plays a key role in metabolic function, facilitating glucose uptake and storage, and is related to physical performance. Recent studies suggest that muscle strength and physical performance were associated with mortality in hemodialysis patients (13–15).
However, they did not comprehensively take into account the various domains of physical performance indicators but only focused on one or two domains of physical performance, such as gait speed. Thus, it remains unclear whether or to what extent physical performance indicators are more independently associated to mortality in hemodialysis patients. In addition, several studies showed that higher muscle mass was independently associated with reduced risks of all-cause mortality in hemodialysis patients [16, 17], but previous findings regarding the relationship between the muscle mass and mortality have been discrepant [8, 18]. Thus, more evidence is required to explore the associations between muscle mass, muscle strength and physical performance with mortality.
The purpose of this study was to identify the relationships between muscle mass, muscle strength and physical performance with mortality in patients on hemodialysis. Additionally, we aimed to examine which measure(s) was/were the most prominent in this relationship and could, accordingly, be relevant to be used in the clinical screening of hemodialysis patients. A broad understanding and addressing this are important for health care providers and policy makers in response to huge health care challenges.
## Participants and study design
This is a multicenter study including hemodialysis patients from eight hemodialysis centers in Tianjin and Shanghai between December 2019 and April 2021 at baseline. Patients were eligible to participate if they were over 18 years of age, had received maintenance hemodialysis for at least 3 months and were able to provide informed consent. Exclusion criteria was described as follows: [1] unable to measure the body composition; [2] inability to perform the handgrip strength test or the physical performance test; [3] patients with visual impairment or hearing impairment difficulties; [4] unable to communicate with researchers or provide informed consent. The final study sample comprised 923 subjects (Figure 1). All patients provided informed consent prior to enrollment in the study. This study was approved by the Ethics Committee of Shanghai University of Medicine and Health Sciences.
**Figure 1:** *Flowchart of study participants.*
## Baseline variable
All patients were invited to a face-to-face interview to answer a standardized questionnaire. Socio-demographic characteristics (including age, gender, post-dialysis weight, dialysis vintage and education level), health behaviors (including smoking and drinking) and condition of chronic diseases were considered as covariates. The short form of the International Physical Activity Questionnaire (IPAQ) was used to assess the physical activity [19]. Comorbidity was evaluated by the Charlson comorbidity index [20]. All blood samples were drawn pre-dialysis. Details of measurement methods have been described in our recent study [21, 22].
## Assessment of muscle mass
Bioelectrical impedance analysis (BIA) (InBody S10; Biospace, Seoul, Korea) was used to measure muscle mass in the pre-dialysis period [23]. Patients were placed in a supine position at least 10 min before assessment. Muscle mass was evaluated as the skeletal muscle index (SMI), calculated as the relative skeletal muscle mass index divided by the square of height [9].
## Assessment of muscle strength
A dynamometer was used to assess the muscle strength on the non-fistula prior to a dialysis session (GRIP-D; Takei Ltd, Niigata, Japan). Patients were asked to make maximum effort twice, and the result of the strongest handgrip strength was used in the analysis. For patients with indwelling dialysis catheters, we tested handgrip strength with the dominant hand.
## Evaluation of physical performance
Physical performance was measured via gait speed, Timed Up and Go Test (TUGT) and short physical performance battery (SPPB). We used the four-meter walking test to assess the gait speed. Patients were asked to stand and walk a distance of eight meters at a normal gait speed, and the time taken for the middle four meters was recorded. The patient was allowed to use a walking aid device [24]. TUGT required a person to stand up from a chair, then walk 3 m, turn around, walk back and sit down again [25]. The SPPB consists of three sequential tests that assess semi-tandem and tandem balance test, gait speed, and 5-sit-to-stand test. The total score ranges from 0 to 12 points, and 12 showing the best physical function performance to 0 showing inability to do the tests [26]. All these physical performance tests were performed before a dialysis session.
## Statistical analyses
Baseline characteristics and clinical parameters are expressed as the means ± standard deviations (SDs) or as the numbers of patients and percentages. Analysis of independent t-test and chi-square test were used to compare variables between patients alive and deceased. Cox regression analyses are used to explore the association between muscle mass, muscle strength and physical performance with mortality, presented as hazard ratios with $95\%$ confidence intervals. Kaplan-Meier curves were generated to assess the probabilities of the patient outcomes according to categorical of SPPB, and the Cox proportional hazards model was used for further multivariate adjustments with possible confounders including age, sex, BMI, IPAQ, Kt/v, albumin, hemoglobin, smoking, fall history, depression, malnutrition, and Charlson comorbidity index. The C -index was defined as the area under the ROC curves between individual measurement predictive probabilities for mortality. We further used C-index to identify the physical performance best correlated with all-cause mortality. Statistical analyses were performed using IBM SPSS Statistics v26.0 (SPSS Inc., Chicago, Illinois, United States). $P \leq 0.05$ was considered to indicate statistical significance.
## Baseline characteristics
The baseline characteristics of the study patients were presented in Table 1. Among the 923 patients (565 men), the mean age was 61.3 ±12.7 years. The median dialysis vintage was 45.12 months (range 22.19–92.80 months), and the mean BMI was 23.34 ± 3.82 kg/m2. Deceased patients were significantly older, and they tend to fall, depressed, malnourished and smoke ($P \leq 0.05$). Furthermore, BMI, IPAQ, handgrip strength, gait speed, TUGT, SPPB, hemoglobin levels, albumin levels and Kt/v were significantly lower in deceased patients than in patients alive ($P \leq 0.05$).
**Table 1**
| Characteristics | Total | Alive | Deceased | P value |
| --- | --- | --- | --- | --- |
| | (n = 923) | (n = 844) | (n = 79) | |
| Age (y) | 61.3 ± 12.7 | 60.8 ± 12.7 | 67.0 ± 11.3 | < 0.001 |
| Male (%) | 565 (61.2) | 512 (60.7) | 53 (67.1) | 0.262 |
| Dry weight (kg) | 62.95 ± 12.88 | 63.18 ± 12.70 | 60.46 ± 14.48 | 0.072 |
| BMI (kg/m2) | 23.34 ± 3.82 | 23.44 ± 3.78 | 22.25 ± 4.10 | 0.008 |
| Vintage (months) | 45.12 (22.19, 92.80) | 45.83 (23.13, 94.80) | 39.2 (17.37, 70.13) | 0.087 |
| IPAQ (Met-min/wk) | 1,386 (594, 3,066) | 1,386 (693, 3,273) | 660 (0, 1,533) | < 0.001 |
| Education (%) | | | | 0.062 |
| Less than high school | 214 (23.2) | 189 (22.4) | 25 (31.6) | |
| High school or higher education | 709 (76.8) | 655 (77.6) | 54 (68.4) | |
| Drinking (%) | 11 (1.2) | 11 (1.3) | 0 (0.0) | 0.421 |
| Smoking (%) | 200 (21.7) | 180 (21.3) | 20 (25.3) | < 0.001 |
| SMI (kg/m2) | 7.00 ± 1.21 | 7.02 ± 1.20 | 6.77 ± 1.35 | 0.077 |
| Handgrip strength (kg) | 24.81 ± 8.80 | 25.17 ± 8.74 | 20.93 ± 8.53 | < 0.001 |
| Gait speed (m/s) | 0.97 ± 0.31 | 0.99 ± 0.29 | 0.79 ± 0.36 | < 0.001 |
| TUGT(s) | 10.26 ± 7.90 | 9.76 ± 6.51 | 15.56 ± 15.77 | < 0.001 |
| SPPB | 9.57 ± 2.96 | 9.80 ± 2.73 | 7.05 ± 4.04 | < 0.001 |
| Fall history (%) | 329 (35.6) | 288 (34.1) | 41 (51.9) | 0.002 |
| Depression (%) | 388 (42.0) | 340 (40.3) | 48 (60.8) | < 0.001 |
| Malnutrition (%) | 252 (27.3) | 214 (25.4) | 38 (48.1) | < 0.001 |
| Number of medications (n) | 4.49 ± 2.45 | 4.46 ± 2.44 | 4.72 ± 2.55 | 0.371 |
| Charlson comorbidity index | 3.87 ± 1.72 | 3.80 ± 1.71 | 4.58 ± 1.66 | < 0.001 |
| Laboratory parameters | Laboratory parameters | Laboratory parameters | Laboratory parameters | Laboratory parameters |
| Hemoglobin (g/dL) | 110.78 ± 16.29 | 111.25 ± 15.96 | 105.79 ± 18.92 | 0.004 |
| Albumin (g/L) | 39.41 ± 3.61 | 39.59 ± 3.44 | 37.47 ± 4.66 | < 0.001 |
| PTH (pg/dL) | 356.86 ± 329.28 | 361.83 ± 330.38 | 300.76 ± 313.36 | 0.129 |
| Calcium (mg/dL) | 2.28 ± 0.26 | 2.27 ± 0.26 | 2.31 ± 0.31 | 0.270 |
| Phosphorus (mg/dL) | 1.93 ± 0.65 | 1.94 ± 0.64 | 1.88 ± 0.81 | 0.449 |
| Kt/v | 1.36 ± 0.33 | 1.37 ± 0.33 | 1.27 ± 0.29 | 0.013 |
## Physical performance is associated with poor survival
During a median follow-up of 14 months (10th percentile-90th percentile, 12–17 months), 79 patients ($8.6\%$) died. The associations between muscle strength and different measurements of physical performance and mortality are presented in Table 2. After adjustments for potential confounders (age, sex, BMI, IPAQ, Kt/v, albumin, hemoglobin, smoking, fall history, depression, malnutrition, and Charlson comorbidity index), handgrip strength (HR = 0.96, $95\%$ CI 0.92–0.99), gait speed (HR = 0.40, 0.18–0.92), TUGT (HR = 1.03, 1.01–1.04) were significantly associated with depressive symptoms. In the adjusted Cox regression model by SPPB categories, patients with very low (score 0–3) had significantly higher risks of death (HR = 3.98, 1.99–7.95). Survival curves of patients by SPPB categories are shown in Figure 2. Survival curves differed significantly at the log-rank test ($P \leq 0.001$).
## Predictive values of physical performance measurements for mortality
With regard to model discrimination, the C-index of the physical performance (C-index for TUGT: 0.678; SPPB: 0.709) were significantly higher than the muscle strength (C-index for handgrip strength: 0.635) (TUGT vs. handgrip strength: $$P \leq 0.046$$; SPPB vs. handgrip strength: $$P \leq 0.020$$), and there was no significant between the handgrip strength and gait speed (C-index for handgrip strength: 0.635; gait speed: 0.678; $$P \leq 0.195$$, Table 3). However, there was no significant difference among the different measurements of physical performance in the values of C-index for mortality.
**Table 3**
| Variables | C-index | SE | P | P.1 | P.2 |
| --- | --- | --- | --- | --- | --- |
| Handgrip strength | 0.635 (0.603, 0.666) | 0.0329 | Ref | | |
| Gait speed | 0.678 (0.646, 0.708) | 0.0332 | 0.195 | Ref | |
| TUGT | 0.700 (0.669, 0.729) | 0.0314 | 0.046 | 0.293 | Ref |
| SPPB | 0.709 (0.679, 0.738) | 0.0317 | 0.020 | 0.121 | 0.637 |
## Discussion
This prospective cohort study demonstrated the influence of muscle strength and physical performance on the mortality risk in patients on maintenance hemodialysis, and supports the physical performance had superior prognostic discrimination for mortality, which deserved as an effective, costless and easily feasible screening strategy in this population. In contrast, no association was observed between muscle mass and mortality.
In line with our findings, recent studies suggest that muscle strength and physical performance were associated with mortality in hemodialysis patients [13, 14]. These studies didn't comprehensively take into account the various domains of physical performance indicators but only focused on one or two domains of physical performance, such as gait speed. Our study also identified that very low physical performance in the SPPB, as well as gait speed and TUGT, were associated with lower survival and a higher risk of death. In addition, we observed that there was no association between muscle mass and mortality in our study, which was consistent with previous studies [8, 18]. However, conflicting results have also been reported. Yajima et al. [ 16] and Fukasawa et al. [ 17] demonstrated that higher muscle mass was independently associated with reduced risks of all-cause mortality in hemodialysis patients. The main reasons for the differences are geographical differences and small population samples. In their study, the sample sizes were 162 and 81 respectively, which were significantly lower than the sample sizes of this study. Recent study has shown a strong association between sarcopenia and mortality in patients undergoing hemodialysis [27], so the association between muscle mass, one of the primary diagnostic factors for sarcopenia, and mortality needs to be further confirmed in the future.
Although the clinical relevance of muscle strength and physical performance as predictors of mortality in hemodialysis patients has been documented in previous studies, to date there have been no direct comparisons of the two tests. Interestingly, our results suggest that physical performance had superior prognostic discrimination for mortality than muscle strength. The possible reason may be that muscle function of lower extremities might be more important than that of the upper extremities regarding the patients' adverse outcomes. Previous research reported a discrepancy in upper and lower strength in a CKD cohort study [28], and other studies also revealed that muscle function in the lower extremities but not in the upper extremities was related to overall physical performance [29], suggesting the clinical importance of lower extremity performance. Furthermore, Johansen et al. found that physical performance such as gait speed declined frequently while handgrip strength didn't change over time among the hemodialysis patients [30]. In that study, physical performance was the strongest predictor of mortality, which is similar to our results. Thus, we believe that monitor physical performance has the potential to be a valuable tool for continuous risk stratification of hemodialysis population.
Regarding the prognostic discrimination of different physical performance indicators for mortality, although SPPB showed the highest C index, it was not significantly different from gait speed and TUGT. The SPPB is an easy-to-apply instrument that includes balance, gait and lower strength, and has been used to evaluate the level of physical performance in different settings [31, 32]. Our results show that patients with very low (score 0–3) by SPPB categories had significantly higher risks of death (HR = 3.98, 1.99–7.95). The cutoff points could help to identify hemodialysis with a higher risk of death at an early stage, given the easy applicability of the SPPB. Of note, SPPB includes three subtests, which would also take longer time to test than gait speed and TUGT. In the future, we need to consider whether isolated gait speed and TUGT would be useful to establish the predictive power for mortality, because there are advantages regarding time and costs to performing one test in an isolated manner compared to the entire SPPB.
Our study has several strengths. This is a multicenter study that comprehensively consider the association of muscle mass, muscle strength and various domains of physical performance indicators with mortality in hemodialysis population. In addition, most recognized confounders were taken into account into Cox regression models to analyze the independent association of physical performance and mortality in this study. Despite extensive efforts to curb study limitations, some limitations of this study should be considered. There is a concern about selection bias because patients who were incapable of performing the muscle strength and physical performance tests were excluded from our study. Second, our study was limited to Chinese patients on hemodialysis, thereby limiting the generalizability of our findings to the broader international hemodialysis population.
## Conclusion
Our study suggest that muscle strength and physical performance rather than muscle mass were significantly associated with all-cause mortality in hemodialysis patients. Furthermore, physical performance had superior prognostic discrimination for mortality than muscle strength, which is effective, costless and easily feasible screening strategy for better patient assessment and individualized care.
## Data availability statement
The datasets presented in this article are not readily available because our database is still expanding. All the multi-center hemodialysis centers have signed the data confidentiality agreement, so we are very sorry that we cannot upload it to the public database. But if you have any questions about the data, please write to us, and we will be happy to answer them for you. Requests to access the datasets should be directed to QG, [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Shanghai University of Medicine and Health Sciences. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. 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
Study concept and design: XC, PH, and KZ. Acquisition, analysis, and interpretation of data: NL, ZS, FC, and XF. Drafting of the work: XC and PH. Critical revision of the manuscript: ZL, CY, and QG. 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: 'Evolution of public health policy on healthcare self-management: the case
of Ontario, Canada'
authors:
- Amélie Gauthier-Beaupré
- Craig Kuziemsky
- Bruno J. Battistini
- Jeffrey W. Jutai
journal: BMC Health Services Research
year: 2023
pmcid: PMC10011770
doi: 10.1186/s12913-023-09191-3
license: CC BY 4.0
---
# Evolution of public health policy on healthcare self-management: the case of Ontario, Canada
## Abstract
### Background
As people live longer, they are at increased risk for chronic diseases and disability. Self-management is a strategy to improve health outcomes and quality of life of those who engage in it. This study sought to gain a better understanding of the factors, including digital technology, that affect public health policy on self-management through an analysis of government policy in the most populous and multicultural province in Canada: Ontario. The overarching question guiding the study was: What factors have influenced the development of healthcare self-management policies over time?
### Methods
Archival research methods, combining document review and evaluation, were used to collect data from policy documents published in Ontario. The documents were analyzed using the READ approach, evaluated using a data extraction table, and synthesized into themes using the model for health policy analysis.
### Results
Between January 1, 1985, and May 5, 2022, 72 policy documents on self-management of health were retrieved from databases, archives, and grey literature. Their contents largely focussed on self-management of general chronic conditions, while $47\%$ ($$n = 18$$/72) mention diabetes, and $3\%$ ($$n = 2$$/72) focussed solely on older adults. Digital technologies were mentioned and were viewed as tools to support self-management in the context of healthcare delivery and enhancing healthcare infrastructure (i.e., telehealth or software in healthcare settings). The actors involved in the policy document creation included mostly Ontario government agencies and departments, and sometimes expert organizations, community groups and engaged stakeholders. The results suggest that several factors including pressures on the healthcare system, hybrid top-down and bottom-up policymaking, and political context have influenced the nature and implementation timing of self-management policy in Ontario.
### Conclusions
The policy documents on self-management of health reveal a positive evolution of the content discussed over time. The changes were shaped by an evolving context, both from a health and political perspective, within a dynamic system of interactions between actors. This research helps understand the factors that have shaped changes and suggests that a critical evidence-based approach on public health policy is needed in understanding processes involved in the development of healthcare self-management policies from the perspective of a democratic governing system.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-023-09191-3.
## Background
Life expectancy continues to increase globally, leading to an aging population that will crest in the next generation. Between 2000 and 2019, there was an increase in global life expectancy from 66.8 years to 73.4 years, respectively [1]. In Canada, the life expectancy increased to reach an 86-year-old average in either sex [2]. For many, it may afford increased time to spend with loved ones and more time to participate in various social and physical activities. However, the increase in life expectancies also comes with an increased risk of developing chronic diseases and comorbidities that may result in disability [2]. Living with chronic diseases and disability can lead to various disabling conditions that often trigger some level of healthcare self-management. Van de Velde et al. [ 3] define self-management as “the intrinsically controlled ability of an active, responsible, informed, and autonomous individual to live with the medical, role and emotional consequences of his chronic condition(s) in partnership with his social network and the healthcare provider(s)” (p.10). Chronic disease self-management is an important aspect of tertiary prevention, whose goal is to achieve a return to maximal function [4], and has only recently become part of urgent public health policy. Addressing self-management of disease promotes healthy living and well-being, which is one of the United Nations’ sustainable development goals [5], endorsed by Canada who has committed to advancing these goals [6].
Self-management can empower older adults to overcome barriers of an overwhelmed and underfinanced healthcare and social system, and leave them better equipped to face challenges in their everyday lives. Interventions to promote self-management, by developing abilities of patients (i.e., education, training and support), have shown to improve health outcomes and reduce healthcare utilization [7]. Conversely, this may reduce experiences of disability that these individuals would otherwise encounter which would make them – to some degree—stronger. Similarly, self-management of disability involves learning to live amidst the disability and forcing to find solutions to increase quality of life while still having to deal with everyday challenges of the disability. By self-managing diseases and disabilities, individuals can enhance their sense of autonomy and dignity, and thereby promote their mental and physical well-being [8]. As an enabler to self-management, digital technology can support activities such as exercising, healthy eating, medication management, monitoring of signs and symptoms, and problem-solving (in cases of distress for example) [9–12].
For Canadians, opportunities to become involved in self-management are numerous and often supported via programming such as adaptations of the Stanford Chronic Disease Self-Management Program [13]. For example, several provinces integrate the Stanford Chronic Disease Self-Management Program into community-based programs or deliver it through local health authorities [14]. While self-management support programs exist, they are not fully integrated within the healthcare system because healthcare providers have limited knowledge about their availability or how to refer their patients to them [15], which limits their effectiveness and ability to meaningfully contribute to improving the quality of life for older adults. Through the years, there have been several attempts by provinces and territories (editor’s note: Canada has 13 separate healthcare systems) to better integrate self-management into healthcare systems and in the lives of Canadians by developing policy actions on the issue, but research has shown that efforts are either disease-specific or embedded in population-wide approaches [14]. Uneven and inequitable implementation of self-management programming and supports limits the impact and reach of such efforts. For example, a focus placed specifically on diabetes management, may have limited impact for those who need to self-manage other chronic conditions or disabilities and functional limitations that may be linked to advanced age. In addition, policies that are focussed on the general population may have limited impact for certain segments of the population, such as older adults, that may have special concerns, needs and sets of difficulties in performing daily activities. This focus and perspective points to larger issues with the processes of policymaking regarding self-management in Canada. To address these issues, we need to undertake an in-dept scan of current and historical policies to understand how a variety of approaches to policymaking came about and to identify which factors have led to advancements (or not) in the area. As per the model for health policy analysis [16], several contextual elements (such as political regimes) and many diverse stakeholders influence how a policy is implemented into society. For example, elements of context such as increased concerns with rising cases for chronic conditions, the political or economic system in a country at a specific time, or major societal events (e.g., the period studied here, COVID-19 pandemic) can all shape the evolution of policies on health self-management. The agenda of the actors involved in the policy creation could also influence the content and way in which policies are developed and implemented.
To develop effective policies on self-management that improve the quality of life for older adults with chronic diseases, developed comorbidities and disability, there is a need to understand why and how governments have undertaken policymaking on those needs. For the purpose of this analysis, Ontario will be used as a case study because it has worked to advance health promotion and prevention initiatives for as long as 35 years [17]. This paper examines the evolution of policies for self-management of health in Ontario to understand how the content has evolved based on context and actors involved in policy creation.
The research question for our study was: What factors have influenced the development of healthcare self-management policies over time?
Sub-questions:How have policies and policy-related documents on self-management of health evolved over time in terms of their content and timing of major events and political timing in Ontario?What elements of context have influenced policies on self-management of health in Ontario?What actors were involved and how did the actors frame health self-management in the creation of policies in Ontario?
## Methodological approach
The study was conducted using the READ approach to document analysis as it sets out a series of systematic procedures to gather, review and evaluate health policy documents [18]. The READ approach was used in previous research to ensure rigour in analyzing the health policy documents [18, 19]. The four steps in this approach were developed to ensure a rigorous process throughout document analysis, and include: 1) readying the materials, 2) extracting the data, 3) analyzing the data, and 4) distilling the findings [18]. The four steps of the READ approach are described below in how they were applied to this study.
While this study follows a rigorous process for collecting, analyzing, interpreting, and presenting the findings, it is positioned and co-constructed with the author’s view and perceptions of the documents. For example, the authors kept reflexive notes as they were coding the documents. It was noted that authors had a particular interest in the role of technology in supporting self-management. As such, they have added a code about technology to document the role of technology to support self-management as discussed in policy documents and this addition is also reflected in the findings of the study.
## Defining policy documents
In this study, archival research was used to identify and document the evolution of self-management policy in Ontario. Initially, policy documents were defined as a “formal statement that defines priorities for action, goals and strategies, as well as accountabilities of involved actors and allocation of resources” (p.94) [20]. While we were able to retrieve a few policy documents as per the definition, we decided to broaden our scope to other policy-related documents that were relevant to the topic of interest. More broadly, documents were included in this study (i) if they discussed self-management, (ii) if it was either a legislative document (including a policy), a strategic or action plan, a report (including environmental scans), an evidence brief, a set of guidelines or recommendations, a memo, a news media release, a fact sheet, or a framework for action, and (iii) if it was developed and published by or with a department of the Ontario Government.
## Collection of policy documents
The search strategy was developed with the help from an information specialist at the University of Ottawa. A diverse set of platforms were included to ensure a comprehensive identification and retrieval of relevant policy documents (Additional file 1). The following sources were searched to locate relevant policy documents between January 1, 1985, and May 5, 2022: 1) the Archives of Ontario and Legislative Library of Ontario platforms to get direct access to government archives; 2) the Government of Ontario webpages to identify documents that would not be indexed in the archive databases; and 3) health and policy-specific databases to identify supplemental policy-related documents that were relevant to self-management of health. The Health Systems Evidence (HSE) repository was also verified to retrieve any additional materials not found in the other platforms and databases. For the search in the Archives of Ontario and Legislative Library of Ontario, we obtained documents that discussed self-management in the context of health and healthcare. We established a search approach with a librarian from the Archives of Ontario to ensure that we accessed all relevant available collections and gathered a comprehensive set of documents relating to self-management. The first author of this publication conducted the searches and identified the relevant documents. The documents were included if the titles and summaries discussed self-management in the context of health. For the search in the government of Ontario website, we used a keyword search through various websites’ search engines. The keywords included “self-management”, “self-care”, “self-monitoring”, and “self-efficacy”. The search results were screened and reviewed by team members who identified results that discussed self-management in the context of health. For the search in academic databases, 8 databases were selected based on their scope and the type of content that they include ensuring that they were likely to publish policy documents. They included CINAHL (EBSCO), EMBASE (OVID), ProQuest Politics Collection (ProQuest), Canadian Business and Current Affairs Database (ProQuest), Canadian Public Policy Collection (Scholars Portal Books, the Canadian Periodical Index (CPI.Q), Academic Search Complete (EBSCO) and the Government and Legislative Libraries Online Publications Portal (GALLOP). The search strategy included keywords and database-specific thesaurus words on self-management, disease and disability, and Ontario (Additional file 1).
The policy documents were screened and assessed for eligibility (Fig. 1). They were included in the study if they were policy documents and developed by or with a department of the Ontario government. Fig. 1PRISMA flow diagram for archival research. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71. For more information, visit: http://www.prisma-statement.org/
## Extraction, analysis and evaluation of policy documents
Data extraction, analysis and evaluation were performed using a modified version of the data extraction spreadsheet developed in a study on the Integrated Community Case Management of Childhood Illness Policies [21], as cited in Dalglish, S. L., Khalid, H. and McMahon, S. A. [18]. We used an excel spreadsheet where specific information about policy documents were recorded. These included descriptive information on the document authors, date of publication, type, and objective. A summary of the documents, information on the evidence cited and any relevant information on budget used in the creation of the document were also recorded. Additional details about the labels assigned are presented below.
## Step 3: analyzing the data
We used an adapted analytical model for health policy analysis (Fig. 2) to frame our analysis and evaluation of the policy documents. Walt and Gibson [16] position and define their model for policy analysis using examples from developed countries to amplify the detriment of one-sided policy models. However, the applicability and operationalisation of their model for policy analysis to developed countries is without contestation as it offers a holistic mechanism to assess various components of health policies. Walt and Gibson [16] suggested that, in the reform of health policies, too much of a focus is put on the content of the policies, ignoring that other dimensions such as context, process and actors all shape how changes in health policy occur. For example, external pressures from health advocacy/pressure groups, not-for-profits, charities, community organizations, personal experiences and interests with ideologies, cannot be ignored as they shape and create how policies are developed and implemented [22]. While the four dimensions of the model for health policy analysis are unique, they do not exist without pressures from others and are therefore interrelated [16]. Policies are highly influenced by the context within which they being developed without neglecting that there were, is, will ever be, a pervasion between the political and cultural factors of the moment [16]. In addition, the actors that are creating or influencing the policy shape policymaking from a variety of angles based on their own presumptions and assumptions. Pressures from actors advocating for change and those developing the policy are all influencing the end result of the policy. For this reason, we evaluated policy documents by reporting on the context, actors and content to better grasp and understand the political dimensions of policy changes. The process dimension, however, could not easily be identified solely from reading the documents since the process of policymaking is often not stated in these documents; therefore, it was omitted from the analysis. In addition, a review of documents did not allow to clearly understand and delineate the concrete influence of actors on content and context on actors as it requires a deeper understanding of the process for policy development. For this reason, actors are represented as a separate section in the results. By analyzing three dimensions of the policies (content, context and actors) and how they are outlined in the policy document, we will be better equipped to understand how healthcare self-management has been shaped in the context of Ontario. Fig. 2An adapted model for health policy analysis. This simple adapted analytical model for conducting policy analysis incorporates key concepts that need to be considered in the design of policies: Context, process, actors and content. Adapted from “Reforming the health sector in developing countries: The central role of policy analysis” by G. Walt and L. Gilson, 1994, Health Policy and Planning, 9[4], p. 354. Copyright 1994 by Oxford University Press *Document analysis* was used to interpret the documents for the three domains of interest. We used a deductive-inductive approach [23] where we identified preliminary codes based on the research questions and refined the codes as we were coding documents for the 3 domains until themes emerged and were defined [24]. The content of the documents was thematically analyzed by identifying key elements about self-management (frequency of mention, definition of self-management, approach to self-management [i.e., development of personal skills, collaboration between various actors, creation of educational resources, etc.]), identifying mentions of and the role of digital technology in self-management, and identifying the chronic diseases and disabilities of interest. For the context dimension, we have assigned contextual labels by identifying the political lead during document release, major events happening around the release date of the documents (i.e., COVID-19 pandemic), and whether there were significant financial implications around document release (i.e., budget funding in support of a policy). Specifically, the labels were: political lead, major events, and health-related budget. This step involved looking at supplementary files since contextual information was usually omitted or not disclosed in the policy documents. To do so, we searched the government of Ontario website for any information relating to political leads and budget, and searched media for major events at the time of the document release. Finally, the actors’ dimension was thematically analyzed using the following codes: implications from groups of actors (government sectors, community organizations, experts and engaged stakeholders), target groups for measures discussed in the policy, and intended users of the policy documents. During analysis, the data was sorted using multiple filters such as year of publication, political context and topics of interest to identify trends and emerging themes.
## Step 4: distilling the findings
As a final step, the documents were organized into a timeline based on their date of release to better visualize the historical evolution of self-management in health-related policy and what political party was governing Ontario alongside the 35 years. This was done using the program XMind. The timeline supplements the sorting action that was performed in the late stages of analysis and allowed to visually represent some of the major findings.
## Results
A thorough search of archival policy documents on self-management in Ontario led to the retrieval of 72 documents. Documents included publicly available research and government reports ($$n = 25$$), news media ($$n = 16$$), information sheets ($$n = 12$$), frameworks and policies ($$n = 6$$), and webpages ($$n = 13$$). Descriptive information was gathered and organized in an excel spreadsheet (as demonstrated in Table 1) to better represent the diversity and characteristics of documents retrieved. Table 1Example data extraction of policy documentsDocument titleDate of publicationAuthorsDocument typeDocument type and objectiveDirections from a Local Scan: Self-Management and Empowering the Person Living with Diabetes in the North Simcoe Muskoka Local Health Integration Network (LHIN)November 2009North Simcoe Muskoka LHINReportReport summarizing local health status on chronic conditions and findings from a report completed by a member of the North Simcoe Muskoka Chronic Disease Prevention and Management Regional Action GroupGuide to Chronic Disease Management and PreventionSeptember 27, 2005Family Health Teams, Ministry of Health and Long-Term CareGuidelines/ RecommendationsThis guide has been developed to assist groups that are forming Family Health Teams to plan chronic disease management and prevention programs for their patients. The guide is intended as a companion to the Guide to Strategic and Program Planning, which provides an overview of the strategic and program planning processWe're here to help you live well with diabetesJuly 2010Ministry of Health and Long-Term CareFact SheetFact sheet with information about how to manage diabetes and the supports available to do soOntario's Action Plan to Transform Healthcare in LondonMarch 9, 2012Ministry of Health and Long-Term CareNews mediaNews release announcing the Action Plan for Health Care in Ontario specific to London, Ontario *Document analysis* allowed researchers to identify several common themes for content, actors, and context. While results are reported as distinct from one another, the section on the context shows how all components of the model for health policy analysis are intertwined and influence one another.
## Content
The policy documents reveal important characteristics about focus areas and approaches to policymaking on self-management of health. Several themes identified by the researchers are described below.
## Singular disease focus
The entirety of documents retrieved focussed on the management and care for chronic diseases. The documents focussed on sharing details about resources available in the community, promoting programs and services, providing evidence on chronic disease management, or underlining guidance, recommendations and frameworks to support effective chronic disease management in Ontario. The scope of the policy documents varied with some focussing on a single chronic condition and others discussed chronic conditions generally. The chronic condition that was the focus for most of the policy documents was diabetes ($$n = 21$$; $29\%$). In total, diabetes was mentioned in close to half of all policy documents on self-management ($$n = 34$$; $47\%$). Other documents were either targeting the whole population, specific to people living with chronic diseases (group as a whole) or specific to people living with other conditions (i.e., stroke, asthma, chronic kidney disease, chronic pain, chronic obstructive pulmonary disease (COPD) and heart failure, plaque psoriasis, hip and knee replacement) (Table 2). In addition to focussing on diverse chronic conditions, 11 documents were specific to people of certain age groups (children and youth ($$n = 9$$) or older adults ($$n = 2$$)) and 11 documents were specific to certain areas of the province (i.e., people living in a Southwest Local Health Integration Network (LHIN) ($$n = 1$$)).Table 2Target population group within policy documents on self-managementTarget population groupNumber (n)All18People living with chronic diseases22People living with diabetes21People who have had a stroke2People living with chronic kidney disease2People living with COPD or who have had heart failure2People who have had hip and knee replacement1People living with asthma1People living with plaque psoriasis1People living with chronic pain2TOTAL72
## Central role of individuals with chronic diseases
The concept of self-management evoked in the policy documents was mainly related to the personal skills that people living with chronic conditions should have to be able to support their daily life, sometimes ignoring the role of family and caregivers. There was a tendency to put self-management as the responsibility of the one with a chronic condition but with great emphasis on self-management supports (i.e., community supports and healthcare provider support). These included providing the ability for people with chronic diseases to receive adequate training, education materials, and information resources on how to effectively manage their condition. The role of healthcare professionals (such as primary care providers) was also mentioned as being essential and favouring effective and purposeful self-management by people living with chronic conditions. The central responsibility, however, remained mostly on the individual living with a chronic condition, with the support of their family/friends/community entourage.
## Lack of digital technology integration
Considering the increase and evolution in the use of digital technology for healthcare, we retrieved specific details on the mentions of technology within the policy documents. While sometimes mentioned, the documents that did include digital technology did not always effectively link self-management with technology. They were mentioned sporadically and mostly in the presentation of specific initiatives and programs, and in relation to its benefits for enhancing effectiveness in healthcare settings. For example, a specific technology was promoted in the showcase of innovative programming that included a technological component [25]. Such technology was also mentioned in a review of best practices as one of the best mechanisms to influence health risk behaviours [26]. It was also re-acquired by several news media outlets and other documents, such as a newsletter about Ontario Diabetes Strategy, where they mentioned that there would be support for the adoption of new information technologies [27]. Most mentions about digital technologies were in the “Preventing and Managing Chronic Diseases: Ontario’s Framework” from May 2007 [28]. In this document from the Ministry of Health and Long-Term care, technology was mentioned for its opportunity, as a connected digital tool, to allow for telehealth in clinical settings and where providers have increased access to software to support decision-making [28].
## Absence of research evidence in policy
Research evidence, in the form of citations of peer-reviewed scientific literature, was noticeably lacking within the policy documents. The documents that cited research evidence were in the form of guidelines ($$n = 9$$; $13\%$), recommendations ($$n = 9$$; $13\%$), or reports ($$n = 25$$; $35\%$). The evidence cited included statistics on chronic diseases in Ontario and in specific regions of the province. Some news media did also include some research evidence. Similarly, few documents mentioned the use of theories and models to frame their narratives. Some of the well-known Canadian models and frameworks cited included the Ottawa Charter for Health Promotion [29], Ontario’s Chronic Disease Prevention and Management Framework [28] and British Columbia’s Expanded Care Model [30]. In addition, certain documents mentioned international models and approaches such as the Chronic Care Model [31] and the Stanford Chronic Disease Self-Management Program [13]. Some documents also referred to smaller-scale programs implemented in other countries around the world.
## Actors
Actors of policy documents can be viewed as two-fold: those who develop and implement the policy and those who will be the beneficiaries or end users of the policy. In both cases, document analysis revealed the diversity of actors involved or impacted by the policy documents. Key themes identified by the researchers are described below.
## Range of actors involved in policy development
The lead authors for the policy documents were numerous and varied between different Ontario government ministries, agencies, LHINs, and research centres. Close to three quarters of all documents retrieved were led and authored by varying structures of the Ontario government. The former Ministry of Health and Long-Term Care (now separate ministries) led one third of all publications that were authored by the Government of Ontario. Other Government of Ontario-lead policy documents were authored by Health Quality Ontario, specific LHINs, the Ministry of Children, Community and Social Services or the Ontario government generally without mention of specific departments or agencies. Our review identified that in addition to the lead authors, actors from a variety of sectors were consulted during document creation and contributed to the document. They included large not-for-profit organizations, community organizations and engaged individual stakeholders (advocates or researchers). For large organizations, those involved had specific knowledge about the health issue being discussed in the policy document. For example, for policy documents on diabetes, the Canadian Diabetes Association was usually cited as a contributor in the document. For community organizations, their role in the development of the document was mainly in a consultation role where they were able to share best practices from work happening in their communities. Finally, our review of the documents also indicated that individuals, as engaged stakeholders, were involved in a consultative role where they shared their real-life opinions and dialogue with others on a specific topic of interest. However, some documents did not explicitly cite what actors were involved in the document development.
## General public as end users
The intended users of the policy documents were numerous. Some policy documents were specifically intended for policymakers and researchers while most of them targeted the general public (people living in Ontario), with some policy documents targeting specific groups like people living with diabetes in Ontario or healthcare providers in Ontario. These policy documents were mainly for information-sharing purposes and provided information about work being done in collaboration with and by the Government of Ontario. This was the case for much of the documents pertaining to the Ontario Diabetes Strategy where there was a significant focus on promotting good practices and new developments (i.e., programming).
## Timeline and evolution over time
While many policy documents on healthcare self-management have been published, content has changed significantly over time. Using the health policy model [16], we were able to identify some contextual factors that may have influenced the content and focus of these documents. In a timeline, three of the four major components of the model for health policy analysis (content, context, and actors) were evaluated (Fig. 3).Fig. 3Timeline of policy documents per health topic, political lead, and digital technology considerations In short, the first policy document on self-management was published in October 2000 and focussed on asthma. A change in focus occurred shortly after, where chronic diseases more generally became of greater importance. This remained constant throughout time but some specific conditions, such as diabetes, received attention at different moments in time because of how fast they were growing (i.e., $69\%$ increase in 10 years for diabetes) and how they are associated to expensive healthcare costs [32]. Self-management, initially viewed as being the responsibility of the individual, changed through time and became the responsibility of a larger team where care and management were seen as a collaborative effort that involved several professionals, community services, and key tools such as technology. Finally, time has also presented a change in the collaborative nature of policymaking. There was a clear shift from top-down to a hybrid between top-down and bottom-up healthcare governance approach to policymaking.
## Context
Through an analysis of context, we have identified themes that help to explain the reforms that occurred in content and actors dimensions of self-management policies in Ontario. As mentioned by Walt and Gilson [16], the interplay between each dimension is critical due to the influence that one dimension puts on others (i.e., influences from actors on the content; influences from context on actors and content; etc.). To do so, we have conducted an evaluation of policy documents to identify influences from context on the different aspects. The contextual factors that have influenced policy development over time are described in more detail below.
## Pressures on healthcare system: increasing burden of chronic diseases and shifting models of care
First, as mentioned above, October 2000 represents the date when the first policy document on self-management was published by the former Ministry of Health and Long-Term Care. This report from the chief medical officer of health focussed on illustrating the burden of asthma on Ontarians and placing asthma as an important public health concern in Ontario [33]. At that time, asthma was viewed as an important public health concern in Ontario especially for children and adults due to increased absenteeism in school and from work [33]. In that document, self-management was mentioned in one specific section and the emphasis was put on the need for individuals to have self-management plans to help them manage various aspects of their conditions such as symptoms [33]. The self-management strategies identified in this publication were specific to asthma and did not include mention of other chronic conditions with similar affects. The document creation was informed by a steering committee composed of individuals working in the Ministry of Health and Long Term Care, in local health units, in various healthcare associations, and included academic research scientists. Since the release of this first publication, publications on self-management changed in focus significantly. The focus went from asthma to chronic disease management and prevention, at-large, when the Ministry of Health and Long-Term care published a guide on chronic disease management and prevention five years later, in September 2005 [34]. This guidance document was developed to assist groups forming the initial Family Health Teams (FHTs) to plan chronic disease management and prevention programs for patients [34]. When analyzing the self-management components in the document, it became clear that an increased emphasis was placed on the need to have supportive healthcare systems that promote patients’ self-management. The policy documents even demonstrated how self-management roles and responsibilities changed from the being on the individual to becoming an interdisciplinary collaboration between various stakeholders that function in tandem to create a good functioning health management environment. For example, Ontario’s Framework for preventing and Managing Chronic Disease [28] showed a shift in focus toward developing a system that was collaborative and where interdisciplinary efforts were made to promote patient empowerment and increase patient education. The increase in care personalization movements across time [35], where patients are involved in their care through participatory medicine or patient empowerment initiatives, also helped to explain this shift in policy focus. In addition, supports in the form of information technologies have also been added as relevant tools for self-management around 2005. They were being promoted in various documents as effective tools to support some components of self-management such as being connected with professionals for consultations, but mostly its benefits aimed at more effective healthcare system management. Overall, results pointed to an evolving understanding that chronic disease management required an interdisciplinary approach that involves the individual, healthcare professionals, community supports, and innovative technologies which leads to patient empowerment and effective self-management.
## Political context
Looking deeper at the political context, the first publication about chronic diseases self-management generally was a guide on chronic disease management and prevention published in September 2005 by a Conservative government in Ontario [34]. It was followed by several publications published under a Liberal government in Ontario until 2018. During the lead of the Liberal party in Ontario, healthcare budgets shifted from focussing on increasing investments in healthcare facilities [36] to targeting spending on FHT, and contributing investments in medical technologies and home care. Chronic disease management and prevention continued to be the focus in all publications under the Liberal government until the most recent one in 2019 [37]. However, diabetes specifically gained traction in many publications since 2006. Several reports, news media and information sheets have focussed largely on diabetes, its impact, its management, and promoting exemplar programming that address challenges in daily lives of people living with diabetes. This specific focus lines up with the development of the Ontario Diabetes Strategy in July 2008. In addition, other chronic diseases received attention in policy documents since 2009. They include plaque psoriasis, chronic pain, chronic kidney disease, dementia, COPD, stroke, heart failure and epilepsy. The context underlying choices of these conditions could be explained by increases in prevalence, death, and hospitalizations for some conditions (epilepsy, dementia, chronic kidney disease, cardiovascular diseases, and COPD), and spendings over 10.5 billion dollars annually in direct healthcare costs [38, 39]. Contextually, these also align with funding for public health including health promotion and prevention initiatives, as outlined in budget documents from 2005 onward under the lead of the Liberal party in Ontario.
## Hybrid top-down and bottom-up policymaking: collaborative policy development
Looking specifically at policy development, the actors involved in document creation have somewhat changed over time. In the initial documents from 2000 to 2007, the contributors originated mainly from within the Government of Ontario (i.e., Ministry of Health and Long-Term Care, Ministry of Health Promotion, etc.). In the following years, however, the actors involved shifted and showed an engagement of smaller government organizations including specific LHINS, academic research centres, individual health experts, and associations from the community. Such consultations with stakeholders (individuals, community organizations and diverse government agencies or departments) remained constant thereafter. This indicates a trend toward using both bottom-up and top-down governance models and allows for better collaboration and partnership between relevant stakeholders. Some documents did not include information on who was consulted in the development of the documents while others provided detailed information.
Overall, several contextual factors impacted policies on health self-management in Ontario. There is a strong interplay between content, context and actor components of policymaking which shaped the result and how policies ultimately impacted those engaging in self-management of health. Factors that impacted policies on self-management of health in Ontario include pressures on healthcare systems, the political context, and hybrid policymaking.
## Discussion
An analysis of Ontario government policy documents about healthcare self-management from October 2000 to June 2019 identified the following key events. The first published document, led by the government of Ontario, focussed on asthma and proposed some tips on how individuals could better self-manage their condition. This document, which was published under the lead of a Conservative party in Ontario, placed individuals living with asthma at the forefront of self-management and as holding responsibility for doing so. In subsequent years and with a change to a Liberal government, the focus shifted to general chronic conditions. Not long after, in 2006, there was an increase in publications on self-management of diabetes more specifically. This specific focus aligned with the release of the Ontario Diabetes Strategy in 2008. From there, publications that focussed on general chronic diseases and diabetes self-management portrayed self-management as a holistic activity that involved various individuals and disciplines in the support system. In addition, leads and collaborators of the publications included people that were closer to the “ground” and from more regional and community backgrounds. Several other publications were released, in an ad hoc manner, on other chronic diseases, and around 2005, documents were starting to include mentions of information technologies as innovative tools to support self-management.
Factors that appeared and identified to be most influential on the nature and timing of these policies were pressures on the healthcare system and healthcare transformation, hybrid top-down and bottom-up policy development, and political context (Fig. 4).Fig. 4Factors that shape health self-management in Ontario Changes in the content within the policy documents were linked to pressures that impacted the healthcare system. With a focus on chronic diseases and projections that chronic diseases would account for millions of deaths around the world [40], Ontario was no stranger to the increased burden of chronic diseases on the healthcare system. The focus of the Government of Ontario toward self-management of general chronic diseases demonstrated a better understanding about the magnitude and impact of chronic diseases on the healthcare system. Additionally, the increased number of publications that focussed on diabetes since 2006 related with the increase in cases of type 2 diabetes in developed countries [41]. In the early 2000s, Ontario even surpassed global rates reported by the World Health Organization (WHO) for the prevalence of diabetes [41], which amplified the need for government leaders to take action. For type 2 diabetes, where lifestyle and healthy living behaviours can fend its offset, action by public health leaders and governments were foreseeable and desirable. In 2008, the government released the “Ontario Diabetes Strategy” which aimed to prevent, manage and treat diabetes, and provided millions of dollars in investments over a four years [32]. This demonstrated that the focus of the content within the policy documents were recognized from real and existing pressures of diabetes on the healthcare system. Seventeen years after its implementation, however, diabetes care and management can still be improved by creating policies that effectively supports its self-management [42]. Throughout time, the focus of the system has evolved from a medicalized system where there was a predominant focus on the diagnosis and treatment of pathological and biological issues, to one that promotes greater autonomy, health promotion and population health strategies [43]. As demonstrated in the policy documents retrieved, patient empowerment and supportive environments became integral parts of the management of chronic diseases. The reviewed literature further demonstrated how patients could and should be seen as equals in the caring for their conditions where both healthcare professionals and patients are viewed as experts in their respective areas and cannot function without one another: healthcare professionals bridge gaps in health literacy while patients are the experts on a personal level [44]. As pointed out in a report from the World Health Organisation in 2002, “optimal [health] outcomes occur when a healthcare triad [(including patients, healthcare professionals and community supports)] is formed” (p. 7) [45]. In Ontario, these concepts of collaboration and patient empowerment were integrated in policies throughout time, which suggests a willingness of the policy system to evolve toward a more collaborative and personalized model of care. While pressures on the healthcare system (increasing burden of chronic diseases and shifting models of care) and transformation of healthcare have shaped policy development, innovation in technology seemed to have had limited effects. The early 2000s have marked significant advancements in the digital technological sphere where many devices such as smartphones, tablets, and social media were born and refined. However, policies on self-management during that time missed key developments that could have facilitated and supported self-management in a more connected way. As mentioned in a scoping review by Jacelon, Gibbs and Ridgway [46], significant work on technology to support self-management was being done all around the world. Some benefits of technology-supported self-management include enhancing the healthcare system and narrowing the distance between patients and healthcare professionals by allowing them to be connected to one another more easily and rapidly [46], and increasing competence and illness management for patients [47]. These benefits, however, may have been suppressed by limits in the technological infrastructure such as limited access to computers and internet during that period [46]. In Ontario, barriers of access to digital technological infrastructure (due to cost of technology and its infrastructure or fluctuating digital literacy) may help to explain the delay in having technologies that support self-management included in policies. Since the start of the present decade, Ontario made significant investments in digitalization, positioning technologies for self-management as having a much clearer role within the system. In addition, the recent COVID-19 pandemic has exacerbated the need to have technologies to support individuals who are managing chronic conditions. During the pandemic, technology has proven to allow for continued healthcare services, improve health outcomes, physical and mental health, and enhance social connectedness of many individuals who are managing their chronic conditions, including older adults [48–51]. Therefore, digital technology has now become an integral and ineluctable part in every policy on self-management.
The results of this study portray important changes in how policy were developed through time. First, when policies on health self-management were first developed in 2000, policy documents seemed to have taken more of a top-down approach where policymakers were the sole drivers of policy development. However, the shift toward a hybrid approach to policymaking that includes both a top-down complemented by bottom-up approach demonstrated a willingness to consider field experts and lived experience in policy development. As stated in work by Sabatier [52], bottom-up approaches involve a network of actors that are actually involved in the execution of policies and programs. Through ongoing consultations with lower levels of decisions-making (bottom-up), involving community organizations and expert stakeholders, novel policies become more adapted to the context of communities. A hybrid healthcare governance model can provide significant benefits and improvement to healthcare [53]. This hybrid model is demonstrated well in the Ontario Diabetes Strategy, where it is driven by the Ontario government but with working-level policymakers and external organizations are involved throughout. The documents demonstrated that much of the hands-on work of the Governemnt of Ontario for the Ontario Diabetes Strategy was developed and ran by regional and community-level organizations. Second, ways by which policies are developed are not only the reflection of who is consulted and involved, but also influenced by the political context. For most of the years during which self-management publications were released, the Ontario government was under the lead of a Liberal party. The in-depth analysis of the budgets from the Liberal government between 2003 and 2018 revealed that there were significant investments in health promotion and prevention programs. Funding in these areas demonstrated a willingness for a liberal government to support prevention and management initiatives which include self-management. In 2005, the Liberal government even established the "Ministry of Health Promotion" as a way to promote healthy choices and healthy lifestyles for Ontarians [54]. This political willingness to promote healthy lifestyles corroborated with increased policies on self-management of chronic diseases.
Finally, our analysis revealed extensive foci for self-management of chronic diseases generally. It became evident that while not all chronic diseases had received attention in policies in Ontario, the general concept of chronic diseases was an area of great interest. This has implications in that not all individuals would adequately benefit from supports in self-managing daily difficulties because policies and related services would take a more generic approach. Furthermore, self-management is never discussed in terms of disabilities or functional limitations related to aging. While disabilities and functional limitations related to aging may have different implications for individuals, similar strategies as the ones used in chronic disease self-management could support older adults living with disabilities or functional limitations to have an improved quality of life. Finally, limited discussions on the role of technologies in supporting self-management reveals that uptake of innovation to support self-management has been slow. This leaves room for the Ontario government to exploit new and effective avenues to improve self-management supports for Ontarians.
## Limitations
The study has several limitations that rely merely on the nature of the data retrieved. The data collected were policy documents published in online archival repositories, websites, or databases. While the documents retrieved offer a comprehensive overview of the policies on self-management and their evolution over time, they may not adequately portray the full picture for policymaking on the issue. Retrieval of such documents also came as a challenge which could have led to documents being missed. In certain cases, documents may have been in other formats and not available via online repositories.
Regarding the framework of analysis for this study, we have selected the approach which we believe would allow for a more comprehensive analysis of the documents. While many frameworks for analysis focus on evaluating the content of policies, they would omit critical components such as actors and context which all contribute to shaping policy development [16]. Unfortunately, the process with which policies have been implemented could not be analyzed in this study due to the nature of the data collected but other critical components of policy development (content, context and actors) were analyzed in detail. For actors and context components, our analytical approach included the retrieval of supplementary documentation (i.e. budget documents) and identifying critical authorship and consultations mechanisms which offers a limited view of the full historical policymaking approach. These limits will be addressed in future steps of the project which include consultations with current policymakers working in the field of self-management. For these reasons, Walt and Gilson’s [16] model for health policy analysis was selected for the analysis and evaluation of the data, and was the best suited to answer the research question.
## Future research
Future research should continue to document the evolution of self-management policy in Ontario and evaluate the effects of the factors explored in this study (shown in Fig. 4). In addition, future research could focus on evaluating the process for developing and implementing policies on self-management as this component could not be assessed from the retrieval of documents alone. This would require that other means of data (i.e., interview data) to complement the results found from documents. Finally, research that investigates present approaches for developing, implementing, and evaluating self-management policies could help to better understand current practices for supporting self-management from the perspective of the system more broadly, and offer a more updated and accurate picture of policymaking in the current context.
## Take-home messages
Healthcare self-management is a concept that first appeared in Ontario policy documents in 2000.Healthcare self-management policies in Ontario have focussed largely on chronic diseases and diabetes, without consideration for people living with disabilities and functional limitations that come with age. Digital technology has received limited attention in policy documents for its potential to support self-management of chronic diseases while significant technological advancements have been made in this area. Several factors have been influential the evolution of self-management policies in Ontario including changes in pressures on the healthcare system and healthcare transformation, hybrid top-down and bottom-up policymaking, and the political context.
## Supplementary Information
Additional file 1. Detailed search strategy.
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|
---
title: Improving bowel preparation for colonoscopy with a smartphone application driven
by artificial intelligence
authors:
- Yan Zhu
- Dan-Feng Zhang
- Hui-Li Wu
- Pei-Yao Fu
- Li Feng
- Kun Zhuang
- Zi-Han Geng
- Kun-Kun Li
- Xiao-Hong Zhang
- Bo-Qun Zhu
- Wen-Zheng Qin
- Sheng-Li Lin
- Zhen Zhang
- Tian-Yin Chen
- Yuan Huang
- Xiao-Yue Xu
- Jing-Zheng Liu
- Shuo Wang
- Wei Zhang
- Quan-Lin Li
- Ping-Hong Zhou
journal: NPJ Digital Medicine
year: 2023
pmcid: PMC10011797
doi: 10.1038/s41746-023-00786-y
license: CC BY 4.0
---
# Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence
## Abstract
Optimal bowel preparation is a prerequisite for a successful colonoscopy; however, the rate of inadequate bowel preparation remains relatively high. In this study, we establish a smartphone app that assesses patient bowel preparation using an artificial intelligence (AI)-based prediction system trained on labeled photographs of feces in the toilet and evaluate its impact on bowel preparation quality in colonoscopy outpatients. We conduct a prospective, single-masked, multicenter randomized clinical trial, enrolling outpatients who own a smartphone and are scheduled for a colonoscopy. We screen 578 eligible patients and randomize 524 in a 1:1 ratio to the control or AI-driven app group for bowel preparation. The study endpoints are the percentage of patients with adequate bowel preparation and the total BBPS score, compliance with dietary restrictions and purgative instructions, polyp detection rate, and adenoma detection rate (secondary). The prediction system has an accuracy of $95.15\%$, a specificity of $97.25\%$, and an area under the curve of 0.98 in the test dataset. In the full analysis set ($$n = 500$$), adequate preparation is significantly higher in the AI-driven app group (88.54 vs. $65.59\%$; $P \leq 0.001$). The mean BBPS score is 6.74 ± 1.25 in the AI-driven app group and 5.97 ± 1.81 in the control group ($P \leq 0.001$). The rates of compliance with dietary restrictions (93.68 vs. $83.81\%$, $$P \leq 0.001$$) and purgative instructions (96.05 vs. $84.62\%$, $P \leq 0.001$) are significantly higher in the AI-driven app group, as is the rate of additional purgative intake (26.88 vs. $17.41\%$, $$P \leq 0.011$$). Thus, our AI-driven smartphone app significantly improves the quality of bowel preparation and patient compliance.
## Introduction
Recent global estimates of cancer incidence and mortality place colorectal cancer (CRC) as the fourth most prevalent and second deadliest cancer worldwide1. The combination of a well-defined precursor lesion and a long preclinical course makes CRC an ideal candidate for cancer prevention screening2, the principal method of which is colonoscopy. However, many factors can impact the accuracy of colonoscopy, especially the quality of bowel preparation3. Optimal bowel preparation, a prerequisite for a successful colonoscopy, includes an appropriate volume of purgatives, appropriate timing of purgative consumption, and at least 3 days of dietary restrictions4,5. Consequently, the rate of inadequate bowel preparation is as high as 20–$25\%$6,7. Inadequate bowel preparation often results from an unwillingness to follow the preparation instructions, difficulties following the prescribed diet, or inability to tolerate the full course of purgatives8. Therefore, it is necessary to reinforce patient education on bowel preparation.
Enhanced education significantly improves the quality of bowel preparation for colonoscopy and increases patient willingness to undergo bowel preparation9. Bowel preparation education can be reinforced by visual aids and directed reminders. Visual aids include patient educational booklets10, cartoon visual aids11, and nurse-delivered education with brochures12. Directed reminders include short messages13, telephone-based instructions14, social media platforms15, and smartphone apps16,17.
The existing education approaches are effective but have certain drawbacks. The most important limitation is that they often have fixed schedules and educational content that does not account for differences in bowel preparation status (i.e., adequate or inadequate). Ideally, patients should receive personalized reminder messages or educational content reflecting their current bowel preparation status. Furthermore, patients often have difficulty evaluating the adequacy of their bowel preparation. Visual aids, such as photographs of “clean” and “dirty” feces in the toilet, may be useful, but they cannot cover all situations and are often difficult for older patients to use. Accurate, real-time evaluation of bowel preparation status would be useful in generating personalized instructions.
Artificial intelligence (AI) has the potential to overcome certain clinical/human obstacles by enabling real-time diagnosis or guidance in many fields of medicine, including endoscopy18–22, where it may be the solution for real-time evaluation of outpatient bowel preparation. Our preliminary experiments reveal that AI technology can be used to evaluate bowel preparation status according to the appearance of feces in the toilet, and an AI-driven smartphone app may provide more personalized and accurate enhanced instructions to improve bowel preparation compared with traditional education approaches. The AI system acts as an evaluator, the results of which inform the smartphone app’s personalized reminders.
In this study, we create an AI-based bowel preparation prediction system for outpatients scheduled for colonoscopy to predict bowel preparation quality in real time by evaluating photos of feces in the toilet. Based on this prediction system, we create an AI-driven smartphone app to provide personalized enhanced instructions to improve the patients’ bowel preparation. After creating the app, we conduct a prospective multicenter study to evaluate the impact of this AI-driven smartphone app on the quality of bowel preparation in colonoscopy outpatients.
## Performance of the AI-based bowel preparation evaluation system
After 350 epochs of training, the model converged well and showed satisfactory performance on the test dataset (Supplementary Information 1). Although ShuffleNet v2 is a lightweight network, it achieved an accuracy of $95.15\%$ (Supplementary Table 2). The specificity was $97.25\%$, indicating that the model can accurately identify photographs that represent inadequate bowel preparation. The area under the receiver operating characteristics curve (AUC) was 0.98 in the test dataset (Supplementary Fig. 5).
## Patient characteristics
Overall, 578 patients were scheduled for colonoscopy examination during the study period (Table 1). After excluding 54 patients who met the exclusion criteria or declined to participate, 524 eligible individuals were randomized to the control group or the AI-driven app group. Twenty-four individuals cancelled their colonoscopy appointment and did not reschedule. Ultimately, 500 participants—247 in the control group and 253 in the AI-driven app group—were enrolled and included in the full analysis set (FAS) (Fig. 1). After excluding patients who did not use the app correctly, 225 patients were included in the AI-driven app group (per-protocol set [PPS]).Table 1Baseline characteristics of the total analysis population. CharacteristicAI-driven app group ($$n = 253$$)Control group ($$n = 247$$)Age, years, mean ± SD51.40 ± 12.5653.35 ± 14.03Men, n (%)126 (49.80)114 (46.15)Body mass index, kg/m2, mean ± SD23.60 ± 3.8623.68 ± 3.45ASA class, n (%) I186 (73.52)172 (69.64) II67 (26.48)75 (30.36)Indication, n (%) CRC screening72 (28.46)72 (29.15) Surveillance after the previous colonoscopy30 (11.86)31 (12.55) Diagnostic151 (59.68)144 (58.30)Prior colonoscopy, n (%)92 (36.36)94 (38.06)Previous surgery (abdominal or pelvic), n (%)45 (17.79)54 (21.86)Medical history, n (%) Diabetes mellitus13 (5.14)15 (6.07) Hypertension30 (11.86)40 (16.24) Chronic constipation50 (19.76)51 (20.65) Liver cirrhosis7 (2.77)12 (4.86)Education level, n (%) University graduation137 (54.15)123 (49.80) High school graduation71 (28.06)70 (28.34) Middle or elementary school45 (17.79)54 (21.86)Marital status, n (%) Single/widowed28 (11.07)22 (8.91) Married/partnership225 (88.93)225 (91.09)ASA American Society of Anesthesiologists, CRC colorectal cancer, SD standard deviation. Fig. 1Flowchart of the clinical trial. AI artificial intelligence, App application.
## Outcomes of bowel preparation and colonoscopy
In the FAS analyses (Table 2), the rate of adequate bowel preparation (Boston Bowel Preparation Scale [BBPS] score≥6) was significantly higher in the AI-driven app group than in the control group (88.54 vs. $65.59\%$, $P \leq 0.001$). The PPS analyses revealed similar results for the primary outcome: $89.78\%$ of patients in the AI-driven app group and $65.59\%$ of patients in the control group achieved adequate bowel preparation ($P \leq 0.001$). Both the FAS and PPS analyses showed that the rate of excellent bowel preparation (BBPS score≥8) was significantly higher in the AI-driven app group than in the control group (FAS: 27.67 vs. $19.84\%$, $$P \leq 0.040$$; PPS: 28.44 vs. $19.84\%$, $$P \leq 0.029$$).Table 2The rate of adequate and excellent bowel preparation. OutcomesAI-driven app groupControl groupRate differenceP valueNn(%, $95\%$CI)Nn(%, $95\%$CI)%; $95\%$CIRate of adequate bowel preparation (primary outcome)FAS253224 (88.54, 83.95–92.19)247162 (65.59, 59.30–71.49)22.95;15.43–30.20<0.001PPS225202 (89.78, 85.06–93.41)247162 (65.59, 59.30–71.49)24.19;16.57–31.42<0.001Rate of excellent bowel preparationFAS25370 (27.67, 22.25–33.62)24749 (19.84, 15.05–25.36)7.83;0.07–15.450.040PPS22564 (28.44, 22.65–34.82)24749 (19.84, 15.05–25.36)8.61;0.59–16.570.029FAS full analysis set, PPS per-protocol set, CI confidence interval.
The mean BBPS score was 6.74 ± 1.25 in the AI-driven app group and 5.97 ± 1.81 in the control group ($P \leq 0.001$) (Table 3). The BBPS scores were also significantly higher in the AI-driven app group for each colon segment (left, transverse, and right). The cecal intubation rate was $100\%$ in both groups. The AI-driven app group had a shorter cecal intubation time (5.06 ± 2.07 min vs. 5.86 ± 2.85 min, $P \leq 0.001$). The mean withdrawal time and colonoscopy time were similar between the groups. No serious complications or adverse events were reported during the study period. Overall, 381 polyps were detected within the study, including 223 adenomas and 42 advanced adenomas. The polyp detection rate (PDR), adenoma detection rate (ADR), and advanced adenoma detection rate (aADR) were not significantly different between the groups. Table 3Effect of AI-driven app on the outcome of bowel preparation and colonoscopy. OutcomesAI-driven app group ($$n = 253$$)Control group ($$n = 247$$)Difference ($95\%$ CI)P valueTotal BBPS score, mean ± SD6.74 ± 1.255.97 ± 1.810.77 (0.49–1.04)<0.001BBPS score in different colon segments, mean ± SD Right2.07 ± 0.651.70 ± 0.840.37 (0.24–0.50)<0.001 Transverse2.36 ± 0.572.15 ± 0.720.21 (0.09–0.32)<0.001 Left2.30 ± 0.562.12 ± 0.660.18 (0.07–0.29)<0.001Polyp detection rate114 ($45.06\%$)98 ($39.68\%$)$5.38\%$(−$3.54\%$–$14.19\%$)0.223Adenomas detected rate70 ($27.67\%$)56 ($22.67\%$)$5.00\%$(−$2.90\%$–$12.80\%$)0.198Advanced adenomas detected rate16 ($6.32\%$)10 ($4.05\%$)$2.27\%$(−$2.00\%$–$6.55\%$)0.252Successful cecal intubation253 ($100\%$)247 ($100\%$)--Cecal intubation time (min), mean ± SD5.06 ± 2.075.86 ± 2.85−0.80 (−1.23– −0.36)<0.001Withdrawal time (min), mean ± SD7.63 ± 3.687.36 ± 3.300.27 (−0.35–0.88)0.392Colonoscopy time (min), mean ± SD12.70 ± 4.3213.22 ± 4.60−0.52 (−1.31–0.26)0.188CI confidence interval, SD standard deviation, BBPS Boston bowel preparation scale.
## Bowel preparation process
The effects of app usage on the bowel preparation process are shown in Table 4. The FAS analyses revealed no significant differences in scheduled colonoscopy time between the groups ($$P \leq 0.082$$). Compared with the control group, the AI-driven app group had significantly higher rates of compliance with dietary restrictions (93.68 vs. $83.81\%$, $$P \leq 0.001$$) and purgative instructions (96.05 vs. $84.62\%$, $P \leq 0.001$). The proportion of patients who consumed additional polyethylene glycol (PEG; total >3 L) was higher in the AI-driven app group than in the control group (26.88 vs. $17.41\%$, $$P \leq 0.011$$). In the subgroup analysis of patients who consumed additional PEG, bowel preparation was adequate and perfect in 95.59 and $27.94\%$ of patients in the AI-driven app group and only in 65.11 and $13.95\%$ of patients in the control group, respectively. Table 4Effect of AI-driven app on the procedure of bowel preparation. OutcomesAI-driven app group ($$n = 253$$)Control group ($$n = 247$$)P valueScheduled colonoscopy time, n (%)0.082 8:30–11:30203 (80.24)182 (73.68) 13:30–16:3050 (19.76)65 (26.32)Compliance with dietary restrictions, n (%)237 (93.68)207 (83.81)0.001Compliance with purgative instructions, n (%)243 (96.05)209 (84.62)<0.001Additional purgative, n (%)68 (26.88)43 (17.41)0.011Willingness to undergo repeat bowel preparation, n (%)5.06 ± 2.07204 (82.59)0.002Sleep quality, n (%)0.006 Worse than usual153 (60.47)178 (72.06) Same as usual100 (39.53)69 (27.94) The percentage of patients who reported “good as usual” sleep quality during bowel preparation was higher in the AI-driven app group than in the control group (39.53 vs. $27.94\%$, $$P \leq 0.006$$). The proportion of participants willing to undergo repeat bowel preparation was also higher in the AI-driven app group than in the control group (91.70 vs. $82.59\%$, $$P \leq 0.002$$). In the AI-driven app group, $91.70\%$ of patients reported that they would be willing to use the app again, and $86.16\%$ said they would recommend it to an acquaintance.
In the safety set (SS) analyses, the overall adverse event rates were 28.06 and $22.27\%$ in the app and control groups, respectively ($$P \leq 0.136$$). There were no significant differences in abdominal pain, abdominal distention, or nausea/vomiting between the groups ($$P \leq 0.942$$).
## Subgroup analysis
The results of the subgroup analysis (Fig. 2) demonstrate a significantly higher rate of adequate bowel preparation in the AI-driven app group in most subgroups, except among those scheduled for afternoon colonoscopy and those who did not comply with the dietary restrictions or purgative instructions. Fig. 2Subgroup analysis of factors associated with the rate of adequate bowel preparation. BMI body mass index, CRC colorectal cancer.
## Discussion
In this study, we successfully established an AI-driven smartphone app to aid in colonoscopy bowel preparation. The AI system evaluated bowel preparation status based on photographs of feces in the toilet and provided real-time binary predictions of adequate or inadequate preparation. The app then used these evaluation results to produce personalized messages to improve bowel preparation.
In the clinical trial portion of the study, we found that digitally reinforced patient guidance via the AI-driven app improved the quality of bowel preparation for colonoscopy. Previous studies reported that reinforced education led to satisfactory bowel preparation and achieved a high ADR14,16. By contrast, we found no significant difference in PDR and ADR between the groups despite the clear difference in bowel preparation quality. One explanation may be that the sample size (which was based on the rate of adequate bowel preparation) was too small to detect significant differences in these rates.
The AI-driven smartphone app has several advantages over the existing reinforced education methods. Most importantly, the app can deliver personalized patient education with suggestions to improve bowel preparation based on real-time evaluations by the AI system. The recommendation to consume additional purgative may be the most important suggestion associated with adequate bowel preparation. In the AI-driven app group, $26.88\%$ of patients consumed additional purgative based on personalized suggestions provided by the app. By contrast, only $17.41\%$ of patients in the control group took additional purgative based on their own judgment. Previous studies reported that 4-L split-dose (2 + 2 L or 3 + 1 L) PEG was superior to other bowel preparation methods4,23,24. However, because of their smaller body size, lower body weight, and different dietary habits, a 4-L PEG volume may be poorly tolerated by the Chinese population25. Accordingly, the volume and effectiveness of pre-procedure purgatives must be balanced, considering the exact timing of administration and the selection of patients. Among the patients in the AI-driven app group who took additional purgatives, 95.59 and $27.94\%$ achieved adequate and excellent bowel preparation, respectively. These exceptional results were likely at least partly due to the personalized suggestions delivered by the app.
Another advantage of the app is that it engages patients in the real-time evaluation of their bowel preparation, which is a prerequisite for following suggestions for improvement: when patients know the status of their bowel preparation, they will be more likely to follow suggestions to improve it. The app also improves compliance with dietary restrictions and purgative instructions because it sends patients enhanced education during the bowel preparation process. Both compliance rates were higher in the AI-driven app group in the current study.
A third advantage of our AI system is that patients can use it without an Internet connection, which is important considering the potentially poor network connectivity when individuals are using the toilet. Furthermore, the app stores feces photographs on the user’s smartphone and deletes them after bowel preparation; only the binary prediction results are uploaded to the cloud server for recording. This design avoids transmitting the photographs themselves, thereby guaranteeing patient privacy. To ensure that smartphones could complete the classification task, we utilized the extremely lightweight but efficient convolutional neural network ShuffleNet v2, which runs smoothly on most Android and iOS smartphones and classifies photos within seconds. The group convolution operation and channel shuffle mechanism significantly compressed the size of the neural network and reduced the computing cost26.
Compared with the control group, most subgroups performed better with the guidance of the AI-driven smartphone app. The rate difference was enlarged between certain subgroups, however, indicating that some people might benefit more from the AI-driven smartphone-guided bowel preparation method. The rate difference in older adults was higher, so this bowel preparation method might be especially user-friendly for older patients compared with traditional patient education. We noted a similar result among patients with few educational qualifications; the combination of visual aids and directed reminders in the app may be more acceptable than verbal instructions for these individuals. Furthermore, we found the app to be more beneficial for individuals with no prior colonoscopy than for those with colonoscopy experience, which illustrates that this bowel preparation method can be applied to individuals with no bowel preparation experience.
The study has limitations. First, only smartphone users (with Android- or iOS-compatible smartphones) could participate in this study. Therefore, our results may not be applicable to those without regular smartphone access or use. A study published in 2014 using a mobile social media app to improve bowel preparation revealed that among all patients considered for the study, 1039 ($44.7\%$) were excluded because of a lack of convenient access to a smartphone16. This may lead to bias, especially because it can exclude many older patients. However, in 2022, most people can now obtain a smartphone at a relatively reasonable price. The COVID-19 pandemic may have further promoted the use of smartphones because quick response (QR) codes became essential for many social activities, including infection-related questionnaires, before entering the hospital. In the current study, only five patients were excluded for lack of access to a smartphone. Moreover, regarding cost-effectiveness, it could be postulated that apps require limited resources and are easily accessible to individuals of all socioeconomic levels, although these issues require further study. Second, the rate of adequate bowel preparation in the control group was lower than $80\%$, which we used in the sample size calculation. The main reason is that our sample size calculation considered both outpatients and inpatients, whereas we included only outpatients in this study. Outpatient education is time-consuming but ineffective in most centers. Third, the smartphone app did not allow outpatients to ask specific questions, in contrast to traditional in-person consultations. To counter this limitation, we have collected frequently asked questions and written specific, detailed answers. We will add a new section to the app for these questions and answers and update them frequently. Fourth, the split-dose PEG used in this study was the currently recommended regimen, but patients from different countries or regions may have different bowel preparation regimens. Thus, a multilingual app with different bowel preparation regimens for colonoscopy is a desirable tool for the future.
In conclusion, this study demonstrated that our AI-driven smartphone-guided bowel preparation app effectively improved bowel preparation quality in patients scheduled for an outpatient colonoscopy. This AI system may help patients engage in the bowel preparation process and ultimately lead to higher rates of adequate bowel preparation.
## Methods
This study consisted of two parts: the establishment of an AI-driven smartphone app and a clinical trial evaluating the app. The entire study protocol was approved by the institutional review board of Zhongshan Hospital (B2020-297R) and registered at the Chinese Clinical Trial Registry (ChiCTR2000040306). All authors had access to the study data and reviewed and approved the final manuscript.
## Creation of the AI-based bowel preparation prediction system
Figure 3 presents the process of creating the AI system. Pictures of feces in the toilet after bowel preparation were used to construct the bowel preparation prediction system. Patients scheduled to undergo colonoscopy were enrolled from November 2020 to January 2021. The 3-L split-dose PEG (Heshuang, Wanhe Pharmaceutical Co, Shenzhen, China) method was used in this part of the study27. Patients were asked to take photographs of the residue in the toilet every time they used the toilet after taking all doses of the purgative. The entire colonoscopy procedure was video-recorded, and the BBPS score was determined. Adequate bowel preparation was defined as a total score ≥6 plus all segment scores ≥2 during withdrawal of the colonoscope after cecal intubation, in accordance with the BBPS guide25,27. Based on these criteria, the patients were divided into adequate and inadequate bowel preparation groups. Fig. 3Entire process of creating the artificial intelligence-based bowel preparation prediction system. AUC area under the receiver operating characteristics curve, Conv convolution.
If a patient’s bowel preparation status was inadequate, all their uploaded photographs were labeled as “inadequate.” If their bowel preparation status was considered adequate, the first photograph was labeled as “inadequate,” the last uploaded photograph was labeled as “adequate,” and the intermediate photographs of the series were discarded because the corresponding preparation status could not be confirmed. The photographs were randomly divided at the patient level into a training set (~$80\%$ of the images) and a test set (~$20\%$ of the images). In total, 5,362 photographs were selected from 992 patients to develop our AI-based bowel preparation prediction method. The data distribution is shown in Supplementary Table 1.
Supplementary Information 1 details the neural network architecture. The training image set was used to train ShuffleNet, and the early stopping strategy was used to avoid over-fitting by monitoring the model’s performance on the internal validation dataset. Image data augmentation, including random cropping and rotation, was performed to increase the generalization and robustness of the model. We evaluated the performance of the classification of the test set by calculating the accuracy, sensitivity, specificity, and AUC.
## Establishment of the AI-driven smartphone app
The AI-based prediction system was integrated into a smartphone app called Qing Chang (version 2.0, provided by Henan Xuanweitang Medical Information Technology Co., China). The design and workflow are detailed in Supplementary Information 2. The app has three main functions. First, it collects patient information, including the patient’s colonoscopy appointment time, the hospital or medical center, and other medical diseases, and uses this information to schedule the entire bowel preparation process. Second, the app delivers personalized reinforced education to the patients, starting prior to the scheduled colonoscopy. The content of the patient education is shown in Supplementary Information 3. Each part contains at least one short video or article focused on improving bowel preparation, all of which were reviewed by two senior endoscopists (Ping-Hong Zhou and Quan-Lin Li). The third function of the app is to evaluate the status of bowel preparation and give the users personalized improvement suggestions based on the AI system’s prediction of bowel preparation quality. Supplementary Fig. 1 presents the schematic overview of the AI-driven app, and Supplementary Fig. 2 shows the workflow of the AI system integrated into the app.
## Clinical trial design
A prospective, multicenter, endoscopist-masked randomized controlled study was conducted between September 2021 and January 2022 at four different endoscopy centers: Zhongshan Hospital, Zhengzhou Central Hospital, Central Hospital of Minhang District, and Xian Central Hospital. The trial protocol is presented in the Supplementary material. The study performance and reporting followed the Consolidated Standards of Reporting Trials, including the use of a flowchart (Fig. 1) to track participants.
## Patient recruitment
Outpatients between 18 and 75 years of age scheduled for routine diagnostic colonoscopy during the study period were eligible for this study. For inclusion in the study, each patient was required to own a smartphone that could access the app.
The exclusion criteria were as follows: (i) previous bowel surgery; (ii) gastroparesis or gastric outlet obstruction; (iii) known or suspected intestinal obstruction or perforation; (iv) severe chronic renal failure (creatinine clearance <30 mL/min); (v) severe congestive heart failure (New York Heart Association class III or IV); (vi) current pregnancy or breastfeeding; (vii) toxic colitis or megacolon; (viii) poorly controlled hypertension (systolic blood pressure >180 mm Hg and/or diastolic blood pressure >100 mm Hg); (ix) moderate or massive active gastrointestinal bleeding (>100 mL/day); (x) major psychiatric illness; (xi) allergy to the study purgatives; (xii) inability to use the smartphone app; or (xiii) inability or unwillingness to provide informed consent.
## Randomization and masking
When scheduling the colonoscopy appointment at the outpatient visit, patients were interviewed by a research assistant not involved in the examination procedures. Written informed consent was obtained from all patients. The assistant explained the aims of the study and collected demographic and medical information on a data collection sheet. The eligible participants were randomized into the control group or the AI-driven app group (i.e., the AI-driven bowel preparation group) in a 1:1 ratio by block randomization with stratification by center. The random allocation table was generated with SAS 9.4 software, and the randomization masking was implemented with an opaque envelope. At least 50 patients were included from each center. Patients were informed of their group assignment and required not to reveal it. None of the attending endoscopists were aware of the patients’ group assignments.
## Education and instructions on the bowel preparation process
When scheduling the colonoscopy during the outpatient visit, all patients were educated about the importance of adequate bowel preparation before the colonoscopy. The standard patient education for bowel preparation included dietary recommendations and information about the use of purgatives, potential adverse drug reactions, and management of inadequate bowel preparation.
All individuals received instructions for a 3-day low-residue diet and a prescription for electrolyte powder (PEG) (Heshuang, Wanhe Pharmaceutical Co). Four liters of PEG were given to each patient during recruitment. Patients began drinking the first liter of PEG at 20:00 on the day before the procedure at a rate of 250 mL every 15 min. On the day of the procedure, patients were instructed to consume 2 L PEG 4–6 h before the examination. The remaining liter of PEG served as a remedial measure for inadequate bowel preparation. Patients enrolled in the control group decided whether this additional 1 L PEG was required using their own judgment. They were told that if their feces were liquid and contained no obvious particles, according to the reference photographs, their bowel preparation was adequate.
Patients in the AI-driven app group were given a link to download the app. The total number of app users and the process of app usage were tracked. The app sent the patients education and reinforcement reminders with suggestions for bowel preparation and indicated whether they should consume the additional 1 L PEG.
## Colonoscopy
All colonoscopy examinations were performed at 8:30–11:30 or 13:30–16:30. Each examination was performed by an endoscopist with a minimum experience of 3000 endoscopic examinations. The insertion goal was to achieve cecal intubation as quickly as possible. The entire colonoscopy procedure was video-recorded. Polyp removal and biopsies were performed during the withdrawal of the scope.
## Outcomes and data collection
The primary outcome was the percentage of patients with adequate bowel preparation, defined as a total BBPS score ≥6 plus all segment scores ≥2. Two endoscopists masked to the entire clinical research reviewed the videos and assigned the BBPS score. If they did not reach a consensus, another senior endoscopist made the final decision.
The secondary outcomes were the total BBPS score, the BBPS score in each colon segment, the rate of patients with perfect bowel preparation (BBPS score ≥8), the rates of compliance with dietary restrictions and purgative instructions, cecal intubation time, colonoscope withdrawal time, PDR, ADR, and aADR. Compliance with dietary restrictions was defined as following the diet instructions and not consuming banned foods. Compliance with purgative instructions was defined as taking the correct volume of purgatives at the correct starting time. PDR was defined as the percentage of patients with ≥1 polyp. ADR was defined as the percentage of patients with ≥1 adenoma. Advanced adenomas were defined as adenomas with an endoscopic size of ≥10 mm, high-grade dysplasia, or villous features.
Patients were also asked to rate their sleep quality during the bowel preparation process as the same as usual or worse than usual and share whether they would be willing to undergo bowel preparation for a repeat colonoscopy in the future.
## Statistical analysis and sample size calculation
The rate of adequate bowel preparation at our endoscopic centers is ~$80\%$. We expected the app to increase this percentage to $90\%$. To detect this difference with a significance level (α) of 0.05 and a power of $80\%$ using a two-tailed test, we calculated that ~394 patients were required for this study. Considering that ~$20\%$ of patients cancel their colonoscopy appointment, we estimated that 500 patients would be required to detect a significant difference in the primary outcome.
The FAS consisted of all participants except those who canceled their colonoscopy appointment after randomization. Patients in the AI-driven app group were expected to take photographs for bowel preparation prediction and browse more than half of the educational content on the app. A research assistant checked and recorded the usage of the app from each patient’s smartphone after the completion of the colonoscopy. Patients who did not use the app in this manner were excluded from the PPS. The SS included all patients who underwent colonoscopy with a safety assessment during the trial. The rates of adequate and excellent bowel preparation were analyzed in the FAS and PPS, and other secondary endpoints were analyzed in the FAS. The safety of the intervention was analyzed in the SS. All statistical analyses were performed with SAS software (version 9.4) at the 0.05 significance level unless otherwise noted. Because the secondary outcomes were considered exploratory, we did not correct the statistical multiplicity generated by multiple outcomes.
Continuous data were presented as the mean ± standard deviation and analyzed using Student’s t-test, and categorical data were presented as the number (percentage) and analyzed with the chi-square test or Fisher’s exact test. The rate and its $95\%$ confidence interval (CI) for each group were estimated using the Clopper–Pearson method. The rate difference between the two groups and its $95\%$CI were calculated using the Newcombe–Wilson method with a continuity correction. The rate of adequate bowel preparation was also compared between the study group and the control group in some subgroups.
## Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
## Supplementary information
Supplemental Material Reporting Summary The online version contains supplementary material available at 10.1038/s41746-023-00786-y.
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|
---
title: Association of Participation in a Value-Based Insurance Design Program With
Health Care Spending and Utilization
authors:
- Hui Zhang
- David W. Cowling
journal: JAMA Network Open
year: 2023
pmcid: PMC10011939
doi: 10.1001/jamanetworkopen.2023.2666
license: CC BY 4.0
---
# Association of Participation in a Value-Based Insurance Design Program With Health Care Spending and Utilization
## Abstract
This cohort study evaluates the association of participation in a value-based insurance design program in California with health care spending and utilization.
## Key Points
### Question
Was participation in a California public payer’s value-based insurance design (VBID) program associated with desired changes in health care spending and utilization?
### Findings
This retrospective cohort study included 94 127 enrollees in commercial health plans. The VBID cohort was associated with significantly higher spending on or use of primary care physicians and immunizations, lower inpatient admissions and surgical procedures, and similar changes in overall spending compared with a non-VBID cohort in 2019 or 2020.
### Meaning
Participation in this VBID program was associated with positive targeted changes in outpatient and inpatient spending and utilization without increasing total costs.
### Importance
Value-based insurance design (VBID) has mostly been used in improving medication use and adherence for certain conditions or patients, but its outcomes remain uncertain when applied to other services and to all health plan enrollees.
### Objective
To determine the association of participation in a California Public Employees’ Retirement System (CalPERS) VBID program with its enrollees’ health care spending and utilization.
### Design, Setting, and Participants
A retrospective cohort study with difference-in-differences propensity-weighted 2-part regression models was performed in 2021 to 2022. A VBID cohort was compared with a non-VBID cohort both before and after VBID implementation in California in 2019 with 2 years’ follow-up. The study sample included CalPERS preferred provider organization continuous enrollees from 2017 through 2020. Data were analyzed from September 2021 to August 2022.
### Exposures
The key VBID interventions include [1] if selecting and using a primary care physician (PCP) for routine care, PCP office visit copayment is $10 (otherwise, PCP office visit copayment is $35 as for specialist visit); and [2] annual deductibles reduced by a half through completion of the following 5 activities: annual biometric screening, influenza vaccine, nonsmoking certification, second opinion for elective surgical procedures, and disease management participation.
### Main Outcomes and Measures
The primary outcome measures included annual per member total approved payments for multiple inpatient and outpatient services.
### Results
The 2 compared cohorts of 94 127 participants (48 770 were female [$52\%$]; 47 390 were younger than 45 years old [$50\%$]) had insignificant baseline differences after propensity-weighting adjustment. The VBID cohort had significantly lower probabilities of inpatient admissions (adjusted relative odds ratio [OR], 0.82; $95\%$ CI, 0.71-0.95), and higher probabilities of receiving immunizations (adjusted relative OR, 1.07; $95\%$ CI, 1.01-1.21) in 2019. Among those with positive payments, VBID was associated with higher mean total allowed amounts for PCP visits in 2019 and 2020 (adjusted relative payments ratio, 1.05; $95\%$ CI, 1.02-1.08). There were no significant differences for inpatient and outpatient combined totals in 2019 and 2020.
### Conclusions and Relevance
The CalPERS VBID program achieved desired goals for some interventions with no added total costs in its first 2 years of operation. VBID may be used to promote valued services while containing costs for all enrollees.
## Introduction
Value-based insurance design (VBID) is a demand-side strategy to increase health plan member use of high value care and reduce low value care usage, primarily through cost-sharing tiers to direct member choice of preferred health care services, medications, and clinicians.1,2,3,4,5 VBID encourages members to engage in healthy activities and manage chronic conditions while discouraging unnecessary or unwarranted health care choices to improve member health and reduce total health care costs in the long term.
VBID has been implemented as a cost-sharing model mostly for certain prescription drugs or treatments for specific diseases or chronic conditions or select patient populations. Most studies have found VBID was associated with desired changes in targeted utilization and medication adherence, but its association with clinical outcomes and health care spending remain uncertain.6,7,8,9,10 VBID studies have been criticized for lacking rigorous study designs and statistical methods to help draw valid causal relationship conclusions.7,11,12,13 We examined a VBID program that was launched by the California Public Employees’ Retirement System (CalPERS) in one of its preferred provider organization (PPO) commercial health plans in 2019. The VBID program was mainly targeted at enhancing primary care for its PPO health plan enrollees. The program’s objectives are to provide economic incentives for members to receive high-value coordinated care at the right place and time through a personal primary care physician (PCP); to increase member engagement in health care decisions and reward members for engaging in healthy activities; and to improve outcomes and lower costs and premiums in the long term.
PPO enrollees can choose any in-network physicians when seeking care, which allows freedom of choice of clinician but also increases the risk of fragmented care and higher costs. The CalPERS VBID PPO personal PCP model aims to provide its members a place where their health issues will first be treated by their PCPs, who will guide them through the health care system when referrals to specialists are needed. PCPs also facilitate dialogue between members and clinicians, fostering member participation in decision-making about their own health care, and providing opportunities for disease prevention and health promotion as well as early detection and treatment of conditions, while controlling or reducing costs in the long term.14 The primary VBID interventions include a $10 office visit copayment if a member selects and uses a personal PCP for routine care, including mental health and substance use physician visits. Otherwise, a $35 office visit copayment is required for a PCP visit, the same as a specialist visit. Additionally, annual deductibles are reduced from $1000 to $500 for individuals and from $2000 to $1000 for families through completion of the following 5 activities with a $100 credit each for individuals (credit doubled for families): annual biometric screening, annual influenza vaccine, nonsmoking certification, second opinion for elective surgical procedures, and disease management participation. Finally, there is a waiver of inpatient co-insurance ($20\%$) for delivery for expectant mothers enrolled in a healthy mother’s program, which provides tools and resources needed for a healthy pregnancy and delivery.
The objective of this study is to evaluate the association of participation in the CalPERS VBID program with health care costs and utilization by its enrollees in 2019 and 2020. We expected a significant VBID association with some of the target services but were unsure about its association with short-term total cost savings.
## Methods
This cohort study was reviewed as minimal risk and waived for informed consent by the Committee for the Protection of Human Subjects, the institutional review board for the California Health and Human Services Agency. This report follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
The VBID interventions were implemented in one of CalPERS PPO health plans, which we label as VBID PPO. We selected another CalPERS PPO plan that did not implement the VBID interventions for comparison, which we call non-VBID PPO. The VBID PPO and non-VBID PPO plan had the same benefit design in terms of copayment, coinsurance, maximum out-of-pocket (OOP) limit, and pharmacy coverage before VBID implementation. The non-VBID PPO had a broader provider (clinicians and hospital systems, etc.) network and higher premium than the VBID PPO, whose premium was set even lower in 2019. After VBID implementation, the VBID PPO annual deductible and PCP visit copayments changed as described previously, but there were no differential changes in copayments for specialist and emergency department visits, coinsurance rate for inpatient hospital admission, annual OOP maximum, and prescription drug coverage and copayments (eTable 1 in Supplement 1).
The VIBD and non-VBID PPO plans are offered during the annual open enrollment period for CalPERS members to compare and choose. The VBID PPO plan provided a mobile health consumer app to increase member engagement through personalized communication and education about the VBID-incentivized activities. CalPERS members also have access to online materials about the VBID PPO incentives, in addition to member outreach and provider communication. The VBID PPO health plan reported that until the fourth quarter of 2019, less than $30\%$ of members had attributed PCPs. For the 5 incentivized activities, the uptake rates for disease management participation and a second opinion for surgery reached more than $90\%$. More than half of VBID PPO members received an influenza vaccine, and up to $70\%$ finished a biometric screening and nonsmoking certification. Only $17\%$ of expectant mothers enrolled in a healthy mother’s program.
We applied a retrospective cohort study design and a difference-in-differences (DID) approach to compare the relative annual changes in cost and use before and after the 2019 VBID implementation between a cohort of VBID PPO continuous enrollees and a cohort of non-VBID PPO continuous enrollees from 2017 through 2020. The data sources included CalPERS commercial PPO California member enrollment and claims data.
The outcome measures are total allowed payments per member per year for inpatient and outpatient health services. Allowed payments are negotiated prices between health plans and providers, which are often much lower than list prices or charges by providers. The control variables include member age group, sex, region, family relationship and tier, employer type, retirement status, clinical risk score categorical group in 2017 and 2018, and year indicator.
## Statistical Analysis
A 2-part DID generalized linear model (GLM) was applied: the first logit GLM model predicts whether an enrollee would have any use or positive payments or not, and the second log-gamma GLM model predicts conditional positive payments. All regression models are adjusted for a VBID PPO indicator, its interaction with a year indicator (using 2018 as the reference year), and the control variables, with the average treatment effect on the treated (ATT) weighting and enrollee-cluster robust SEs. The exponentiated coefficients of the interaction terms from the VBID and year indicators are the multiplicative DID estimators of interest: relative odds ratio in the logit GLM model and relative payments ratio in the log-gamma GLM model.15 ATT weighting is based on propensity scores predicted from a logistic regression of the probability of selecting the VBID PPO cohort on the control variables. ATT weighting sets weights of the treatment group as 1s and weights of the control group as the products of propensity scores and inversed (1-propensity scores) to weight the control group equivalent to the treatment group.
Inpatient cesarean-section measure was analyzed for female participants aged between 15 and 55 years old. A sensitivity analysis was performed for female participants with any inpatient deliveries.
All statistical hypothesis tests were 2-sided tests with a priori significance level of.05 using STATA version 14.2 (StataCorp) and SAS Enterprise Guide version 8.3 (SAS Institute). Data were analyzed from September 2021 to August 2022.
## Results
The study population consisted of 94 127 CalPERS PPO health plan non-Medicare members; $50\%$ [48 770] were younger than 45 years old and $52\%$ [47 390] were female. The VBID PPO longitudinal cohort enrollees were significantly younger, more likely to be male, dependent, not single, employed by the state or public agencies, not retired, living in Northern California other than the Bay Area or Sacramento, or healthier in 2017 and 2018 when compared with the non-VBID PPO cohort. However, all significant differences became insignificant after the ATT weighting adjustment, which helped balance the control variables and improved the comparability of the VBID and non-VBID PPO cohorts (Table 1).
**Table 1.**
| Characteristic | VBID [n = 24 498], No. (%) | Non-VBID (n = 69 629) | Non-VBID (n = 69 629).1 | Non-VBID (n = 69 629).2 | P valuea |
| --- | --- | --- | --- | --- | --- |
| Characteristic | VBID [n = 24 498], No. (%) | No. | % | % | P valuea |
| Characteristic | VBID [n = 24 498], No. (%) | No. | Before ATT weighting | After ATT weighting | P valuea |
| Age, y | | | | | |
| 0-14 | 4885 (19.9) | 9884 | 14.2 | 20.2 | .26 |
| 15-24 | 3735 (15.3) | 10 764 | 15.5 | 15.1 | .26 |
| 25-34 | 2317 (9.5) | 2761 | 4.0 | 10.1 | .26 |
| 35-44 | 4521 (18.5) | 8523 | 12.2 | 18.5 | .26 |
| 45-54 | 4832 (19.7) | 14 649 | 21.0 | 19.3 | .26 |
| 55-64 | 3836 (15.7) | 19 268 | 27.7 | 15.3 | .26 |
| ≥65 | 372 (1.5) | 3780 | 5.4 | 1.5 | .26 |
| Sex | | | | | |
| Female | 12 404 (50.6) | 36 366 | 52.2 | 50.5 | .78 |
| Male | 12 094 (49.4) | 33 263 | 47.8 | 49.5 | .78 |
| Relation | | | | | |
| Dependent | 8646 (35.3) | 20 958 | 30.1 | 35.4 | .98 |
| Spouse | 4890 (20.0) | 15 807 | 22.7 | 19.9 | .98 |
| Subscriber | 10 962 (44.8) | 32 864 | 47.2 | 44.7 | .98 |
| Tier | | | | | |
| 2-Party | 4284 (17.5) | 15 915 | 22.9 | 17.1 | .30 |
| Family | 16 355 (66.8) | 41 279 | 59.3 | 66.7 | .30 |
| Single | 3859 (15.8) | 12 435 | 17.9 | 16.2 | .30 |
| Employer | | | | | |
| California State University | 2200 (9.0) | 11 224 | 16.1 | 9.1 | .16 |
| Public agency | 7551 (30.8) | 18 904 | 27.2 | 31.7 | .16 |
| School | 2430 (9.9) | 10 865 | 15.6 | 9.9 | .16 |
| State | 12 317 (50.3) | 28 636 | 41.1 | 49.3 | .16 |
| Member status | | | | | |
| Active | 22 962 (93.7) | 59 103 | 84.9 | 93.9 | .57 |
| Retired | 1536 (6.3) | 10 526 | 15.1 | 6.2 | .57 |
| Region | | | | | |
| Bay Area | 2141 (8.7) | 11 962 | 17.2 | 8.7 | .49 |
| Los Angeles area | 3486 (14.2) | 17 919 | 25.7 | 14.1 | .49 |
| Other Northern California | 11 972 (48.9) | 17 629 | 25.3 | 49.7 | .49 |
| Other Southern California | 5643 (23.0) | 17 300 | 24.9 | 22.7 | .49 |
| Sacramento area | 1256 (5.1) | 4819 | 6.9 | 4.9 | .49 |
| Risk score 2017b | | | | | |
| 0-0.9 | 17 587 (71.8) | 40 811 | 58.6 | 72.3 | .80 |
| 1-1.9 | 3600 (14.7) | 12 878 | 18.5 | 14.5 | .80 |
| 2-3.9 | 2172 (8.9) | 9422 | 13.5 | 8.7 | .80 |
| 4-8.9 | 886 (3.6) | 4846 | 7.0 | 3.6 | .80 |
| ≥9 | 253 (1.0) | 1672 | 2.4 | 1.0 | .80 |
| Risk score 2018 | | | | | |
| 0-0.9 | 17 171 (70.1) | 39 794 | 57.2 | 70.7 | .68 |
| 1-1.9 | 3655 (14.9) | 12 892 | 18.5 | 14.7 | .68 |
| 2-3.9 | 2314 (9.5) | 9720 | 14.0 | 9.2 | .68 |
| 4-8.9 | 1034 (4.2) | 5325 | 7.7 | 4.1 | .68 |
| ≥9 | 324 (1.3) | 1898 | 2.7 | 1.3 | .68 |
The unadjusted per-member per-year mean total allowed payments for the VBID PPO were substantially lower than the non-VBID PPO, but with parallel pre-VBID trends. Many spending measures seem to be influenced substantially by the COVID-19 pandemic in 2020, which varied by service setting and type (Figures 1, 2, and 3).
**Figure 1.:** *Unadjusted Yearly Trends of Selected per Member Annual Category Spending Measures by CohortsWhiskers indicate 95% CI of mean. VBID indicates value-based insurance design.* **Figure 2.:** *Unadjusted Yearly Trends of Selected per Member Annual Out-of-Pocket (OOP) Spending Measures by CohortsWhiskers indicate 95% CI of mean. VBID indicates value-based insurance design.* **Figure 3.:** *Unadjusted Yearly Trends of Selected per Member Annual Service Spending Measures by CohortsWhiskers indicate 95% CI of mean. PCP indicates primary care physician; VBID, value-based insurance design.*
The 2-part GLM regression-model-adjusted DID estimates are reported in Table 2. Compared with the non-VBID PPO cohort, the VBID PPO enrollees had significantly lower probability of inpatient hospital surgical admission in 2019 (adjusted relative odds ratio [OR], 0.74; $95\%$ CI, 0.59-0.93; $$P \leq .01$$). The VBID PPO cohort also had a lower probability of total inpatient hospital admission and paying admission OOP in 2019 (adjusted relative OR, 0.82; $95\%$ CI, 0.71-0.95; $$P \leq .008$$). The VBID PPO cohort had significantly higher chances of receiving immunizations in 2019 (adjusted relative OR, 1.07; $95\%$ CI, 1.01-1.21; $$P \leq .01$$), seeing a PCP in 2020 (adjusted relative OR, 1.05; $95\%$ CI, 1.01-1.10; $$P \leq .02$$), and paying for an outpatient visit OOP in 2020.
**Table 2.**
| Model and measure | 2019 | 2019.1 | 2019.2 | 2020 | 2020.1 | 2020.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Model and measure | Relative odds ratio (95% CI) | SE | P value | Relative odds ratio (95% CI) | SE | P value |
| Logit GLM | | | | | | |
| Total allowed payment | | | | | | |
| Total combined | 0.99 (0.93-1.05) | 0.03 | .74 | 1.01 (0.95-1.07) | 0.03 | .86 |
| Inpatient admission | 0.82 (0.71-0.95) | 0.06 | .007 | 0.89 (0.77-1.03) | 0.07 | .12 |
| Outpatient visit | 1.00 (0.95-1.06) | 0.03 | .94 | 1.03 (0.98-1.09) | 0.03 | .23 |
| Prescription drug | 0.97 (0.93-1.01) | 0.02 | .13 | 0.99 (0.95-1.03) | 0.02 | .63 |
| OOP | | | | | | |
| Combined | 1.01 (0.95-1.06) | 0.03 | .83 | 1.05 (0.99-1.11) | 0.03 | .08 |
| Admission OOP | 0.82 (0.71-0.95) | 0.06 | .008 | 0.88 (0.76-1.02) | 0.07 | .09 |
| Visit OOP | 1.01 (0.96-1.06) | 0.03 | .66 | 1.07 (1.01-1.12) | 0.03 | .01 |
| Prescription OOP | 0.97 (0.93-1.01) | 0.02 | .14 | 1.03 (0.98-1.07) | 0.02 | .25 |
| Inpatient | | | | | | |
| Inpatient cesarean section | 0.77 (0.45-1.32) | 0.21 | .34 | 0.78 (0.46-1.34) | 0.21 | .37 |
| Inpatient surgical | 0.74 (0.59-0.93) | 0.09 | .01 | 0.85 (0.67-1.08) | 0.1 | .19 |
| Outpatient | | | | | | |
| PCP office visit | 0.99 (0.95-1.03) | 0.02 | .69 | 1.05 (1.01-1.10) | 0.02 | .02 |
| Specialist office visit | 0.97 (0.93-1.01) | 0.02 | .20 | 1.00 (0.96-1.04) | 0.02 | .95 |
| Psychiatrist outpatient | 1.00 (0.94-1.06) | 0.03 | .97 | 0.99 (0.93-1.06) | 0.03 | .82 |
| Emergency department | 1.07 (1.00-1.15) | 0.04 | .06 | 1.01 (0.94-1.09) | 0.04 | .73 |
| Laboratory test | 0.97 (0.93-1.01) | 0.02 | .12 | 1.02 (0.97-1.06) | 0.02 | .50 |
| Immunization | 1.07 (1.01-1.12) | 0.03 | .01 | 1.03 (0.98-1.08) | 0.03 | .28 |
| Log-Gamma GLM | Relative payments ratio (95% CI) | SE | P value | Relative payments ratio (95% CI) | SE | P value |
| Total allowed payment | | | | | | |
| Total combined | 1.03 (0.95-1.13) | 0.05 | .46 | 1.12 (0.96-1.31) | 0.09 | .15 |
| Inpatient admission | 0.91 (0.74-1.12) | 0.1 | .37 | 1.02 (0.77-1.34) | 0.14 | .89 |
| Outpatient visit | 1.05 (0.98-1.12) | 0.04 | .17 | 1.03 (0.95-1.13) | 0.05 | .45 |
| Prescription drug | 1.08 (1.00-1.17) | 0.04 | .06 | 1.10 (1.00-1.23) | 0.06 | .06 |
| OOP | | | | | | |
| Combined | 0.98 (0.94-1.01) | 0.02 | .22 | 0.98 (0.93-1.03) | 0.02 | .37 |
| Admission OOP | 0.97 (0.87-1.08) | 0.05 | .59 | 0.90 (0.81-1.00) | 0.05 | .05 |
| Visit OOP | 0.98 (0.94-1.02) | 0.02 | .29 | 0.98 (0.93-1.03) | 0.03 | .50 |
| Prescription OOP | 1.02 (0.99-1.05) | 0.02 | .12 | 1.04 (1.00-1.08) | 0.02 | .03 |
| Inpatient | | | | | | |
| Inpatient cesarean section | 1.29 (1.01-1.66) | 0.16 | .04 | 0.99 (0.77-1.27) | 0.13 | .92 |
| Inpatient surgical | 0.90 (0.66-1.24) | 0.15 | .54 | 1.17 (0.77-1.77) | 0.25 | .47 |
| Outpatient | | | | | | |
| PCP office visit | 1.02 (1.00-1.05) | 0.01 | .04 | 1.05 (1.02-1.08) | 0.01 | .000 |
| Specialist office visit | 1.01 (0.97-1.04) | 0.02 | .71 | 1.01 (0.97-1.05) | 0.02 | .56 |
| Psychiatrist outpatient | 1.03 (0.95-1.11) | 0.04 | .53 | 1.02 (0.93-1.11) | 0.05 | .67 |
| Emergency department | 1.02 (0.93-1.12) | 0.05 | .72 | 1.03 (0.94-1.13) | 0.05 | .47 |
| Laboratory test | 0.97 (0.91-1.04) | 0.03 | .42 | 1.11 (1.01-1.22) | 0.05 | .04 |
| Immunization | 1.03 (0.98-1.08) | 0.02 | .28 | 1.01 (0.96-1.07) | 0.03 | .60 |
Among those with any utilization or positive allowed payments, the VBID PPO cohort had significantly higher mean total allowed payments per member per year for PCP office visits in both 2019 and 2020 (adjusted relative payments ratio, 1.05; $95\%$ CI, 1.02-1.08; $P \leq .001$). The VBID PPO cohorts also had higher mean payments for laboratory test and medication OOP in 2020, but lower mean OOP payments for inpatient admission in 2020 (adjusted relative payments ratio, 0.90; $95\%$ CI, 0.81-1.00; $$P \leq .05$$). The VBID and non-VBID PPO cohorts had no significant differences for the following utilization and payment measures in both 2019 and 2020: inpatient and outpatient combined, outpatient, prescription drug, OOP combined, emergency department, specialist, and psychiatrist visit.
We provide the estimated overall marginal effects in dollars from the fitted 2-part GLM models in eTable 2 in Supplement 1. The differential changes in per-member mean annual payments are small for most measures. Note that although the marginal effects are intuitively appealing in original measurement units, they are not unbiased DID estimates as the interaction item coefficients for GLMs.15 The VBID PPO subcohort, female and aged between 15 and 55 years old, had higher mean total inpatient cesarean-section positive payments than their non-VBID PPO counterpart in 2019 (Table 2). The sensitivity analysis for those with inpatient delivery yielded very similar results or conclusions (eTable 3 and 4 in Supplement 1).
The coefficients of interactions of the VBID PPO indicator and year indicator before 2019 in the 2-part DID GLMs are the model tests for parallel pre-VBID trends assumption for utilization and payments.15 They were insignificant for almost all measures after ATT weighting (eTable 5 in Supplement 1).
## Discussion
In this cohort study, we found that in 2019 or 2020, participation in the CalPERS VBID PPO was associated with [1] higher probability of PCP use, immunizations, and outpatient OOP payments, and lower probability of inpatient surgical and total admissions and OOP payments; [2] higher mean positive spending for PCPs, laboratory tests, inpatient cesarean deliveries and medication OOP payments; and lower inpatient OOP payments; and [3] similar total health care spending changes in both years, suggesting the VBID program has the potential to promote valued health care services while controlling costs for CalPERS PPO plan members.
The VBID association with some measures, such as PCP visits, immunizations, and inpatient surgical admissions, are as expected, while others are more nuanced. For example, the VBID PPO cohort experienced a higher chance of paying for outpatient visits OOP, but they also had lower positive outpatient OOP allowed payments (Table 2) and lower overall annual outpatient OOP allowed payments, although most are not significant (eTable 2 in Supplement 1). The VBID PPO cohort experienced a lower chance of paying for inpatient admissions OOP, lower positive inpatient OOP payments (Table 2), and lower overall annual inpatient OOP payments (eTable 2 in Supplement 1). These results suggest that even though the VBID PPO may be associated with a higher chance of outpatient OOP payments, it may come with lower annual OOP payments for both outpatient and inpatient services (Table 2; eTable 2 in Supplement 1).
Another unexpected result concerns cesarean sections. The low uptake rate for the healthy mother’s program makes drawing meaningful conclusions difficult regarding the VBID PPO higher positive mean total allowed payments for inpatient cesarean deliveries in a single year. Although statistically insignificant, the VBID PPO had a lower probability of inpatient cesarean deliveries than the non-VBID PPO (Table 2; eTable 3 in Supplement 1). Additionally, there were no essential differences in marginal effects on spending (eTable 2 and 4 in Supplement 1). It should be noted that the healthy mother’s program has no explicit goal of reducing cesarean delivery. Similarly, the VBID program’s significant association with positive laboratory test payments and medication OOP payments in 2020 may be due to the COVID-19 pandemic, more primary care interventions, or some other cause. Additional years of observation and study are warranted to further explore and confirm the real long-term VBID association with these measures.
The CalPERS VBID program needs improvement in scope and depth. One area is to enhance its member engagement. The high uptake rates in disease management participation and second opinion for surgery were primarily because VBID PPO members would automatically receive these incentives until they actively declined them. A similar approach may be used to address the low PCP uptake rate. For example, a PCP would be matched to a member if they do not choose a PCP upon open enrollment, but they still have the freedom to change or drop their assigned PCP anytime over the course of the year.
Improving VBID PPO member engagement with PCPs and participation in the incentivized activities should help achieve the program’s objectives. CalPERS could further increase the VBID PPO copayment differential between PCP and specialist visits by eliminating the copayment for assigned PCP office visits, thereby incentivizing VBID PPO members to select and use PCPs, promoting primary care and reducing expensive specialty and emergent care.16,17,18,19 Similarly, the VBID PPO may also automatically enroll all expectant mothers who do not explicitly decline program participation.
Another area for improvement is to target prescription drugs for high prevalence diseases, chronic conditions, or high-risk patients (eg, heart diseases, diabetes, hypertension, hyperlipidemia, and so on).10,20,21,22,23 Many VBID studies have found that lowering or eliminating cost sharing for prescription drugs would increase medication use, adherence, and spending for targeted diseases or conditions, although with uncertainty in total health care savings.6,9,11,19,24,25,26,27,28,29,30,31,32,33,34,35,36,37 CalPERS could consider combining reference pricing for prescription drugs with VBID to control total medication spending. For example, eliminating drug copayments if members choose the referenced prescription drugs for targeted diseases or conditions to lower member medication OOP payments and improve medication adherence. These measures will increase the operational complexity of administering pharmacy benefits and may face resistance from health plans and pharmacy benefit managers, which could be addressed and resolved during contract solicitation, negotiation, and renewal phases.
Increasing cost sharing for low-value care to reduce health care waste and cost may be another option for CalPERS to expand its VBID scope but with many challenges.13,38,39 Most VBID applications and studies have focused on decreasing cost sharing for high-value care and few have looked at increasing cost sharing for low-value care although the latter has been advocated since VBID’s inception.40,41,42 There are professional, ethical, and potentially legal issues regarding who and how to define, measure, and generalize usually situational health care as low value care, which may also increase administrative, operational, and benefit complexity, as well as member confusion.43,44,45,46,47 What’s more, its association with reducing total cost may not come to fruition in the short term. One empirical study found that substantially increased cost sharing for low-value services was associated with reduced targeted use but not with total cost savings.48 The literature suggests that clinician-based interventions are more effective than consumer-based interventions, and multicomponent interventions in addressing both clinician and patient roles have the greatest potential for reducing low-value care.13,39
## Limitations
Our VBID study focused on the cost and utilization of general health care providers and services in a large health benefit public purchaser’s commercial PPO health plans using rigorous methods and recent data, but with limitations, nevertheless. We applied a fixed cohort approach by requiring continuous enrollment in the VBID PPO or non-VBID PPO from 2017 through 2020 to reduce favorable selection bias due to lower VBID PPO premium and VBID incentives attracting healthy enrollees in 2019 and 2020.15 However, we may not exclude selection bias on unobserved factors without randomization even though we applied a DID ATT weighted regression adjustment. Therefore, this study cannot tell whether VBID associations with lower inpatient admissions and surgical procedures were from reduced unnecessary procedures or from limited access, although the former was more likely than the latter for these PPOs. Our study findings may not be generalizable to other states’ commercial health plans and the Medicare or Medicaid populations. The study time frame is short, and the COVID-19 pandemic had a shock on the 2020 results, which led to a lack of consistent patterns for many significant findings over the first 2 years. We also did not examine clinical outcomes, which warrant further research. The marginal effect sizes of most outcome measures are financially minimal. It may take a longer time and additional incentives targeting medications for certain conditions or populations for the CalPERS VBID PPO to materialize substantial cost savings. However, the VBID literature to date seem to imply that VBID is more effective in redistributing and enhancing health care value than reducing total cost.6,28,49
## Conclusions
In this study, the CalPERS VBID program was associated with desired changes for some interventions with no added total costs in its initial 2 years of operation: higher PCP and immunization utilization or spending, and lower inpatient total and surgical admissions and OOP payments. However, due to the small marginal effect sizes, the COVID-19 pandemic in 2020 and inconsistent results across years, additional years of observation and study are warranted to assure persistent VBID association. CalPERS may need to adjust current VBID interventions and keep exploring, implementing, and evaluating other VBID incentives and benefit designs to promote value while controlling cost for its health plan members.50
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|
---
title: 'Selection of and Response to Physical Activity–Based Social Comparisons in
a Digital Environment: Series of Daily Assessment Studies'
journal: JMIR Human Factors
year: 2023
pmcid: PMC10012003
doi: 10.2196/41239
license: CC BY 4.0
---
# Selection of and Response to Physical Activity–Based Social Comparisons in a Digital Environment: Series of Daily Assessment Studies
## Abstract
### Background
Innovative approaches are needed to understand barriers to and facilitators of physical activity among insufficiently active adults. Although social comparison processes (ie, self-evaluations relative to others) are often used to motivate physical activity in digital environments, user preferences and responses to comparison information are poorly understood.
### Objective
We used an iterative approach to better understand users’ selection of comparison targets, how they interacted with their selected targets, and how they responded to these targets.
### Methods
Across 3 studies, different samples of insufficiently active college students used the Fitbit system (Fitbit LLC) to track their steps per day as well as a separate, adaptive web platform each day for 7 to 9 days ($$n = 112$$). The adaptive platform was designed with different layouts for each study; each allowed participants to select their preferred comparison target from various sets of options, view the desired amount of information about their selected target, and rate their physical activity motivation before and after viewing information about their selected target. Targets were presented as achieving physical activity at various levels below and above their own, which were accessed via the Fitbit system each day. We examined the types of comparison target selections, time spent viewing and number of elements viewed for each type of target, and day-level associations between comparison selections and physical activity outcomes (motivation and behavior).
### Results
Study 1 ($$n = 5$$) demonstrated that the new web platform could be used as intended and that participants’ interactions with the platform (ie, the type of target selected, the time spent viewing the selected target’s profile, and the number of profile elements viewed) varied across the days. Studies 2 ($$n = 53$$) and 3 ($$n = 54$$) replicated these findings; in both studies, age was positively associated with time spent viewing the selected target’s profile and the number of profile elements viewed. Across all studies, upward targets (who had more steps per day than the participant) were selected more often than downward targets (who had fewer steps per day than the participant), although only a subset of either type of target selection was associated with benefits for physical activity motivation or behavior.
### Conclusions
Capturing physical activity–based social comparison preferences is feasible in an adaptive digital environment, and day-to-day differences in preferences for social comparison targets are associated with day-to-day changes in physical activity motivation and behavior. Findings show that participants only sometimes focus on the comparison opportunities that support their physical activity motivation or behavior, which helps explain previous, equivocal findings regarding the benefits of physical activity–based comparisons. Additional investigation of day-level determinants of comparison selections and responses is needed to fully understand how best to harness comparison processes in digital tools to promote physical activity.
## Background
Engaging in regular physical activity (PA) has wide-ranging and meaningful benefits for physical and mental health [1-3]. Although activity of moderate to vigorous intensity confers unique cardiovascular protection [4], lighter-intensity activity is linked to positive outcomes and is recommended to promote health [5,6]. Conversely, physical inactivity is a key contributor to many of the leading causes of death in the United States and worldwide, including cardiovascular disease and cancer [7-9]. Identifying determinants of PA engagement has been a research priority for several decades and has informed a myriad of prevention and intervention efforts [10]. However, despite these efforts, adults in the United States rarely engage in sufficient PA to protect their health; recent estimates indicate that only $50\%$ meet recommended levels of PA [11], although estimates vary by calculation approach [12]. Consequently, there is a clear need for work that can offer additional insights into PA barriers and facilitators—particularly those that could inform PA promotion efforts on a large scale.
Digital tools such as web platforms and mobile apps show promise for maximizing accessibility to PA resources as they are available for use as needed and can respond to varying contexts in daily life. Specifically, these tools can harness the power of the social environment to support PA by connecting individuals with other users without requiring synchronous interaction [13]. For example, social comparison processes can be activated by sharing PA data between users as captured by a wearable monitor [14]. Exposure to others’ PA behavior allows users to evaluate their own PA relative to that of others [15] using features such as leaderboards and competitive challenges [16,17]. Upward comparison, via exposure to someone doing better with PA (eg, with more steps per day), can inspire the comparer to reach the upward target’s level and provide guidance for how to achieve a similar outcome [18]. Downward comparison, via exposure to someone doing worse with PA (eg, with fewer steps per day), can prompt the comparer to avoid becoming like the downward target to maintain their status [19,20]. Social comparison is expected to work in these ways to motivate users to maintain or increase their PA [21,22].
As a result, features of digital PA tools that activate social comparison processes are popular and have received considerable attention [14,23]. Literature in this area shows some evidence that social comparisons affect PA motivation and behavior (via digital tools and more broadly [24-26]). For instance, direct access to information about others’ PA behavior results in attending more group exercise classes than access to discussions with others about PA (to facilitate social support [27]). However, the effects of comparisons in both upward and downward directions on PA outcomes are heterogeneous and poorly understood. Some people experience decreased PA motivation or behavior in response to social comparisons, including those that are self-selected from a range of options [27-29].
Furthermore, responses to comparisons of PA (with respect to motivation and behavior) are not necessarily consistent for the same person across time; a person may respond positively at some times and negatively at others depending on the daily context [30]. In addition to the direction of a comparison (upward vs downward), a feature that may affect a comparison’s proximal influence on PA outcomes is its scale, or the relative distance between the comparer and target. Comparisons to others doing just a little bit better or worse than the self may have the biggest impact as the target’s outcome seems achievable (upward) or imminent (downward) and the comparer is motivated to improve or maintain their status [15,31,32]. In contrast, comparisons to others who are doing much better or worse may be demotivating as the target’s outcome seems unattainable (upward) or unlikely (downward).
Despite the ubiquity of social comparison features in digital tools to promote PA, the optimal approach to activating comparison processes in a digital environment is not clear. Allowing users to select their preferred comparison target appears to be more effective for promoting PA than restricting exposure to a single (nonpreferred) target [33], and many digital comparison opportunities allow the user to select or focus on a subset of targets from a range of options (eg, leaderboards). However, as noted, even self-selection often results in negative responses. Specifically, there is a current need for additional insights into users’ comparison selections, their interactions with these selections, and the extent to which users respond positively (vs negatively) to their selections in a digital PA environment.
## Aims of This Study
Given the availability of digital features that activate social comparison processes to promote PA and the equivocal nature of evidence in this area, there is a need for an improved understanding of PA-based comparison selections and responses in a digital environment. Additional information in this domain could elucidate the nature of PA-based comparison processes and help identify the comparisons that are associated with benefits for PA outcomes (vs harms). The aims of this study were to describe PA-based comparison selections (direction and scale) and examine day-level associations between comparison selections and PA outcomes (motivation and behavior), both overall and for within-person differences across days. To achieve these aims, we used data from an existing 3-study series that allowed participants to select a PA-based comparison target from different sets of options with respect to direction and scale. PA motivation was assessed both before and after comparison exposure each day, and PA behavior was captured in steps per day using the Fitbit platform (Fitbit LLC).
## Study Series Overview
As part of a larger series of studies to investigate the potential for personalizing social comparison opportunities in the context of a social exergame [34-37], participants in each study completed 7 to 28 total days of data collection. In studies 2 and 3, the first 9 days constituted an exploratory period during which all participants selected from various sets of comparison options; the following days introduced a personalized experimental manipulation for half of the participants based on random assignment. This report describes a set of secondary analyses that examine comparison selections, interactions with these selections via a web platform, and associated consequences for PA motivation and behavior during only the initial 7- or 9-day exploratory period in each study.
## Consistent Components Across Studies
Across studies, participants were recruited from the Drexel University undergraduate student participant pool using both in-class recruiting and a web-based study scheduling platform (Sona Systems). Students were eligible to participate if they were aged ≥18 years, had daily access to a desktop or laptop computer, self-reported that PA was important to them, and had access to a Fitbit account or were willing to create one. Use of either a Fitbit wearable device or the Fitbit smartphone app was acceptable. Students were excluded if they had a medical condition that limited their ability to engage in moderate- or vigorous-intensity PA or were under medical advisement to avoid moderate or vigorous PA.
## Participants—Study 1
Of the 11 undergraduate students who expressed interest in participating, 6 ($55\%$) enrolled in this initial pilot phase. In total, $17\%$ ($\frac{1}{6}$) of the participants did not complete any days of data collection and were excluded, resulting in a sample of 5 students. The average participant took 4690 (SE 1767.99) steps per day during the study period. All participants were undergraduate students aged ≥18 years; however, further demographic data were not collected during this initial pilot.
## Participants—Study 2
Through rolling recruitment over the course of 2 months, 119 students expressed interest in participating. Of these 119 students, 66 ($55.5\%$) did not complete the required days of data collection, resulting in 53 ($44.5\%$) participants who enrolled in study 2. The sample comprised $57\%$ ($\frac{30}{53}$) women and was racially representative of an undergraduate population, with most participants identifying as White ($\frac{28}{53}$, $53\%$) or Asian ($\frac{13}{53}$, $25\%$; see Table 1 for further demographic information). The average participant took 6376 (SE 351.43) steps per day during the baseline study period.
**Table 1**
| Demographics | Demographics.1 | Study 1a (n=5) | Study 2 (n=53) | Study 3 (n=54) |
| --- | --- | --- | --- | --- |
| Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) | Gender, n (%) |
| | Women | —b | 30 (57) | 37 (69) |
| | Men | — | 23 (43) | 17 (31) |
| Age (years), mean (SD; range) | Age (years), mean (SD; range) | ≥18 | 22.45 (7.40; 18-53) | 20.31 (2.93;18-36) |
| Race, n (%) | Race, n (%) | Race, n (%) | Race, n (%) | Race, n (%) |
| | White | — | 28 (53) | 23 (43) |
| | Asian | — | 13 (25) | 22 (41) |
| | Multiracial | — | 5 (9) | 4 (7) |
| | Black | — | 4 (8) | 2 (4) |
| | Other | — | 2 (4) | 2 (4) |
| | American Indian or Alaska Native | — | 1 (2) | 0 (0) |
| | Prefer not to say | — | 0 (0) | 1 (2) |
| Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) | Ethnicity, n (%) |
| | Hispanic or Latino | — | 3 (6) | 7 (13) |
| | Not Hispanic or Latino | — | 49 (92) | 47 (87) |
| | Not reported | — | 1 (2) | 0 (0) |
## Participants—Study 3
Through rolling recruitment over 3 months, 90 students expressed interest in participating. Of these 90 students, 35 ($39\%$) did not complete the required days of data collection, resulting in 54 ($60\%$) participants who enrolled in study 3. Most of the participants were women ($\frac{37}{54}$, $69\%$) and the majority of students identified as White ($\frac{23}{54}$, $43\%$) or Asian ($\frac{22}{54}$, $41\%$; see Table 1 for further details). The average participant took 3609 (SE 339.32) steps per day during the baseline study period.
## Social Comparison Selections
As described in the following sections, participants were asked to select user profiles to view each day from a range of options that represented upward and downward comparisons relative to their own PA behavior. They could select multiple profiles each day to view partial information but could only select 1 profile to view in full. Telemetry built into the web application tracked the participants’ navigation of the web app, including the profiles they viewed (in part or in full), the time spent viewing profiles, and the fields they chose to observe for their full selected profile. Comparison selections were operationally defined with respect to the total number each day, the time spent viewing profiles, the number of profile elements viewed, and the direction and scale of the profile selected for full viewing.
## Motivation to Exercise
Participants in studies 2 and 3 self-reported their immediate motivation to exercise at the start and end of their participation each day (ie, before and after their comparison selections and exposure). Responses to the following statement—“Overall, I would rate my current motivation to exercise as...”—were rated on a scale from 1 (very low motivation) to 5 (very high motivation) at each time point. This approach to assessing motivation was guided by previous work in this area, including prior work by the investigators [28,38].
## PA Behavior
To maximize accessibility, activity behavior was defined as total steps per day; steps are a commonly used metric to evaluate PA behavior and are associated with health outcomes [39]. Daily step count totals were measured using data pulled from either a Fitbit wearable device or the Fitbit MobileTrack smartphone app. The app is synced to a participant’s accelerometer on their smartphone, which shows validity for assessing steps across devices and operating systems [40]. Of note, we allowed for heterogeneity in the device used to measure daily steps to enhance the generalizability of findings across individuals with and without the means to purchase a wrist-worn device. This approach has been used in prior work, which shows that Fitbit devices and the MobileTrack app do not generate meaningfully different step estimates [41]. Fitbit step data from the previous day were synced with the study website and then displayed to participants when they logged into the study platform each day.
## Procedures
After completing a web-based screening survey to determine eligibility, eligible individuals provided electronic informed consent and were then directed to a second web page where they completed a battery of global self-report questionnaires (not included in this report). Participants were then given a username and log-in for the daily web-based activity, where on first log-in, they were directed to authenticate a Fitbit account with our web platform so that daily steps could be retrieved. Starting the following day (which allowed for the sign-up day’s steps to be used in the first session), the user was introduced to the relevant activities described in the following sections. Users were asked to log in and complete a session once per day; the time of day was not specified.
Upon log-in, the web server queried the user’s steps for the previous day via the Fitbit application programming interface (API). If it was detected via the API call that Fitbit did not yet have a full account of the previous day’s steps, the web application directed the participant to open the Fitbit app on their mobile device to prompt a data upload. Of note for study 2, there was a short period during data collection (3 days) in which the Fitbit server was not properly syncing with the study website. As a result, participants’ steps displayed upon logging in represented steps from the last successful sync rather than from the previous day’s true step count. This error was remedied on the day it was identified.
## Overview
As in several previous studies, opportunities to make social comparisons came through viewing profiles of individuals described as similar to the participant [42]. After completing the motivation assessment, participants in each study had the opportunity to select one or more profiles to view. These profiles described other individuals who had recently engaged in more or less PA than the participant to represent upward or downward comparison targets at a range of distances from the participant’s own recent PA behavior. Profile options included only minimal information, including only their username (eg, “dmf25”) and step total. Participants were able to click on multiple selections to learn additional information but could only select 1 profile to view in full.
## Study 1
Study 1 was designed as a proof-of-concept pilot to ensure that the systems worked correctly and that the platform could detect participants’ navigation behavior. Participants were asked to engage in a 5-minute session on the web platform once per day for 7 days. After logging in each day, participants were greeted with their own step total for the previous day, as tracked by their Fitbit device or app. This was posted next to 4 profiles of “other users,” which were created by the system; 2 presented upward comparisons (ie, with step totals of $110\%$ and $130\%$ of the participant’s steps from the day before), and 2 presented downward comparisons (ie, with step totals of $90\%$ and $70\%$ of the participant’s steps from the day before; Figure 1). In each case, a margin of –$2\%$ to +$2\%$ was applied as noise to protect against potential identification of the study’s aim.
As noted, participants could select multiple profiles to learn additional information about the users, including their city of residence and favorite location to exercise (as shown in Figure 2). However, they would have to select 1 profile to view in full to complete the task for the day. Upon selecting a profile to view in full, participants viewed a page containing a user’s demographics (eg, age, sex, and profession), physical appearance (eg, height and weight), exercise preferences (eg, preferred forms of PA), and other personal information (eg, hobbies; Figure 3).
**Figure 1:** *View of the study web page that included 4 comparison targets to select from.* **Figure 2:** *View of the Overview study web page, in which a profile has been initially selected but not yet selected to view in full. Participants could still go back and peruse other profiles to select from before selecting their final profile for full details (comparison target). Avg: average.* **Figure 3:** *Once committing to a profile during a daily session, participants are taken to a Details page that lists full information regarding the profile.*
Of the 5 individuals who participated in the initial proof-of-concept test, 4 ($80\%$) completed the expected daily uses of the web platform (ie, 6-7 within 19 days of enrollment); 1 ($20\%$) participant completed 2 daily uses during the allotted time frame. Participants elected to view the full profile for the first user they selected on $71\%$ ($\frac{20}{28}$ selections) of days. Across days, participants spent an average of 40 (range 3.3-145) seconds on their selected full profile and clicked on an average of 5 (range 0-29) profile elements. Less than $40\%$ of the variability in both the amount of time each participant spent on their selected profiles and the number of elements they elected to view was attributable to stable, between-person differences (ICC=0.28 and 0.36, respectively), suggesting considerable within-person variability in these behaviors across days ($P \leq .001$ in all cases).
Selecting to view the full profile of upward comparison targets was considerably more frequent than selecting downward targets, with upward targets representing $75\%$ ($\frac{21}{28}$) of the observed selections. The most popular selection was the user with $130\%$ of the participant’s own steps from the previous day ($\frac{13}{28}$, $46\%$ of selections; Table 2). Relative to all other choices, participants spent slightly longer viewing targets with $110\%$ of their own steps from the previous day (contrast $B = 18.53$, SE 12.51 seconds; F6=2.19; $$P \leq .19$$) but clicked on more profile elements when viewing targets with $90\%$ of their own steps from the previous day (contrast $B = 8.18$, SE 3.31 clicks; F6=2.47; $$P \leq .05$$). Within-person, neither the amount of time spent viewing profiles nor the number of profile elements viewed were associated with steps per day ($$P \leq .53$$, $$P \leq .99$$ respectively). However, participants took nearly 4000 more steps on days when they selected upward targets than on days when they selected downward targets (F1,3=5.31; $$P \leq .10$$), with the most steps occurring on days when they selected targets with $110\%$ of their own steps from the previous day (Table 3).
**Table 3**
| Type of target | Frequency, n (%) | Steps per day, B (SE) |
| --- | --- | --- |
| 70% | 3 (11) | 4023.82 (2927.76) |
| 90% | 4 (14) | 1448.51 (2665.40) |
| 110% | 8 (29) | 7152.59 (2321.05) |
| 130% | 14 (50) | 6081.24 (2050.10) |
| Downward (70% or 90%) | 7 (25) | 2241.73 (2358.08) |
| Upward (110% or 130%) | 21 (75) | 6403.27 (2015.87) |
## Study 2
The goal of study 2 was to examine patterns of user profile selection (ie, comparison targets) and response with respect to PA motivation and behavior. A revised web platform facilitated engagement in a daily, 2-minute task involving the selection of potential social comparison targets (9 days total). After logging in each day, participants viewed a page displaying their step count from the previous day (as collected from the Fitbit API either via a Fitbit wearable device or a smartphone step tracker synced to the Fitbit app). After reporting their initial motivation to exercise (1-5 rating scale), participants were presented with 4 profiles of other “users” of the application, as in study 1. However, instead of offering a consistent set of profiles with respect to step total (ie, $70\%$, $90\%$, $110\%$, and $130\%$ of the participant’s own steps), participants were assigned to one of the following profile sets each day: [1] all 4 profiles lower than the participant’s (downward options only) at $90\%$, $80\%$, $70\%$, and $60\%$ of the participant’s own step total from the previous day; [2] a mix of profiles—2 downward (lower than the participant’s own step total from the previous day at $90\%$ and $80\%$) and 2 upward (higher than the participant’s own step total from the previous day at $110\%$ and $120\%$); and [3] all 4 profiles higher than the participant’s (upward options only) at $110\%$, $120\%$, $130\%$, and $140\%$ of the participant’s own step total from the previous day.
In each case, a margin of –$2\%$ to +$2\%$ was applied as noise to protect against potential identification of the study’s aim. After viewing their selected full profile, participants were asked to report their exercise motivation a second time (1-5 rating scale).
Similar to study 1, participants elected to view the full profile for the first user they selected on the vast majority of days ($\frac{425}{472}$, $90\%$ of selections). Across days, participants spent an average of 18 (range 1.4-130) seconds on their selected full profile and clicked on an average of 9 (range 0-64) profile elements. Most of the variability in both the amount of time each participant spent with their selected profiles and the number of elements they elected to view was attributable to stable, between-person differences (ICC=0.53 and 0.63, respectively), although both showed evidence of fluctuation for the same person across days ($P \leq .001$ for both within-person variance components). Men spent slightly longer viewing each profile and selected to view more profile elements than women ($$P \leq .09$$ and $$P \leq .13$$, respectively); both behaviors were also positively associated with age ($$P \leq .02$$ and $$P \leq .02$$, respectively). However, neither time spent viewing nor the number of elements selected meaningfully differed based on racial/ethnic identification, the set of profile options presented, or the type of target selected ($$P \leq .63$$, $$P \leq .11$$, $$P \leq .39$$, $$P \leq .36$$, $$P \leq .91$$, $$P \leq .56$$, respectively).
Upward comparison target selections were slightly more frequent than downward comparison target selections, representing $54.2\%$ ($\frac{258}{476}$) of all final profile selections. However, overall, the most popular comparison target selection for viewing the full profile were downward targets at $90\%$ of the participant’s steps from the previous day (Table 4). On days when only downward target options were presented, participants most often selected the target with the step count closest to their own (ie, $90\%$ of their steps from the previous day); this trend was reversed on days when only upward target options were presented (ie, $140\%$ of their steps from the previous day, the farthest from their own). When presented with both upward and downward target options, they selected the target with the highest overall step count (ie, $120\%$ of their steps from the previous day).
Average change in motivation from before to after selection was slightly positive across the days ($B = 0.10$, SE 0.05), with considerable within-person variability (ICC=0.18). The lowest increases in motivation occurred on days when only downward target options were presented (Table 4). Interestingly, participants showed decreases in motivation to exercise only on days when they selected targets with $60\%$ and $110\%$ of their own steps from the previous day (Table 4). These represented the farthest downward and closest upward targets from their own steps, respectively. Participants showed increases in motivation on days when they selected all other targets (contrast F409=5.38; $$P \leq .02$$; sr=0.32), and this trend did not change when controlling for the set of target options shown.
With respect to steps per day, participants took approximately 540 fewer steps on days when both downward and upward target selections were presented relative to only upward or only downward targets (contrast F417=−3.80; $$P \leq .05$$). Steps were highest on days when participants selected targets most distant from themselves in both directions—they took approximately 725 more steps on days when they selected targets with $60\%$ and $140\%$ of their own steps from the previous day relative to targets closer to their own steps (contrast F409=3.76; $$P \leq .05$$). As noted, participants did not always select targets that led to increases in motivation to exercise. Within-person, neither motivation nor steps differed based on the amount of time spent viewing the selected profile or the number of profile elements viewed ($$P \leq .28$$, $$P \leq .21$$, $$P \leq .81$$, $$P \leq .90$$, respectively). However, controlling for their typical change in motivation to exercise from before to after comparison, on days when participants were more (vs less) motivated than usual after viewing their selected target, they engaged in more steps (F1,418=9.24; $$P \leq .003$$).
**Table 4**
| Unnamed: 0 | Unnamed: 1 | Frequency, n (%) | Change in motivation to exercise, B (SE) | Steps per day, B (SE) |
| --- | --- | --- | --- | --- |
| Type of target selected | Type of target selected | Type of target selected | Type of target selected | Type of target selected |
| | 60% | 34 (7.2) | −0.04 (0.11) | 6932.36 (597.06) |
| | 70% | 32 (7.2) | 0.11 (0.11) | 6215.89 (605.29) |
| | 80% | 51 (10.8) | 0.21 (0.09) | 5697.40 (515.19) |
| | 90% | 100 (21.2) | 0.02 (0.07) | 6356.75 (418.57) |
| | 110% | 84 (17.8) | −0.01 (0.08) | 6078.63 (444.95) |
| | 120% | 93 (19.7) | 0.14 (0.07) | 6447.72 (424.89) |
| | 130% | 21 (4.4) | 0.20 (0.14) | 6515.59 (733.75) |
| | 140% | 60 (12.7) | 0.19 (0.09) | 6965.72 (733.75) |
| Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown |
| | Downward only | 159 (33.7) | 0.04 (0.06) | 6573.20 (367.93) |
| | Downward and upward (2 each) | 159 (33.7) | 0.12 (0.06) | 6020.79 (366.82) |
| | Upward only | 159 (33.7) | 0.11 (0.06) | 6556.29 (368.49) |
## Study 3
The purpose of study 3 was to examine the translation of the profile selection platform to a gamified context, whereby participants were assigned to teams of 3 users. A further revised version of the web application allowed participants to view other users’ PA behavior and personal information (representing comparison targets) using a new format. As in study 2, participants were asked to log in and report their initial exercise motivation (1-5 rating scale). They then viewed brief descriptions of 2 additional profiles (as opposed to 4 in studies 1 and 2) in leaderboard format and were asked to select 1 to view additional information (Figure 4).
After selecting a profile, participants could view a subset of personal information (Figure 5); this view retained their own step total from the previous day to facilitate comparison with the selected user. Participants could access a full Details page once they selected a final profile to view in full.
However, unlike in the previous studies, step totals for other users in study 3 included data from other participants completing their data collection at the same time (ie, user data that were not created by the platform). Each participant was randomly assigned to a team with another user who began the study at the same time; these participants each saw the other’s step totals as 1 of their 2 profile options. The third user profile displayed in each session was generated and assigned by the platform, selected from the following options: [1] the third profile showed a step total $20\%$ lower than the lower of the 2 live participants, and the individual participant had either the most steps or was in the middle; [2] the third profile showed a step total between that of the 2 live participants, and the individual participant had either the most or the least steps; and [3] the third profile showed a step total $20\%$ higher than the higher of the 2 live participants, and the individual participant had either the least steps or was in the middle.
In each case, a random noise factor of –$2\%$ to +$2\%$ was added to obscure our process. This approach was designed to test manipulations of the game environment for the 2 live participant teammates by showing a fabricated third user who might provide an optimal comparison experience for the live teammates.
Across the studies, the distances between the user’s steps and the target’s steps (eg, $80\%$ and $140\%$) were guided by the principle of offering a realistic range of options and by relevant literature. Specifically, there is evidence supporting the Köhler effect and “motivation gain” in a team game environment that shows that participants’ performance improves with a teammate who performs approximately $20\%$ better than they do [43,44]. Under conditions in which users in this study saw both upward and downward targets as options, −$20\%$ was offered for symmetry. Other options were selected to retain realism while capturing distances from the user’s own steps that would be perceptible and large enough to show differences in associations with motivation or behavior. In study 3, the design particulars (ie, percentages below, between, or above 2 real users) resulted in a larger range and set of targets. A summary of each study design is presented in Table 2.
**Figure 4:** *Options for selecting from 2 user profiles, listing them and the user in descending order and representing their step totals visually (ie, a leaderboard format).* **Figure 5:** *Initial profile view in study 3. Avg: average.* TABLE_PLACEHOLDER:Table 2 Participants elected to view the full profile for the first user they selected on $96.9\%$ ($\frac{375}{387}$ selections) of occasions. Across days, participants spent an average of 72 (range 1-351) seconds on their selected full profile and clicked to view an average of 12 (range 0-54) profile elements. As in study 2, although the amount of time each participant spent with their selected profiles and the number of elements they elected to view were fairly stable (ICC=0.58 and 0.65, respectively), they showed some variation for the same person across days (within-person variance components; $P \leq .001$ in all cases). The time spent viewing profiles and the number of profile elements selected were again positively associated with age ($$P \leq .04$$ and $$P \leq .03$$, respectively), although neither behavior was associated with the set of profile options presented, whether the selected profile represented an upward or downward target, or whether the selected profile was of the fabricated user versus the real participant ($$P \leq .63$$, $$P \leq .75$$, $$P \leq .88$$, $$P \leq .92$$, $$P \leq .14$$, $$P \leq .80$$, respectively). However, unlike in study 2, neither the amount of time spent on the selected profile nor the number of profile elements selected differed by gender or racial/ethnic identification ($$P \leq .93$$, $$P \leq .34$$, $$P \leq .93$$, $$P \leq .35$$, respectively).
The method used to generate profiles in study 3 resulted in participant selections of comparison targets ranging from $0\%$ to 20,$610\%$ of their steps from the previous day. This represented selections of users with step totals ranging from 0 to 21,132 steps, with 88 selections of users who had <1000 steps and 27 selections of users with >10,000 steps. *This* generated >90 individual categories of selection, with most of these categories representing upward targets (ie, the selected users had more steps than the participants on the previous day). For ease of interpretation, upward selections were recategorized by percentages of the participants’ steps, as shown in Table 5. Participants selected the fabricated user on most days ($\frac{210}{387}$, $54.3\%$); they were more likely to choose the fabricated user when they selected upward (vs downward) targets (F1,336=4.44; $$P \leq .04$$) and were least likely to choose the fabricated user when that user was shown as last on the leaderboard (F2,335=10.20; $P \leq .001$).
As in studies 1 and 2, upward selections were more frequent than downward selections and represented $57.1\%$ ($\frac{221}{387}$) of all targets selected. However, unlike in study 2, the most popular choice overall was upward at $120\%$ of the participants’ steps from the previous day ($\frac{55}{387}$, $14.2\%$ of selections; Table 5). Users with $120\%$ of the participants’ steps from the previous day represented $41.4\%$ ($\frac{53}{128}$) of all selections on days when the fabricated participant was at the top of the leaderboard but <$1\%$ ($\frac{1}{127}$, $0.8\%$ and $\frac{1}{132}$, $0.8\%$) of selections on days when the fabricated user was second or third. Close in overall frequency of selections were users with $80\%$ of the participant’s steps (as in study 2; $\frac{41}{387}$, $10.6\%$ of selections) and users with $200\%$ to $999\%$ of the participant’s steps ($\frac{41}{387}$, $10.6\%$ of selections). Of note, selecting to view the profile for a user with the same number of steps the participant had on the previous day occurred on $2.3\%$ ($\frac{9}{387}$) of the days.
**Table 5**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | Frequency, n (%) | Frequency, n (%).1 | Change in motivation to exercise, B (SE) | Change in motivation to exercise, B (SE).1 | Steps per day, B (SE) |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Type of target | Type of target | Type of target | Type of target | Type of target | Type of target | Type of target | Type of target |
| | 0% | 10 (2.6) | 10 (2.6) | −0.07 (0.27) | −0.07 (0.27) | 3838.82 (1160.46) | 3838.82 (1160.46) |
| | 10% | 8 (2.1) | 8 (2.1) | −0.09 (0.30) | −0.09 (0.30) | 2635.07 (1137.18) | 2635.07 (1137.18) |
| | 20% | 13 (3.4) | 13 (3.4) | 0.06 (0.24) | 0.06 (0.24) | 3964.32 (922.67) | 3964.32 (922.67) |
| | 30% | 6 (1.6) | 6 (1.6) | 0.18 (0.35) | 0.18 (0.35) | 3697.40 (1277.26) | 3697.40 (1277.26) |
| | 40% | 5 (1.3) | 5 (1.3) | −0.74 (0.38) | −0.74 (0.38) | 3263.22 (1391.01) | 3263.22 (1391.01) |
| | 50% | 21 (5.4) | 21 (5.4) | 0.24 (0.20) | 0.24 (0.20) | 3004.35 (874.38) | 3004.35 (874.38) |
| | 60% | 21 (5.4) | 21 (5.4) | 0.39 (0.20) | 0.39 (0.20) | 3404.35 (1127.69) | 3404.35 (1127.69) |
| | 70% | 19 (4.9) | 19 (4.9) | 0.50 (0.21) | 0.50 (0.21) | 3221.15 (1130.97) | 3221.15 (1130.97) |
| | 80% | 41 (10.6) | 41 (10.6) | −0.03 (0.16) | −0.03 (0.16) | 2243.21 (901.90) | 2243.21 (901.90) |
| | 90% | 22 (5.7) | 22 (5.7) | 0.10 (0.20) | 0.10 (0.20) | 3440.08 (877.97) | 3440.08 (877.97) |
| | 100% | 9 (2.3) | 9 (2.3) | 0.32 (0.29) | 0.32 (0.29) | 3337.31 (1432.54) | 3337.31 (1432.54) |
| | 110% | 14 (3.6) | 14 (3.6) | 0.07 (0.24) | 0.07 (0.24) | 3598.14 (1053.33) | 3598.14 (1053.33) |
| | 120% | 55 (14.2) | 55 (14.2) | −0.09 (0.14) | −0.09 (0.14) | 3454.85 (747.45) | 3454.85 (747.45) |
| | 130% | 9 (2.3) | 9 (2.3) | 0.28 (0.29) | 0.28 (0.29) | 3221.15 (1130.97) | 3221.15 (1130.97) |
| | 140% | 11 (2.8) | 11 (2.8) | −0.11 (0.26) | −0.11 (0.26) | 2422.84 (1127.69) | 2422.84 (1127.69) |
| | 150% | 4 (1) | 4 (1) | 0.16 (0.42) | 0.16 (0.42) | 3448.54 (1551.14) | 3448.54 (1551.14) |
| | 160% | 6 (1.6) | 6 (1.6) | −0.25 (0.35) | −0.25 (0.35) | 3891.26 (1299.39) | 3891.26 (1299.39) |
| | 170% | 10 (2.6) | 10 (2.6) | 0.06 (0.27) | 0.06 (0.27) | 3345.57 (1093.40) | 3345.57 (1093.40) |
| | 180% | 5 (1.3) | 5 (1.3) | 0.14 (0.38) | 0.14 (0.38) | 5536.70 (1382.12) | 5536.70 (1382.12) |
| | 190% | 6 (1.6) | 6 (1.6) | 0.51 (0.35) | 0.51 (0.35) | 1878.49 (1378.49) | 1878.49 (1378.49) |
| | 200% | 5 (1.3) | 5 (1.3) | 0.04 (0.38) | 0.04 (0.38) | 2900.83 (1372.52) | 2900.83 (1372.52) |
| | 110%-199% | 18 (4.7) | 18 (4.7) | −0.12 (0.21) | −0.12 (0.21) | 3675.08 (938.78) | 3675.08 (938.78) |
| | 200%-999% | 41 (10.6) | 41 (10.6) | 0.29 (0.16) | 0.29 (0.16) | 2949.21 (781.46) | 2949.21 (781.46) |
| | 1000%-1999% | 8 (2.1) | 8 (2.1) | −0.10 (0.31) | −0.10 (0.31) | 3915.49 (1299.92) | 3915.49 (1299.92) |
| | >2000% | 20 (5.2) | 20 (5.2) | −0.16 (0.21) | −0.16 (0.21) | 3771.33 (962.49) | 3771.33 (962.49) |
| Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown | Type or types of target options shown |
| | Participant either first or second on leaderboard (fabricated user was third or last) | 127 (32.8) | 127 (32.8) | 0.05 (0.11) | 0.05 (0.11) | 3510.65 (667.93) | 3510.65 (667.93) |
| | Participant either first or third (last) on leaderboard (fabricated user was second) | 132 (34.1) | 132 (34.1) | 0.17 (0.11) | 0.17 (0.11) | 3033.56 (669.24) | 3033.56 (669.24) |
| | Participant either second or third (last) on leaderboard (fabricated user was first) | 128 (33.1) | 128 (33.1) | 0.02 (0.11) | 0.02 (0.11) | 3573.50 (668.77) | 3573.50 (668.77) |
| Selected fabricated user | Selected fabricated user | Selected fabricated user | Selected fabricated user | Selected fabricated user | Selected fabricated user | Selected fabricated user | Selected fabricated user |
| | No | 177 (45.7) | 177 (45.7) | 0.06 (0.11) | 0.06 (0.11) | 3248.29 (653.82) | 3248.29 (653.82) |
| | Yes | 210 (54.3) | 210 (54.3) | 0.09 (0.10) | 0.09 (0.10) | 3463.86 (642.90) | 3463.86 (642.90) |
Average change in motivation to exercise from before to after selection was again positive across days but extremely small ($B = 0.08$, SE 0.51), although within-person variability was predominant (ICC=0.04). Increases in motivation were largest on days when participants selected users with $190\%$ of their steps from the previous day, followed by users with $70\%$ of their steps from the previous day (Table 5). Participants’ motivation decreased on days when they selected upward targets with steps farthest from their own (ie, >$2000\%$ of their steps from the previous day) as well as on days when they selected users with $10\%$, $40\%$, $80\%$, $120\%$, and $160\%$ of their steps from the previous day; the greatest decreases were seen on days with selections of $40\%$ of the participants’ own steps from the previous day. Change in motivation was highest on days when the fabricated user was placed between a given participant and the other real participant on the leaderboard relative to days when the fabricated user appeared above or below both real participants (contrast F335=2.34; $$P \leq .12$$; sr=0.17). Change in motivation did not meaningfully differ between days when participants selected an upward or downward target (collapsed across percentage categories; F47=.97; $$P \leq .34$$) or between days when they selected the fabricated user versus the other live participant (F46=.00; $$P \leq .98$$).
With respect to steps per day, participants took approximately 500 fewer steps on days when the fabricated user was placed between themselves and the other real participant on the leaderboard relative to days when the fabricated user appeared above or below both real participants (contrast F303=2.89; $$P \leq .09$$). Steps did not meaningfully differ between days when participants did and did not select to view the profile of the fabricated user (F46=.56; $$P \leq .46$$). Although steps also did not differ overall based on the comparison direction and scale of the selected profile ($$P \leq .90$$, $$P \leq .99$$, respectively), interestingly, steps were highest on days when participants selected users with $180\%$ of their own steps from the previous day (approximately 5500 steps) and lowest on days when they selected users with $190\%$ of their own steps from the previous day (approximately 1900 steps; Table 5). Steps also did not meaningfully differ between days when participants selected an upward versus a downward target (collapsed across percentage categories; $$P \leq .90$$).
Neither motivation nor steps were associated with daily fluctuation in the amount of time each participant spent on their selected profiles or the number of elements they elected to view (within-person; $$P \leq .60$$, $$P \leq .64$$, $$P \leq .38$$, $$P \leq .34$$, respectively). Finally, although the within-person association between participants’ motivation and steps per day was not significant (F304=1.11; $$P \leq .29$$), it was noteworthy that the direction of the association was negative—unlike in study 2, on days when they were more motivated than usual after viewing their selected profile, participants took fewer steps than usual (B=−186.84, SE 177.65).
## Statistical Analyses
All analyses were conducted using SAS (version 9.4; SAS Institute). Missing data were minimal; data were missing for $20\%$ ($\frac{7}{35}$) of days in study 1 (because of low compliance from 1 participant), $1\%$ ($\frac{5}{477}$) of days in study 2, and $4.9\%$ ($\frac{24}{486}$) of days in study 3. Additional data were removed from relevant analyses where unreasonable values were observed, including values for time spent viewing profiles (>6 minutes; 4 observations) and steps per day (<100; 38 observations). The resulting data sets for studies 2 and 3 included 472 and 387 observations, respectively. These data sets afforded power of >0.80 for the primary, within-person tests described in this section (α of.05 [45]), although we emphasize effect sizes throughout—PA is described in steps per day, and all other associations are described using semipartial correlation coefficients (sr). Between-person tests were included to describe potential trends only as power was limited by modest sample sizes.
We first used empty models to calculate intraclass correlation coefficients (ICCs) to determine the proportion of variance attributable to between-person stability in the outcomes of interest. This included participant navigation behavior when interacting with comparison target profiles (time spent viewing the selected profile and number of elements viewed) and PA outcomes (motivation to exercise and steps per day), which were treated as continuous in all models. Motivation was not assessed in study 1; total steps per day were assessed in all 3 studies. Change in motivation in studies 2 and 3 was calculated by subtracting motivation before profile selection from motivation after selection.
Our first aim was to describe PA-based comparison selections, including participant navigation of the platform and the comparison direction and scale of the selected profile. To address this aim, we initially examined whether gender, racial/ethnic identification, and age (age treated as continuous and centered at the grand mean) differentially predicted navigation behavior. We then used descriptives to examine the frequencies of user profile selections in categories, representing the user’s steps as a percentage of the participant’s steps from the previous day (rounded to the nearest 10). The direction and scale of comparison targets (profiles) selected (all studies), the direction or directions of targets presented using randomization (studies 2 and 3), and whether the selected profile represented the other active participant or the fabricated user (study 3) were treated as categorical and subsequently used as predictors of PA outcomes.
Our second aim was to examine day-level associations between comparison selections and PA outcomes (motivation to exercise and steps per day). Analyses used multilevel modeling techniques using SAS PROC MIXED with restricted maximum likelihood estimation to address the nested data structure (ie, days nested within individuals). Gender, racial/ethnic identification, and age were used as covariates in all multilevel models (studies 2 and 3), with comparison target direction and scale (all studies), the randomized set of targets (studies 2 and 3), and fabricated user versus not (study 3) as predictors of PA outcomes. Although users accessed the platform at a range of times across the days of observation in each study, sensitivity analyses showed that the time of day at which users accessed the platform was not associated with any of our outcomes of interest and did not meaningfully change the results or conclusions reported in the next section. For parsimony, we reported the results of all tests without time of day as an additional covariate.
Finally, new navigation behavior and motivation variables were created for studies 2 and 3: between- and within-person variance were distinguished by calculating each person’s mean across days (between-person) and the difference between this person’s mean and the response on a given day (within-person; ie, person-mean centering [46]). This allowed for testing whether steps per day were associated with within-person fluctuation in navigation behavior or motivation, controlling for typical navigation behavior or typical change in motivation from before to after comparison.
## Ethics Approval
All procedures were approved by the institutional review board of Drexel University (approval 1901006917).
## Informed Consent and Compensation
All participants provided documentation of informed consent. Compensation for participation was provided through either extra credit in college courses or electronic gift cards depending on individual preference.
## Principal Findings
Social comparison processes can be activated to promote PA in digital environments, although individuals’ interactions with and responses to self-selected comparison targets in this context are poorly understood. As social comparison features are already built into many existing digital PA tools [14,16,23], this series of studies was designed to provide additional information about this important aspect of digital PA promotion. We created unique web-based platforms to capture individuals’ selections of social comparison targets, their interactions with information about the selected targets, and their subjective responses to the selected targets over 7 to 9 days, as well as their PA behavior on each of these days. We observed several similarities and differences across these studies that can shed additional light on this area.
First, participants chose to view the full profile of the first participant they selected on the vast majority of days ($71\%$-$97\%$), although many participants explored other profiles before returning to and settling on the first one they had selected. Participants also interacted with the platform and their selected profiles differently across days. They did not merely settle into a pattern of the same behavior each day despite the consistency and simplicity of the task. This underscores the appeal of PA-based comparisons and their potential to sustain engagement with digital tools, although additional testing over longer periods is needed.
Second, in both studies where demographic information was collected, older participants spent more time viewing profiles and selected more profile elements to view than younger participants. This stands in contrast to existing cross-sectional evidence, which suggests that older people are less interested in comparisons than younger people [47]. It is possible that our findings reflect a general tendency among older people to pay more attention to their participation in research than younger people [48]. Alternatively, it is possible that cross-sectional, retrospective self-evaluations of comparison activity do not align with observable behavior; this potential discrepancy is worthy of further investigation given that social comparison is often captured using global self-report measures [49,50]. Also noteworthy is that, although the participants’ ages in these studies ranged from 18 to 56 years, we recruited students enrolled in college who were predominantly in their early 20s. As such, associations with age warrant further investigation. Other observations of differences in behavioral interactions with social comparison information based on demographics (eg, gender) were not consistent across the studies in this series, although the power for these comparisons was limited.
Third, across all studies, the profiles of upward comparison targets were selected for full viewing more often than those of downward comparison targets. This was not an artifact of randomized exposure—each participant had an equal number of opportunities to select upward and downward targets. Moreover, participants tended to select upward targets that were distant from themselves (ie, those who had many more steps than they had) rather than upward targets closer to themselves. Selecting to make upward comparisons, particularly when a range of options is available, is often motivated by a desire for self-improvement [51,52]. Given that participants in these studies indicated that PA is important to them, selecting targets doing extremely well with PA offered an opportunity to learn information from that target that could support achievement of a similar high status [53]. For example, participants could learn new ways to be active from the profiles of very active participants, giving them opportunities to set PA goals to model the target.
However, despite the relative popularity of upward targets, participants also frequently selected downward targets and tended to select downward targets close in steps to their own (vs more distant from their own). Self-selection of downward targets is often motivated by a desire for self-enhancement [51,52]; seeing oneself as doing better than someone else in a valued domain can be satisfying and provide an emotional boost. The variety of selections across days may indicate day-to-day variability in participants’ needs and immediate goals that could be met with comparison opportunities [54,55].
Importantly, participants did not always select the target that was most useful with respect to either subjective PA motivation or PA behavior—many selections were associated with decreases in motivation, low PA engagement, or both. Similarly, a participant’s change in PA motivation as a result of viewing their selected comparison target was not consistently associated with their PA behavior. Subsets of previous work in this area show important aspects of comparisons that may help explain these findings and, thus, warrant further consideration. One is that people do not always select the comparison opportunities that fulfill either self-improvement or self-enhancement goals; at times, their intentions are to confirm that their own situation is bad or could worsen or to justify not making difficult behavior changes such as increasing their PA (eg, “I’m already doing better than someone else, so I’m doing fine” [56,57]). Even when they do have positive, goal-oriented intentions for selecting particular comparison opportunities (eg, to learn important information or to feel better), their expectations are not always met by the target provided [58]. In such situations, the comparison opportunity may actually lead to negative outcomes.
In addition, the affective consequences and behavioral correlates of a social comparison selection opportunity may depend on how the comparer interprets the information they receive. The Identification-Contrast Model of comparison processes [59] proposes that the comparer can focus on either similarities or differences between themselves and a target (reflecting identification with vs contrast against the target, respectively). Identifying with an upward target highlights the possibility that the comparer can achieve similar (better) outcomes, and contrasting against a downward target highlights the comparer’s current success (as the outcome could be worse). Conversely, identifying with a downward target suggests that the comparer’s situation is bad or may become worse; contrasting against an upward target highlights the comparer’s inferiority and suggests that the likelihood of achieving similar success is low. In the context of PA and similar comparisons of health behaviors, there is recent evidence showing that greater (vs less) identification with active others is associated with more frequent attendance to exercise classes [60], and identification and contrast processes moderate the association between the type of target selected (upward vs downward) and motivation to engage in healthy behavior [28]. Identification and contrast with respect to both upward and downward comparisons are also known to differ between people and show evidence of fluctuation for the same person over time [61-63]. Thus, in this series of studies, the high day-to-day variability in participants’ PA outcomes that were not fully explained by the direction or scale of the selected target may be due to individual or day-level differences in the extent of identification or contrast with the target. Assessment of these processes in future work could more fully explicate the complexity of social comparison and its optimal use to promote PA engagement. As discussed further in this section, to effectively isolate the source of this variability, removing potential noise coming from variability in the time of day of social comparison selections and exposure would be optimal in future studies.
Finally, we observed differences in findings between studies that may generate additional hypotheses to be tested in future work. For example, PA motivation in response to viewing the selected comparison target was positively associated with within-person behavior in study 2 but not in study 3. Study 2 presented the list of target selection options and the selected target’s step total side by side with the participant’s step total from the previous day. In contrast, study 3 presented social comparison target selection options in a leaderboard format such that the participant saw a visual representation of their rank against the 2 other users. These differences may affect the psychological dynamics of comparison selections and their associations with PA motivation and behavior, in general or for specific individuals. Target selection options in study 3 also included both a real participant and a fabricated user, where the ultimate goal was to determine the optimal placement of the fabricated user to balance the comparison effects on both of the real users. In this study, PA motivation increased the most on days when the fabricated user was in the middle of the leaderboard (between the 2 real users), but steps were lowest on these days. The leaderboard and balance approach may have blunted the potential negative effects of comparisons but also blunted some positive effects.
Participants who enrolled in study 2 were also noticeably more active than those who enrolled in study 3 (and study 1); relative to the US guideline of achieving 10,000 steps per day [6], the average activity level was moderate in study 2 and low in study 3 (and study 1). It is possible that the general correspondence between PA motivation and behavior is stronger for those who are moderately active than for those who are inactive in that those who are moderately active are better able to enact their PA motivation. Distinctions between studies could be due to participant characteristics, study design, or a combination of both. As a result, it is not yet clear whether one study design is more useful than another for activating beneficial PA-based social comparisons or whether there is a subset for whom one is superior to another.
## Strengths and Limitations of This Research
This series of studies has several strengths. Specifically, all 3 studies used objective assessment of comparison target (profile) selection, interactions with the target (ie, time spent viewing and number of profile elements viewed), and PA behavior (steps per day) across several days. Studies 2 and 3 also captured motivation to exercise both before and after target selection using a momentary item that was tested in previous work [28,38]. Retention of enrolled participants was high across studies, with minimal missing data. In addition, we used a multilevel analytic approach that allowed for maximizing the utility of intensive repeated assessments, with insights into daily behavior across participants as well as within-person associations across days. Finally, we took an iterative approach such that the platforms used in each study were slightly different with respect to the comparison target options to allow for preliminary comparisons between and across studies. Although the sample sizes in each study were modest and do not afford definitive conclusions about the sources of divergent results, observations of consistency and inconsistency across studies provide a strong foundation for hypothesis-driven research on a larger scale.
In addition to modest sample sizes, several other limitations are noteworthy. Participants’ access to the web platform was not restricted to a particular time of day or constrained to be consistent for the same participant across days. Consequently, participants may have taken part at varying times of day (eg, before vs midway through vs after engaging in most of their steps for that day). Although participants’ comparisons were anchored to their steps for the previous day, which were already completed, and controlling for time of day did not alter our findings, this inconsistency could mask any effects of social comparison selections on motivation or PA behavior for the current day by allowing for considerable noise between and within participants. In addition, the precision of PA behavior captured likely varied by participant as some used wearable PA monitors (eg, Fitbit wristbands) whereas others used less sensitive smartphone accelerometers. Assessment of PA motivation and behavior was also misaligned—motivation referred to “exercise” (ie, structured bouts of sustained, moderate– to vigorous–intensity movement), and behavior was captured with respect to steps (ie, overall movement at any intensity, including light activity). Although motivation did predict within-person behavior in study 2, this discrepancy may further help explain the lack of association in study 3. Future work should ensure that assessments of cognitive determinants of PA and PA behavior refer to the same behavioral outcomes.
Finally, participants were all students enrolled in college courses who reported that PA was important to them. This ensured that the dimension of comparison (PA) was relevant to the participants [15]. The average participant in each study also fell far short of US recommendations for PA behavior (ie, 10,000 steps per day), suggesting that participants generally represented individuals who could benefit from increasing PA—a target population of interest. However, recruitment from college courses and requiring participants to endorse a preexisting interest in PA resulted in samples of well-educated, motivated, and predominantly White young adults. As noted, there is existing evidence indicating that younger adults report more interest in and show stronger responses to social comparison information than older adults [47]. This may limit the effectiveness of social comparison processes as a PA promotion tool for younger adults, who already tend to be more active than older adults in the United States [11]. These are common problems in digital health research, particularly early-stage work. Additional attention needs to be paid to recruiting and retaining diverse samples to fully understand the range of PA social comparison preferences and responses that may be useful for promoting PA.
## Conclusions
Despite these limitations, these findings have several important implications. With respect to platform interface design, users show interest in viewing the profiles of other users and engage with profile content when the initial information available offers social comparison opportunities. Furthermore, as social comparison target selections are often not associated with benefits for PA motivation or behavior, the current real-world conditions for digital PA promotion tools (which offer unrestricted access to other users [14]) do not appear to meet users’ needs. Outcomes could be improved with subtle manipulation of comparison target options. These exploratory findings show that constraining users’ PA-based social comparison options and changing their options across days (with respect to direction and scale) is both feasible and acceptable, with high completion rates. An important next step is to identify the people and immediate contexts for which certain types of comparisons are optimal (eg, older vs younger adults, men vs women, or high vs low precomparison motivation) to allow for systems to offer the PA-based social comparison opportunities that are most likely to benefit users in their daily lives.
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|
---
title: 'A Decentralized Marketplace for Patient-Generated Health Data: Design Science
Approach'
journal: Journal of Medical Internet Research
year: 2023
pmcid: PMC10012005
doi: 10.2196/42743
license: CC BY 4.0
---
# A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach
## Abstract
### Background
Wearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers.
### Objective
We aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace.
### Methods
We used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system.
### Results
We designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study.
### Conclusions
A decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems.
## Background
Pervasive devices and wearables create health data that can be combined with electronic health record data to improve disease predictability. Such data can be used to create a patient-centric health system in addition to managing population health [1,2]. There are limited examples of patient-generated health data (PGHD) in clinical settings; however, recent advances in predictive analytics and health informatics have found numerous uses for such data. For example, mobile data may be used to predict and provide early warning signs of diseases such as hypertension, diabetes, cancer, and other heart ailments [3]. PGHD assets can become important value-adding differentiators for health care–related businesses, adding value across the health care value chain [4]. However, the design of centralized warehouses to support clinical and translational research suffers from many challenges, including “organization of data,” “access control,” “oversight and governance,” “sharing of data,” “service management between different bodies such as informatics and bio-statisticians,” and “technology challenges of maintenance, upgradation, and storage” [5]. In addition, Kruse and Goswamy [1] describe various challenges with data structure organization, validation, security, and privacy. PGHD available for real-time analysis may be challenging because device manufacturers often control all data supply, or data are often deleted because edge devices (mobile and pervasive) are not designed to include long-term memory storage [6].
Mainstream clinical health care repositories, such as a research patient data repository (RPDR) and health information exchanges (HIEs), are examples of large complex data warehouses often governed by consortiums. RPDRs specify rules for data collection and access among members, which are focused on the clinical data field [4,5]. In the RPDR, health care data storage and analysis are distributed among consortium members, with specific well-vetted guidelines for data access. Gagalova and Elizalde [5] describe the creation of an integrated data repository with the following steps: data extraction, deidentification, ID assignment, transformation, ontology mapping, linkage, and loading into warehouses, among the stages for data retrieval. Recent innovations in web service–based application programming interfaces (APIs) and the evolution of standards have provided standards such as Fast Healthcare Interoperability Resources, which enable third-party systems to access clinical health care data [2]. However, these mechanisms depend on the ability of independent data stores, hospital systems, and data intermediaries to satisfy legal mandates. Access mechanisms cannot be applied to patient-generated data where data are stored by device manufacturers or third-party vendors [6].
Preaggregated anonymized health data sets are available for sale and subscription through Amazon Web Services such as Qiagen [7], IBM Watson [8], Medisafe [9], and Annotate-it [10]. Such data can be used for analysis in several domains, such as cardiology or pathology, to discover and predict diseases using sophisticated machine learning models. Centralized data stores, such as research data repository and HIEs, are alternatives, but hospital systems usually store clinical data, not PGHD [3]. In addition, PGHD data sets need not provide the necessary provenance (eg, one cannot request the source or transmission records for data because they are subscription-based). Similarly, it would be difficult to verify the recency of such data because they are already curated from publicly available information or by the firm offering subscription-based services. Prior research has recommended standardizing formats for data storage to exchange health care data (such as the Health Level Seven [international standards for transfer of clinical and administrative health data]) and to create APIs such as Fast Healthcare Interoperability Resources that can seamlessly operate across clinical systems; accomplishing such a standard would need legal mandates [11].
This paper proposes, designs, and provides a proof-of-concept implementation for a secure public blockchain infrastructure–based PGHD marketplace that can address several issues concerning data reliability, privacy, provenance, and availability. In this paper, we proposed a user-level encryption schema that enables a seamless exchange and monetization of health data by creators. Users are incentivized to produce high-quality data sets on the supply side of such a marketplace. On the demand side, users experience reduced search costs and can locate and trade with high-quality data providers at a lower price because of competition and choice. In this study, we examined the following research questions: We argue that a marketplace approach can be a panacea for many health data–quality concerns and issues through [1] market-induced competition in a decentralized marketplace resulting in increased availability, [2] backed by privacy and an encryption schema that protects data provider privacy and ownership, and [3] a reputation mechanism for data sets and market participants, while [4] simultaneously enabling monetary incentives for participants, including the infrastructure provider or marketplace creators. Next, we examined data storage and access challenges.
## Overview
In a health data marketplace, different sellers, buyers, and (value-added) service providers congregate to cocreate value for the entire ecosystem [12]. Users who own health care record data can assign agents to operate on their behalf or directly benefit economically by having the ability to sell such data [13]. Data aggregators, health care data repository owners, or storage providers can monetize health data by enabling value-added services, such as applying intelligent data analytics and prescriptive or diagnostic machine learning technologies to their data [14]. A PGHD marketplace has to adhere to the legal requirements of privacy and data access [6]. However, substantial private trade in health care technology, curated data sets, and secondary uses of such data sets have existed for a time. Private entities with resources, that is, both human resources and financial and technical know-how, have been able to arbitrage the advantages of such PGHD data sets by solving unique predictive problems.
On the one hand, technology has enabled autonomous driving with high accuracy [15]; on the other hand, it is not yet possible for automated disease diagnosis or prediction without specialist intervention from data. The lack of automated diagnosis from PGHD data increases the costs of diagnosis, not to mention delays in diagnosis [10]. In addition, such asymmetrical market power between resourceful players and smaller health care analytics startups can reduce the discovery time for newer data-driven models for diagnosis [16]. Often, health data sets are expensive and do not provide any value to creators. For example, the health data set for predicting heart disease costs US $500 per hour for use on Amazon Sage Maker.
On the seller’s side, data providers, aggregators, or intermediaries cannot monetize the precious data created. Another issue is that of provenance, where it is not possible for the analyst or others to truly validate or ascertain, under confidentiality, the creator of such data. Similarly, on the buyer’s side, small- and medium-scale businesses and research projects that need large data sets to perform experimental analysis face an entry barrier because of the lack of data provenance [16]. Clinical studies are backed by stringent data disclosure and ethics reviews, where such reviews provide value in preventing data fabrication and unethical uses of data. Applying similar stringent data disclosure standards to collect and access PGHD may be possible if a marketplace approach is used, wherein users are compensated for sharing their own data [17], and moderation mechanisms filter out fabricated data. In many fields of medicine and health care, such as digital pathology, the lack of a large corpus of data for training algorithms in image detection and pattern analysis, owing to lack of data, is challenging. However, recent improvements in using patient health data are visible in research done by Google Inc [18] and Apple Inc [19]. The lack of automation increases the cost of care and, in many cases, prevents improvements to health care that are technically feasible yet lack data accessibility, data provenance, and data quality [20-22]. Next, we discuss the key properties of a PGHD marketplace.
We used the design science research methodology [28,29], commonly used in information systems and computer science, to design and validate the decentralized marketplace. The following are 3 phases in the design science research method: O’Donoghue et al [33] discussed various trade-offs to be managed adaptively to improve electronic medical record utility and argued that although these trade-offs can result in improved blockchain security, some of these features could affect scalability. Kumar and Bharti [34] summarized 10 different approaches for encrypting IPFS data records using various encryption methods and described different storage solutions. In addition, a recent work by Lin and Zhang [35] proposed an approach to create a directory-based file system and to use the bit swap protocol built on the IPFS to transfer encrypted records among users. As a technology, we could apply any of the 10 encryption approaches. We chose a modified version of the multiparty authentication and re-encryption oracle suggested by Battah et al [32], who released their full code. In brief, the activity diagram for the encryption schema is shown in Figure 5. The main entities in the multisignature system are multiparty authentication servers, the re-encryption oracle, the data owner, and the data requester.
The data owner (seller) uploads the data and agrees with access requirements posed by the multiparty authenticator or multiparty authentication server. The data owner registers the address of the data (which is the hash of the data) on the blockchain by minting the token once the multiparty authentication server and encryption oracle encrypt the data. There is always a shared wallet between the multiparty authenticator and the data owner on the system, which is used to encrypt the data (once the data owner submits the symmetrical key–encrypted data onto the IPFS). This second stage ensures that the data can be securely decrypted and re-encrypted using another pair of keys without access to the original data owner.
**Figure 5:** *Activity diagram for data encryption flow in the data marketplace with buyer and seller. DR: data requestor; IPFS: interplanetary file system.*
Furthermore, the data owner (seller) creates a smart contract that contains the hash of the mentioned components to act as the address of the data by minting the NFT as per the ERC-721 protocol. Once a sale is finalized (or a purchase action occurs), the data owner creates a re-encryption key from the public key of the data requester (buyer) and its own private key to send to the re-encryption oracle. This symmetrical key is then used by the re-encryption oracle and is shared with the buyer. Once the data are downloaded from the IPFS, the requester downloads the encrypted data, encrypted symmetrical keys, and the hash of the file. Subsequently, it decrypts the symmetrical key along with the data using its private key and decrypts the data again with that symmetrical key. The data requester (buyer) can then either choose to relist these data or use them for the analysis.
## Properties of a Decentralized PGHD Marketplace
The unique properties of a PGHD marketplace include its ability to preserve data privacy, access control, data storage, and fault tolerance. Buyers who purchase and use such data to develop useful classification algorithms monetize the data. In addition, such analytics enable various auxiliaries, such as analytics for diagnoses, disease prediction, and gamification of health care services [23]. Blockchains are a new distributed and decentralized technology used to address the challenges of data standardization, system interoperability, security, privacy, and accessibility [24]. Before the advent of blockchains, providing anonymized, privacy-controlled single points of access for different data sources for each user was a challenging problem [25]. We present the design and implementation of a decentralized blockchain-based marketplace. A decentralized marketplace enables faster matching of buyers and sellers of data, seamless transaction efficiency, and institutional infrastructure features, such as provenance, privacy, access control, and perennial storage [12].
## Scope of the Marketplace
Figure 1 describes the 2 sides of such a marketplace and the actors in the marketplace.
**Figure 1:** *Decentralized health care data marketplace.*
Marketplaces are 2-sided, with buyers on one side and sellers on the other. Buyers can purchase data to modify, analyze, and sell downstream or use it for research and other purposes. The buyer side consists of service providers, such as data aggregators, individual patients who can share personal health care data, firms that provide predictive analytics for data, and application developers or researchers or data scientists who analyze data and add value. The buyer side could also consist of specialists who resell data, data aggregators, game developers, and research institutions. The scope of the data seller entails only PGHD, wherein the patient is responsible for creating such data using personal devices. Others, such as health research institutions, web service providers, and data aggregators, form a part of the supply chain wherein the patient authorizes them to intervene. Table S1 in Multimedia Appendix 1 describes the differences between centralized health data stores and decentralized PGHD data marketplaces targeted in our design.
The burden of the cost of data storage for centralized and managed health information systems such as the RPDR or HIEs usually falls on the patient or the end user [23]. A marketplace is not feasible in such data architectures because HIEs specifically cater to clinical health care data not PGHD data. Table S2 in Multimedia Appendix 1 describes the differences between decentralized PGHD data stores and HIEs and integrated data repositories.
Centralized data stores often do not cater to PGHD, which can come from either the patient’s own health device or from another device, such as a publicly available blood pressure monitor, commonly found in grocery stores. However, very often, such data can provide valuable insights into user health and when services are aggregated into apps, such as the one by Google [18] or by Sleep Tracker [26].
Blockchains have been shown to provide various benefits when user data are involved, allowing users to store large quantities of data [6]. However, such benefits are not transferred to pervasive devices and ubiquitous applications that are designed with security, access, privacy, and performance considerations. Prior health care research on data at health care exchanges, tamper-proofing data, and securing data has demonstrated benefits in the context of health care [21]. In the subsequent section, we discuss the data-quality dimensions pertaining to health care data and how a decentralized marketplace addresses quality issues. There are three main dimensions to data quality in decentralized marketplaces: [1] information quality, [2] security, and [3] communication.
Information quality refers to the following 7 characteristics: Security refers to the following 4 characteristics: Data communication refers to the following 3 characteristics:
## Phase 1: Problem Definition and Importance of Solving
Beinke and Fitte [28] discuss that blockchain technology offers the possibility to verify transactions through a decentralized network and identified 34 stakeholder-specific requirements. Although their proposed blockchain-based architecture caters to electronic health records, certain requirements to support PGHD marketplaces are extracted and summarized in the following goals, along with the justification in the subsequent section.
The design goals of PGHD marketplace are as follows:
## Phase 2: Design and Implementation
We proposed an approach using nonfungible token (NFT) standards (Ethereum Request for Comments [ERC]-721 and ERC-1155) optimized for PGHD data and propose the creation of decentralized health care marketplaces where there are sellers, buyers, and value-added service providers, among others (Figure 1). Each participant in the marketplace, that is, seller, buyer, or value-added service provider, is identified by their wallet addresses (a modified version of their public key on the blockchain) [30]. Marketplace participants adhere to privacy, data security, and other features required by laws, such as HIPAA and General Data Protection Regulation. Subramanian and Subramanian [31] described a digital pathology system using an interplanetary file system (IPFS) and Ethereum. We used a similar strategy for our design, except that we built a full marketplace based on smart contracts with user encryption of data, the IPFS to store the data, and the web3 interface to enable interactions between buyers and sellers. We reduced the transaction fees needed to operate a public blockchain infrastructure to a few cents on Ethereum version 2 (proof of stake) [28].
## A Decentralized PGHD Data Marketplace Using Smart Contracts Using NFT Standards, IPFS, and MongoDB
The subsequent section is an overview of key technologies used to create our decentralized marketplace, based on NFTs. First, we examined how the blockchain network enables a decentralized marketplace. Then, we studied the principles of Ethereum-based smart contracts. Finally, we analyzed how decentralized markets powered by Ethereum-based smart contracts enable NFT markets to make them function. The Ethereum blockchain enables a wide range of transactions via smart contracts and self-executable Turing-complete programs, which run on the Ethereum virtual machine and maintain a state in their storage. The Ethereum virtual machine has a stack-based architecture and can store things on the stack (eg, using bytecode operations), in memory (eg, temporary variables within functions), or in storage (eg, permanent variables holding database entries). Each smart contract can read and write data only to its smart-data structure. The network consensus mechanism determines which user in the network will append the transactions to the chain as a new block. Ethereum has recently moved to the proof of stake mechanism, which substantially reduces energy consumption [32]. With proof of stake, a network algorithm determines which node will add the block to the chain based on the node’s stake, a combination of parameters, including their account balance. The transaction fee for smart-contract operations, such as minting, transferring data, and creating an on-chain record, is a fraction of a cent on Ethereum proof of stake.
## PGHD as NFTs Listed in Marketplaces
Smart contracts provide an opportunity to develop applications with complex functionalities in a blockchain network. Using Ethereum smart contracts, we implemented the ERC-721 standard with which we can store, mint, list, trade, and burn health care data. We also implemented recurring revenue for data creators and owners and facilitated the provision of quality-of-service paradigms for the market. The life cycle of an NFT is presented in a list here in the context of the tokens on the network. The details of each stage are provided: Figure 2 depicts the different variations of data stored on the blockchain. The metadata separates the ownership of data from the user uploading the data to the marketplace. Buyers of these data can use it to analyze and provide value-added services to end users of the marketplace. They can also reupload data to the marketplace or relist data as is. The marketplace provides financial incentives to data creators and marketplace-hosting agencies to ensure that the system works per design. Similarly, each time a data owner uploads data, they can claim a royalty on each future sale. Similarly, the marketplace wallet can receive a fixed amount of cryptocurrency as a commission per sale, making it financially feasible to maintain future requirements for the platform.
Sellers can set prices for the data sets listed, and once a sale transaction occurs, the cryptocurrency will be transferred to the seller after deducting platform fees and royalty fees preset in the smart contract. The architecture of such a marketplace is illustrated in Figure 3. The PGHD data are stored in the IPFS, and the data identifier CID is stored on Ethereum within a smart contract (ERC-721). The marketplace connects the data creators and the buyers through the IPFS and Ethereum infrastructure. The data are encrypted on the IPFS as per the protocol discussed in the multiparty and encryption schema [32] discussed in subsequent sections.
**Figure 2:** *Blood pressure data, electroencephalograph, and brainwave data pertaining to a patient collected on her own personal devices.* **Figure 3:** *This diagram shows the transactions among data creators, buyers, the interplanetary file system (IPFS), and the blockchain. CID: content identifier; NFT: nonfungible token.*
The PGHD will be stored on the IPFS, and the corresponding token ID will contain the metadata associated with the data owner. Similarly, each time the record or the token changes hands, the token will be transferred to a new owner, and the new owner will access the data. In between the data transfer, the encryption protocol is invoked, which generates a new pair of keys and provides the new owner with the key to decrypt the data. Consequently, the blockchain records the owner of the data, which in turn points to the CID on the IPFS. The marketplace creator can use a database, such as MongoDB, to store the mappings of user wallets, CIDs of data, and corresponding price variables, as in our case. This database is not absolutely essential but can be used to supplement data stored on the blockchain for faster lookup and querying or searching of data to provide ease of use to the user.
Users can upload multiple copies of their data to the IPFS. Each copy of the data must go through the minting workflow. In the minting workflow, data are newly uploaded onto the IPFS and encrypted with a different key. Later, this new IPFS CID will be minted as a separate token for listing. The platform does not restrict offering multiple data sets belonging to the same user. However, marketplace moderation mechanisms can flag duplicates uploaded onto the system or can potentially affect the reputation of the user.
In Figure 4, we list the schematic and user flow of such a PGHD marketplace. Creators of data or owners of digital data, for example, patients and hospital systems, can list their data on the marketplace using an easy-to-use user interface. Sellers are identified on the blockchain through a know-your-customer and antimoney laundering mechanism as well as their wallet addresses associated with their purchases. A preview image illustrates the sample data sets used. The actual data set forms a part of a JSON text entry. The data are stored on the IPFS, a distributed file system hosting peer-to-peer file storage. If the public IPFS is not sufficiently performant, marketplace creators can use layer 2 solutions, such as Filecoin, ArWeave, and Storj. As data scale to petabytes or exabytes, a layer 2 solution will be required because the IPFS may not be performant enough in terms of response times for the download of data unless the marketplace provides its own hosting and pinning service.
Similarly, the buyers of data purchase the data from the owner. In the process, the NFT’s ownership is transferred to the buyer, which is recorded on the blockchain. In addition, we have third-party data validators and analysts such as “value-added service” providers who will purchase the data from the marketplace, perform operations such as data-oriented simulations, data mining, or cleaning of data and relist them or resell them downstream.
**Figure 4:** *Design schematic and architecture of a decentralized marketplace prototype. CID: Content Identifier; IPFS: interplanetary file system; NFT: nonfungible token.*
## Reputation Models for Users and Data Sets
Reputation models enable buyers and sellers to evaluate each other and make informed decisions about transactions:
## Data Fabrication Defense
A platform-level data-correctness strategy includes a combination of reputation mechanism design, statistical validation for data, onboarding validation for the data seller through third-party oracles, and penalization of the vendor upon detection of fraud by third-party vendors. In our design, we enabled the data description metadata entered by the user, which can be used to validate the data by third parties.
## Pricing and Royalty Mechanism for Data
We created 2 smart contracts, one in which the value is transferred between the buyer and seller and another in which a proportion of the sale price at each transaction is transferred to the original creator (owner) of the data. This mechanism gives the data owner a market mechanism and an incentive to offer their data to the marketplace. Royalties to downstream and upstream sellers for personal data incentivize all players in the marketplace.
## User Registration
We registered each user in the marketplace along with the user’s wallet ID and social media profiles to enable the user to list data. The data listed each time can be validated for fictitious or simulated data through a combination of third-party validation oracles and statistical analysis techniques to detect patterns of fraud. Figure 1 shows the user registration flow in the system. Multimedia Appendix 3 provides a video demonstration of the platform using Ethereum.
## Premarket Validation
When the PGHD data record is uploaded onto the IPFS, in the backend, a record on the blockchain will point to the unique CID on the IPFS. If the web service provider or marketplace wants to enable users to transact, the provider can pin the record onto a particular hosted node on the IPFS. Subramanian and Subramanian [31] described IPFS functionality, data storage, and use in the context of digital pathology. We used a similar mechanism for marketplace functionality and data storage, where metadata are stored, specifically pointing to the actual data on the IPFS. The CID pertaining to the metadata will reside in the blockchain record and is minted as an NFT (Figure 6).
Figure 7 shows a screenshot of the user interface wherein users, upon logging into their wallets and identifying themselves, can see all the minted tokens. Each minted token is associated with an IPFS record that contains metadata pertaining to the uploaded data set. Furthermore, Figure 2 shows the interaction wherein the data are purchased using the wallet balance and the transfer of NFT. These metadata are listed in Listing 1 in Multimedia Appendix 1. In addition, when data are uploaded onto the IPFS, the tokens cannot be minted because of issues such as network connectivity, insufficient wallet balance, or high network traffic. The unminted tokens could later be minted by supplying sufficient balance to the user and later be used for listing on the decentralized marketplace.
Figure 8 shows the user interface of the decentralized marketplace displaying the listings. Although this user interface is implemented in HTML or Cascading Style Sheets, the web3 platform responsible for creating the listings platform can also supply a Representational State Transfer API for third parties to create and display listings.
**Figure 6:** *User preregistration with social media profile to check validity. NFT: nonfungible token.* **Figure 7:** *User flow depicting data upload and mint functionality. ETH: Ethereum; NFT: nonfungible token.* **Figure 8:** *The user interface lists all these minted tokens on the network. Each user gets a separate listing, excluding their owned tokens available for sale in the marketplace.*
## Data Categories
There are 3 categories of assets in the marketplace, unique to each wallet. The first category is “minted” NFTs that an owner can list in the marketplace for immediate transactional sale by a different user. Similarly, the second category is “collected NFTs,” which are just collections of digital health data attributed to the user but are not currently listed for sale. The third category of data accessible to the user not minted yet is listed as “unminted.” These records are not yet available on the blockchain for transactions. Code Listing 3 in Multimedia Appendix 1 lists the key functions used to create the listings. The JavaScript interfaces with the IPFS and the web3 smart contract and enables users to mint, list, and purchase tokens.
## HIPAA Support of the PGHD Marketplace
HIPAA requires covered entities to protect individuals’ health records and other identifiable health information by requiring appropriate safeguards to protect privacy and by setting limits and conditions on the uses and disclosures that may be made of such information. Our design, in which personal device-generated data are uploaded into the IPFS, is encrypted and stored on the web. The blockchain provides a web-based transaction history of the data. For example, the minting of the aforementioned token is recorded on the blockchain and can be viewed on the Ethereum blockchain. The 6 aforementioned records that were minted with different Ethereum prices can be located by scanning the contract address on the network. We can examine which wallet transferred the newly created and minted NFT. In addition, each time the data are transferred, the original data owner earns a royalty, and the platform’s wallet also earns a share of the revenues. Figure 9 illustrates the creation of the contract and the set of transactions performed on the same.
In the subsequent section, we provide support for the various directives recommended by HIPAA. Figure S1 in Multimedia Appendix 1 displays the details of the transaction used to transfer the token from one address to another after paying the requisite fees. Note that the transaction uses the TransferNFT function, which transfers ownership from wallet A to wallet B (Figure S1 in Multimedia Appendix 1).
**Figure 9:** *The contract address and transactions are done with respect to the nonfungible token creation.*
## HIPAA Regulations About Device-Generated Data
Our marketplace supports the following requirements with respect to PGHD as follows:
## Results
We evaluated our prototype against the goals set out in the design phase: The marketplace addresses the key requirements and objectives that enable the monetization of health data in a fair and transparent manner. Similarly, it meets the goals set out to achieve. Next, we discuss the limitations of such a marketplace and future work.
## Governance
Decentralized marketplaces require governance structures that are not centrally controlled and managed. Governance structures provide oversight, management control, approvals for enhancements to the platform, reward mechanisms, and a formal structure answerable to the law of the land. A consortium-based approach is recommended wherein representatives of health data providers, buyers, and value-added service providers participate in a voting-based decision-making system. Penalizing collusion can be a deterrent to any attempt to thwart decentralized governance. In a consortium-based governance approach, all stakeholders, including the legal community, public, buyers, and sellers, have a stake in the platform’s decision-making process. Another approach is that of a decentralized autonomous organization, where governance tokens (using smart contracts) could be issued to users participating in the platform’s governance. Although HS has prototyped a token-based governance model for such a marketplace, the complexities in defining briefly such a schema can be the subject of future research.
## Limitations and Future Research
First, the creation of such a marketplace, while allowing the acceleration of data provision in markets, can increase the quantity of data available in marketplaces.
However, excessive data listed in the marketplace can increase the search costs for end users unless the marketplace creator implements a local search. Second, owing to the use of blockchain, the IPFS, and other technologies, where users can upload and store data inexpensively, it is likely that many users could start using such a platform as a data storage device. To solve these issues, platform operators should design and operate recommendation systems that work in tandem with users uploading and trading data, providing ratings and reviews for both data sets and data providers.
Third, the onboarding of data providers should be controlled by firms operating the platform rather than a free-for-all service, where people can use it for various nefarious purposes. This provides additional monetization opportunities for marketplace creators, data providers, or device manufacturers. “ Unminted” data and unlisted data could be reduced to eliminate free renting. Fourth, owing to the decentralized nature of such marketplaces, it is important to realize that decentralization also leads to challenges with account integrity owing to the anonymity provided by the blockchain. Fifth, decentralized marketplaces pose a threat to existing industry structures, where major hardware creators such as Apple and Fitbit (Google Inc) dominate wearables. As a result, conflicts with the survival of such a marketplace could be exacerbated. Sixth, the legal and regulatory implications for a marketplace that trades in PGHD while generating secondary value-added services (such as diagnostic ability) have not yet been investigated in this paper and could be the subject of future research. Seventh, the scalability of the solution when data size exceed petabytes needs to be investigated with layer 2 solutions, such as Filecoin, ArWeave, and Storj. Future research can highlight more performant solutions based on the IPFS.
## Conclusions
In this paper, we proposed, designed, and prototyped a decentralized marketplace for PGHD data. We proposed a mechanism by which different participants, such as data creators, sellers, and value-added service providers, can monetize data transparently. Similarly, our design attempts to support the HIPAA regulations that provide privacy, security, and legal protection to users, platform creators, and other stakeholders in the ecosystem.
Such a marketplace can improve [1] the quality of data available in the marketplace and will ensure that [2] more high-quality data are available for artificial intelligence–driven analysis and diagnosis of diseases. The salient features of our decentralized PGHD marketplace are as follows:
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|
---
title: 'Feasibility and Acceptability of an Internet of Things–Enabled Sedentary Behavior
Intervention: Mixed Methods Study'
journal: Journal of Medical Internet Research
year: 2023
pmcid: PMC10012006
doi: 10.2196/43502
license: CC BY 4.0
---
# Feasibility and Acceptability of an Internet of Things–Enabled Sedentary Behavior Intervention: Mixed Methods Study
## Abstract
### Background
Encouraging office workers to break up prolonged sedentary behavior (SB) at work with regular microbreaks can be beneficial yet challenging. The Internet of Things (IoT) offers great promise for delivering more subtle and hence acceptable behavior change interventions in the workplace. We previously developed an IoT-enabled SB intervention, called WorkMyWay, by applying a combination of theory-informed and human-centered design approaches. According to the Medical Research Council’s framework for developing and evaluating complex interventions such as WorkMyWay, process evaluation in the feasibility phase can help establish the viability of novel modes of delivery and identify facilitators and barriers to successful delivery.
### Objective
This study aims to evaluate the feasibility and acceptability of the WorkMyWay intervention and its technological delivery system.
### Methods
A mixed methods approach was adopted. A sample of 15 office workers were recruited to use WorkMyWay during work hours for 6 weeks. Questionnaires were administered before and after the intervention period to assess self-report occupational sitting and physical activity (OSPA) and psychosocial variables theoretically aligned with prolonged occupational SB (eg, intention, perceived behavioral control, prospective memory and retrospective memory of breaks, and automaticity of regular break behaviors). Behavioral and interactional data were obtained through the system database to determine adherence, quality of delivery, compliance, and objective OSPA. Semistructured interviews were conducted at the end of the study, and a thematic analysis was performed on interview transcripts.
### Results
All 15 participants completed the study (attrition=$0\%$) and on average used the system for 25 tracking days (out of a possible 30 days; adherence=$83\%$). Although no significant change was observed in either objective or self-report OSPA, postintervention improvements were significant in the automaticity of regular break behaviors (t14=2.606; $$P \leq .02$$), retrospective memory of breaks (t14=7.926; $P \leq .001$), and prospective memory of breaks (t14=–2.661; $$P \leq .02$$). The qualitative analysis identified 6 themes, which lent support to the high acceptability of WorkMyWay, though delivery was compromised by issues concerning Bluetooth connectivity and factors related to user behaviors. Fixing technical issues, tailoring to individual differences, soliciting organizational supports, and harnessing interpersonal influences could facilitate delivery and enhance acceptance.
### Conclusions
It is acceptable and feasible to deliver an SB intervention with an IoT system that involves a wearable activity tracking device, an app, and a digitally augmented everyday object (eg, cup). More industrial design and technological development work on WorkMyWay is warranted to improve delivery. Future research should seek to establish the broad acceptability of similar IoT-enabled interventions while expanding the range of digitally augmented objects as the modes of delivery to meet diverse needs.
## Background
In the past decade, ample evidence has accumulated to suggest the unfavorable association between sedentary behavior (SB) and cardiometabolic health, even after adjusting for the amount of exercise [1-3]. Moreover, the amount of sedentary time accumulated in single bouts that last longer than 30 minutes (ie, sustained sedentary bouts) and 60 minutes (ie, prolonged sedentary bouts) adds to the risks, whereas breaks in sedentary time are beneficially associated with metabolic biomarkers [3-5]. With a larger proportion of the workforce employed on sedentary occupations, occupational sitting has become a public health concern in modern Western societies. Based on studies with office-based workers in Australia and the United Kingdom (UK), occupational sitting contributed more than half of total sedentary time on workdays [6-9]. Self-report and accelerometer studies have consistently demonstrated that office workers spend most (varying from $60\%$ to $82\%$ across studies) of their working hours on sitting [10-13]; moreover, office workers’ within-work time is characterized by more sustained ($12\%$-$34.8\%$ of total sitting) and prolonged ($25\%$-$49.8\%$ of total sitting) sedentary bouts with fewer breaks than nonwork time [7,11]. This makes the office-based workplace a priority setting for interventions targeting SB reduction through the promotion of regular break behaviors.
It is challenging to design an intervention that interrupts users at work at opportune moments and encourages them to move around without causing disturbance or annoyance. Internet of Things (IoT) technologies, characterized by ubiquitous sensing, context-aware computing, and embedded interfaces, have shown great promise for delivering just-in-time adaptive interventions to improve health behaviors nonintrusively in everyday settings [14,15], including the workplace [16]. Yet, there is a dearth of theoretically driven development and evaluative work on IoT-enabled health behavior change interventions.
We have previously reported, in detail, the design and development of an IoT-enabled occupational SB intervention called WorkMyWay following the Behavior Change Wheel and human-centered design approach [17]. In this paper, we report the next phase of research, namely the “feasibility phase,” under the framework of the UK Medical Research Council (MRC) for developing and evaluating complex interventions [18]. Emphasis will be placed on evaluating the feasibility and acceptability of the intervention process [19].
## Process Evaluation in the Feasibility Phase
While randomized controlled trials of interventions are important to answer questions on the effectiveness and efficacy of the intervention, translation of the evidence into the diverse settings of everyday practice is often challenged by uncertainties in delivery across contexts [20]. This gives rise to the importance of mixed methods process evaluations to answer questions such as how and under what circumstances an intervention can bring about changes [19].
For research involving automated sensors (eg, accelerometer) either for outcome measurement or for delivering just-in-time adaptive interventions, the quality of tracking has great influence on research and intervention feasibility. As demonstrated by Tang and colleagues [21], adjusting for data incompleteness would significantly affect outcome measures and conclusions about behavior change efficacy. In view of this, the occurrence and severity of data loss caused by technological issues and nonadherence should be routinely monitored and considered as indicators of feasibility in this phase. Moreover, process evaluations can explore contexts in which technological failures are more likely to occur, as this will inform the improvement of protocols and development of strategies to minimize the occurrence and adverse impacts of technological failures. Last but not least, considering the potential of analyzing technology-captured data to understand processes of change and identify active intervention ingredients in future larger-scale evaluations [22], it is important to ascertain, at an early stage, whether system data of satisfactory quality can be collected and used for analysis.
Acceptability should be another area of focus in process evaluations in the feasibility phase [19]. Indeed, acceptability is integral to feasibility, because interventions disfavored by participants are unlikely to be implementable in subsequent trials [23]. This is especially the case for digital behavior change interventions, as the quantity and quality of interventions received by a user are dependent on the extent to which the user likes and engages with the digital technology [24,25].
## Objective of This Study
The objective of this study was to assess the feasibility and acceptability of WorkMyWay in real-life office settings through examining the following: [1] retention, adherence, compliance, and quality of tracking; [2] participants’ experiences of WorkMyWay, including perceived fidelity and quantity of delivery, and contextual factors that would potentially affect the adoption and effectiveness of WorkMyWay; and [3] potential for changes in occupational sitting and physical activity (OSPA) and psychological variables theoretically aligned with the hypothesized mechanisms underpinning the intervention.
## Study Design
This was a mixed methods process evaluation with a single-group pretest-posttest design. Figure 1 visualizes the study procedure and data collected at each stage.
**Figure 1:** *Study procedure and data collected at each stage. OSPA: occupational sitting and physical activity.*
## Ethical Considerations
The study was approved by the Ethics Committee at the School of Computer Science, University of Nottingham (ID 20170920). The information sheet and consent form are enclosed in Multimedia Appendix 1. Study data are all anonymized with individuals represented by participant IDs. A £50 (US $62) Amazon voucher was offered to each participant upon full completion of the study to compensate for their time and feedback.
## Intervention
The WorkMyWay intervention was developed in accordance with the framework of the UK MRC for complex intervention research [26], by following through the process of identifying and summarizing the best available evidence [16], developing a theoretical understanding that is likely to account for the process of change [27], theorizing the intervention in terms of the key behavior change techniques and mechanisms, and involving the target recipients and stakeholders of the intervention throughout the development process [17].
The resulting intervention is complex and has been detailed elsewhere [17], using the TIDieR (Template for Intervention Description and Replication) checklist [28]. In brief, the intervention is centered on an IoT system called WorkMyWay, which consists of a wrist-worn activity monitor, a light-emitting diode (LED) break reminder attached to the user’s own cup or water bottle, and an Android app that communicates with both devices over Bluetooth low-energy connections. The system uses the movement data livestreamed from the wrist device to detect the user’s period of inactivity in real time and deliver 2 major interventional components.
The first interventional component features quick and actionable point-of-behavior prompts delivered during work hours via the LED device attached to the user’s vessel, an object well-integrated into most office workers’ daily routines with strong associations with work break activities. Based on consultations with stakeholders, the following reminder rules were set as default: if the user is inactive for 45-55 minutes, the cup LED turns into an amber breathing light, meaning “you can consider a break now!”; if the user is inactive for 55–60 minutes, it becomes a red breathing light, meaning “you should take a break now!”; and if the period of inactivity exceeds 60 minutes, it turns into a red flashing light, warning the user of the emergence of a prolonged stationary period (Figure 2).
The second component features more detailed and in-depth feedback and rewards delivered via a screen-based medium (the app) that the user engages with after each workday (Figure 3). Consistent with the LED color scheme and mimicking a traffic light system, the app uses amber, red, and green bars to signify normal inactive bouts (ie, bouts of <60 minutes), prolonged inactive bouts (ie, bouts of >60 minutes), and active breaks (ie, ambulatory bouts), respectively.
Regarding intervention delivery, participants were required to first use a lite version of WorkMyWay that only supported tracking while masking all other functionalities from the user for 2 weeks, to obtain baseline SB. This was followed by a 30-minute action planning session where the participant and the researcher (one of the authors) reflected on the baseline data, discussed personal goals, set up action plans, and configured the full WorkMyWay system. Afterward, the participant was provided with the full system for another 6 weeks (intervention period). A weekly reminder email was sent to all participants by the researcher on each Monday morning to enhance adherence.
**Figure 2:** *The tracking and prompting component. LED: light-emitting diode.* **Figure 3:** *The feedback and reward component.*
## Sampling and Recruitment
Feasibility studies do not require formal sample size calculation or power calculation [29]. A sample size of 15 is deemed sufficient to uncover most usability and user experience issues [30], which has been used in prior studies to assess feasibility and acceptability of similar eHealth interventions [31-34]. Hence, we recruited a convenience sample of 15 university-employed office workers from 2 local and geographically adjacent workplaces (a university campus and an acute teaching hospital campus) via staff mailing lists and on-campus posters. Potential participants were directed to an online sign-up form with screening questions assessing the following eligibility criteria: [1] no physical disability prohibiting engagement in light physical activity; [2] employed full-time on a job that involved significant amounts of desk-based work (≥$50\%$ of total office hours); and [3] normally had the discretion over when to take microbreaks on workdays. Those meeting all the aforesaid criteria were contacted by the researcher to schedule a briefing and consent session in their own offices or a nearby meeting room.
## Overview
We used a combination of system logs and surveys for quantitative data collection. Table 1 summarizes key process and outcome measures calculated based on data accessed from the system and processed using Python (Python Software Foundation), a high-level, general-purpose programming language. The following subsections provide a brief explanation.
**Table 1**
| Measures | Measures.1 | Calculation |
| --- | --- | --- |
| Process measures | Process measures | Process measures |
| | Adherence | Tracking days/30 |
| | Quality of tracking | Valid tracking days/tracking days |
| | Compliance | Prompts with a latency of ≤15 minutes/total prompts triggered |
| Objective OSPAa | Objective OSPAa | Objective OSPAa |
| | Daily ambulatory time | Accumulated time spent on bouts classified as “active” by the WorkMyWay algorithm |
| | Daily stationary time | Accumulated time spent on bouts classified as “inactive” by the WorkMyWay algorithm |
| | Number of prolonged stationary bouts | Number of stationary bouts that lasted 60 minutes or above for each day |
| | Duration of prolonged stationary bouts | Accumulated time spent on stationary bouts that lasted 60 minutes or above for each day |
## Process Measures
According to the algorithm we had developed and detailed in a previous article [17], whenever the tracking was on, a period with 0 counts for 40 or more consecutive 15-second epochs (ie, no data for 10 minutes) would be classified as “invalid tracking,” which was likely caused by technological issues or nonwear time; other epochs were all valid tracking time. Tracking days with over 3 hours of valid tracking time and less than 3 hours of invalid tracking time were regarded as “valid tracking days,” whereas the remaining tracking days were classified as “invalid tracking days.” We operationalized each participant’s “quality of tracking” as the percentage of tracking days that were valid (ie, valid tracking days/tracking days × $100\%$), which was an indicator of technological reliability regardless of the participants’ intention to adhere. Individual adherence was operationalized as the number of tracking days out of a possible 30 days. We also measured each participant’s behavioral compliance with the intervention. For analytic purpose, the onset of the ambulatory or active bout following the prompt event was seen as the response to that prompt, even though the initiation of that break could be irrelevant to the prompts. The time elapsed in between the prompting event and the response was calculated as “response latency” and each individual’s “compliance” was measured as the percentage of prompts responded to with a latency of 15 minutes or less.
## Outcome Measures
While behavior change outcomes are not the primary focus of process evaluations, the promise for behavior change can still be examined by observing trends of change in outcome measures and especially psychosocial variables theoretically aligned with the intervention [35].
The following outcome measures on objective OSPA for pre- and postintervention periods were calculated based on the system data using the aforementioned algorithm [17]: daily ambulatory time, daily stationary time (ie, any waking behavior done while lying, reclining, sitting, or standing, with no ambulation, irrespective of energy expenditure [36]), and quantities and durations of prolonged stationary bouts (ie, periods of uninterrupted stationary time that were 60 minutes or above).
In addition to objective outcome measures, a survey (Multimedia Appendix 2) was administered at briefing (preintervention) and debriefing (postintervention) sessions to collect the self-report outcome measures reported in Textbox 1.
## Quantitative Data Analysis
Data on process measures were analyzed with descriptive statistics. Objective OSPA and survey data were imported to SPSS 22.0 (IBM Corp) for inferential statistical analysis. Differences between pre- and postintervention measures were assessed using paired-samples t tests, with statistical significance set at.05.
## Qualitative Data Collection and Analysis
A semistructured interview guide (Multimedia Appendix 3) was developed, informed by the MRC guidance for process evaluation of complex interventions [19], which covered the following topics: participant’s perceived quality and quantity of implementation of various intervention components and contextual factors (ie, facilitators and barriers) influencing the engagement with and effectiveness of WorkMyWay. All interviews were audio recorded with consent and transcribed in verbatim. Data were analyzed for themes related to feasibility and acceptability of the WorkMyWay intervention using a thematic analysis approach [40], which involved familiarization with the data, generating initial codes, searching for themes, reviewing potential themes, defining and naming themes in a code book, final analysis, and write-up. NVivo version 12 (QSR International) was used to facilitate the organization of codes and themes.
## Study Sample
Table 2 presents the characteristics of the sample.
**Table 2**
| Characteristic | Characteristic.1 | Value |
| --- | --- | --- |
| Age (years), mean (SD; range) | Age (years), mean (SD; range) | 40.5 (11.0; 25-63) |
| Gender, n (%) | Gender, n (%) | Gender, n (%) |
| | Male | 3 (20) |
| | Female | 12 (80) |
| Highest education level completed, n (%) | Highest education level completed, n (%) | Highest education level completed, n (%) |
| | University preparatory degree | 2 (13) |
| | Undergraduate degree | 6 (40) |
| | Postgraduate degree | 7 (47) |
| Self-reported occupational time spent (hours), mean (SD; range) | Self-reported occupational time spent (hours), mean (SD; range) | Self-reported occupational time spent (hours), mean (SD; range) |
| | Sitting | 6.2 (1.5; 2.4-8.2) |
| | Standing | 0.9 (1.3; 0-4.8) |
| | Walking | 0.8 (0.6; 0.145-2) |
| | Heavy labor | 0.1 (0.5; 0-1.9) |
| | Total | 8.0 (0.9; 7.25-10) |
| Height (cm), mean (SD; range) | Height (cm), mean (SD; range) | 169.3 (7.5; 155-180) |
| Weight (kg), mean (SD; range) | Weight (kg), mean (SD; range) | 72.0 (13.6; 49-90) |
| BMI (kg/m2), mean (SD; range) | BMI (kg/m2), mean (SD; range) | 25.0 (4.1; 18.4-33.0) |
| | Underweight (≤18.5), n (%) | 1 (7) |
| | Normal (18.5-24.9), n (%) | 5 (33) |
| | Overweight (25-29.9), n (%) | 8 (53) |
| | Obese (≥30), n (%) | 1 (7) |
| Number of officemates, n (%) | Number of officemates, n (%) | Number of officemates, n (%) |
| | 0 | 5 (33) |
| | 1 | 2 (13) |
| | 3 | 5 (33) |
| | >3 | 3 (20) |
## Adherence and Usage
All participants completed the 8-week study protocol ($100\%$ retention), including all measurement and interventional components. Figure 4 provides an overview of the usage data since the installation of WorkMyWay full version. Weeks 1 and 2 (ie, the baseline period) were excluded from the graph, as the lite version of the app was used during that period.
The number of tracking days over the intervention period ranged from 15 to 30 workdays across participants, with a mean of 25 (SD 4) days and a median (25th-75th percentile) of 26 [23-28] days. This meant that the adherence rate ranged from $50\%$ ($\frac{15}{30}$) to $100\%$ ($\frac{30}{30}$) across participants, with a mean adherence rate of $83.3\%$ (SD $14\%$) and a median (25th-75th percentile) of $86.7\%$ ($76.7\%$-$93.3\%$).
Of the 375 total tracking days, 262 ($69.9\%$) were valid tracking days. On those valid days, daily valid tracking time ranged from 182.75 to 632.25 minutes, with a mean of 414.2 (SD 94.6) minutes, or 6.9 (SD 1.6) hours; daily invalid tracking time ranged from 0 to 179.5 minutes, with a mean of 23.35 (SD 37.6) minutes and a median of 0 minutes. Anecdotal reports suggested that invalid tracking was mostly caused by data loss during Bluetooth disconnection, which will be detailed in the “Qualitative Results” section.
The number of valid days tracked over the intervention period ranged from 6 to 26 days across participants, with a mean of 17.5 (SD 5.3) valid tracking days and a median (25th-75th percentile) of 16 (14.5-21.5) days. This yielded a mean quality of tracking of $68.6\%$ (SD $14.9\%$), with a median (25th-75th percentile) of $71.4\%$ ($59.3\%$-$81.1\%$).
After the completion of the 6-week intervention, we offered the option for participants to keep using WorkMyWay; 11 ($73\%$) participants opted in to continue using the devices in their own interests, but 2 of them (P6 and P9) had to stop earlier than they would like to because we ran out of devices for new participants. The main reasons for not opting in (P2, P5, P7, and P15) to poststudy use were [1] leaving the university for a new job ($$n = 1$$), [2] having technical difficulties setting up ($$n = 2$$), and [3] physical discomfort wearing the wristband ($$n = 2$$).
Among the remaining 9 participants (P1, P3, P4, P8, and P10-P14) who could use the devices freely for as long as they wanted, the last of day of use (number of days since the study end) ranged from 8 (P11) to 98 (P4), with a median of 39 and a mean of 44.8 (SD 32.5). Self-directed use after the 6-week intervention generated a further 211 days of tracking and usage data, of which 91 days were valid. As expected, poststudy adherence (mean $55.8\%$, SD $19.3\%$) and quality of tracking (mean $35.7\%$, SD $5.4\%$) were significantly lower than within-study adherence (mean $81.5\%$, SD $15.3\%$) and quality (mean $67.3\%$, SD $5.4\%$), confirmed by paired-samples t tests (t8=3.619; $$P \leq .007$$ for adherence; t8=4.3; $$P \leq .003$$ for quality of tracking).
**Figure 4:** *Usage pattern of the WorkMyWay full version.*
## Prompts Delivery and Compliance
A total of 698 time stamped prompting events were recorded. This meant that each participant would have received 1.8 (SD 1.1) prompts on a typical tracking day. The number of prompts received by each participant over the study period ranged from 13 (P11) to 116 (P3), with a median of 37.
As Figure 5 shows, slightly over one-third of the prompts ($$n = 269$$, $38.5\%$) were responded to within 15 minutes. Within this category, the majority were responded within 5 minutes ($$n = 113$$, $16.2\%$), followed by 5-10 minutes ($$n = 85$$, $12.2\%$) and 10-15 minutes ($$n = 71$$, $10.2\%$).
**Figure 5:** *Latency of responses to LED (light-emitting diode) prompts.*
## Promise for Change
As Table 3 shows, there was no statistically significant pre-post difference in any of the behavioral outcomes. However, postintervention improvements were significant in several psychosocial variables theoretically aligned with the target behavior, namely, automaticity of microbreak behaviors (t14=2.606; $$P \leq .02$$), retrospective memory of breaks (t14=7.926; $P \leq .001$), and prospective memory of breaks (t14=–2.661; $$P \leq .02$$).
**Table 3**
| Measures | Measures.1 | Preintervention, mean (SD) | Postintervention, mean (SD) | Trend (mean difference) | t value (df) | Pvalue |
| --- | --- | --- | --- | --- | --- | --- |
| Objective OSPAa based on tracking data (based on valid days) | Objective OSPAa based on tracking data (based on valid days) | Objective OSPAa based on tracking data (based on valid days) | Objective OSPAa based on tracking data (based on valid days) | Objective OSPAa based on tracking data (based on valid days) | Objective OSPAa based on tracking data (based on valid days) | Objective OSPAa based on tracking data (based on valid days) |
| | Valid tracking time, min/workday | 430.4 (45.2) | 419.7 (51.4) | –10.7 | –0.627 (14) | .54 |
| | Daily stationary, minutes/workday | 355.0 (57.3) | 356.7 (56.3) | 1.7 | 0.115 (14) | .91 |
| | Daily ambulatory, minutes/workday | 75.4 (45.9) | 63.0 (28.7) | –12.4 | –1.288 (14) | .22 |
| | Duration of prolonged stationary bouts, minutes/workday | 176.1 (78.7) | 188.3 (95.3) | 12.1 | 0.591 (14) | .56 |
| | Number of prolonged stationary bouts, n/workday | 1.8 (0.8) | 1.8 (0.7) | –0.05 | –0.252 (14) | .80 |
| Self-report OSPA | Self-report OSPA | Self-report OSPA | Self-report OSPA | Self-report OSPA | Self-report OSPA | |
| | Work time, minutes/day | 482.5 (55.7) | 492.5 (77.5) | 10.1 | 0.569 (14) | .58 |
| | Siting, minutes/day | 369.0 (91.1) | 373.3 (78.8) | 4.3 | 0.209 (14) | .84 |
| | Standing, minutes/day | 56.0 (77.9) | 58.6 (61.2) | 2.6 | 0.138 (14) | .89 |
| | Walking, minutes/day | 49.5 (38.7) | 60.3 (50.6) | 10.8 | 1.131 (14) | .28 |
| | Heavy labor, minutes/day | 7.9 (29.4) | 0.29 (1.1) | –7.6 | –0.998 (14) | .34 |
| Determinants of breaks | Determinants of breaks | Determinants of breaks | Determinants of breaks | Determinants of breaks | Determinants of breaks | |
| | Intention to take regular work breaks | 6.07 (0.89) | 6.20 (0.86) | 0.13 | 0.695 (14) | .49 |
| | Positive outcome expectancy | 6.18 (0.75) | 6.27 (0.63) | 0.08 | 0.673 (14) | .51 |
| | Perceived behavioral control | 6.20 (0.78) | 6.33 (0.82) | 0.13 | 0.487 (14) | .63 |
| | Perceived barrier: heavy workloadb | 5.07 (1.9) | 5.00 (1.91) | –0.07 | –0.163 (14) | .87 |
| | Perceived barrier: discouraging organizational cultureb | 1.80 (0.561) | 1.80 (0.941) | 0.00 | 0.000 (14) | >.99 |
| | Perceived facilitator: organizational culture encouraging breaks | 6.00 (1.00) | 6.07 (0.80) | 0.07 | 0.202 (14) | .84 |
| | Regular microbreak habit (automaticity subscale) | 4.41 (0.71) | 4.85 (0.44) | 0.43 | 2.606 (14) | .02c |
| | Retrospective memory of breaks | 3.47 (1.47) | 6.30 (0.80) | 2.83 | 7.926 (14) | <.001 |
| | Difficulty with remembering to take breaks (prospective memory)b | 5.70 (1.07) | 4.93 (0.92) | –0.77 | –2.661 (14) | .02c |
| Work fatigue | Work fatigue | Work fatigue | Work fatigue | Work fatigue | Work fatigue | |
| | Physical fatigue | 2.14 (0.64) | 2.05 (0.60) | –0.08 | –0.807 (14) | .43 |
| | Mental fatigue | 2.69 (0.96) | 2.61 (0.86) | –0.07 | –0.504 (14) | .62 |
| | Cognitive fatigue | 1.57 (0.54) | 1.78 (0.52) | 0.21 | 1.809 (14) | .09 |
## Overview of Themes
A total of 6 themes were identified through the qualitative analysis. These encompass acceptability of tracking, causes of inaccuracy and solutions, barriers to prompts delivery, mixed attitudes toward the embedded medium for delivering prompts, organizational climate and job characteristics affecting intervention uptake, and interpersonal influences on adherence and compliance. Table 4 presents all themes and subthemes with illustrative quotes. The following subsections provide a brief explanation.
**Table 4**
| Themes and subthemes | Themes and subthemes.1 | Illustrative quotes |
| --- | --- | --- |
| Theme 1: Acceptability of tracking | Theme 1: Acceptability of tracking | Theme 1: Acceptability of tracking |
| | Ease of integration into everyday routines | I think it’s really quite simple to use. You just start and stop. That's how it's supposed work, start tracking and stop tracking. [P2]Pretty easy. I guess I have a set-up routine when I get into my office anyway, get my laptop out, set up. [P4] |
| | Difficulty with remembering to stop tracking | I had no trouble coming in every day and turning it on, but I had a couple of days on which, I went back home with my wrist on me. I was like 'no!'...Once you clicked 'tracking' you forget about it. [P8] |
| | Discomfort of wearing | It just gets sweaty and in a way it’s quite annoying. [P14] |
| | Accuracy of tracking | I think like 90% of the time it was accurate in telling whether I’m active or not. [P10] |
| Theme 2: Causes of inaccuracy and solutions | Theme 2: Causes of inaccuracy and solutions | Theme 2: Causes of inaccuracy and solutions |
| | Inaccuracy caused by data loss | I take my phone when I’m out of the office. But if we just went to the corridor, it was okay to just leave the phone in the office. Sometimes I don’t think it’s recorded things like going to the printer and back from the printer for like 10 or 11 times. I don’t think it had, because it kept saying ‘not connected’. [P13] |
| | Reducing data issues with system updates | They seem really accurate, especially after one update, I can’t remember when it was I updated it. After then it felt really was picking up everything. So I felt like it was quite accurate. [P15] |
| | Inaccuracy related to individual differences and needs | I realized it was quite sensitive because a lot of the stripes were just 1 min. Initially I sat there and thought I haven’t been out of the office. What is it recording? Then I thought, oh, I’ve opened the blind, I’ve got up and put something in the bin. Maybe actually I haven’t physically moved. Then I thought it’s logging that I’m typing. [P7] |
| | Adjusting detection thresholds upon individual requests | I talked to you, if you remember, I had problems with the data not being sent, you restarted it and did something, you also changed the parameters the last time. After that, it was no longer doing that. [P8] |
| Theme 3: Barriers to prompts delivery | Theme 3: Barriers to prompts delivery | Theme 3: Barriers to prompts delivery |
| | Misplacement of the LEDa reminder device | But it's not in a good place on a cup really. It gets in the way. So I tended to use a different cup. [P5] |
| | LED facing away from the participants accidentally | Occasionally I would turn around to look at my bottle and found that I had turned it away from me unconsciously. Then I’ll turn it around and find it flashing. [P6] |
| | Not noticing LED flashing in the periphery of attention | But sometimes when you are concentrating, you don’t really look at things around. [P13] |
| | Disconnection between devices | Although it is there, if it’s not connected for some reason, it doesn’t always light up. [P13] |
| Theme 4: Mixed attitudes toward the embedded medium for delivering prompts | Theme 4: Mixed attitudes toward the embedded medium for delivering prompts | Theme 4: Mixed attitudes toward the embedded medium for delivering prompts |
| | Advantages over vibratory or audible alarms | I got a Garmin watch that buzzes...This (cup device) was a more subtle way of saying, ‘you need to get up’, as opposed to go out buzzing that’s really disturbing to your surroundings. I really like having the visual cue because I feel like it kind of took my attention away from what I was doing and made me physically look away from what I was doing. [P11] |
| | Concerns over disturbance to others | I’m not sure. I’m in two minds. Coz I was gonna say that it would be useful for me to (have) kind of noise, almost vibrate or buzz or something like that. But if it is 2-hour meeting, and I forget to turn it off, then an hour in, it starts making some annoying noise. [P15] |
| | Object cueing and facilitating break activities | Because it reminds you to do something. You can very well take it as an excuse to fill up your water bottle, or take it and drink it and then fill it up again. It worked for me in that way. [P3] |
| | Positive spillover effect on hydration | It was good to make me drink more rather than just get up, coz it gets me a reason to go to the kitchen and fill my bottle. If it wasn’t attached to a bottle, I might not have taken that with me. I’d just go for a wander. So that was good. [P14] |
| | Complexity of managing multiple devices | Maybe just having one device or one thing embedded in an object that just all works together as one. That'll be much better than having all the individual things. [P2] |
| Theme 5: Organizational climate and job characteristic affecting uptake | Theme 5: Organizational climate and job characteristic affecting uptake | Theme 5: Organizational climate and job characteristic affecting uptake |
| | Organizational support | I think this workplace will be happy with it, it's a very flexible department...There is a lot of trust and independent work in timing. I don't think people mind if you get up to go to the bathroom in the middle of a meeting, and things like that. [P4] |
| | Job constraints | But because of the nature of roles, the period of breaks may have to be a bit more controlled. So like student-facing services, they have to be there for particular times, so the breaks are gonna be structured around of their availability and around other’s availability. [P9] |
| | Division of responsibilities for employees’ health-related behaviors | I think the organization doesn’t really mind, or care, either way. They really leave it up to the individuals...It would be nice if they would have some options that we could use. [P12]As I’m the wellbeing lead, anything that encourages staff to take a practice at work, I’m keen on understanding...If you got some summaries of if people actually found it helpful, I’d be quite keen to promote it to university. [P9] |
| Theme 6: Interpersonal influences on adherence and compliance | Theme 6: Interpersonal influences on adherence and compliance | Theme 6: Interpersonal influences on adherence and compliance |
| | Subjective norms on regular break behaviors | It’s a nice environment in that. People are often going out to make a cuppa or asking somebody. Yeah. I think we are all very aware of sitting down all day. [P10] |
| | Object triggering social interactions that promoted breaks | They would go, ‘oh what’s on your water bottle?’ ‘Oh, I’m part of a study’. So, they were interested, and it got them talking. Someone I work with in office could sometimes see the light when she was over at my desk asking me a question or anything, she pointed it out, and we’d be like, ‘oh, maybe we should go get up!” [P11] |
| | Office team participation enabled social comparison and social support | Because we were all in it together. We all had issue. We would sort it out. [P12]It was a reward to think, ‘oh yeah, look, I’ve done this this. I showed my colleagues. Have you done this?’ and we compared it. [P13] |
## Theme 1: Acceptability of Tracking
Most participants reported it was easy to integrate the behavioral tracking into everyday routines and to adhere to the tracking protocol. The email sent by the researcher at the beginning of every workweek was deemed a helpful reminder to recontinue tracking, especially after holidays. Participants found it more difficult to remember to stop tracking at the end of each workday than to start tracking in the morning, because the automated tracking worked unnoticeably at the background throughout the day.
The discomfort of wearing the wristband (eg, “too tight,” “sweaty in summer”) was identified as a barrier to adherence by participants. As a result, some participants proposed new ways of wearing the “wrist” device using clips, pins, and sellotapes (Figure 6) for poststudy use where more flexibility was allowed in the placement of sensors.
Speaking of the value of tracking, most participants were positive toward the function and thought the algorithm was accurate in differentiating activity (ambulatory behavior) and inactivity (stationary behavior).
**Figure 6:** *An alternative way of wearing the tracking device suggested by participants.*
## Theme 2: Causes of Inaccuracy and Solutions
Combining participants’ reports with system logs, perceived inaccuracy occurred mostly during or right after periods of device disconnection when no data were streamed at all. As the MetaWear hardware used for the wrist and cup device was supposed to cache data temporarily during short periods of disconnection and resend data to the app upon reconnection, we told participants they need not take the phone with them unless they were out of the office for 15 minutes or longer. However, the devices did not always reconnect as reliably as expected, even after just brief disconnections.
In addition, participants tended to forget to stop tracking and remove the wrist device at the end of each workday, which also caused data synchronization problems the following day. This was due to a flaw in the hardware—with the wrist device logging data in standalone mode for long periods, the microcontroller could be easily overloaded and crashed. Knowing the aforesaid contexts in which data connection problems were likely to occur, we implemented an important system update to make the app automatically clear cache on the MetaWear board if no data were streamed from the wrist device for 10 minutes after first reconnection request. This modification effectively minimized severity of data loss in case of synchronization issues and greatly enhanced perceived accuracy.
Another source of inaccuracy pertained to the need for personalized threshold for activity detection. Some participants reported the algorithm was too sensitive in picking up movements that participants would not consider as breaks (eg, opening the window blind, sitting and talking with hand gesturing). This issue was rectified by adjusting the detection thresholds upon individual requests. We let the participant know upfront that the researcher could adjust the sensitivity of the break detection setting based on each individual’s experience and preference. Three participants (P4, P7, and P8) requested to have the threshold raised so that the break detection became less sensitive.
## Theme 3: Barriers to Prompts Delivery
Interviews suggested the prompts delivered with the embedded LED (variably called “cup device,” “light” in interviews) did not always reach the participants (ie, low dosage) exactly the way as intended (ie, low fidelity) due to several factors. First, although we had designed WorkMyWay to deliver prompts and cues with an object inherently associated with office work breaks (eg, a cup or glass), a few participants did not follow the instruction to attach the LED reminder to vessels that they normally used for everyday hydration. For example, P5, P7, and P9 reported placing the LED device to one vessel while using another vessel for everyday hydration, because the device was “too cumbersome” and “got in the way.” Second, several participants (P4, P6, P14, and P15) reported accidentally putting down the vessel with the LED facing away from themselves. Third, a lack of attentional resources at work to notice the LED flashing in the periphery of attention was reported as another barrier.
In addition to participants’ behaviors, the unreliable connection between the devices compromised prompts delivery.
## Theme 4: Mixed Attitudes Toward the Embedded Medium for Delivering Prompts
Individual differences existed with respect to the preferred modality and medium of prompting. Some strongly preferred the object-delivered visual prompts to the audible prompts commonly used in commercially available health gadgets, as they thought the LED attached to the object was a “more subtle way” that “effectively directed one’s attention away from they were doing” and “made them physically look away” [P11]. Although some participants did mention vibratory or audible reminders could be more “noticeable,” disturbance to others was raised as a concern.
The idea of integrating prompts and cues for breaks with a break activity–related everyday object was evaluated differently across participants. This approach made a lot of sense and worked well to cue and facilitate breaks for some participants.
In addition, as a positive spillover effect of this medium of delivery, some participants (P1, P2, P3, P12, and P14) reported drinking more liquid. When prompted in interviews, most participants expressed positive attitudes toward the potential addition of technological features to the cup device for tracking, visualizing, and prompting hydration behaviors in the future.
However, several participants reported feeling tired of managing multiple devices, partly because of the unreliable connections between the 3 devices in the current system; a few participants suggested combining the wrist and cup device into 1 to reduce the complexity of system setup.
## Theme 5: Organizational Climate and Job Characteristics Affecting Intervention Uptake
Organizational support was identified as a major facilitator to the uptake of WorkMyWay. All participants in the study thought their employers were happy with the behavioral target (ie, hourly break) promoted by the intervention and permissive of employees’ personal use of technologies as such. However, there were some constraints on break behaviors placed by the nature of the work and the relationships with others involved in the job role.
Different views existed regarding who should be held accountable for employees’ health-related behaviors that occurred in the workplace. Some participants thought the organization and management had “an important role to play.” Although the majority held the view that it should be down to the individual to take care of themselves and to choose the appropriate tools, it would be nice if the organization could offer some options. Encouragingly, one of the participants, who was a senior manager, participated in the study with the interest to source an intervention that could be widely implemented at the university to improve staff well-being.
## Theme 6: Interpersonal Influences on Adherence and Compliance
The subjective norm, or the perception that a majority in the workplace are trying to take regular breaks, was identified as another facilitator to both using WorkMyWay and reducing prolonged SB.
In addition, direct social interactions facilitated the use of WorkMyWay most of the time. For instance, when a participant did not notice the LED reminder, there was the chance that coworkers who happened to see the LED flashes could remind him or her. The physical artifact of the technology also turned out to be a conversation piece to get people talking about well-being in the workplace and sometimes to prompt them to take a break together.
For P12, P13, and P14, who shared the same office, participation as an office team enhanced the use and potentially the effectiveness of the intervention through helping each other with troubleshooting, reminding each other to adhere to the study protocol and to comply with the prompts, comparing each other’s data, and competing for fun.
## Principal Findings
This study evaluated the process of delivering WorkMyWay in real-life office settings. On the basis of participant experiences, an IoT-based intervention consisting of multiple interconnected devices was complex yet manageable in the workplace. Office workers accepted and adopted the WorkMyWay system, as demonstrated with a $100\%$ retention with an 8-week delivery protocol and $83\%$ ($\frac{25}{30}$) adherence on tracking, which were exemplary for technology-based interventions compared with previous studies [31,41]. Bluetooth disconnection was identified as the major issue impacting on the quality of data and fidelity of delivery, echoing observations from other studies on digital interventions [42]. Nonetheless, this did not deter our participants, as $73\%$ ($\frac{11}{15}$) continued to use WorkMyWay in their own interests after the study ended. While behavior change efficacy is beyond the scope of this study, the significant postintervention improvements in psychosocial determinants of occupational SB suggested high potential for behavior change. Those results together have established WorkMyWay as a promising intervention with high potential for long-term adoption and behavior change.
## Potential Intervention Improvements and Broader Research Implications
Our study has revealed various ways in which WorkMyWay can be improved as an intervention, which relate to broader implications for the design and delivery of digital interventions targeting SB, and potentially other health behaviors. The first lesson we can draw from the study concerns the importance of designing intervention delivery technologies with minimal reliance on users’ memories. The delivery of the current version of WorkMyWay required the user to remember to carry phones even on short breaks, to start and stop tracking on a daily basis, to place the cup device within the field of vision, and to make sure the LED was not facing away from them, which induced uncertainties to the quality and quantity of delivery. From there, we see the need for more engineering work to make data synchronization between different devices more reliable and effortless for the users; we also see a greater role for industrial design in the future to improve the presentation of the LED reminders, for example, by making it an LED ring surrounding a vessel so that it is visible from all directions. These are nontrivial aspects that warrant more considerations and investments in the design and development of digital behavior change interventions.
Second, the study highlights the importance of personalization. Indeed, the ability to dynamically adjust the threshold for activity detection by tweaking parameters in the hidden setting menu was an especially useful feature of WorkMyWay and mentioned positively by participants. Personalization should also be supported in the choice and deployment of devices, a need transpired by the fact that participants proposed new ways of wearing tracking devices and placing prompting devices to better fit their individual work practices. This echoes the finding from a previous study that calls for a greater choice of behavior change support tools and devices to be offered to satisfy individualized needs of participants [43]. Despite the proliferation of wearables and IoT technologies, there is a dearth of theoretically informed development of IoT systems for delivering interventions such as the WorkMyWay. Another theory-informed intervention most similar to ours is the Stand More At Work (SMArt Work) intervention [44]. Both interventions feature behavior change techniques such as information about health consequences, prompts and cues, self-monitoring, goal setting, action planning, and feedback on behaviors. Although following broadly similar approaches, the 2 studies embedded prompts and cues into very different everyday artifacts—a cup and a cushion, respectively. One might now ask the question “which of these is the best mode of delivery?” This implies the need for comparative studies of these and potentially other designs before being in a position to roll out an intervention at scale. However, we note an alternative stance, one in which there is no one-size-fits-all intervention design. Rather, interventions may need to be personalized to individuals and contextualized to their particular situations.
Under this view, many potential interventions might be created, for example, by embedding sensors and displays into all manner of everyday objects, tailoring designs to the preferences and contexts of specific individuals. The idea that our interactions with digital technologies should become more personalized and contextualized underlies much research into contextual and ubiquitous computing and its commercial realization in IoT. A third key implication from this research is therefore the need for future research to explore the wider “design space” of possible IoT-enabled behavior change interventions and to deliver generic design guidelines and toolkits for making them (alongside further studies of feasibility and efficacy).
## Strengths and Limitations
A main strength of our study is the mixed method approach that combines system logs, activity tracking data, questionnaires, and interviews to shed light on multiple aspects of the processes of delivering WorkMyWay. We demonstrated the feasibility of using technology-captured data to monitor user adherence, compliance, and the quality and quantity of intervention delivery. This approach is advantageous as it allows implementation issues to be considered in relation to the fidelity of individual component delivery in feasibility studies and causal pathways to be potentially modeled in future larger-scale evaluations [22].
Nevertheless, this study has several limitations that should be noted. For instance, the intervention did not sufficiently target the constructs of knowledge and intentions, even though they were considered important determinants of the target behavior [27]. Instead, we decided to place more emphasis on the constructs less explored in previous research (eg, automaticity, prospective memory, and retrospective memory) and target those with sufficient awareness of the issue in the first place by employing self-selection sampling. Therefore, the demographics of the study sample was very different from that of the general population—$100\%$ ($$n = 15$$) of the participants had higher education qualifications, compared with $42\%$ of the UK working population [45]. The demographics of this sample pointed to the possibility of better health-related knowledge and compliance to healthy lifestyle advice than the average population as indicated in previous research [46]. In addition, recruited from higher-education workplace settings, the participants were very supportive of research and tolerant of technological issues, which might not be the case for average office workers employed by other organizations with very different priorities on their agendas (eg, employer targets and financial profit). Therefore, future studies with more representative samples of office workers from a more diverse range of job roles and organizations especially in the private sector are warranted to establish the broad acceptability of WorkMyWay.
## Conclusions
It is acceptable and potentially feasible to deliver an SB intervention with an IoT system that involves a wearable activity tracking device, an app, and a digitally augmented everyday object (eg, cup). The findings suggest the interventional contents and technological approach of WorkMyWay are viable and it holds great promise to become a successful behavior change intervention. Therefore, it is worth investing in further technological development and industrial design to improve the technology reliability and reduce user burdens. Future research should seek to establish the broad acceptability of similar interventional and technological approaches while expanding the range of digitally augmented objects as modes of delivery to meet diverse needs.
## Data Availability
The system tracking data generated and analyzed during this study are included Multimedia Appendix 4. The questionnaire and interview data are available from the corresponding author on reasonable requests.
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|
---
title: 'Machine Learning for Predicting Micro- and Macrovascular Complications in
Individuals With Prediabetes or Diabetes: Retrospective Cohort Study'
journal: Journal of Medical Internet Research
year: 2023
pmcid: PMC10012007
doi: 10.2196/42181
license: CC BY 4.0
---
# Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study
## Abstract
### Background
Micro- and macrovascular complications are a major burden for individuals with diabetes and can already arise in a prediabetic state. To allocate effective treatments and to possibly prevent these complications, identification of those at risk is essential.
### Objective
This study aimed to build machine learning (ML) models that predict the risk of developing a micro- or macrovascular complication in individuals with prediabetes or diabetes.
### Methods
In this study, we used electronic health records from Israel that contain information about demographics, biomarkers, medications, and disease codes; span from 2003 to 2013; and were queried to identify individuals with prediabetes or diabetes in 2008. Subsequently, we aimed to predict which of these individuals developed a micro- or macrovascular complication within the next 5 years. We included 3 microvascular complications: retinopathy, nephropathy, and neuropathy. In addition, we considered 3 macrovascular complications: peripheral vascular disease (PVD), cerebrovascular disease (CeVD), and cardiovascular disease (CVD). Complications were identified via disease codes, and, for nephropathy, the estimated glomerular filtration rate and albuminuria were considered additionally. Inclusion criteria were complete information on age and sex and on disease codes (or measurements of estimated glomerular filtration rate and albuminuria for nephropathy) until 2013 to account for patient dropout. Exclusion criteria for predicting a complication were diagnosis of this specific complication before or in 2008. In total, 105 predictors from demographics, biomarkers, medications, and disease codes were used to build the ML models. We compared 2 ML models: logistic regression and gradient-boosted decision trees (GBDTs). To explain the predictions of the GBDTs, we calculated Shapley additive explanations values.
### Results
Overall, 13,904 and 4259 individuals with prediabetes and diabetes, respectively, were identified in our underlying data set. For individuals with prediabetes, the areas under the receiver operating characteristic curve for logistic regression and GBDTs were, respectively, 0.657 and 0.681 (retinopathy), 0.807 and 0.815 (nephropathy), 0.727 and 0.706 (neuropathy), 0.730 and 0.727 (PVD), 0.687 and 0.693 (CeVD), and 0.707 and 0.705 (CVD); for individuals with diabetes, the areas under the receiver operating characteristic curve were, respectively, 0.673 and 0.726 (retinopathy), 0.763 and 0.775 (nephropathy), 0.745 and 0.771 (neuropathy), 0.698 and 0.715 (PVD), 0.651 and 0.646 (CeVD), and 0.686 and 0.680 (CVD). Overall, the prediction performance is comparable for logistic regression and GBDTs. The Shapley additive explanations values showed that increased levels of blood glucose, glycated hemoglobin, and serum creatinine are risk factors for microvascular complications. Age and hypertension were associated with an elevated risk for macrovascular complications.
### Conclusions
Our ML models allow for an identification of individuals with prediabetes or diabetes who are at increased risk of developing micro- or macrovascular complications. The prediction performance varied across complications and target populations but was in an acceptable range for most prediction tasks.
## Background
Micro- and macrovascular complications are a major burden for individuals with diabetes, resulting in an increased risk of morbidity and mortality [1]; for example, individuals with diabetes are at increased risk of developing cardiovascular disease (CVD) in comparison with individuals without diabetes [2]. Furthermore, macrovascular complications are responsible for the majority of diabetes-related deaths [3]. Other examples are diabetic retinopathy, which is the primary cause of blindness among adults aged 20 to 74 years [4], and diabetic nephropathy, which is responsible for the majority of new cases of renal failure in the United States [5]. Furthermore, it has been shown that already individuals with prediabetes have an increased risk of developing micro- or macrovascular complications [6,7].
Hence, identifying these individuals (with either prediabetes or diabetes) at increased risk of developing micro- or macrovascular complications is important to allocate treatments, which might prevent the onset of these complications. Importantly, interventions have proven useful in reducing the risk of developing micro- or macrovascular complications in individuals with diabetes [8,9], and targeted interventions to those at highest risk were shown to be more effective than population-wide interventions [10]. Although several risk factors for the different micro- and macrovascular complications are known, identifying those at highest risk is complex, and machine learning (ML) may improve the prediction performance.
## Objectives
Prior studies have used traditional statistical methods (eg, Cox proportional hazards models) to predict complications in individuals with diabetes [11,12]. Such methods typically have a linear structure and thus have the advantage of being interpretable. However, they usually cannot effectively handle high-dimensional data. By contrast, ML allows for modeling more complex (eg, nonlinear) relationships between predictors and outcomes and thus makes effective use of high-dimensional data as in the case of electronic health records (EHRs). Therefore, in recent studies, ML methods were increasingly applied for predicting complications in individuals with diabetes [13-18]. However, these studies suffer from [1] a small and nonrepresentative population (eg, individuals in a single hospital) or [2] a limited number of complications, or [3] they lack important patient information (eg, biomarkers or disease codes). Furthermore, according to a review of prediction models for diabetes complications [19], there exist no prediction models for micro- or macrovascular complications in individuals with prediabetes. However, such prediction models are important because they allow for an earlier intervention (ie, treatment via medications or lifestyle changes) to prevent the onset of micro- or macrovascular complications. Therefore, we aimed to develop ML models (logistic regression and gradient-boosted decision trees [GBDTs]) for predicting micro- and macrovascular complications in individuals with diabetes or prediabetes over a forecast horizon of 5 years.
## Methods
This section was structured in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement for the development of prediction models in medicine [20].
## Source of Data
This is a retrospective analysis, where we analyzed anonymized EHRs from an Israeli health provider [21,22]. The EHRs contain data from multiple centers across Israel and cover the years from 2003 to 2013. The EHRs consist of 6 tables in a longitudinal data format with information on demographics (age and sex), blood pressure, BMI, biomarkers, medications, and disease codes. The baseline year to build the prediction models was 2008. We used a 5‑year forecast horizon, that is, the end of follow-up was in 2013. This forecast horizon was chosen analogous to previous work [11,16-18] because it allows for identification of the individuals at highest risk for developing a micro- or macrovascular complication.
## Participants
We built our prediction models for 2 different target populations, consisting of individuals with either prediabetes or diabetes. Definitions of prediabetes and diabetes were based on laboratory measurements of glycated hemoglobin (HbA1c), recorded disease codes (using the International Classification of Diseases, Ninth Revision [ICD‑9]), and medications. Onset of diabetes was defined by [1] 2 measurements of HbA1c ≥$6.5\%$ (48 mmol/mol), where the onset is then set to the year of the first measurement; [2] an ICD‑9 code corresponding to diabetes (249 or 250); or [3] if any prescription for antidiabetic medication or device for self-measurement of blood glucose was recorded. The list of antidiabetic medications and devices for self-measurement of blood glucose is presented in Multimedia Appendix 1. An individual was considered to have prediabetes if either a single measurement of HbA1c of $5.7\%$ to $6.4\%$ (39 mmol/mol-47 mmol/mol) or an ICD‑9 code corresponding to prediabetes (790.2) was recorded. In addition, individuals with prediabetes were only considered if none of the aforementioned definitions of diabetes were fulfilled.
Inclusion and exclusion criteria were as follows: we selected individuals who were considered to have prediabetes or diabetes in 2008. We only included individuals where information on sex and age was recorded. To account for patient dropout, we only considered individuals where ICD‑9 codes were recorded until 2013. This ensured that, for each individual, an ICD‑9 code corresponding to a possible complication could have been recorded over the entire 5‑year forecast horizon. For nephropathy, we required that either ICD‑9 codes or measurements of serum creatinine or the albumin-to-creatinine ratio in the urine were recorded until 2013. In addition, we only included individuals at baseline who had not been diagnosed with the specific complication beforehand. Hence, the baseline populations differed across complications. A flowchart of the inclusion and exclusion criteria is displayed in Figure 1.
**Figure 1:** *Flowchart of the inclusion criteria. CeVD: cerebrovascular disease; CVD: cardiovascular disease; ICD-9: International Classification of Diseases, Ninth Revision; PVD: peripheral vascular disease.*
The final cohorts consisted of 13,904 individuals with prediabetes and 4259 individuals with diabetes (Figure 1). Of the 13,904 individuals with prediabetes, 2096 ($15.1\%$) developed diabetes within 5 years. Table 1 (individuals with prediabetes) and Table 2 (individuals with diabetes) show the cohort characteristics at baseline in comparison with the characteristics at the time of diagnosis with a certain complication. Across all complications, the 5‑year incidence was smaller for the individuals with prediabetes than for those with diabetes. The cohorts at baseline contained more female individuals than male individuals (diabetes: $56.3\%$ vs $43.7\%$, respectively; prediabetes: $53.3\%$ vs $46.7\%$, respectively). For the macrovascular complications PVD and CVD, this ratio turned the other way: more male individuals developed these complications in both cohorts. The mean age of individuals with prediabetes at baseline was 51.2 (SD 8.7) years and that of individuals with diabetes was 52.9 (SD 9.5) years. Furthermore, BMI, SBP, blood glucose, and HbA1c values were higher at the time of diagnosis of a complication than at baseline. This was observed in both cohorts.
## Outcome
Definitions of micro- and macrovascular complications were based on recorded ICD‑9 codes. We included the following microvascular complications: retinopathy (ICD‑9 codes 250.5 and 362.0), nephropathy (ICD‑9 codes 250.4 and 585), and neuropathy (ICD‑9 codes 250.6 and 357.2). Furthermore, we considered 3 macrovascular complications: peripheral vascular disease (PVD; ICD‑9 codes 250.7, 443.9, and 440), cerebrovascular disease (CeVD; ICD‑9 codes 430, 431, 432, 433, 434, 435, 437, and 438), and CVD (ICD‑9 codes 410, 411, 412, 413, and 414).
For nephropathy, we additionally included the ratio of albumin to creatinine in the urine (albuminuria) and the estimated glomerular filtration rate (eGFR) as disease-defining markers. We considered an individual to have developed nephropathy if 1 measurement of eGFR <60 ml/minute per 1.73 m2 or 2 measurements of the ratio of albumin to creatinine in the urine ≥30 mg/g were recorded [23]. The eGFR has been calculated using the formula from the study by Levey et al [24], which takes serum creatinine, age, sex, and ethnicity as inputs. As the formula only differentiates between a Black ethnicity and a non‑Black ethnicity, we assumed a non-Black ethnicity for all individuals because the EHRs did not cover that information but represent an Israeli population.
## Predictors
The following predictors were used: age, sex, BMI, and blood pressure (systolic blood pressure [SBP] and diastolic blood pressure). We further included predictors from the following categories: biomarkers, medications, and ICD‑9 codes. Feature selection was applied, whereby we selected the most frequent biomarkers, medications, and ICD‑9 codes. Specifically, we added to our predictors the 60 most frequently recorded biomarkers and the 20 most frequently recorded ICD‑9 codes. We did not include the ICD‑9 code 250 (diabetes mellitus) because this code was used in our inclusion criteria to define the diabetes cohort (among other criteria). We grouped the 50 most often prescribed medications into 20 classes (Multimedia Appendix 1) and added them to our predictors. In total, this resulted in 105 predictors (Multimedia Appendix 2).
The predictors were preprocessed by applying the following steps: first, ICD‑9 codes and medications were one-hot encoded, and ICD-9 codes were forward filled to account for the disease history of the individual. Second, we averaged measurements for BMI, blood pressure, and biomarkers if multiple measurements were recorded within 1 year. Third, missing values at the point of evaluation were forward filled from previous years. For the logistic regression, measurements that were still missing were imputed using the median. For the GBDTs, we did not impute missing data because the chosen model can handle missing values automatically. Finally, the predictors were standardized (by removing the mean and dividing by the SD) for the logistic regression. By contrast, standardization is not necessary for the GBDTs.
## Sample Size
The primary end point of this study was the diagnostic accuracy of our ML models to predict micro- and macrovascular complications. Therefore, we included all individuals who fulfilled our inclusion criteria (as specified in the Participants subsection) to maximize the discriminatory power of our models.
## Missing Data
Missing data in the predictors were handled as follows: we only included individuals with recorded age and sex. Hence, no data are missing for these predictors. For numerical values (ie, blood pressure, BMI, and biomarkers), missing values at the baseline were forward filled from the last measurement. For the logistic regression, values that were still missing were imputed using the median. For the GBDTs, no imputation was performed as described previously, but, internally, GBDTs treat missing values as informative and replace them with a dummy variable [25].
## Statistical Analysis Methods
We built separate ML models for each complication and disease state at baseline (either prediabetes or diabetes). Specifically, we used a logistic regression with L1 regularization and GBDTs. Logistic regression is a linear model, which typically performs well in clinical settings [26]. It is inherently interpretable, which is advantageous—and often demanded—for medical predictions [27]. Gradient boosting is an ML technique where a sequence of weak learners (here, decision trees) are sequentially optimized to minimize the prediction errors of the previous weak learners. The final gradient-boosting model consists of an ensemble of weak learners. GBDTs are highly effective in modeling complex, nonlinear relationships and in handling high-dimensional data as in the case of EHRs. Both models were chosen because they are well-established in the medical literature [26,28,29]. Furthermore, this allows us to make direct comparisons between a linear model (logistic regression) and a more flexible model (GBDTs).
The ML models were implemented in Python (version 3.6.9; Python Software Foundation). In particular, we used scikit‑learn (version 0.23.2 [30]) for the logistic regression and the CatBoost package (version 1.0.4 [25]) for the GBDTs.
We applied a nested cross-validation, where we used 5 outer folds to measure the out‑of‑sample performance of the ML models and 4 inner folds to choose the optimal hyperparameters (Multimedia Appendix 3). More specifically, within each training set in the outer fold, an additional 4-fold cross-validation is performed to select the hyperparameters. Thereafter, the model is trained on the training set using these optimal hyperparameters, and the out-of-sample performance is evaluated on the test set corresponding to the current outer fold. This procedure is repeated within each of the 5 folds of the outer cross-validation. As such, nested cross-validation is best practice in ML to optimize the hyperparameter tuning and to assess how well the model generalizes to new data because it ensures that each individual in the data set is used once for measuring the out-of-sample performance [31].
We evaluated the performance of our ML models primarily on the area under the receiver operating characteristic curve (AUROC). We report the mean and the SD of the AUROC across the 5 different test sets generated by the outer cross-validation. For discussing the results, we categorized the AUROC into moderate (0.600-0.700), acceptable (0.700-0.800), and good (0.800-0.900). Additional performance metrics such as area under the precision recall curve, sensitivity, specificity, and balanced accuracy are reported in Multimedia Appendix 4.
The calibration (observed risk vs raw prediction score) of a prediction model is often relevant in medical settings [32]. In contrast to logistic regression, GBDTs may not be well calibrated. Therefore, we applied a post hoc calibration by fitting a logistic regression to the predictions on the validation set. We evaluate the calibration in Multimedia Appendix 5 by plotting the calibration curves and reporting the Brier score [33].
To explain the predictions of the GBDTs, we calculated Shapley additive explanations (SHAP) values [34]. These represent a unified approach for estimating the individual contribution of a predictor to the overall model output. Thus, SHAP values provide a ranking of the most important predictors [34]. Furthermore, SHAP values inform whether larger (smaller) values of a predictor are attributed with an increased risk of developing a certain complication. In addition, we report the coefficients of the logistic regression in Multimedia Appendix 6.
## Risk Groups
We followed best practice to check for potential algorithmic bias [35] and thus added a separate analysis, where we evaluated the performance differences between male and female individuals. For this, we did not train new prediction models on only a male or female population but instead checked the performance of our final models on these subgroups. The results are presented in Multimedia Appendix 7.
## Ethics Approval
This study was approved by the ethics committee of the faculty of mathematics, computer science, and statistics at Ludwig Maximilian University Munich (EK-MIS-2022-116). We report the Minimum Information About Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist [36], which was developed to improve transparent reporting of ML in medicine, in Multimedia Appendix 8 [36].
## Prediction Performance
Figure 2 shows the performance of the logistic regression and the GBDTs for predicting micro- and macrovascular complications in individuals with prediabetes (Figure 2A) and diabetes (Figure 2B) over a 5‑year forecast horizon. For the prediabetes cohort, the mean AUROCs of the respective best model were in a range of 0.681 (SD 0.164) to 0.815 (SD 0.009). For the diabetes cohort, the mean AUROCs spanned over a range of 0.651 (SD 0.043) to 0.775 (SD 0.033). Nephropathy showed the best prediction performance in both cohorts. The prediction performance for macrovascular complications was generally better for individuals with prediabetes, whereas for microvascular complications (except nephropathy), the performance was better for individuals with diabetes. A comparison of the performance differences between logistic regression and GBDTs revealed that it depends on the cohort and the complication which model performs better. However, it can be observed that the prediction performance is comparable between the 2 models for most prediction tasks. Especially because in all cases the error bars are largely overlapping, we argue that no model should be preferred over the other. Additional performance metrics are reported in Multimedia Appendix 4. These metrics also show that both models performed comparably.
**Figure 2:** *Performance of the logistic regression and the gradient boosted decision trees (GBDTs) for predicting micro- and macrovascular complications in (A) individuals with prediabetes or (B) diabetes. We report the mean of the area under the receiver operating characteristic curve (AUROC) across the 5 different test sets. The error bars denote SD. CeVD: cerebrovascular disease; CVD: cardiovascular disease; PVD: peripheral vascular disease.*
In Multimedia Appendix 5, we report the results of the calibration of the GBDTs. In summary, we observe that the GBDTs were already well calibrated before the post hoc calibration. However, in most cases, the calibration improved thereafter.
In Multimedia Appendix 7, we present the prediction performance for male and female individuals. No systematic deviation in performance was observed.
## Model Explainability
Figures 3A and 3B show the 5 most important predictors for all 6 complications for individuals with prediabetes and diabetes, respectively. The HbA1c value is an important predictor for all microvascular complications in both populations, where larger values are related to an increased risk of developing one of these complications. For nephropathy, increased age and large serum creatinine levels are the most important risk factors. In both populations, age is ranked as the most relevant predictor for all macrovascular complications. Hypertension (either determined by ICD‑9 code 401, elevated SBP, or a prescription of beta blockers or calcium channel blockers) is important for predicting PVD and CeVD in both populations. Male sex was identified as a risk factor for developing CVD.
**Figure 3:** *(A) SHAP plots for individuals with prediabetes. (B) SHAP plots for individuals with diabetes. For the SHAP plots, the ranking of the predictors is based on their importance listed in descending order. Each dot represents 1 individual, and its position on the x axis denotes its SHAP value. Elements with a positive (negative) SHAP value pull the prediction toward an increased (decreased) risk of developing a complication. The color of each dot is a representation of the corresponding predictor value, where red indicates a high, blue a low, and gray a missing value. BB: beta blocker; BUN: blood urea nitrogen; CCB: calcium channel blocker; CeVD: cerebrovascular disease; CPK: creatine phosphokinase; CVD: cardiovascular disease; HbA1c: glycated hemoglobin; HDL: high-density lipoprotein; ICD-9 719: other and unspecified disorders of joint; ICD-9 786: symptoms involving respiratory system and other chest symptoms; ICD-9 401: essential hypertension; LDH: lactate dehydrogenase; LDL: low-density lipoprotein; MCH: mean corpuscular hemoglobin; PVD: peripheral vascular disease; SBP: systolic blood pressure; SCr: serum creatinine; SHAP: Shapley additive explanations; UACR: albumin to creatinine ratio in urine; UCr: creatinine in urine.*
## Robustness Checks
We experimented with other model variants to corroborate our findings. First, we tested sequential ML models (ie, recurrent neural networks with gated recurrent units and long short-term memory networks). Such models are hypothesized to improve prediction performance by taking into account the entire patient trajectory [37]. However, in our case, this did not improve the performance in comparison with our models. Second, we experimented with multitask learning where the different predictions are learned jointly. Again, our models were found to be superior. More details and the corresponding results can be found in Multimedia Appendix 9 [37].
## Principal Findings
We developed ML models to predict the risk of developing micro- or macrovascular complications in individuals with prediabetes or diabetes using routinely collected EHRs. Across all microvascular complications, the respective best ML model showed at least an acceptable performance for both cohorts. The only exception was retinopathy within the prediabetes cohort, where the performance was moderate. The reason for this might be the small number of individuals who developed retinopathy within the prediabetes cohort and a 5-year forecast horizon. The prediction performance for nephropathy in individuals with prediabetes showed good performance and, interestingly, thereby a better performance than the respective model for the diabetes cohort. It might be assumed that diabetes-related complications can be predicted with better performance in individuals with diabetes than in a population with prediabetes. This is because diabetes-related complications tend to occur earlier in individuals with diabetes because they have already passed through the stage of prediabetes. However, our diagnosis criteria for nephropathy (eGFR and albuminuria in addition to the ICD‑9 codes) might also include individuals whose nephropathy is not directly linked to prediabetes or diabetes. Furthermore, the prediabetes cohort contains almost 3 times more individuals than the diabetes cohort, which makes it easier for the ML models to learn the specific relationships leading to nephropathy.
For the macrovascular complications, we observed that the performance of the best model for PVD was acceptable in both cohorts. By contrast, for CeVD, the prediction performance was only moderate, which reduces its value for possible application in clinical settings. The best model for CVD for the prediabetes cohort showed a performance between moderate and acceptable (closer to acceptable), whereas for the diabetes cohort, it was slightly below this level. In addition, we observed that all macrovascular complications were easier to predict in individuals with prediabetes. One possible explanation would be that these complications are not as directly related to diabetes as are microvascular complications. The latter depend more on glycemic control (eg, blood glucose and HbA1c levels), whereas macrovascular complications highly depend on additional risk factors (eg, age and blood pressure). In combination with the larger prediabetes cohort, this might be responsible for the better performance.
Furthermore, we observed that the comparative performance of the ML models depends upon the cohort and the specific complication. Overall, both ML models showed a similar prediction performance. Although GBDTs are widely considered to outperform logistic regression, a systematic review has found that a variety of ML models (including GBDTs) do not generally perform better than logistic regression in clinical prediction models [26]. However, in recent studies that used EHRs to build clinical prediction models, GBDTs significantly outperformed logistic regression [28,29]. Hence, it is not surprising that in this study, for some prediction tasks, logistic regression performed better, whereas for others, GBDTs performed better. In 1 case (retinopathy for individuals with diabetes), a large difference (>0.050) in the mean AUROC was observed between the 2 models. However, in this case, the number of outcomes compared with the overall sample size is small, thus resulting in large, overlapping error bars of the AUROCs. Hence, we argue that this difference does not reflect a substantial performance difference between the 2 models.
To identify the most important predictors of the GBDTs, SHAP values were calculated. These revealed that the HbA1c value is an important predictor for all microvascular complications, and higher values are related to an increased risk. This relationship is well known and has been reported previously [4,38,39]. Serum creatinine is relevant for predicting nephropathy in individuals with prediabetes or diabetes. This finding is not surprising because serum creatinine is used to calculate the eGFR, which is one of the variables defining nephropathy. For macrovascular complications, age was identified as the most important predictor. This correlation is well known in the literature [40]. For PVD and CeVD, hypertension was related to an increased risk of developing one of these complications, which has been described previously [41,42].
## Comparison With Prior Work
To the best of our knowledge, no prediction models for micro- and macrovascular complications exist for individuals with prediabetes; hence, a comparison with prior work is not possible. By contrast, several prediction models for individuals with diabetes exist. Two previous studies have used Cox proportional hazards models [11,12]. Tanaka et al [11] built prediction models for coronary heart disease (CHD), stroke, noncardiovascular mortality, overt nephropathy, and retinopathy for a forecast horizon of 5 years. Our prediction models for stroke, nephropathy, and retinopathy outperformed their models; theirs performed better only for CHD and CVD (theirs: 0.725; ours: 0.686). The models by Basu et al [12] estimate the 10-year risk, which makes a direct comparison with our models with a 5-year forecast horizon difficult. In their work, the AUROCs on the internal validation set were often only moderate (ie, 0.550-0.680 for retinopathy, 0.600-0.840 for nephropathy, and 0.570-0.640 for neuropathy). In comparison, our best ML models achieved acceptable performances (ie, mean 0.726, SD 0.069; mean 0.775, SD 0.033; and mean 0.771, SD 0.031, respectively) on these complications for individuals with diabetes. Their model for myocardial infarction (MI) showed a performance similar to ours. For stroke, they reported an AUROC of 0.700 (our best model for CeVD: mean 0.651, SD 0.043).
ML models for predicting microvascular complications in individuals with diabetes were built in the study by Dagliati et al [13]. Therein, the authors reported acceptable performance for retinopathy and moderate performances for nephropathy and neuropathy (using logistic regression and a 5‑year forecast horizon). By contrast, our models showed acceptable performances for these 3 complications. Dworzynski et al [14] built ML models for cardiovascular disease, stroke, and chronic kidney disease with AUROCs of 0.690, 0.720, and 0.770, respectively. In comparison, we report mean AUROCs of 0.686 (SD 0.017), 0.651 (SD 0.043), and 0.775 (SD 0.033) for CVD, CeVD, and nephropathy, respectively. In a study by Ljubic et al [15], recurrent neural networks were used to estimate the 9-year risk for 10 different complications. Therein, separate performances for angina pectoris, ischemic CHD, and MI are reported, which are all grouped together in our outcome CVD. The performances of the models built by Ljubic et al [15] range from acceptable (MI) to good (ischemic CHD), thereby outperforming our model for CVD. Furthermore, their model for PVD outperformed ours (0.738-0.767 vs mean 0.715, SD 0.027, respectively). For nephropathy and neuropathy, our models performed better than theirs (nephropathy: mean 0.775, SD 0.033, vs 0.742-0.768, respectively; neuropathy: mean 0.771, SD 0.031, vs 0.715-0.746, respectively), whereas for retinopathy, their model outperformed ours by a small margin (0.728-0.796 vs mean 0.726, SD 0.069, respectively). Furthermore, a prediction model to estimate the 1‑year risk for chronic kidney disease was reported in the study by Song et al [17]. Therein, the authors state a prediction performance between acceptable and good (closer to good), which is slightly better than the performance of our model for nephropathy. ML models for predicting retinopathy and CVD within 3 years were reported in the study by Ravaut et al [18]. These models showed good performance for retinopathy (ours: acceptable) and acceptable performance for CVD (ours: moderate).
Overall, the prediction performance of our models for individuals with diabetes is comparable to the performances reported in prior work. However, the advantage of our study is that we are the first to also include prediction models for individuals with prediabetes. Prediction models for such a population are relevant because already half of the individuals when diagnosed with type 2 diabetes have had vascular complications [6]. Furthermore, our models are based on EHRs that include a large and representative population. This is because our EHRs contain data from multiple centers across Israel. In addition, our models account for an individual’s personal history by including a large number of predictors from demographics, biomarkers, medications, and comorbidities. In clinical settings, our prediction models could prove useful because they can be derived directly from EHRs and are therefore easily scalable. Furthermore, they allow for an early identification of individuals at risk. For these individuals, treatment could be administered earlier than usual and thus could increase the chances to prevent the complication. However, the prediction performance is in some cases worse than acceptable (AUROC <0.700). The benefit of these models—CeVD for both cohorts, retinopathy for the prediabetes cohort, and CVD for the diabetes cohort—is questionable.
## Limitations
This study has limitations. First, we used EHR data, which may be prone to wrongly reported or missing data. This may explain the small number of individuals who developed a specific complication within 5 years in comparison with individuals in data obtained from specialized diabetes clinics [13]. Moreover, it may also be the reason why our models did not achieve a better predictive performance although the general sample size was large. Furthermore, we only had access to data until 2013. It is possible that the reporting within the EHRs got better over time. Therefore, our analysis might be based on data from times when the quality of EHRs was lower than current standards. By contrast, an advantage of these EHRs is that they typically include measurements and diagnoses across the care continuum. This is due to their origin from a health insurance company. However, we cannot state this with absolute certainty because patients may switch among health care providers or receive treatment abroad. Finally, our EHRs did not contain information regarding living status (eg, income, diet, and physical activity) or sociodemographics (eg, race), which could be relevant predictors for estimating the risk of developing a micro- or macrovascular complication. Future studies may use more recent and more complete EHRs to improve the prediction performance. In addition, adjudicated claims or problem lists could be used to improve the reliability of the diagnosis of micro- and macrovascular complications.
Second, the data only covers an Israeli population. However, our approach could be generalized to other populations as well. This may be addressed in future work and, thereby, our ML models could additionally be validated on an external data set. As our EHRs did not contain information regarding ethnicity but encompass an Israeli population, we had to assume a non-Black ethnicity to calculate the eGFR. Furthermore, we could not assess whether our models perform equally well among different ethnicities.
Third, our definitions of prediabetes or diabetes were only based on HbA1c measurements and recorded ICD-9 codes. We did not consider blood glucose measurements because the EHRs did not provide information on the time point of blood glucose measurement. Therefore, disease-defining fasting values were not available. However, this might also be beneficial because it ensures direct applicability of our ML models to EHRs, where the fasting state is not always recorded.
Fourth, our ML models were not trained on individuals at the time of diagnosis of prediabetes or diabetes but rather at a specific point in time [2008]. The advantage of this approach is that it ensures an application of our ML models to all individuals with prediabetes or diabetes and not only to those who were just recently diagnosed. In addition, in clinical practice, the time point of prediabetes or diabetes diagnosis is often unknown, and the disease may have been already present for several years without being diagnosed.
Fifth, diagnosing nephropathy based on measurements of eGFR and ratio of albumin to creatinine in the urine may identify individuals whose nephropathy is unrelated or not exclusively related to prediabetes or diabetes but developed because of other reasons (eg, hypertension). Nonetheless, because prediabetes or diabetes is a major contributing factor for renal impairment, a correct prediction of nephropathy may be helpful, irrespective of its primary cause.
Sixth and last, we only consider a forecast horizon of 5 years. For the prediabetes cohort, a larger forecast horizon would be useful because diabetes-related complications typically occur later than in individuals with diabetes. Hence, an extension to larger forecast horizons would be an interesting analysis. However, it should be noted that the time span of our data set is not sufficient for such an analysis.
## Conclusions
Micro- and macrovascular complications are a major burden for individuals with prediabetes or diabetes. An early identification of individuals at risk is important because it could help to offer adequate treatment to prevent these complications. In this study, we built ML models to identify individuals with prediabetes or diabetes at high risk of developing micro- or macrovascular complications. For the first time, we showed that ML allows for predicting micro‑ and macrovascular complications in individuals with prediabetes. The prediction performance varied across complications and target populations but was acceptable for most prediction tasks.
## Data Availability
The data set analyzed in this study is not publicly available. To analyze the data, a data access agreement is needed.
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|
---
title: Pregnancy Complications, Correlation With Placental Pathology and Neonatal
Outcomes
authors:
- Maria Teresa Loverro
- Edoardo Di Naro
- Vittorio Nicolardi
- Leonardo Resta
- Salvatore Andrea Mastrolia
- Federico Schettini
- Manuela Capozza
- Matteo Loverro
- Giuseppe Loverro
- Nicola Laforgia
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012052
doi: 10.3389/fcdhc.2021.807192
license: CC BY 4.0
---
# Pregnancy Complications, Correlation With Placental Pathology and Neonatal Outcomes
## Abstract
### Purpose
We aimed to clarify and contribute to a better comprehension of associations and correlations between placental histological findings, pregnancy evolution, and neonatal outcomes.
### Study Design
This is a longitudinal and prospective observational study, performed between May 2015 and May 2019, on 506 pregnant women. Clinical data related to pregnancy outcome, neonatal health status, and placental histology were primarily collected. Twin pregnancies or malformed newborns were excluded and therefore the study was conducted on 439 cases. These cases have been then subdivided into the following study groups: (a) 282 placentas from pathological pregnancies; and, (b) a control group of 157 pregnancies over 33 weeks of gestational age, defined as physiological or normal pregnancies due to the absence of maternal, fetal, and early neonatal pathologies, most of which had undergone elective cesarean section for maternal or fetal indication.
### Results
A normal placenta was present in $57.5\%$ of normal pregnancies and in $42.5\%$ of pathological pregnancies. In contrast, placental pathology was present in $26.2\%$ of normal pregnancies and $73.8\%$ of pathological pregnancies. Comparison of the neonatal health status with the pregnancy outcome showed that, among the 191 newborns classified as normal, 98 ($51.3\%$) were born from a normal pregnancy, while 93 ($48.7\%$) were born from mothers with a pathological pregnancy. Among the 248 pathological infants, 59 ($23.8\%$) were born from a mother with a normal pregnancy, while 189 ($76.2\%$) were born from pregnancies defined as pathological.
### Conclusion
Placental histology must be better understood in the context of natural history of disease. Retrospective awareness of placental damage is useful in prevention in successive pregnancy, but their early identification in the evolving pregnancy could help in association with biological markers or more sophisticated instruments for early diagnosis.
## Introduction
The placenta is a vital structure for fetal development providing the feto-maternal exchanges of nutrients and catabolic products, and it is strictly influenced by the maternal biohumoral environment which might affect in turn, fetal development.
Although research is already attempting to replace the placenta with an artificial organ, we do not know yet the exact functional framework of this complex natural machine that helps women to generate new lives.
Since it is still difficult to explore the human placenta working principles with the current methods of investigation, histological examination, albeit a posteriori, is the only way to understand the complex functions that nature has entrusted to this organ, which functions as lung, kidney, and liver of the fetus, as well as a regulator and stabilizer of fetal biohumoral and endocrine exchange.
Up to the present day, the study of placental histology is the sole instrument enlightening many aspects of placental physiopathology. Nevertheless, it still presents unresolved problems, such as the widespread feedback of classified pathological aspects also in normal pregnancies.
Despite the development and large acceptance of a standardized, reproducible classification system based on previously defined features [1], many details of placental structure and function remain to be elucidated, especially with regard to the influence of maternal conditions and modulation of fetal-neonatal health.
Therefore, pending the development of a machine allowing human embryos to develop outside the womb, it is necessary to understand more about how it works and how we can improve the functioning of the wonderful tool that is the human placenta.
The aim of this study is to investigate the associations between histological examination of the placenta, classified according to the Amsterdam criteria, and the main clinical aspects, both maternal and neonatal, in order to understand to what extent this histological examination reflects placental function and to what extent it can be used for the prognosis and prevention of subsequent pregnancies.
Since the literature has always shown that there is no exact overlap of placental pathological pictures with maternal-fetal-neonatal clinical contexts, stirring up the abovementioned skepticism for the adoption of placental histological findings in clinical practice, we aimed to clarify and contribute to a better comprehension of associations and correlations between placental histological findings, pregnancy evolution, and neonatal outcomes.
## Material and Methods
This is a longitudinal and prospective observational study, performed at the University of Bari “Aldo Moro” between May 2015 and May 2019, on 506 pregnant women enrolled at delivery time after a previous informed consent and ethical committee approval. Clinical data related to pregnancy outcome, neonatal health status, and placental histology were primarily collected. The mode of delivery was mainly cesarean section in $78\%$ (347 cesarean sections and 92 vaginal deliveries) of cases. Twin pregnancies or malformed newborns (67 placentas) were excluded and therefore the study was conducted on 439 cases.
These cases have been then subdivided into the following study groups: (a) 282 placentas from pathological pregnancies; and, (b) a control group of 157 pregnancies over 33 weeks of gestational age, defined as physiological or normal pregnancies due to the absence of maternal, fetal, and early neonatal pathologies, most of which had undergone elective caesarean section for maternal or fetal indication. All these placentas were processed and analyzed by a single operator (RL), who was blinded to maternal and neonatal outcomes and classified them according to the Amsterdam criteria. In addition, placental, cord, and membrane sampling was performed according to the Amsterdam Placental Workshop Group Consensus Statement on placental sampling.
Pregnancies and the respective newborns were classified as normal or pathological based on the presence of one of the criteria listed in Table 2 for the mother and in Table 3 for the newborn.
Placental histology has also been classified as normal or pathological, in accordance with the Amsterdam classification, as shown in Table 4.
For each type of pathology, the incidence and respective statistical significance of the association with placental pathology has been calculated.
## Statistical Analysis
Statistical analysis was performed using “association analysis” in order to check whether the distribution of the attributes of a certain variable could be defined as dependent or independent from the one of another variable. This analysis was conducted using Pearson’s χ 2 test and the contingency coefficient and Cramer’s V, which offer a standardized measure of the association level between the variables. Moreover, in order to verify which of the modalities of matched and compared variables has determined the strongest degree of association between them, the cell χ 2 tests have been calculated for each cell of intersection of the contingency tables used.
## Definition of Variables
Two subgroups of pregnancies have been identified: physiological pregnancies and pathological pregnancies, characterized by abnormal conditions that could impair the health or the life of the mother and fetus, such as maternal diabetes, oligohydramnios (AFI <5), clinically significant uterine blood loss, central placenta previa, preeclampsia, stained amniotic fluid, intrauterine fetal death, hypertension, intrauterine growth retardation, and premature rupture of membranes.
In turn, also newborns were divided into normal and pathological, according to the presence or absence of neonatal weight lower than 1,500 g, neonatal sepsis, respiratory distress, neonatal resuscitation, small gestational age (SGA), neonatal hospitalization longer than 10 days, preterm infant, and neonatal deaths. A SGA fetus was defined as a fetus with an estimated fetal weight or an abdominal circumference below the 10th percentile of given reference ranges.
The placentas have also been divided into normal and pathological. Each pathological placenta was grouped by a single pathologist (RL) into the main groups, according to the Amsterdam classification [1] and corresponding to: infection, maternal malperfusion, fetal malperfusion, chorangiosis, intervillous hemorrhage, and other.
The histological results of the placenta corresponding to maternal or neonatal data were then associated with each corresponding pregnancy.
## Results
Among the 439 pregnancies considered eligible for analysis, 157 ($35.8\%$) were physiological pregnancies randomly enrolled in 4 years and 282 ($64.2\%$) were pathological pregnancies, with a mean annual enrolment of 72 physiological pregnancies treated in the Department of Obstetrics and Gynecology (Table 1).
**Table 1**
| Placenta | Pregnancy | Pregnancy.1 | Pregnancy.2 | Newborns | Pregnancy.3 | Pregnancy.4 | Pregnancy.5 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Placenta | Normal | Pathological | Total | Newborns | Normal | Pathological | Total |
| Normal | 77 (57.5%) (49%) | 57 (42.5%) (20.2%) | 134 (100%) (30.5%) | Normal | 98 (51.3%) (62.4%) | 93 (48.7%) (33%) | 191 (100%) (43.5%) |
| Pathological | 80 (26.2%) (51%) | 225 (73.8%) (79.8%) | 305 (100%) (69.5%) | Pathological | 59 (23.8%) (37.6%) | 189 (76.2%) (67%) | 248 (100%) (56.5%) |
| Total | 157 (35.8%) (100%) | 282 (64.2%) (100%) | 439 (100%) (100%) | Total | 157 (35.8%) (100%) | 282 (64.2%) (100%) | 439 (100%) |
A normal placenta was present in $57.5\%$ of normal pregnancies and in $42.5\%$ of pathological pregnancies.
In contrast, placental pathology was present in $26.2\%$ of normal pregnancies and $73.8\%$ of pathological pregnancies.
Comparison of the neonatal health status with the pregnancy outcome showed that, among the 191 newborns classified as normal, 98 ($51.3\%$) were born from a normal pregnancy, while 93 ($48.7\%$) were born from mothers with a pathological pregnancy.
Among the 248 pathological infants, 59 ($23.8\%$) were born from a mother with a normal pregnancy, while 189 ($76.2\%$) were born from pregnancies defined as pathological (Table 1).
The incidence of the various pregnancy diseases detected in the 282 pathological pregnancies is shown in Table 2. The most frequent pregnancy pathology was the premature rupture of membranes before 37 weeks (72 cases, $25.5\%$), followed by 55 cases ($19.5\%$) of IUGR and then 44 cases of hypertension ($15.6\%$), 27 cases ($9.6\%$) of oligohydramnios (AFI <5), in the absence of premature rupture of membranes), 23 cases of gestational diabetes (8,$2\%$), 15 cases of central placenta previa ($5.3\%$), 13 cases of preeclampsia ($4.6\%$), 13 cases of meconium stained amniotic fluid grade II or III ($4.6\%$), 12 cases of profuse blood loss due to partial or total placental abruption ($4.3\%$), and, finally, 8 cases ($2.8\%$) of intrauterine fetal death.
**Table 2**
| Pregnancy complications | Number (%) |
| --- | --- |
| Maternal diabetes | 23 (8.2%) |
| Oligohydramnios (AFI <5) | 27 (9.6%) |
| Uterine blood loss from placental abruption | 12 (4.3%) |
| Placenta previa/accreta | 15 (5.3%) |
| Preeclampsia | 13 (4.6%) |
| Stained amniotic fluid | 13 (4.6%) |
| Stillbirth | 8 (2.8%) |
| Maternal hypertension | 44 (15.6%) |
| Fetal intrauterine growth retardation IUGR | 55 (19.5%) |
| P-PROM premature rupture of membrane (<37 weeks) | 72 (25.5%) |
| Total pathological pregnancies | 282 (100%) |
Among neonatal complications (Table 3), the most frequent has been the birth of 68 premature neonates ($27.4\%$), 64 small gestational age (SGA) infants ($25.8\%$), followed by 36 cases ($14.5\%$) of infants in need of primary resuscitation, 29 cases ($11.7\%$) of respiratory distress, 23 cases of sepsis ($9.3\%$), 19 cases ($7.7\%$) of neonatal hospitalisation longer than 10 days, and 9 ($3.6\%$) cases of neonatal death.
**Table 3**
| Neonatal complications | Number (%) |
| --- | --- |
| Premature neonates | 68 (27.4%) |
| Neonatal sepsis | 23 (9.3%) |
| Neonatal respiratory distress | 29 (11.7%) |
| Neonatal resuscitation | 36 (14.5%) |
| Small for gestational age neonate | 64 (25.8%) |
| Neonatal hospitalization longer than 10 days | 19 (7.7%) |
| Neonatal death | 9 (3.6%) |
| Total pathological births | 248 (100%) |
With regard to placental histology, 134 pregnancies ($30.5\%$) had a normal placenta, while 305 ($69.5\%$) had placentas defined as pathological due to histological lesions (Table 1), among them 80 ($26.2\%$) were from physiological pregnancies and 225 ($79.8\%$) from pathological pregnancies.
Among placental lesions (Table 4), the most frequent observed condition is maternal malperfusion found in 144 patients ($47.2\%$), followed by infections diagnosed in 71 patients ($23.3\%$), chorangiosis and intervillous hemorrhage, both identified in 29 patients ($9.5\%$), fetal malperfusion in 8 cases ($2.6\%$), and, finally, different types of pathologies, defined here as “other” in 24 placentas ($7.9\%$).
**Table 4**
| Types of placental histologic anomaly | Number (%) |
| --- | --- |
| Infection | 71 (23.3%) |
| Maternal malperfusion | 144 (47.2%) |
| Fetal malperfusion | 8 (2.6%) |
| Chorangiosis | 29 (9.5%) |
| Intervillous hemorrhage | 29 (9.5%) |
| Others | 24 (7.9%) |
| Total pathological placentas | 305 (100%) |
## Analysis of Association of Placental Histology, Pregnancy, and Neonatal Outcomes
In order to understand whether the results of histological examination of the placenta were significantly associated with pregnancy or neonatal outcomes, an association analysis was conducted between the type of pregnancy (physiological or pathological), placental histology (normal or pathological) and early neonatal health conditions (normal or pathological).
Preliminarily, the analysis of association between pregnancy and neonatal outcomes has been performed, displaying a good level of association between the type of pregnancy and neonatal outcomes, as confirmed by both the χ 2 tests ($p \leq 0.0001$) and the contingency coefficient and Cramer’s V, confirming at the same time a logical assumption that a physiological pregnancy is associated with a disease-free placenta.
Subsequently, the analysis of the degree of association between the pregnancy outcome and the neonatal diseases considered here showed that normal infants displayed a significant degree of association with normal pregnancies (χ 2 = 12.91, p-value = 0.0003) and that, among all neonatal diseases, infants with SGA reported a significant degree of association with pathologic pregnancies (χ 2 = 4.69, p-value = 0.0303).
Indeed, SGA has a significantly higher incidence in pathological pregnancies ($19.5\%$) than in physiological pregnancies ($5.7\%$), while healthy neonates have a significantly higher incidence in normal pregnancies ($62.4\%$) compared with pathological pregnancies ($33\%$).
The other neonatal pathologies, however, do not present statistically significant differences between pathological and normal pregnancies.
To be precise, premature neonates are present in $12.7\%$ of physiological pregnancies and $17\%$ of pathological pregnancies, while neonatal sepsis shows an incidence of $3.8\%$ in normal pregnancies and $6\%$ in pathological pregnancies. In addition, the incidence of neonatal respiratory distress resulted higher in pathological pregnancies ($7.8\%$) than in physiological pregnancies ($4.5\%$), although the difference is not significant.
Finally, the incidence of neonatal resuscitation is substantially comparable between normal and pathological pregnancies ($8.3\%$ and $8.2\%$, respectively), while neonatal hospitalization longer than 10 days showed a higher incidence in pathological pregnancies ($5.7\%$) than in physiological ones ($1.9\%$), without a significant difference probably due to small number of neonates experiencing a long hospital stay after a normal pregnancy.
Likewise, intrauterine deaths reported a relatively higher incidence in pathological pregnancies ($2.8\%$) than in normal pregnancies (only 1 case, $0.6\%$) (Table 5).
**Table 5**
| Neonatal conditions | Normal pregnancy | Pathological pregnancy |
| --- | --- | --- |
| Premature neonates | 20 (12.7%) | 48 (17%) |
| Sepsis | 6 (3.8%) | 17 (6%) |
| Respiratory distress at birth | 7 (4.5%) | 22 (7.8%) |
| Neonatal intensive care | 13 (8.3%) | 23 (8.2%) |
| SGA Small gestational age | 9 (5.7%) | 55 (19.5%) |
| Neonatal hospitalization >10 days | 3 (1.9%) | 16 (5.7%) |
| Neonatal death | 1 (0.6%) | 8 (2.8%) |
| Normal neonates | 98 (62.4%) | 93 (33%) |
| Total | 157 (100%) | 282 (100%) |
## Analysis of Association Between Neonatal Outcomes and Maternal Diseases
The analysis of the association between neonatal outcomes and maternal pathologies appeared significant, as shown both by the χ 2 tests ($p \leq 0.0001$) and by the contingency coefficient and Cramer’s V.
Therefore, the distribution and significance of the association between the different components of the two variables under consideration was specifically analyzed (Table 6), reflecting a significant association between specific neonatal outcomes and maternal pathologies, both displaying significantly higher joint frequencies than simple action due to chance.
**Table 6**
| Neonatal outcome | Pregnancy complications | Pregnancy complications.1 | Pregnancy complications.2 | Pregnancy complications.3 | Pregnancy complications.4 | Pregnancy complications.5 | Pregnancy complications.6 | Pregnancy complications.7 | Pregnancy complications.8 | Pregnancy complications.9 | Pregnancy complications.10 | Pregnancy complications.11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Neonatal outcome | Maternal diabetes | Oligohydramnios | Uterine bleeding | Placenta previa | Pre-eclampsia | Meconium stained amniotic fluid | Intrauterine fetal death | Maternal hypertension | IUGR | Normal pregnancies | PROM | Total |
| Premature neonates | – | 6 (8.82%) (22.22%) | 4 (5.88%) (33.33%) | 4 (5.88%) (26.67%) | 6 (8.82%) (46.15%) | 1 (1.47%) (7.69%) | – | 9 (13.24%) (20.45%) | 5 (7.35%) (9.09%) | 20 (29.41%) (12.74%) | 13 (19.12%) (18.06%) | 68 (15.5%) |
| Sepsis | 1 (4.4%) (4.4%) | 2 (8.7%) (7.4%) | – | – | 1 (4.4%) (7.7%) | 2 (8.7%) (15.4%) | – | 3 (13%) (6.8%) | – | 6 (26.1%) (3.8%) | 8 (34.8%) (11.1%) | 23 (5.2%) |
| Neonatal respiratory distress | 3 (10.3%) (13%) | 1 (3.5%) (3.7%) | – | 3 (10.3%) (20%) | 3 (10.3%) (23.1%) | – | – | 3 (10.3%) (6.8%) | 3 (10.3%) (5.5%) | 7 (24.1%) (4.5%) | 6 (20.7%) (8.3%) | 29 (6.6%) |
| Neonatal intensive care | 1 (2.8%) (4.4%) | 2 (5.6%) (7.4%) | – | 2 (5.6%) (13.3%) | 1 (2.8%) (7.7%) | 1 (2.8%) (7.7%) | – | 4 (11.1%) (9.1%) | 6 (16.7%) (10.9%) | 13 (36.1%) (8.3%) | 6 (16.7%) (8.3%) | 36 (8.2%) |
| SGA | 3 (4.7%) (13%) | 5 (7.8%) (18.5%) | 2 (3.1%) (16.7%) | 1 (1.6%) (6.7%) | 1 (1.6%) (7.7%) | – | – | 7 (10.9%) (15.9%) | 25 (39.1%) (45.5%) | 9 (14.1%) (5.7%) | 11 (17.2%) (15.3%) | 64 (14.6%) |
| Neonatal hospitalization >10 days | – | 1 (5.3%) (3.7%) | – | – | 1 (5.3%) (7.7%) | 2 (10.5%) (15.4%) | – | 4 (21.1%) (9.1%) | 6 (31.6%) (10.9%) | 3 (15.8%) (1.9%) | 2 (10.5%) (2.8%) | 19 (4.3%) |
| Neonatal death | – | – | – | – | – | – | 8 (88.9%) (100%) | – | – | 1 (11.1%) (0.6%) | – | 9 (2.1%) |
| Normal | 15 (7.9%) (65.2%) | 10 (5.2%) (37%) | 6 (3.1%) (50%) | 5 (2.6%) (33.3%) | – | 7 (3.7%) (53.9%) | – | 14 (7.3%) (31.8%) | 10 (5.2%) (18.2%) | 98 (51.3%) (62.4%) | 26 (13.6%) (36.1%) | 191 (43.5%) |
| Total | 23 (5.2%) (100%) | 27 (6.2%) (100%) | 12 (2.7%) (100%) | 15 (3.4%) (100%) | 13 (3%) (100%) | 13 (3%) (100%) | 8 (1.8%) (100%) | 44 (10%) (100%) | 55 (12.5%) (100%) | 157 (35.7%) (100%) | 72 (16.4%) (100%) | 439 (100%) (100%) |
Specifically, a high association was identified between the intrauterine diagnosis of small gestational age and the neonatal diagnosis of intrauterine growth retardation (IUGR) (χ 2 = 34.69, p-value <0.0001), confirming the well-known predictive value of prenatal biometric ultrasound screening. Significant has proven to be the association between normal pregnancies and normal neonates (χ 2 = 11.61, p-value = 0.0007), between premature neonates and preeclampsia (χ 2 = 7.60, p-value = 0.0058), between neonatal hospital stay longer than 10 days and IUGR intrauterine growth retardation (χ 2 = 5. 27, p-value = 0.0217), respiratory neonatal distress and preeclampsia (χ 2 = 5.16, p-value = 0.0231), neonatal sepsis and PROM premature rupture of membrane (χ 2 = 4.5, p-value = 0.0339), and, finally, respiratory distress at birth and placenta previa (χ 2 = 3.93, p-value = 0.0475).
The correlation of other possible correlated variants in the present study did not reveal any significant degree of association.
## Analysis of Association Between Placental Pathology and Pregnancy Outcomes
Therefore, considering that, overall, the incidence of neonatal pathologies is correlated to the presence or absence of pregnancy pathology, we wondered if and to what extent this association is influenced by or could be related with a normal or pathological placenta. For this reason, we have analyzed the association between the placental histology, classified according to the Amsterdam criteria, the pregnancy outcome and the neonatal health status.
This analysis has demonstrated the presence of a significant association between placental pathology and the pregnancy outcome, as confirmed by the χ 2 test ($p \leq 0.0001$) and the Contingency Coefficient and Cramer’s V.
Therefore, the detailed analysis of the distribution of the placental pathologies related to the course of pregnancy (Table 7), has highlighted a high degree of association between normal placentas and physiological pregnancies (χ 2 = 17. 64, p-value < 0.0001), between placental maternal malperfusion and pathological pregnancies (χ 2 = 8.78, p-value = 0.0030) and between intervillous hemorrhage and physiological pregnancies (χ 2 = 4.24, p-value = 0.0396).
**Table 7**
| Placental pathologies | Normal pregnancies | Pathological pregnancies |
| --- | --- | --- |
| Infection | 25 (15.9%) | 46 (16.3%) |
| Maternal malperfusion | 23 (14.7%) | 121 (42.9%) |
| Fetal malperfusion | 2 (1.3%) | 6 (2.1%) |
| Chorangiosis | 5 (3.2%) | 24 (8.5%) |
| Intervillous hemorrhage | 17 (10.8%) | 12 (4.3%) |
| Other | 8 (5.1%) | 16 (5.7%) |
| Normal placenta | 77 (49%) | 57 (20.2%) |
| Total | 157 (100%) | 282 (100%) |
Overall, the absence of placental abnormalities was found in $49\%$ of normal pregnancies, compared with $20.2\%$ of pathological pregnancies.
Maternal malperfusion is present in $14.7\%$ of physiological pregnancies and $42.9\%$ of pathological pregnancies, making it the most important placental lesion with a possible active role in determining the pregnancy outcome. Its presence in a non-negligible percentage in physiological pregnancies deserves careful consideration and calls, perhaps, for a better pathological significance.
Finally, intervillous hemorrhage is present in $10.8\%$ of normal pregnancies and $4.3\%$ of pathological pregnancies. This difference leads increasingly more to considering intervillous hemorrhage as a paraphysiological phenomenon of normal pregnancy arrangement, whose intensity is not such as to result in fetal hypoxia.
As far as other placental pathologies are concerned, they do not show significant differences between normal and pathological pregnancies. Among these we note that chorangiosis is present in $3.2\%$ of normal pregnancies and in $8.5\%$ of pathological pregnancies, infection in $15.9\%$ of physiological pregnancies and in $16.3\%$ of pathological pregnancies, fetal malperfusion in $1.3\%$ of physiological pregnancies and in $2.1\%$ of pathological pregnancies. Finally, placental pathologies defined as “other” have a $5.1\%$ incidence in physiological pregnancies and $5.7\%$ in pathological pregnancies (Table 7).
## Analysis of Association Between Placental and Maternal Pathologies
In order to further investigate the interrelations between the factors examined here, it appeared important to conduct an association analysis between placental pathologies and pregnancy pathologies.
The degree of association between the two variables was significant, as shown both by the χ 2 tests ($p \leq 0.0001$) and by the contingency coefficient and Cramer’s V.
The analysis of the distribution of the different modalities of the two variables analyzed (Table 8), displayed significant associations between certain types of placental disorders and maternal diseases, corresponding to significantly higher joint frequencies than the simple action by chance.
**Table 8**
| Placental disorders | Normal pregnancy | Pregnancies complicated by maternal disease | Pregnancies complicated by maternal disease.1 | Pregnancies complicated by maternal disease.2 | Pregnancies complicated by maternal disease.3 | Pregnancies complicated by maternal disease.4 | Pregnancies complicated by maternal disease.5 | Pregnancies complicated by maternal disease.6 | Pregnancies complicated by maternal disease.7 | Pregnancies complicated by maternal disease.8 | Pregnancies complicated by maternal disease.9 | Pregnancies complicated by maternal disease.10 | Pregnancies complicated by maternal disease.11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Placental disorders | Normal pregnancy | Maternal diabetes | Oligoidramnios | Uterine bleeding | Placenta previa | Preeclampsia | Meconium-stained amniotic fluid | Intrauterine fetal death | Maternal hypertension | IUGR | Normal pregnancy | P-PROM | Total |
| Infection | 25 (35.2%) (15.9%) | – | 6 (8.5%) (22.2%) | 2 (2.8%) (16.7%) | 3 (4.2%) (20.0%) | – | 5 (7.0%) (38.5%) | 3 (4.2%) (37.5%) | 3 (4.2%) (6.8%) | 2 (2.8%) (3.6%) | 25 (35.2%) (15.9%) | 22 (31.0%) (30.6%) | 71 (16.2%) |
| Maternal malperfusion | 23 (16.0%) (14.7%) | 5 (3.5%) (21.7%) | 7 (4.9%) (25.9%) | 4 (2.8%) (33.3%) | 2 (1.4%) (13.3%) | 11 (7.6%) (84.6%) | 2 (1.4%) (15.4%) | 2 (1.4%) (25.0%) | 34 (23.6%) (77.3%) | 34 (23.6%) (61.8%) | 23 (16.0%) (14.7%) | 20 (13.9%) (27.8%) | 144 (32.8%) |
| Fetal malperfusion | 2 (25.0%) (1.3%) | – | 2 (25.0%) (7.4%) | – | – | – | – | – | – | 4 (50.0%) (7.3%) | 2 (25.0%) (1.3%) | – | 8 (1.8%) |
| Chorangiosis | 5 (17.2%) (3.2%) | 13 (44.8%) (56.5%) | 2 (6.9%) (7.4%) | – | – | – | 1 (3.5%) (7.7%) | – | 2 (6.9%) (4.6%) | 4 (13.8%) (7.3%) | 5 (17.2%) (3.2%) | 2 (6.9%) (2.8%) | 29 (6.6%) |
| Intervillous hemorrhage | 17 (58.6%) (10.8%) | 2 (6.9%) (8.7%) | 1 (3.5%) (3.7%) | – | 2 (6.9%) (13.3%) | 1 (3.5%) (7.7%) | 1 (3.5%) (7.7%) | – | 2 (6.9%) (4.6%) | – | 17 (58.6%) (10.8%) | 3 (10.3%) (4.2%) | 29 (6.6%) |
| Other | 8 (33.3%) (5.1%) | – | 3 (12.5%) (11.1%) | 1 (4.2%) (8.3%) | 2 (8.3%) (13.3%) | 1 (4.2%) (7.7%) | 1 (4.2%) (7.7%) | 2 (8.3%) (25.0%) | – | 1 (4.2%) (1.8%) | 8 (33.3%) (5.1%) | 5 (20.8%) (6.9%) | 24 (5.5%) |
| Normal placenta | 77 (57.5%) (49.0%) | 3 (2.2%) (13.0%) | 6 (4.5%) (22.2%) | 5 (3.7%) (41.7%) | 6 (4.5%) (40.0%) | – | 3 (2.2%) (23.1%) | 1 (0.8%) (12.5%) | 3 (2.2%) (6.8%) | 10 (7.5%) (18.2%) | 77 (57.5%) (49.0%) | 20 (14.9%) (27.8%) | 134 (30.5%) |
| Total | 157 (35.8%) (100%) | 23 (5.24%) (100%) | 27 (6.2%) (100%) | 12 (2.7%) (100%) | 15 (3.4%) (100%) | 13 (3.0%) (100%) | 13 (3.0%) (100%) | 8 (1.8%) (100%) | 44 (10.0%) (100%) | 55 (12.5%) (100%) | 157 (35.8%) (100%) | 72 (16.4%) (100%) | 439 (100%) (100%) |
In particular, a high association was found between chorioangiosis and maternal diabetes (χ 2 = 86.75, p-value < 0.0001), maternal malperfusion and maternal hypertension (χ 2 = 26.53, p-value < 0.0001), normal placenta and normal pregnancies (χ 2 = 17. 64, p-value < 0.0001), maternal malperfusion and IUGR (χ 2 = 14.12, p-value = 0.0002), maternal malperfusion and preeclampsia (χ 2 = 10.64, p-value = 0.0011), infection and PROM (χ 2 = 9.21, p-value = 0. 0024), fetal malperfusion and IUGR (χ 2 = 8.97, p-value = 0.0028), other placental diseases and fetal demise (χ 2 = 5.58, p-value = 0.0181), fetal malperfusion and oligohydramnios (χ 2 = 4. 62, p-value = 0.0316), intervillous hemorrhage and normal pregnancies (χ 2 = 4.24, p-value = 0.0396), and infection and amniotic fluid meconium stained (χ 2 = 3.99, p-value = 0.0457).
The other possible associations did not show a significant degree of association.
## Analysis of Association Between Neonatal Outcomes and Placental Disease
The analysis of the association between neonatal outcomes and placental disease showed a significant degree results, as shown by both χ 2 tests ($p \leq 0.0001$), contingency coefficient and Cramer’s V.
A specific analysis of the distribution of placental pathologies in relation to neonatal pathologies (Table 9) reveals that some of them are significantly higher than a simple action of chance.
**Table 9**
| Neonatal outcomes | Placental pathologies | Placental pathologies.1 | Placental pathologies.2 | Placental pathologies.3 | Placental pathologies.4 | Placental pathologies.5 | Placental pathologies.6 | Placental pathologies.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Neonatal outcomes | Infection | Maternal malperfusion | Fetal malperfusion | Chorangiosis | Intervillous hemorrhage | Other | Normal placenta | Total |
| Premature neonates | 15 (22.1%) (21.2%) | 25 (36.8%) (17.3%) | 1 (1.5%) (12.5%) | 2 (2.9%) (6.9%) | 9 (13.2%) (31.3%) | 2 (2.9%) (8.3%) | 14 (20.6%) (10.5%) | 68 (100%) (15.5%) |
| Sepsis | 15 (65.2%) (21.1%) | 5 (21.7%) (3.5%) | – | 1 (4.3%) (3.5%) | – | 2 (8.7%) (8.3%) | – | 23 (100%) (5.2%) |
| Neonatal respiratory distress | 3 (10.3%) (4.2%) | 10 (34.5%) (6.9%) | – | 4 (13.8%) (13.8%) | 2 (6.9%) (6.9%) | 3 (10.3%) (12.5%) | 7 (24.1%) (5.2%) | 29 (100%) (6.6%) |
| Neonatal intensive care | 6 (16.7%) (8.5%) | 12 (33.3%) (8.3%) | – | 1 (2.8%) (3.5%) | 1 (2.8%) (3.5%) | 3 (8.3%) (12.5%) | 13 (36.1%) (9.7%) | 36 (100%) (8.2%) |
| SGA small gestational age | 6 (9.4%) (8.5%) | 33 (51.6%) (22.9%) | 4 (6.3%) (50.0%) | 5 (7.8%) (17.2%) | 3 (4.7%) (10.3%) | 1 (1.6%) (4.2%) | 12 (18.8%) (9.0%) | 64 (100%) (14.6%) |
| Neonatal hospitalization >10 days | 2 (10.5%) (2.8%) | 8 (42.1%) (5.6%) | 1 (5.3%) (12.5%) | 1 (5.3%) (3.5%) | – | 1 (5.3%) (4.2%) | 6 (31.6%) (4.5%) | 19 (100%) (4.3%) |
| Neonatal death | 3 (33.3%) (4.2%) | 3 (33.3%) (2.1%) | – | – | – | 2 (22.2%) (8.3%) | 1 (11.1%) (0.8%) | 9 (100%) (2.1%) |
| Normal | 21 (11.0%) (29.6%) | 48 (25.1%) (33.3%) | 2 (1.0%) (25.0%) | 15 (7.9%) (51.7%) | 14 (7.3%) (48.3%) | 10 (5.2%) (41.7%) | 81 (42.4%) (60.5%) | 191 (100%) (43.5%) |
| Total | 71 (16.2%) (100%) | 144 (32.8%) (100%) | 8 (1.8%) (100%) | 29 (6.6%) (100%) | 29 (6.6%) (100%) | 24 (5.5%) (100%) | 134 (30.5%) (100%) | 439 (100%) (100%) |
In particular, there is a high association between neonatal sepsis and placental infections (χ 2 = 34.21, p-value < 0.0001), together with a similar significant association between normal neonates and normal placentas (χ 2 = 8.84, p-value = 0. 0030), small gestational age and fetal malperfusion (χ 2 = 6.89, p-value = 0.0087), small gestational age and maternal malperfusion (χ 2 = 6.87, p-value = 0.0088), neonatal deaths and the grouping of other placental diseases named “other” (χ 2 = 4.62, p-value = 0.0316), and between premature neonates and intervillous hemorrhage (χ 2 = 7.60, p-value = 0.0058).
For the remaining matched pairs of modalities, there was no significant degree of association between neonatal outcomes and the presence of placental pathologies.
## Discussion
The role of human placenta continues to be the subject of interpretative debate, despite the fact that ever greater progress is being made in the development of an artificial placenta [2].
For this reason, the histological study of the placenta continues to have poor implication in clinical practice and a difficult place in forensic medicine.
Human placenta is a key organ in “programming the fetus for later disease” [3], and, as a consequence, it might be even more important in programming newborn disease.
The primary and useful approach in placental clinical application is the study of its histological lesions. However, despite the large number of articles published and the widespread acceptance of the “Amsterdam Classification,” this precious diagnostic tool has not found a wide application into neonatal and in obstetric care of successive pregnancies yet.
This difficulty may be due to various reasons such as the finding, albeit in varying percentages, of the same lesion in both normal and pathological pregnancies, leading to a nonunique clinical interpretation.
Moreover, although there is an adequate nosology of placental lesions, the multifactorial nature of pregnancy evolution does not allow us to understand to what extent the maternal pathophysiology can affect placental function and, above all, to what extent pathological placenta can influence fetal-neonatal health.
Many pathological maternal and neonatal conditions have been associated with pathological placental histology, although few are based on reliable association analyses.
The instrument adopted here to better understand the relationship between the maternal and placental pathology, as well as those between placental and neonatal diseases, is the analysis of association between variables and, specifically in our case, between maternal, placental, and neonatal pathology (4–8).
The association analysis, adopted in the present study, has provided us with data that may help to better understand the clinical use of histological findings and, at the same time, may shed further light on the significance of these lesions in both physiological and pathological pregnancy.
## Influence of Placental Pathology on Pregnancy Outcome
In pregnancies classified as normal, only $49\%$ did not display any placental alterations, while $15.9\%$ of placentas had infections, $14.7\%$ maternal malperfusion, and $10.3\%$ intervillous hemorrhage, to mention just the most representative.
Although present in a nonnegligible proportion and significantly lower than the respective incidence of pathological pregnancies ($73.8\%$), this percentage of pathological placentas in normal pregnancy could be interpreted as paraphysiological which, for reasons still unknown, does not result in a clinical pathology. Nevertheless, the nonnegligible extent of these lesions poses a problem, addressed in the literature mainly for placental infections, of graduation of the intensity of the lesion, the real functional placental impairment.
In our experience, only intervillous hemorrhage is the histological lesion significantly present (χ 2 = 4.24, p-value = 0.0396) in normal pregnancies. This incidence is significantly higher than the one of pathological pregnancies ($4.3\%$), reaffirming the quasi-physiological significance of this lesion. Presumably, the other lesions that remain nonsignificantly associated are not so frequent or intense enough to induce a pathological outcome of the pregnancy.
In order to better understand the reciprocal influence of pregnancy disorders, placental abnormalities, and neonatal disease, we have analyzed the relative mutual association and the reciprocal influence.
In the grouping of pathological pregnancies, the analysis of the association between the various types of placental and maternal pathologies demonstrated a statistical significance among some of the variables taken into account, due to higher joint frequencies than chance, as shown in Table 8.
In the context of maternal pathology, placenta previa and uterine bleeding are characterized by and have in common many placental lesions, such as infection, maternal malperfusion, intervillous hemorrhage, as well as some placental lesions grouped under the name of “others,” although without any significantly characterizing incidence compared with physiological pregnancies.
The small group of cases with intrauterine fetal death is characterized by a significant association only with the group of other placental pathologies (“other”) (χ 2 = 5.58, p-value = 0.0181), underlining the great variability of causes of intrauterine death in the third trimester of pregnancy and, therefore, the need to perform an autopsy in any case to clarify the fairly heterogenous pathogenetic mechanism.
The sonographic diagnosis of oligohydramnios also displayed a significant association with malperfusion (χ 2 = 4.62, p-value = 0.0316), while meconium-stained amniotic fluid (χ 2 = 3.99, p-value = 0.0457) has been significantly associated with placental infection and intervillous hemorrhage, suggesting in both cases the presence of unquantifiable reduced oxygenation of the fetus by the placenta.
Preeclampsia has been significantly associated to the maternal malperfusion (χ 2 = 10.64, p-value = 0.0011) as a characterizing placental lesion. The key role of the altered placental vascularization is also confirmed by the significant association between the intrauterine diagnosis of intrauterine growth retardation (IUGR) and maternal malperfusion (χ 2 = 14.12, p-value = 0.0002), underlining the usefulness of a very early diagnosis of impaired placental function.
Association between fetal IUGR and fetal malperfusion appear highly significant (χ 2 = 8.97, p-value = 0.0028) underlying the crucial role of placental disorders in fetal growth defects due to chronic fetal hypoxia. The poor villi development with a consequent hypoplasia and poor branching of the villous tree, the increased syncytial nodes, and formation of vasculo-syncytial membranes, are indeed the true architectural elements, distinctive of the vascular-based fetal growth pathologies, characterizing the fetal malperfusion.
It would therefore be desirable to identify this condition at an early stage, in order to implement all the therapeutic measures that research is trying to validate.
The significant association between the reduction of amniotic fluid and the placental fetal vascular malperfusion (χ 2 = 4.62, p-value = 0.0316), emphasizes a reduced or absent perfusion of the villous parenchyma, characterized by vascular stasis, ischemia, and in some cases, thrombosis, associated to reduced placental function.
A striking high-degree association, particularly significant, has been the one between gestational diabetes and placental chorangiosis (χ 2 = 86.75, p-value < 0.0001) found in $56.5\%$ of our gestational diabetes cases.
Chorangiosis is characterized by an increase in the number of fetal capillaries in the terminal villi in noninfarcted areas of the placenta.
It represents a compensation mechanism in some cases of chronic hypoxia, which, strangely enough, is present from the early stages of pregnancy in the presence of completely asymptomatic glycemic dysmetabolism. This coincidence is clinically unexpected but seems plausible from a biological point of view.
Indeed, pregnancies complicated by diabetes show an increase in human placental lactogen already at the very early stages of pregnancy with the induction of a “diabetogenic state,” leading to a higher glucose availability for the fetus [9]. This is associated to an increased production of insulin-like growth factor and growth factors promoting tissue growth [9], and this mechanism may be the basis for the placental changes observed in chorangiosis.
It would therefore be desirable to diagnose dysmetabolism in a very early stage of pregnancy, also by means of the tools offered by recent omics sciences [10].
The analysis of the distribution of the different modalities of the two variables analyzed, maternal and placental pathologies (Table 8) has revealed further significant associations. In fact, the association between premature rupture of membranes and placental infection (χ 2 = 9.21, p-value = 0.0024), corresponds to significantly higher joint frequencies than the simple action by chance.
Premature rupture of membranes may result from placental-fetal inflammation syndrome, maternal placental malperfusion, or both, irrespective of gestational age and of the interval between rupture of membranes and time of delivery [11].
Preterm birth has resulted to be significantly associated with certain Amsterdam classification placental pathologies, manly with acute and chronic inflammation and/or altered early maternal perfusion, often resulting from chorioamnionitis.
According to the literature, the incidence of placental infections, presenting as chorioamnionitis, occurs in $32\%$ of preterm deliveries, compared with $10\%$ of normal pregnancies [12].
As far as the problem of premature birth is concerned, we detected a $23\%$ rate of placental infection, without any difference between normal and pathological pregnancies (Table 7). Moreover, our results did not identify any significant association between placental infection and premature birth, which is inconsistent with the literature [13, 14]. These differences may be related to the mode of recruitment (pregnancies over 32 weeks) and, in large part, elective cesarean section.
## Vascular Implications in Placental Pathology and Pregnancy Outcomes
Maternal vascular malperfusion has been reported in the literature with a reported incidence varying between $35\%$ [15], $19.7\%$ [14], and $1.5\%$ [16].
In our experience, maternal malperfusion has been present in $47.2\%$ of cases, accounting for a large number of pathological pregnancies, especially IUGR and preeclampsia, enrolled in the present study.
Therefore, inadequate remodeling of the spiral artery or pathology of the spiral arteries (decidual vasculopathy), is the main factor inducing a placental picture of maternal vascular malperfusion, strictly associated with preeclampsia, IUGR [17], with possible development of cerebral palsy or neurocognitive abnormalities at school age, especially in very low birth weight infants (<1 kg) [18].
For this reason and the potential severity of the outcomes mentioned above, there has been in the last decades an effort to prevent or reduce the inadequate remodeling of the spiral arteries primarily by the administration of low-dose aspirin during pregnancy (19–21).
## Correlation of Early Neonatal Disease to Placental Pathology
The present study demonstrates unequivocally that in the absence of placental lesions, not only does pregnancy develop in a normal manner but newborns are also healthy.
As shown by the association analysis, a normal placenta is associated with a high probability to deliver a healthy baby (χ 2 = 8.84, p-value = 0.0030), underlining the active role of the placenta during pregnancy in preserving the fetal well-being.
In contrast, but according to the same approach, neonatal sepsis is strongly and significantly associated with placental infection (χ 2 = 34.21, p-value < 0.0001).
In our experience, no placental lesion has resulted to be significantly associated with an increased incidence of perinatal asphyxia, and this reflects the vagueness of the literature data, according to which perinatal asphyxia has been associated with placental lesions affecting the vascular supply to the fetus, such as umbilical cord complications (interrupted velamentous vessels, cord laceration, hypercoiled cord, cord hematoma), chorioamnionitis with fetal vasculitis, fetal thrombotic vasculopathy, ascending intrauterine infection, and reduced maternal vascular perfusion with low Apgar scores at 1 and 5 min [22, 23].
Therefore, we can reaffirm that the role of placental lesions in perinatal asphyxia with neonatal death exists, but it is not exclusive, considering that the role of chronic hypoxic trauma in utero is difficult to be evaluated by histological examination, as it is related to a functional factor represented by the ability of the placenta to compensate the hypoxic conditions.
Maternal malperfusion was significantly associated with a neonatal diagnosis of small gestational age at birth (χ 2 = 6.87, p-value = 0.0088).
Neonatal sepsis is significantly associated with increased incidence of placental inflammation, classified as villitis of unknown origin and chorioamnionitis [24, 25].
In our experience, neonatal sepsis was significantly associated with placental infection, which was present in $65.2\%$ of neonatal sepsis (χ 2= 34.21, p-value < 0.0001), showing a strong dependence between the two conditions.
Similarly, there is a significant association between neonatal deaths and the grouping of placental pathologies called “other” (χ 2 = 4.62, p-value = 0.0316), although placental and neonatal infection were present with an important, but nonsignificant, incidence of $33.3\%$. Therefore, we believe that the limitation of significance is due to a nonhomogeneous group of placental lesions and to the small number of cases, even though placental pathology has a primary role in the case of neonatal death, clearly in the context of a multifactorial nature of this event.
## Conclusion
In conclusion, placental histology must be better understood in the context of natural history of the disease.
Retrospective awareness of placental damage is useful in prevention of successive pregnancy, but their early identification in evolving pregnancy could be very useful if we have biological markers or more sophisticated instruments for early diagnosis.
As a result, we believe that the efforts of research should be directed towards identification of technological innovations, capable of identifying early placental pathologies. Unfortunately, this will not happen without the help of robust biomarkers, capable of predicting the onset of the placenta disease early in pregnancy. Future research in these areas will be critical for filling this gap and will open a new era of placental therapy.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author Contributions
MTL: writing original manuscript. EDN: supervision. VN:data analysis. LR: data collection. SAM: writing original manuscript. FS: data curation. MC: data collection. ML: data analysis. GL: supervision. NL: supervision.
## 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: Clinical and biochemical characteristics of diabetic ketoacidosis in adults
with type 1 or type 2 diabetes at a tertiary hospital in the United Arab Emirates
authors:
- Raya Almazrouei
- Amatur Rahman Siddiqua
- Mouza Alnuaimi
- Saif Al-Shamsi
- Romona Govender
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012054
doi: 10.3389/fcdhc.2022.918253
license: CC BY 4.0
---
# Clinical and biochemical characteristics of diabetic ketoacidosis in adults with type 1 or type 2 diabetes at a tertiary hospital in the United Arab Emirates
## Abstract
### Background
Diabetes ketoacidosis (DKA) is a well-known acute complication of diabetes. This study aims to describe the sociodemographic, clinical, and biochemical characteristics of adult patients with different diabetes types and DKA severities attending a tertiary hospital in the UAE.
### Methods
We retrospectively extracted sociodemographic, clinical, and laboratory data from the electronic medical records of 220 adult patients with DKA admitted to Tawam Hospital between January 2017 and October 2020.
### Results
The average age was 30.6 ± 16.6 years of whom $54.5\%$ were women, $77.7\%$ were UAE nationals and $77.9\%$ were Type 1 diabetes (T1DM). $12.7\%$ were newly diagnosed with diabetes. Treatment noncompliance ($31.4\%$), and infection ($26.4\%$) were the main precipitating factors. Most patients presented with moderate DKA ($50.9\%$). Compared to T1DM, patients with Type 2 diabetes (T2DM) were older (53.6 vs 23.9 years, $p \leq 0.001$), had longer hospital stay (12.1 days vs 4.1 days, $p \leq 0.001$), had more complications ($52.1\%$, vs $18.9\%$ $p \leq 0.001$), and a higher mortality rate ($6.3\%$ vs $0.6\%$, $$p \leq 0.035$$). Patients with severe DKA had a shorter diabetes duration compared to mild and moderate DKA (5.7 vs 11.0 vs 11.7 years, respectively, $$p \leq 0.007$$), while complications were significantly lower in the mild group compared to both the moderate and severe groups ($11.6\%$ vs $32.1\%$ vs $33.3\%$, respectively).
### Conclusion
The risk of DKA is higher for patients with T1DM than for those with T2DM. The clinical characteristics and outcomes of patients with T2DM differ from those with T1DM highlighting the importance of educating all patients about DKA.
## Introduction
Diabetes mellitus (DM) is a metabolic disease characterized by hyperglycemia due to the impairment in insulin secretion or insulin resistance. The two main types of DM are type 1 diabetes (T1DM) and type 2 diabetes (T2DM). While T1DM is more common in the young age group (childhood and adolescence), T2DM is encountered more with increasing age. In the Middle East and North Africa (MENA) region, the 2021 prevalence of diabetes is $16.2\%$ (8.5–18.3) and the United Arab Emirates (UAE) belongs to this region [1]. The prevalence of diabetes in the UAE is alarming with age-adjusted comparative prevalence of diabetes of $16.4\%$ in 2021, which is projected to increase to $18.1\%$ by 2045 [2]. No solid data are available about T1DM prevalence in UAE.
DM, together with its macrovascular and microvascular complications, is associated with an increased risk of morbidity and mortality. Diabetic ketoacidosis (DKA), described as one of the most severe acute life-threatening complications, has an overall mortality rate ranging from $0.2\%$ to $2\%$, with persons at the highest end of the range residing in developing countries [3]. DKA is characterized by biochemical abnormalities including hyperglycemia, ketonemia, and metabolic acidosis and is usually diagnosed in patients with T1DM (incidence, 50–100 episodes per 1,000 patients) but is a less frequent complication of T2DM with an incidence rate of 4.6–8 episodes per 1,000 patients [4]. A local single-center retrospective study showed that T1DM constitutes $61\%$ of DKA admissions compared to $27.1\%$ for T2DM [5], and as expected, those with T2DM are older [6]. The most common precipitating causes of DKA in most studies were nonadherence to therapy, infections, and undiagnosed diabetes with data from the UAE reporting $31.4\%$, $22.7\%$, and $12\%$, respectively [5, 7, 8]. Only a few studies have explored the clinical and biochemical characteristics of DKA in the MENA region, including in the UAE. As far as we are aware, none of these studies have assessed DKA characteristics according to the diabetes type and the degree of DKA severity. Thus, we aimed to describe the clinical and biochemical characteristics, and the severity of DKA among adults with T1DM or T2DM admitted to a tertiary care hospital in the UAE.
## Methods
We reviewed records of all patients aged 16 years (local cutoff age for adult service) and older admitted with DKA to Tawam Hospital (Al Ain, UAE) from January 2017 to October 2020. We retrieved the records using ICD 10 CM codes (E10.10, E10.11, E13.10, and E13.11) for DKA episodes. Prior to April 2020, Tawam was the designated hospital for Emirati nationals, employees, and a few other medical insurance holders, while non-Emirati nationals and other individuals with different medical insurance types were seen at another public hospital. The protocol used at Tawam Hospital for DKA diagnosis and treatment is derived from international guidelines [9, 10]. Using the American Diabetes Association (ADA) criteria, the DKA severity was classified according to the pH value as mild (pH 7.3–7.25), moderate (pH 7.24–7), or severe (pH <7) [9]. The diabetes types were diagnosed on the basis of history and/or autoantibodies statuses. We extracted data on patients’ sociodemographic characteristics, initial clinical and chemical findings on admission, and DKA outcomes from the electronic medical records. We also retrieved data on comorbidities and complications from the medical notes and confirmed them using the list of persistent and acute problems in the electronic medical records. Finally, for every case, we recorded the hemoglobin A1c (HbA1c) value on admission or within the preceding 3 months. The Tawam Human Research Ethics Committee approved the study (MF2058-2022-826) and waived the requirement for consent due to the retrospective nature of the study, the anonymity of the data collection, and the lack of interventions.
## Statistical analyses
We described categorical variables as proportions and continuous variables as means ± standard deviations (SDs). The means between two groups were compared using the Student’s t-test, and the means among more than three groups were compared using the one-way ANOVA and the least significant differences test. In addition, we applied Fisher’s exact test (two-tailed) to compare proportions. All results were performed using SPSS for Windows v28.0 (IBM, Armonk, NY, USA). p-values < 0.05 (two-sided) were considered as statistically significant.
## Results
We collected data from 220 consecutive patients with DKA at Tawam Hospital. Table 1 lists the patients’ sociodemographic characteristics upon admission. The average age was 30.6 ± 16.6 years (range, 16–87 years); $54.5\%$ ($\frac{120}{220}$) of patients were women; $77.7\%$ ($\frac{171}{220}$) were Emirati nationals. Overall, $77.9\%$ ($\frac{169}{217}$) of the patients had T1DM, and $22.1\%$ ($\frac{48}{217}$) had T2DM. On admission, $12.7\%$ ($\frac{28}{220}$) of the patients were newly diagnosed as having diabetes. The mean duration of diabetes in patients diagnosed prior to admission was 10.4 ± 9.3 years, and $62.3\%$ ($\frac{134}{215}$) had had previous DKA admissions. The proportion of patients with a previous history of DKA was significantly lower in non-Emirati individuals than in Emirati nationals ($28.3\%$ vs. 71.6, $p \leq 0.001$). Figure 1 depicts the increase in monthly DKA admissions after April 2020 compared to matched months over the previous years due to service reallocations during the SARS-CoV-2 pandemic. Figure 2 shows the increase in DKA episodes in individuals with T2DM over the study period.
The initial clinical and biochemical findings recorded in DKA admissions are described in Table 2. The top two precipitating factors were treatment noncompliance ($31.4\%$), and infections ($26.4\%$; mostly respiratory and urinary tract infections). We found 10 patients with T2DM and SGLT2 (sodium/glucose cotransporter 2) inhibitor-associated DKA. On admission, the mean random serum glucose level was 24.0 ± 8.0 mmol/L and the mean HbA1c level was 11.0 ± $2.7\%$. Table 3 presents the DKA outcomes in this cohort. The mean length of hospital stay was 5.8 ± 8.8 days, and the total in-hospital mortality rate among patients was 4 ($1.8\%$). We found $31.4\%$ ($\frac{69}{220}$) mild, $50.9\%$ ($\frac{112}{220}$) moderate, and $17.7\%$ ($\frac{39}{220}$) severe DKA cases.
The clinical characteristics of the diabetes type groups in the study population are shown in Table 4. Patients with T1DM were significantly younger than those with T2DM (23.9 vs. 53.6 years, $p \leq 0.001$). Among Emirati patients with DKA, most of them had T1DM, whereas T2DM was more common among the non-Emirati patients with DKA. Most patients with T1DM ($76.4\%$) had a previous history of DKA, as compared to only $17\%$ of the patients with T2DM ($p \leq 0.001$). The proportion of patients with microvascular and macrovascular complications, and chronic kidney disease (CKD), was higher in the T2DM group, and most patients requiring ICU admission belonged to the T2DM group as well. The average lengths of stay in hospital were 12.1 days for patients with T2DM and 4.1 days for patients with T1DM ($p \leq 0.001$). The mortality rate was highest in the T2DM group at $6.3\%$, whereas the recorded mortality rate in patients with T1DM was $0.6\%$ ($$p \leq 0.035$$). The proportions of complications observed in patients with T1DM ($18.9\%$) and T2DM ($52.1\%$) differed significantly ($p \leq 0.001$).
**Table 4**
| Characteristic | Type 1 (n = 169) | Type 2 (n = 48) | p-value |
| --- | --- | --- | --- |
| Age (years), mean ± SD | 23.9 ± 7.7 | 53.6 ± 18.9 | <0.001 |
| Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) |
| FemaleMale | 96 (56.8)73 (43.2) | 24 (50.0)24 (50.0) | 0.416 |
| New diagnoses, n (%) | 20 (11.8) | 7 (14.6) | 0.623 |
| Nationality, n (%) | Nationality, n (%) | Nationality, n (%) | Nationality, n (%) |
| EmiratiNon-Emirati | 140 (82.8)29 (17.2) | 30 (62.5)18 (37.5) | 0.005 |
| Diabetes duration (years), mean ± SD (n = 170) | 9.3 ± 6.4 | 15.4 ± 16.2 | 0.049 |
| Baseline medication | Baseline medication | Baseline medication | Baseline medication |
| Metformin, n (%)*Sulfonylurea, n (%)DDP4i, n (%)*SGL2i, n (%)GLP-1 agonist, n (%)Insulin, n (%) | 1 (0.6)0 (0.0)1 (0.6)0 (0.0)0 (0.0)149 (88.2) | 24 (50.0)10 (20.8)15 (31.3)10 (20.8)5 (10.4)26 (54.2) | <0.01<0.01<0.01<0.01<0.01<0.01 |
| Previous DKA, n (%) (n = 212) | 126 (76.4) | 8 (17.0) | <0.001 |
| Comorbidities | Comorbidities | Comorbidities | Comorbidities |
| Microvascular complications, n (%)Macrovascular complications, n (%)CKD, n (%)Active Cancer, n (%) | 32 (18.9)0 (0.0)6 (3.6)1 (0.6) | 18 (37.5)8 (16.7)11 (22.9)5 (0.4) | 0.011<0.001<0.0010.002 |
| Initial blood test results | Initial blood test results | Initial blood test results | Initial blood test results |
| HbA1c (%) ± SD (n = 206)Serum glucose (mmol/L) ± SDSerum Na (mmol/L) ± SD | 11.2 ± 2.824.2 ± 7.9132.7 ± 4.6 | 10.2 ± 2.323.0 ± 7.6133.5 ± 6.2 | 0.0180.3450.378 |
| DKA severity, n (%) | DKA severity, n (%) | DKA severity, n (%) | DKA severity, n (%) |
| MildModerateSevere | 60 (35.5)79 (46.7)30 (17.8) | 9 (18.8)31 (64.6)8 (16.7) | 0.060 |
| Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis |
| ICU admission, n (%)Time to DKA resolution, (h) ± SD(n = 212)Length of hospital stay, (days) ± SDIn-hospital mortality, n (%)Mechanical ventilation, n (%)Complications, n (%) | 119 (70.4)17.7 ± 14.4 4.1 ± 4.51 (0.6)2 (1.2)32 (18.9) | 41 (85.4)24.1 ± 19.7 12.1 ± 15.33 (6.3)9 (18.8)25 (52.1) | 0.0410.040 <0.0010.035<0.001<0.001 |
Table 5 shows the comparison in clinical characteristics among the mild, moderate, and severe groups of patients with DKA. Most patients with mild DKA were women, whereas most in the moderate group were men. We found a shorter duration of diabetes in patients with severe DKA than in those with mild and moderate DKA (5.7 vs. 11.0 vs. 11.7 years, respectively, $$p \leq 0.007$$). Interestingly, the mild DKA group had a higher proportion of patients with previous DKA episodes than the moderate DKA group ($72.1\%$ vs. $53.7\%$).
**Table 5**
| Characteristic | Mild (n = 69) | Moderate (n = 112) | Severe (n = 39) | p-value |
| --- | --- | --- | --- | --- |
| Age (years), mean ± SD | 28.8 ± 13.7 | 32.9 ± 18.6 | 27.5 ± 14.3 | 0.117 |
| Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) |
| FemaleMale | 46 (66.7)23 (33.3) | 54 (48.2)58 (51.8) | 20 (51.3)19 (48.7) | 0.047a |
| Nationality, n (%) | Nationality, n (%) | Nationality, n (%) | Nationality, n (%) | Nationality, n (%) |
| EmiratiNon-Emirati | 57 (82.6)12 (17.4) | 85 (75.9)27 (24.1) | 29 (74.4)10 (25.6) | 0.479 |
| Insurance status (yes), n (%) | 63 (91.3) | 93 (83.0) | 32 (82.1) | 0.244 |
| Job status, n (%) (n = 204) | Job status, n (%) (n = 204) | Job status, n (%) (n = 204) | Job status, n (%) (n = 204) | Job status, n (%) (n = 204) |
| StudentEmployedUnemployed | 31 (48.4)24 (37.5)9 (14.1) | 43 (41.7)29 (28.2)31 (30.1) | 21 (56.8)10 (27.0)6 (16.2) | 0.099 |
| Diabetes type, n (%) (n = 217) | Diabetes type, n (%) (n = 217) | Diabetes type, n (%) (n = 217) | Diabetes type, n (%) (n = 217) | Diabetes type, n (%) (n = 217) |
| Type 1Type 2 | 60 (87.0)9 (13.0) | 79 (71.8)31 (28.2) | 30 (78.9)8 (21.1) | 0.060 |
| Diabetes duration (years), mean ± SD (n = 171) | 11.0 ± 7.2 | 11.7 ± 11.3 | 5.7 ± 3.6 | 0.007bc |
| Previous DKA, n (%) (n = 215) | 49 (72.1) | 58 (53.7) | 27 (69.2) | 0.033a |
| Presenting symptoms | Presenting symptoms | Presenting symptoms | Presenting symptoms | Presenting symptoms |
| Nausea and vomiting, n (%)Abdominal pain, n (%)Shortness of breath, n (%)Polyuria and polydipsia, n (%)Weight loss, n (%)Impaired loss of consciousness, n (%)Fever, n (%)Newly diagnoses, n (%) | 48 (69.6)39 (56.5)9 (13.0)13 (18.8)4 (5.8)2 (2.9)15 (21.7)10 (14.5) | 80 (71.4)62 (55.4)8 (7.1)19 (17.0)6 (5.4)15 (13.4)17 (15.2)14 (12.5) | 28 (71.8)24 (61.5)14 (35.9)5 (12.8)1 (2.6)14 (35.9)1 (2.6)4 (10.3) | 0.9800.827<0.001bc 0.7410.843<0.001bc 0.017b 0.892 |
| Vital signs at presentation | Vital signs at presentation | Vital signs at presentation | Vital signs at presentation | Vital signs at presentation |
| Weight (kg) ± SDBMI (kg/m²) ± SD (n = 212)SBP (mmHg) ± SDHeart rate (beats per minute) ± SDRespiratory rate (breaths per minute) ± SD | 64.2 ± 20.224.9 ± 7.2119.6 ± 23.5106.0 ± 23.521.0 ± 7.0 | 64.0 ± 18.724.0 ± 6.5122.4 ± 21.6113.9 ± 20.321.7 ± 4.5 | 61.3 ± 17.423.1 ± 5.9130.6 ± 23.7126.5 ± 15.628.3 ± 6.9 | 0.7050.3840.049bc <0.001abc <0.001bc |
| Initial blood test results | Initial blood test results | Initial blood test results | Initial blood test results | Initial blood test results |
| Na (mmol/L) ± SDK (mmol/L) ± SDCL (mmol/L) ± SDUrea (mmol/L) ± SDHCO3 (mmol/L) ± SDAnion gap (mmol/L) ± SDSerum glucose (mmol/L) ± SDHbA1c (%) ± SD (n = 208) | 132.7 ± 3.74.1 ± 0.697.3 ± 6.45.4 ± 4.214.4 ± 3.121.1 ± 5.423.3 ± 8.810.6 ± 2.9 | 133.3 ± 5.24.5 ± 0.997.8 ± 7.08.3 ± 8.78.8 ± 3.626.7 ± 7.223.7 ± 7.610.7 ± 2.4 | 131.7 ± 5.94.7 ± 1.097.3 ± 9.96.1 ± 3.04.3 ± 1.630.2 ± 6.326.2 ± 7.112.6 ± 2.7 | 0.2010.002ab 0.8640.016a <0.001abc <0.001abc 0.166<0.001bc |
| Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis | Outcomes of diabetes ketoacidosis |
| ICU admission, n (%)Time to DKA resolution, (h) ± SD(n = 215)Length of hospital stay, (days) ± SDIn-hospital mortality, n (%)Mechanical ventilation, n (%)Complications, n (%) | 30 (43.5)11.1 ± 9.9 4.4 ± 3.70 (0.0)1 (1.4)8 (11.6) | 94 (83.9)21.1 ± 16.5 7.1 ± 11.54 (3.6)8 (7.1)36 (32.1) | 39 (100.0)28.0 ± 16.7 4.7 ± 4.70 (0.0)2 (5.1)13 (33.3) | <0.001abc <0.001abc 0.086a 0.3030.2250.003ab |
In subgroup analysis stratified by sex, among the women with T1DM, the largest DKA group comprised mild cases when compared to the moderate and severe groups (91.3 vs. 68.5 vs. $85\%$, respectively, $$p \leq 0.014$$), whereas in men, all groups had similar (percentages).
As expected, the severe DKA group had a significantly higher proportion of patients presenting with shortness of breath, impaired level of consciousness (LOC), and ICU (intensive care unit) admissions than the mild and moderate groups. In addition, systolic blood pressure, heart rate, respiratory rate, anion gap, and HbA1c values were higher, and the time to DKA resolution was significantly longer in patients with severe DKA than in those with mild or moderate DKA.
The mean length of hospital stay was significantly longer in the moderate group than in the mild group (7.1 vs. 4.4 days), while compared to both the moderate and severe groups, the proportion of patients with complications was significantly lower in the mild group ($32.1\%$ vs. $33.3\%$ vs. $11.6\%$, respectively).
## Discussion
Traditionally, DKA has been a characteristic complication of T1DM [9] and was once a hallmark for differentiating between cases of T1DM and T2DM. However, with $90\%$–$95\%$ of patients diagnosed with T2DM [11] and with DKA occasionally associated with T2DM, we therefore reviewed DKA hospital admissions for patients with both T1DM and T2DM. In this study, $77.9\%$ of the patients admitted with DKA were patients with T1DM; however, we noted clinical and biochemical differences in DKA between the patients admitted with T1DM and those admitted with T2DM. Patients with T2DM admitted with DKA were older compared to patients with T1DM with longer disease duration and higher comorbidities. Most patients with T2DM were admitted with moderate DKA ($64.6\%$) and $85.4\%$ required ICU admission. We also found statistically significant differences in the outcomes of DKA according to the patients’ DM type with longer hospital stay, recovery time, and higher in-hospital mortality rate at $6.3\%$ ($\frac{3}{48}$) in patients with T2DM compared to T1DM. These findings demonstrate the importance of alerting physicians to early recognition of DKA in at-risk patients with T2DM.
DKA accounts for $14\%$ of the hospital admissions for patients with DM worldwide [3]. Almost one-quarter of the DKA admissions in this study were for patients with T2DM, and we observed a steady increase in DKA admissions for individuals with T2DM from 2017 to 2020. These findings are supported by other studies [5, 12], whereas results from Saudi Arabia (a country within the MENA region) tended to be much lower at $7\%$ [13], and about the same time, a study in Malaysia reported that at least half of their DKA admissions ($51.5\%$) were patients with T2DM [13]. This confirms the existence of epidemiological variability by country (13–16), and this may be reflective of the prevalence of diabetes in that population [17]. The reasons for this variability are multifactorial and multidimensional [18], and they may be influenced by socioeconomic conditions, ethnicity, health systems, body mass index (lower BMI), infections, and delayed management [19]. In our cohort, the highest proportion of T2DM patients with DKA was encountered in the year 2020. This is likely explained by the service redistribution during the COVID-19 pandemic in our city. We previously reported the DKA admissions in our COVID-19-free hospital during the pandemic, and these patients were found to be older with a higher proportion of newly diagnosed diabetes and T2DM, and the majority were non-Emirati nationals (Arabs $17.9\%$ vs. $12.7\%$ and South Asian $20.9\%$ vs. $3.8\%$) [20] when compared to prior to the pandemic [21]. Additionally, we identified a small number of T2DM patients with DKA related to SGLT2i, which is an increasingly recognized risk with SGLT2 inhibitor use [22]. In the 10 DKA cases of T2DM patients who were using SGLT2 inhibitors, 6 had concomitant infections, 1 had insulin omission, and there were no other concomitant risks in 3 patients. There were no T1DM patients using SGLT2 inhibitors.
Sixteen percent of all global diabetes-related fatalities are due to DKA [3]. Our in-hospital mortality rate for patients with T2DM was $6.3\%$, while the overall mortality rate was $1.8\%$. Interestingly, all the patients with T2DM and DKA who died were classified as having moderate DKA. The overall mortality rates varied across different regions of the world, with some documenting lower rates than those in our study [23, 24] and others reporting higher rates [25, 26]. Although speculative, the factors likely associated with the increased risk of mortality in our patients with T2DM when compared to those with T1DM are older age, the presence of comorbidities (macrovascular complications and CKD), high ICU admission rates, long lengths of stay, long times to recovery, and extended mechanical ventilation needs. Similar results were obtained from a study in Pakistan reporting higher mortality in older patients with concomitant comorbidities and T2DM [24]. Interestingly, the presence of severe comorbidities has been found to be a significant independent predictor for mortality in patients with DKA [25, 27]. These factors along with the fact that the moderate DKA group constitutes the majority of DKA cases in T2DM ($64.6\%$) in our cohort could explain the high mortality with the moderate DKA. These findings highlight the importance of vigilant glycemic control monitoring in older patients presenting with T2DM and underlying comorbidities.
With regard to the severity of DKA, most of our DKA admissions were classified as moderate ($50.9\%$) with severity differences noted according to sex and disease duration. Almost $84\%$ of the patients in our cohort were admitted to the ICU. Despite this finding being in concordance with a study by Rashid et al. [ 21], as expected, other studies showed higher rates for ICU admissions for patients with severe DKA [25, 26]. A previous study suggests that classifying DKA correlates with the duration of in-hospital stay, requirement of ICU care, and mortality, and is thus a valuable tool to predict outcomes [23]. In our cohort, the severe DKA cases were associated with longer time to DKA resolution with no difference in length of hospital stay and complication compared to the moderate DKA group. Additionally, there was no difference between the DKA severity groups in regard to in-hospital mortality and the need for mechanical ventilation. These could be related to several factors including hospital-specific indications for ICU admission in DKA cases, patient’s age, and the presence of concomitant comorbidities.
## Limitations
We are aware of the limitations of this study. This was a retrospective observational study limited to patients selected on the basis of electronic medical records from a single-center hospital. Thus, generalizations to the general population should be approached with caution. A study on the pre-pandemic DKA episodes of patients in the other public facility would probably yield comparatively interesting results, given the differences in the cohorts of patients attending the two facilities.
## Conclusion
The main finding of this study is that patients with uncontrolled DM are at risk of being hospitalized with DKA irrespective of their DM type. T1DM are at a higher risk for DKA but those with T2DM are older with more comorbidities and have a higher mortality. Among Emirati patients with DKA, most patients had T1DM, whereas T2DM was more common among non-Emirati patients with DKA, which highlights the ethnic difference in DKA risk with T2DM. SGLT2 inhibitor use in T2DM is a newly contributing factor to DKA in the recent years that needs to be identified especially with expanding indication for non-glycemic use. DKA is a life-threatening but avoidable complication where complications are higher in moderate and severe groups in our cohort. Therefore, physicians and patients should strive towards optimal long-term DM management by taking preventive measures such as self-management education and education regarding DKA risk in both DM types mainly in ethnicities with higher risk and in SGLT2 inhibitor users.
## 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 Tawam Human Research Ethics Committee (MF2058-2022-826). Written informed consent was waived due to the retrospective nature of the study, the anonymity of the data collection, and the lack of interventions.
## Author contributions
All authors were involved in the data collection and the manuscript drafting and finalizing. RA, AS and MA collected the data. SA-S performed the statistical analysis. RA, SA-S and RG provided intellectual input and wrote the manuscript. All the authors reviewed the final draft and have accepted responsibility for its entire content and approved its submission
## 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: Cold Sensitivity in Ulnar Neuropathy at the Elbow - Relation to Symptoms and
Disability, Influence of Diabetes and Impact on Surgical Outcome
authors:
- Malin Zimmerman
- Hanna Peyron
- Ann-Marie Svensson
- Katarina Eeg-Olofsson
- Erika Nyman
- Lars B. Dahlin
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2021
pmcid: PMC10012061
doi: 10.3389/fcdhc.2021.719104
license: CC BY 4.0
---
# Cold Sensitivity in Ulnar Neuropathy at the Elbow - Relation to Symptoms and Disability, Influence of Diabetes and Impact on Surgical Outcome
## Abstract
Cold sensitivity, an abnormal response to exposure to cold, is debilitating. It often affects people with nerve injuries and diabetes. Knowledge about the occurrence and prognostic impact of cold sensitivity in people with ulnar neuropathy at the elbow (UNE) is limited. We aimed to investigate the occurrence of cold sensitivity in UNE in relation to disability, the influence of diabetes and impact on surgical outcome. Data concerning 1270 persons operated on for UNE from 2010-2016 from the Swedish National Register for Hand Surgery (HAKIR) were matched with data from the Swedish National Diabetes Register (NDR). Disability and symptoms were assessed preoperatively, and at three and 12 months postoperatively using QuickDASH and a symptom-specific survey (HQ-8) containing one item regarding cold sensitivity. Differences regarding grade of cold sensitivity, occurrence of diabetes, QuickDASH scores and HQ-8 scores were studied. A linear regression analysis was performed to predict surgical outcome based on preoperative cold sensitivity. The mean age of the cases was 52 ± SD 14 years and $48\%$ were women. Preoperatively, 427 answered the questionnaire. Severe cold sensitivity was present in $\frac{140}{427}$ ($33\%$) cases, moderate in $\frac{164}{427}$ ($38\%$) and mild in $\frac{123}{427}$ ($29\%$) cases. Cases with severe preoperative cold sensitivity reported higher QuickDASH scores at all times compared to cases with mild cold sensitivity. Relative change in QuickDASH scores over time did not differ between the groups. Cases with diabetes reported worse cold sensitivity preoperatively, but not postoperatively. All HQ-8 items improved with surgery, but cases with severe cold sensitivity reported worse persisting symptoms. Cold sensitivity is a major problem among those with UNE and an even greater preoperative problem among people with diabetes. It is associated with more symptoms and disability pre- and post-operatively. All cases, regardless of preoperative degree of cold sensitivity improve with surgery.
## Introduction
Cold sensitivity, an abnormal painful response to cold exposure, is a debilitating condition, which often occurs after hand trauma, as a result of peripheral nerve injuries, and in carpal tunnel syndrome (CTS) [1]. Cold sensitivity is also a symptom of ulnar neuropathy at the elbow (UNE) [2]. UNE has an incidence rate of 21-$\frac{30}{100}$ 000 person-years [3, 4]. It occurs when the ulnar nerve becomes compressed at elbow level for example beneath the Osborne’s ligament. When the nerve is compressed disruptions occur in the intraneural microcirculation, leading to epineural oedema and dynamic ischemia, inhibition of axonal transport and subsequent structural changes in the nerve fibres with axonal degeneration. The ischemia triggers early symptoms of UNE, such as paraesthesia and numbness, and later permanent sensory and motor loss may be seen, possibly caused by axonal degeneration [5]. The structural axonal alterations in UNE, which may involve nerve fibres of different sizes, i.e. myelinated and non-myelinated nerve fibres, and their relation to the pathophysiology of cold sensitivity, remain unknown [6]. Cold sensitivity is more common in people with diabetes, BMI >25, rheumatic diseases and in women [2, 7]. It is associated with reduced quality of life and characterized by pain, stiffness, weakness, numbness and colour changes in the affected body part [8, 9]. The causes for the occurrence of cold sensitivity in UNE and the influence of diabetes as well as the effects of surgery are not entirely clear. The aim of this study was to investigate the presence of cold sensitivity in those with UNE at the elbow requiring surgical treatment, its relation to other symptoms and disability, the influence of gender and diabetes, as well as the effects of surgical treatment.
## Data Sources
All patients ≥18 years operated on for UNE (identified through the ICD-10 code G562 and surgical KKÅ97 codes ACC53, ACC43 or NCK19) from 2010-2016 registered in the Swedish National Quality Register for Hand Surgery (HAKIR) were included in the study. Patients completed the Swedish version of the QuickDASH questionnaire [10] and a symptom questionnaire (HAKIR questionnaire-8, HQ-8) before and three and 12 months after surgery [11]. If the questionnaires were not digitally returned after two days, one reminder was sent by text message [12]. The HQ-8 questionnaire comprises eight questions concerning: pain on load; pain on motion without load; pain at rest; stiffness; weakness; numbness; cold sensitivity; and ability to perform daily activities [11]. The answers to each question are reported on a Likert scale. Check boxes are marked with 0, 10, 20…100; i.e. 100 signifies the worst possible perceived symptom. Based on answers to the relevant question in HQ-8, we have earlier defined three degrees of severity of cold sensitivity; mild (score <30), moderate (30–70) and severe (>70) [1]. The QuickDASH is an 11-item questionnaire which provides overall information about the patient’s perceived disability in upper extremity musculoskeletal disorders. It is a shortened version of the well-established 30-item DASH-questionnaire and is considered similarly accurate [13]. Each question is scored one to five. A total score for the entire questionnaire ranges from zero to 100, where 100 signifies the worst possible disability. The presence of diabetes at surgery and preoperative data on HbA1c levels were collected from the NDR. The NDR was started in 1996 and today includes the majority of people with diabetes in Sweden [14]. Personal identification numbers were used to link data from the HAKIR and the NDR.
## Statistics
Normally distributed data are presented as mean ± standard deviation (SD); non-parametric data as a median [interquartile range, IQR] and nominal data as a number (%). For comparisons of nominal data, we used the Chi-squared test. The Kruskal Wallis test and Mann-Whitney U test were used for group comparisons. Linear regression analysis was used to study the effect of cold sensitivity on QuickDASH scores and the effect of diabetes on cold sensitivity scores. The linear regression analyses were adjusted for age, sex and diabetes (when applicable). Spearman’s correlation was used to assess correlation between preoperative cold sensitivity and preoperative total QuickDASH score. We interpreted an r-value of 0.30-0.70 as a moderate correlation and >0.70 as a strong correlation. Each operated arm was considered a separate statistical entity. A p-value of <0.05 was considered statistically significant. IBM SPSS Statistics (version 27; SPSS Inc., Chicago, IL, USA) was used for all statistical calculations and spider diagrams were created in Microsoft Excel for Mac, version 16.16.27 (Microsoft).
## Ethics
The Regional Ethics Review Board in Lund, Sweden approved the study ($\frac{2016}{931}$, $\frac{2018}{57}$ and $\frac{2018}{72}$). Everyone provides informed consent before being registered in HAKIR and NDR.
## Study Population
In total, 1354 surgeries for UNE were identified in HAKIR during the given period. Eight cases were excluded as they were under 18 years of age. As 76 persons were operated on bilaterally, our study included 1346 cases, from 1270 people, where the ulnar nerve was subjected to surgery at the elbow. Mean age at surgery was 52 ± SD 14 years and $\frac{649}{1346}$ ($48\%$) were women (Table 1). Concomitant procedures were performed in $\frac{201}{1245}$ ($16\%$) cases (data were missing in one case). The most common concomitant procedures were carpal tunnel release ($\frac{85}{201}$, $42\%$), trigger finger release ($\frac{15}{201}$, $7\%$) and surgery for ulnar nerve compression in Guyon’s canal ($\frac{14}{201}$, $7\%$). More than half of the cases, $\frac{735}{1346}$ ($55\%$), underwent surgery during the winter period (October-Mars), and $\frac{611}{1346}$ ($45\%$) during the summer period (April-September).
**Table 1**
| Unnamed: 0 | All (n = 1346) | No diabetes (n = 1186) | Diabetes (n = 160) | P-value |
| --- | --- | --- | --- | --- |
| Age, years | 52 ± 14 | 51 ± 14 | 60 ± 11 | <0.0001 |
| Women, n (%) | 649 (48) | 586 (49) | 63 (39) | 0.018 |
| Concomitant procedures, n (%) | 116 (9) | 92 (8) | 24 (15) | 0.225 |
| Mild cold sensitivity (<30) preoperative n (%) | 123/427 (29) | 115/384 (30) | 8/43 (19) | 0.16 |
| Moderate cold sensitivity (30–70) preoperative n (%) | 164/427 (38) | 153/384 (40) | 11/43 (26) | 0.071 |
| Severe cold sensitivity (>70) preoperative n (%) | 140/427 (33) | 116/384 (30) | 24/43 (56) | 0.001 |
| Preoperative cold sensitivity score | 60 [20-80] (n=427) | 55 [20-80] (n=384) | 76 [39-91] (n=43) | 0.006 |
| 3 months postoperative cold sensitivity score | 20 [1-60] (n=319) | 20 [1-60] (n=282) | 20 [4-54] (n=37) | 0.69 |
| 12 months postoperative cold sensitivity score | 30 [3-70] (n=305) | 30 [3-70] (n=265) | 40 [1-70] (n=40) | 0.82 |
| Change in cold sensitivity score 0-12 months | 3 [-5-31] (n=113) | 2 [-5-31] (n=101) | 16 [-14-52] (n=12) | 0.63 |
## Responders vs. Non-Responders
Response rates preoperatively and at three and 12 months postoperatively were $\frac{451}{1346}$ ($34\%$), $\frac{325}{1277}$ ($25\%$) and $\frac{307}{1081}$ ($28\%$), respectively. There were no differences in diabetes prevalence between responders and non-responders at any time point (data not shown). At three months postoperatively, responders were slightly older than non-responders. At 12 months postoperatively, there were more women among the responders than among non-responders and responders were a little older than non-responders (data not shown).
## Persons With and Without Diabetes
The 1346 cases included in the cohort comprised $\frac{160}{1346}$ ($12\%$) persons with diabetes at the time of surgery (Table 1). Those with diabetes reported greater preoperative cold sensitivity scores (76 [39-91]) than those without diabetes (55 [20-80]; $$p \leq 0.006$$), but there were no postoperative differences (Table 1). In the linear regression analysis, diabetes predicted a higher preoperative cold sensitivity score [unstandardized B 12.5 (1.7-23.7), $$p \leq 0.023$$)], but diabetes had no effect on postoperative scores (three months postoperative unstandardized B -0.71 (-12.1-10.7); $$p \leq 0.90$$ and 12 months postop -2.15 (-13.5-9.2); $$p \leq 0.71$$). We found no differences in HbA1c values, retinopathy frequency, BMI or duration of diabetes between persons with diabetes and moderate or severe cold sensitivity compared to those with diabetes and mild cold sensitivity (data not shown).
## Severity of Cold Sensitivity
Compared to cases with mild cold sensitivity, cases with severe cold sensitivity scored higher on the QuickDASH questionnaire at three and 12 months postoperatively (Table 2). Change in QuickDASH scores from preoperative to postoperative did not differ between groups (Table 2). In the linear regression analysis, moderate and severe preoperative cold sensitivity predicted higher preoperative QuickDASH scores compared to mild cold sensitivity [unstandardized B 11.8 ($95\%$ CI 7.2-16.4); $p \leq 0.0001$ and 23.3 (18.3-28.2); $p \leq 0.0001$]. Severe preoperative cold sensitivity predicted higher QuickDASH scores at three [unstandardized B 21.6 (10.7-32.5); $p \leq 0.0001$] and 12 months postoperatively [unstandardized B 18.3 (6.9-29.8); $$p \leq 0.002$$] than mild preoperative cold sensitivity. Moderate, compared to mild, preoperative cold sensitivity did not predict higher QuickDASH scores at three months [unstandardized B 6.5 (-3.7-16.7; $$p \leq 0.21$$], but did at 12 months postoperatively [unstandardized B 11.3 (0.24-22.3; $$p \leq 0.045$$]. All regressions were adjusted for sex, age at surgery and diabetes at surgery. Cold sensitivity scores did not differ between persons with concomitant nerve surgery and the rest of the study population at any time point.
**Table 2**
| Characteristics and QuickDASH | Mild cold sensitivity (n = 139) | Moderate cold sensitivity (n = 148) | Severe cold sensitivity (n = 140) | P-value |
| --- | --- | --- | --- | --- |
| Age, years | 52 [43–61] | 51 [41–61] | 55 [44–64] | 0.06 |
| Women n (%) | 55 (40) | 66 (45) ns | 81 (58)B | 0.007 |
| Diabetes n (%) | 9 (6) | 10 (7) ns | 24 (17)B | 0.003 |
| QuickDASH preoperatively | 32 [18–52](n=139) | 50 [36–64]A (n=147) | 63 [50–75]B (n=136) | <0.0001 |
| QuickDASH 3 months postoperative | 11 [2–33](n=42) | 25 [9–44]A (n=46) | 45 [19–69]B (n=42) | <0.0001 |
| QuickDASH 12 months postoperative | 15 [7–46](n=34) | 34 [17–53]A (n=37) | 45 [23–70] ns(n=39) | 0.003 |
| Change in QuickDASH0–12 months | 8 [-3–20](n=34) | 7 [-1– 21](n=37) | 14 [0–27](n=39) | 0.373 |
## Cold Sensitivity, QuickDASH and HQ-8
We found a moderate correlation between preoperative cold sensitivity and preoperative total QuickDASH score ($r = 0.47$; $p \leq 0.0001$). Preoperatively, persons with moderate and severe cold sensitivity scored higher on all HQ-8 items than those with mild cold sensitivity (Table 3 and Figure 1A). Three and 12 months postoperatively, persons with moderate and severe cold sensitivity still scored higher on most of the HQ-8 items compared to those with mild cold sensitivity (Table 3 and Figures 1B, C). At 12 months postoperatively, the cold sensitivity item was the only HQ-8 item with significantly higher scores in the severe group compared to the moderate group (Table 3).
## Discussion
Cold sensitivity is a common symptom in people requiring surgical treatment for UNE and an even greater preoperative problem among those with diabetes. Since we saw an association and correlation between preoperative cold sensitivity and high preoperative QuickDASH score, our proposal is that cold sensitivity is strongly related to impaired hand function. Overall, this corresponds well to other studies investigating the impact of cold sensitivity [6, 8]. People with moderate or severe cold sensitivity before surgery had worse symptoms and more disabilities after surgery than those who reported mild cold sensitivity preoperatively. However, the change in QuickDASH scores from preoperative to postoperative did not differ between groups, indicating that the absolute improvement after surgical treatment is similar. Hence, there is no reason to avoid surgery in persons with severe cold sensitivity, but they should be informed about the risk of enduring symptoms. People with diabetes did not exhibit more extensive symptoms of cold sensitivity after surgery, in contrast to surgically-treated carpal tunnel syndrome, where those with type 1 and 2 diabetes have more cold sensitivity symptoms at one year, but not at five, postoperatively [15, 16], indicating that people with diabetes have a delayed recovery.
To achieve the best possible symptom relief after surgery, early surgery in persons with UNE and moderate or severe cold sensitivity might be preferable, in order to hinder the progression of cold sensitivity and restore hand function as soon as possible. Further, we want to emphasize that persons with UNE benefit from surgery irrespective of the grade of cold sensitivity and, therefore, surgery should be considered even in cases with a severe level of cold sensitivity.
Cold sensitivity in nerve compression syndromes seems more common than was previously thought. If our study is assumed to represent all persons with UNE, $67\%$ suffer from moderate or severe cold sensitivity, which is a substantial number. In a previous study, we investigated cold sensitivity in carpal tunnel syndrome (CTS), also by using QuickDASH and HQ-8 questionnaires. The corresponding percentage of people with CTS with moderate and severe cold sensitivity preoperatively was $60\%$ [1]. This indicates a similarly high occurrence of cold sensitivity in the two most common nerve compression syndromes in the upper extremity. Another small study ($$n = 100$$), using the Cold Intolerance Symptom Severity (CISS) questionnaire to evaluate the occurrence of cold sensitivity in both UNE and CTS, found that $52\%$ of those included suffered from cold sensitivity [2]. To our knowledge, few studies have been performed on the presence of cold sensitivity in nerve compression syndromes. Since QuickDASH, which is often used clinically, does not evaluate cold sensitivity, there is a risk that cold sensitivity symptoms go unrecorded. Further, as the occurrence of cold sensitivity among people with conservative treatment is unknown this could be the subject for future research.
People with diabetes suffered from worse cold sensitivity preoperatively than those without diabetes. There were also significantly more persons with diabetes in the group with severe cold sensitivity than in the groups of mild and moderate cold sensitivity. These findings correspond to the findings of Wendt et al. [ 2], who concluded that people with diabetes in UNE or CTS report worse cold sensitivity. It is possible that diabetes increases symptoms of cold sensitivity in the presence of a nerve compression lesion. Persons with and without diabetes recovered after surgery to the same extent regarding cold sensitivity symptoms. Surgical treatment for UNE may thus be recommended in persons with diabetes and cold sensitivity.
According to Swedish studies, cold sensitivity affects 5-$14\%$ of the normal population, with the highest prevalence in the northern, and generally colder, parts of Sweden [17, 18]. It is plausible that the same should apply to people with UNE and cold sensitivity. Since our study included persons from the whole of Sweden, the risk of selection bias is reduced thus strengthening our findings. It would also be interesting to compare our results with a study performed in a warmer country.
Despite our research being based on the largest available register in Sweden concerning UNE, the main limitation of our study is the low response rate. Probably, this is partly due to the difficulty in motivating people to answer questionnaires. As sample size decreases, sub-analyses become more difficult to power properly, and a low response rate elevates the risk of selection bias. Motivation might drive some people to be more likely to participate than others. For instance, those with worse symptoms may be more prone to respond preoperatively, and postoperatively; it is possible that very pleased or dissatisfied people are more interested in answering surveys. Unfortunately, this potential bias is hard to fully evaluate in retrospect. Instead, we compared the characteristics of responders and non-responders. We saw no differences preoperatively, thus the surveys seem to be equally accessible for all ages. However, since scoring on QuickDASH differs between sexes and ages, the increased age and the majority of women among responders postoperatively might skew the results. To circumvent that as far as possible, we adjusted the linear regression analysis for sex and age. We also had no data on preoperative neurography testing; thus, no information about the presence of axonal degeneration. In future studies, it could be valuable to study the correlation between preoperative neurography values and other parameters e.g. function of different types of nerve fibres with various diameters, and cold sensitivity as well as prediction of outcome.
## Conclusion
Cold sensitivity is common in people with UNE and those with diabetes report even more preoperative problems than the rest of the population. The severity of cold sensitivity improves after surgical treatment of UNE in all patients regardless of diabetes. A higher preoperative degree of cold sensitivity is associated with more perceived disability both pre- and post-operatively.
## Data Availability Statement
Public access to data is restricted by the Swedish Authorities (Public Access to Information and Secrecy Act; https://government.se/information-material/$\frac{2009}{09}$/public-access-to-information-and-secrecy-act/), but data can be available for researchers after a special review that includes approval of the research project by both an Ethics Committee and the authorities’ data safety committees.
## Ethics Statement
The studies involving human participants were reviewed and approved by Regional Ethical Review Board in Lund, Sweden $\frac{2016}{931}$, $\frac{2018}{57}$ and $\frac{2018}{72.}$ The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
All authors interpreted the data and critically reviewed the report. MZ and HP collected and analyzed the data as well as drafted the first manuscript. A-MS and KE-O were responsible for collecting the data from the diabetes register. MZ, EN, and LD generated the hypothesis and outline of the project. A-MS and KE-O contributed to hypothesis generation and to writing the manuscript. All authors fulfilled the criteria for authorship. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by grants from Lund University, the Swedish Diabetes Foundation, Kockska stiftelsen, Skåne University Hospital, and by ALF Grants in Region Skåne and Region Östergötland (register number LIO-823361), Sweden.
## 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: Assessment of Serum Free Light Chains as a Marker of Diabetic Nephropathy;
A Cross-Sectional Study in the Kumasi Metropolis
authors:
- Elizabeth Sorvor
- William K. B. A. Owiredu
- Perditer Okyere
- Max Efui Annani-Akollor
- Sampson Donkor
- Richard Bannor
- Felix B.K. Sorvor
- Richard K.D. Ephraim
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012065
doi: 10.3389/fcdhc.2022.881202
license: CC BY 4.0
---
# Assessment of Serum Free Light Chains as a Marker of Diabetic Nephropathy; A Cross-Sectional Study in the Kumasi Metropolis
## Abstract
### Aims
Although traditional tests such as serum urea, creatinine, and microalbuminuria have been widely employed in the diagnosis of diabetic nephropathy, their sensitivity and accuracy are limited because kidney damage precedes the excretion of these biomarkers. This study investigated the role of serum free light chains in the disease manifestation of diabetic nephropathy.
### Materials and Methods
Using a cross-sectional design we recruited 107 diabetes mellitus out-patients who visited the Diabetes and Renal Disease Clinics at the Komfo Anokye Teaching Hospital, Manhyia District Hospital, and Suntreso Government Hospital all in Ghana from November 2019 to February 2020. Five [5] mls of blood was collected from each participant and analyzed for fasting blood glucose (FBG) urea, creatinine, immunoglobulin free light chains. Urine samples were obtained and analyzed for albumin. Anthropometric characteristics were also measured. Data were analyzed using descriptive analysis, analysis of variance (ANOVA) test, Tukey HSD post hoc, and Kruskal Wallis test. Chi-squared test was used to examine if there are significant associations with the indicators of interest. In addition, Spearman’s correlation was used to test for associations between appropriate variables. Receiver operating characteristic analysis (ROC) was also performed to assess the diagnostic performance of free light chains.
### Results
The mean age of studied participants was 58.2 years (SD: ± 11.1), $63.2\%$ were females and most of the participants were married ($63.0\%$). The mean FBG of the studied participants was 8.0mmol/L (SD: ± 5.86), and the average duration of diabetes mellitus (DM) was 11.88 years (SD: ± 7.96). The median serum Kappa, Lambda, and Kappa: Lambda ratios for the studied participants were 18.51 (15.63-24.18), 12.19(10.84-14.48), and 1.50(1.23-1.86) respectively. A positive correlation was observed between albuminuria and; Kappa (rs=0.132; $$p \leq 0.209$$), and Lambda (rs=0.076; $$p \leq 0.469$$). However, a negative correlation was observed between albuminuria and K: L ratio (rs=-0.006; $$p \leq 0.956$$).
### Conclusions
The current study observed an increasing trend in the levels of free light chains and degree of diabetic nephropathy, although not statistically significant. The exploration of serum free light chains as a better marker of diabetic nephropathy showed very promising results but further studies are required to elucidate its predictive value as a diagnostic tool for diabetic nephropathy.
## Introduction
Diabetic nephropathy is one of the most significant complications of diabetes mellitus and it is known to be responsible for about 40-$50\%$ of the cases of end-stage kidney failure [1]. The prevalence of diabetic nephropathy is increasing with the diabetes mellitus epidemic; one-third to half of the patients with diabetes mellitus develop kidney problems [2]. Diabetic nephropathy is linked with end-stage kidney failure, cardiovascular disease, and increased healthcare costs [3]. Diabetic nephropathy is more prevalent among patients with diabetes mellitus in Africa and Ghana for that matter [4, 5] compared with their counterparts in the developed world [2] as a result of delayed diagnosis, limited screening, and diagnostic resources, poor glycaemic control and inadequate treatment at an early stage [6, 7].
Although diabetic nephropathy has been traditionally known to be a non-immune disease, recent studies have shown that immunity and inflammation are vital factors in its development and progression [8, 9]. Free light chains are low molecular weight proteins and by-products of immunoglobulin synthesis (10–12). Free light chains are produced in excess during immunoglobulin synthesis and are cleared from the serum by the kidneys; therefore a decrease in glomerular filtration rate leads to an increase in free light chains [12]. There are two forms of free light chains namely, kappa(k) and lambda(λ) [13]. The serum concentration of free light chains depends on the balance between production and clearance by the kidneys [13]. Free light chains have been shown to have a stronger relationship with renal inflammation than other inflammation markers like CRP (13–15).
Although microalbumin has been widely employed in the diagnosis of diabetic nephropathy, its sensitivity and accuracy are limited because kidney damage precedes albumin excretion [8, 16]. Recent studies have demonstrated that the free light chain is a better marker for the risk of developing kidney damage than other markers of kidney function [10]. A study conducted in the United Kingdom demonstrated that serum concentrations of free light chains increased progressively with chronic kidney disease (CKD) stage and also correlated strongly with renal function tests including cystatin C [17]. Another study carried out in the UK among type two diabetes mellitus patients indicate significantly elevated levels of free light chains (FLC) before the development of overt renal impairment, signifying their possible role in predicting early diabetic nephropathy [13, 18, 19].
Although some studies have been conducted to explore the efficacy of free light chains in the diagnosis of kidney diseases, only a few have been done in Africa and for that matter Ghana. There is also limited information on this marker with diabetic nephropathy. This current study, therefore, seeks to investigate serum free light chain as a marker in the diagnosis of diabetic nephropathy among diabetes mellitus patients in the Ghanaian context.
## Study Design, Sampling Technique, Study Area, and Population
This cross-sectional study was conducted from November 2019 to February 2020. The study comprised a total of 107 diabetes mellitus patients aged between eighteen [18] to eighty-five years [85]. The study was conducted in three major hospitals in the Kumasi metropolis; Komfo Anokye Teaching Hospital (KATH), Suntreso Government Hospital, and Manhyia Government Hospital. The sample population included diabetes mellitus out-patients without nephropathy, and clinically diagnosed diabetic nephropathy patients attending the Diabetes Clinic and Renal Disease Clinics in the hospitals listed as well as those who qualified per the inclusion criteria and who consented to be part of the study. All diabetic and non-diabetic patients who did not meet the age criteria and other characteristics of the study and did not consent to the study were excluded from the study. In addition, all study participants who had recent infections, as well as multiple myeloma, were excluded from the study.
Sample size calculation The following formula was used to estimate the sample size for the study.
Where: n = sample size $Z = 1.96$ is the standard score for the confidence interval of $95\%$; p = expected prevalence; d = allowable margin of error = $5\%$ or 0.05. According to a recent unpublished study carried out in the Ashanti region of Ghana, the prevalence of diabetic nephropathy is $11\%$. Substituting these values into the equation gives a sample size of 150 study participants. To provide for a $10\%$ nonresponse rate, 165 participants were recruited for the study. Of the 165 eligible participants recruited using non randomized purposive sampling, 107 finally participated and provided complete data. Fifty eight [58] patients could not participate due to their inability to provide complete set of data [36] and not giving informed consent [22].
## Data Collection
A validated questionnaire was administered to obtain socio-demographic data including age, marital status, sex, education, and occupation from each participant. Information on the medical history including duration and family history of diabetes mellitus as well as side effects of the regular medication of the participants was also collected using their medical records. Anthropometric variables such as height, weight, waist, and hip circumference were measured using a tape measure and an electronic weighing scale, and BMI was calculated. Blood pressure was measured using a sphygmomanometer.
## Blood and Urine Collection and Laboratory Assessments
5ml of blood was collected from each participant through a venipuncture into gel separator tubes. The gel separator tube was centrifuged to obtain the serum and then further aliquoted into 1 ml Eppendorf tubes and stored at -20°C until assayed. In addition, spot urine samples were collected from study participants. The urine samples were analyzed for albumin.
Serum kappa(ĸ) and Lambda(ʎ) free light chain concentrations were measured using the ELISA technique manufactured by Melson Shanghai chemical Ltd, China. The reference interval used was ĸ 3.3 -19.4mg/L; ʎ 5.7 -26.3mg/L and ĸ/ʎ 0.26 -165 [19]. Creatinine and urea were analyzed using the BT 1500 automated chemistry analyzer (Biotecnica Instrument) based on the Jaffe and glutamate dehydrogenase technique respectively [20], and fasting blood glucose (FBG) was also measured [21].
Urine chemistry analysis was carried out using the dipstick and urine albumin was measured using a chemistry analyzer (Le Scientific Horizon 850, USA),. Microalbumin (MALB) reagent [Biobase Industry (Shandong) Co., Ltd.], which consisted of two reagents (R1 and R2) was used in the estimation of microalbuminuria. Participants were categorized as normal, microalbuminuria and macroalbuminuria using the KDIGO criteria; <30 mg/g = normal; 30-300 mg/g = microalbuminuria; >300 mg/g = macroalbuminuria [22]. The creatinine-based Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation was used to estimate GFR for the participants [23, 24].
## Statistical Analysis
Data was recorded into an excel sheet and analyzed using Statistical Packaged for the Social Sciences, SPSS (SPSS version 22.0). Normally distributed continuous data were analyzed using analysis of variants (ANOVA) test and Tukey HSD post hoc. Data that were not normally distributed were presented as median and compared by the Kruskal Wallis test. In the case of categorical variables, the chi-square test was used to examine if there are significant associations with the indicators of interest. In addition, Spearman’s correlation was used to test for associations between appropriate variables.
Receiver Operating Characteristic Analysis (ROC) was performed to assess the diagnostic performance of appropriate variables. Statistically significant associations were determined using the conventional α = 0.05 significance level. Graphical representations of variables were presented with histograms, scatter plots, box plots, and line graphs.
## Sociodemographic Characteristics
This study involved 107 T2DM patients; $36.8\%$ males and $63.2\%$ females. The average age of the entire population was 58.20 ± 11.08 years with most of the participants being married ($63.0\%$). Basic education was the highest level of education attained by most of the study participants ($52.6\%$), followed by secondary ($32.0\%$), and tertiary ($15.5\%$). Christianity was the predominant religion ($78.1\%$) among the study population. Most of the participants ($52.9\%$) were self-employed, followed by those on pension ($19.5\%$), the unemployed ($17.2\%$), and public sector workers (10.3). The prevalence of microalbuminuria in this study was $79.4.4\%$. The results are presented in Table 1.
**Table 1**
| Characteristic | Characteristic.1 | Total | Non-DN | DN | P-value |
| --- | --- | --- | --- | --- | --- |
| | | 107 (100.0) | 22 (20.6) | 85 (79.4) | |
| Age | | 58.20 ± 11.08 | 61.73 ± 13.19 | 57.27 ± 10.35 | 0.093 |
| | < 40 yrs | 5 (4.7) | 0 (0.0) | 5 (6.0) | |
| | 40 – 49 yrs | 13 (12.3) | 4 (18.2) | 9 (10.7) | |
| | 50 – 59 yrs | 42 (39.6) | 7 (31.8) | 35 (41.7) | |
| | 60 – 69 yrs | 30 (28.3) | 6 (27.3) | 24 (28.6) | |
| | ≥ 70 yrs | 16 (15.1) | 5 (22.7) | 11 (13.1) | |
| Gender | Male | 39 (36.8) | 7 (31.8) | 32 (38.1) | 0.587 |
| | Female | 67 (63.2) | 15 (68.2) | 52 (61.9) | |
| Marital status | Single | 23 (23.0) | 5 (23.8) | 18 (22.8) | 0.922 |
| | Married | 63 (63.0) | 14 (66.7) | 49 (62.0) | |
| | Divorced | 11 (11.0) | 2 (9.5) | 9 (11.4) | |
| | Co-habitation | 1 (1.0) | 0 (0.0) | 1 (1.3) | |
| | Widowed | 2 (2.0) | 0 (0.0) | 2 (2.5) | |
| Education | No education | 51 (52.6) | 12 (63.2) | 39 (50.0) | 0.351 |
| | Basic | 31 (32.0) | 6 (31.6) | 25 (32.1) | |
| | Secondary | 15 (15.5) | 1 (5.3) | 14 (17.9) | |
| | Tertiary | | | | |
| Religion | Christianity | 82 (78.1) | 17 (77.3) | 65 (78.3) | 0.916 |
| | Islam | 23 (21.9) | 5 (22.7) | 18 (21.7) | |
| Occupation | Unemployed | 15 (17.2) | 4 (20.0) | 11 (16.4) | 0.737 |
| | Self-employed | 46 (52.9) | 10 (50.0) | 36 (53.7) | |
| | Public sector | 9 (10.3) | 1 (5.0) | 8 (11.9) | |
| | Pensioner | 17 (19.5) | 5 (25.0) | 12 (17.9) | |
## Anthropometric Characteristics and Medical History
The average weight, height, and BMI of the study population were 70.00kg, 161.87cm, and 26.86 kg/m2 respectively. Most of the respondents had a high waist-to-hip ratio ($83.3\%$) as well as a high waist-to-height ratio ($76.4\%$) using the WHO recommended reference range (W/H Male, 0.9 or less; female 0.85 or less, W/Ht 0.5 or less); with 90.80cm average waist circumference and 95.56cm average hip circumference. The mean FBG of the study population was 8.02 mmol/L (reference range 3.6-6.4) and the average duration of DM was 11.88 years. The majority of the participants were hypertensive ($73.5\%$), had a family history of DM ($63.8\%$), and also presented with high FBG levels ($62.0\%$). The mean systolic and diastolic blood pressures were 136.53 mmHg and 87.78 mmHg correspondingly. No significant association was observed between albuminuria and medical history as well as all the anthropometric characteristics of the study participants (Tables 2 and 3).
## Estimated Glomerular Filtration Rate and CKD Stages Among Study Participants
The average eGFR of the studied participants was 64.80 ± 3.29 ml/min/1.73m2. A significant association was observed between eGFR and DN ($p \leq 0.0001$) across the studied participants (Figure 1). Estimated glomerular filtration rate (eGFR) was significantly lower among patients with macroalbuminuria (25.38 ± 8.23 ml/min/1.73m2) compared to those with microalbuminuria (71.25 ± 3.90 ml/min/1.73m2; $p \leq 0.0001$) and non-DN (67.27 ± 4.91 ml/min/1.73m2; $$p \leq 0.001$$). The prevalence of significant CKD (stages 3-5) among the study population was $44.3\%$; where a larger proportion of the participants ($17.9\%$) were in stage 3a, followed by stage 3b ($10.4\%$), stage 5 ($9.4\%$) and stage 4 ($6.6\%$). Most of the respondents were in stage 2 ($34.0\%$), and stage 1 ($21.7\%$) as shown in Figure 1.
**Figure 1:** *Estimated glomerular filtration rate (A) and CKD stages among the respondents (B).*
## Free Light Chain Profile of Study Participants
The median serum Kappa, Lambda, and Kappa: Lambda ratios for studied participants were 18.51(15.63-24.18), 12.19(10.84-14.48), and 1.50(1.23-1.86) respectively. An increasing trend in Kappa levels was observed with increasing nephropathy; where participants with macroalbuminuria had higher Kappa levels (21.86) followed by those with microalbuminuria (18.42) and then non-DN respondents (16.37), albeit not statistically significant (Figure 1A; $$p \leq 0.191$$). A similar increasing trend was observed for K: L ratio concerning the degree of nephropathy; where participants with macroalbuminuria had a higher K: L ratio (1.56) followed by those with microalbuminuria (1.51) and non-DN respondents (1.42), with no statistical significance (Figure 2; $$p \leq 0.637$$). Nevertheless, an inconsistent trend was observed for Lambda levels; where non-DN respondents had higher levels (13.16) than those with microalbuminuria (11.95), with macroalbuminuria patients recording the highest Lambda levels (35.15). No statistical significance was observed between DN and serum levels of Lambda (Figure 2; $$p \leq 0.782$$).
**Figure 2:** *Free light chain profile of the study participants showing (A) (Median Kappa), (B) (Median Lambda), (C) (Median Kappa:Lambda ratio).*
A positive correlation was observed between albuminuria and; Kappa (rs=0.132; $$p \leq 0.209$$), and Lambda (rs=0.076; $$p \leq 0.469$$). However, a negative correlation was observed between albuminuria and K: L ratio (rs= -0.006; $$p \leq 0.0.956$$). A negative correlation was observed between eGFR and; Kappa (rs= -0.187; $$p \leq 0.072$$), Lambda (rs= -0.092; $$p \leq 0.379$$) and K: L ratio (rs= -0.103; $$p \leq 0.324$$).
## Diagnostic Performance of Free Light Chains
Receiver operating characteristic analysis of Kappa levels showed an optimum cut-off value of 16.46, corresponding to $70.90\%$ sensitivity and $53.30\%$ specificity and an area under the curve of 0.599 to predict albuminuria (i.e. microalbuminuria or macroalbuminuria). For Lambda, a cut-off value of 11.17, a sensitivity of $65.80\%$, a specificity of $40.00\%$, and an AUC of 0.487 were observed to differentiate non-DN status and those with albuminuria. Moreover, Kappa: Lambda ratio also showed a cut-off of 1.43 with a corresponding sensitivity of $65.80\%$, specificity of $53.30\%$, and an AUC of 0.565.
Kappa depicted a sensitivity of $71.10\%$, a specificity of $41.80\%$, a cut-off of 16.83, and an AUC of 0.594 to predict CKD among the studied participants. A slightly higher sensitivity of $73.70\%$ was observed for Lambda, with corresponding specificity, cut-off, and AUC of $41.80\%$, 11.17, and 0.601 respectively to differentiate respondents with CKD from those without CKD. Also, the K: L ratio showed a sensitivity of $64.10\%$, a specificity of $63.20\%$, an AUC of 0.510, and a cut-off value of 1.46 to differentiate respondents with CKD from those without CKD (Table 3). The various ROC curves are presented in Figure 3.
**Figure 3:** *Receiver operating characteristic (ROC) curves for Kappa (A), Lambda (B), and K: L ratio (C) for predicting albuminuria; and Kappa (D), Lambda (E), and K: L ratio (F) for predicting CKD.*
## Discussion
This study evaluated serum free light chains as a marker of diabetic nephropathy. Our findings showed that the concentration of serum Kappa(K) and Lambda(ʎ) increased progressively with diabetic nephropathy (K: rs=0.132; $$p \leq 0.209$$ and ʎ: rs=0.076; $$p \leq 0.469$$), albeit not statistically significant. In addition, serum free light chains showed a positive correlation with serum creatinine and a negative correlation with estimated GFR. Our investigation of the diagnostic performance of the free light chains using ROC analysis indicated $70.90\%$ and $53.30\%$ sensitivity and specificity respectively for Kappa, On the other hand sensitivity of $65.80\%$, and specificity of $40.00\%$ for Lambda for predicting DN.
The availability of immunoassays in the quantification of the concentration of serum free light chains has made it possible for its use in routine diagnosis and follow up [25, 26]. We used these assays to measure the concentration of free light chains in a population of diabetic nephropathy patients and describe the association between serum free light chains, renal function, and disease state.
There was an increase in the median serum concentration of K with regards to the severity of nephropathy, with macroalbuminuric patients having higher levels compared with microalbuminuric and normoalbuminuric patients. On the other hand, an inconsistent trend of λ concentration was observed, in that participants without diabetic nephropathy had higher levels (13.16mg/L) compared with microalbuminuric patients, though macroalbuminuria patients recorded the highest λ levels (35.15mg/L). However, generally there was a positive correlation between serum free light chain concentration and diabetic nephropathy, although not statistically significant. Hutchison et al., in their assessment of serum free light chains in type 2 diabetic patients (South-Asian and Caucasian) observed significantly elevated concentration, even in patients with normal renal function, which is contrary to what was found in this study [19]. Other studies involving CKD patients found elevated levels of free light chains across CKD stages [14, 19]. Fraser et al. in a review also found that elevated concentration of serum FLC is independently associated with a higher risk of ESRD in patients with chronic kidney disease [27]. The reticuloendothelial system becomes an increasingly significant route for FLC clearance when renal clearance decreases accounting for the elevated levels of free light chains associated with the severity of diabetic nephropathy seen in this current study. Excessive free light chain synthesis and subsequent filtration through renal glomeruli can damage renal tubules, resulting in tubular dysfunction, whereas renal failure from any cause can boost serum FLC concentrations due to a lower filtration rate [28].*The* general increase in serum FLC concentrations as seen in this study and other studies may play a pathophysiological function in the course of diabetic nephropathy [19], which will require further scientific inquiry. The findings of this study highlight the importance of further research into the role of FLC in diabetic nephropathy and its application in risk stratification.
We also observed that FLC correlated with markers of kidney function, a positive and a negative correlation was found with serum creatinine concentration and estimated GFR respectively, which was consistent with findings from other studies [12, 14, 15, 19, 29]. FLC levels in the plasma rise in patients with impaired renal function [10]. The current study have demonstrated that the serum concentrations of FLC in patients with diabetic nephropathy increases as renal function declines, making it a probable biomarker of diabetic nephropathy. When compared to other inflammation markers such as CRP, raised FLCs may be a better indicator of the risk of renal injury and have a greater link with renal inflammation [14, 15].
Receiver Operating Characteristic (ROC) was used to determine the diagnostic performance of FLC in detecting DN and CKD. There is a paucity of information on the use of FLC in the diagnosis of diabetic nephropathy in literature and there is little evidence of its use as a predictive technique for early detection of diabetic nephropathy. The sensitivity and specificity scores obtained with regards to FLC in this study are quite low and do not offer a good predictive value for predicting diabetic nephropathy and CKD. There is therefore the need for further studies to ascertain the role of FLC in the pathophysiology of diabetic nephropathy and its usefulness as a biomarker of early diabetic nephropathy. The study however has some limitations. Our findings, though relevant, is limited by the cross sectional design we employed instead of a case control study hence the presence of a significant number of the participants in the later stages of CKD. Again, the cross-sectional design of this study is an intrinsic limitation since it does not allow to estimate the true predictive role of these markers on the onset of DN, as patients already have diabetes mellitus.
## Conclusion
The current study observed an increasing trend in the levels of free light chains and degree of diabetic nephropathy, although not statistically significant. The exploration of serum free light chains as a better and early marker of diabetic nephropathy showed very promising results but further studies are required to elucidate its predictive value as a diagnostic tool for nephropathy.
## 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 Research and Development Unit of Komfo Anokye Teaching Hospital (KATH) Kumasi, Ghana, with reference KATHIRB/AP/$\frac{080}{20.}$ The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
ES, WO, PO and RE designed the study and supervised the work. ES, FS, PO and SD were in charge of major parts of technical aspects of work and participated in the writing of the manuscript. ES, RB and PO participated in the technical work and participated in the interpretation of data. ES, RE, FS participated in the manuscript writing. All authors read and approved the final 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: Periodic health checkups reduce the risk of hospitalization in patients with
type 2 diabetes
authors:
- Hidetaka Hamasaki
- Hidekatsu Yanai
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2023
pmcid: PMC10012066
doi: 10.3389/fcdhc.2023.1087303
license: CC BY 4.0
---
# Periodic health checkups reduce the risk of hospitalization in patients with type 2 diabetes
## Abstract
### Introduction
Periodic health checkups (PHCs) represent a unique system in Japan that is useful for the early detection of lifestyle-related diseases and cardiovascular diseases (CVDs). This study aims to investigate the association of PHCs with the hospitalization risk of patients with type 2 diabetes mellitus (T2DM).
### Methods
A retrospective cohort study was conducted from April 2013 to December 2015 and included participant information such as CVD history, lifestyle, and whether PHC was conducted in addition to regular medical examinations. Difference in clinical data between patients with and without PHC was examined. Furthermore, Cox regression analysis was performed to investigate the independent association of PHCs with hospitalization.
### Results
Herein, 1,256 patients were selected and followed up for 2.35 ± 0.73 years. In the PHC group, body mass index, waist circumference, proportion of patients with a history of CVD, and number of hospitalizations were lower than those in the non-PHC group. Furthermore, the PHC group exhibited a significant association with lower hospitalization risk (hazard ratio = 0.825; $95\%$ confidence interval, 0.684 to 0.997; $$p \leq 0.046$$) in the Cox model.
### Conclusion
This study revealed that PHCs minimized the risk of hospitalization in patients with T2DM. Furthermore, we discussed the effectiveness of PHCs in enhancing health outcomes and reducing health care costs in such patients.
## Introduction
Periodic health checkups (PHCs) that are usually conducted annually represent a unique system in Japan created for the early detection of lifestyle-associated diseases and cancer [1]. The goal of PHCs is to extend the healthy life span of individuals via early detection and treatment of serious ailments, such as cancer and cardiovascular diseases (CVDs), which is the presumed advantage of conducting PHCs. The rate of cancer detection, including that of suspected cancer cases and confirmed cancer diagnoses in examinees, has been estimated as follows: lung cancer, $0.41\%$ and $6.65\%$; gastric cancer, $0.7\%$ and $11.36\%$; and breast cancer, $1.34\%$ and $16.23\%$; respectively [1]. However, overdiagnosis or under diagnosis of the target disease constitute a disadvantage of PHCs [1]. However, the effectiveness of PHCs from the perspective of public health and health economics remains unclear. Hackl et al. [ 2] reported that a general health screening program substantially increased both inpatient and outpatient health care costs in the short-term, reduced outpatient health care cost in the medium-term, and demonstrated no effect on both health care cost and health status in examinees in the long-term. A meta-analysis of 17 randomized trials also showed that health checkups exhibited no effect on all-cause mortality, CVD death rate, and the risk of reducing ischemic heart disease and stroke [3]. Thus, the overall effectiveness of PHCs is controversial. However, the efficacy of this system also depends on the method and frequency of application and the appropriate selection of individuals [4].
Cost escalation of health care is a serious problem worldwide. The global economic burden of T2DM will increase to >$2 trillion by 2030 [5]. The treatment costs of each patient with T2DM and CVDs increase by $3418–$9705 annually compared with patients with T2DM without CVDs [6]. The total cost of cancer treatment has inflated faster than cancer incidence among Europeans [7]. Hence, it is important to detect such serious diseases at an early stage to prevent disease progression and reduce health care costs.
Patients with T2DM exhibit physically weakness [8], susceptibility to infections [9], high prevalence of CVDs [10], and increased risk of cancer incidence [11]; thus, PHCs may contribute to the reduction of health care costs by the early detection and treatment of such serious diseases requiring hospitalization. However, to the best of our knowledge, no studies have investigated the association of PHCs with the hospitalization risk of patients with T2DM. Therefore, this study aims to analyze the association of PHC with the hospitalization risk of T2DM at a regional core hospital in Japan.
## Study design and subjects
This retrospective cohort study was conducted at National Center for Global Health and Medicine (NCGHM), Kohnodai Hospital, a public, secondary care hospital in Japan. Between April 2013 and December 2015, patients who came to the outpatient department and were diagnosed with T2DM were enquired regarding their medical history. Furthermore, data regarding diabetes and lifestyle-related behaviors after obtaining informed consent from the patients were collected from the Biobank for Metabolic Disorders in the NCGHM Kohnodai Hospital [12] and retrospectively analyzed. As a standard guideline, patient information, such as CVD history, smoking habit, drinking habit, exercise regularity, and whether PHCs were conducted in addition to regular medical examinations, was collected. Regular medical examinations included parameters such as physical measurements of the body, blood pressure (BP) measurement, vision and hearing assessment, routine blood and urine examination, chest X-ray, and electrocardiogram, which were performed at first visit. Patients were excluded if their age was <20 years. Participants were recommended to consume a strict low-calorie diet of 25–30 kcal/kg of ideal body weight at the very first visit by a certified nutritionist. The dietary adherence of the patients was confirmed at every consultation, and the patients followed the diet throughout the research duration. All the participants were assessed and followed up till the end of follow-up up (May 2016) or death. At the end of the follow-up, information regarding hospitalization was collected from the medical record review. The number of hospitalizations was calculated for all the participants. PHCs, which were the primary outcome analyzed, were associated with minimized the hospitalization risk of patients with T2DM. The impact of PHCs on the occurrence of CVDs and all-cause mortality was also examined.
The research design was certified by the Medical Ethics Committee of the NCGHM (Reference No. NCGM-G-002052), this research was conducted as per the Declaration of Helsinki.
## PHCs in Japan
PHCs are performed voluntarily and separately from regular medical examinations for patients with T2DM as well as healthy individuals. In Japan, company employees and students have the provision of availing PHCs annually. In addition, Japanese individuals aged between 40 and 74 years are encouraged to undergo annual health checkups that include physical body measurements and examination, BP measurement, blood examination (serum triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, aspartate aminotransferase, alanine aminotransferase, γ-glutamyl transpeptidase, and plasma glucose or hemoglobin A1c [HbA1c]) and urine examination (protein and glucose) to prevent the incidence and progression of lifestyle-associated diseases, such as diabetes mellitus, hypertension, and dyslipidemia [13]. Although the Japanese health insurance system does not cover these PHCs, companies and schools cover the costs of the PHCs of their employees and students, respectively. In addition, individuals can avail free PHC services provided by some health insurances schemes (e.g., National Health Insurance). The components of the examinations vary depending on the type of health checkup. For example, a complete comprehensive health checkup known as “Ningen Dock” is conducted in Japan. It includes detailed radiological investigations, such as ultrasonography, computed tomography, magnetic resonance imaging, and endoscopic investigations, in addition to routine blood and urine examinations [1].
## Anthropometric values and physiological measurements
A rigid stadiometer (TTM stadiometer; Tsutsumi Co., Ltd., Tokyo, Japan) was employed to document the height of the participants and a standard calibrated scale (AD-6107NW; A&D Medical Co., Ltd., Tokyo, Japan) was used to determine the body weight (BW). Body mass index (BMI) was calculated via the standard formula: BW (kg) divided by the square of individual height measured (m). Waist circumference (WC) was measured in centimeter using an inch tape at the umbilical level at the end of expiration from a standing position. A standard automatic sphygmomanometer (HBP-9020; Omron Co., Ltd, Tokyo, Japan) was used to measure the BP in a seated position and sedentary state.
## CVD history, smoking and drinking habits, and physical activity assessment
A trained professional from the Clinical Research Center of the NCGHM at Kohnodai Hospital recorded the CVD history of the patients, comprising information such as stroke, nonfatal coronary artery or peripheral artery disease, and foul habits, including smoking and alcoholism, during the baseline evaluation. To quantify the smoking habits of the participants, their Brinkman index was calculated as the number of cigarettes smoked per day multiplied by the number of years of smoking [14]. Alcohol consumption (AC) was estimated by the type of liquor and amount consumed daily. Furthermore, we enquired regarding the exercise regularity and type, such as jogging, cycling, and calisthenics, of the participants. Using the available information, we calculated the daily exercise duration. Additionally, patients were asked regarding their daily walk-time, except volitional exercise.
## Blood examination
We measured the blood glucose and HbA1c of the participants at the time of enrollment. The glomerular filtration rate (eGFR) of the participants was documented using the revised equation exclusively adjusted for the Japanese patients [15].
## Sample size
As sample size estimation is very vital for such studies, we employed post-hoc sample size estimation using a command comparing survival curves between two independent groups in EZR [16]. During this study period, assuming that the hospitalization rates of patients who did and did not undergo PHCs were $38.2\%$ and $48.2\%$, respectively, the groups were of equal size (1:1 ratio), and the two-tailed level was 0.05. The final sample size was 730 observations needed for a power of $80\%$. This indicates that the selected sample size had the required power to detect the association between PHCs and hospitalization risk.
## Statistical analysis
Data analyses were conducted using SPSS version 25 (IBM Co., Ltd., Chicago, IL). Continuous variables were presented as the mean ± standard deviation (SD). Student’s t test or the Mann–Whitney test, depending on whether the variables followed normal or non-normal distribution, was preferred to find differences between patients who did and did not undergo PHCs, respectively. Categorical variables were expressed as numbers and compared using χ2 test. The Cox proportional hazard equation was used to examine the independent associations of hospitalization, CVD occurrence, and death with PHCs. We included age, gender, BMI, CVD history, AC, Brinkman index for smoking habit, exercise time, systolic BP (SBP), diastolic BP (DBP), blood glucose, HbA1c, and eGFR in the Cox model. The significance level for the study was fixed at <0.05.
## Results
The study recruited 1,256 patients with T2DM, of which 695 were men, 561 were women, and 557 ($44.3\%$) had undergone a PHC. The mean age and BMI were 63.7 ± 13.9 years and 25.5 ± 5.5 kg/m2, respectively. Patient characteristics are presented in Table 1.
**Table 1**
| Demographics | Unnamed: 1 |
| --- | --- |
| N | 1256 |
| Age (years) | 63.7 (13.9) |
| Gender (male/female) | 695/561 |
| Exercise time (min/day) | 16.0 (45.9) |
| AC (g ethanol per day) | 18.4 (32.3) |
| Smoking habit (Brinkman index) | 329.6 (550.2) |
| History of CVDs | 174 |
| Stroke | 91 |
| Myocardial infarction | 92 |
| Peripheral artery disease | 10 |
| Duration of T2DM (years) | 11.7 (11.0) |
| Anthropometric data | Anthropometric data |
| BMI (kg/m2) | 25.5 (5.5) |
| WC (cm) | 92 (13.7) |
| Physiological and biochemical data | Physiological and biochemical data |
| SBP (mmHg) | 131.2 (19.9) |
| DBP (mmHg) | 73.6 (14.2) |
| Plasma glucose (mg/dL) | 159.4 (64.0) |
| HbA1c (%) | 7.5 (1.7) |
| Estimated glomerular filtration rate (mL/min/1.73 m2) | 73.0 (23.6) |
BMI, WC, number of patients with CVD history, and number of hospitalizations during the study period were lower in the PHC group than in the non-PHC group. Furthermore, AC, exercise time, and walking time were higher in the PHC group than in the non-PHC group. On an average, patients without PHC were admitted once to our hospital during the study period (Table 2).
**Table 2**
| Unnamed: 0 | With periodic health checkups | Without periodic health checkups | p |
| --- | --- | --- | --- |
| N | 557 | 699 | – |
| Age (years) | 63.8 (12.9) | 63.7 (14.6) | 0.99 |
| Gender (Male/Female) | 250/307 | 311/388 | 0.91 |
| BMI (kg/m2) | 25.0 (5.2) | 25.8 (5.7) | 0.013 |
| WC (cm) | 90.5 (13.3) | 93.2 (14.0) | 0.001 |
| Duration of diabetes (years) | 11.5 (10.9) | 11.9 (11.1) | 0.54 |
| AC (g/day in ethanol consumption) | 19.3 (33.3) | 17.6 (31.5) | 0.032 |
| Smoking habits (Brinkman index) | 305.6 (523.4) | 348.9 (570.5) | 0.063 |
| Exercise time (min/day) | 18.7 (46.9) | 13.9 (15.0) | 0.003 |
| Walking time (min/day) | 31.4 (47.5) | 22.4 (39.9) | <0.001 |
| SBP (mmHg) | 131.5 (19.3) | 130.9 (20.4) | 0.48 |
| DBP (mmHg) | 74.4 (14.0) | 72.9 (14.3) | 0.069 |
| Plasma glucose (mg/dL) | 157.5 (64.8) | 160.8 (63.4) | 0.17 |
| HbA1c (%) | 7.5 (1.7) | 7.5 (1.8) | 0.60 |
| eGFR (mL/min/1.73 m2) | 74.0 (21.9) | 72.1 (24.8) | 0.17 |
| History of CVDs (yes/no) | 62/495 | 112/580 | <0.001 |
| Number of hospitalizations per patient | 0.7 (1.5) | 1.0 (2.1) | <0.001 |
During a mean follow-up of 857 ± 267 days, 20 patients ($1.6\%$) died, 14 ($1.1\%$) sustained cardiovascular events, and 550 ($43.8\%$) were admitted to our hospital. The number of patients who were hospitalized at least once was 213 ($38.2\%$) in the PHC group and 337 ($48.2\%$) in the non-PHC group. The total number of hospitalizations was 1092, of which 382 belonged to the PHC group and 710 to the non-PHC group. Of these, 378 ($34.6\%$) belonged to the diabetes, endocrinology, and metabolism ward, 165 ($15.1\%$) to the surgery ward, 135 ($12.4\%$) to the internal medicine ward, 85 ($7.8\%$) to the ophthalmology ward, 79 ($7.2\%$) to the gastroenterology ward, 65 ($6.0\%$) to the hepatology ward, 36 ($7.5\%$) to the cardiology ward, 27 ($2.5\%$) to the respiratory medicine ward, and 122 ($11.1\%$) to other wards, such as neurology, rheumatology, and psychiatry.
Furthermore, Cox proportional hazard analyses with adjustment for age, gender, BMI, CVD history, drinking habit, smoking habit, exercise time, SBP, DBP, blood glucose, HbA1c, and eGFR showed that PHCs exhibited a significant impact on patient hospitalization risk (hazard ratio [HR] = 0.825; $95\%$ confidence interval [CI], 0.684–0.997; $$p \leq 0.046$$); however, there was no significant association of PHCs with CVDs and all-cause mortality (Table 3).
**Table 3**
| Unnamed: 0 | Hospitalization | Hospitalization.1 | Hospitalization.2 | CVDs | CVDs.1 | CVDs.2 | All-cause mortality | All-cause mortality.1 | All-cause mortality.2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | HR | 95% CI | p | HR | 95% CI | p | HR | 95% CI | p |
| Age (per 1 year increase) | 1.021 | 1.011–1.031 | <0.001 | 1.112 | 1.022–1.210 | 0.014 | 1.035 | 0.980–1.093 | 0.21 |
| Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender | Gender |
| Male | 1.179 | 0.955–1.457 | 0.13 | 1.432 | 0.389–5.274 | 0.59 | 0.347 | 0.097–1.245 | 0.10 |
| Female | (reference) | | | (reference) | | | (reference) | | |
| BMI (per 1 unit increase in kg/m2) | 1.019 | 0.999–1.040 | 0.063 | 1.123 | 0.996–1.266 | 0.058 | 0.810 | 0.696–0.942 | 0.006 |
| History of CVDs | History of CVDs | History of CVDs | History of CVDs | History of CVDs | History of CVDs | History of CVDs | History of CVDs | History of CVDs | History of CVDs |
| Yes | 1.304 | 1.028–1.654 | 0.029 | 14.999 | 4.063–55.375 | <0.001 | 0.276 | 0.034–2.225 | 0.23 |
| No | (reference) | | | (reference) | | | (reference) | | |
| AC (per 1 g/day increase in ethanol consumption) | 1.002 | 1.000–1.005 | 0.074 | 0.997 | 0.968–1.026 | 0.81 | 1.013 | 1.004–1.022 | 0.006 |
| Smoking habit (per 1 unit increase in Brinkman index) | 1.000 | 1.000–1.000 | 0.55 | 0.999 | 0.997–1.001 | 0.21 | 1.001 | 1.000–1.002 | 0.001 |
| Exercise time (per 1 min/day increase) | 0.998 | 0.996–1.000 | 0.093 | 1.009 | 1.000–1.018 | 0.057 | 0.994 | 0.979–1.010 | 0.49 |
| Systolic blood pressure (per 1 mmHg increase) | 1.004 | 0.998–1.011 | 0.19 | 1.012 | 0.973–1.052 | 0.56 | 0.991 | 0.953–1.030 | 0.64 |
| Diastolic blood pressure (per 1 mmHg increase) | 0.994 | 0.984–1.003 | 0.18 | 1.040 | 0.972–1.112 | 0.26 | 0.995 | 0.939–1.055 | 0.88 |
| Plasma glucose (per 1 mg/dL increase) | 1.001 | 1.000–1.003 | 0.084 | 0.996 | 0.982–1.010 | 0.59 | 1.010 | 1.001–1.019 | 0.027 |
| HbA1c (per 1% increase) | 1.340 | 1.256–1.429 | <0.001 | 1.070 | 0.633–1.808 | 0.80 | 0.832 | 0.540–1.282 | 0.41 |
| eGFR (per 1 unit increase in mL/min/1.73 m2) | 1.001 | 0.996–1.006 | 0.61 | 1.020 | 0.987–1.055 | 0.24 | 0.980 | 0.956–1.004 | 0.11 |
| Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup | Periodic health checkup |
| Yes | 0.825 | 0.684–0.997 | 0.046 | 2.297 | 0.667–7.910 | 0.19 | 1.461 | 0.495–4.316 | 0.49 |
| No | (reference) | | | (reference) | | | (reference) | | |
## Discussion
We demonstrated that PHCs can remarkably lower the hospitalization risk of patients with T2DM. Although PHCs were not correlated with CVDs and all-cause mortality, the primary result of the study suggests that PHCs improved health outcomes in patients with T2DM. To the best of our knowledge, this is the first study to demonstrate that all-cause hospitalization could be prevented in patients with T2DM who have undergone a PHC in addition to regular medical examinations.
A previous systematic review and meta-analysis concluded that general PHCs were not beneficial for reducing all-cause mortality and cardiovascular events; however, the studies included were from Western countries (United States, United Kingdom, Denmark, Sweden, Belgium, Poland, Italy, and Montenegro) [3]. Ethnic differences should be considered while evaluating the effectiveness of PHCs on health outcomes in patients with T2DM. The average BMI of Japanese individuals with T2DM was 24.8 kg/m² in 2021 [17]. The prevalence of a BMI of ≥30 kg/m² varies by country and ranges from $3.7\%$ in Japan to $38.2\%$ in the United States [18]. Obese individuals are susceptible to stress-induced eating habits, with a greater preference for high-sugar and high-fat foods [19], which deteriorate metabolic health and results in the development of diseases that require hospitalization. Hence, evidence regarding the effectiveness of PHCs for health in Western countries is not always applicable to Eastern countries such as Japan. In this regard, the findings of the present study are important.
Generally, patients who regularly visit hospitals for chronic diseases undergo PHCs voluntarily in Japan; thus, health consciousness and literacy could also influence their behavior. A systematic review reported that health literacy was a significant determinant of obesity [20]. Moreover, adequate health literacy was inversely related to physical inactivity (odds ratio [OR] = 0.48; $95\%$ CI, 0.39–0.59) in individuals with CVDs [21], and patients with diabetes who did not adequately understand health information were sedentary (OR = 3.43; $95\%$ CI, 2.14–5.51) [22]. Herein, patients in the PHC group were less obese and more active than patients in the non-PHC group. Furthermore, rate of history of CVD was lesser in the PHC group than in the non-PHC group. These results suggest that patients who underwent PHC exhibited high health literacy and consciously attempted to prevent obesity through increasing daily physical activity, thereby resulting in primary prevention activities against CVDs. Health literacy also constitutes an important factor for the self-management of diabetes in patients with T2DM [23]; however, there was no significant difference in HbA1c levels between the groups in this study. The reason behind why chronic glycemic control did not vary between the groups cannot be explained based on the findings of the present study. However, glucose fluctuation, which cannot be assessed by HbA1c levels, appears to encourage the development of cardiovascular events in patients with diabetes than chronic hyperglycemia [24]. Furthermore, high plasma glucose levels have been independently associated with inadequate health literacy in patients with T2DM [25]. Hence, patients in the non-PHC group could have possibly experienced hypoglycemia and hyperglycemia more often compared with patients in the PHC group. Further studies are warranted to characterize the relationship between PHCs and the rate of health literacy and glycemic control.
A higher AC in the PHC group than in the non-PHC group is inconsistent with the finding that the patients who underwent PHCs were less obese and more active than the patients who did not undergo PHC. However, previous studies have indicated that AC impacted the levels of physical activity in a positive manner [26]. Additionally, AC was 19.3 and 17.6 g/day in the PHC and non-PHC groups, respectively, which is not harmful for cancer, CVDs, and mortality in Japanese people [27]. Furthermore, even after adjusting AC in the Cox model, PHC was significantly associated with hospitalization risk in this study. Therefore, we propose that PHCs are not directly related to increase in AC.
Treatment costs vary depending on the cause of hospitalization, type and severity of disease, patient condition, and type of health care organization. For example, hospitalization due to uncontrolled diabetes costs ~¥40,000 ($296 at the current exchange rate) per day in our organization [28]. Considering that the average length of a hospital stay due to diabetes is 13–14 days in our organization [28], the hospitalization cost is >¥50,0000 ($3,703 at the current exchange rate). Conversely, the cost of PHCs also varies significantly depending on the components of examination. For example, a nationwide annual health checkup to prevent the incidence and progression of lifestyle-associated diseases costs ~¥7,000 ($55 at the current exchange rate) per examination [29], whereas a comprehensive health checkup in our organization, which includes chest X-ray, abdominal ultrasonography, gastroscopy, and some cancer screening tests, in addition to routine blood and urine examinations costs ¥57,000 ($422 at the current exchange rate) for men and ¥64,000 for women ($474 at the current exchange rate) [30]. Herein, PHC was equated with a $17.5\%$ reduction in the risk of all-cause hospitalization in patients with T2DM, which is comparable with >¥87,500 ($648 at the current exchange rate) when converted to hospitalization costs due to diabetes. This study is not a randomized controlled trial; however, if the number of patients who would need intervention is calculated as follows: 1/(0.482 − 0.382) = 10, we need to provide PHCs to 10 patients to prevent one additional hospitalization in this study cohort. Thus, comprehensive health checkup may not be cost effective; however, providing a general PHC annually might be cost effective for the managing patients with T2DM who are at a higher risk of hospitalization. Furthermore, the cost of hospitalization due to ischemic heart disease with percutaneous coronary intervention (¥1,100,000; $8,148 at the current exchange rate), acute cerebral infarction (¥1,190,000; $8,814 at the current exchange rate), and cancers, such as lung cancer with chemotherapy (¥610,000; $4,519 at the current exchange rate), are higher than that of hospitalization due to diabetes [28]. The recurrence risk of CVDs was significantly high (HR = 14.999; $95\%$ CI, 4.063–55.375; $p \leq 0.001$) in this study (Table 3). Patients with T2DM exhibit a high prevalence of CVDs [6], and an increased risk of liver, pancreatic, and endometrial cancers [11]; therefore, PHCs that focuses on cardiovascular risks and early detection of such cancers may be beneficial for cost-reduction as well as for improving health and life expectancy in such patients.
However, PHC is not necessary for every healthy individual. Current evidence suggests that regular nationwide health checkups are neither beneficial nor cost effective to improve health outcomes [31]. Compared with other developed countries, such as the United Kingdom, Canada, and Sweden, PHCs in Japan cover an unusually wide range and high volume of the population, and it is unclear whether all examinations contribute to the overall health of the population and provide financial benefits [31]. The target population for PHCs should be narrowed down to ensure that health care providers can effectively and cost-efficiently apply the results of PHCs in primary and secondary prevention activities for CVDs and cancers. In addition, we need to establish nationwide standardized screening programs that include what, when, to whom, and how PHCs should be administered by considering national and international evidence and best practices to efficiently use limited medical resources [31]. In this regard, the findings of this study are highly suggestive. At least, we might as well consider the adoption of PHCs as a secondary prevention strategy in the management of patients with T2DM.
Reducing inequities in health and raising health awareness across all social strata are critical for improving health in patients with T2DM. In Japan, company employees and students can avail PHCs every year at the expense of the company or school, respectively. However, the unemployment rate has been positively associated with reduced PHCs [32]. Furthermore, women with lower socioeconomic statuses are less likely to avail cancer screening examinations, which cost ¥500−¥1,000 with municipal subsidies [33]. Individuals with low socioeconomic statuses generally exhibit a higher risk of smoking, drinking, physical inactivity, poor nutrition, and not undergoing PHCs [32, 34, 35]. Therefore, it is also vital that health promotion policies for PHCs target patients with T2DM with low socioeconomic statuses.
There are several limitations of this study that need to be addressed. First, we did not investigate the components of PHCs. Whether or not the participants have undergone a PHC was gathered from their response to the question “Do you have a PHC except regular medical examinations for diabetes?” Hence, information regarding the type of PHC; for example, general health checkup, cancer screening test, or comprehensive PHC, was lacking. Second, we did not assess the detailed causes of the hospitalizations; for example, the name and severity of the disease. We can speculate that a certain number of hospitalizations were related to diabetes because one third of the total hospitalizations were in the diabetes, endocrinology, and metabolism ward; however, how PHCs were related with the heightened risk of hospitalizations due to a specific disease remains unknown. Third, we assessed the adherence of the participants to the diet therapy by asking the question “Do you stick to the diet therapy?” at the regular medical examinations approximately once a month; however, the adherence rate of the diet therapy should be measured via a specific tool (e.g., diet diary). Fourth, the socioeconomic status and educational level of the participants were not investigated. Individuals with high risk factors, such as smoking, physical inactivity, and low education and socioeconomic statuses, are less likely to attend PHCs than others [28, 29]. Such demographic data should be incorporated in future studies.
## Conclusion
PHCs exhibit a significant role in minimizing the hospitalization risk of patients with T2DM. PHCs may be useful in improving health outcomes of patients and reduce health care costs when targeting individuals with high risk factors, such as T2DM. Health policy makers need to improve PHC programs by including what, when, to whom, and how PHCs should be administered. Furthermore, a discussion should be conducted to reach a consensus regarding how the results of PHCs can be effectively applied to effectively patient management.
## 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 Medical Ethics Committee of the National Center for Global Health and Medicine (Reference No. NCGM-G-002052). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
HH conducted the study, performed the data analyses, drafted the manuscript, and revised the manuscript. HY critically reviewed the manuscript and the scientific interpretations of study results. 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: Dapagliflozin, as Add-on Therapy in Type 2 Diabetes Patients, Is Associated
With a Reduction in Albuminuria and Serum Transaminase Levels
authors:
- Silas Benjamin
- Manjunath Ramanjaneya
- Alexandra E. Butler
- Imran Janjua
- Firjeeth Paramba
- Jafer Palaki
- Aisha Al Kubaisi
- Prem Chandra
- Ibrahem Abdalhakam
- Nasseer Ahmad Massodi
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2021
pmcid: PMC10012067
doi: 10.3389/fcdhc.2021.733693
license: CC BY 4.0
---
# Dapagliflozin, as Add-on Therapy in Type 2 Diabetes Patients, Is Associated With a Reduction in Albuminuria and Serum Transaminase Levels
## Abstract
### Introduction
SGLT-2 inhibitors are shown to be nephroprotective, slowing progression of nonalcoholic steatohepatitis (NASH) in addition to improving glycemic control in patients with type 2 diabetes (T2D). To date, no real-life clinical data is available on the effect of SGLT-2 inhibitors on urine albumin-creatinine ratio (ACR) and liver enzymes in a Middle Eastern population. Therefore, we evaluated the effect of dapagliflozin (DAPA) on urine ACR, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) when added to standard therapy for T2D.
### Methods
This is an observational study of 40 patients with T2D in whom DAPA was added to their existing anti-diabetic regimen to improve glycemic control. The primary outcomes were changes in serum transaminase level and urine albumin-to-creatinine ratio (ACR). Secondary outcomes include changes in glycosylated hemoglobin (HbA1C), body mass index (BMI), oral hypoglycemic agents and insulin dose.
### Results
Whole group analysis showed a reduction in ALT ($p \leq 0.0001$), (AST) ($$p \leq 0.009$$), ACR ($$p \leq 0.009$$) and BMI ($p \leq 0.0001$) following DAPA treatment. Further sub-group analysis showed that patients on insulin and DAPA combination had a reduction in ACR ($$p \leq 0.0090$$), ALT ($$p \leq 0.0312$$), BMI ($$p \leq 0.0007$$) and HbA1c ($p \leq 0.0001$) compared to the sulfonylurea and DAPA combination group. In the sulfonylurea and DAPA combination group, there was a reduction in the sulfonylurea requirement following DAPA therapy ($$p \leq 0.0116$$), with reductions in ALT ($$p \leq 0.0122$$), AST ($$p \leq 0.0362$$), BMI ($$p \leq 0.0026$$) and HbA1c ($p \leq 0.0001$) but with no change in ACR ($$p \leq 0.814$$).
### Conclusion
In routine clinical practice, the addition of DAPA to standard medical therapy is well tolerated and beneficial for T2D patients and is associated with a reduction of ALT and ACR.
## Introduction
The relationship between diabetes and heart disease is well recognized and, as reported in the Framingham heart study, there is a ~2-3 fold increased risk of arteriosclerosis in patients with type 2 diabetes (T2D) [1]. In the United Kingdom Prospective Diabetes Study (UKPDS), poor glycemic control in patients with T2D was associated with an increased risk of diabetes complications and, with each percentage drop in HbA1c, there was a $14\%$ risk reduction in myocardial infarction [2]. Diabetes is the leading cause for end stage renal disease (ESRD) and microalbuminuria is recognized as an independent risk factor for cardiovascular disease in patients with T2D [3]. The annual rate of progression from normo-albuminuria to microalbuminuria in T2D is about $2\%$ per year, $2.8\%$ per year for progression from micro- to macroalbuminuria and $2.3\%$ per year for progression from macroalbuminuria to elevated plasma creatinine or renal replacement therapy [4]. As diabetic nephropathy progresses from the normoalbuminuric stage to ESRD, the annual death rate due to cardiovascular disease also increases [4].
Non-alcoholic fatty liver disease (NAFLD) is a spectrum of liver disease with high prevalence in T2D patients. The prevalence of NAFLD in T2D is 2-fold higher than in non-diabetic patients; conversely, the risk of developing T2D increases by 5-fold in patients with NAFLD [5, 6]. Moreover, NAFLD can increase the risk of complications in T2D and, reciprocally, the presence of T2D in patients with NAFLD can enhance the progression to fibrosis [7]. Various molecular and metabolic changes which occur in a genetically predisposed individual contribute to the pathogenesis of NAFLD; based on euglycemic clamp studies, the pathogenesis of NAFLD, and its association with insulin resistance and hyperinsulinemia, indicates that it is a feature of metabolic syndrome [8].
Although many anti-diabetic agents have been tested for efficacy against NAFLD, lifestyle modification remains the main therapeutic option, as there is no specific medication licensed for its treatment. A calorie-restricted diet along with exercise has shown histological improvement in the liver in NAFLD patients [9].
Randomized clinical studies in non-diabetic patients with thiazolidinediones, highly selective agonists for peroxisome proliferator-activated receptor gamma (PPARϒ) which sensitize adipose tissue to insulin actions and increase uptake of fatty acids in the liver, failed to show an improvement in liver enzyme levels, insulin resistance or a reduction in steatosis or inflammation. Thiazolidinediones also failed to show improvement in histological findings [10].
The glucagon-like peptide-1 (GLP-1) analogue, liraglutide, has been shown to improve insulin sensitivity in hepatocytes and adipose tissue in hyperinsulinemic euglycemic clamp studies [11]. The Lifestyle, Exercise and Nutrition (LEAN) study, a 48-week randomized, double blind, placebo-controlled study in patients with non-alcoholic steatohepatitis (NASH) showed histological resolution of NASH when these patients were treated with liraglutide 1.8 mg once daily compared to placebo [12].
Sodium-glucose co-transporter 2 (SGLT-2) inhibitors are a new class of oral hypoglycemic agents used to lower blood glucose in T2D patients. These drugs have shown a reduction in oxidative stress and inflammatory markers and improved plasma levels of aminotransferase, steatosis, inflammation and fibrosis in animal models of NAFLD [13]. The Effect of Empagliflozin on Liver Fat (E-Lift) trial showed that treatment with empagliflozin reduced liver fat [magnetic resonance imaging (MRI)-derived proton density fat fraction (MRI-PDFF)] and alanine transferase (ALT) levels in patients with T2D and NAFLD [14].
The current study is an observational analysis of patients with regular follow up in our diabetic clinic. We observed a high prevalence of class 1 obesity in our patients, with a mean BMI of 32.6 ± 6 kg/m2 in our cohort, and prior radiological assessment was not done to exclude NAFLD or NASH. Previous epidemiological studies from Qatar have shown an overall prevalence of Metabolic Syndrome of $48.8\%$ [15]. The high prevalence of obesity and elevated ALT [5] is associated with increased risk of T2D and prompted us to look at the effect of DAPA on liver enzymes and ACR.
Our study objective was to determine the effect on liver enzymes and urine albumin-creatinine ratio (ACR) when DAPA as added-on to existing antidiabetic medications in patients with T2D. This study was done in a real-life clinic setting serving a Middle Eastern population.
## Study Design
Observational data was collected from patients with T2D, who were initiated on DAPA 10 mg as add-on therapy to their existing antidiabetic agent from June 2017 to September 2018. All patients enrolled in this study had standard diabetes care to discuss lifestyle and diet with diabetes educators, and patients were advised to continue with their normal routine activities. A total of 40 patients were included in the study. All patients were seen in the diabetic clinic at Hamad Medical Corporation. Patient data was collected within 6-months prior to initiation of DAPA and 12-14 months post-treatment with DAPA. All patients were on established antidiabetic medications prior to initiation of DAPA therapy. In our cohort, 18 patients were taking a sulphonylurea ($45\%$), 39 patients were taking metformin ($98.0\%$), 26 patients were taking a DPP4 inhibitor ($65.0\%$) and 10 patients ($25\%$) were taking a GLP-1 agonist prior to initiation of DAPA therapy, and 2 patients were started on a GLP-1 agonist at 7 months after initiation of DAPA. Eighteen patients ($45\%$) were on insulin therapy. Five patients were on a combination of insulin and oral triple therapy (sulphonylurea + DPP4 inhibitor + metformin and insulin). Seven patients were on a GLP-1 agonist and insulin. Eighteen patients were on a combination of an oral hypoglycemic agent (sulphonylurea + DPP4 inhibitor + metformin). Demographic and biochemical data collected included weight, BMI, HbA1c, lipid profile, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and urine ACR. Patients were reviewed in clinic every 4 months and blood biochemistry was repeated at 0, 6 and 12 months. Data was collected retrospectively between June 2017 and September 2018 and, for statistical purposes, initial data (prior to initiation of DAPA) and final data at 12 months was analyzed. The primary outcome was to measure change in liver enzymes and urine ACR following initiation of DAPA as add-on therapy to existing antidiabetic medications. Secondary outcomes were reduction in hypoglycemic agents and glycosylated hemoglobin (HbA1C).
Patients were included if they were >20 years of age, had documented T2D and were newly initiated on DAPA as add-on therapy to their existing antidiabetic medications that was not changed for at least 4 months prior to DAPA initiation. Patients were excluded if they had DAPA therapy initiated prior to participation in the current study or those lost to follow up. In addition, patients with pre-existing liver disease such as hepatitis, autoimmune liver disease, alcoholic liver disease, or any known drug induced liver disease were also excluded from the study. All patients enrolled in the study denied consumption of alcohol.
## Statistical Analysis
Based on clinical observations prior to the initiation of this study, we noted that mean change in AST ranged from 7 to 10 U/L and in ALT from 3 to 7 U/L and therefore we computed sample size using effect size (changes in the mean AST is 8 U/L, statistical power $90\%$ and level of significance $5\%$, therefore the sample size required was 35 participants). Descriptive statistics and Means ± Standard Deviations (SD) were calculated for all continuous variables in the study. Paired t-tests were performed to assess the mean differences and fold changes following DAPA treatment and to calculate the significance before and after the DAPA treatment. All statistical analysis was done using statistical analysis SAS version 9.4 software. A statistical significance level (P-value) of <0.05 was considered as significant.
## Results
Between June 2017 and September 2018, 40 patients with T2D were initiated on DAPA as add-on therapy to their existing antidiabetic agents to achieve better glycemic control in the study participants. Baseline characteristics are shown in Table 1. Mean age was 51.3 ± 9.7 years, BMI 32.3 ± 6.0, HbA1c 9.1 ± $1.1\%$ and duration of diabetes 10.7 ± 5.4 years.
**Table 1**
| Unnamed: 0 | Whole group analysis (n = 40). | Whole group analysis (n = 40)..1 | Whole group analysis (n = 40)..2 |
| --- | --- | --- | --- |
| | Pre-DAPA/baselineMean (S.D) | Post-DAPAMean (S.D) | P |
| Sex (male/female) | 24/16 | – | |
| Age (Y) | 51.3 (9.7) | – | |
| Duration of diabetes (years) | 10.7 (5.4) | – | |
| DAPA (months of treatment) | 10.2 (2.7) | – | |
| WT (KG) | 88.7 (17.5) | 85.3 (16.4) | *** |
| BMI (kg/m2) | 32.3 (6.0) | 30.9 (5.4) | *** |
| SBP (mmHg) | 132.3 (16.4) | 124.8 (22.4) | NS |
| DBP (mmHg) | 75.7 (12.4) | 71.0 (9.8) | NS |
| HbA1c (%) | 9.1 (1.1) | 7.4 (0.9) | *** |
| Creatinine (µmol/L) | 77.4 (15.5) | 74.0 (17.9) | NS |
| ACR (mg/mmol) | 15.0 (42.1) | 11.1 (28.3) | ** |
| TCH (mmol/L) | 4.2 (0.8) | 4.1 (0.8) | NS |
| HDL-C (mmol/L) | 1.4 (2.0) | 1.2 (0.4) | NS |
| LDL-C (mmol/L) | 2.4 (0.7) | 2.3 (0.7) | NS |
| TG (mmol/L) | 1.4 (0.7) | 1.4 (0.7) | NS |
| ALT (U/L) | 43.2 (43.3) | 28.9 (13.1) | *** |
| AST (U/L) | 33.9 (37.0) | 23.1 (5.6) | ** |
For the whole group, mean baseline ALT was 43.2 ± 43.3 U/l and mean AST was 33.9 ± 37.0 U/l. Following add-on therapy with DAPA, ALT and AST decreased to 28.9 ± 13.1 U/l ($p \leq 0.0001$) and 23.1 ± 5.6 U/l ($p \leq 0.009$), respectively. Following DAPA as add-on therapy for a duration of 12 months, there was a significant reduction in ALT ($p \leq 0.0001$) and AST ($p \leq 0.009$) for the whole group (Table 1 and Figure 1). Eleven patients had an elevation of ALT more than 41 U/L (reference range 0-41 U/L); it was noted that these 11 patients had a mean ALT of 59.5 ± 15.6 U/L and this was reduced following DAPA treatment to 43.4 ± 12.6 U/L (P value= 0.004). Subgroup analysis of patients on insulin and DAPA showed a trend towards reduction in AST and significant reduction in ALT (Table 2 and Figure 2).
**Figure 1:** *Effect of 12 months DAPA treatment as add-on therapy on HbA1c, BMI, ALT, AST and creatinine/albumin ratio whole group analysis. Significant differences were determined by paired t-test with fold difference changes following DAPA therapy **p < 0.01; ***p < 0.0001.* TABLE_PLACEHOLDER:Table 2 **Figure 2:** *Effect of 12 months DAPA treatment as add-on therapy on ALT, AST, ACR and HbA1c subgroup analysis fold changes. (A) ALT reduction in insulin and DAPA group, (B) ALT reduction in sulfonyl and DAPA combination group, (C) AST reduction in insulin and DAPA group, (D) AST reduction in sulfonyl and DAPA combination group, (E) ACR reduction in insulin and DAPA group, (F) ACR reduction in sulfonyl and DAPA combination group. (G) HbA1c reduction in insulin and DAPA group, (H) ALT reduction in sulfonyl and DAPA combination group. Significant differences were determined by paired t-test with fold difference changes following DAPA therapy *p < 0.05; **p < 0.01; ***p < 0.0001; ns, non significant.*
Baseline mean urine ACR for the whole group ($$n = 40$$) was decreased from 15.0 mg/mmol to 11.1 mg/mmol [$p \leq 0.009$] following treatment with DAPA. For the whole group, the addition of DAPA to existing therapy did improve HbA1c with a reduction from 9.1 ± $1.1\%$ to 7.4 ± $0.9\%$ ($p \leq 0.0001$). Subgroup analysis of patients who were on combination of insulin therapy and metformin therapy ($$n = 18$$) showed a significant reduction in HbA1c from 9.6 ± $1.1\%$ to 7.6 ± $1.0\%$ ($p \leq 0.0001$). Furthermore, there was a trend towards reduction in the required total daily dose of insulin in this group, although this did not reach statistical significance. Subgroup analysis of patients who were on a sulfonylurea ($$n = 18$$) showed significant reductions in HbA1c ($p \leq 0.0001$). After 6 months of DAPA therapy, sulphonylurea ($p \leq 0.05$) was discontinued in 5 patients as they maintained good glycemic index. For the whole group, the addition of DAPA to existing therapy also showed a significant reduction in BMI ($p \leq 0.0001$). Subgroup analysis showed a significant reduction in BMI for the patients on insulin therapy ($p \leq 0.0007$).
Further analysis of DAPA response in T2D patients based on duration of diabetes of less than or greater than 10 years showed that ALT reduction was greater in less than 10 years group ($p \leq 0.0001$) compared to greater than 10 years group ($p \leq 0.05$). AST reduction was also greater in less than 10 years group ($p \leq 0.001$) compared to greater than 10 years group (ns). ACR ($p \leq 0.0001$) and BMI reduction was only observed in those patients who were diagnosed with T2D more than 10 years. However, improvement in HbA1c was similar in both the groups (Figure 3).
**Figure 3:** *Effect of DAPA treatment as add-on therapy on ALT, AST, ACR, HbA1c and BMI were measured in T2D patients who were diagnosed with diabetes less than 10 year and more than 10 years of duration and represented as fold changes. (A) ALT changes, (B) AST changes, (C) ACR changes, (D) HbA1c changes, (E) BMI changes. Significant differences were determined by paired t-test with fold difference changes following DAPA therapy *p < 0.05; **p < 0.01; ***p < 0.0001; ns, non significant.*
## Discussion
A cross sectional epidemiological study from Qatar showed a $16\%$ prevalence of T2D. There is an epidemic of obesity in Qatar which is closely associated with metabolic syndrome and NAFLD [15]. In our study, the mean BMI was 32.3 kg/m2 which indicates a high prevalence of class 1 obesity associated with T2D with raised ALT. The imbalance between insulin secretion and insulin sensitivity results in hepatic insulin resistance which is well known in T2D [16]. A personalized patient centered approach should be taken when initiating or optimizing therapy in patients with T2D. Newer antidiabetic medications have additional therapeutic benefits other than lowering blood sugars; these effects may be synergistic effects or may work through different pathophysiological mechanisms [17]. When intensifying treatment, there is also an increased risk of hypoglycemia especially as add-on therapy to insulin or sulphonylurea. Non-insulin dependent therapies, such as SGLT-2 inhibitors, have a reduced risk of hypoglycemic incidents in T2D patients. For this reason, these medications may become a first choice, or a second choice added to metformin for treatment of T2D in the future.
In our group of patients with T2D with a mean duration of 10 years, addition of DAPA to existing anti-diabetic medications lowered ALT and AST significantly. The fold change reduction in ALT was noticeably higher in the group with duration of diabetes less than 10 years as compared to those with T2D duration of greater than 10 years. In a community based prospective study by Cho et al, the investigators found that a raised ALT level was associated with a 2-fold increase in the risk of T2D. Hepatosteatosis and insulin resistance in obese type 2 diabetes causes increased influx of fatty acid, which triggers inflammatory cytokines and hepatocyte destruction which can cause high liver enzymes [5]. The effect of DAPA and other SGLT-2 inhibitors in randomized control trials has shown a disease modifying effect on patients with NAFLD in addition to its blood glucose lowering benefits. DAPA has been shown to reduce hepatocyte injury biomarkers, plasma fibroblast growth factor (FGF21), cytokeratin (CK) 18-M30 and CK 18-M65 which could explain the reduction in liver enzymes [18]. Patients with adiposity-related hepatic insulin resistance have impaired beta cell dysfunction prior to diagnosis of T2D [19, 20]. We observed that early initiation of DAPA as add-on therapy caused a significant reduction in ALT and a possible decrease in hepatic insulin resistance. The benefits of the addition of an SGLT-2 inhibitor were observed within the first 12 months of therapy in a real-life routine diabetic follow up clinic. Microalbuminuria in T2D is an early indicator of systemic vasculopathy, causing progressive renal damage, and is the leading cause of chronic kidney disease. In addition to the glucose lowering effects of SGLT-2 inhibitors, this class of drugs has other glucose independent effects which alter renal hemodynamics and reduce intraglomerular pressure [21].
In this study, addition of DAPA significantly reduced microalbuminuria in the whole group, though the reduction was more pronounced in patients with a duration of diabetes greater than 10 years. Most patients in our study group ($82\%$) were already established on an ACE inhibitor prior to DAPA therapy initiation. Despite this, we observed a further reduction in microalbuminuria when DAPA was added to existing antidiabetic medications. A similar prospective randomized control trial showed a $36\%$ reduction in 24-hour urine albumin excretion and this effect was reproduced when patients were re-exposed to DAPA 10 mg, indicating a true response to this class of antidiabetic medications [22]. A more recent DAPA-CKD study showed benefit of adding DAPA to existing therapy in reducing the decline of eGFR and all-cause mortality in patients with diabetes and non-diabetic kidney disease [23].
Significant improvements in HbA1c and body weight were found when DAPA was added to existing antidiabetic medications and DAPA was well tolerated in our group of patients. Improvement in glycemic control and additional benefits of add-on therapy with DAPA were noted in patients with a duration of diabetes more than 10 years. Meta-analysis of randomized control studies in which SGLT-2 inhibitors were compared to placebo or as add-on therapy to existing medications showed long term additional benefits beyond glycemic control [24]. DAPA was well tolerated when added-on to other antidiabetic medications. There was a reduction in dosage of insulin and sulphonylureas which are known to cause weight gain. No significant hypoglycemia was observed requiring either hospitalization or discontinuation of the medication. Genitourinary infections are reported as a common side effects in other large, randomized control trials with SGLT-2 inhibitors; however, in our observational study over 12 months, none of the patients reported severe genitourinary infection requiring discontinuation of the medication.
A strength of this study was that it was undertaken in a real-life clinical setting. Limitations include limited patient data capture and the small sample size. Most of the patients were on multiple medications and therefore it is not possible to eliminate the influence of the various classes of anti-diabetic medications on our study outcomes. We were aware that other medications such as GLP agonists have been shown to reduce liver enzymes. Interestingly, addition of DAPA as add-on therapy further reduced ALT and ACR. Given the nature of the study, it was not possible to eliminate all confounding factors. However, all the patients in our study were on a stable dose of medications for at least 4 months preceding the initiation of DAPA. Due to the nature of the study, we did not do radiological imaging to diagnose NAFLD or NASH prior to study initiation. However, participants in our study are likely to have undiagnosed NASH based on their risk profile and previous epidemiological studies from Qatar [15]. Previous studies have already established the safety of combining DAPA as add-on therapy to other antidiabetic medications. Safety assessments after initiation of DAPA to existing antidiabetic therapy were not specifically done for this study. Patients were reviewed at 4 monthly intervals and advised to report any concerns following initiation of DAPA.
In conclusion, in a real-life clinical setting, the addition of SGLT-2 inhibitors is well tolerated and can be combined with all classes of antidiabetic medications in patients with T2D. SGLT-2 inhibitors, in addition to their benefits in improving glycemic control and weight, as add-on therapy it is associated with a reduction ACR and liver enzymes. To date, this is the first study looking at real life clinical data in a Middle Eastern T2D population.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics Statement
Protocols were approved by Institutional Review Boards of the Hamad Medical Corporation, Qatar. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author Contributions
SB submitted the proposal to medical research center (MRC) at Hamad Medical Corporation and was the principle investigator for this study. MR and PC performed statistical analysis, prepared graphs, and contributed to writing the manuscript. AB researched the data and assisted with writing the manuscript. FP, IJ, JP, NM, IA, AK and SB designed the experiments, supervised progress revised and approved the final version of the article.
## Funding
This study was supported by funding received from medical research Centre, Hamad Medical corporation (MRC-01-18-322). We would like to thank medical research Centre, Hamad Medical corporation for article processing fees support.
## 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: Screening of latent tuberculosis infection among patients with diabetes mellitus
from a high-burden area in Brazil
authors:
- Amanda Vital Torres
- Raquel da Silva Corrêa
- Maria de Fátima Bevilacqua
- Luana Cristina França do Prado
- Flavia Miranda Gomes de Constantino Bandeira
- Luciana Silva Rodrigues
- Marilia Brito Gomes
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012069
doi: 10.3389/fcdhc.2022.914574
license: CC BY 4.0
---
# Screening of latent tuberculosis infection among patients with diabetes mellitus from a high-burden area in Brazil
## Abstract
Although several cohort studies have raised the important association between diabetes mellitus (DM) and latent tuberculosis infection (LTBI), evidences are limited and controversial. Furthermore, it is well documented that the poor glycemic control may exacerbate the risk for active TB. Thus, the monitoring of diabetic patients living in high-incidence areas for TB is an important concern in views of available diagnostic tests for LTBI. In this cross-sectional study, we estimate the association of DM and LTBI among diabetic patients classified as type-1 DM (T1D) or type-2 DM (T2D) living in Rio de Janeiro, RJ, Brazil – considered a high TB burden region of these country. Non-DM volunteers were included as endemic area healthy controls. All participants were screened for DM using glycosylated-hemoglobin (HbA1c) and for LTBI using the QuantiFERON-TB Gold in Tube (QFT-GIT). Demographic, socioeconomic, clinical and laboratorial data were also assessed. Among 553 included participants, 88 ($15.9\%$) had QFT-GIT positive test, of which 18 ($20.5\%$) were non-DM, 30 ($34.1\%$) T1D and 40 ($45.4\%$) T2D. After adjustments for potential baseline confounders, age, self-reported non-white skin color and an active TB case in the family were significantly associated with LTBI among the studied population by using a hierarchical multivariate logistic regression analysis. Additionally, we verified that T2D patients were able to produce significant increased interferon-gamma (IFN-γ) plasma levels in response to Mycobacterium tuberculosis-specific antigens, when compared to non-DM individuals. Altogether, our data showed an increased prevalence of LTBI among DM patients, albeit non-statistically significant, and point out to important independent factors associated with LTBI, which deserve attention in monitoring patients with DM. Moreover, QFT-GIT test seems to be a good tool to screening LTBI in this population, even in a high TB burden area.
## Introduction
Diabetes Mellitus (DM) is a chronic disease with an increasing worldwide prevalence. Nowadays, the disease can be considered an epidemic clinical condition and a huge public health problem because it may lead to a loss in quality of life and early mortality [1, 2]. Considering the increased life expectancy reached in the last decades among people with diabetes, a rise in the occurrence of diabetes-related chronic complications is also expected, mainly due to long lasting exposure to hyperglycemia [3]. Such complications may include macroangiopathy and microangiopathy (retinopathy, nephropathy and neuropathy) that can compromise an individual’s health and imply in higher expenditures by the public health system. A prompt diagnosis and an adequate treatment can lead to higher chances of obtaining a good glycemic control, which in turn has been proven to reduce diabetes-related complications [4]. In addition to these diabetes-related complications, it is currently known that high levels of blood glucose may result in altered immune responses leading to higher susceptibility to several infections, including tuberculosis (TB) [5, 6].
Tuberculosis (TB) is an infectious disease, caused by the *Mycobacterium tuberculosis* (Mtb), which affects primarily the lungs, but can also reach other organs. According to the World Health Organization (WHO), 10 million new cases are diagnosed and 1.2 million of deaths worldwide are registered every year, indicating that TB is a serious public health issue [7]. Brazil remains included in the 30 high TB/TB-HIV coinfection burden countries, and in 2019 were registered 66,819 new cases with an incidence rate of 31.6 per 100,000 inhabitants, and 4,500 deaths (mortality rate of 2.2 per 100,000), pointing out Acre, Amazonas and Rio de Janeiro which have shown incidence coefficients above of the national average [8]. According WHO, less than half of the cases are notified, which shows a weakness in policies regarding the disease control. This serious condition is due to an increase in poverty, poor distribution of family income and increased urbanization. Many risk factors have been associated to TB, such as male gender, smoking, alcoholism, low body weight, renal diseases, contact with people who have TB, social class and DM. In particular, Brazil has a high prevalence of both diseases (DM and TB), being of paramount importance to investigate reliable and practical diagnostic methods [7].
It is estimated that around one-quarter of the world’s population is asymptomatically infected by Mtb, a condition called latent tuberculosis infection (LTBI) and in which individuals are in a high risk of active TB development. Although in an intriguing and poorly understood way, only 5-$10\%$ of the people with LTBI progress to the active form of the disease [9], it is a consensus that cell immunity is the main mechanism involved in the protection against Mtb, being interferon-gamma (IFN-γ) production and predominantly type I immune response considered as biomarkers of the protection against TB [10]. In views of this last aspect, immunological tests such as tuberculin skin test (TST) and interferon-gamma release assays (IGRA) represent an indirect response to Mtb infection currently used to LTBI diagnosis, even though they are not considered as “gold standard”. Moreover, their performances are limited or compromised due to immunological status and/or populations from high TB burden areas [11].
The present study intended to determine the prevalence of LTBI among type-1 (T1D) and type-2 DM (T2D) patients living in Rio de Janeiro, a Brazilian State that occupies the 2th place in number of TB cases in Brazil showing an incidence rate of 60 per 100,000 [8], by using the commercial IGRA QuantiFERON-TB Gold in Tube (QFT-GIT). Demographic, socioeconomic, clinical and laboratorial data were also assessed to analyze the association with LTBI among study population.
## Study design and participants
This was a cross-sectional study conducted with consecutive patients classified as type-1 DM (T1D) and type-2 DM (T2D) patients who were receiving health care from the Brazilian National Health System (SUS), from June 2015 to June 2017 at the Diabetes Unit in the Policlínica Piquet Carneiro, Rio de Janeiro State University (PPC/UERJ), Rio de Janeiro, RJ, Brazil. DM patients were diagnosed according to American Diabetes Association (ADA) criteria [12]. All T1D patients have been in continuous use of insulin since their diagnosis, and with at least 6 months of follow-up at this diabetes center. As control subjects, healthy volunteers that were medical staff (administration, nurses or physicians) or blood donors from the Herbert de Souza Hemotherapy Center at Pedro Ernesto University Hospital (HUPE/UERJ) were included in the study and attested/named as non-DM group. The exclusion criteria consisted in HIV seropositive patients, pregnant women, lactating women, presence of acute or chronic infectious, patients with diabetic ketoacidosis in the prior three months to the assessment and patients that had difficulties in walking and moving or to go to the hospital for medical care. Blood samples were collected from all participants aiming to measure glycated hemoglobin (HbA1c) and to detect LTBI by using QuantiFERON-TB Gold in Tube (QFT-GIT) test. Individuals with HbA1c at 5.7 – $6.5\%$ or indeterminate QFT-GIT were also excluded. The protocols were approved by the Pedro Ernesto University Hospital Ethics Committee (number 686.651) and all participants signed a written informed consent for the study.
## Measures and definitions
Glycemic control in patients with DM were determined by HbA1c levels measured in whole blood collected in ethylenediamine tetra-acetic acid (EDTA) tubes and processed using high-performance liquid chromatographic method (HPLC, Bio-Rad Laboratories, Hercules, California, USA). Adequate glycemic control was defined as the presence of HbA1c levels < $7.0\%$ (58 mmol/mol) [12], and inadequate glycemic control was defined as HbA1c levels ≥ $7.0\%$ (58 mmol/mol). All non-DM participants had HbA1c levels ≤ $5.6\%$.
The presence of LTBI was determined using the QFT-GIT test (Cellestis Limited, Australia) according to the manufacturer’s instructions. Briefly, 1 mL of whole blood was draw into the three QFT-GIT tubes pre-coated with saline (Nil, negative control), Mtb-specific antigens (ESAT-6, CFP-10 and TB 7.7), or mitogen (positive control) and incubated for 18-24 h at 37°C. After centrifugation, the supernatant was collected and stored frozen at -20°C until IFN-γ measurement, which was done by an enzyme-linked immunosorbent assay (ELISA) from QFT-GIT kit. IFN-γ-Mtb-specific levels were calculated by subtracting of the values obtained from Mtb antigen tubes minus Nil/control tube. QFT-GIT result was defined as positive when IFN-γ in response to Mtb-specific antigens were ≥ 0.35 IU/ml and IFN-γ levels in response to mitogen (mitogen tube minus Nil/control tube) ≥ 0.5 IU/mL. Indeterminate result was defined as IFN-γ of Nil/control > 8.0 IU/mL or positive control value < 0.5 IU/mL. Results were calculated using the manufacturer’s QFT-GIT software.
## Questionnaire
Gender, current age, age at diagnosis, DM time duration, presence of comorbidities, self-reported skin color (according to the Instituto Brasileiro de Geografia e Estatística, IBGE) [13], employment status and years of formal education provided verbally as well as housing conditions and TB exposure history were assessed using medical records or a questionnaire applied by individual interviews during a clinical visit.
Economic status was defined according to the Brazilian Economic Classification Criteria [14], which is based on educational status and house-income and possession of certain house appliances. The following classes of economic status were considered for this analysis: high, middle, low and very low. Regions of housing was defined based on pragmatic areas at Rio de Janeiro State, Brazil.
Body mass index (BMI) was classified as normal, overweight, and obese according to WHO criteria: underweight BMI <18.5 kg/m2, normal weight ≥ 18.5 to < 25 kg/m2, overweight ≥ 25 to < 30kg/m2 and obese as BMI ≥ 30 k/gm2 [15]. Excess use of alcoholic beverages and current smoking status were defined by self-reported (daily or occasional current user).
## Statistical analysis
A Mann-Whitney or Kruskal-Wallis followed by Dunn’s correction tests were used to compare variables with nonparametric distribution. A T-test or ANOVA (with Sidak correction) was used for parametric distribution. We used Pearson’s univariate correlation when applicable. Categorical variables were reported as percentage and Chi-square tests were used for comparison.
To further explore the association between LTBI (QFT-GIT positivity) and DM status a multivariable hierarchical logistic regression (Backward Wald model) was performed to determine which variables could be associated with the presence of QFT-GIT positivity as dependent variable. To select the independent variables, we chose those with statistical significance in an exploratory analysis or those with clinical plausibility. Afterwards, the order of entry into the model was initially demographic and social data [age, gender, self-reported color-race (stratified as white and non-white), economic classes, years of study] followed by clinical data (DM status, use of statin and antihypertensive drugs and finally data related to TB, such as familiar case of TB). DM status entered in the model first as yes/no and secondly stratified as controls (non-DM), T1D and T2D patients. The model fit was assessed through a Hosmer and Lemeshow and Omnibus test. The calculated Nagelkerke R2 and the odds ratio (OR) with a $95\%$ confidence interval (CI) were expressed as indicated. All statistical analyzes were performed with a $95\%$ confidence interval (CI) and the significance level was P ≤ 0.05. Statistical Package for Social Sciences (SPSS) version 16.0. GraphPad Prism version 9 was used for graphic illustrations.
## Overview of the study population
Figure 1 shows the flow chart of the study design. A total of 615 volunteers was recruited at the Health Complex of Rio de Janeiro State University (UERJ), Rio de Janeiro, RJ, Brazil. We excluded participants presenting HIV, syphilis and hepatitis B or C ($$n = 7$$), those who had an indeterminate QFT-GIT ($$n = 2$$) or who did not take this test ($$n = 1$$). Also, all non-DM subjects showing HbA1c levels among 5.7 – $6.5\%$ were excluded ($$n = 52$$). Therefore, a final study population consisted of 553 participants, categorized as follow: i) non-DM ($$n = 154$$); ii) T1D ($$n = 201$$) and iii) T2D ($$n = 198$$).
**Figure 1:** *Flow chart of the study design. A total of 615 individuals were recruited for the study. Of these, 553 represented the final study population based on the eligible criteria and were categorized as follow, non-DM subjects (N = 154), type-1 DM (N = 201) and type-2 DM (N = 198). DM, diabetes mellitus; HbA1c, glycosylated hemoglobin; QFT-GIT, QuantiFERON-TB Gold in Tube; n, sample number.*
Baseline characteristics including demographics, socioeconomic, clinical and laboratorial features of the study population are shown in Table 1. T1D patients had higher levels of fasting glycemia (197.89 ± 105.58 mg/dL) and HbA1c levels (9.0 ± $2.1\%$) than patients with T2D (154.29 ± 66.08 mg/dL; 8.33 ± $1.93\%$, respectively). Patients with T2D were more likely to be older (58.5 ± 9.79 years) and have overweight ($35.9\%$) or obesity ($47\%$) compared to those T1D or non-DM participants. Also, among T2D, $66.7\%$ reported as non-white, $16.7\%$ as current users of cigarettes and $30.8\%$ of the patients were retired. Most of the participants reported not being a current smoker ($89.3\%$) or alcohol user ($70.5\%$).
**Table 1**
| Variable | Non-DM(n = 154) | Type-1 DM(n = 201) | Type-2 DM(n = 198) | P value |
| --- | --- | --- | --- | --- |
| Demographic characteristics | Demographic characteristics | Demographic characteristics | Demographic characteristics | Demographic characteristics |
| Age, y | 34.6 ± 12.9 | 32.5 ± 15.0 | 57.70 ± 10.7 | < 0.001 |
| Gender, (%) | Gender, (%) | Gender, (%) | Gender, (%) | Gender, (%) |
| Male | 86 (55.8) | 97 (50.0) | 103 (50.2) | 0.580 |
| Self-reported skin color, n (%) | Self-reported skin color, n (%) | Self-reported skin color, n (%) | Self-reported skin color, n (%) | Self-reported skin color, n (%) |
| White | 95 (61.7) | 79 (40.7) | 67 (32.7) | < 0.001 |
| Non-white | 59 (38.3) | 115 (59.3) | 138 (67.3) | |
| Socioeconomic characteristics | Socioeconomic characteristics | Socioeconomic characteristics | Socioeconomic characteristics | Socioeconomic characteristics |
| Years of study a , n (%) | Years of study a , n (%) | Years of study a , n (%) | Years of study a , n (%) | Years of study a , n (%) |
| 0 < 12 | 18 (11.7) | 75 (38.7) | 133 (64.9) | < 0.001 |
| > 12 | 136 (88.3) | 119 (61.3) | 72 (35.1) | |
| Occupation, n (%) | Occupation, n (%) | Occupation, n (%) | Occupation, n (%) | Occupation, n (%) |
| Unemployed | 7 (4.4) | 26 (13.4) | 51 (24.9) | < 0.001 |
| Employed | 120(75) | 99 (51.0) | 90(43.9) | |
| Student | 32 (20.0) | 60 (30.9) | 3 (1.5) | |
| Retired | 1 (0.6) | 9 (4.5) | 61 (29.8) | |
| Economic class b , n (%) | Economic class b , n (%) | Economic class b , n (%) | Economic class b , n (%) | Economic class b , n (%) |
| Very low | 12 (7.8) | 22 (11.3) | 41 (20.0) | < 0.001 |
| Low | 107 (69.5) | 135 (69.6) | 143 (69.8) | |
| Middle | 35 (22.7) | 37 (19.1) | 21 (10.2) | |
| Regions/Housing, n (%) | | | | 0.142 |
| RJ city/Metropolitan | 152 (98.7) | 199 (99.0) | 204 (99.5) | |
| Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics | Clinical characteristics |
| Duration of diabetes, y | | 14.5 ± 10.3 | 12.0 ± 7. 7 | < 0.001 |
| Fasting glycemia (mg/dL) | Fasting glycemia (mg/dL) | Fasting glycemia (mg/dL) | Fasting glycemia (mg/dL) | Fasting glycemia (mg/dL) |
| Mean ± SD | | 197.89 ± 105.58 | 154.29 ± 66.08 | < 0.001 |
| HbA1c, (%) | HbA1c, (%) | HbA1c, (%) | HbA1c, (%) | HbA1c, (%) |
| Mean ± SD | 5.18 ± 0.34 | 9.1 ± 2.10 | 8.3 ± 1.9 | < 0.001 |
| < 7.0 | | 37 (18.4) | 50 (25.30) | < 0.001 |
| HbA1c, (mmmol/mol) | 33.3 ± 2.1 | 76.0 ± 10.2 | 67.2 ± 10.0 | < 0.001 |
| BMI (kg/m2) | 26.2 ± 4.3 | 23.9 ± 4.3 | 26.7 ± 4.3 | |
| Underweight (< 18.5) | 0 | 21 (10.8) | 2 (1.0) | < 0.001 |
| Normal weight (18.5 – 24.9) | 68 (44.2) | 97 (50.0) | 33 (16.1) | |
| Overweight (25 – 29.9) | 62 (40.3) | 59 (30.4) | 74 (36.1) | |
| Obesity (≥ 30.0) | 24 (15.6) | 17 (8.8) | 96 (46.8) | |
| Current smoker, yes, n (%) | 12 (7.8) | 15 (7.7) | 33 (16.1) | 0.001 |
| Alcohol use, yes, n (%) | 51 (33.1) | 35 (18.0) | 46 (22.4) | < 0.001 |
| Medicine/drugs, yes, n (%) | Medicine/drugs, yes, n (%) | Medicine/drugs, yes, n (%) | Medicine/drugs, yes, n (%) | Medicine/drugs, yes, n (%) |
| Hypoglycemicagents | 0 | 43 (22.2) | 165 (80.5) | < 0.001 |
| Antihypertensives | 12 (7.8) | 57 (29.4) | 146 (71.2) | < 0.001 |
| Insulin | 0 | 194 (100.0) | 82 (40.0) | < 0.001 |
| Corticoid | 1 (0.6) | 7 (3.6) | 11 (5.4) | 0.052 |
| Statin | 1 (0.6) | 42 (21.6) | 141 (69.8) | < 0.001 |
| BCG vaccine, yes, n (%) | 142 (92.2) | 186 (95.9) | 184 (89.8) | 0.01 |
| Family TB Case, yes, n (%) | 26 (16.9) | 43 (22.2) | 69 (33.8) | 0.001 |
| TB Household contact, yes, n (%) | 9 (5.8) | 15 (7.7) | 23 (11.2) | 0.2 |
Regarding the origin of residence and some housing conditions, we verified that the majority of the study population were from very low or low socioeconomic classes’ mainly patients with T2D ($89.8\%$). The most common areas of origin were from Rio de Janeiro districts/*Metropolitan area* ($99.3\%$). Around one third of all DM patients reported to have a TB case in the family. However, individuals who were in household contact with TB case were not statistically significant among the groups (Table 1).
## Prevalence of latent tuberculosis infection among study population
Based on the QFT-GIT positivity, the overall prevalence of LTBI among study population was $15.9\%$ ($$n = 88$$), of which 18 ($20.5\%$) were non-DM, 30 ($34.1\%$) T1D and 40 ($45.5\%$) T2D (Table 2). However, we did not observe a significant association of QFT-GIT positivity with DM status. As shown in Figure 2, an increasing prevalence of LTBI, albeit non-statistically significant, was observed in all DM ($17.5\%$ [$$n = 70$$]) or when DM group was stratified at T1D ($15\%$) and T2D ($20.2\%$) in comparison to non-DM group ($11.7\%$) (Figures 2A, B).
As depicted in Table 2, among all analyzed variables, LTBI was significantly associated with individuals more likely to be older (mean age = 47.8 ± 17.0 years; $p \leq 0.001$), non-white self-report skin color ($72.7\%$; $$p \leq 0.001$$), use of antihypertensive drugs ($51.1\%$; $$p \leq 0.01$$) and the presence of TB case ($35.2\%$; $$p \leq 0.04$$). In the hierarchical multivariable logistic model examining all selected independent variables, we observed that there was a significant association between LTBI prevalence and age (adjusted odds ratio [aOR] 1.018, $95\%$ confidence interval [CI] 1.004 – 1.033; $$p \leq 0.01$$), self-reported skin color non-white (aOR 2.38, $95\%$ CI 1.427 – 3.999; $$p \leq 0.001$$) and a TB case in family (aOR 1.638, $95\%$ CI 0.999 – 2.710; $$p \leq 0.05$$). No association was noted concerning the other variables, including status of DM (Table 3). All the independent variables which entered in the model could explain $7.6\%$ (Nagelkerke R-squared) of a given patient having QFT-GIT positivity.
**Table 3**
| Variable | aOR (95% CI) | P value |
| --- | --- | --- |
| Age | 1.018 (1.004 – 1.033) | 0.01 |
| Self-reported skin color, non-white | 2.389 (1.427 – 3.999) | 0.001 |
| Family TB case, Yes | 1.638 (0.999- 2.710) | 0.05 |
Additionally, based on the survey response, we observed that among non-DM participants who were QFT-GIT-positives, $22.2\%$ ($\frac{4}{18}$) reported a TB case in the family. Among T1D patients QFT-GIT-positive, $40\%$ ($\frac{12}{30}$) reported a TB case in the family and $13.3\%$ ($\frac{4}{30}$) had a household contact with a TB index case. Finally, in the T2D group who were positive for QFT-GIT, $40.0\%$ ($\frac{16}{40}$) individuals reported a TB case in the family and $17.5\%$ ($\frac{7}{40}$) of them had a household contact with a TB index case (data not shown).
## Interferon-gamma-Mtb-specific plasma levels among diabetic patients
As a last exploratory analysis, we investigated the IFN-γ levels produced in response to Mtb specific-antigens in plasma supernatants from QFT-GIT. Significant increased IFN-γ levels were observed in DM patients in comparison to non-DM subjects (0.37 IU/mL versus 0.20 IU/mL, respectively; $$p \leq 0.0026$$) (Figure 3A). However, when DM group was stratified, only T2D showed significant IFN-γ-Mtb-specific levels in comparison to non-DM (0.50 IU/mL; $$p \leq 0.0019$$) (Figure 3B).
**Figure 3:** *Interferon-gamma plasma levels in response to Mtb-specific antigens by non-diabetic and diabetic patients. Whole blood from study population was stimulated or not with Mtb-specific antigens (ESAT-6, CFP-10 and TB7.7) by using QuantiFERON-TB Gold in Tube (QFT-GIT) assay for 18- 24h at 37°C. A) IFN-g-Mtb-specific levels were compared among study population: (A) non-DM and pooled MD (T1D and T2D patients) or (B) non-DM, T1D and T2D. IFN-γ. Mtb-specific levels were determined by subtraction between Mtb-specific antigens minus control tube (Nil). Each dot represents an individual value. DM, Diabetes Mellitus; T1D, type-1 DM; T2D, type-2 DM.*
In conclusion, our data have shown an increased prevalence of LTBI among DM patients by using IGRA test QFT-GIT, and it points to a significant association with sociodemographic and status of exposition to TB cases as important risk factors for LTBI. Moreover, a quantitative analysis from QFT-GIT revealed that T2D patients produced significant high levels of IFN-γ in response to Mtb-specific antigens.
## Discussion
Despite of DM is a well-recognized risk factor for active tuberculosis, there are heterogeneous and limited evidences in the literature for their association with latent TB infection, LTBI [16, 17]. To our best knowledge, the present study is the first one to compare the prevalence of LTBI in individuals with and without DM from a high endemic area in Brazil, the Rio de Janeiro State. Our data showed that albeit non-significant, the prevalence of LTBI among T1D and T2D individuals was increased in comparison to non-DM individual. QFT-GIT positivity was associated with sociodemographic determinants such as age and self-reported skin color (non-white). As expected, having an active TB case in the family was also an important significant variable associated with LTBI. Interestingly, IFN-γ Mtb-specific levels were significantly increased in patients with T2D. Altogether, these results revealed important variables to take into account in the management of DM patients in the screening of LTBI.
DM and TB can affect each other by many mechanisms including monocyte traffic modulation, phagocytosis, cytokine production as well as altered functions of the innate and adaptative immune cells [18]. DM increases the risk of TB as well as its severity in case of active disease and has a negative impact on public health, especially in countries where both conditions are strongly predominant [19]. The hyperglycemic state is strongly related to the alteration in the expression of receptors of activation and recognition, also for the phagocytic and microbicidal activity of cells of the innate immune system, as well as in the production of cytokines/chemokines and mechanisms of activation of cells of adaptive immunity, mainly in cellular response that are determinant for the response to Mtb [20].
There are no gold standard tests for the diagnosis of LTBI, and those available consist of indirect measures of the cellular immune response to Mtb antigens as the tuberculin skin test (TST) and the IGRAs (T-SPOT and QFT-GIT). It is well documented that prior BCG vaccination, nontuberculous mycobacterium (NMT) or others mycobacteria such as M. leprae (etiological agent of leprosy) infection are important causes of false-positive TST results [11]. However, TST is widely used in Brazil and other countries due to be considered an inexpensive and accessible method for LTBI detection in adults and active disease in children [21]. In the other hand, IGRAs offers numerous advantages over TST, such as reducing professional interference, measuring the specific response to Mtb antigens that are not shared with BCG or most species of NMT [22], in addition to requiring only one visit to the laboratory to be performed. However, it consists of a high-cost test and needs a laboratory structure to be performed [11]. In this study, we decided to evaluate the QFT-GIT, the only commercial IGRA available in Brazil, based on their advantages over the TST. All individuals in the present study showed higher levels of IFN-γ in response to mitogen stimuli, which guarantee the validation of the QFT-GIT (data not shown).
Patients with DM recruited for this study were from the Diabetes Outpatient Clinic of the PPC/UERJ, which receives patients from all over the State of Rio de Janeiro, but mostly residents in Rio de Janeiro city, followed by metropolitan regions including Baixada Fluminense and other regions. A predominance of active TB in young adult men (24-35 years) is well established [7, 23]. However, studies aiming to establish the predominance of this age range regarding LTBI are scarce and the majority of these studies have been performed in individuals with risk factors such as HIV positivity, health care professionals, people that have close contact with infected patients, use of TNF blockers (24–29). Although some of these studies have identified male gender risk factor for LTBI [29, 30], in our study gender was not related to LTBI (Table 2). Further studies are necessary to establish the relationship between LTBI and gender in Brazilian population. Interestingly, Brazilian cross-sectional studies have demonstrated an age transition of TB disease incidence as well as LTBI prevalence among healthy individuals to over forty-fifty years [30, 31]. Our results are consistent with these particular studies since that QFT-GIT positive individuals identified in the present study presented a mean age of 47.8 (Table 2).
The increased risk of LTBI infection among patients with DM in TB-endemic areas is recognized, but yet displaying limited and controversial evidences regarding several aspects, such as the choice or available LTBI diagnostic test, analytical methods employed, study population and TB setting [32]. Some evidences from literature support this affirmative, as well as our data which have shown increased proportion of LTBI among DM patients, although not statistically significant. An epidemiological study carried out in Malaysia, a Southeast Asian country with a high incidence of TB ($\frac{92}{100}$,000 population) observed a similar prevalence of LTBI among diabetic ($28.5\%$) and non-diabetic subjects ($29.2\%$) [33]. In addition, even in a high endemic setting, no significant differences were observed in the proportion of LTBI between TB household contacts and individuals without previous TB exposure. A systematic review and metanalysis including 13 observational studies, which were conducted in high-risk populations, such as household contacts of active pulmonary TB, immigrants and immunocompromised patients, revealed a small statistically significant association between DM and risk for LTBI [34]. It is worth to note that the studies included in this metanalysis not necessarily show a specific cohort composed by DM patients, as addressed in the present one. Moreover, DM diagnosis was self-reported in the majority of the studies and LTBI investigation was based on different immunological methods, such as TST, QFT-GIT and T.SPOT. Also, status or type of DM is not mentioned. Thus, we emphasize that there are few studies addressing this subject in regions with high incidence of TB. The present study suggests that in Brazil, and particularly in the Rio de Janeiro where there is a high incidence of TB [8], the presence of DM may not represent an isolated risk factor for LTBI and other variables, probably related to social determinants such as those described below, should be investigated.
There is substantial evidence that social variables such as being illiterate, unemployed and belonging to low income stratum are strongly associated with TB [35]. TB, poverty and poor access to health care services are strongly linked, which could be of great concern when TB is associated with DM. Nevertheless, a previous study has shown that the prevalence of the TB-DM association was quite similar in both developing and developed countries [36, 37]. We have shown that the majority of patients with DM in this study presented 6–12 years of study, considered a medium degree of school attendance, which showed a significant association with LTBI.
Our findings showed a strong association of LTBI among individuals who reported the presence of TB cases in the family. This highlights the need of effective and low cost LTBI screening in those who were known to be exposed to a major risk factor for early LTBI and also active cases detection in this population [38]. As we know, patients in contact with TB are included in the high risk group for developing the active form of the disease [17]. TB is acquired not only where people live but also where they work or socialize and poverty seems to influence the risk of progression to the disease as well as the risk of infection [37]. Socioeconomic characteristics and less favorable neighborhoods where patients reside are independent risk factors for TB [38]. Although, the prevalence of TB in metropolitan areas in RJ is higher, we did not observe significant differences regarding the QFT-GIT positivity among different regions of RJ.
In agreement with our data, which points out an increased prevalence of LTBI among T2D patients (Table 2 and Figure 2), some recent studies also have been reported the association of T2D patients and risk factors of LTBI [39, 40]. Also, we have observed that T2D patients have produced significant high levels of IFN-γ in response to Mtb-specific antigens from QFT-GIT when compared to non-DM individuals (Figure 3B). Active TB patients with DM display higher levels of circulating type-1 (Th1) cytokines such as IFN-γ and tumor necrosis factor (TNF) in comparison to TB patients without DM [41], while LTBI in the presence of DM or pre-DM were associated with reduced levels of these important mediators which are involved in the control of Mtb [42]. A recent study conducted in Brazil have shown altered surface molecules (HLA-DR, CD80 and CD86), cytokine/chemokine production and diminished bacterial clearance in monocyte-derived macrophages from T2D infected with Mtb clinical isolates in comparison to healthy individuals [43], suggesting that this group of DM patient may failure in the control of Mtb infection. Alternatively, the Mtb infection may influence the adipose tissue toward to pro-inflammatory cytokines production which in turn impact host metabolic homeostasis [44], adding new views of TB-DM interaction.
Particular strengths of our study are the population of diabetes cases in a large sample of Brazilian patients with T1D and T2D from a wide range of ethnic groups according to self-reported color-race, diagnosed in a community setting. All participating individuals followed a uniform and standardized protocol and similar to other population studies, we used a clinical definition of T1D and T2D assigned by healthcare providers and supported by the quantification of fasting plasma glucose and HbA1c. There are some limitations in our study that must be mentioned such as the low availability of TST test during the recruitment period of the study, which is widely distributed by the Health Ministry in Brazil. This fact did not allow us to perform additional comparisons with QFT-GIT in the studied population. Moreover, we could not associate the QFT-GIT findings with clinical evaluation and chest x-ray performance in order to obtain a greater follow-up of the studied population.
In conclusion, although we did not find a statistically significant association between DM and LTBI, this study revealed increased proportion of LTBI prevalence among DM patients from a high-endemic area in Brazil. Sociodemographic variables such as age, self-reported non-white skin color and the presence of TB index case in the family were highlighted as independent risk factors for LTBI. Overall, our data provide important evidences for guide LTBI screening programs among diabetic patients in high-burden TB setting.
## 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 Hospital Universitário Pedro Ernesto, Universidade do Estado do Rio de Janeiro. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AT, RS, MB and LF contributed with sample collection and processing, experiments, rationale for the study and manuscript preparation. LR and MB designed the study. Project supervision was performed by LR and MB. LR and MB were responsible for funding acquisition. AT, FG, RS, LF were responsible for participants’ recruitment. AT, RS, LR and MB contributed for data analysis and graph generation. AT, LR and MB analyzed the results. LR and MB were responsible for manuscript revision. AT, RS, LR and MB were responsible for writing the original manuscript draft of the manuscript while MB, LF and FG were in charge of revising it. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by grants awarded by the Fundação Carlos Chagas Filho de Amparo Pesquisa do Estado do Rio de Janeiro to MBG (FAPERJ; E-$\frac{26}{010.001534}$/2014). AT was a recipient of a fellowship from CNPq.
## Acknowledgments
The authors are grateful to physicians and nursing staff from Diabetes Unit, PPC/HUPE/UERJ and also to Dr. Walter Costa and Dr. Ana Paula Gomes from TB outpatient at HUPE/UERJ, and Carlos Antonio Negrato for editing the text.
## 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: 'Psychosocial Impact of the COVID-19 Pandemic on People With Type 1 Diabetes:
Results of an Ecological Momentary Assessment Study'
authors:
- Fabienne Schmid
- Andreas Schmitt
- Norbert Hermanns
- Bernhard Kulzer
- Dominic Ehrmann
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012070
doi: 10.3389/fcdhc.2022.834643
license: CC BY 4.0
---
# Psychosocial Impact of the COVID-19 Pandemic on People With Type 1 Diabetes: Results of an Ecological Momentary Assessment Study
## Abstract
### Aims
Psychological distress due to living with diabetes, demanding self-management tasks, impacts on life, and risks of complications is common among people living with diabetes. COVID-19 could pose a new additional risk factor for psychological distress in this group. This study aimed to analyze levels of COVID-19-related burdens and fears, variables explaining these levels, and associations with the concurrent 7-day COVID-19 incidence in people with type 1 diabetes (T1D).
### Methods
A total of 113 people with T1D ($58\%$ women; age: 42.3 ± 9.9 years) participated in an ecological momentary assessment (EMA) study between December 2020 and March 2021. The participants reported daily levels of COVID-19-related burdens and fears over 10 consecutive days. Global ratings of COVID-19-related burdens and fears were assessed using questionnaires, as were current and previous levels of diabetes distress (PAID), acceptance (DAS), fear of complications (FCQ), depressive symptoms (CES-D), and diabetes self-management (DSMQ). Current levels of diabetes distress and depressive symptoms were compared with pre-pandemic ratings gained during an earlier study phase. Associations between burdens and fears, psychosocial and somatic aspects, and the concurrent 7-day incidence rate were analyzed using multilevel regression.
### Results
Diabetes distress and depressive symptoms reported during the pandemic were comparable to pre-pandemic levels (PAID: $$p \leq .89$$; CES-D: $$p \leq .38$$). Daily EMA ratings reflected relatively low mean COVID-19-related burdens and fears in everyday life. However, there was substantial day-to-day variation per person indicating higher burdens on specific days. Multilevel analyses showed that daily COVID-19-related burdens and fears were significantly predicted by pre-pandemic levels of diabetes distress and diabetes acceptance but were not associated with the concurrent 7-day incidence rate nor with demographic and medical variables.
### Conclusions
This study observed no increase in diabetes distress and depressive symptoms during the pandemic in people with T1D. The participants reported low to moderate levels of COVID-19-related burdens. COVID-19-related burdens and fears could be explained by pre-pandemic levels of diabetes distress and acceptance but not by demographic and clinical risk variables. The findings suggest that mental factors may constitute stronger predictors of COVID-19-related burdens and fears than objective somatic conditions and risks in middle-aged adults with T1D.
## Introduction
The COVID-19 pandemic is a global health threat on a scale not seen in many years. While any person can be severely affected by the virus, people with pre-existing health problems or chronic conditions are at particularly elevated risk [1]. One such risk group is people with type 1 diabetes [2]. It has been demonstrated that suboptimal glucose control and pre-existing long-term complications of diabetes increase the risk of a severe clinical course of COVID-19. A recent study found that the odds of hospitalization 14 days after a positive test were 3.9 times higher in people with type 1 diabetes than in comparable persons without diabetes [2]. Additionally, people with type 1 diabetes may be at higher risk for infectious diseases, including respiratory tract infections, thus the risk of infection with COVID-19 might also be increased [3]. Furthermore, meta-analyses showed a nearly twofold increase in mortality risk for COVID-19-infected people with diabetes vs. without [4, 5].
In addition, adverse effects of the COVID-19 pandemic on psychological well-being and mental health have been observed. Systematic reviews and meta-analyses have shown that between $28\%$ and $34\%$ of people reported increased depressive symptoms due to the pandemic [6, 7]. In people with chronic diseases, the prevalence of anxiety and depressive symptoms even be increased to $55\%$ [6]. Furthermore, several studies reported increases in mental symptoms in people with diabetes during the pandemic. Fisher et al. [ 8] found that $67\%$ of people with type 1 diabetes reported higher diabetes distress than before the pandemic. A study by Moradian et al. [ 9] with German people with type 1 and type 2 diabetes suggests increases in depressive symptoms, anxiety, and psychological distress during the pandemic compared to before, however, using a retrospective evaluation. Moreover, Joensen et al. [ 10] showed that diabetes distress was positively associated with greater worries about COVID-19 and diabetes in people with type 1 and type 2 diabetes. A study by Brailovskaia et al. [ 11] demonstrated that depressive symptoms were positively associated with psychological distress caused by the pandemic. Finally, Sauchelli et al. [ 12] found that the confidence in diabetes self-management decreased during the pandemic and people reported that their needs for assistance and support were not sufficiently met by the diabetes healthcare system.
The psychological repercussions of COVID-19 in people with diabetes are particularly concerning considering the potential effects on diabetes outcomes. Depressive disorders as well as elevated diabetes distress have been frequently associated with detrimental effects on self-care behavior, glycemic control, and quality of life [13]. Existing evidence suggests that depression and diabetes distress may have increased during the pandemic. Thus, it is important to understand the psychological impacts and risks that the COVID-19 pandemic poses on people with type 1 diabetes including people’s subjective daily experiences of the pandemic.
We re-examined a sample of people with type 1 diabetes, who had participated in an observational study regarding psychosocial aspects of living with diabetes before the COVID-19-pandemic, during the pandemic. Experiences of burdens and fears due to COVID-19 were captured using questionnaires. Levels of diabetes distress and depressive symptoms as well as fear of complications, acceptance, and self-management were assessed and compared to the pre-pandemic assessment.
Furthermore, we aimed to analyze the subjective experience of COVID-19-related burdens and fears in everyday life. Therefore, we applied ecological momentary assessment (EMA) to assess the day-to-day COVID-19-related experiences. EMA is a methodology allowing the continued daily sampling of participants’ experiences in their everyday life [14].
Finally, we aimed to determine predictors of COVID-19-related burdens and fears, including medical risk factors, psychological aspects, and the concurrent 7-day incidence rate. To our knowledge, this is the first study in people with diabetes that analyzes the associations of the objective risk of infection with COVID-19 (i.e., 7-day incidence rate) with the subjective experience (i.e., burdens and fears) on that day, longitudinally over several days.
## Materials and Methods
The present study was a follow-up of participants of the DIA-LINK Study, a prospective observational study on affective conditions in type 1 diabetes, which was conducted before the COVID-19 pandemic started in Germany. The DIA-LINK study is described in detail elsewhere [14]. In short, participants were recruited at a large diabetes clinic in Germany. Participants had to be between 18 and 70 years of age, have type 1 diabetes, and were stratified based on elevated depressive symptoms and diabetes distress levels. Participation in the study went over three months including the baseline assessment, an EMA phase, and a follow-up after three months. This follow-up was used as baseline time point in the current analysis. The study was approved by the Ethics Committee of the German Psychological Society (DGPs) (file number NH082018). The follow-up survey, focusing on participants’ burdens and fears due to the COVID-19 pandemic, which constitutes the basis of the present research, was conducted between December 2020 and March 2021, usually about one year after participation in the original DIA-LINK Study.
## Participant Enrollment
Of the 203 participants of the original DIA-LINK Study, those who had consented to be contacted for a follow-up were informed about the present COVID follow-up via email, mail, or telephone. Interested persons were then informed about the follow-up survey, both orally and in writing, and written informed consent was obtained prior to inclusion. A total of 113 former study participants took part in this COVID follow-up. Actual assessment then took place via online questionnaires and via EMA.
## Assessments
All participants had completed a questionnaire package and interview prior to the beginning of the pandemic as part of their original participation in the DIA-LINK Study. HbA1c had been determined at the same time in a central laboratory from venous blood samples.
In the COVID follow-up, participants were surveyed using EMA over a period of 10 consecutive days. The 10-day period was chosen as it was considered long enough for gaining generalizable results and short enough to avoid participation rejection due to overly high effort. Also, the period should include both week and weekend days to reflect daily patterns of variations. For the EMA, a smartphone app (“mEMA”; Ilumivu Software for Humanity, North Carolina) was installed on the participants’ personal smartphones. Burdens and fears due to the COVID-19 pandemic were assessed each day as part of the evening assessment. A questionnaire survey including a set of questionnaires and specific COVID-19-related questions was administered online at the end of the 10-day EMA period. The most recent HbA1c value was requested personally as part of a telephone interview referring to the most recent estimation as documented in the participants’ diabetes booklets. The following variables were measured before the beginning of the COVID-19 pandemic (baseline): The following variables were measured as part of the assessment during the pandemic (COVID follow-up):
## Statistical Analyses
For each of the EMA items, the mean of response scores over the 10 days was calculated for each person (e.g., mean burden level per person). Furthermore, for each item, the mean score across all participants was calculated (e.g., mean burden level in the sample).
In addition, the average course of the EMA item scores over the 10 study days (1–10) was examined. For this purpose, the mean value of each item was calculated for each EMA day (1–10).
To reflect the day-to-day variability of responses of each participant, the coefficient of variation was calculated per person and item. The extent of day-to-day variability across participants is given as mean, median, and $25\%$ and $75\%$ percentiles of the coefficients of variation per item.
To examine possible changes in questionnaire scores, sum scores before and during the pandemic were compared using Student’s t-test.
To assess the associations between EMA-based ratings of COVID-19-related burden and the concurrent 7-day incidence rate, multilevel modelling with the participant as the nesting factor was used. Analyses were conducted separately with each EMA item as dependent variable and the 7-day incidence rate on that day as within-level predictor. In the first step, the within factor 7-day incidence rate was entered. In the second step, the medical and demographic risk factors for severe course of COVID-19 were added as between-level predictors, that is, age, sex, BMI, smoking, diabetes duration, presence of diabetes complications, presence of other comorbidities (e.g., cancer), and HbA1c. Finally, in the third step, psychosocial/psychobehavioral predictors were added: diabetes distress (PAID), depressive symptoms (CES-D), diabetes acceptance (DAS), diabetes self-management (DSMQ), and fear of complications (FCQ). The questionnaire scores from before the pandemic were used for the analyses. In each analysis, we controlled for study day and first autoregressive parameter. Bayes estimation was used and raw estimates as well as standardized coefficients (β) are reported.
## Characteristics of the Study Sample
A total of 113 people with type 1 diabetes participated in the COVID follow-up. The sample characteristics are displayed in Table 1. Sixty-six participants ($58.4\%$) were women. The mean age was 43.7 (± 12.0) years. The mean duration of diabetes was 21.6 (± 12.2) years. Fifty-seven persons ($50.4\%$) were diagnosed with at least one long-term complication of diabetes, mostly diabetic neuropathy and/or retinopathy. The mean HbA1c value was $7.8\%$ (± 1.2) or 61.5 (± 13.3) mmol/mol, respectively.
**Table 1**
| Variable | Participants (N = 113) |
| --- | --- |
| Age (years) | 43.7 ± 12.0 (22–70) |
| Female sex | 66 (58.4%) |
| Smoking | 20 (17.7%) |
| BMI (kg/m²) | 27.0 ± 4.9 (18.2–43.9) |
| Living alone | 27 (23.9%) |
| Persons in household (number) | 2.4 ± 1.1 (1–6) |
| Years of education | 13.3 ± 2.4 (9–18) |
| Diabetes duration (years) | 21.6 ± 12.2 (2–50) |
| With long-term complications * Retinopathy * Neuropathy * Nephropathy * Foot syndrome * Cardiovascular disease * Arterial vascular disease | 29 (25.7%)40 (35.4%)4 (3.5%)2 (1.8%)1 (0.9%)4 (3.5%) |
| With other serious diseases * Liver disease * Cancer (past) | 6 (5.3%)2 (1.7%) |
| Had severe hypoglycemia requiring assistance in the past year | 15 (13.3%) |
| Had ketoacidosis with medical treatment in the past year | 7 (6.2%) |
| HbA1c in % HbA1c in mmol/mol | 7.8 ± 1.2 (5.5–13.0)61.5 ± 13.3 (36.6–118.6) |
| PAID score (0–100) | 32.4 ± 17.6 (1–71) |
| CES-D score (0–60) | 17.9 ± 10.7 (0–44) |
| DSMQ score (0–10) | 6.6 ± 1.5 (3.1–9.1) |
| FCQ score (0–18) | 8.2 ± 4.3 (0–18) |
| DAS score (0–30) | 21.1 ± 7.2 (0–30) |
| Perceived burden due to the COVID-19 pandemic (questionnaire item) | 4.99 ± 3.13 (0–10) |
| Perceived threat from the COVID-19 (questionnaire item) | 5.04 ± 3.11 (0–10) |
| Perceived likelihood of becoming infected later in the pandemic (questionnaire item) | 4.29 ± 2.53 (0–10) |
| Perceived risk of severe course if infected (questionnaire item) | 5.00 ± 2.97 (0–10) |
Using the COVID-19-specific questionnaire, the perceived burden due to the COVID-19 pandemic was rated 4.99 ± 3.13 on a scale of 0−10. The perceived threat from COVID-19 was rated with 5.04 ± 3.11 on average. Other risks such as the perceived risk of becoming infected during the pandemic (4.29 ± 2.53) and the risk of severe clinical course if infected (5.00 ± 2.97) were rated similarly (also rated on the questionnaire).
DIA-LINK study participants who did not attend the follow-up survey, compared to those who did (present sample), were significantly younger, more likely to live alone, had higher HbA1c, higher diabetes distress, and more acute complications (i.e., diabetic ketoacidosis) according to baseline assessments (at the time of enrolment) (all p ≤.036; data not shown).
## Depression and Diabetes Distress Levels Before and During the Pandemic
Figure 1 shows the scores of depressive symptoms and diabetes distress before and during the COVID-19 pandemic. Interestingly, neither diabetes distress nor depressive symptoms differed at the time point during the pandemic from the time point before the pandemic (all p ≥.38). The mean PAID value was 32.2 ± 18.1 before pandemic and 32.4 ± 17.6 during the pandemic ($$p \leq .89$$). The average CES-D score remained stable at 17.1 ± 10.9 before the pandemic and 17.9 ± 10.7 during the pandemic ($$p \leq .38$$). In addition, no significant changes were observed for the other questionnaires (Figure 1).
**Figure 1:** *Changes in questionnaire scores before vs. during the COVID-19 pandemic.*
## EMA Period: Mean Levels of COVID-19-Related Burdens and Fears
In the daily assessment (EMA), participants reported a mean of 2.3 ± 2.3 (scale: 0−10) regarding burden due to worries about COVID-19 and health. The burden due to COVID-19-related restrictions was rated as 2.9 ± 2.4 on average. The fear of getting infected with COVID-19 was rated with a mean of 1.9 ± 2.0. The fear of family members or friends getting infected with the virus was rated with a mean of 2.3 ± 2.3. Figure 2 depicts the course of COVID-19-related burdens (Figure 2A) and fears (Figure 2B) together with the corresponding incidence rates across the study period. Burdens due to worries and restrictions increased toward January 2021 and declined afterward with the nadir in mid-February (Figure 2A). Fears of getting infected also showed a slight increase in December 2020 with a steady decline toward March 2021 (Figure 2B). Burdens and fears seemed to increase toward April 2021.
**Figure 2:** *Course of COVID-19-related burdens (A) and fears (B) displayed against concurrent incidence rates over the study period.*
## EMA Period: Variability of Burdens and Fears due to COVID-19
The mean day-to-day variability (coefficient of variation) per person of the burden due to worries about COVID-19 and health was 1.14 and indicates that the score varied by $114\%$ around the mean from day to day. Twenty-five percent of individuals had a coefficient of variation of ≤ 0.48 on the question regarding burden due to worries about COVID-19 and health over the 10 days and can be considered relatively stable with respect to their worries. For $25\%$ of all participants, the coefficient of variation was ≥ 1.58, indicating highly fluctuating worry. The coefficient of variation of burden due to COVID-related restrictions was 0.92, indicating that the rating varied by $92\%$ from day to day.
The rating of fear of getting infected with COVID-19 varied from day to day by $114\%$ around the mean. Twenty-five percent of participants had a coefficient of variation ≤ 0.53. In contrast, $25\%$ had a value ≥ 1.38, indicating highly variable anxiety. The mean coefficient of variation of fear of family members or friends getting infected with COVID-19 was 0.97.
Overall, substantial day-to-day variation per person was observed. The results are displayed in Figure 3.
**Figure 3:** *Boxplots displaying variability (CV) of COVID-19-related burdens and fears. Data are bowled line = median; box upper line = 75%; low line = 25%; lower end line = minimum; upper end line = maximum.*
## Associations of COVID-19 Burden and Fear Ratings with Risk Factors and the 7-Day Incidence Rate
Table 2 shows the associations of COVID-19 burdens and fears and 7-day incidence rate. Neither burden due to COVID-19-related restrictions nor burden due to worries about COVID-19 and health, fear of getting infected or the fear of family members/friends getting infected were significantly associated with the concurrent 7-day incidence rate (all β < 0.08).
**Table 2**
| Unnamed: 0 | Burden due to COVID-19-related restrictions | Burden due to COVID-19-related restrictions.1 | Burden due to worries about COVID-19 and health | Burden due to worries about COVID-19 and health.1 | Fear of getting infected with COVID-19 | Fear of getting infected with COVID-19.1 | Fear of family members or friends getting infected with COVID-19 | Fear of family members or friends getting infected with COVID-19.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Predictor | Estimate (95% CI) | ß | Estimate (95% CI) | ß | Estimate (95% CI) | ß | Estimate (95% CI) | ß |
| Step 1 - only within | Step 1 - only within | Step 1 - only within | Step 1 - only within | Step 1 - only within | Step 1 - only within | Step 1 - only within | Step 1 - only within | Step 1 - only within |
| 7-day COVID incidence | 0.000 (-0.006 - 0.006) | 0.01 | 0.003 (0.000 - 0.006) | 0.08 | 0.002 (-0.002 - 0.008) | 0.06 | 0.002 (-0.004 - 0.009) | 0.06 |
| Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors | Step 2 - within + demographic & medical risk factors |
| 7-day COVID incidence | 0.003 (-0.002 - 0.009) | 0.07 | 0.001 (-0.004 - 0.006) | 0.03 | 0.001 (-0.003 - 0.006) | 0.03 | 0.003 (-0.004 - 0.007) | 0.09 |
| Age | -0.021 (-0.060 - 0.021) | -0.10 | 0.02 (-0.02 - 0.06) | 0.11 | 0.019 (-0.015 - 0.053) | 0.11 | 0.037 (-0.004 - 0.078) | 0.19 |
| Female sex | 0.542 (-0-323 - 1.388) | 0.11 | 0.517 (-0.348 - 1.330) | 0.11 | 0.244 (-0.502 - 0.990) | 0.06 | 0.803 (-0.063 - 1.705) | 0.17 |
| BMI | 0.049 (-0.046 - 0.139) | 0.10 | 0.072 (-0.021 - 0.159) | 0.14 | 0.029 (-0.053 - 0.107) | 0.07 | 0.035 (-0.063 - 0.147) | 0.08 |
| Smoking | 0.498 (-0.878 - 1.909) | 0.08 | 0.969 (-0.302 - 2.326) | 0.16 | 0.838 (-0.241 - 2.066) | 0.16 | 0.765 (-0.370 - 1.972) | 0.12 |
| Diabetes duration | 0.043 (-0.001 - 0.081) | 0.21 | 0.031 (-0.012 - 0.070) | 0.15 | 0.040 (0.002 - 0.073) | 0.22 | 0.040 (0.004 - 0.079) | 0.21 |
| With long-term complications | -0.464 (-1.190 - 0.076) | -0.15 | -0.289 (-1.014 - 0.255) | -0.10 | -0.208 (-0.824 - 0.267) | -0.08 | -0.215 (-0.875 - 0.332) | -0.08 |
| With other chronic diseases | 1.276 (-.0368 - 3.274) | 0.13 | 0.892 (-0.692 - 2.728) | 0.09 | 0.986 (-0.417 - 2.581) | 0.11 | 1.263 (-0.557 - 2.928) | 0.12 |
| HbA1c | -0.025 (-0.487 - 0.346) | -0.01 | -0.122 (-0.565 - 0.218) | -0.06 | -0.135 (-0.508 - 0.173) | -0.08 | -0.084 (-0.425 - 0.196) | -0.04 |
| Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors | Step 3 - plus psychosocial risk factors |
| 7-day COVID incidence | 0.001 (-0.005 - 0.006) | 0.02 | -0.001 (-0.005 - 0.004) | -0.02 | 0.000 (-0.004 - 0.004) | 0.001 | -0.001 (-0.007 - 0.002) | -0.04 |
| Age | -0.022 (-0.067 - 0.024) | -0.09 | 0.014 (-0.028 - 0.056) | 0.06 | 0.008 (-0.027 - 0.046) | 0.04 | 0.033 (-0.003 - 0.075) | 0.15 |
| Female sex | 0.484 (-0.481 - 1.383) | 0.09 | 0.271 (-0.602 - 1.105) | 0.05 | 0.084 (-0.674 - 0.801) | 0.02 | 0.465 (-0.348 - 1.263) | 0.09 |
| BMI | 0.035 (-0.071 - 0.148) | 0.06 | 0.052 (-0.044 - 0.156) | 0.09 | 0.015 (-0.069 - 0.102) | 0.03 | 0.036 (-0.052 - 0.113) | 0.07 |
| Smoking | -0.048 (-1.135 - 1.121) | -0.006 | 0.175 (-0.839 - 1.275) | 0.02 | 0.066 (-0.840 - 0.993) | 0.01 | 0.244 (-0.967 - 1.492) | 0.04 |
| Diabetes duration | 0.035 (-0.007 - 0.077) | 0.14 | 0.023 (-0.014 - 0.061) | 0.09 | 0.030 (-0.001 - 0.063) | 0.15 | 0.037 (0.001 - 0.075) | 0.17 |
| With long-term complications | -0.432 (-1.083 - 0.152) | -0.13 | -0.349 (-0.940 - 0.202) | -0.11 | -0.308 (-0.819 - 0.161) | -0.11 | -0.276 (-0.771 - 0.268) | -0.09 |
| With other chronic diseases | 1.483 (-0.577 - 3.304) | 0.12 | 0.747 (-1.147 - 2.400) | 0.06 | 0.746 (-0.924 - 2.124) | 0.08 | 0.741 (-0.857 - 2.474) | 0.07 |
| HbA1c | 0.066 (-0.317 - 0.416) | 0.03 | -0.026 (-0.385 - 0.262) | -0.01 | -0.029 (-0.352 - 0.233) | -0.01 | -0.077 (-0.429 - 0.233) | -0.04 |
| Diabetes distress score (pre-pandemic) | 0.074 (0.026 - 0.113) | 0.45 | 0.093 (0.050 - 0.127) | 0.58 | 0.069 (0.032 - 0.099) | 0.53 | 0.068 (0.033 - 0.100) | 0.48 |
| Depressive symptoms score (pre-pandemic) | 0.001 (-0.044 - 0.047) | 0.004 | -0.008 (-0.049 - 0.033) | -0.03 | -0.007 (-0.041 - 0.027) | -0.03 | -0.004 (-0.046 - 0.039) | -0.02 |
| Diabetes acceptance score (pre-pandemic) | 0.171 (0.067 - 0.264) | 0.36 | 0.173 (0.080 - 0.255) | 0.37 | 0.146 (0.065 - 0.219) | 0.38 | 0.101 (0.010 - 0.190) | 0.25 |
| Diabetes self-management score (pre-pandemic) | -0.135 (-0.462 - 0.229) | -0.07 | -0.001 (-0.283 - 0.326) | -0.001 | 0.027 (-0.223 - 0.308) | 0.02 | 0.097 (-0.175 - 0.382) | 0.05 |
| Fear of complications score (pre-pandemic) | -0.093 (-0.237 - 0.047) | -0.14 | -0.006 (-0.130 - 0.120) | -0.01 | 0.071 (-0.032 - 0.183) | 0.13 | 0.061 (-0.066 - 0.168) | 0.11 |
| Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) | Variation explained by each model (R² ´[95% CI]) |
| Step 1 - only within | 0.071 (0.027 - 0.130) | 0.071 (0.027 - 0.130) | 0.092 (0.05 - 0.164) | 0.092 (0.05 - 0.164) | 0.092 (0.045 - 0.155) | 0.092 (0.045 - 0.155) | 0.047 (0.020 - 0.103) | 0.047 (0.020 - 0.103) |
| Step 2 - within + demographic & medical risk factors | within: 0.072 (0.029 - 0.128) | within: 0.072 (0.029 - 0.128) | within: 0.093 (0.045 - 0.136) | within: 0.093 (0.045 - 0.136) | within: 0.091 (0.038 - 0.138) | within: 0.091 (0.038 - 0.138) | within: 0.047 (0.016 - 0.092) | within: 0.047 (0.016 - 0.092) |
| Step 2 - within + demographic & medical risk factors | between: 0.192 (0.062 - 0.343) | between: 0.192 (0.062 - 0.343) | between: 0.187 (0.066 - 0.329) | between: 0.187 (0.066 - 0.329) | between: 0.189 (0.070 - 0.345) | between: 0.189 (0.070 - 0.345) | between: 0.207 (0.092 - 0.362) | between: 0.207 (0.092 - 0.362) |
| Step 3 - plus psychosocial risk factors (pre-pandemic) | within: 0.068 (0.028 - 0.108) | within: 0.068 (0.028 - 0.108) | within: 0.095 (0.049 - 0.158) | within: 0.095 (0.049 - 0.158) | within: 0.087 (0.043 - 0.139) | within: 0.087 (0.043 - 0.139) | within: 0.042 (0.014 - 0.084) | within: 0.042 (0.014 - 0.084) |
| Step 3 - plus psychosocial risk factors (pre-pandemic) | between: 0.531 (0.281 - 0.677) | between: 0.531 (0.281 - 0.677) | between: 0.606 (0.369 - 0.715) | between: 0.606 (0.369 - 0.715) | between: 0.580 (0.351 - 0.697) | between: 0.580 (0.351 - 0.697) | between: 0.484 (0.296 - 0.633) | between: 0.484 (0.296 - 0.633) |
The addition of clinical and demographic risk factors in step 2 yielded a slight improvement of explained variation of burdens and fears (Table 2). Simply, fear of getting infected as well as fear of family members/friends getting infected were associated with diabetes duration (β > 0.21) in this step.
When adding psychosocial risk factors, the explained variation was significantly increased (Table 2). Between $48\%$ and $61\%$ of the variation of each aspect could be explained by the models. All COVID-19 items were significantly and positively associated with pre-pandemic levels of diabetes distress (PAID) (all β > 0.45) and diabetes acceptance (DAS) (all β > 0.25). Higher daily COVID-19-related burdens and fears were significantly predicted by higher diabetes distress before the pandemic. Furthermore, higher daily COVID-19-related burdens and fears were also predicted by higher diabetes acceptance scores notably. In contrast, no demographic or medical variable, except diabetes duration for the fear of infection of family members, was significantly associated with COVID-19-related burdens and fears in the third step.
## Main Findings
The present study found no evidence of increased levels of depressive symptoms and diabetes distress during the COVID-19 pandemic in people with type 1 diabetes. The mean day-to-day ratings of COVID-19-related burdens ranged at a rather low to moderate level. The intra-individual variability of these burdens and concerns were considerable. Elevated diabetes distress and higher diabetes acceptance significantly and independently predicted higher COVID-19-related burdens, whereas the concurrent 7-day incidence rate was not significantly associated.
On average, there was no indication of an increase of diabetes distress and depressive symptoms during the COVID-19 pandemic compared to before in this group of middle-aged adults with type 1 diabetes. This result differs from previous study findings which suggest higher rates of depressive symptoms in the general population [6, 7] as well as higher diabetes distress and depressive symptoms in people with type 1 and type 2 diabetes [8, 9] during the pandemic. On the other hand, the lack of increase in depressive symptoms and diabetes distress is in line with a study by Sacre et al. [ 24] that also found no increase in people with type 2 diabetes during the pandemic. A possible explanation for the different results in this study compared to Fischer et al. [ 8] could be the higher mean age of their sample, possibly associated with more COVID-19-related burdens and fears. Furthermore, their study was conducted at an earlier stage of the pandemic at which people with diabetes may have been less habituated to the restrictions and burdens due to COVID-19 [25]. Differences to the study by Moradian et al. [ 9] could be explained by the retrospective evaluation of mental health (depressive symptoms, anxiety, and psychological distress before the pandemic) after the pandemic had begun, which could have overestimated the effect. The lack of change in diabetes distress and depressive symptoms in our study was mirrored by the lack of significant changes in diabetes self-management, fear of complications, and diabetes acceptance notably.
The average daily reported COVID-19-related burdens and fears were lower than those assessed via questionnaire. This effect is frequently observed in EMA studies, indicating that questionnaire-assessed burden ratings are usually higher than the day-to-day reported ratings due to more global evaluations and generalization [26, 27].
In the daily assessment over 10 days, the mean levels of burdens and fears were relatively low. However, the individual participant’s burden and fear ratings varied significantly from day-to-day, suggesting that clinically relevant burdens and fears may have been experienced on specific, while not all, days. COVID-19-related burdens and fears showed some level of trend that seemed to follow the daily 7-day incidence rates. However, on a within-person level, there was no evidence of an association of subjective burdens and fears due to COVID-19 and the concurrent objective incidence rate. This analysis showed the benefit of the EMA approach, as objective and subjective risk could be analyzed concurrently daily. Since the burdens and fears were not associated with the 7-day incidence rate in this study, it would be of interest for further research to identify the impacts that lead to greater fluctuations of burdens.
Diabetes distress and diabetes acceptance before the pandemic were the strongest predictors of COVID-19-related burdens and fears. They remained significant even when controlling for more traditional risk factors such as HbA1c and long-term complications. Diabetes distress and acceptance also seemed more relevant for explaining COVID-19-related burdens and fears than the 7-day incidence rate on the respective day. This suggests a partial independence of burdens and fears due to COVID-19 from rather objective risk markers. The finding that higher acceptance of diabetes was related to higher COVID-19-related burdens seems surprising at first look because diabetes acceptance is negatively related with diabetes distress [23, 28]. However, considering the objectively higher risks of COVID-19 for people with type 1 diabetes [2], this result may suggest that people who accept their diabetes are also more likely to accept the associated health risks. We hypothesize that low diabetes acceptance, in contrast, might represent rejection and avoidance of dealing with the associated risks for COVID-19. Their perceived personal threat and burden as well as their perceived risk of a severe course if infected might therefore be less pronounced. Further research will be needed to better understand these relations.
The 7-day incidence rate, demographic, and clinical risk factors for COVID-19 infection contributed little to the prediction of COVID-19-specific burdens and fears. It seems that objective risk factors for severe disease progression were less relevant in creating COVID-19-related burdens and fears than psychological aspects such as diabetes-related emotional concerns and integration of diabetes into daily life. Persons reporting higher distress due to their chronic condition also experienced higher burden due to the COVID-19 pandemic. This suggests an overarching way of dealing with stress that can have positive and negative effects, respectively, on both diabetes distress and COVID-19 burden.
## Limitations and Strengths
When interpreting the results, the following limitations must be considered. The conservative findings regarding diabetes distress and depressive symptoms during the pandemic as well as COVID-19-related burdens and fears should be interpreted against the specific characteristics of the study sample, that is, middle-aged adults with type 1 diabetes with relatively good overall health on average. Self-selection may have occurred during recruitment. Compared to the main study, individuals who participated in the follow-up survey had lower HbA1c levels, less diabetes distress, and were less likely to live alone at the time of the original DIA-LINK Study. These are factors that might contribute to lower COVID-19-related burdens and fears. Furthermore, the DIA-LINK Study sample was mainly recruited at a tertiary diabetes center; thus, the sample may not represent people with diabetes in primary care. Comparisons of the changes in diabetes distress and depressive symptoms over time with a control group without diabetes might support a better understanding of the possible impacts, but due to the design of the DIA-LINK Study, controls were not available. Comparisons of the present data with data from the general population would be of great interest; thus, further research will be needed. Finally, the specific time point of the follow-up survey within the pandemic should be considered: the survey was conducted during a period of higher incidence, mainly during the third wave. At that time, lockdown regulations and contact restrictions were in place for the second time in Germany. In addition, the first vaccine against the virus had been approved, which might have led to hopeful expectations. It is unclear to which extent these results can be generalized to other periods, for instance, with lower incidence rates.
Strengths of this study are the assessment of daily impacts of COVID-19 using EMA, probably yielding higher ecological validity than global questionnaire ratings, as well as the direct comparison of depression and diabetes distress levels during the pandemic with pre-pandemic values of the same individuals. Furthermore, fluctuations in COVID-19-related burdens and fears could be made visible via EMA demonstrating the additional information compared to single spot questionnaire assessment.
## Conclusions
In summary, the results show substantial day-to-day variability of COVID-19-related burdens and fears in this sample of people with type 1 diabetes. Although the levels of burdens and fears were rather modest on average, clinically relevant levels were experienced on specific days. The findings regarding predictors of COVID-19 burdens and fears suggest that diabetes-specific psychological factors and subjective experiences may be more relevant in explaining burdens and fears than objective health aspects and risk factors for a severe COVID-19 course. The findings highlight the importance of mental factors in dealing with COVID-19 and suggests the need for a psychosocial approach to reducing burdens and worries due to the pandemic in addition to information/education about a person’s individual risk to foster realistic expectations and corresponding feelings.
## Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. The dataset analyzed for this study is restricted by the German Federal Data Protection Act (BDSG) and will be made available upon reasonable request to the corresponding author.
## Ethics Statement
The studies involving human participants were reviewed and approved by Ethics committee of the German Psychological Society. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
FS: collected the data; analyzed and interpreted the data; drafted the manuscript. AS: planned and designed the study; collected the data; discussed the findings; revised the manuscript. NH: planned and designed the study; discussed the findings; revised the manuscript. BK: planned and designed the study; discussed the findings. DE: planned and designed the study; analyzed and interpreted the data; revised the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by the German Center for Diabetes Research (DZD) [grant number 82DZD11A02]. The funders were not involved in decisions regarding study design; collection, analysis, and interpretation of data; writing of the report; and submission of the article 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: 'Stress hyperglycemia, Diabetes mellitus and COVID-19 infection: The impact
on newly diagnosed type 1 diabetes'
authors:
- Ioanna Farakla
- Theano Lagousi
- Michael Miligkos
- Nicolas C. Nicolaides
- Ioannis-Anargyros Vasilakis
- Maria Mpinou
- Maria Dolianiti
- Elina Katechaki
- Anilia Taliou
- Vasiliki Spoulou
- Christina Kanaka-Gantenbein
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012074
doi: 10.3389/fcdhc.2022.818945
license: CC BY 4.0
---
# Stress hyperglycemia, Diabetes mellitus and COVID-19 infection: The impact on newly diagnosed type 1 diabetes
## Abstract
Several recent studies have documented an increased incidence of newly diagnosed type 1 Diabetes (T1D) cases in children and adolescents during the COVID-19 pandemic and a more severe presentation at diabetes onset. In this descriptive study, we present the experience of the Diabetes Centre of the Division of Endocrinology, Diabetes, and Metabolism of the First Department of Pediatrics of the National and Kapodistrian University of Athens Medical School at “Aghia Sophia” Children’s Hospital in Athens, Greece, concerning new cases of T1D diagnosis during the COVID-19 pandemic (March 2020- December 2021). Patients who had already been diagnosed with T1D and needed hospitalization due to poor control during the pandemic have been excluded from this study. Eighty- three children and adolescents with a mean age of 8,5 ± 4.02 years were admitted to the hospital due to newly diagnosed T1D during this 22 months’ period in comparison to 34 new cases in the previous year. All patients admitted during the pandemic with a new diagnosis of T1D, presented in their majority with DKA (Ph: 7.2) representing an increase of new severe cases in comparison to previous years (Ph 7.2 versus 7.3, p value: 0.021, in the previous year), [p-value: 0.027]. 49 cases presented with DKA, of which 24 were characterized moderate and 14 severe DKA ($28.9\%$ and 16,$9\%$, respectively), while 5 patients newly diagnosed, needed to be admitted to the ICU to recover from severe acidosis. Whether a previous COVID- 19 infection could have been the triggering factor is not supported by the SARS-Cov2 specific antibodies analysis in our cohort of patients. As far as HbA1c is concerned there was no statistically significant difference between the pre COVID-19 year and the years of the pandemic ($11.6\%$ versus $11.9\%$, p- value: 0.461). Triglycerides values were significantly higher in patients with new onset T1D during COVID-19 years compared to those before the pandemic (p value= 0.032). Additionally, there is a statistically significant correlation between Ph and Triglycerides for the whole period 2020-2021 (p-value<0.001), while this correlation is not significant for the year 2019. More large- scale studies are required to confirm these observations.
## Introduction
The Corona Virus Disease-19 (COVID-19) has been first described as a severe form of pneumonia reported in Wuhan, China in the late 2019 [1]. The COVID-19 has been subsequently spread worldwide, so that on March 11th, 2020, the World Health Organization has declared it as a pandemic [2]. Since the initial description of the disease, caused by the SARS-CoV2 virus, more that 6 million people worldwide lost their lives [3], and the disease is continuously spreading worldwide with a frightening velocity, while new mutations account for the continuously evolving clinical outcome [4].
Several underlying medical conditions, such as chronic pulmonary disease, arterial hypertension, cardiovascular events but also obesity and Type 2 Diabetes (T2D) pose an increased risk for severe COVID-19 outcome. The severe course of the COVID-19 infection in case of T2D is mainly attributed to the inflammatory state that characterizes obese patients with T2D leading to aggravation of insulin resistance, hyperglycemia with resulting glucotoxicity, increased oxidative stress and endothelial dysfunction, all contributing to increased morbidity [5]. On the other hand, it was observed that young patients with T1D were not represented among the patients affected by COVID-19 and, therefore, many articles appeared, especially during the first waves of the pandemic, suggesting that patients with T1D may even be spared from the SARS-CoV2 infection [6].
Moreover, most countries have implemented a range of public health care interventions and government regulations to mitigate the transmission of SARS-CoV-2. This situation significantly reduced pediatric emergency department (ED) access, most likely due to the fear of infection of their children, while awaiting to get examined [7]. As a result, Based on international reports from pediatric hospitals, it has been observed that newly diagnosed cases of T1D came to medical attention or seek medical care at a more advanced stage with severe dehydration and ketoacidosis and it was concluded that during the COVID-19 pandemic patients were reluctant to attend hospitals due to the fear of contamination with SARS-CoV2. Furthermore, there is a universal concern that the new coronavirus SARS-CoV2 may induce the autoimmune destruction of insulin-producing pancreatic beta cells through binding to the ACE-2 receptor expressed on the surface of the pancreatic beta cells, representing thus an environmental triggering factor for T1D initiation (8–11).
Diabetic ketoacidosis (DKA) is an avoidable complication of T1D if signs and symptoms of diabetes mellitus are recognized early, and the goal of the medical community worldwide is to diagnose T1D at an early stage before the exhaustion of beta cell reserve and the resulting occurrence of severe ketoacidosis [12]. DKA not only places increased burden on the healthcare system and, in some cases, may even require intensive care support in a tertiary care setting, but, more importantly, is associated with advanced exhaustion of the beta cell secretory capacity, associated with an earlier occurrence of long-term diabetes complications [13]. Moreover, during the pandemic period, it was important to minimize avoidable admissions to intensive care units, when the public health system worldwide was meant to spare medical staff and ICU coverage for the patients affected by severe COVID-19. Studies during the COVID-19 pandemic have reported an increase in pediatric cases of T1D presenting with DKA in many countries such as Italy, UK, USA, Canada, Southern Turkey (9–11, 14–23). A study in an Australian pediatric tertiary center reported a significant increase in presentation of severe DKA in a paediatric population with newly diagnosed T1D during the Covid -19 pandemic [11].
The aim of the current study was therefore to present the experience of the Diabetes Centre of the Division of Endocrinology, Diabetes, and Metabolism of the First Department of Pediatrics of the National and Kapodistrian University of Athens Medical School at “Aghia Sophia” Children’s Hospital in Athens, Greece, regarding both the incidence as well as the severity of new cases of T1D during the first two years of the COVID-19 pandemic, namely from March 2020 to December 2021, in comparison to the previous year 2019 and, furthermore, to compare these data with the existing literature data from other countries during the pandemic.
## Patients cohort
All children and adolescents aged <18 years with the initial diagnosis of T1D hospitalized at the “Aghia Sophia” Children’s Hospital (Athens, Greece) between March 2020 and December 2021 were included in the study. The number and data concerning DKA presentation of these patients were compared to the respective cases of the 12 months’ period preceding the pandemic.
## Ethical considerations
The study was approved by the “Aghia Sophia” Children’s Hospital Ethics Committee for Human Research. Written informed consent was obtained from the parents of the patients before their participation in the study.
## Clinical and laboratory parameters
Demographic and clinical data (age, date at diagnosis, personal and family history for severe diseases, family history of T1D), as well as auxological data, including height, weight, body mass index (BMI) and pubertal stage of the participants were collected. Endocrinological and biochemical data including HbA1c, initial venous blood gas analysis (pH, bicarbonate), serum glucose, C-peptide, and type 1 diabetes-associated antibodies [anti-glutamic acid decarboxylase antibodies, islet antigen 2, islet cell and insulin antibodies] were also determined. Based on the International guidelines of the International Society for pediatric and Adolescent diabetology (ISPAD) on DKA [24], DKA was categorized into three groups according to the severity: mild (pH: 7,2-7,3 and/or bicarbonate: 10-15 mmol/L), moderate: (pH: 7,1-7,2 and/or bicarbonate: 5-10 mmol/L) and severe (pH: <7,1 and/or bicarbonate: <5 mmol/L). The autoimmune etiology was confirmed by the presence of at least two or more autoantibodies for T1D.
## Serum detection of antibodies against the SARS-CoV2
To investigate whether the newly diagnosed patients with T1D have been previously infected with the SARS-CoV2 virus, antibodies against the SARS-CoV2 were assessed in all patients through ELISA by the use of an “in-house” ELISA, using the combination of the Receptor Binding Domain (RBD) and S2 domain within S protein and the whole N protein as capture antigens. This “in-house” ELISA has been previously developed in our Laboratory, with comparable, and even higher, sensitivity and specificity with other commercially available ELISA kits ($92\%$ and $97\%$ respectively) [26].
Briefly, 96-well plates (Nunc Maxisorp, Rochester, NY, USA) were coated with RBD 2.5 µg/mL, S2 1 µg/mL and N 1.5 µg/mL suspended in Phosphate Buffered Saline (PBS). After blocking with PBS containing $2\%$ Bovine Serum Albumin (BSA) at 37°C for 30 minutes, diluted serum samples ($$n = 65$$), in a dilution $\frac{1}{100}$ in $2\%$ BSA PBS were added and incubated for 1 hour at 37°C. Each serum sample was also evaluated against BSA ($0.01\%$ PBS) to eliminate non-specific binding. Alkaline phosphatase conjugated goat anti-human IgG (Jackson ImmunoResearch Laboratories, $\frac{1}{3000}$) antibody diluted in PBS/BSA was used to reveal specific human antibodies (IgG). Antibody-binding was assessed with the substrate 4-nitrophenyl-phosphate-disodium salt hexahydrate (Sigma Chemicals) at 405 nm (Chromate reader, Awareness Technology). R3022, a human SARS-CoV antibody previously determined to cross-react with SARS-CoV-2 was used as a positive control. The cut-off value has been previously determined as the mean plus 2 standard deviations (SD) of a pool of pre-COVID-19 controls [26]. Age-matched healthy children who were regularly followed-up at the Pediatric Department of our Hospital were used as controls ($$n = 120$$). Data was analyzed in Prism (GraphPad).
## Statistical analysis
Qualitative variables are presented as absolute and relative (%) frequencies. Quantitative variables are presented using measures of location (i.e. mean, median) and measures of dispersion (i.e. SD, min, max). To compare qualitative variables between the 2 groups, chi-square test (exact) was implemented. For quantitative variables, we used Mann-Whitney test for between groups comparisons.
A two-tailed p-value<0.05 was considered statistically significant. For the statistical analysis we used IBM SPSS v. 26 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp.).
## Clinical and laboratory characteristics
A total of 83 children (42 boys, 52,$1\%$), with a mean age of 8.5 years (± 4.02 years) had been hospitalized at the “Aghia Sophia” Children’s Hospital between March 2020 and December 2021, i.e., during the 22 months covering the 3 waves of the pandemic period, while during the preceding pre-pandemic year [2019] a total of 35 children with the new diagnosis of T1D had been admitted to the hospital (25 boys, $71.4\%$). Of the 83 patients newly diagnosed with T1D during the 22 months’ period of the pandemic from March 2020 to December 2021, DKA was present in 53 participants (63,$9\%$), that was not statistically different to the DKA percentage of the pre- covid period ($48.6\%$), [p value: 0.151]. Among the patients who presented with DKA at T1D onset, 11 patients presented with mild DKA (13,$3\%$), 24 with moderate DKA (28,$9\%$) and 14 patients with severe DKA (16,$9\%$) (Table 1) concerning four patients who have been initially diagnosed with diabetic ketoacidosis in other health centers and were subsequently transferred to our hospital for further treatment and diabetes education, unfortunately no data on their initial blood gas were available in order to classify them in a group of mild, moderate or severe DKA. It is worth mentioning that 5 of the participants necessitated hospitalization in the Intensive Care Unit (ICU) due to their severe ketoacidosis and poor general condition.
**Table 1**
| Variables | Group | Levels | Ν | % | p-value* |
| --- | --- | --- | --- | --- | --- |
| Gender | 2019 | Male | 25 | 71.4 | 0.043 |
| Gender | 2019 | Female | 10 | 28.6 | 0.043 |
| Gender | 2020-21 | Male | 42 | 50.6 | 0.043 |
| Gender | 2020-21 | Female | 41 | 49.4 | 0.043 |
| Season | 2019 | 1 | 8 | 22.9 | 0.072 |
| Season | 2019 | 2 | 5 | 14.3 | 0.072 |
| Season | 2019 | 3 | 7 | 20.0 | 0.072 |
| Season | 2019 | 4 | 11 | 31.4 | 0.072 |
| Season | 2019 | Missing values | 4 | 11.4 | 0.072 |
| Season | 2020-21 | 1 | 20 | 24.1 | 0.072 |
| Season | 2020-21 | 2 | 24 | 28.9 | 0.072 |
| Season | 2020-21 | 3 | 26 | 31.3 | 0.072 |
| Season | 2020-21 | 4 | 12 | 14.5 | 0.072 |
| Season | 2020-21 | Missing values | 1 | 1.2 | 0.072 |
| DKA | 2019 | Yes | 17 | 48.6 | 0.151 |
| DKA | 2019 | No | 17 | 48.6 | 0.151 |
| DKA | 2019 | Missing values | 1 | 2.9 | 0.151 |
| DKA | 2020-21 | Yes | 53 | 63.9 | 0.151 |
| DKA | 2020-21 | No | 29 | 34.9 | 0.151 |
| DKA | 2020-21 | Missing values | 1 | 1.2 | 0.151 |
| DKA severity | 2019 | 0 | 17 | 48.6 | 0.219 |
| DKA severity | 2019 | 1 | 6 | 17.1 | 0.219 |
| DKA severity | 2019 | 2 | 7 | 20.0 | 0.219 |
| DKA severity | 2019 | 3 | 2 | 5.7 | 0.219 |
| DKA severity | 2019 | Missing values | 3 | 8.6 | 0.219 |
| DKA severity | 2020-21 | 0 | 29 | 34.9 | 0.219 |
| DKA severity | 2020-21 | 1 | 11 | 13.3 | 0.219 |
| DKA severity | 2020-21 | 2 | 24 | 28.9 | 0.219 |
| DKA severity | 2020-21 | 3 | 14 | 16.9 | 0.219 |
| DKA severity | 2020-21 | Missing values | 5 | 6.0 | 0.219 |
| antiGAD < 5 | 2019 | Positive | 19 | 54.3 | 1.0 |
| antiGAD < 5 | 2019 | Negative | 9 | 25.7 | 1.0 |
| antiGAD < 5 | 2019 | Missing values | 7 | 20.0 | 1.0 |
| antiGAD < 5 | 2020-21 | Positive | 53 | 63.9 | 1.0 |
| antiGAD < 5 | 2020-21 | Negative | 23 | 27.7 | 1.0 |
| antiGAD < 5 | 2020-21 | Missing values | 7 | 8.4 | 1.0 |
| IAA | 2019 | Positive | 20 | 57.1 | 0.181 |
| IAA | 2019 | Negative | 8 | 22.9 | 0.181 |
| IAA | 2019 | Missing values | 7 | 20.0 | 0.181 |
| IAA | 2020-21 | Positive | 44 | 53.0 | 0.181 |
| IAA | 2020-21 | Negative | 35 | 42.2 | 0.181 |
| IAA | 2020-21 | Missing values | 4 | 4.8 | 0.181 |
| ICA | 2019 | Positive | 13 | 37.1 | 0.501 |
| ICA | 2019 | Negative | 16 | 45.7 | 0.501 |
| ICA | 2019 | Missing values | 6 | 17.1 | 0.501 |
| ICA | 2020-21 | Positive | 25 | 30.1 | 0.501 |
| ICA | 2020-21 | Negative | 43 | 51.8 | 0.501 |
| ICA | 2020-21 | Missing values | 15 | 18.1 | 0.501 |
| ZnT8 | 2019 | Positive | 10 | 28.6 | 0.281 |
| ZnT8 | | Negative | 9 | 25.7 | 0.281 |
| ZnT8 | | Missing values | 16 | 45.7 | 0.281 |
| ZnT8 | 2020-21 | Positive | 46 | 55.4 | 0.281 |
| ZnT8 | | Negative | 22 | 26.5 | 0.281 |
| ZnT8 | | Missing values | 15 | 18.1 | 0.281 |
## Seasonality of new diagnosis of T1D before and during the pandemic
We studied the seasonality of new diagnosis of T1D one year before and for 22 months’ period during the pandemic as a whole and separately. An increase of new cases of T1D during summer and autumn was observed, as far as the pandemic years are concerned ($28.9\%$ & $31.3\%$ respectively) versus pre-COVID19 year ($14.3\%$ & $20\%$). In contrast, based on the data from the pre-COVID19 year, most of the cases with new onset T1D were documented during winter for the last year prior to the pandemic ($31.4\%$ vs $14.5\%$) (Figure 1). Furthermore, we observed differences in seasonality within the two years of the pandemic. In the first year, the increased frequency was noted during autumn ($39\%$), while in the second year most cases presented during summer ($33.3\%$) (Figure 2).
**Figure 1:** *Boxplot of Ph for the 2 groups of interest. P-value: 0,021.* **Figure 2:** *Boxplot of Tg (triglycerides) for the 2 groups of interest. P-value: 0,032. Values with a circle denote outliers, while values with an asterisk denote extreme outliers.*
## Baseline laboratory characteristics before and during the pandemic
No differences were found regarding total cholesterol (CHOL), HDL and LDL (Table 2). There were statistically significant differences in Ph and Triglycerides (Tg) between the two groups. In specific, Ph was lower in the pandemic group (p-value: 0.021), while Tg were higher in the pandemic group (p- value: 0.032) (Table 2). There is a statistically significant correlation between Ph and Tg for the Covid-19 period (p value < 0.001) while this is not significant for year 2019 (p value: 0.387) (Table 3 and Figures 3, 4). As far as HbA1c is concerned there was no statistically significant difference between the pre COVID-19 year and the years of the pandemic ($11.6\%$ versus $11.9\%$, p- value: 0.461). ( Table 2)
During the pandemic, all patients tested for Covid- 19 with rapid antigen test at admission and antibodies against SARS-CoV-2 were assessed. If they had symptoms suggesting Covid -19 disease (like fever, abdominal pain, vomiting, etc.) they underwent PCR for SARS- CoV-2. Rapid tests were negative for all patients. Immunoactivity against SARS-COV2 in patients with newly diagnosed T1D and healthy children was evaluated using an “in house” ELISA where the combination of three different antigens (RBN, S2 and N) in their soluble form was used as capture antigens [26]. Three patients out of the 83 that were evaluated recognized Covid -19 antigens and were classified as positive, implying that they have successfully recovered from COVID-19 disease 10 days to 3 weeks prior to the admission for T1D. The SARS – COV2 antibody test was positive for six controls (out of 120 assessed) as well. Three of them were confirmed to have positive PCR, prior to admission, while the rest of them denied experiencing COVID-19 symptoms, implying that this positivity was most likely associated with the previous asymptomatic infection. The rate of positivity did not significantly differ between the two groups ($p \leq 0.05$). Notably, 3 patients were considered negative for COVID-19 based on ELISA results, although they had a positive PCR-test confirming a recent COVID-19 disease upon admission. This discrepancy between the positive PCR testing and the negative SARS-CoV2 antibodies titer may be explained as most COVID-19 patients elicit specific antibodies against SARS- CoV 2 14 days post disease onset, although several discrepancies have been reported [27] (Figure 5).
## Discussion
In the current study, we present the experience of the Diabetes Centre of the Division of Endocrinology, Diabetes, and Metabolism of the First Department of Pediatrics of the National and Kapodistrian University of Athens Medical School at “Aghia Sophia” Children’s Hospital in Athens, Greece, regarding number and severity of newly diagnosed children and adolescents with T1D during the first 22 months’ period of the COVID-19 pandemic. From March 2020 until December 2021, 83 children and adolescents with a mean age of 8.5 years (± 4.2 years) were admitted to the hospital due to newly diagnosed T1D. The absolute number of newly diagnosed cases of T1D was higher compared to that of the previous 12 months (83 new cases in 2020-2021, in more detail 42 the first year and 41 the second-year, vs 35 cases in 2019). The number of patients with more severe DKA at presentation was not statistically significantly increased ($16.9\%$ presenting with DKA in 2020-2021 vs. $5.7\%$ presenting with DKA during the previous non-pandemic year, p- value: 0.219) although the mean value of blood pH during the 2020-2021 22 months' pandemic period (pH: 7.2) was statistically lower that the mean blood pH value (pH: 7.3) of the pre covid- 19 year.
Our findings concerning the severity of DKA at presentation of new cases of T1D do not agree with those of similar studies carried out in many European and non-European countries during the COVID-19 pandemic reporting an increase of pediatric cases of T1D presenting with DKA like Italy, Germany, UK, USA, Southern Turkey, and Australia (9, 10, 14–24, 28–52). This apparent discrepancy between our observation and the literature data concerning severity DKA at presentation could probably be attributed to the smaller sample size of our cohort not allowing to unravel subtle differences. Since the observation of a greater severity of DKA diabetes onset as well as presumed overall increase of new T1D cases following the pandemic year is still an evolving phenomenon, only large scale multi-center studies could better clarify this issue in the future. Nevertheless, we have also observed that Ph on admission was statistically significantly lower during the pandemic years. ( p value = 0.021). ( Figure 3; Table 2) The majority of the 83 patients hospitalized in our hospital with a new diagnosis of T1D, mainly presented with DKA ($63.9\%$). Most DKA cases had moderate or severe DKA (28,$9\%$ and 16,$9\%$, respectively), while 5 patients needed to be admitted to the ICU to recover from severe acidosis. No differences were found regarding auto- antibodies for T1D since all newly diagnosed patients with T1D demonstrated positivity for at least two T1D-specific autoantibodies. ( Table 1) *Whether a* previous COVID- 19 infection could have been the triggering factor is not supported by the SARS-CoV2 specific antibodies analysis in our cohort of patients. The SARS-CoV2 antibody test was positive in 3 patients with T1D and 6 controls, therefore demonstrating that the positivity rate did not significantly differ between the two groups. Thus, our data did not support a clear association between previous SARS-CoV2 infection and T1D onset, similarly to previous reports [53]. ( Figure 5) Not only infectious agents but also psychosocial stressors are known to play a fundamental role in the pathophysiology of T1D initiation, since several physical catastrophic events have been reported to be followed by an increased number of newly diagnosed cases of T1D [54, 55]. However, in the current study no questionnaires investigating the psychological impact of the pandemic per se but also of the lockdown have been used, and thus no conclusions on that topic can be drawn. We speculate that public health measures implemented during the pandemic may have provoked stress, anxiety and fear to the children and adolescents and resulted to the emergence of autoimmune-mediated diabetes initiation, however, no data from the current study are available to assess such a possible association.
**Figure 3:** *Barplot of season for the 2 groups of interest.*
In our study, we have observed a more aggressive progress of T1D during the pandemic, since 5 of the participants needed to be admitted to the ICU department in contrast with only one patient the year before the pandemic. Two studies, so far, have documented that DKA presentation was more severe during the pandemic, compared to the pre-COVID-19 period [9, 52]. This observation might be attributed to the delay of patients seeking medical care and attending hospitals due to the fear of contamination with SARS-CoV2 and/or the possible more severe autoimmunity.
Previous studies have shown an association between lipid profile impairment and T1D onset [56]. In our study, triglycerides values were significantly higher in patients with new onset T1D during COVID-19 years compared to those before the pandemic (p value= 0.032). ( Figure 4; Table 2) Additionally, there is a statistically significant correlation between Ph and Tg for the whole period 2020-2021 (p-value<0.001), while this correlation is not significant for the year 2019. Total, LDL and HDL cholesterol were similar between the pandemic period and the pre- pandemic period. The same findings of a significant negative correlation between blood pH and triglycerides levels during the pandemic years in the absence of such a correlation in the preceding year have already been reported by Mastromauro C., et al. [ 57] pointing towards an additional role of lifestyle factors during the pandemic as an aggravating factor for the triglycerides’ elevation. Based on both our own data as well as the data reported from the Italian experience, it might, therefore, be suggested that there is not only an association between lipid profile impairment and severity of DKA in newly diagnosed T1D, but, furthermore, that also lifestyle changes during the COVID- 19 pandemic might have affected the lipid profile of newly diagnosed T1D cases.
**Figure 4:** *Barplot of season for the 3 groups of interest.*
Furthermore, according to these accumulated data, in our cohort an increased number of newly diagnosed cases of T1D was observed during the summer and autumn months. Moreover, we observed differences in seasonality during the pandemic period in comparison to the pre-pandemic year. ( Figures 1, 2) In the first year, the increased frequency was observed during autumn ($39\%$), after the first wave, while in the second year most cases presented during summer ($33.3\%$), after the second wave. So, after the first wave there was a time lag of around 6 months between the initiation of the pandemic and the newly diagnosed T1D cases, resulting at a peak of newly diagnosed T1D cases during autumn, while, after the second wave of the pandemic, there was another peak in the incidence of new T1D cases after a lag time of around 6-8 months. It is well known that a clear elapse time between the effect of a triggering factor for the initiation of the beta cell destruction of at least 6 months up to two years is occurring until the overt diabetes onset. Therefore, it is quite plausible that the first wave of the pandemic resulted in the first peak of T1D onset after a time lag period of around 6 months, while the second peak in the occurrence of new cases of T1D may both be attributed to previous COVID-19 infection that occurred during the previous 6-15 months before the diabetes onset. Although such an elapse time fits well to the time reported between the triggering factor and diabetes initiation, however, the lack of documentation of a previous infection with the SARS-CoV2 virus, as suggested by the non-significant presence of SARS-CoV2 antibodies among the patients who presented with new T1D, renders the previous SARS-CoV2 infection less probable to initiate the autoimmune destruction of the beta cells. Although several studies have attempted to demonstrate a pattern of seasonality, collected data are still controversial. Nevertheless, it has been reported that the incidence rate of new diagnosis of T1D reaches a peak during the winter months as was also the case in our new T1D patients in the year preceding the pandemic [58]. According to the previous and current data from our institution the seasonal distribution of diagnosis of T1D during the pandemic has changed and perhaps depends on the severity of the pandemic. In a recent study from Greece, Kostopoulou et al. investigated the impact of COVID-19 on new-onset of T1D in children and adolescents hospitalized in the two children’s hospitals in Patras, Greece, during the pandemic. They found a gradual increase of the incidence of new diagnosis of T1D from spring to winter, compared to the pre-COVID-19 year, where spring and autumn months displayed the lower rate of newly diagnosed cases of T1D. We may further speculate that the increased number of T1D diagnosis during autumn may have occurred since the parents of the patients were reluctant to attend hospitals due to fear of contamination with SARS-CoV2 even for routine blood exams and attended the hospitals at a more advanced stage of diabetes. Indeed, several studies have reported a reduction in the number of visits at the Emergency Department both for children and adults during the first wave of the pandemic [25, 52, 59, 60].
Our study has several limitations. There is a limited number of participants from a single pediatric center in Athens, Greece; therefore, no safe conclusions can be drawn.
Furthermore, the cellular and molecular involvement of SARS-CoV2 infection in the pathogenesis of T1D could not be confirmed by the implementation of an “in house” ELISA with comparable sensitivity with commercially available SARS- COV2 diagnostic ELISA kits [50] that has been used to assess seroprevalence of SARS- COV2 in our patients. [ Figure 5] Therefore, further, larger, and multicenter studies are needed to draw safe conclusions. Nevertheless, our study showed a different pattern of seasonality of T1D cases and a rather severe presentation of new cases of T1D. The elapse time between the COVID-19 pandemic waves and the higher incidence of new T1D cases ranged from 6 to 9 months, a period already reported between the effect of a triggering factor to the outbreak of overt diabetes.
In conclusion, we could not confirm a statistically significant higher incidence of newly diagnosed T1D cases during the pandemic, although a trend was obvious, but a seasonality pattern that is reminiscent of a lag period of 6-9 months between the separate COVID-19 waves and the respective peaks in the incidence of new T1D cases has been observed. Although we could not confirm an etiological link between previous SARS-CoV2 infection and resulting diabetes autoimmunity, as witnessed by the lack of SARS-CoV2-specific seropositivity, it is however plausible that the psychological burden of the lockdown and social isolation imposed to children during these individual waves of the pandemic may have triggered beta cell auto-immunity that resulted in new T1D onset.
Since the possible association between the Covid -19 pandemic and T1D cases is still an envolving phenomenon, only prospective, multicenter, large-scale studies could clarify this issue in the future.
**Figure 5:** *Prevalence of antibodies against the combination of the RBD and S2 domain within S protein and the whole N protein in sera from 65 patients and 120 controls, using the “in-house” ELISA. The cut-off value had been previously determined using the mean optical density plus 2 standard deviations (SDs) of pre-pandemic controls, represented by the dotted line. Black symbols represent the antibody-binding as Optical Density (OD) value of each serum at 405 nm. The rate of positivity did not differ significantly between the two groups (p >0.05). ns: Not Statistically Significant.*
## 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 Aghia Sophia Children’s Hospital, Athens, Greece. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
CKG and IF conceived and designed the study. IF coordinated the participants’ recruitment. IF, NN, I-AV, MMp, MD, EK and AT contributed to the collection of clinical characteristics, as well as endocrinological and biochemical parameters. TL performed the ELISA experiments under the supervision of VS. MMi performed the statistical analysis. NN, IF, TL and MMi wrote the original draft of the manuscript. CKG edited the final version of the manuscript. All authors approved the submitted version.
## Acknowledgments
Special thanks to the families of the patients and the patients per se.
## 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: Apparent Insulin Deficiency in an Adult African Population With New-Onset Type
2 Diabetes
authors:
- Davis Kibirige
- Isaac Sekitoleko
- Priscilla Balungi
- William Lumu
- Moffat J. Nyirenda
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012075
doi: 10.3389/fcdhc.2022.944483
license: CC BY 4.0
---
# Apparent Insulin Deficiency in an Adult African Population With New-Onset Type 2 Diabetes
## Abstract
Identifying patients with new-onset type 2 diabetes who have insulin deficiency can aid in timely insulin replacement therapy. In this study, we measured fasting C-peptide concentration to assess endogenous insulin secretion and determine the prevalence and characteristics of patients with insulin deficiency in adult Ugandan patients with confirmed type 2 diabetes at presentation.
### Methods
Adult patients with new-onset diabetes were recruited from seven tertiary hospitals in Uganda. Participants who were positive for the three islet autoantibodies were excluded. Fasting C-peptide concentrations were measured in 494 adult patients, and insulin deficiency was defined as a fasting C-peptide concentration <0.76 ng/ml. The socio-demographic, clinical, and metabolic characteristics of participants with and without insulin deficiency were compared. Multivariate analysis was performed to identify independent predictors of insulin deficiency.
### Results
The median (IQR) age, glycated haemoglobin (HbA1c), and fasting C-peptide of the participants was 48 [39-58] years,10.4 (7.7-12.5) % or 90 [61-113] mmol/mol, and 1.4 (0.8-2.1) ng/ml, respectively. Insulin deficiency was present in 108 ($21.9\%$) participants. Participants with confirmed insulin deficiency were more likely to be male ($53.7\%$ vs $40.4\%$, $$p \leq 0.01$$), and had a lower body mass index or BMI [$p \leq 0.001$], were less likely to be hypertensive [$$p \leq 0.03$$], had reduced levels of triglycerides, uric acid, and leptin concentrations [$p \leq 0.001$]), but higher HbA1c concentration ($$p \leq 0.004$$). On multivariate analysis, BMI (AOR 0.89, $95\%$ CI 0.85-0.94, $p \leq 0.001$), non-HDLC (AOR 0.77, $95\%$ CI 0.61-0.97, $$p \leq 0.026$$), and HbA1c concentrations (AOR 1.08, $95\%$ CI 1.00-1.17, $$p \leq 0.049$$) were independent predictors of insulin deficiency.
### Conclusion
Insulin deficiency was prevalent in this population, occurring in about 1 in every 5 patients. Participants with insulin deficiency were more likely to have high HbA1c and fewer markers of adiposity and metabolic syndrome. These features should increase suspicion of insulin deficiency and guide targeted testing and insulin replacement therapy.
## Introduction
Measurement of fasting, random, or stimulated C-peptide concentrations, as an indicator of pancreatic beta-cell insulin secretion, is recommended in patients with new-onset diabetes to identify specific diabetes subtypes (type 1 and type 2 diabetes and maturity-onset diabetes of the young), patients that require timely insulin replacement therapy, and also to predict response to oral hypoglycaemic agents [1, 2]. In patients with type 1 or type 2 diabetes and absolute insulin deficiency, early initiation of insulin therapy at diagnosis helps in achieving early optimal glycaemic control, preserves and improves beta-cell mass and function, in addition to averting diabetic ketoacidosis, and early onset of diabetes complications [3].
While fasting or random C-peptide concentrations are commonly measured to guide the management of diabetes in high-income countries, it remains a less available and expensive test in resource-constrained settings like sub-Saharan Africa (SSA). Initiation of insulin or switching from oral hypoglycaemic agents to insulin therapy in adult patients with new-onset or long-standing diabetes and presumed insulin deficiency in such settings is based on specific clinical suspicion. This has potential limitations, in part because there is little data on the prevalence, characteristics, and clinical correlates of insulin deficiency in adult patients with new-onset type 2 diabetes in SSA.
To address this evidence gap, we undertook this sub-study which was part of the Uganda Diabetes Phenotype (UDIP) study that aimed to investigate the manifestation of diabetes in adult Ugandan patients with recently diagnosed diabetes. In this sub-study, we specifically sought to determine the prevalence, characterisation, and predictors of insulin deficiency in an adult population with newly diagnosed type 2 diabetes in Uganda.
## Study Setting, Duration, and Participants
The UDIP study was carried out in seven tertiary public and private hospitals located in Central and Southwestern Uganda between February 2019 and October 2020. These hospitals predominantly serve the surrounding urban, peri-urban, and rural populations and have once weekly outpatient diabetes clinics for long-term management of adult patients with diabetes.
A total of 494 participants were recruited from these outpatient diabetes clinics. The inclusion criteria were patients aged ≥18 years with a recent diagnosis of diabetes (<3 months since diagnosis), initiated on any glucose-lowering treatment or treatment naïve, and tested negative for the three measured islet autoantibodies (defined as a concentration of autoantibodies against glutamic acid decarboxylase-65 [GADA], zinc transporter 8 [ZnT8-A], and tyrosine phosphatase [IA-2A] of ≤34U/ml, ≤67.7 U/ml, and ≤58 U/ml, respectively).
Participants who were critically ill and required urgent hospital admission were not immediately recruited into the study. Enrolment into the study was done later, at least two weeks after discharge from the hospital (but within three months of diagnosis), when they re-attended the outpatient diabetes clinic in a more clinically stable state. Pregnant women with new-onset diabetes were excluded from the study.
## Assessment of Socio-Demographic, Clinical, and Biophysical Characteristics
Participants were recruited in the study after an overnight fast of a minimum of eight hours. Using pre-tested case report forms, we collected information on relevant socio-demographic and clinical characteristics (age at diagnosis, gender, residence, smoking and alcohol ingestion habits, history of admission at the time of diagnosis, presence of urine or serum ketosis on admission, co-existing medical conditions, and glucose-lowering diabetes therapies initiated).
This was followed by resting blood pressure and anthropometric measurements (weight, height, waist circumference or WC, hip circumference or HC, body mass index or BMI, waist: hip circumference ratio or WHR, and waist circumference: height ratio or WHtR).
## Assessment of Metabolic Characteristics and Laboratory Measurements
Following standardised procedures, a fasting venous blood sample was drawn for measurement of blood glucose (FBG), glycated haemoglobin (HbA1c), insulin, C-peptide, lipid profile, uric acid, leptin, and three islet autoantibodies (GADA, ZnT8-A, and IA-2A). This was followed by a 75-gram oral glucose tolerance test (OGTT), with venous blood samples being drawn again 30 and 120 minutes following glucose solution ingestion to determine the serum glucose, insulin, and C-peptide concentrations at those two time-points.
All the above tests were performed at the ISO-certified clinical chemistry laboratory at the Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe Uganda using electro-chemiluminescence immunoassays manufactured by Roche diagnostics Limited, Germany on a Cobas 6000 C-model SN 14H3-15 machine (Hitachi High Technologies Corporation, Tokyo Japan). Pancreatic autoantibody testing was done using autoantibody ELISA kits from RSR Limited (Cardiff CF14 5DU, UK).
To determine the insulin resistance (homeostatic model assessment-2 [HOMA2] insulin resistance or HOMA2-IR) and the pancreatic beta-cell function (HOMA2-%B), we used the online HOMA2 calculator by the Diabetes Trial Unit of the University of Oxford, Oxford UK [4]. Pancreatic beta-cell function was also assessed using an optimal marker of beta-cell function- the oral insulinogenic index (IGI) using the formula: IGI = δ insulin (30 min insulin - 0 min insulin in µU/ml)/δ glucose (30 min glucose - 0 min glucose in mmol/l) [5]. We used the online quantitative insulin sensitivity check index (QUICKI) calculator to calculate the QUICKI using the fasting serum glucose and insulin concentrations [6].
## Definition of Insulin Deficiency
Insulin deficiency was defined as a fasting C-peptide concentration <0.76 ng/ml (equivalent to 0.25 nmol/l or 250 pmol/l). Absolute insulin deficiency or requirement was defined as a fasting C-peptide concentration <0.24 ng/ml (equivalent to 0.08 nmol/l or 80 pmol/l) [1, 2].
## Statistical Analysis
The categorical and continuous variables were expressed as proportions and medians with inter-quartile range (IQR), respectively. The prevalence of insulin deficiency was expressed as a frequency. Differences in the socio-demographic, clinical, anthropometric, and metabolic characteristics of participants with and without insulin deficiency were analysed using the x 2 test for categorical data and the Kruskal Wallis test for continuous data. Multivariate analysis was performed to identify predictors of insulin deficiency. All analyses were done using STATA statistical software version 15 College Station, TX: StataCorp LLC.
## Ethical Approval
This study was approved by the Research Ethics Committee of Uganda Virus Research Centre, Entebbe Uganda (GC/$\frac{127}{18}$/$\frac{05}{650}$) and the Uganda National Council of Science and Technology (HS 2431). Administrative clearance was also obtained from all participating study sites. All enrolled study participants provided written informed consent to participate in the study.
## Baseline Characteristics of All Study Participants
The socio-demographic, clinical, anthropometric, and metabolic characteristics of all study participants are summarised in Table 1.
**Table 1**
| Characteristic | All study participants (n=494) | Insulin deficient, (n=108, 21.9%) | Insulin sufficient, (n=386, 78.1%) | P value |
| --- | --- | --- | --- | --- |
| Socio-demographic and clinical | | | | |
| Age, years | 48 (39-58) | 48 (39-58) | 48 (39-57) | 1.00 |
| Gender Male | 214 (43.3) | 58 (53.7) | 156 (40.4) | 0.01 |
| Female | 280 (56.7) | 50 (46.3) | 230 (59.6) | |
| Residence UrbanRural | 364 (73.8)127 (26.2) | 79 (73.2)29 (26.8) | 285 (74.0)98 (26.0) | 0.73 |
| Current alcohol ingestion | 112 (22.9) | 21 (20.0) | 91 (23.6) | 0.69 |
| Past and current history of smoking | 39 (7.9) | 13 (2.6) | 26 (5.3) | 0.16 |
| History of admission at diagnosis | 199 (40.5) | 55 (50.9) | 144 (37.5) | 0.02 |
| Presence of urine or serum ketosis on admission | 69 (30.7) | 27 (45.8) | 42 (25.3) | 0.03 |
| Glucose-lowering drugs used** | | | | |
| Diet alone | 18 (3.6) | 3 (2.8) | 15 (3.9) | 0.78 |
| Metformin | 398 (80.6) | 71 (65.7) | 327 (84.7) | <0.001 |
| SU | 189 (38.3) | 34 (31.5) | 155 (40.2) | 0.10 |
| Insulin | 132 (26.7) | 48 (44.4) | 84 (21.8) | <0.001 |
| Medical comorbidities | | | | |
| Hypertension | 171 (34.6) | 28 (25.9) | 143 (37.1) | 0.03 |
| HIV infection | 58 (11.7) | 10 (9.3) | 48 (12.4) | 0.37 |
| Tuberculosis | 2 (0.4) | 1 (0.3) | 1 (0.9) | 0.39 |
| Systolic BP, mmHg | 126 (115-137) | 122 (111-131) | 126 (117-140) | 0.05 |
| Diastolic BP, mmHg | 84 (77-91) | 81 (75-86) | 85 (77-92) | 0.02 |
| Anthropometry | | | | |
| BMI, kg/m2 | 27.5 (23.5-31.5) | 24.3 (21.0-28.2) | 28.1 (24.6-32.1) | <0.001 |
| BMI in kg/m2 < 25 | 161 (32.9) | 57 (52.8) | 104 (27.2) | <0.001 |
| WC, cm | 96.0 (87.0-104.5) | 88.9 (81.5-96.5) | 98.5 (91.0-106.4) | <0.001 |
| HC, cm | 103.0 (96.0-111.5) | 98.0 (90.1-106.5) | 104.0 (96.5-113.0) | <0.001 |
| WHR | 0.92 (0.88-0.96) | 0.91 (0.87-0.95) | 0.93 (0.88-0.97) | 0.06 |
| WC: height ratio | 0.59 (0.53-0.65) | 0.54 (0.50-0.59) | 0.61 (0.55-0.66) | <0.001 |
| Metabolic | | | | |
| TC, mmol/l | 4.0 (3.3-5.0) | 3.7 (3.1-4.6) | 4.1 (3.3-5.1) | 0.03 |
| HDLC, mmol/l | 1.0 (0.7-1.2) | 0.9 (0.7-1.2) | 1.0 (0.8-1.2) | 0.68 |
| TGL, mmol/l | 1.3 (1.0-1.8) | 1.2 (0.9-1.7) | 1.4 (1.0-1.9) | 0.02 |
| LDLC, mmol/l | 2.6 (1.9-3.4) | 2.4 (1.7-3.0) | 2.7 (2.0-3.5) | 0.14 |
| Non-HDLC, mmol/l | 3.0 (2.4-3.8) | 2.7 (2.1-3.6) | 3.1 (2.5-3.9) | 0.01 |
| Uric acid, µmol/l | 273.0 (222.0-335.0) | 240.5 (197.5-294.5) | 284.0 (232.0-342.0) | <0.001 |
| Leptin, pmol/l | 2538.3 (619.8-5495.5) | 858.0 (279.0-2834.5) | 3207.5 (904.5-6276.0) | <0.001 |
| HbA1c, % | 10.4 (7.7-12.5) | 11.5 (8.9-13.4) | 10.0 (7.4-12.2) | 0.004 |
| HbA1c, mmol/mol | 90 (61-113) | 102 (75-123) | 86 (57-110) | 0.004 |
| Fasting glucose, mmol/l | 8.5 (6.2-13.4) | 10.4 (7.0-15.2) | 8.1 (6.0-12.7) | 0.001 |
| Fasting serum insulin, µU/ml | 5.9 (3.0-10.6) | 2.2 (0.9-3.9) | 7.3 (4.5-12.2) | <0.001 |
| Fasting C-peptide, ng/ml | 1.4 (0.8-2.1) | 0.5 (0.2-0.6) | 1.7 (1.3-2.4) | <0.001 |
| 30-minute glucose, mmol/l | 13.0 (9.9-18.3) | 15.9 (11.3-20.8) | 12.4 (9.7-16.9) | <0.001 |
| 30-minute insulin, µU/ml | 11.1 (5.5-22.5) | 3.7 (1.7-7.4) | 14.4 (7.7-25.3) | <0.001 |
| 30-minute C-peptide, ng/ml | 2.1 (1.1-3.3) | 0.7 (0.4-1.1) | 2.5 (1.7-3.8) | <0.001 |
| 120-minute glucose, mmol/l | 17.2 (12.4-23.4) | 20.9 (16.0-28.1) | 16.2 (11.6-22.4) | <0.001 |
| 120-minute insulin, µU/ml | 13.8 (6.9-27.5) | 5.7 (2.1-11.0) | 17.2 (8.8-33.5) | <0.001 |
| 120-minute C-peptide, ng/ml | 2.8 (1.5-4.8) | 1.11 (0.5-1.8) | 3.3 (2.1-5.3) | <0.001 |
| HOMA2-IR | 1.2 (0.8-2.0) | 0.8 (0.6-1.3) | 1.3 (0.8-2.1) | 0.01 |
| QUICKI | 0.35 (0.31-0.42) | 0.41 (0.36-0.52) | 0.34 (0.31-0.39) | <0.001 |
| HOMA2-%B | 43.1 (20.7-77.6) | 15.4 (9.2-36.1) | 46.1 (22.5-79.8) | 0.007 |
| Oral IGI | 1.3 (0.5-3.9) | 0.5 (0.2-1.0) | 1.8 (0.6-4.6) | <0.001 |
| TNF-α | 24.5 (19.5-34.0) | 22.0 (17.8-35.0) | 25.2 (20.0-34.0) | 0.06 |
| IL-1β | 16.0 (14.0-20.2) | 15.8 (13.8-20.0) | 16.0 (14.0-20.3) | 1.00 |
| IL-6 | 23.0 (15.0-43.5) | 24.0 (15.0-44.5) | 23.0 (15.0-40.3) | 0.78 |
The median (IQR) age, BMI, HbA1c, and fasting C-peptide of the participants was 48 [39-58] years, 27.5 (23.5-31.5) kg/m2, 10.4 (7.7-12.5) % or 90 [61-113] mmol/mol, and 1.4 (0.8-2.1) ng/ml, respectively, with $56.7\%$ of participants being female. About $41\%$ of participants reported a history of admission at the time of diagnosis with $30.7\%$ of those admitted presenting with urine or serum ketosis. The majority of the participants were initiated on metformin, either as monotherapy or in combination with other therapies ($80.6\%$), with only $26.7\%$ initiated on insulin therapy.
## Prevalence of Insulin Deficiency
One hundred and eight participants had insulin deficiency, corresponding to a prevalence of $21.9\%$. Absolute insulin deficiency or requirement was noted in 26 ($5.3\%$) participants.
## Socio-Demographic, Clinical, Anthropometric, and Metabolic Characterisation of Participants With and Without Insulin Deficiency
The socio-demographic, clinical, anthropometric, and metabolic characteristics of participants with and without insulin deficiency are summarised in Table 1.
Compared with those who were insulin sufficient, participants with confirmed insulin deficiency were more likely to be male ($53.7\%$ vs $40.4\%$, $$p \leq 0.01$$), to be admitted at the time of diagnosis ($50.9\%$ vs $37.5\%$, $$p \leq 0.02$$), to present with urine or serum ketosis on admission ($45.8\%$ vs $25.3\%$, $$p \leq 0.03$$), and to be initiated on insulin at diagnosis ($44.4\%$ vs $21.8\%$, $p \leq 0.001$). No statistically significant differences were noted in the age at diagnosis between the two groups.
Participants with insulin deficiency had statistically significant higher glycaemic indices (FBG-10.4 [7.0-15.2] vs 8.1 [6.0-12.7] mmol/l, $$p \leq 0.001$$, HbA1c- 11.5 [8.9-13.4] vs 10 [7.4-12.2] % or 102 [75-123] vs 86 [57-110] mmol/mol, $$p \leq 0.004$$ and 30-minute glucose- 15.9 [11.3-20.8] vs 12.4 [9.7-16.9] mmol/l, $p \leq 0.001$) and a lower HOMA2-IR (0.8 [0.6-1.3] vs 1.3 [0.8-2.1], $$p \leq 0.01$$). In addition, these participants had statistically significant lower markers of pancreatic beta cell function like HOMA2-B%, oral IGI, 30-and 120-minute C-peptide concentrations ($p \leq 0.001$).
Compared with those who were insulin sufficient, participants with insulin deficiency were less likely to have hypertension comorbidity ($25.9\%$ vs $37.1\%$, $$p \leq 0.03$$) and had statistically significant lower median BMI (24.3 [21.0-28.2] vs 28.1 [24.6-32.1] kg/m2, $p \leq 0.001$), WC (88.9 [81.5-96.5] vs 98.5 [91.0-106.4] cm, $p \leq 0.001$), triglyceride (1.2 [0.9-1.7] vs 1.4 [1.0-1.9] mmol/l, $$p \leq 0.02$$), non-high-density lipoprotein cholesterol or non-HDLC (2.7 [2.1-3.6] vs 3.1 [2.5-3.9] mmol/l, $$p \leq 0.01$$), uric acid (240.5 [197.5-294.5] vs 284 [232-342] µmol/l, $p \leq 0.001$), and leptin (858 [279-2834.5] vs 3207.5 [904.5-6276], $$p \leq 0.001$$) concentrations.
## Clinical Predictors of Insulin Deficiency
Table 2 shows the independent predictors of insulin deficiency. On multivariate analysis, BMI (AOR 0.89, $95\%$ CI 0.85-0.94, $p \leq 0.001$), non-HDLC concentration (AOR 0.77, $95\%$ CI 0.61-0.97, $$p \leq 0.026$$), and HbA1c level (AOR 1.08, $95\%$ CI 1.00-1.17, $$p \leq 0.049$$) were noted to independently predict insulin deficiency in this study population.
**Table 2**
| Characteristic | AOR (95% CI) | P-value |
| --- | --- | --- |
| Age at diagnosis, years | 0.99 (0.97-1.01) | 0.53 |
| Male gender | 1.30 (0.77-2.20) | 0.32 |
| Body mass index, kg/m2 | 0.89 (0.85-0.94) | <0.001 |
| Non-HDLC concentration | 0.77 (0.61-0.97) | 0.03 |
| HbA1c, % | 1.08 (1.00-1.17) | 0.05 |
## Discussion
To our knowledge, this is the first study to robustly investigate the frequency, characterisation, and predictors of insulin deficiency in an adult African population with newly diagnosed confirmed type 2 diabetes. We demonstrate that the prevalence of insulin deficiency is relatively high, occurring in about 1 in every 5 adult patients with newly diagnosed type 2 diabetes. Absolute insulin deficiency or requirement, similar to what is described in most patients with type 1 diabetes, was also prevalent in our study population. The documented high prevalence of insulin deficiency in these participants with newly diagnosed type 2 diabetes is less likely to be due to pancreatic beta-cell exhaustion related to the natural disease progression, seen in patients with long-standing diabetes.
Studies that have evaluated pancreatic beta-cell function using fasting or random C-peptide measurement in adult populations with newly diagnosed type 2 diabetes in SSA are limited. The few available studies have been conducted in adult patients with long-standing type 2 diabetes, reporting a wide variation in the documented prevalence of insulin deficiency ranging from $7\%$ to $73\%$ (7–11). Except for one study which used a lower cut-off of fasting C-peptide concentration of <0.2 nmol/l (0.6 ng/ml) to define low C-peptide levels [7], the rest of the above studies used a cut-off of <0.3 nmol/l (0.9 ng/ml).
Varying degrees of insulin deficiency have been reported in Asian and Caucasian adult populations with new-onset type 2 diabetes, further underscoring phenotypic differences across ethnicities. Despite using varied parameters to define insulin deficiency (fasting and stimulated C-peptide, HOMA2-B%, and insulin sensitivity or HOMA2-S) and fasting C-peptide thresholds, a lower prevalence of insulin deficiency was reported in an adult South Korean ($4.4\%$) [12] and Danish population ($9.6\%$) [13] with new-onset presumed type 2 diabetes. Similar to our study findings, the Swedish All New Diabetics in Scania (ANDIS) cohort [14] and the India-Scotland Partnership for precision medicine in diabetes (INSPIRED) Indian cohort [15] also reported a relatively high frequency of insulin deficiency of $17.5\%$ and $26.2\%$, respectively. Only the study performed on 500 adult patients with new-onset diabetes in Yemen reported a higher prevalence of insulin deficiency of $43.6\%$, based on HOMA2-B% and S [16].
In this study, participants who were insulin-deficient were more likely to be of the male gender, with a history of being admitted with ketosis and initiated on insulin therapy at the time of diagnosis of diabetes. A higher prevalence of insulin deficiency in male patients with type 2 diabetes has also been reported in two large studies conducted in Indian populations with long-standing type 2 diabetes [15, 17]. Similar to what we observed in our study, male participants had a lower mean BMI compared to their female counterparts in one study that investigated novel diabetes subgroups in Indians with young-onset diabetes [17]. The reasons to explain this clinical observation are not well-known and ought to be investigated.
In this study, we also demonstrated that insulin deficiency is associated with high glycaemic levels and reduced markers of adiposity and metabolic syndrome. An increase in BMI reduced the odds of insulin deficiency by $11\%$. A similar direct relationship between fasting C-peptide, as a measure of pancreatic beta-cell secretory function, and BMI has been reported in similar studies investigating pancreatic beta-cell function in African and Caucasian patients with long-standing and recently diagnosed diabetes (7–9, 18, 19). In addition to its role in regulating glucose homeostasis, insulin is an anabolic hormone that promotes lipogenesis and protein synthesis through the tyrosine kinase receptor pathway [20]. Insulin deficiency, therefore, would result in an increased lipolysis, diffuse muscle wasting, and a low BMI phenotype as clinical hallmarks.
The association between an increase in HbA1c concentrations and insulin deficiency as shown in our study has been replicated in most studies, further highlighting the key role of insulin in glucose homeostasis [8, 9, 19, 21].
To our knowledge, non-HDLC concentration, as an indicator of increased cardiovascular disease risk, has not been reported in any study as a predictor of reduced pancreatic beta-cell secretory dysfunction. Conversely, an increase in non-HDLC concentrations reduced the odds of insulin deficiency in our study population. Most studies have reported a close relationship between increased TC, LDLC, TGL, and low HDLC concentrations and reduced insulin secretion by the pancreatic beta-cells (19, 22–24). This variation in study findings may be related to differences in diabetes phenotypes across populations. Participants with confirmed insulin deficiency in our study had a favourable lipid profile pattern (lower TC, LDLC, TGL, and non-HDLC concentrations).
Disorders in lipid metabolism have been implicated in pancreatic beta-cell dysfunction through their adverse effects of inhibiting glucokinase activity, glucose-stimulated insulin secretion, pancreatic beta-cell proliferation, in addition to inducing beta-cell apoptosis, and suppressing transcription of the pre-proinsulin gene [25, 26].
The reasons explaining the high frequency of insulin deficiency in our study population are unknown but may be related to structural pancreatic defects (presence of a naturally existing small beta-cell mass), local environmental (in-utero and early childhood chronic infections and malnutrition), and genetic factors that influence the capacity for beta-cell expansion (replication or neogenesis) or secretory function (27–30). The developmental origins of health and disease (DOHaD) concept emphasises the direct influence of chronic infections and undernutrition on structural development and physiologic function of most critical body organs during the early stages of life and development, hence increasing the future risk of metabolic conditions like type 2 diabetes [31]. Undernutrition and chronic infections remain common in SSA and could explain the high rates of pancreatic beta-cell dysfunction and insulin deficiency noted in adult African patients with type 2 diabetes (32–36).
The African continent also has diverse populations with marked genetic heterogeneity. Black Africans may have susceptibility genes that directly affect pancreatic beta-cell mass and secretory function hence increasing the risk of developing type 2 diabetes. Two significant susceptibility genes that have been discovered to be highly prevalent in Western and Eastern African patients with type 2 diabetes and are worth mentioning are the Zinc Finger RANBP2-Type Containing 3 (ZRANB3) gene, which increases apoptosis resulting in reduced pancreatic beta-cell mass [37] and the transcription factor 7-like 2 (TCF7L2) gene, known to affect pancreatic beta-cell secretory function [38].
Because our study population did not include participants positive for islet autoantibodies and participants with insulin deficiency had a favourable lipid profile pattern (low TC, TGL, LDLC, and non-HDLC concentrations) and low circulating pro-inflammatory cytokines (IL-6, TNF-α, and IL-1β), we hypothesise that the observed insulin deficiency may be related to a structural defect (inadequate functional pancreatic beta-cell mass) either due to prior early-life environmental insults or genetic influences affecting the pancreatic beta-cell growth or regeneration and insulin secretion, as opposed to increased pancreatic beta-cell apoptosis due to lipotoxicity, increased endoplasmic reticulum stress, or inflammation.
In the context of SSA, communicable diseases like tuberculosis and HIV infection have been implicated in causing acute and chronic pancreatitis, hence reduced pancreatic beta-cell mass and consequent dysglycaemia [39, 40]. Cases of brittle diabetes due to tuberculosis of the pancreas have been reported in SSA, a region with one of the highest burden of TB globally [39]. In our study, pre-existing HIV and tuberculosis infections were only reported in $11.7\%$ and $0.4\%$ of participants, respectively with no differences between those who were insulin deficient and sufficient.
## Strengths and Limitations of the Study
Our study had several strengths. It recruited a substantially large number of patients with incident adult-onset diabetes without markers of islet autoimmunity from seven tertiary hospitals. Recruiting newly diagnosed adult patients rules out the possibility that the insulin deficiency observed in this study population is due to pancreatic beta-cell mass exhaustion which is part of the natural progressive nature of type 2 diabetes. We performed additional metabolic tests to further evaluate pancreatic beta-cell function in this study population like 30-and 120-minute C-peptide measurement, oral insulinogenic index, and HOMA2-%B calculation. To further characterise insulin deficiency in our study population, we performed additional relevant metabolic and immunological tests like leptin, uric acid, and pro-inflammatory cytokines. Few studies have performed these tests as part of their metabolic and immunologic characterisation.
Despite these strengths, the study had some limitations. Participants were recruited only from tertiary hospitals, which may introduce a selection bias and limit the generalisability of the findings to the adult Ugandan population with diabetes. Despite this limitation, it is important to note that the majority of patients self-refer to these tertiary hospitals for long-term diabetes management. Being cross-sectional in design, the study offers no evidence of a temporal relationship between type 2 diabetes and insulin deficiency.
## Conclusions
The relatively high prevalence of insulin deficiency in this Ugandan population with recently diagnosed adult-onset type 2 diabetes has significant clinical practice and policy implications in Uganda. It underscores the need for targeted assessment of pancreatic beta-cell function at diagnosis of diabetes, especially in patients with high glycaemic levels and reduced features of adiposity and metabolic syndrome. This will be important in identifying patients that require immediate insulin replacement therapy.
## 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 Uganda Virus Research Centre, Entebbe Uganda and the Uganda National Council of Science and Technology. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
DK oversaw the entire data collection process and wrote the initial draft of the manuscript. IS performed the statistical analysis and reviewed all the versions of the manuscript. WL contributed to the discussion and reviewed all the versions of the manuscript. MN supervised this work, collectively contributed to the research idea, and reviewed all the versions of the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This study was funded by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement for the Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine Uganda Research Unit, Entebbe Uganda (Project Reference: MC_UP_$\frac{1204}{16}$), and the National Institute for Health Research (NIHR) ($\frac{17}{63}$/131).
## 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/fcdhc.2022.944483/full#supplementary-material
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title: “It Just Kind of Feels Like a Different World Now:” Stress and Resilience for
Adolescents With Type 1 Diabetes in the Era of COVID-19
authors:
- Maeve B. O’Donnell
- Marisa E. Hilliard
- Viena T. Cao
- Miranda C. Bradford
- Krysta S. Barton
- Samantha Hurtado
- Brenda Duran
- Samantha Garcia Perez
- Kiswa S. Rahman
- Samantha Scott
- Faisal S. Malik
- Daniel J. DeSalvo
- Catherine Pihoker
- Chuan Zhou
- Abby R. Rosenberg
- Joyce P. Yi-Frazier
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012077
doi: 10.3389/fcdhc.2022.835739
license: CC BY 4.0
---
# “It Just Kind of Feels Like a Different World Now:” Stress and Resilience for Adolescents With Type 1 Diabetes in the Era of COVID-19
## Abstract
### Purpose
The COVID-19 pandemic has been a major stressor for adolescents. Given the unique implications of the pandemic for youth with type 1 diabetes (T1D), who already navigate multiple stressors as a function of their chronic condition, we aimed to describe the impact of the pandemic on adolescents with T1D and describe their coping strategies and resilience resources.
### Research Method
In a 2-site (Seattle WA, Houston TX) clinical trial of a psychosocial intervention targeting stress/resilience, adolescents 13-18 years old with T1D ≥ 1 year and elevated diabetes distress were enrolled August 2020 – June 2021. Participants completed a baseline survey about the pandemic, including open-ended questions about the effects of the pandemic, what was helping them navigate, and how it impacted T1D management. Hemoglobin A1c (A1c) was extracted from clinical records. Free text responses were analyzed using an inductive content approach. Survey responses and A1c were summarized using descriptive statistics and associations were assessed by Chi-squared tests.
### Results
Adolescents ($$n = 122$$) were $56\%$ female. $11\%$ of adolescents reported diagnosis of COVID-19 and $12\%$ had a family member/other important person die from COVID-19 complications. Adolescents described Social Relationships, Personal Health/Safety Practices, Mental Health, Family Relationships, and School to be primary areas affected by COVID-19. Helpful resources included: Learned Skills/Behaviors, Social Support/Community, and Meaning-Making/Faith. Among participants indicating that the pandemic had an impact on their T1D management ($$n = 35$$), the most commonly described areas were: Food, Self-Care, Health/Safety, Diabetes Appointments, and Exercise. Compared to adolescents who reported minimal difficulty managing T1D during the pandemic ($71\%$), those reporting moderate to extreme difficulty ($29\%$) were more likely to have A1C ≥ $8\%$ ($80\%$ vs. $43\%$, $p \leq .01$).
### Conclusions
Results underscore the pervasive impact of COVID-19 on teens with T1D across multiple major life domains. Their coping strategies aligned with stress, coping, and resilience theories and suggest resilient responses in the face of stress. Despite experiencing pandemic-related stressors in many areas, diabetes-related functioning was relatively protected for most teens, highlighting their diabetes-specific resilience. Discussing the pandemic impact on T1D management may be an important focus for clinicians, especially for adolescents with diabetes distress and above-target A1C.
## Introduction
Adolescents with type 1 diabetes (T1D) are disproportionately affected by stress. Over one-third report high stress about their diabetes [1], with serious implications for mental and physical heath. High diabetes distress is associated with higher A1c and an increased risk of developing psychological disorders [2, 3]. The COVID-19 pandemic is widely recognized as a global and potentially traumatic stressor [4] and has been linked with high rates of stress, loneliness, and increased risk of depression for teens [5]. Little is known, however, about the impact for teens managing a chronic health condition, such as T1D.
While there is no existing literature on the impact of the COVID-19 pandemic on the psychosocial well-being of adolescents with diabetes, research in adults with diabetes (both type 1 and type 2) suggests a significant impact on psychosocial health and diabetes management. Due, likely in part, to the increased risk of morbidity and mortality from COVID-19 [6, 7], adults with diabetes were more worried about contracting COVID-19 than their peers without chronic disease [8, 9]. Increased pandemic worry, in turn, was associated with poorer psychosocial health and feelings of isolation and loneliness [10]. Further, nearly half of adults with diabetes reported that the pandemic made diabetes management more difficult [11], and higher A1c was observed in those who reported less physical activity and an unhealthy diet during the pandemic [12]. Increases in diabetes-related stress during the pandemic were also linked with higher A1c [11]. To date, similar information has not been reported in teens, which represents a critical gap in the literature considering that prior to the pandemic over $80\%$ of U.S. adolescents with T1D were not meeting glycemic targets [13].
The current study was designed to describe the impact of the COVID-19 pandemic on psychosocial health and diabetes management in an adolescent population with T1D and elevated diabetes distress. Specifically, we aimed to explore the effects of the COVID-19 pandemic on teens with T1D, to understand what coping strategies they used to manage stress, and to describe the impact of the pandemic on diabetes management. We anticipated that teens with T1D would report that the COVID-19 pandemic significantly impacted various aspects of their lives and their T1D self-management.
## Materials and Methods
Data for this study were part of a larger set of baseline measures for a psychosocial intervention trial for teens with T1D and elevated diabetes distress that was ongoing during the start of the COVID-19 pandemic (clinicaltrials.gov registration: NCT03847194). Quantitative and qualitative data were collected using REDCap electronic data capture tools hosted at the University of Washington [14, 15]. This research was approved by the relevant Institutional Review Boards.
## Participants
Participants were eligible for the trial if they: 1) were aged 13-18 years old, 2) had a duration of T1D ≥ 12 months, 3) reported elevated diabetes distress within the prior 12 months (Problem Area in Diabetes Scale-Teen Version [PAID-T] [16] score ≥30), 4) spoke English fluently, and 5) were cognitively able to participate in intervention sessions and complete written surveys. Participants were screened through the diabetes/endocrinology clinics at their respective institutions, and recruitment primarily occurred via phone/video chat, although there were options for recruitment during outpatient or telehealth visits as was desired/appropriate. For participants under 18, written assent from the participant and written consent from the parent/legal guardian was provided. For participants aged 18, written consent was provided. We added a questionnaire related to the COVID-19 pandemic to baseline surveys in August 2020 and administered it to all newly enrolled study participants through June 2021. Participants were provided 6 weeks to complete their baseline survey and A1c. Participants received a monetary incentive ($20) for completion of their baseline survey and were eligible for further incentives for subsequent surveys (up to $80). Participants ($$n = 122$$) completed the COVID-19 questionnaire as part of baseline measures prior to randomization and intervention.
## Measures
Demographic data were collected via a self-report survey that participants completed as part of baseline measures. Health insurance, diabetes duration, A1c, insulin pump use, and CGM use were abstracted from electronic health records.
The Problem Area in Diabetes Scale-Teen Version (PAID-T) was utilized as a screening measure to assess the self-perceived emotional burden of living with diabetes [16, 17]. The 14-item scale is the only measure of diabetes distress developed and validated purposely for use with teens. Patients responded on a 6-point Likert scale (1=not a problem, 6=serious problem), and higher scores represent greater distress.
The COVID-19 questionnaire is a 26-item self-report questionnaire developed for this study to assess perceived impact of the COVID-19 pandemic on participants. These items were adapted from other measures of COVID-19 stress and impact that were newly developed at the start of the pandemic, including the COVID-19 Exposure and Family Impact Survey [18] (CEFIS-19) and the COVID-19 Impact Measure [19]. Items from these measures most relevant to the study aims were utilized, and minor wording changes, as were appropriate for the target population, were made by content knowledge experts on the study team.
In the first 11 questions, items assess worry/anxiety related to COVID-19, life events as a result of COVID-19 (e.g., missed school), lifestyle changes, and known COVID-19 symptoms/diagnosis for self, family, and important others. Sample items included: “Overall, how worried or anxious have you been about the COVID-19 pandemic?” and “How have changes in your life caused by COVID-19 impacted you?” Two open-ended question allowed for participants to comment on their general experiences of the COVID-19 pandemic, including: 1) *Tell us* about other effects of COVID-19 on yourself and/or your family, both negative and/or positive; 2) *What is* helping you through the COVID-19 pandemic? The following 15-items focused on participants’ appraisal of how the COVID-19 pandemic had affected their T1D management. 14 of the items were on a 5-point Likert scale (1=not at all; 5=extremely) and pertained to key domains of diabetes management. Items included the same stem (“Since the COVID-19 pandemic, I…) and were phrased in both negative (“…have struggled to properly manage my diabetes.”) and positive directions (“…have found it easier to manage my diabetes.”). Domains included overall management, food/eating, physical activity, diabetes supplies, blood glucose variability, access to healthcare team, and family management. The remaining question was open-ended and asked participants: 3) In what ways has COVID-19 impacted your T1D management?
## Analysis Plan
Following the Standards for Reporting Qualitative Research (SRQR) [20], the qualitative study team included researchers with training in psychology (MO, MH, VC, JYF), endocrinology (FM, DD), medical anthropology and global health (KR), and health services science with expertise in qualitative research (KB, FM). Three members of the study team (MO, VC, KR) were trained to code the qualitative data under the supervision of the team’s qualitative lead (KB). The lead coder (MO) met with the qualitative lead to discuss a data analysis plan, to share codes, and to get feedback on the process. The lead coder periodically shared results, received feedback about codes, and identified themes with members of the investigator team (JYF, MH, FM, DD). The full writing group provided input into interpretation of codes to assure clinical relevance.
At the time of data analysis, existent literature on the COVID-19 pandemic’s impact on teens with T1D was lacking. As such, we decided to take an inductive qualitative approach and used conventional content analysis [21]. Each question was individually analyzed and open coding was conducted to create a codebook for each of the three questions. The lead coder generated initial codebooks from the response data. The three coders (MO, VC, KR) independently applied the codebook categories to all of the responses in separate Microsoft Excel spreadsheets and the coding team met regularly to identify discrepancies among coders and refine the codebook (e.g., by adding new codes for ideas that were not captured by existing codes). Multiple codes could be applied to a single response. Since both Question 1 (Effects of COVID-19) and Question 3 (COVID 19’s Impact on T1D management) generated both positively and negatively worded responses, after primary codes were applied, responses were coded with sub-codes of “positive” or “negative” if the participant added a decipherable valence to their response.
After each iteration, each coder independently recoded the transcripts using the updated codebook. This process continued until there were minimal (<5) discrepancies across all three coders for both codes and sub-codes. Any discrepancies that could not be resolved within the coding team were escalated to the team’s qualitative lead for adjudication. Once final coding occurred, codes across all three questions were tallied to identify the most widely endorsed code categories within each response set. The full research team then considered how the codes related to one another, to theories of resilience, and to team members’ clinical experiences with teens with T1D during the pandemic to identify meaningful themes.
Survey items regarding COVID-19 impacts on diabetes management were collapsed from a 5-point Likert scale to 3 categories (Not at all/Slightly, Moderately, Very/Extremely) based on distributions for ease in interpretation and presentation. Two questions (“I have struggled to properly manage my diabetes” and “I have noticed more fluctuations/variability in my blood glucose levels”) were dichotomized as Not at all/*Slightly versus* Moderately/Very/Extremely because the distribution suggested a natural division between people who experienced little to no difficulty versus those reporting greater impact. A1c at enrollment was categorized as <$8\%$ vs. ≥$8\%$ as an indicator for elevated A1c. Participant demographics, A1c and survey responses related to general impacts of COVID-19 were summarized descriptively using frequencies and percentages for categorical variables and means with standard deviations for quantitative variables. Associations between survey responses and A1c levels (<$8\%$ vs. ≥$8\%$) were assessed using Chi square tests.
## Participant Demographics
Participants ($$n = 122$$) were $56\%$ Female, $2\%$ American Indian/Alaska Native, $5\%$ Asian, $11\%$ Black/African-American, $1\%$ Native Hawaiian/Pacific Islander, $80\%$ White, and $7\%$ Other (participants could endorse multiple racial identities). $18\%$ of the sample indicated Hispanic/Latino ethnicity. Participants had a mean A1c of $8.5\%$ ($2.1\%$), $71\%$ used an insulin pump, and $76\%$ used a CGM (Table 1).
**Table 1**
| Demographic Characteristics | Unnamed: 1 |
| --- | --- |
| Age 13-17 years | 88% |
| Age 18 years | 12% |
| Age in years, median (IQR) | 15 (14-16) |
| Gender | |
| Male | 40% |
| Female | 56% |
| Other | 4% |
| Race* | |
| American Indian/Alaska Native | 2% |
| Asian | 5% |
| Black/African American | 11% |
| Native Hawaiian/Pacific Islander | 1% |
| White | 80% |
| Other | 7% |
| Ethnicity | |
| Hispanic/Latino Ethnicity | 18% |
| Public Health Insurance | 29% |
| Site | |
| A | 52% |
| B | 48% |
| Diabetes Characteristics | |
| A1c, mean ± SD | 8.5 ± 2.1 |
| Duration in years, median (IQR) | 5.9 (3.4-8.9) |
| Pump use | 71% |
| CGM use | 76% |
## General Impact of COVID-19
Prevalence of COVID-related events. Regarding incidents related to COVID-19, $11\%$ ($\frac{13}{122}$) of teens in this sample reported a diagnosis of COVID-19 themselves, and $2\%$ ($\frac{2}{122}$) were hospitalized. One half ($\frac{63}{122}$) of teens had a family member or other important person diagnosed, $19\%$ ($\frac{23}{122}$) had a family member or other important person who was hospitalized, and $12\%$ ($\frac{15}{122}$) of teens reported that they had a family member or other important person in their life die due to COVID-19.
Personal impacts and responses to COVID-19. On the quantitative survey, $44\%$ ($\frac{54}{122}$) of teens reported that they were not at all or slightly worried/anxious about the COVID-19 pandemic, $37\%$ ($\frac{45}{122}$) reported that they were moderately worried/anxious and $19\%$ ($\frac{23}{122}$) reported that they were very or extremely worried/anxious. In response to the open-text question regarding negative or positive effects of COVID-19 on the teen or their family (Q1), there were 14 code categories (Table 2). The most frequently observed code categories were: Family Relationships ($$n = 37$$), School Changes ($$n = 21$$), Personal Health and Safety Practices ($$n = 21$$), Social Relationships ($$n = 18$$), and Mental Health ($$n = 17$$) (Figure 1). Changes in family relationships was a widely endorsed effect of COVID-19, although some teens found both positive and negative effects. For example, one 13-year-old gender non-binary teen noted, “Staying at home together all the time has caused tension between my family, but we have also grown closer.” Several teens noted the changes to their schooling, which were described as overwhelmingly negative. A 14-year-old male teen simply described, “School in my opinion is worse now (with it being online)…” Participants also discussed negative effects to their social relationships and mental health. One 13-year-old female teen described both noting, “There are not any positive effects. I can’t see my friends and my dog got an … injury that we can’t get treated because of COVID. There is nothing to look forward to. Every day is the same … my mental health has worsened…”
In response to the question of “*What is* helping you through the COVID-19 pandemic?” ( Q2), there were 12 code categories (Table 2). Most commonly, teens reported that Relationships ($$n = 65$$) and Stress *Management via* Entertainment, Hobbies, and Exercise ($$n = 43$$) were helping them through. For example, a 14-year-old female teen described that “being able to still talk to people I love through facetime and text” was helping them get through, while another 16-year-old female teen described multiple behavioral strategies, “Increased free time to do more exercise and hobbies. Running and hiking have been good stress-relievers. I have also had more time for baking and reading.” Six participants noted that there was nothing that was helping them through the COVID-19 pandemic.
From the codes, we identified overarching resilience themes consistent with stress and coping [22] and resilience theories [23, 24]. These themes were: 1) Internal Resilience Resources, which referred to personal learned skills and behaviors (i.e., what helped the teen navigate COVID-19), 2) External Resilience Resources, which referred to social support and community resources (i.e., who helped the teens navigate COVID-19) and 3) Existential Resilience Resources, which referred to meaning-making, faith, religious, and spiritual resources (i.e., finding a why in navigating COVID-19) (Figure 2).
**Figure 2:** *Themes and code categories of teen-reported resilience with example quotes.*
## Impact of COVID-19 on T1D Management
On the quantitative survey, the majority of teens ($71\%$) reported that they were not at all or only slightly affected by COVID-19 in terms of properly managing their diabetes. Most teens endorsed that they had continued access to their diabetes care team ($70\%$) and that they were not arguing with their parents more about diabetes during the COVID-19 pandemic ($70\%$) (Figure 3 and Supplemental Table 1). Of the $29\%$ of teens who experienced increased (moderate to extreme) difficulty were more likely to have A1c ≥ $8\%$ ($80\%$ versus $43\%$, $p \leq .01$). The $42\%$ of teens who reported greater fluctuations in blood glucose levels also were more likely to have A1c ≥ $8\%$ ($67\%$ vs. $43\%$, $$p \leq .01$$) (Figure 4).
**Figure 3:** *Negative and positive impacts of COVID-19 on diabetes management. Numbers shown are percentages.* **Figure 4:** *Percentages with <8% and ≥8% A1C among subgroups of respondents who did or did not report problems with diabetes management since the COVID-19 pandemic. P values are from Chi square tests.*
In response to the open-text question of, “In what ways has COVID impacted your T1D management?,” there were 10 code categories (Table 2). Most commonly, when asked in an open-ended fashion, teens reported that there was no effect of COVID-19 on their diabetes management ($$n = 50$$). The next most widely endorsed domain was blood glucose management ($$n = 18$$). For example, one 16-year-old male teen noted, “I’ve had more time to focus on my blood sugars,” while another 16-year-old gender non-binary teen described, “It’s [COVID-19] also been hard emotionally and diabetes management is much harder when it’s difficult to find the energy to care about blood sugars.” If participants indicated a positive or negative valence to their response, results were plotted in Figure 5. Overall, for those whose T1D management was affected by the COVID-19 pandemic, participants described more negative impacts than positive impacts.
**Figure 5:** *Positive and negative teen comments about COVID-19's impact on Type 1 Diabetes management. Numbers shown are counts.*
## Discussion
The purpose of this study was to explore the effects of the COVID-19 pandemic, to understand what coping strategies were utilized, and to describe the impact of the pandemic on diabetes management for a diverse sample of teens with T1D and elevated diabetes distress participating in a clinical trial. There were pervasive impacts of the COVID-19 pandemic in our sample—the majority of teens reported moderate to high anxiety about the pandemic and had direct knowledge of an important person in their lives having COVID-19. More than 1 in 10 teens in our study were diagnosed themselves or had a family member or other important person die due to COVID-19. Teens also highlighted stressors in a wide range of areas due to the COVID-19 pandemic, including most commonly with family, engaging in personal health and safety practices (e.g., social distancing), in their social relationships, school, and in their own mental health—all of which are life domains that contain social and relational elements. This finding is in line with other emergent research that suggests that teens have experienced more family conflict and difficulty navigating peer relationships during the COVID-19 pandemic [25, 26]. COVID-19 and associated safety measures (e.g., quarantine) have had a crushing social impact globally [27], which may be especially concerning for adolescents who are at a key time of social and emotional development [28]. This may be a driving cause as to why teens are at risk of developing mental health symptoms during the COVID-19 pandemic [29].
Despite the widespread impact of the COVID-19 pandemic on many parts of life, most teens generated specific strategies, skills, and resources that were helping them to navigate the COVID-19 pandemic. Thematically, many of these resilience strategies were consistent with existent theories of stress, coping, and resilience literature, which suggest that teens, especially in the context of chronic disease, will accumulate and apply resources to navigate challenges as they arise (22–24). Some theories of resilience further suggest categories of resilience resources, which fall into individual, community, and existential domains [23, 30]. Teens reported strategies across all these domains, demonstrating pervasive use of internal, external, and existential resilience resources; this provides support that employing resilience resources is both attainable and a “universal” response to stress [23]. This pattern is particularly notable and supportive of resilience, given that these were teens with elevated diabetes distress. Teens in our sample predominantly reported engaging in personal behavioral strategies, such as using technology or pursuing hobbies, and/or relying on existing social support structures. This finding provides continued rationale for stress management and resilience interventions that bolster personal and existential resources for high-risk groups. These intrapersonal skills can be utilized in multiple settings and life domains, which aligns with teens’ reports that they felt stress in multiple arenas of their lives throughout the COVID-19 pandemic.
Contrary to the literature describing adults with diabetes [11], for many teens with T1D, diabetes management was not one of the major sources of stress and was not significantly impacted by the pandemic. This suggests that teens’ perceived diabetes resilience was high during the pandemic, even higher than what has been reported in adult populations with diabetes [11]. Although conclusions cannot be drawn from this study about why this is the case, it is plausible that aspects of the COVID-19 pandemic response in the United States may have facilitated resilience in diabetes management for some. For example, teens may have had more hands-on parental involvement and support for daily diabetes management tasks while at home and may have spent less time in environments that introduce barriers to consistent diabetes self-management (e.g., school, sports, social gatherings, etc.). Together, changes in daily routines may have reduced vulnerability to blood glucose variability and made it easier for teens and families to manage diabetes. This finding is consistent with positive psychology and diabetes literature that suggests that people with diabetes draw on strengths and exhibit resilience during times of stress [24, 31].
Although not the predominant experience of the teens in this study, it was notable that over a quarter of participants reported serious impacts of the COVID-19 pandemic on diabetes management. Further, many of these participants were already struggling with diabetes management given our finding that adolescents in this subgroup were more likely to have higher A1c levels. This aligns with data demonstrating associations between stress, diabetes distress, and A1c generally [2] and extends the results to stress specifically related to the COVID-19 pandemic [11]. Our results suggest that teens may be more likely to feel the effects in the following areas: blood glucose management, self-care, exercise, and mental health. With this knowledge, diabetes teams can identify target areas for intervention and collaborate with patients and families to find workable solutions in light of the specific stressors the teen may be facing.
## Limitations
The nature of this study is descriptive and limits the extent to which we may be able to make any causal inference about this population. Additionally, patients completed the COVID-19 survey as early as August 2020 and as late as June 2021. Major shifts in the COVID-19 pandemic had occurred prior to the start of the study period and occurred during the study period, such as the availability of the COVID-19 vaccine, which may have differentially impacted participant responses on the COVID-19 questionnaire. In addition, throughout our data collection period, there were several notable co-occurring stressors, such as racial tensions in summer 2020, political unrest related to the 2020 election, and multiple climate disasters in our respective regions, any of which could have impacted stress and coping but were not assessed in this study.
Further, due to the free response nature of the qualitative questions, responses were often brief, and we were unable to seek clarification about their responses or follow up with probing questions. This limited our ability to deeply explore the impact of COVID-19 and contributes only a basic understanding of what teens with T1D were experiencing. It is also possible that some patients felt uncomfortable sharing about sensitive topics in this format, which may have restricted the range of responses.
This study was conducted at two large academic pediatric diabetes centers in urban centers of two different areas of the United States (Pacific Northwest and Gulf Coast). While this allowed for a culturally, racially, ethnically, and socio-economically diverse sample, the results may not be generalizable to adolescents who live in other areas or whose care is delivered in other settings.
## Future Research and Clinical Directions
To more fully understand the phenomena observed here, future studies should include qualitative interviews about stress and resilience both generally and related to specific adverse circumstances, such as future public health crises. Interviews may provide more detailed data about which types of stressors tend to derail T1D management and how teens cope with those stressors.
This study may help to inform stress management and resilience interventions for teens with T1D. Such interventions may benefit from building on teens’ existing coping skills (e.g., behavioral and social support strategies) and introducing intrapersonal and existential/meaning-making skills, which were less common in our sample. Given the social nature of many of the teens’ stressors, they may benefit from additional support and resources when re-integrating into contexts that were paused during the COVID-19 pandemic. From a strengths-based perspective, it may be valuable to help teens recognize the ways they already successfully manage stress, both in general and specific to their diabetes management, and to promote recognition of which resilience resources benefit them most and when.
Finally, results from this study highlight the possible care needs for teens with T1D who are both stressed about their diabetes and experience difficulty managing their disease. Future studies may systematically explore this sub-group’s experience of the COVID-19 pandemic. Clinical diabetes teams may consider specifically including questions about the COVID-19 pandemic or other life stressors in clinic surveys, including if and how the COVID-19 pandemic has affected their care routines. Teens in this group may benefit from increased access to services and tailored health interventions to address stress and diabetes management. Existent strengths-based intervention for teens with diabetes [32, 33], which both explore strengths and identify areas for growth, may be particularly beneficial for this higher risk group.
## Conclusions
The COVID-19 pandemic has undoubtedly made an impact on teens with T1D, and our quantitative and qualitative findings reveal that teens with T1D felt the effects of COVID-19 predominantly in social aspects of their lives. Despite significant changes to major domains of their lives, many teens reported that their T1D management was protected and they described using coping strategies that were helping them through this stressful time, demonstrating diabetes resilience. However, for those whose T1D management was negatively impacted by COVID-19, higher A1c was more common, suggesting a need for focused follow-up by diabetes care teams.
## 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 Seattle Children’s Institutional Review Board (IRB of Record); Baylor College of Medicine Institutional Review Board (Relying Site). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin or self (if participant had reached the age of majority).
## Author Contributions
All authors contributed to either data collection and/or interpretation. JY-F is principal investigator of this study and oversaw all aspects of the research. MO’D was involved in data collection, analysis, and crafted an initial draft of this paper. MH, FM, DD, and AR are co-investigators of the study and have contributed to study design, oversight, and editing. MB and CZ are biostatisticians and contributed to quantitative analysis. KB is a qualitative expert and contributed to oversight of qualitative analyses. All authors have reviewed this manuscript and approved the submitted version.
## Funding
This study was funded by the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Project Number: R01DK121224.
## Conflict of Interest
DD serves as an independent consultant for Dexcom and Insulet outside the submitted work.
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/fcdhc.2022.835739/full#supplementary-material
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|
---
title: Aspalathin-rich green rooibos tea in combination with glyburide and atorvastatin
enhances lipid metabolism in a db/db mouse model
authors:
- Oelfah Patel
- Christo J. F. Muller
- Elizabeth Joubert
- Bernd Rosenkranz
- Johan Louw
- Charles Awortwe
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012079
doi: 10.3389/fcdhc.2022.963489
license: CC BY 4.0
---
# Aspalathin-rich green rooibos tea in combination with glyburide and atorvastatin enhances lipid metabolism in a db/db mouse model
## Abstract
Rooibos (Aspalathus linearis), an indigenous South African plant and its major flavonoid, aspalathin, exhibited positive effects on glycemia and dyslipidemia in animal studies. Limited evidence exists on the effects of rooibos extract taken in combination with oral hypoglycemic and lipid-lowering medications. This study investigated the combined effects of a pharmaceutical grade aspalathin-rich green rooibos extract (GRT) with the sulfonylurea, glyburide, and atorvastatin in a type 2 diabetic (db/db) mouse model. Six-week-old male db/db mice and their nondiabetic lean db+ littermates were divided into 8 experimental groups ($$n = 6$$/group). Db/db mice were treated orally with glyburide (5 mg/kg bodyweight), atorvastatin (80 mg/kg bodyweight) and GRT (100 mg/kg bodyweight) as mono- and combination therapies respectively, for 5 weeks. An intraperitoneal glucose tolerance test was conducted at 3 weeks of treatment. Serum was collected for lipid analyses and liver tissues for histological examination and gene expression. A significant increase in the fasting plasma glucose (FPG) of the db/db mice compared to their lean counterparts (from 7.98 ± 0.83 to 26.44 ± 1.84, $p \leq 0.0001$) was observed. Atorvastatin reduced cholesterol (from 4.00 ± 0.12 to 2.93 ± 0.13, $p \leq 0.05$) and triglyceride levels (from 2.77 ± 0.50 to 1.48 ± 0.23, $p \leq 0.05$). In db/db mice, the hypotriglyceridemic effect of atorvastatin was enhanced when combined with both GRT and glyburide (from 2.77 ± 0.50 to 1.73 ± 0.35, $$p \leq 0.0002$$). Glyburide reduced the severity and pattern of steatotic lipid droplet accumulation from a mediovesicular type across all lobular areas, whilst combining GRT with glyburide reduced the abundance and severity of lipid droplet accumulation in the centri- and mediolobular areas. The combination of GRT, glyburide and atorvastatin reduced the abundance and severity of lipid accumulation and the intensity score compared to the administered drugs alone. The addition of either GRT or glyburide in combination with atorvastatin had no effect on blood glucose or lipid profiles, but significantly reduced lipid droplet accumulation.
## Introduction
In Africa, 19 million people are living with diabetes, of which 4.6 million people are affected in South Africa alone [1]. In type 2 diabetes elevated glucose levels in conjunction with increased triglyceride (TG) levels, exacerbate insulin resistance, dyslipidemia and cellular damage that complicates treatment regimens. Insulin resistance and dyslipidemia can cause impaired cardiac function, cardiovascular disease [2] and atherosclerosis [3]. The cumulative increase of cholesterol, triglycerides (TG), and low-density lipoproteins (LDL-C) are indicators of hepatic steatosis, leading to a potential lipotoxic state [3, 4], liver cirrhosis, and hepatocellular carcinoma [5]. Therefore, statin therapy in conjunction with sulfonylureas is recommended for diabetic patients by most clinical practice guidelines [6].
Sulfonylureas are among the oldest drug classes in use for the management of type 2 diabetes. Sulfonylureas enhance insulin secretion in response to elevated glucose levels by blocking ATP-sensitive potassium channels in pancreatic beta cells [7]. Improving glycemic control through the stimulation of insulin secretion also suppresses hepatic gluconeogenesis resulting in better glycemic control [8]. In vitro studies of isolated human islets suggest that prolonged use of sulfonylureas may be toxic to beta cells, by inducing beta cell apoptosis and loss of beta cell mass [9]. In clinical trials such as the U.K. Prospective Diabetes Study (UKPDS) and Diabetes Outcome Progression Trial (ADOPT), the findings showed that sulfonylureas initially increase early-phase insulin secretion in response to oral glucose tolerance tests, thus leading to a more rapid rate of deterioration of beta cell function and overall glycemic control compared to treatment with metformin, thiazolidinediones or insulin therapy [10, 11].
Statins lower LDL-C by competitively inhibiting 3-hydroxy-3-methyl-glutaryl coenzyme-A (HMG-CoA) reductase in the liver [12]. Through the inhibition of the mevalonate pathway, statins also present with cholesterol-independent pleiotropic effects such as inhibiting macrophage inflammatory activity, endothelial cell function and vascular smooth muscle cell proliferation, as well as the reduction in several cellular biosynthetic pathways including those involved in glucose homeostasis (13–15).
The effective long-term management of T2D remains a therapeutic challenge. Monotherapies supplemented with natural medicines or phytoconstituents have shown appreciable improvements in the blood glucose levels in diabetic patients, as they interact with multiple pathways simultaneously [16].
Polyphenols are known to have anti-diabetic and lipid-lowering activities [17, 18]. In addition, these biologically active compounds not only protect vulnerable cells such as pancreatic beta cells against increased oxidative stress and inflammation associated with insulin resistance and obesity, but they also affect genes and proteins that regulate both glucose and lipid metabolic pathways [19, 20]. The glucose-lowering effects of rooibos extracts and aspalathin (a C-glucosyl dihydrochalcone), its major compound, have been demonstrated in vitro (21–26) and in vivo (22, 23, 25–29). Using a crossover design study in humans, Francisco [30] found that when consuming a standardised fat meal with commercial soda (control group) or a rooibos beverage (treatment group), rooibos treatment lowered blood glucose levels at 2 hours and 6 hours (-$22\%$ and -$18\%$, respectively) post-ingestion when compared to the baseline ($T = 0$) value. Marnewick et al. [ 31] studied the effects of rooibos on oxidative stress and biochemical parameters in adults at risk of cardiovascular disease over a 6-week study period. The study demonstrated that consuming six cups of rooibos tea per day for 6 weeks caused a $14.4\%$ marked decrease in serum glucose levels although this was not statistically significant.
Given the growing interest in the health-promoting effects of herbal products, including rooibos, herbal medicines and supplements are often used together with pharmaceutical therapeutics and have been estimated to be as high as $35\%$. This study, therefore, aimed to determine the effects of combining a pharmaceutical-grade green rooibos extract (GRT) with the hypoglycemic drug, glyburide, and the dyslipidemic drug, atorvastatin in a diabetic db/db mouse model.
## Rooibos extract
A pharmaceutical-grade aspalathin-rich green rooibos extract (GRT), containing $12\%$ aspalathin, was used in the study. Characterization of its Z-2-(β-D-glucopyranosyloxy)-3-phenylpropenoic acid content and that of the major flavonoids was described previously [32, 33].
## Animals and diet
Male 6-week-old db/db mice were bred and housed at the Primate Unit Delft Animal Centre (PUDAC) under a 24 hr light/dark cycle in a temperature-controlled room with food and water ad libitum. The Ethics Committee for Research on Animals (ECRA) of the South African Medical Research Council (SAMRC) approved all procedures involving animals (ECRA approval reference $\frac{04}{15}$). Mice were divided into 8 experimental groups ($$n = 6$$ per group), receiving GRT (100 mg/kg BW), glyburide (5 mg/kg BW), and atorvastatin (80 mg/kg BW) as mono-, co-therapies of GRT and atorvastatin, and the combination of GRT, atorvastatin, and glyburide. Db/db and db+ (BKS.Cg-Dock7m +/+Leprdb J) controls received $0.1\%$ dimethyl sulfoxide and Dulbecco’s phosphate-buffered saline. Mice were treated daily for 5 weeks. Bodyweight and fasting blood glucose (FPG) measurements via tail prick using a glucometer (One-Touch Select, M-Kem pharmacy, South Africa) were determined weekly. After 3 weeks of treatment, an intraperitoneal glucose tolerance test was conducted by injecting mice peritoneally with 0.2 g of glucose/ml/100 g bodyweight. Liver tissues were excised, weighed, and collected. Tissues collected for histology were fixed in formalin, embedded, and stained for further analyses. *For* gene expression analyses, tissues were stored in RNAlater.
## mRNA expression
Total RNA was extracted from mouse liver tissue using the RNeasy kit (ThermoFischer Scientific Inc., Waltham, MA, USA). Tissues were homogenised using a TissueLyser (Qiagen GmbH, Hilden, Germany), centrifuged at 13,500 g for 3 min, and the extracted RNA purified using the RNeasy kit according to the manufacturer’s instructions. RNA concentration and purity were quantified using a Nanodrop One spectrophotometer (Thermo Electron Scientific Instruments LLC, Madison, WI, USA). The RNA quality was determined using an Agilent 2100 Bioanalyser (Agilent Technologies, Santa Clara, CA, USA) where the inclusion criterium was RIN > 7. A Turbo DNase kit (ThermoFischer Scientific Inc. Waltham, MA, USA) was used to remove genomic DNA. RNA samples were converted to cDNA using the High-Capacity Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Quantitative real-time PCR was performed on the ABI 7500 Instrument (ThermoFischer Scientific Inc. Waltham, MA, USA) using the standard curve method. Predesigned and optimised TaqMan gene expression probes for Apoa1 (Mm00437569_m1), Fasn (Mm00662319_m1), Pparγ (Mm00440940_m1), Pparα (Mm00440939_m1), Scd1 (Mm00772290_m1), Srebp1 (Mm00550338_m1), Gsk3β (Mm00444911_m1), Fabp1 (Mm00444340_m1), Irs2 (Mm03038438_m1), Slc2a2 (Mm00446229_m1) were used for differential gene expression (ThermoFisher Scientific Inc. Waltham, MA, USA). Gene expression data were normalised to β-actin and hypoxanthine-guanine phosphoribosyltransferase (HPRT) as housekeeping genes.
## Biochemistry measurements
Blood samples were collected in BD Vacutainer® SST gel tubes, centrifuged at 4000 rpm for 15 mins at 4 °C and the sera stored at -80˚C until assayed. To determine total cholesterol, high-density lipoprotein cholesterol (HDL-C), LDL-C, and TG contents, all samples were analyzed by Pathcare (Dietrich, Voigt, Mia & Partners, N1 City, Cape Town, SA), a pathology laboratory accredited by the South African National Accreditation System (SANAS). Briefly, total cholesterol was determined using the cholesterol esterase enzymatic method. A coupled enzymatic reaction using adenosine triphosphate (ATP) as an agent was used to determine the triglyceride (TG) content. A cholesterol esterase/cholesterol oxidase method, based on an enzyme chromogen system for quantification, was used to determine high-density (HDL-C) and low-density lipoprotein cholesterol (LDL-C) content, respectively.
## Histological assessment
Formalin fixed paraffin embedded sections of liver tissue were stained with hematoxylin-eosin (H&E) and scored histologically for steatotic changes. Hepatic steatosis of the obese diabetic db/db mice was assessed histologically by an experienced histologist, blinded to the treatment groups, using a steatotic severity scoring system adapted from Trak-Smayra et al. [ 34] and Liang et al. [ 35]. Steatosis was assessed for lipid accumulation in the liver lobules by type (microvesicular, presence of minute cytoplasmic lipid droplets around a centrally positioned nucleus; mediovesicular, several medium-sized lipid vacuoles present in the cytoplasm of hepatocytes; and macrovacuolar, single large cytoplasmic lipid vacuole displacing the nucleus to the periphery of the hepatocytes), grade or severity [0 < $5\%$, grade 1 (5–$33\%$), grade 2 (34–$66\%$) and grade 3 (> $66\%$), zonal predominance [periportal (zone 1), mediolobular (zone 2) or centrilobular (zone 3)]. Samples were blind-coded and randomly assessed to avoid observational bias by the histologist.
## Statistical analysis
Data are presented as the means ± SEM and were compared using one-way ANOVA with Tukey and Dunnett’s post-hoc tests with $p \leq 0.05$ considered statistically significant. Statistical analyses were performed using GraphPad Prism® version 7.03 (GraphPad Software Inc., San Diego, CA, United States).
## Blood glucose levels
The effects of GRT as either a mono- and/or co-treatment with glyburide, and/or atorvastatin, were orally tested in 6-week-old db/db mice. Fasting blood glucose levels of obese db/db (control) mice were significantly increased compared to their lean (db +) counterparts (from 7.98 ± 0.83 to 26.44 ± 1.84, $p \leq 0.0001$) (Figure 1A). Interestingly, after 5 weeks of treatment, glyburide alone did not improve fasting blood glucose levels nor intraperitoneal glucose tolerance ($$p \leq 0.129$$), or in combination with GRT and/or atorvastatin ($$p \leq 0.818$$, Figures 1A, B), respectively.
**Figure 1:** *The effect of GRT, Glyb and Ator mono - or combination therapy on glycaemia as assessed by (A) fasting plasma glucose (FPG) and (B) intraperitoneal glucose tolerance test (IPGTT). IPGTT and FPG were assessed after 3 and 5 weeks of treatment, respectively. N = 5-6/group. #p < 0.05; ##p < 0.01 and ####p < 0.0001 vs db+
(One-Way ANOVA followed by Tukey post-hoc test). GRT, green rooibos extract; Glyb, glyburide; and Ator, atorvastatin.*
## Body, liver, and adipose weight
The body, liver, and retroperitoneal fat (RF) weights of the obese db/db mice were significantly increased compared to their lean db + littermates (Table 1). GRT (from 1.35 ± 0.35 to 0.67 ± 0.04, $p \leq 0.001$) and glyburide (from 1.35 ± 0.35 to 0.78 ± 0.05, $p \leq 0.001$) monotherapy, and GRT with glyburide and atorvastatin (from 1.35 ± 0.35 to 0.48 ± 0.02, $p \leq 0.0001$) as a combined therapy significantly reduced RF weights, without affecting body or liver weight (Table 1).
**Table 1**
| Unnamed: 0 | db+ | db/db | GRT (100 mg/kg) | Glyb (5 mg/kg) | GRT (100 mg/kg) + Glyb (5 mg/kg) | Ator (80 mg/kg) | GRT (100 mg/kg) + Ator (80 mg/kg) | GRT (100 mg/kg) + Glyb(25 mg/kg) + Ator (80 mg/kg) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Bodyweight (g) | 25.00 ± 0.86 | 40.36 ± 1.33**** | 42.32 ± 0.82#### | 38.83 ± 0.98#### | 39.30 ± 1.19#### | 40.00 ± 0.67#### | 41.70 ± 0.82#### | 38.60 ± 1.75#### |
| Liver (g) | 1.35 ± 0.04 | 2.46 ± 0.07**** | 2.67 ± 0.13#### | 2.40 ± 0.08#### | 2.41 ± 0.16#### | 2.41 ± 0.08#### | 2.69 ± 0.04#### | 2.34 ± 0.09#### |
| RF (g) | 0.09 ± 0.01 | 1.35 ± 0.35**** | 0.67 ± 0.04**,# | 0.78 ± 0.05**,#### | 0.47 ± 0.02**** | 0.45 ± 0.04**** | 0.51 ± 0.05**** | 0.48 ± 0.02**** |
## Blood lipid levels
Following treatment, serum TG, cholesterol, and LDL-C levels were significantly increased in db/db mice compared to the db + mice (Figures 2A–C). Atorvastatin expectedly reduced total cholesterol (from 4.00 ± 0.12 to 2.93 ± 0.13, $p \leq 0.0001$) and TG (from 2.77 ± 0.50 to 1.48 ± 0.23, $p \leq 0.0001$) compared to the untreated control (Figures 2A, B). In combination, GRT and atorvastatin treatment reduced TG levels (from 2.77 ± 0.50 to 2.05 ± 0.20, $p \leq 0.01$, Figure 2B). GRT monotherapy, the combination of GRT with glyburide, and GRT combined with glyburide and atorvastatin, reduced serum TGs (from 2.77 ± 0.50 to 1.73 ± 0.35, $$p \leq 0.001$$; 2.77 ± 0.50 to 2.68 ± 0.23, $$p \leq 0.0002$$; and 2.77 ± 0.50 to 1.96 ± 0.30) levels (Figure 2B).
**Figure 2:** *The effect of GRT, Glyb, and Ator mono- and combination treatments on serum lipid contents. Serum lipid contents of (A) cholesterol, (B) TG, (C) LDL-C were measured after 5 weeks of treatment. N = 5-6/group. ##p < 0.01, ###p < 0.0001 and ####p < 0.0001 denotes treatment vs db+ (non-diabetic control); **p < 0.01, ***p < 0.0001, and ****p < 0.0001 denotes treatment vs db/db (diabetic control)(One-Way ANOVA followed by Tukey post-hoc test). Abbreviations: TG, triglyceride; LDL-C, low density lipoprotein cholesterol, GRT, green rooibos extract; Glyb, glyburide; and Ator, atorvastatin.*
## Hepatic glucose and lipid gene expression
mRNA expression of genes involved in lipid metabolism, lipid transport, insulin signalling, glucose metabolism, and lipogenesis in the liver were investigated. GRT, glyburide and atorvastatin monotherapies did not affect the expression of selected genes presented in Table 2. GRT in combination with atorvastatin significantly upregulated the expression of ApoA1 (8.3-fold, $$p \leq 0.0006$$), Fabp (11.5-fold, $$p \leq 0.006$$), Fasn (5.4-fold, $p \leq 0.05$), Gsk3β (4.6-fold, $$p \leq 0.002$$), Irs2 (4.3-fold, $$p \leq 0.003$$), Pparγ (27.9-fold, $$p \leq 0.009$$), Pparα (11.8-fold, $$p \leq 0.002$$), Scd1 (10.0-fold, $$p \leq 0.004$$), Srebp1 (11.5-fold, $$p \leq 0.004$$), and Scl2a2 (6.8-fold, $$p \leq 0.0001$$) (Table 2). Whereas GRT combined with glyburide and atorvastatin increased the expression of Fasn (5.4-fold, $$p \leq 0.029$$) only (Table 2).
**Table 2**
| Groups | db+ | db/db | GRT (100 mg/kg) | Glyb (5 mg/kg) | GRT (100 mg/kg) + Glyb (5 mg/kg) | Ator (80 mg/kg) | GRT (100 mg/kg) + Ator (80 mg/kg) | GRT (100 mg/kg) + Glyb (5 mg/kg) + Ator (80 mg/kg) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Apoa1 | 0.2 | 4.9 | ↓ 2.2 | ↓ 4.7 | 1.3 | ↓ 2.2 | ↑ 8.3*** | ↓ 1.7 |
| Fabp | 0.6 | 1.6 | 1.2 | ↑ 3.3 | ↑ 1.9 | 1.4 | ↑ 11.5*** | ↑ 1.8 |
| Fasn | 0.6 | 1.8 | ↑ 2.9 | ↑ 2.4 | 1.3 | ↑ 3.1 | ↑ 5.4* | ↑ 5.4 |
| Gsk3β | 0.9 | 1.0 | 1.4 | 1.4 | ↑ 3.1 | ↑ 5.5 | ↑ 4.6** | 1.5 |
| Irs2 | 0.5 | 2.1 | 1.3 | ↓ 1.6 | ↑ 5.8 | ↑ 4.6 | ↑ 4.3** | 1.1 |
| Pparγ | 0.3 | 3.9 | 1.4 | ↓ 2.3 | 1.3 | ↓ 1.6 | ↑ 27.9** | ↓ 3.7 |
| Pparα | 1.1 | 1.4 | 1.3 | ↑ 3.9 | 1.1 | 1.5 | ↑ 11.8** | ↑ 2.2 |
| Scd1 | 0.3 | 3.2 | ↑ 3.3 | ↑ 9.9 | 1.3 | 1.2 | ↑ 10.0** | ↓ 2.8 |
| Srebp1 | 0.7 | 1.4 | 1.3 | ↑ 4.8 | ↑ 1.8 | 1.4 | ↑ 11.5** | ↑ 2.4 |
| Slc2a2 | 0.7 | 1.5 | 1.3 | ↑ 1.9 | 1.4 | 1.5 | ↑ 6.8*** | ↑ 1.7 |
## Histological scoring
The histological scoring of steatotic severity and type in untreated obese db/db mice confirmed the predominance of mediovesicular steatosis pattern (intensity score of 3) present in all hepatic acinar lobular zonal areas. After 5 weeks of treatment, GRT and atorvastatin reduced the scoring intensity from a predominantly mediovesicular lipid droplet type, to a mixed micro- and mediovesicular pattern (intensity score of 2), limited to the centrilobular (zone 3) and mediolobular (zone 2) areas. Glyburide treatment had no effect on the zonal severity and type of lipid steatosis, whilst combining GRT with glyburide reduced the abundance and severity to a mixed micro- and mediovesicular lipid droplet pattern mostly limited to the centrilobular (zone 3) and mediolobular (zone 2) areas. Similarly, the combination of GRT, glyburide and atorvastatin reduced the abundance and severity of lipid accumulation as well as the intensity score compared to monotherapies alone (Table 3 and Figure 3).
## Discussion
Diabetic dyslipidemia presents with increased TGs, low HDL-C, and high LDL-C levels, often necessitating the combination of oral hypoglycemic drugs with lipid-lowering drugs to improve the clinical outcomes of type 2 diabetic patients. Hypertriglyceridemia is common in patients with T2D and places them at increased risk of CVD. In addition, hypertriglyceridemia is strongly associated with a host of other potential risk factors, including obesity, insulin resistance and increased levels of apolipoprotein [36]. In rodents, increased rates of fatty acid synthesis lead to the development of hepatic steatosis. In an insulin-resistant state, the influx of fatty acids from adipocytes increases de novo lipogenesis whilst decreasing fatty acid oxidation, resulting in TG accumulation in the liver [37]. Combination therapies have proven more effective in treating multi-factorial metabolic conditions, including the management of lipid disorders.
Glyburide, belonging to the sulfonylurea class of insulin secretagogues, lowers blood glucose levels by stimulating beta cells to produce and secrete more insulin [38]. Our results showed that glyburide alone or combined with GRT and/or atorvastatin did not affect blood glucose levels of obese db/db mice. This finding is inconsistent with other studies in diabetic animals and humans that showed reductions of FPG levels in glyburide-treated groups (39–42). In diabetic Wistar rats, Neerati and Gade [43] observed that combined administration of glyburide with atorvastatin enhanced the reduction in blood glucose levels. Albeit in different models, one such study showed that after 4 weeks of treatment, glyburide at 5 mg/kg reduced FPG in an alloxan-induced diabetic mouse model as well as improved the clearance of blood glucose at each time point monitored versus vehicle-treated diabetic mice [42]. Db/db mice are obese, highly insulin - and leptin-resistant and spontaneously and progressively develop worsening diabetes over time culminating in beta cell mass depletion. Hence, the persistent increased demand for insulin leads to the hyperactivity of beta cells which in the long-term, results in a dysfunctional secretory response to glucose [44], thus providing a possible explanation for the lack of effect of glyburide observed in this study. Surprisingly, however, the combined effect of GRT and atorvastatin improved glucose levels in these obese db/db mice. This finding could be attributed to the effect of GRT, in improving inflammatory conditions and insulin resistance. There is accumulating evidence to suggest an emerging role of inflammation as an early driving factor for the development of insulin resistance and type 2 diabetes. Moreover, treatment of obese mice with statins over several weeks was sufficient to decrease insulin-induced glucose uptake in adipose tissue. Importantly, treatment with statins lead to dysregulated insulin signaling in explanted adipose tissue, whilst insulin signaling was maintained in the adipose tissue of mice deficient in NLR Family Pyrin Domain Containing 3 (NLRP3), and in explants treated with glyburide [45], a NLRP3 inflammasome inhibitor.
Atorvastatin is currently the most-prescribed cholesterol-lowering agent for treating elevated levels of LDL-C, TG, and cholesterol. Although statins lower cardiovascular risk, they may also promote adverse effects such as new-onset diabetes, myopathy, rhabdomyolysis, and induce hepatotoxicity [46, 47]. As expected, in this obese diabetic db/db mouse model atorvastatin monotherapy reduced TG levels. This is in accordance with literature that showed reduced TG levels of atorvastatin administered at either low (10 mg/kg) or high doses (80 mg/kg) [48, 49]. The enhanced decrease in TG levels induced by glyburide co-therapy may, at least in part, be due to the glyburide-induced insulin release that activated lipoprotein-lipase and the hydrolysis of TGs [40]. This effect was further enhanced in combination with GRT and glyburide. Co-administration of GRT with atorvastatin did not improve cholesterol, LDL-cholesterol, or TG levels relative to atorvastatin alone. With the addition of glyburide to GRT and atorvastatin, an additive reduction in TG levels was observed. In part, the enhanced effect could be due to the drug-drug or herb-drug interactions of glyburide and/or GRT on the pharmacokinetics of atorvastatin, respectively. This was evidenced by decreasing the metabolic clearance and increasing the Cmax of glyburide thereby effectively increasing its therapeutic effect [43]. Previously, we showed that GRT inhibits the activity of CYP2C9 and CYP3A4, enzymes involved in the metabolism of glyburide and atorvastatin, suggesting that further herb-drug interactions are likely when GRT is co-administered with glyburide and atorvastatin [33]. This could have added to the improvement of glycemia by the combination of GRT and atorvastatin. In addition, the added positive effects on body and liver weight together with lower TG levels induced by the combination of GRT, glyburide and atorvastatin are an important outcome of this study.
In the liver, statin treatment is associated with worsening glycemic control [50] and a small incremental increase in fasting blood glucose levels [51]. A known characteristic of insulin resistance and T2D is the elevation of hepatic gluconeogenesis contributing to hyperglycemia [52]. Atorvastatin treatment upregulates thyroid hormone-responsive spot 14 protein (THRSP) expression, a small protein found in the liver that is predominantly expressed in lipid-producing tissues. This protein has been implicated in the regulation of lipogenic processes by controlling the expression of fatty acid synthase (Fasn) and sterol regulatory element binding protein (Srebp) lipogenic genes [53, 54], thereby increasing free fatty acids (FFAs) in the liver, which can contribute to the development of steatosis. In this study, atorvastatin reduced mediovesicular lipid accumulation around the centrilobular area, whilst significantly increasing both Fasn and *Srebp* gene expression when combined with GRT. Srebp regulates hepatic de novo lipogenesis by insulin and mediates the synthesis of fatty acids and TGs [37]. In rodent models of insulin resistance and obesity, increased rates of hepatic fatty acid synthesis contribute to the development of hepatic steatosis [55]. There is a direct relationship between body fat levels, leptin, and insulin resistance, with the leptin receptor deficient db/db mouse presenting with alterations in lipid metabolism and liver function including steatosis [56]. Hence hyperleptinemia could account for the resultant increase in Srebp expression lipogenesis and steatosis. In steatotic livers, stearoyl-CoA desaturase (Scd) regulates monounsaturated fatty acid (MUFA) synthesis whilst preventing the progression of steatosis to non-alcoholic steatohepatitis (NASH). High Scd1 expression is correlated with metabolic diseases such as obesity and insulin resistance, whereas low levels are protective against these metabolic disturbances [57]. Scd1 protection is through channeling saturated fatty acids into MUFAs, which are easily incorporated into TGs [58]. The reduction in FPG, TGs and lipid accumulation observed within the same treatment group could lessen steatosis and decrease insulin resistance. A study by Layman et al. [ 59] also showed that GRT alone displayed hepatoprotective effects by reducing hepatic steatosis.
Furthermore, Slc2a2 and Irs2 were both upregulated by this combined treatment group, coupled with the reduction in FPG, which could infer an increase in insulin signaling by the activation of IRS/PI3K/AKT in the liver. A study reported that the abnormal accumulation of TG in the diabetic liver is due to the simultaneous activation of lipogenesis and gluconeogenesis, leading to excessive lipid production [60]. PPARα, a transcriptional factor predominantly expressed in the liver, plays a key role in maintaining lipid homeostasis through the regulation of various enzymes in lipid and glucose metabolism [61]. The current study demonstrated that GRT combined with atorvastatin upregulated the mRNA expression of PPARα in the liver of obese diabetic db/db mice. The modulating effects of PPARα on lipid metabolism and inflammation may explain our finding that PPARα activation modulated dyslipidemia in this diabetic mouse model.
Despite the unexplained increase in some hepatic lipogenic genes in these obese diabetic db/db mice, the reduction in TGs by GRT and atorvastatin is favorable. Clinically, although statins can harm glycemia, the suppression of hypertriglyceridemia and the reduction in steatosis could help to improve glycemic control in patients with T2D [62]. This study suggests that the addition of GRT enhances therapeutic potential in the obese diabetic db/db mouse, however, clinical studies are needed to further validate these findings. Moreover, in the current study, glyburide failed to influence glucose levels, and as glyburide is an NLRP3 inflammasome inhibitor, the combined effect of glyburide and statins on adipose tissue should therefore be further explored.
## Conclusion
This study demonstrated that co-therapy of GRT with atorvastatin and/or glyburide, enhanced the glucose and lipid-lowering effects of obese diabetic db/db mice, which was associated with effects on hepatic steatosis and retroperitoneal fat accumulation. These results further demonstrate that the combination of GRT together with conventional treatments such as hypoglycemic and hypolipidemic medications may have beneficial effects on type 2 diabetes.
## Data availability statement
The original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding author.
## Ethics statement
This animal study was reviewed and approved by the Ethics Committee for Research on Animals (ECRA) of the South African Medical Research Council. The study was carried out in accordance with the recommendations of the Ethics Committee for Research on Animals (ECRA) of the SouthAfrican Medical Research Council (ref. $\frac{04}{15}$).
## Author contributions
OP, CM, EJ, BR, and CA participated in research design. OP conducted laboratory experiments. OP and CM performed data analysis. OP, CM, EJ, BR, and CA wrote or contributed to the writing of the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This research was funded in part by the National Research Foundation (NRF) Thuthuka Programme (Grant 99381 to OP), the Japan Society for the Promotion of Science/NRF Research Cooperation Programme (NRF Grant 108667 to EJ) and the Biomedical Research and Innovation Platform of the South African Medical Research Council. Afriplex GRT™ was provided by Afriplex, Paarl, South Africa.
## Acknowledgments
Special thanks to the staff of PUDAC and BRIP, especially Joritha van Heerden and Desmond Linden for their assistance with the animal work.
## 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: COVID-19 Pandemic Influence on Diabetes Management in Croatia
authors:
- Ivan Cerovečki
- Marija Švajda
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2021
pmcid: PMC10012086
doi: 10.3389/fcdhc.2021.704807
license: CC BY 4.0
---
# COVID-19 Pandemic Influence on Diabetes Management in Croatia
## Abstract
### Aim
The study aims to investigate the effects of the COVID-19 pandemic on diabetes management and diabetes patients’ healthcare utilization patterns in Croatia.
### Methods
Using data contained in the Croatian diabetes registry (CroDiab), Central Health Information System of the Republic of Croatia (CEZIH), and the Croatian hospitalization database (BSO), indicators including the total number of registered diabetes patients, number of newly diagnosed diabetes cases, number of diabetes-related primary care visits and hospitalizations, and key diabetes control indicators were analyzed. Yearly values from 2017 until 2020 were compared.
### Results
The age-adjusted prevalence rate increased significantly from 2017 until 2019 (2017: 6,$\frac{858}{100}$,000; 2018: 7,$\frac{053}{100}$,000; 2019: 7,$\frac{160}{100}$,000). In 2020 the age-adjusted prevalence rate was 7,$\frac{088}{100}$,000, but the decrease was insignificant compared to 2019. The age-adjusted rate of new cases decreased from 2017 until 2019 (2017: $\frac{910}{100}$,000; 2018: $\frac{876}{100}$,000; 2019: $\frac{845}{100}$,000), with a significant decrease in 2020 ($\frac{692}{100}$,000) compared to 2019. The number of diabetes panels increased from 2017 [117,676] to 2018 [131,815], with a slight decrease in 2019 [127,742] and a sharp decrease in 2020 [104,159]. A similar trend was observed regarding the numbers of diabetes patients with panels, visits to primary healthcare providers for diabetes-related problems and diabetes patients who visited their primary healthcare provider. A slightly different trend was observed regarding diabetes-related hospitalizations. In 2017 there were 91,192 diabetes-related hospitalizations; the number decreased to 83,219 in 2018, increased again to 102,087 in 2019 and decreased to 85,006 in 2020. The number of hospitalized diabetes patients displayed a similar tendency.
### Conclusion
The COVID-19 pandemic has had a negative effect on the utilisation of healthcare by diabetes patients, which may have long-term consequences for their general health.
## Introduction
Since its beginning in late 2019 in the People’s Republic of China, the COVID-19 pandemic has been causing major disruptions to healthcare systems worldwide, regarding both healthcare providers and healthcare users [1]. Significant numbers of patients in need of hospital treatment for intermediate or severe clinical forms of COVID-19 necessitated the diversion of financial, technological and human resources to intensive care and associated stationary care units, whereas patients were advised to avoid seeking medical care unless it was strictly necessary, leading to irregular provision of most routine regular medical services [2]. An analysis conducted by the Health Foundation determined a sharp decrease in the number of consultations, referrals, vaccinations and other primary healthcare use indicators during the initial lockdown imposed during the spring 2020 in the United Kingdom [3]. Another study of the impact of the COVID-19 epidemic on the follow-up and control of chronic diseases conducted in Spain found a significant reduction in quantitative healthcare quality indicators during the same period in Catalonia [4]. Urgent medical care was found to be affected in some countries as well, as studies conducted in the United States, Italy, Spain and Hong Kong reported decreases of the number of hospitalizations related to urgent cardiologic and neurologic conditions with concurrent increases in respective mortality and complication rates (5–9).
The first case of COVID-19 in Croatia, confirmed by real-time polymerase chain reaction testing, was reported on February 25, 2020. By December 31, 2020, 210,837 cases of COVID-19 were recorded in Croatia with 3,920 deaths and a case-fatality ratio of $1.9\%$ [10]. Besides posing a burden on the hospital system, the COVID-19 epidemic also compromised the provision of primary healthcare, as indicated by primary healthcare reports published by the Croatian Institute of Public Health. A significant decrease in the number of contacts, examinations and consultations with primary healthcare providers was recorded in the spring months (March-June) of 2020 during the initial lockdown imposed by Croatian health authorities; the number of patients seeking primary healthcare services increased thereafter during the autumn months (September-December) of 2020, coinciding with the peak of second wave of the COVID-19 epidemic [11, 12].
The prevalence of diabetes mellitus (DM) in Croatia had been estimated by previous research at $8.9\%$ of the adult population, although recent epidemiological data set the estimate as high as 500,000 patients [13, 14]. Considering the growing number of DM patients in the country, the Croatian Ministry of Health has adopted the National Diabetes Program for the timeframe 2015 - 2020 and other strategic documents aiming to promote and facilitate early discovery and prevention of DM. The majority of preventive activities described in the aforementioned documents were intended to be implemented foremost in the primary healthcare sector, defined by the National Diabetes Program as the organizational and operational groundwork for all DM-related preventive actions [15]. However, the diversion of resources during the COVID-19 epidemic may have affected the provision of routine healthcare services for DM patients, as well as their disease management capabilities.
The aim of this study was to investigate how the COVID-19 pandemic affected diabetes management and diabetes patients’ healthcare utilization patterns in Croatia.
## Materials and Methods
Various sources were used to extract the data on DM prevalence and incidence, the number of primary healthcare visits and hospitalizations labelled with ICD-codes pertaining to DM, and disease control indicators among DM patients in Croatia in the period 2017 - 2020. The Croatian national diabetes registry (CroDiab) and the Croatian hospitalization database (BSO) are administered by the Croatian Institute of Public Health, whereas the access to the Central Health Information System of the Republic of Croatia (CEZIH), containing information on all healthcare service provided by primary healthcare practitioners in Croatia, was granted to the Croatian Institute of Public Health by the Croatian Health Insurance Fund. The data on DM incidence and prevalence were extracted from the CroDiab registry. Data on DM-related primary healthcare visits were extracted by retrieving all records labelled with ICD-10 codes E10 - E14 in the period 2017 - 2020 from the CEZIH database. The data on DM-related hospitalizations were collected by extracting all records labelled with ICD-10 codes E10 - E14 in the same period from the BSO database. The data on DM control indicators in the studied period was extracted using disease control panels for DM patients, which are recorded in the CEZIH database.
A descriptive statistical data analysis was performed thereafter. The comparison of key disease control indicators (total cholesterol, fasting glucose, HbA1c %, and systolic blood pressure) across years 2017 - 2020 was performed using the Friedman nonparametric test. Crude prevalence, age-standardized prevalence and new cases’ rates were determined and the resulting data were standardized with regard to the European Standard Population [2013]. The comparison of age-standardized rates across years and the determination of the ratio of age-standardized rates across years were performed using Smith’s formulas. The values of the empirical significance level p of 0.05 ($p \leq 0.05$) were considered statistically significant. The SPSS 21 software package was used for the data analysis.
## Results
The total number of patients registered in the CroDiab registry increased from 2017 until 2019 (297,298 registered patients in 2017; 307,200 in 2018; 313,625 in 2019). In 2020 the total number of registered patients decreased to 310,212. The number of new patients registered in the CroDiab registry displayed a slightly decreasing trend from 2017 until 2019 (39,414 in 2017; 37,987 in 2018; 36,749 in 2019); in 2020 it decreased sharply to 30,026.
The age-adjusted prevalence rate increased significantly from 2017 until 2019 (2017: 6,$\frac{858}{100}$,000; 2018: 7,$\frac{053}{100}$,000; 2019: 7,$\frac{160}{100}$,000). In 2020 age-adjusted prevalence rate was 7,$\frac{088}{100}$,000, but the decrease was insignificant compared to 2019. The age-adjusted rate of new cases decreased from 2017 until 2019 (2017: $\frac{910}{100}$,000; 2018: $\frac{876}{100}$,000; 2019: $\frac{845}{100}$,000), with a significant decrease in 2020 ($\frac{692}{100}$,000) compared to 2019.
The number of DM control panels increased from 2017 [117,676] until 2018 [131,815], with a slight decrease in 2019 [127,742] and a sharp decrease in 2020 [104,159]. A similar trend was observed regarding the numbers of patients with completed DM panels, DM-related visits to primary healthcare providers and DM patients who visited their primary healthcare provider. With particular regard to the number of completed DM panels and the number of DM-related visits to primary care providers per quarters, as shown in Figures 1 and 2, there was a significant decrease in Q2-2020 compared to Q1-2020.
**Figure 1:** *Number of completed diabetes control panels and patients with completed diabetes control panels per yearly quarter 2017 - 2020.* **Figure 2:** *Number of diabetes-related primary healthcare visits per yearly quarter 2017 - 2020.*
A slightly different trend was observed regarding the number of DM-related hospitalizations, as shown in Figure 3. In 2017 there were 91,192 DM-related hospitalizations; the number of such hospitalizations decreased to 83,219 in 2018, increased again to 102,087 in 2019 and decreased to 85,006 in 2020. The number of patients undergoing DM-related hospital treatment displayed a similar tendency. As shown in Figure 2, the DM-related hospitalization rate during the second quarter of 2020 was nearly half the rate in the first quarter.
**Figure 3:** *Number of diabetes-related hospitalisations and patients hospitalised for diabetes per yearly quarter 2017 - 2020.*
Key disease control indicators (total cholesterol, fasting glucose, HbA1c %, and systolic blood pressure) were compared across the studied years using the Friedman nonparametric test. The test revealed statistically significant differences in all observed indicators ($p \leq 0.001$ for all indicators). As shown in Figures 4 and 5, average systolic blood pressure and total cholesterol values were continuously decreasing in observed period. Highest average fasting glucose values were recorded in 2017, whereas the lowest average values were recorded in 2019 and 2020 (Figure 6). However, average HbA1c % values were similar in 2017 and 2020, with the lowest average values recorded in 2019 (Figure 7).
**Figure 4:** *Average systolic blood pressure values in diabetes patients in Croatia per year, 2017 - 2020.* **Figure 5:** *Average total cholesterol values in diabetes patients in Croatia per year, 2017 - 2020.* **Figure 6:** *Average fasting glucose values in diabetes patients in Croatia per year, 2017 – 2020.* **Figure 7:** *Average HbA1c percentages in diabetes patients in Croatia per year, 2017 - 2020.*
## Discussion
The results of this study corroborate the hypothesized negative effect of the COVID-19 epidemic, the associated lockdowns and the disruptions to healthcare provision on both early disease discovery and disease management in DM patients. The number of newly-recorded DM patients in Croatia in 2020 was the lowest recorded in several years; in the absence of other factors which may influence the incidence of DM in the general population, the diminished accessibility of medical services to patients seeking healthcare for DM-related symptoms appears as a plausible explanation. The prevalence rate of DM in Croatia recorded its first decrease in years as well. Moreover, the DM-related hospitalization rate during the second quarter of 2020 was nearly half the rate in the first quarter, implying that a significant number of DM patients in need of stationary treatment (diagnostic or therapeutic) may have been left untreated. Regarding health monitoring provided to known DM patients, the number of disease control panels, comprising indicators such as glycaemia, HbA1c concentration etc., and provided by primary healthcare physicians, steadily decreased during the first two quarters of 2020, with a total reduction of approximately $20\%$ in the second quarter of 2020 in comparison with the last quarter of 2019. Similar trends have been observed in other countries, such as United States and Japan [16, 17].
Besides aggravating the provision of healthcare, lockdowns associated with the COVID-19 pandemic may have been another detrimental factor for DM patients due to the suspension of recreational activities in communities and the general discouragement of outdoor activities possibly involving social contact by health authorities. Despite their efficiency in mitigating the transmission of COVID-19, such policies led large numbers of DM patients to spend a greater proportion of their time indoors, which in turn reflected on their physical activity patterns, as well as their diet and psychological condition. Instances of earlier research have associated disaster-related experiences in DM patients with a deterioration of disease control indicators such as HbA1c percentages (18–20). The results of research on DM disease control during the COVID-19 pandemic published so far further substantiate these findings [21, 22].
With regard to disease control indicators, this study revealed a steadily decreasing trend in both average cholesterol levels and systolic blood pressure values in studied individuals across the studied interval, including the year 2020. However, the average value of HbA1c % stagnated in 2020 in comparison with 2019 in the studied population, whereas the average fasting glucose concentration increased significantly in 2020 with regard to the previous year. This apparent inconsistency may be associated with the limited accessibility of healthcare services for DM patients, as a significant number of DM patients requiring healthcare services may have been prevented from attending their appointments due to lockdowns and travel restrictions, particularly patients with impaired physical mobility or those experiencing socio-economic deprivation; results of a recent Scottish study affirm this observation [23].
The chronic nature of diabetes mellitus (DM) as a metabolic disease and its pathophysiology require constant monitoring of glycaemia, as well as regular professional medical surveillance and care to prevent disease complications. The adverse effects of unregulated dysglycemia on the function of the human immune system have been well established, making DM one of the best-known risk factors for poor outcome in infectious diseases [24]. A number of studies published heretofore has confirmed the association of poor COVID-19 clinical outcomes and previously existing chronic medical conditions, including DM (25–27). In this regard, a previous study on the association of comorbidities and COVID-19 disease outcomes in Croatia found DM patients to have significantly higher mechanical ventilation support and case fatality rates [28]. Moreover, uncontrolled hyperglycemia has been found to have a negative effect on the immunological response consequent to COVID-19 vaccination, posing an additional risk for DM patients in case of COVID-19 infection [29].
Considering the somewhat unpredictable further evolution of the COVID-19 pandemic, new methods of providing healthcare to patients with chronic diseases will have to be investigated and put into use as soon as feasible to mitigate the effects of limited availability of medical services and patient mobility. Tele-health services designed in this respect for DM patients offer significant prospects for better disease control both in the short and long term [30]. Furthermore, their implementation may decrease the costs of healthcare for DM patients, being of special importance due to the growing DM prevalence rate and enabling the diversion of financial resources to other segments of patient care.
The limitations of this study are related to the data sources used to conduct this study. All DM patients listed in the CroDiab registry have had their diagnosis confirmed by means of laboratory tests and relevant diagnostic criteria prerequisite for inclusion in the CroDiab patient registry. In some situations, however, ICD-10 codes relating to DM (E10 - E14) may have been erroneously attributed in patient records contained in CEZIH or BSO databases due to oversight or inadvertence. In this regard, diagnoses attributed during primary healthcare visits or hospital treatment might not necessarily correspond with the medical reason for the visit or hospitalization; however, the effect of misattribution errors is likely to be negligible. Furthermore, seasonal variation in the number of patient visits to primary healthcare providers and hospitalizations should be taken into account when analyzing healthcare utilization patterns, warranting future research to analyze longer timeframes to exclude any confounding factors.
The COVID-19 epidemic in Croatia compromised the provision of healthcare to all patients diagnosed with chronic illnesses, including DM patients. Reductions in the number of patient visits, patient consultations and completed DM control panels with primary healthcare providers were recorded, as well as reductions in numbers of DM-related hospitalizations. Considering the possible consequences of compromised disease control, further attention is necessary to accommodate the existing limitations of healthcare accessibility and provide for new methods to provide adequate DM patient care.
## Data Availability Statement
The data analyzed in this study is subject to the following licenses/restrictions: Datasets contain confidential patient information. Requests to access these datasets should be directed to IC, [email protected].
## Author Contributions
IC and MŠ conceived and designed the study. MŠ acquired the data. IC and MŠ analyzed and interpreted the data. IC and MŠ drafted the manuscript. IC and MŠ critically revised the manuscript for important intellectual content. IC and MŠ gave approval of the version to be submitted; IC and MŠ agree to be accountable for all aspects of 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: 'A Self-Report Measure of Diabetes Self-Management for Type 1 and Type 2 Diabetes:
The Diabetes Self-Management Questionnaire-Revised (DSMQ-R) – Clinimetric Evidence
From Five Studies'
authors:
- Andreas Schmitt
- Bernhard Kulzer
- Dominic Ehrmann
- Thomas Haak
- Norbert Hermanns
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012087
doi: 10.3389/fcdhc.2021.823046
license: CC BY 4.0
---
# A Self-Report Measure of Diabetes Self-Management for Type 1 and Type 2 Diabetes: The Diabetes Self-Management Questionnaire-Revised (DSMQ-R) – Clinimetric Evidence From Five Studies
## Abstract
### Aims
Measurement tools to evaluate self-management behavior are useful for diabetes research and clinical practice. The Diabetes Self-Management Questionnaire (DSMQ) was introduced in 2013 and has become a widely used tool. This article presents a revised and updated version, DSMQ-R, and evaluates its properties in assessing self-management practices in type 1 diabetes (T1D) and type 2 diabetes (T2D).
### Methods
The DSMQ-R is a multidimensional questionnaire with 27 items regarding essential self-management practices for T1D and T2D (including diabetes-adjusted eating, glucose testing/monitoring, medication taking, physical activity and cooperation with the diabetes team). For the revised form, the original items were partially amended and the wording was updated; eleven items were newly added. The tool was applied as part of health-related surveys in five clinical studies (two cross-sectional, three prospective) including a total of 1,447 people with T1D and T2D. Using this data base, clinimetric properties were rigorously tested.
### Results
The analyses showed high internal and retest reliability coefficients for the total scale and moderate to high coefficients for the subscales. Reliability coefficients for scales including the new items were consistently higher. Correlations with convergent criteria and related variables supported validity. Responsiveness was supported by significant short to medium term changes in prospective studies. Significant associations with glycemic outcomes were observed for DSMQ-R-assessed medication taking, glucose monitoring and eating behaviors.
### Conclusions
The results support good clinimetric properties of the DSMQ-R. The tool can be useful for research and clinical practice and may facilitate the identification of improvable self-management practices in individuals.
## Introduction
Diabetes mellitus is a chronic metabolic disease characterized by elevated blood glucose levels due to absolute [type 1 diabetes (T1D)] or relative [type 2 diabetes (T2D)] insulin deficiency [1]. The International Diabetes Federation estimates that 537 million adult people (20–79 years) are currently living with diabetes worldwide; the number is expected to rise to 643 million by 2030 [2]. Diabetes care aims to help people with diabetes achieve near-normal glycemic levels in order to reduce the risk of long-term (e.g., vascular) complications of diabetes while avoiding acute metabolic risks and preserving best possible quality of life [3].
The key factor to achieving good glycemic levels is the person with diabetes’s self-management of their condition. People with diabetes may need to control carbohydrate intake via their selection of foods, adapt eating behaviors with regard to glycemic load, fats and healthy nutrition, manage blood glucose using glucose-lowering medications, monitor glucose levels using blood tests or sensors, engage in sufficient physical exercise (to optimize glycemia, manage weight or maintain good health) and arrange their activities around current glycemic levels and treatment requirements, as recommended by current guidelines (4–6). Where rapid acting insulin is used (to cover glucose rises after meals), estimating carbohydrate loads of the meals, dose-adjusting insulin doses and correcting elevated glucose levels are additional required practices of daily diabetes self-management.
Persistent or recurrent hyperglycemia increases the risk for developing serious long-term complications of diabetes such as diabetic retinopathy, neuropathy, nephropathy and foot syndrome; further, suboptimal glycemic management is associated with increased risks of acute metabolic complications such as severe hypoglycemia or severe hyperglycemia with the risk of ketoacidosis or hyperosmolar coma (7–9). Therefore, the adoption and maintenance of functional self-management behaviors to achieve good glycemic outcome is decisive for maintaining good health and preventing complications and morbidity [10]. However, evidence supports that people with diabetes’ self-management practices and overall performance are often improvable [11, 12]; this may be particularly true for people with comorbid mental conditions such as depression and diabetes-specific distress (13–15).
Since self-management is the decisive determinant of the course of diabetes, reflecting/monitoring relevant behaviors in individuals to identify areas of potential improvement and offer suitable education and support may be useful for routine clinical practice. The assessment and evaluation of diabetes self-management behaviors may be of particular interest in people with persistent suboptimum diabetes outcomes where possible problems and barriers are to be detected. Furthermore, measuring self-management may be required as part of research where facilitators and barriers to optimal diabetes care, including mental factors, shall be analyzed [e.g. [15, 16]] or effects of interventions (e.g., diabetes self-management education) are to be evaluated. Thus, suitable measurement tools are required.
Several systematic reviews of available measurement tools for diabetes self-management confirm that many different tools have been developed; however, most instruments have been applied in limited numbers of studies and the testing of measurement properties was often limited, with few scales meeting rigorous appraisal criteria, according to the reviewers’ conclusions (17–20). These problems may limit the available tools’ usability for research and practice.
In 2013, the Diabetes Self-Management Questionnaire [DSMQ [21]] was introduced to provide a multidimensional measure of diabetes self-management behaviors relevant for the control of glycemia in both major types of diabetes and to overcome limitations of contemporary questionnaires [e.g. [22]]. In direct comparisons, the DSMQ explained significantly more glycemic variation than an established standard self-care scale [21, 23]. Since then, it has been translated into diverse languages and used in many studies, supporting its potential value for research and practice. A recent systematic review listed the DSMQ as one of only three scales on diabetes self-management which met the COSMIN (COnsensus-based Standards for the selection of health Measurement Instruments) guidelines for measurement tools that can be recommended for use and results obtained with can be trusted [20].
However, technological innovations such as continuous glucose monitoring and automatic insulin delivery have changed terms and expressions in diabetes care. Furthermore, a shift in diabetes-related language has taken place [24]. Also, some specific self-management aspects should be better covered by the tool. For these reasons, a revision of the DSMQ was needed. The present article presents a revised and updated version of the tool and rigorous testing of its clinimetric properties and functions. Experiences with the tool’s use within five clinical studies provides a broad evidence base to inform about its characteristics and potentials.
## Diabetes Self-Management Questionnaire (DSMQ)
The DSMQ is a multidimensional questionnaire consisting of self-descriptive statements from the person’s point of view (Table 1). Respondents are asked to reflect their self-management behaviors over the past weeks and rate to which extent each statement applies to them. An eight-week reference period was chosen to cover behaviors explaining present HbA1c; however, a shorter period (e.g., four weeks) might support the reflection of short-term changes, thus adaption of the instruction, where needed, might be considered. Responses are given on a four-point scale (from 0–’does not apply to me’ to 3–’applies to me very much’). Item scores are summed to scale scores reflecting the following specific activities: adjusting one’s diet towards diabetes (subscale ‘eating behavior’), taking medications consistently (subscale ‘medication taking’), testing/monitoring blood glucose or interstitial glucose (subscale ‘glucose monitoring’), being physically active to improve diabetes and health (subscale ‘physical activity’) and interacting with one’s diabetes-treating physician/healthcare professionals (subscale ‘cooperation with diabetes team’). A total score as a global measure of diabetes self-management can be calculated. Raw sum scores are transformed to a range from 0–10 for better interpretability and comparability (by dividing the raw sum score by the maximum possible sum of the scale [i.e., item number * 3] and multiplying with 10; details on scoring in Supplementary Table 1). The tool contains positively and negatively keyed items for greater validity and reliability (e.g., avoidance of one-sided, biased responses); negatively keyed items are reverse-scored before summing, thus higher scale scores reflect more optimal behavior. Since its introduction in 2013, the tool has been widely adopted and used for research and practice across countries and languages (Supplementary Table 2).
**Table 1**
| Original version with 16 items | Original version with 16 items.1 | Unnamed: 2 | Revised version with 27 items | Revised version with 27 items.1 |
| --- | --- | --- | --- | --- |
| No. | Item | Level of revision1 | No. | Item |
| 1 | I check my blood sugar levels with care and attention. (gm)2 | ≈ | 1 | I check my glucose levels with care and attention. (gm)2 |
| 2 | The food I choose to eat makes it easy to achieve optimal blood sugar levels. (eb) | ≈ | 2 | The foods I choose to eat make it easy for me to achieve good glucose levels. (eb) |
| 3 | I keep all doctors’ appointments recommended for my diabetes treatment. (cdt) | < | 3 | I regularly see the doctor (/diabetes specialist) regarding my diabetes. (cdt) |
| 4 | I take my diabetes medication (e.g. insulin, tablets) as prescribed. (mt)3 | ≈ | 4 | I take my diabetes medication (e.g. insulin, tablets) consistently and reliably. (mt)3 |
| 5 | Occasionally I eat lots of sweets or other foods rich in carbohydrates. (eb)r | ≈ | 5 | I occasionally eat large amounts of sweets or other foods rich in carbohydrates. (eb)r |
| 6 | I record my blood sugar levels regularly (or analyse the value chart with my blood glucose meter). (gm)2 | < | 6 | I keep a diary/log of my glucose levels to inform and improve my diabetes management. (gm)2 |
| 7 | I tend to avoid diabetes-related doctors’ appointments. (cdt)r | < | 7 | I tend to avoid seeing the doctor (/diabetes specialist) regarding my diabetes. (cdt)r |
| 8 | I do regular physical activity to achieve optimal blood sugar levels. (pa) | < | 8 | I am regularly physically active to improve my diabetes and health. (pa) |
| 9 | I strictly follow the dietary recommendations given by my doctor or diabetes specialist. (eb) | < | 9 | I follow the current dietary recommendations for people with diabetes (e.g. given to me by my doctor or diabetes specialist). (eb) |
| 10 | I do not check my blood sugar levels frequently enough as would be required for achieving good blood glucose control. (gm)2r | ≈ | 10 | I do not check my glucose levels frequently enough for achieving good blood glucose control. (gm)2r |
| 11 | I avoid physical activity although it would improve my diabetes. (pa)r | ≈ | 11 | I avoid physical activity although it would be good for my diabetes. (pa)r |
| 12 | I tend to forget to take or skip my diabetes medication (e.g. insulin, tablets). (mt)3r | ≈ | 12 | I tend to forget or skip taking my diabetes medication (e.g. insulin, tablets). (mt)3r |
| 13 | Sometimes I have real ‘food binges’ (not triggered by hypoglycemia). (eb)r | = | 13 | Sometimes I have real ‘food binges’ (not triggered by hypoglycemia). (eb)r |
| 14 | Regarding my diabetes care, I should see my medical practitioner(s) more often. (cdt)r | < | 14 | Regarding my diabetes, I should see my doctor (/diabetes specialist) more often. (cdt)r |
| 15 | I tend to skip planned physical activity. (pa)r | < | 15 | I am less physically active than would be good for my diabetes. (pa)r |
| 16 | My diabetes self-care is poor. (ts)r | = | 20 | (see below) |
| | | / | 16 | I could improve my diabetes self-care considerably. (ts)r |
| | | / | 17 | I estimate the carbohydrate content of my meals/foods (to improve my diabetes control). (eb) |
| | | / | 18 | I eat without regard to my diabetes. (eb)r |
| | | / | 19 | I check and discuss my diabetes treatment with the doctor (/diabetes specialist) regularly. (cdt) |
| 16 | (see above) | = | 20 | My diabetes self-care is poor. (ts)r |
| | | / | 21 | I check my glucose levels before each meal.* |
| | | / | 22 | I adjust my insulin doses to the carbohydrate content of my meals.* |
| | | / | 23 | I adjust the timing of my insulin injections to the start of my meals.* |
| | | / | 24 | I adjust my insulin doses according to the current glucose levels and preceding or planned activities.* |
| | | / | 25 | I correct elevated glucose levels consistently whenever necessary.* |
| | | / | 26 | I carry fast carbohydrates to enable quick treatment of low blood glucose.* |
| | | / | 27 | In case of low blood glucose, I take appropriate amounts of carbohydrates to avoid causing high blood glucose.* |
## Original Version
The original version of the DSMQ consists of 16 items (Table 1) which were developed and selected in a systematic, iterative process: A set of newly developed and qualitatively piloted items were initially tested on a sample of 110 people and successively excluded until only those with good properties remained [21]. The resulting questionnaire was then administered to 261 people with T1D or T2D to evaluate measurement properties against a convergent standard measure; results supported reliability and validity [21]. A subsequent study yielded further supportive evidence [23].
## Revision
Reasons for the revision were: i) wording considered as improvable in single items, ii) findings suggesting limited reliability for the ‘cooperation with diabetes team’ subscale in some studies and iii) practices of dose-adjusting insulin injections and correcting glucose levels (where intensive insulin treatment applies) being insufficiently covered. The original scale was amended accordingly, that is: i) items were updated to conform with new technologies such as continuous glucose monitoring (CGM) and data management software; the potentially misleading term ‘blood sugar levels’ was replaced with ‘glucose levels’, referring to both blood and interstitial glucose; some items were revised to avoid compliance-oriented expressions (e.g., ‘strictly follow’ or ‘as prescribed’); ii) the ‘cooperation with diabetes team’ items were harmonized and one additional item was added to improve reliability; iii) seven items covering practices of intensive insulin treatment were added as an optional extra. Item-level amendments are given in detail in Supplementary Table 3; old and new items are compared in Table 1. In summary, two items remained unchanged, seven items were slightly revised and seven items were significantly altered but the essential meaning was kept (Table 1). The original item order was kept, except for item 16 which was repositioned as number 20. A total of eleven items were newly added, thereof four regarding general behaviors (no. 16–19) and seven (no. 21–27) regarding intensive insulin treatment practices specifically (e.g., adjusting insulin; correcting glucose levels), the latter given in a separate section with specific instruction.
The DSMQ-R thus contains a total of 27 items, 20 on general behaviors relevant for most people with diabetes and seven on specific insulin treatment behaviors. A total score is estimated using the 20 general items; where applicable, a 27-item total score including the optional items can be calculated. The subscale ‘eating behavior’ contains now six items and the subscale ‘cooperation with diabetes team’ four; ‘medication taking’, ‘glucose monitoring’ and ‘physical activity’ remain unchanged with two, three and three items, respectively; two of the 20 general items request global statements and are included in the total scale only (Table 1).
## Study Design and Data Collection
This evaluation of the DSMQ-R includes T1D and T2D. The analyzed data were acquired as part of five clinical studies, three cross-sectional, two prospective, conducted between 2015 and 2021. All studies were ethically approved and carried out in accordance with the Declaration of Helsinki. All participants provided written informed consent.
## Variables and Measurements
Besides the DSMQ-R, the following variables were assessed as part of the studies: Glycemic outcome: Glycated hemoglobin (HbA1c) was estimated from venous blood samples taken at the same time as the questionnaire assessments in all studies. HbA1c was usually estimated in a central laboratory (at the Diabetes Center Mergentheim) using high performance liquid chromatography (performed with the Bio-Rad Variant II Turbo analyzer in studies 2 and 3 and the Tosoh Automated Glycohemoglobin Analyzer HLC-723G11 in studies 4 and 5), meeting IFCC standard [laboratory normal range 4.3–$6.1\%$ (24–43 mmol/mol)]; study 1 included four different laboratory cites.
Study 4 additionally assessed glycemic levels over four weeks using intermittently scanned CGM. The following CGM-derived parameters were calculated: mean sensor glucose (in mg/dl), time in range (% values between 70–180 mg/dl, 3.9–10 mmol/l), time below range (% values <70 mg/dl, <3.9 mmol/l), time above range (% values >180 mg/dl, >10 mmol/l), and glucose variability [coefficient of variation (CV)].
Diabetes self-care activities: The 10-item Summary of Diabetes Self-Care Activities Measure [SDSCA [22, 30]] was applied as a convergent measure of diabetes self-management in study 2. The tool requests on how many days of the past week the person engaged in healthy eating, exercising, blood sugar testing and foot care. Responses are averaged to scales (e.g., Diet, Exercise, Blood Sugar Testing) with scores ranging from 0–7 and higher values reflecting more frequent activity.
Diabetes distress and diabetes-specific problems: The 20-item Problem Areas in Diabetes Scale (PAID) measuring diabetes-related distress [31] was applied in all studies. The questionnaire requests ratings of diabetes-specific emotional problems on a five-point scale (0–’not a problem’ to 4–’serious problem’). The item scores are summed and transformed to a total score ranging from 0–100; higher scores reflect higher distress; scores ≥40 suggest meaningful distress [32]. In study 1, the 5-item short form [PAID-5 [33]] was used.
In studies 2–5, the Diabetes Distress Scale [DDS [34]] or T1-Diabetes Distress Scale [T1-DDS [35]] was administered in addition to the PAID. The DDS/T1-DDS items address a range of diabetes-specific problems; however, it also includes items and scales whose relations to the construct of diabetes distress have been questioned [14, 32, 36]. Therefore, we did not estimate a total score but rather selected specific items whose contents regarding self-management-related problems could be used for the correlation analysis (i.e., DDS items 6, 8 and 12 on ‘not testing blood sugars frequently enough’, ‘often failing with diabetes routine’ and ‘not sticking closely enough to a good meal plan’, and T1-DDS items 2, 8, 12, 23 and 28 on ‘not eating as carefully as one should’, ‘not taking as much insulin as one should’, ‘not checking blood glucose as often as one should’, ‘eating being out of control’ and ‘not giving diabetes as much attention as one should’); these aspects were assessed as convergent criteria for corresponding DSMQ-R scales. Items regarding doctor-related problems (i.e., DDS item 15 on ‘not having a doctor who one can see regularly about diabetes’ and T1-DDS items 7 and 18, ‘can’t tell diabetes doctor what is really on my mind’, ‘diabetes doctor doesn’t really understand what it’s like to have diabetes’) were used for correlation with the DSMQ-R scale ‘cooperation with diabetes team’. Responses in the DDS/T1-DDS are given on a six-point scale (1–’not a problem’ to 6–’a very serious problem’), thus higher scores reflect greater problems.
Diabetes acceptance, a measure of psychological adjustment to living with diabetes, was assessed using the Diabetes Acceptance Scale (DAS); in studies 1–3, the full 20-item version was used, in studies 4–5, the 10-item short form [25]. The items request aspects of acceptance and integration (e.g., ‘I accept diabetes as part of my life’) versus avoidance, neglect and demotivation (e.g., ‘I avoid dealing with topics related to diabetes’). Responses are given on a four-point scale (0–’never true for me’ to 3–’always true for me’). Item scores are summed so that higher scores reflect higher acceptance (range 0–60). Higher acceptance scores have been associated with more optimal self-management [25, 37]. Besides the total score, items specifically related to treatment motivation (e.g., ‘I have difficulties to motivate myself to perform good diabetes self-care’) and treatment neglect (e.g., ‘I neglect diabetes self-care because I want to avoid topics related to diabetes’) were aggregated to subscales (Cronbach’s α=0.71 and 0.83, respectively).
Diabetes treatment satisfaction was measured using the Diabetes Treatment Satisfaction Questionnaire (DTSQ) in study 1, including six satisfaction-related items and a 7-point scale (0–’very dissatisfied’ to 6–’very satisfied’). Items are summed to a total score from 0–64; higher scores reflect higher satisfaction [38]. Higher treatment satisfaction was expected to be associated with more optimal treatment behavior (DSMQ-R).
Depressive symptoms were assessed in all studies due to their high prevalence in diabetes as well as the studies focusing on depression and mental health. Studies included either the Patient Health Questionnaire-9 (PHQ-9) or the Center for Epidemiologic Studies Depression Scale (CES-D); both have excellent properties [39]. The PHQ-9 assesses the nine symptoms of major depression according to DSM-5 during the past two weeks. Responses are given on a four-point scale (0–’not at all’ to 3–’nearly every day’). Total score range is 0–27; higher scores indicate more symptoms. The CES-D assesses 20 depressive symptoms during the past week; responses are given on a four-point scale (0–’rarely or none of the time’ to 3–’most or all of the time’), resulting in a total score from 0–60 (higher scores=more symptoms). Depressive symptoms have been consistently associated with less optimal self-management across behaviors [e.g. [13]].
Daily diabetes problems/burdens: The DIA-LINK studies included a smartphone-based EMA with daily diabetes-related questions over 17 days [29]. Items constituting likely correlates of the DSMQ-R were used as convergent criteria (e.g., ‘How much have you felt guilty when neglecting your diabetes treatment today?’; full item details in Supplementary Table 4). Responses were given on a scale from 0–’not at all’ to 10–’very much’. Daily responses were averaged.
Demographic and person-related variables comprised sex, age, BMI, diabetes type, diabetes duration and treatment regimen. Long-term and acute complications of diabetes (study 1) were based on medical examinations, laboratory assessments and interviews (assessed were diabetic retinopathy, neuropathy, nephropathy, foot syndrome; treated ketoacidosis, past 12 months). Mean numbers of daily insulin injections (where applicable) and daily glucose tests or scans/readings as well as frequencies of diabetologist visits per past six months were assessed in face-to-face interviews.
## Statistical Analyses
Statistical analyses were performed using SPSS 26.0.0 (IBM SPSS Statistics). P values < 0.05 (two-tailed) were considered to indicate statistical significance. For the DSMQ-R, total and subscale scores were calculated as per scoring instruction (Supplementary Table 1) with scores ranging between 0 and 10. Negatively-keyed items were reverse-scored so that higher scale scores suggest more optimal behavior. Where applicable, a 27-item total score was calculated in addition to the 20-item total; yet the optional items were not included in subscale scores to warrant comparisons of scores between subgroups. Measurement functions were analyzed according to clinimetric criteria [40]. Internal reliability was analyzed using Cronbach’s α; since potential preference of McDonald’s ω over α has been discussed [41], ω was additionally estimated [using Hayes’ OMEGA macro for SPSS [41]]. Reproducibility was tested using retest correlations in the prospective studies. Construct validity was evaluated via correlations with convergent measures and related variables to develop a nomological network. Since adjusting eating behaviors towards diabetes, taking medications consistently and checking glucose levels regularly can be expected to result in better glycemic levels, associations between the corresponding DSMQ-R scales and glycemic outcomes were analyzed as indicators of validity. Similarly, associations with acute and long-term complications were assessed in study 1. Further, associations between the DSMQ-R scales and convergent measures of self-care activities, treatment satisfaction, treatment motivation and neglect as well as diabetes acceptance, diabetes distress and depressive symptoms were analyzed. Structural validity was assessed using confirmatory factor analyses (AMOS 26.0.0, IBM SPSS Statistics). Model fit was evaluated according to Comparative Fit Index (CFI) ≥ 0.95, Tucker Lewis Index (TLI) ≥ 0.95, Standardized Root Mean Square Residual (SRMR) ≤ 0.08 and Root Mean Square Error of Approximation (RMSEA) ≤ 0.06. Responsiveness, the ability to detect change, was assessed via changes of the DSMQ-R scales in prospective studies, given as Cohen’s d. Where applicable, changes were compared between treatment groups (i.e., study 2, with participants randomized to either depression treatment or diabetes care as usual).
## Sample Characteristics
The sample characteristics are given in Table 2. Studies 1–3 had mixed samples including people with T1D and T2D (T1D being overrepresented in line with secondary and tertiary care enrolment), study 4 and 5 assessed only T1D or T2D, respectively. Sample sizes varied between 180 and 588. Study 1 contained a more general sample, whereas other studies overrepresented people with specific mental aspects: study 2 contained people with current depressive symptoms, study 3 contained people with a history of depressive symptoms and studies 4 and 5 included majorities with either depressive symptoms or diabetes distress. All samples had a wide age range with a mean age between 45 and 53 years, except for study 4 (T1D only) whose sample’s mean age was 39 years. The mean diabetes duration reflected relatively long-standing diabetes throughout. HbA1c levels were generally elevated with mean values around 7.8 to $9.3\%$ (62 to 78 mmol/mol) across the studies.
**Table 2**
| Studies | n | Sample characteristics | DSMQ-R scales | N items (item numbers) | Mean ±SD scale scores | Mean ±SD scale scores.1 | Internal reliability coefficients (Cronbach’s α [McDonald’s ω]) | Internal reliability coefficients (Cronbach’s α [McDonald’s ω]).1 | Test-retest reliability coefficients (Pearson’s r) | Test-retest reliability coefficients (Pearson’s r).1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | | T1D | T2D | T1D | T2D | T1D | T2D |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | 20-item total score | 20 (1–20) | 6.4±2.0 | 6.4 ±1.5 | 0.92 (0.92) | 0.84 (0.84) | | |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | 27-item total score1 | 27 (1–27) | 6.4 ±2.1 | 6.6 ±1.61 | 0.94 (0.95) | 0.90 (0.90)1 | | |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | Eating behavior | 6 (2,5r,9,13r,17,18r) | 5.6 ±2.2 | 5.5 ±2.0 | 0.81 (0.81) | 0.75 (0.76) | | |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | Medication taking | 2 (4,12r) | 8.1 ±2.8 | 8.7 ±2.0 | 0.84 (n/a) | 0.66 (n/a) | | |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | Glucose monitoring | 3 (1,6,10r) | 6.4 ±3.2 | 6.9 ±2.9 | 0.82 (0.83) | 0.78 (0.79) | | |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | Physical activity | 3 (8,11r,15r) | 5.8 ±2.9 | 4.8 ±2.6 | 0.84 (0.84) | 0.72 (0.72) | | |
| Study 1: Cross-sectional questionnaire study (2015–16) | 588.0 | Adults with T1D or T2DØ age: 49.5 ±15.2 (range 18–82) years55.6% women n=333 with T1D n=255 with T2D (thereof 87 with MDI1)Ø DM duration: 15.7 ±11.2 yearsØ HbA1c: 8.2% ±1.6 (66 mmol/mol ±18)Ø PHQ-9 depression score: 7.7 ±5.6 | Cooperation with diabetes team | 4 (3,7r,14r,19) | 7.8 ±2.3 | 8.0 ±1.8 | 0.78 (0.78) | 0.56 (0.56) | | |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | 20-item total score | 20 (1–20) | 5.4 ±2.1 | 5.4 ±1.7 | 0.91 (0.91) | 0.87 (0.86) | 0.62 | 0.48 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | 27-item total score1 | 27 (1–27) | 5.4 ±2.0 | 5.7 ±1.71 | 0.93 (0.93) | 0.91 (0.90)1 | 0.61 | 0.621 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | Eating behavior | 6 (2,5r,9,13r,17,18r) | 4.7 ±2.2 | 4.2 ±2.1 | 0.78 (0.79) | 0.77 (0.76) | 0.57 | 0.46 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | Medication taking | 2 (4,12r) | 7.4 ±2.9 | 7.9 ±2.5 | 0.80 (n/a) | 0.81 (n/a) | 0.62 | 0.54 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | Glucose monitoring | 3 (1,6,10r) | 4.9 ±3.3 | 5.4 ±3.6 | 0.81 (0.82) | 0.89 (0.90) | 0.53 | 0.73 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | Physical activity | 3 (8,11r,15r) | 5.1 ±3.3 | 4.3 ±2.7 | 0.87 (0.87) | 0.76 (0.77) | 0.68 | 0.23 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months | 198.0 | Adults with T1D or T2D with elevated depressive symptomsØ age: 45.4 ±13.6 (range 18–69) years57.6% women n=131 with T1D n=67 with T2D (thereof 40 with MDI1)Ø DM duration: 15.8 ±9.6 yearsØ HbA1c: 9.3% ±1.4 (78 mmol/mol ±15)Ø CES-D depression score: 24.0 ±11.0 | Cooperation with diabetes team | 4 (3,7r,14r,19) | 7.0 ±2.8 | 7.8 ±2.3 | 0.85 (0.85) | 0.73 (0.73) | 0.46 | 0.54 |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | 20-item total score | 20 (1–20) | 6.7 ±1.6 | 6.3 ±1.7 | 0.88 (0.88) | 0.89 (0.89) | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | 27-item total score1 | 27 (1–27) | 6.9 ±1.6 | 6.5 ±1.61 | 0.91 (0.91) | 0.91 (0.91)1 | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | Eating behavior | 6 (2,5r,9,13r,17,18r) | 5.8 ±1.8 | 5.3 ±2.1 | 0.72 (0.73) | 0.78 (0.80) | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | Medication taking | 2 (4,12r) | 8.3 ±2.2 | 8.6 ±2.4 | 0.73 (n/a) | 0.72 (n/a) | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | Glucose monitoring | 3 (1,6,10r) | 7.0 ±2.6 | 6.6 ±3.0 | 0.72 (0.74) | 0.80 (0.80) | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | Physical activity | 3 (8,11r,15r) | 6.0 ±3.0 | 4.5 ±2.8 | 0.88 (0.88) | 0.86 (0.87) | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study | 298.0 | Adults with T1D or T2D and a history of depressive symptomsØ age: 52.3 ±13.3 (range 23–77) years57.7% women n=191 with T1D n=107 with T2D (thereof 79 with MDI1)Ø DM duration: 21.6 ±11.1 yearsØ HbA1c: 7.8% ±1.1 (62 mmol/mol ±12)Ø PHQ-9 depression score: 8.2 ±5.0 | Cooperation with diabetes team | 4 (3,7r,14r,19) | 8.4 ±2.1 | 8.2 ±1.9 | 0.80 (0.80) | 0.65 (0.66) | | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | 20-item total score | 20 (1–20) | 5.8 ±1.8 | | 0.88 (0.88) | | 0.66 | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | 27-item total score | 27 (1–27) | 6.0 ±1.7 | | 0.91 (0.91) | | 0.64 | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | Eating behavior | 6 (2,5r,9,13r,17,18r) | 4.6 ±2.1 | | 0.74 (0.75) | | 0.66 | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | Medication taking | 2 (4,12r) | 7.4 ±2.6 | | 0.78 (n/a) | | 0.53 | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | Glucose monitoring | 3 (1,6,10r) | 5.8 ±2.8 | | 0.69 (0.72) | | 0.55 | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | Physical activity | 3 (8,11r,15r) | 5.5 ±3.2 | | 0.87 (0.87) | | 0.69 | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months | 203.0 | Adults with T1D, majority with diabetes distress and/or depressive symptomsØ age: 38.6 ±12.8 (range 18–69) years58.1% women100% with T1DØ DM duration: 18.5 ±11.7 yearsØ HbA1c: 8.7% ±1.9 (71 mmol/mol ±21)Ø CES-D depression score: 21.3 ±11.4Ø PAID diabetes distress score: 40.1 ±18.8 | Cooperation with diabetes team | 4 (3,7r,14r,19) | 7.9 ±2.4 | | 0.83 (0.83) | | 0.59 | |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | 20-item total score | 20 (1–20) | | 5.4 ±1.8 | | 0.89 (0.89) | | 0.58 |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | 27-item total score1 | 27 (1–27) | | 5.7 ±1.81 | | 0.92 (0.92)1 | | 0.571 |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | Eating behavior | 6 (2,5r,9,13r,17,18r) | | 4.3 ±2.2 | | 0.80 (0.80) | | 0.56 |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | Medication taking | 2 (4,12r) | | 7.5 ±2.9 | | 0.82 (n/a) | | 0.55 |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | Glucose monitoring | 3 (1,6,10r) | | 5.1 ±3.1 | | 0.79 (0.80) | | 0.48 |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | Physical activity | 3 (8,11r,15r) | | 4.2 ±3.1 | | 0.85 (0.86) | | 0.62 |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months | 180.0 | Adults with T2D, majority with diabetes distress and/or depressive symptomsØ age: 52.9 ±9.8 (range 23–70) years38.9% women100% with T2D (thereof 121 with MDI1)Ø DM duration: 12.1 ±8.0 yearsØ HbA1c: 9.1% ±1.7 (76 mmol/mol ±19)Ø CES-D depression score: 22.2 ±12.1Ø PAID diabetes distress score: 41.7 ±19.1 | Cooperation with diabetes team | 4 (3,7r,14r,19) | | 8.1 ±2.0 | | 0.73 (0.74) | | 0.50 |
## Internal Reliability
Cronbach’s α of the 20-item total scale varied from 0.88–0.92 (mean=0.90) in T1D and from 0.84–0.89 (mean=0.87) in T2D across studies. Coefficients were slightly higher for the 27-item total scale, where applicable (Table 2). For the subscales, mean coefficients α for T1D (T2D) were: ‘eating behavior’=0.76 (0.78), ‘medication taking’=0.79 (0.75), ‘glucose monitoring’=0.76 (0.82), ‘physical activity’=0.87 (0.80) and ‘cooperation with diabetes team’=0.82 (0.67). McDonald’s ω yielded consistent results (Table 2). Direct comparisons of scale reliabilities estimated including the newly added items versus original ones only yielded consistently higher reliability coefficients for the new scales (Supplementary Table 5).
## Reproducibility
Retest correlations over three to six months reflected sufficient intra-individual stability of the measurement over time. Mean correlations for 20-item total scale were 0.64 in T1D and 0.53 in T2D; mean correlations for the subscales were from 0.53–0.69 in T1D and from 0.43–0.61 in T2D (Table 2).
## Construct Validity
Correlations with convergent criteria were generally in line with expectations towards validity of the measurement and a meaningful nomological network.
Total scale: Higher DSMQ-R total scores (suggesting more optimal self-management) were consistently associated with better HbA1c values across studies and diabetes types; however, the sizes of associations varied (e.g., from -0.29 to -0.57, mean = -0.41, in T1D and from -0.20 to -0.36, mean=-0.30, in T2D; 20-item total). Higher DSMQ-R total scores were also associated with lower mean sensor glucose, more time in range, less time above range and lower glucose variability in T1D (study 4). Further, higher DSMQ-R total scores were associated with lower rates of long-term complications and less events of ketoacidosis (T1D). DSMQ-R total scores were highly positively associated with convergent measures of treatment motivation, treatment satisfaction and self-management performance according to the SDSCA questionnaire and corresponding DDS/T1-DDS items (Table 3); and highly negatively with items reflecting suboptimal treatment behavior. In studies 4 and 5, significant correlations with EMA items reflecting self-management were observed. Finally, higher DSMQ total scores were seen in people with better mental health, lower diabetes distress and less depressive symptoms.
**Table 3**
| Studies | DSMQ scales | Convergent criteria/outcome variables | Correlations (Pearson’s r) with criteria/outcomes | Correlations (Pearson’s r) with criteria/outcomes.1 | Baseline to follow-up changes (Cohen’s d) per scale | Baseline to follow-up changes (Cohen’s d) per scale.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | | | T1D | T2D | T1D | T2D |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | HbA1c | -0.57‡ | -0.36‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | Ketoacidosis past year (yes) | -0.22‡ | | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | With complications2 (yes) | -0.17† | -0.20† | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | Treatment satisfaction (DTSQ score) | 0.63‡ | 0.53‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | Treatment motivation (DAS subscale) | 0.68‡ | 0.56‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | Treatment neglect (DAS subscale) | -0.77‡ | -0.68‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | Diabetes distress (PAID-5 score) | -0.42‡ | -0.27‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 20-item total score | Depressive symptoms (PHQ-9 score) | -0.49‡ | -0.43‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | HbA1c | -0.57‡ | -0.49‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | Ketoacidosis past year (yes) | -0.21† | | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | With complications2 (yes) | -0.17† | -0.25* | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | Treatment satisfaction (DTSQ score) | 0.64‡ | 0.53‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | Treatment motivation (DAS subscale) | 0.71‡ | 0.61‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | Treatment neglect (DAS subscale) | -0.76‡ | -0.69‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | Diabetes distress (PAID-5 score) | -0.47‡ | -0.40‡ | | |
| Study 1: Cross-sectional questionnaire study (2015–16); n=588 PWD (333 with T1D, 255 with T2D) | 27-item total score1 | Depressive symptoms (PHQ-9 score) | -0.53‡ | -0.48‡ | | |
| | Eating behavior | HbA1c | -0.43‡ | -0.31‡ | | |
| | Medication taking | HbA1c | -0.55‡ | -0.42‡ | | |
| | Glucose monitoring | HbA1c | -0.53‡ | -0.15* | | |
| | Physical activity | BMI | -0.10 | -0.28‡ | | |
| | Cooperation with diabetes team | Self-reported n of diabetologist visits past year | 0.14* | 0.09 | | |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 20-item total score | HbA1c | -0.36‡ | -0.30* | Total sample: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.66). EG: 6.0 ±1.8 to 6.8 ±1.6† (d=0.50), CG: 5.7 ±1.9 to 6.8 ±1.4‡ (d=0.82), p (time*group)=0.66 | Total sample: 5.3 ±1.5 to 6.1 ±1.6† (d=0.51). EG: 5.2 ±1.3 to 5.9 ±1.5† (d=0.51), CG: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.53), p (time*group)=0.89 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 20-item total score | Summary of diabetes self-care activities past week (SDSCA total score) | 0.76‡ | 0.77‡ | Total sample: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.66). EG: 6.0 ±1.8 to 6.8 ±1.6† (d=0.50), CG: 5.7 ±1.9 to 6.8 ±1.4‡ (d=0.82), p (time*group)=0.66 | Total sample: 5.3 ±1.5 to 6.1 ±1.6† (d=0.51). EG: 5.2 ±1.3 to 5.9 ±1.5† (d=0.51), CG: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.53), p (time*group)=0.89 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 20-item total score | Often failing with diabetes routine (DDS item 8) | -0.69‡ | -0.54‡ | Total sample: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.66). EG: 6.0 ±1.8 to 6.8 ±1.6† (d=0.50), CG: 5.7 ±1.9 to 6.8 ±1.4‡ (d=0.82), p (time*group)=0.66 | Total sample: 5.3 ±1.5 to 6.1 ±1.6† (d=0.51). EG: 5.2 ±1.3 to 5.9 ±1.5† (d=0.51), CG: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.53), p (time*group)=0.89 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 20-item total score | Diabetes distress (PAID score) | -0.28‡ | -0.24* | Total sample: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.66). EG: 6.0 ±1.8 to 6.8 ±1.6† (d=0.50), CG: 5.7 ±1.9 to 6.8 ±1.4‡ (d=0.82), p (time*group)=0.66 | Total sample: 5.3 ±1.5 to 6.1 ±1.6† (d=0.51). EG: 5.2 ±1.3 to 5.9 ±1.5† (d=0.51), CG: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.53), p (time*group)=0.89 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 20-item total score | Diabetes acceptance (DAS score) | 0.51‡ | 0.40† | Total sample: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.66). EG: 6.0 ±1.8 to 6.8 ±1.6† (d=0.50), CG: 5.7 ±1.9 to 6.8 ±1.4‡ (d=0.82), p (time*group)=0.66 | Total sample: 5.3 ±1.5 to 6.1 ±1.6† (d=0.51). EG: 5.2 ±1.3 to 5.9 ±1.5† (d=0.51), CG: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.53), p (time*group)=0.89 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 20-item total score | Depressive symptoms (PHQ-9 score) | -0.28† | -0.22 | Total sample: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.66). EG: 6.0 ±1.8 to 6.8 ±1.6† (d=0.50), CG: 5.7 ±1.9 to 6.8 ±1.4‡ (d=0.82), p (time*group)=0.66 | Total sample: 5.3 ±1.5 to 6.1 ±1.6† (d=0.51). EG: 5.2 ±1.3 to 5.9 ±1.5† (d=0.51), CG: 5.8 ±1.9 to 6.8 ±1.5‡ (d=0.53), p (time*group)=0.89 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 27-item total score1 | HbA1c | -0.39‡ | -0.32* | Total sample: 5.8 ±1.9 to 6.9 ±1.4‡ (d=0.72). EG: 6.0 ±1.8 to 6.9 ±1.5‡ (d=0.59), CG: 5.6 ±2.0 to 6.9 ±1.4‡ (d=0.85), p (time*group)=0.49 | Total sample1: 5.6 ±1.6 to 6.4 ±1.6† (d=0.57). EG: 5.3 ±1.1 to 6.5 ±1.2‡ (d=1.44), CG: 6.1 ±2.1 to 6.2 ±2.1 (d=0.06), p (time*group)=0.15 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 27-item total score1 | Summary of diabetes self-care activities past week (SDSCA total score) | 0.73‡ | 0.76‡ | Total sample: 5.8 ±1.9 to 6.9 ±1.4‡ (d=0.72). EG: 6.0 ±1.8 to 6.9 ±1.5‡ (d=0.59), CG: 5.6 ±2.0 to 6.9 ±1.4‡ (d=0.85), p (time*group)=0.49 | Total sample1: 5.6 ±1.6 to 6.4 ±1.6† (d=0.57). EG: 5.3 ±1.1 to 6.5 ±1.2‡ (d=1.44), CG: 6.1 ±2.1 to 6.2 ±2.1 (d=0.06), p (time*group)=0.15 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 27-item total score1 | Often failing with diabetes routine (DDS item 8) | -0.70‡ | -0.49‡ | Total sample: 5.8 ±1.9 to 6.9 ±1.4‡ (d=0.72). EG: 6.0 ±1.8 to 6.9 ±1.5‡ (d=0.59), CG: 5.6 ±2.0 to 6.9 ±1.4‡ (d=0.85), p (time*group)=0.49 | Total sample1: 5.6 ±1.6 to 6.4 ±1.6† (d=0.57). EG: 5.3 ±1.1 to 6.5 ±1.2‡ (d=1.44), CG: 6.1 ±2.1 to 6.2 ±2.1 (d=0.06), p (time*group)=0.15 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 27-item total score1 | Diabetes distress (PAID score) | -0.31‡ | -0.05 | Total sample: 5.8 ±1.9 to 6.9 ±1.4‡ (d=0.72). EG: 6.0 ±1.8 to 6.9 ±1.5‡ (d=0.59), CG: 5.6 ±2.0 to 6.9 ±1.4‡ (d=0.85), p (time*group)=0.49 | Total sample1: 5.6 ±1.6 to 6.4 ±1.6† (d=0.57). EG: 5.3 ±1.1 to 6.5 ±1.2‡ (d=1.44), CG: 6.1 ±2.1 to 6.2 ±2.1 (d=0.06), p (time*group)=0.15 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 27-item total score1 | Diabetes acceptance (DAS score) | 0.51‡ | 0.33* | Total sample: 5.8 ±1.9 to 6.9 ±1.4‡ (d=0.72). EG: 6.0 ±1.8 to 6.9 ±1.5‡ (d=0.59), CG: 5.6 ±2.0 to 6.9 ±1.4‡ (d=0.85), p (time*group)=0.49 | Total sample1: 5.6 ±1.6 to 6.4 ±1.6† (d=0.57). EG: 5.3 ±1.1 to 6.5 ±1.2‡ (d=1.44), CG: 6.1 ±2.1 to 6.2 ±2.1 (d=0.06), p (time*group)=0.15 |
| Study 2: ‘Depression and Diabetes Control Trial’ (2016–17), prospective randomized trial, retest after six months n=198 PWD (131 with T1D, 67 with T2D) | 27-item total score1 | Depressive symptoms (PHQ-9 score) | -0.31‡ | -0.13 | Total sample: 5.8 ±1.9 to 6.9 ±1.4‡ (d=0.72). EG: 6.0 ±1.8 to 6.9 ±1.5‡ (d=0.59), CG: 5.6 ±2.0 to 6.9 ±1.4‡ (d=0.85), p (time*group)=0.49 | Total sample1: 5.6 ±1.6 to 6.4 ±1.6† (d=0.57). EG: 5.3 ±1.1 to 6.5 ±1.2‡ (d=1.44), CG: 6.1 ±2.1 to 6.2 ±2.1 (d=0.06), p (time*group)=0.15 |
| | Eating behavior | HbA1c | -0.34‡ | -0.01 | Total sample: 5.0 ±2.2 to 5.7 ±1.9† (d=0.37). EG: 5.2 ±2.3 to 5.8 ±2.1 (d=0.30), CG: 4.9 ±2.2 to 5.7 ±1.8* (d=0.42), p (time*group)=0.93 | Total sample: 3.9 ±1.8 to 5.0 ±1.8† (d=0.59). EG: 3.7 ±1.8 to 4.6 ±1.8† (d=0.51), CG: 4.3 ±2.0 to 5.9 ±1.8* (d=0.71), p (time*group)=0.68 |
| | Eating behavior | Healthy eating past week (SDSCA General Diet scale) | 0.67‡ | 0.65‡ | Total sample: 5.0 ±2.2 to 5.7 ±1.9† (d=0.37). EG: 5.2 ±2.3 to 5.8 ±2.1 (d=0.30), CG: 4.9 ±2.2 to 5.7 ±1.8* (d=0.42), p (time*group)=0.93 | Total sample: 3.9 ±1.8 to 5.0 ±1.8† (d=0.59). EG: 3.7 ±1.8 to 4.6 ±1.8† (d=0.51), CG: 4.3 ±2.0 to 5.9 ±1.8* (d=0.71), p (time*group)=0.68 |
| | Eating behavior | Not sticking to a good meal plan (DDS item 12) | -0.57‡ | 0.54‡ | Total sample: 5.0 ±2.2 to 5.7 ±1.9† (d=0.37). EG: 5.2 ±2.3 to 5.8 ±2.1 (d=0.30), CG: 4.9 ±2.2 to 5.7 ±1.8* (d=0.42), p (time*group)=0.93 | Total sample: 3.9 ±1.8 to 5.0 ±1.8† (d=0.59). EG: 3.7 ±1.8 to 4.6 ±1.8† (d=0.51), CG: 4.3 ±2.0 to 5.9 ±1.8* (d=0.71), p (time*group)=0.68 |
| | Medication taking | HbA1c | -0.43‡ | -0.36† | Total sample: 7.8 ±2.7 to 8.8 ±1.8‡ (d=0.47). EG: 7.9 ±2.6 to 8.8 ±1.8* (d=0.43), CG: 7.7 ±2.9 to 8.8 ±1.9† (d=0.49), p (time*group)=0.74 | Total sample: 8.0 ±2.5 to 8.6 ±2.1‡ (d=0.27). EG: 7.9 ±2.5 to 8.4 ±2.2 (d=0.23), CG: 8.3 ±2.5 to 9.0 ±1.9 (d=0.28), p (time*group)=0.51 |
| | Medication taking | Self-reported n of daily insulin injections | 0.27† | -0.14 | Total sample: 7.8 ±2.7 to 8.8 ±1.8‡ (d=0.47). EG: 7.9 ±2.6 to 8.8 ±1.8* (d=0.43), CG: 7.7 ±2.9 to 8.8 ±1.9† (d=0.49), p (time*group)=0.74 | Total sample: 8.0 ±2.5 to 8.6 ±2.1‡ (d=0.27). EG: 7.9 ±2.5 to 8.4 ±2.2 (d=0.23), CG: 8.3 ±2.5 to 9.0 ±1.9 (d=0.28), p (time*group)=0.51 |
| | Glucose monitoring | HbA1c | -0.29† | -0.39† | Total sample: 5.5 ±3.4 to 7.3 ±2.5‡ (d=0.61). EG: 5.6 ±3.3 to 7.0 ±2.7† (d=0.48), CG: 5.3 ±3.5 to 7.5 ±2.4‡ (d=0.73), p (time*group)=0.20 | Total sample: 5.3 ±3.6 to 6.9 ±3.0‡ (d=0.64). EG: 4.7 ±3.6 to 6.6 ±3.0‡ (d=0.78), CG: 6.8 ±3.5 to 7.8 ±3.1* (d=0.39), p (time*group)=0.90 |
| | Glucose monitoring | SMBG past week (SDSCA Blood Sugar Testing scale) | 0.59‡ | 0.81‡ | Total sample: 5.5 ±3.4 to 7.3 ±2.5‡ (d=0.61). EG: 5.6 ±3.3 to 7.0 ±2.7† (d=0.48), CG: 5.3 ±3.5 to 7.5 ±2.4‡ (d=0.73), p (time*group)=0.20 | Total sample: 5.3 ±3.6 to 6.9 ±3.0‡ (d=0.64). EG: 4.7 ±3.6 to 6.6 ±3.0‡ (d=0.78), CG: 6.8 ±3.5 to 7.8 ±3.1* (d=0.39), p (time*group)=0.90 |
| | Glucose monitoring | Not testing sugar frequently enough (DDS item 6) | -0.74‡ | -0.69‡ | Total sample: 5.5 ±3.4 to 7.3 ±2.5‡ (d=0.61). EG: 5.6 ±3.3 to 7.0 ±2.7† (d=0.48), CG: 5.3 ±3.5 to 7.5 ±2.4‡ (d=0.73), p (time*group)=0.20 | Total sample: 5.3 ±3.6 to 6.9 ±3.0‡ (d=0.64). EG: 4.7 ±3.6 to 6.6 ±3.0‡ (d=0.78), CG: 6.8 ±3.5 to 7.8 ±3.1* (d=0.39), p (time*group)=0.90 |
| | Glucose monitoring | Self-reported n of daily SMBG checks | 0.36‡ | 0.61‡ | Total sample: 5.5 ±3.4 to 7.3 ±2.5‡ (d=0.61). EG: 5.6 ±3.3 to 7.0 ±2.7† (d=0.48), CG: 5.3 ±3.5 to 7.5 ±2.4‡ (d=0.73), p (time*group)=0.20 | Total sample: 5.3 ±3.6 to 6.9 ±3.0‡ (d=0.64). EG: 4.7 ±3.6 to 6.6 ±3.0‡ (d=0.78), CG: 6.8 ±3.5 to 7.8 ±3.1* (d=0.39), p (time*group)=0.90 |
| | Physical activity | BMI | -0.05 | -0.35† | Total sample: 5.1 ±3.0 to 5.4 ±3.0 (d=0.13). EG: 5.2 ±2.7 to 5.5 ±2.8 (d=0.12), CG: 4.9 ±3.3 to 5.3 ±3.3 (d=0.19), p (time*group)=0.99 | Total sample: 4.2 ±2.4 to 3.9 ±2.6 (d=0.10). EG: 4.2 ±2.3 to 4.2 ±2.7 (d=0.00), CG: 4.4 ±2.7 to 3.2 ±2.1 (d=0.37), p (time*group)=0.23 |
| | Physical activity | Physical exercise past week (SDSCA Exercise scale) | 0.73‡ | 0.72‡ | Total sample: 5.1 ±3.0 to 5.4 ±3.0 (d=0.13). EG: 5.2 ±2.7 to 5.5 ±2.8 (d=0.12), CG: 4.9 ±3.3 to 5.3 ±3.3 (d=0.19), p (time*group)=0.99 | Total sample: 4.2 ±2.4 to 3.9 ±2.6 (d=0.10). EG: 4.2 ±2.3 to 4.2 ±2.7 (d=0.00), CG: 4.4 ±2.7 to 3.2 ±2.1 (d=0.37), p (time*group)=0.23 |
| | Cooperation with diabetes team | Self-reported n of diabetologist visits past half year | 0.20* | 0.35† | Total sample: 7.9 ±2.3 to 8.5 ±1.7* (d=0.28). EG: 8.0 ±2.3 to 8.5 ±1.9 (d=0.22), CG: 7.8 ±2.4 to 8.4 ±1.5 (d=0.28), p (time*group)=0.89 | Total sample: 8.1 ±2.1 to 8.1 ±2.1 (d=0.00). EG: 8.1 ±1.8 to 8.3 ±1.8 (d=0.11), CG: 8.1 ±2.7 to 7.7 ±2.7 (d=0.17), p (time*group)=0.35 |
| | Cooperation with diabetes team | Not having a diabetes doctor (DDS item 15) | -0.30† | -0.57‡ | Total sample: 7.9 ±2.3 to 8.5 ±1.7* (d=0.28). EG: 8.0 ±2.3 to 8.5 ±1.9 (d=0.22), CG: 7.8 ±2.4 to 8.4 ±1.5 (d=0.28), p (time*group)=0.89 | Total sample: 8.1 ±2.1 to 8.1 ±2.1 (d=0.00). EG: 8.1 ±1.8 to 8.3 ±1.8 (d=0.11), CG: 8.1 ±2.7 to 7.7 ±2.7 (d=0.17), p (time*group)=0.35 |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 20-item total score | HbA1c | -0.41‡ | -0.34‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 20-item total score | Often failing with diabetes routine (DDS item 8) | -0.64‡ | -0.72‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 20-item total score | Diabetes distress (PAID score) | -0.32‡ | -0.34‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 20-item total score | Diabetes acceptance (DAS score) | 0.65‡ | 0.61‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 20-item total score | Depressive symptoms (PHQ-9 score) | -0.24† | -0.21* | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 27-item total score1 | HbA1c | -0.42‡ | -0.24* | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 27-item total score1 | Often failing with diabetes routine (DDS item 8) | -0.61‡ | -0.67‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 27-item total score1 | Diabetes distress (PAID score) | -0.26‡ | -0.29† | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 27-item total score1 | Diabetes acceptance (DAS score) | 0.63‡ | 0.57‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | 27-item total score1 | Depressive symptoms (PHQ-9 score) | -0.21† | -0.26* | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | Eating behavior | HbA1c | -0.29‡ | -0.35‡ | | |
| Study 3: Five-year FU of the DIAMOS-ECCE HOMO cohort (2017–18), cross-sectional study n=298 PWD (191 with T1D, 107 with T2D) | Eating behavior | Not sticking to a good meal plan (DDS item 12) | 0.50‡ | 0.54‡ | | |
| | Medication taking | HbA1c | -0.36‡ | -0.35‡ | | |
| | Medication taking | Self-reported n of daily insulin injections | 0.01 | 0.06 | | |
| | Glucose monitoring | HbA1c | -0.36‡ | -0.23* | | |
| | Glucose monitoring | Not testing sugar frequently enough (DDS item 6) | -0.67‡ | -0.72‡ | | |
| | Glucose monitoring | Self-reported n of daily glucose checks/scans/ readings | 0.33‡ | 0.39‡ | | |
| | Physical activity | BMI | -0.26‡ | -0.25* | | |
| | Cooperation with diabetes team | Not having a diabetes doctor (DDS item 15) | -0.31‡ | -0.14 | | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | HbA1c | -0.29‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Not giving diabetes as much attention as one should (T1-DDS item 28) | -0.61‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Diabetes distress (PAID score) | -0.27‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Diabetes acceptance (DAS-10 score) | 0.40‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Depressive symptoms (CES-D score) | -0.32‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Feeling guilty for neglecting diabetes treatment (mean of daily EMA ratings) | -0.42‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Feeling overwhelmed by diabetes treatment (mean of daily EMA ratings) | -0.23† | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Mean sensor glucose (4 weeks) | -0.30‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Time-below-range (<70 mg/dl) | -0.06 | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Time-in-range (70–180 mg/dl) | 0.31‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Time-above-range (>180 mg/dl) | -0.27‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 20-item total score | Glucose variability (CV) | -0.27‡ | | 5.8 ±1.7 to 6.4 ±1.5‡ (d=0.38) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | HbA1c | -0.33‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Not giving diabetes as much attention as one should (T1-DDS item 28) | -0.61‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Diabetes distress (PAID score) | -0.29‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Diabetes acceptance (DAS-10 score) | 0.41‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Depressive symptoms (CES-D score) | -0.33‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Feeling guilty for neglecting diabetes treatment (mean of daily EMA ratings) | -0.44‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Feeling overwhelmed by diabetes treatment (mean of daily EMA ratings) | -0.24† | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Mean sensor glucose (4 weeks) | -0.33‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Time-below-range (<70 mg/dl) | -0.04 | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Time-in-range (70–180 mg/dl) | 0.34‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Time-above-range (>180 mg/dl) | -0.30‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| Study 4: ‘DIA-LINK1’ (2019–20), prospective observational study, retest after three months n=203 PWT1D | 27-item total score1 | Glucose variability (CV) | -0.28‡ | | 6.1 ±1.7 to 6.6 ±1.4‡ (d=0.37) | |
| | Eating behavior | HbA1c | -0.08 | | 4.6 ±2.2 to 5.2 ±1.9‡ (d=0.35) | |
| | Eating behavior | Not eating as carefully as one should (T1-DDS item 2) | -0.55‡ | | 4.6 ±2.2 to 5.2 ±1.9‡ (d=0.35) | |
| | Eating behavior | Feeling that eating is out of control (T1-DDS item 23) | -0.48‡ | | 4.6 ±2.2 to 5.2 ±1.9‡ (d=0.35) | |
| | Medication taking | HbA1c | -0.36‡ | | 7.6 ±2.5 to 8.2 ±2.4† (d=0.25) | |
| | Medication taking | Time-in-range (70–180 mg/dl) | 0.33‡ | | 7.6 ±2.5 to 8.2 ±2.4† (d=0.25) | |
| | Medication taking | Not taking as much insulin as one should (T1-DDS item 8) | -0.45‡ | | 7.6 ±2.5 to 8.2 ±2.4† (d=0.25) | |
| | Medication taking | Self-reported n of daily insulin injections | 0.22† | | 7.6 ±2.5 to 8.2 ±2.4† (d=0.25) | |
| | Glucose monitoring | HbA1c | -0.42‡ | | 6.1 ±2.7 to 6.8 ±2.3‡ (d=0.29) | |
| | Glucose monitoring | Time-in-range (70–180 mg/dl) | 0.36‡ | | 6.1 ±2.7 to 6.8 ±2.3‡ (d=0.29) | |
| | Glucose monitoring | Not checking glucose level as often as one should (T1-DDS item 12) | -0.70‡ | | 6.1 ±2.7 to 6.8 ±2.3‡ (d=0.29) | |
| | Glucose monitoring | Self-reported n of daily glucose checks/scans/ readings | 0.40‡ | | 6.1 ±2.7 to 6.8 ±2.3‡ (d=0.29) | |
| | Physical activity | BMI | -0.15* | | 5.5 ±3.2 to 5.6 ±3.0 (d=0.04) | |
| | Cooperation with diabetes team | Can’t tell diabetes doctor what is on mind (T1-DDS item 7) | -0.27‡ | | 8.1 ±2.2 to 8.3 ±1.9 (d=0.11) | |
| | Cooperation with diabetes team | Diabetes doctor doesn’t understand what it’s like to have diabetes (T1-DDS item 18) | -0.20† | | 8.1 ±2.2 to 8.3 ±1.9 (d=0.11) | |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | HbA1c | | -0.20† | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | Often failing with diabetes routine (DDS item 8) | | -0.40‡ | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | Diabetes distress (PAID score) | | -0.33‡ | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | Diabetes acceptance (DAS-10 score) | | 0.46‡ | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | Depressive symptoms (CES-D score) | | -0.23† | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | Feeling guilty for neglecting diabetes treatment (mean of daily EMA ratings) | | -0.43‡ | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 20-item total score | Self-rated quality of self-management overall (mean of daily EMA ratings) | | 0.47‡ | | 5.4 ±1.9 to 6.4 ±1.8‡ (d=0.54) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | HbA1c | | -0.25† | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | Often failing with diabetes routine (DDS item 8) | | -0.43‡ | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | Diabetes distress (PAID score) | | -0.39‡ | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | Diabetes acceptance (DAS-10 score) | | 0.54‡ | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | Depressive symptoms (CES-D score) | | -0.37‡ | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | Feeling guilty for neglecting diabetes self- management (mean of daily EMA ratings) | | -0.37‡ | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| Study 5: ‘DIA-LINK2’ (2020–21), prospective observational study, retest after three months n=180 PWT2D | 27-item total score1 | Self-rated quality of self-management overall (mean of daily EMA ratings) | | 0.44‡ | | 5.8 ±1.9 to 6.9 ±1.7‡ (d=0.61) |
| | Eating behavior | HbA1c | | -0.08 | | 4.2 ±2.3 to 5.6 ±2.3‡ (d=0.61) |
| | Eating behavior | Not sticking to a good meal plan (DDS item 12) | | -0.53‡ | | 4.2 ±2.3 to 5.6 ±2.3‡ (d=0.61) |
| | Eating behavior | Diet promoting good glucose control (EMA ratings) | | 0.44‡ | | 4.2 ±2.3 to 5.6 ±2.3‡ (d=0.61) |
| | Eating behavior | Diet beneficial for weight (EMA ratings) | | 0.46‡ | | 4.2 ±2.3 to 5.6 ±2.3‡ (d=0.61) |
| | Medication taking | HbA1c | | -0.22† | | 7.4 ±3.0 to 8.1 ±2.3† (d=0.26) |
| | Medication taking | Self-reported n of daily insulin injections | | 0.00 | | 7.4 ±3.0 to 8.1 ±2.3† (d=0.26) |
| | Medication taking | Medical diabetes treatment appraisal (EMA ratings) | | 0.33‡ | | 7.4 ±3.0 to 8.1 ±2.3† (d=0.26) |
| | Glucose monitoring | HbA1c | | -0.08 | | 5.3 ±3.2 to 6.8 ±2.8‡ (d=0.50) |
| | Glucose monitoring | Not testing sugar frequently enough (DDS item 6) | | -0.57‡ | | 5.3 ±3.2 to 6.8 ±2.8‡ (d=0.50) |
| | Glucose monitoring | Self-reported n of daily glucose checks/scans/readings | | 0.41‡ | | 5.3 ±3.2 to 6.8 ±2.8‡ (d=0.50) |
| | Physical activity | Level of physical activity contributing to good diabetes management (EMA ratings) | | 0.58‡ | | 4.1 ±3.1 to 5.1 ±3.0‡ (d=0.36) |
| | Physical activity | BMI | | -0.31‡ | | 4.1 ±3.1 to 5.1 ±3.0‡ (d=0.36) |
| | Physical activity | HbA1c | | -0.15* | | 4.1 ±3.1 to 5.1 ±3.0‡ (d=0.36) |
| | Cooperation with diabetes team | Not having a doctor for diabetes (DDS item 15) | | -0.39‡ | | 8.2 ±1.9 to 8.1 ±1.9 (d=0.05) |
Subscales: The subscales ‘eating behavior’, ‘medication taking’ and ‘glucose monitoring’ showed significant associations with corresponding convergent criteria for diabetes-adjusted eating (e.g., SDSCA scale on healthy eating, DDS/T1-DDS items regarding sticking to a good meal plan and eating carefully), medication taking (e.g., T1-DDS item on insulin taking, mean number of daily insulin injections in T1D), glucose monitoring (e.g., SDSCA scale on blood sugar testing, DDS/T1-DDS items on glucose checking, mean number of daily glucose checks/scans). Each of the scales showed significant associations with better HbA1c in several studies, however not all. The subscale ‘physical activity’ showed high correlations with the convergent SDSCA scale on past-week physical exercise and small-to-moderate associations with BMI. The subscale ‘cooperation with diabetes team’ showed significant correlations with self-reported frequencies of diabetologist visits as well as corresponding DDS/T1-DDS items on doctor-related problems. ‘ Eating behavior’, ‘medication taking’ and ‘physical activity’ were also significantly associated with corresponding EMA ratings in studies 4 and 5.
## Structural Validity
Confirmatory factor analyses supported a five-factor structure representing the five subscales with excellent fit to the data for both T1D and T2D (Supplementary Figures 1–2). One-factor models representing the total scale showed good fit as well; however, with slightly lower fit indices and lower factor loadings (Supplementary Figures 3–6).
## Responsiveness
The ability to detect change was supported by significant changes over time in the total score and most subscale scores in the prospective studies. Greater changes were seen in the total scale and ‘eating behavior’ and ‘glucose monitoring’ subscales, while changes in ‘medication taking’ were modest and changes in ‘physical activity’ and ‘cooperation with diabetes team’ were small or lacking (Table 3). Between-group comparisons for people receiving depression treatment versus diabetes care as usual in study 2 suggested similar changes in DSMQ-R scores without significant differences between the groups at six-month follow-up.
## Main Findings
The evaluation of the DSMQ-R using data from diverse studies suggests very good properties in measuring diabetes self-management behavior in both T1D and T2D according to clinimetric criteria [40]. Results suggest that the tool has good reliability, validity and responsiveness to change. The terms and expressions used in the questionnaire were updated to conform with modern diabetes-related language. The revised scales with newly added items showed higher internal reliability than the original version’s item sets.
The DSMQ-R total scale constitutes a reliable and valid measure of overall self-management. Yet it is a global measure; thus assessing the specific behaviors using the subscales may be preferred and even necessary for understanding individual aspects. For the subscales, however, differential properties and options should be considered: First, the numbers of items per scale differ which may affect reliability of the measurement. In this evaluation, most subscales yielded satisfactory to good reliability estimates; however, lower reliability coefficients were seen for subscales with fewer items (e.g., medication taking) in some of the studies. Furthermore, coefficients varied across studies and patient groups, suggesting that the utilization of subscales in research might benefit from affirming reliability within a given study data set. Notably, despite specific revisions and improved internal reliability, the ‘cooperation with diabetes team’ subscale still showed subthreshold reliability coefficients in two of five studies for T2D; yet not for T1D.
Reliability coefficients were mostly slightly higher in T1D subsamples compared to T2D which is in line with previous findings [21]. This might be explained by more diverse treatment regimens and practices in T2D; for instance, prescribed medications may be diverse (oral drugs, insulin and/or incretin mimetics), glucose testing may or may not be required and dietary recommendations may vary in relevance and function. This might also explain higher associations between the DSMQ-R scales and HbA1c in T1D [consistent with previous findings [23]], where glycemic outcomes directly depend on the consistent coordination and adjustment of carbohydrate intake, activities and insulin doses; whereas in T2D, glycemic control may rely more on diet and activity and less on glucose checking and meal-specific decisions (depending on the treatment regimen); also, residual insulin action may stabilize glycemic levels and reduce hyperglycemia.
It should be noted that two-sided questioning (using both positively and negatively keyed items) may lower internal consistency as observed in some DSMQ-R subscales; at the same time, higher validity is achieved and response bias is prevented. From a clinimetric perspective, a varied assessment using items covering different aspects from different sides is more important than a highly homogeneous measurement [40].
Validity of the scale measurement was supported by high correlations with convergent scores and items from other questionnaires. However, as self-report is prone to bias, associations with objective measures constitute another important source of information. Thus, the widely consistent associations between DSMQ-R scales and HbA1c (as well as CGM-derived glucose parameters) across studies may be seen as extra evidence favoring validity.
Relatively good explanation of variation in HbA1c was already observed in our previous studies for both T1D and T2D [21, 23]. This might be explained by i) the reflection of behaviors over a broader, more representative reference period and ii) the items requesting behavioral evaluations (e.g., ‘with care and attention’) rather than behavior frequency (e.g., ‘on how many days…?’ as in the SDSCA). On the other hand, three studies using the DSMQ with non-Western samples (42–44) and one Hungarian study [45] have reported lower associations with HbA1c, suggesting caution against generalization across cultures.
Validity of the measurement was also supported by the structural representation of assessed contents (i.e., items and scales) in the factor analyses with good model fit for both T1D and T2D.
Change scores reflecting improvements in DSMQ-R-assessed behaviors supported good responsiveness of the measurement. In study 4, similar changes were seen for people randomized to depression treatment versus diabetes care as usual; this could be explained by both groups receiving treatment with beneficial effects on self-management behavior. The tool’s ability to detect change is also supported by findings from international studies using the DSMQ which found significant self-management improvement over time and between-group differences in randomized trials (46–49); notably, observed changes in DSMQ scores by group were often accompanied by parallel changes in HbA1c, which might be taken as evidence supporting validity of the changes [46, 48, 50]. With regard to responsiveness and the tool’s reference period (eight weeks), a shorter period might facilitate the detection of short-term changes, thus adapting the instruction (e.g., four weeks), where needed, may be considered.
In terms of item amendments (e.g., revised wording), the DSMQ-R probably constitutes a relevant improvement. However, since most revisions were minor and item concepts were kept equivalent, the original 16-item version is basically included in the revised form. Estimation of scales as for the original version, where needed (e.g., to compare scores with former study results), would still be possible.
## Limitations and Strengths
The inferences drawn from this research are qualified by the following limitations: first, four of the studies whose data were analyzed here focused on diabetes-comorbid mental conditions, thus rates of depressive symptoms and/or diabetes distress were elevated and the samples may not be representative for the general population with diabetes (i.e., risk of spectrum bias). Second, we assessed cross-sectional associations between self-reported behavior and diabetes outcomes, thus inferences towards causation are not possible; in fact, associations with glycemic outcomes might be bidirectional; for instance, knowing of glycemic levels (e.g., last HbA1c) might influence self-management self-appraisal in the questionnaire. Third, the study samples were recruited within secondary or tertiary care, thus samples may not represent the primary care population; based on this, people with T2D assessed here used advanced medical treatments often including insulin and even basal-bolus therapy with multiple daily injections, whereas people with diet-and-exercise regimens and/or oral antidiabetic treatment alone were less represented.
The strengths of the evaluation may be seen in the standardized assessment using validated scales and items, temporal coincidence of questionnaire self-reports, interviews and laboratory measures and the inclusion of multiple methods including CGM and EMA for the assessment of convergent criteria. Furthermore, the stratified analyses for T1D and T2D using sufficiently large samples support evaluation for both major types of diabetes. Due to potential advantages of McDonald’s ω over Cronbach’s α [41], we calculated both estimates, yielding highly consistent results. Finally, the evaluation across different study samples, both general and specific, yields a more comprehensive and representative total evidence base; the fact that indices of reliability and validity, including associations with clinical criteria, were relatively consistent across studies may favor generalizability.
## Conclusions
In summary, the results support good clinimetric properties of the DSMQ-R. The tool can be used for research and clinical practice. It may help understand barriers and facilitators of functional self-management in T1D and T2D, facilitate the identification improvable practices in individuals and monitor behavior change following treatment in practical care or research trials.
## 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 German Psychological Society or the Ethics Committee of the State Medical Chamber of Baden-Wuerttemberg. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
AS: developed and revised the DSMQ; planned and designed study 1; co-planned and designed studies 2–5; collected the data; performed the evaluation, analyzed and interpreted the data; wrote the manuscript. BK and NH: planned and designed studies 2–5; discussed the findings and revised the manuscript. DE: planned and designed studies 4–5; co-planned and designed studies 2–3; discussed the findings and revised the manuscript. TH: discussed the findings and revised the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
Study 1 was supported by the German Diabetes Foundation (DDS) (grant number 375.10.15). Studies 2–3 were supported by the German Center for Diabetes Research (DZD) (grant number 82DZD01102). Studies 4–5 were supported by the German Center for Diabetes Research (DZD) [grant number 82DZD11A02]. The funders were not involved in decisions regarding study design; collection, analysis and interpretation of data; writing of the report; and submission of the article for publication.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcdhc.2021.823046/full#supplementary-material
## Abbreviations
CES-D, Center for Epidemiologic Studies Depression Scale; CG, control group; CV, coefficient of variation; DAS, Diabetes Acceptance Scale; DDS, Diabetes Distress Scale; DM, diabetes mellitus; DSMQ, Diabetes Self-Management Questionnaire; DTSQ, Diabetes Treatment Satisfaction Questionnaire; EG, experimental group; EMA, ecological momentary assessment; HbA1c, glycated hemoglobin; iscCGM, intermittently scanned continuous glucose monitoring; MDI, multiple daily (insulin) injections; PAID, Problem Areas in Diabetes Scale; PHQ-9, Patient Health Questionnaire-9; PWD, people with diabetes; rtCGM, real-time continuous glucose monitoring; SDSCA, Summary of Diabetes Self-Care Activities; SMBG, self-monitoring of blood glucose; T1-DDS, Type 1 Diabetes Distress Scale; T1D, type 1 diabetes; T2D, type 2 diabetes.
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|
---
title: 'A nonlinear associations of metabolic score for insulin resistance index with
incident diabetes: A retrospective Chinese cohort study'
authors:
- Zhuangsen Chen
- Caiyan Huang
- Zhongyu Zhou
- Yanrong Zhang
- Mingyan Xu
- Yingying Tang
- Lei Fan
- Kun Feng
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2023
pmcid: PMC10012088
doi: 10.3389/fcdhc.2022.1101276
license: CC BY 4.0
---
# A nonlinear associations of metabolic score for insulin resistance index with incident diabetes: A retrospective Chinese cohort study
## Abstract
### Background
The Metabolic score of insulin resistance (METS-IR) has recently been accepted as a reliable alternative to insulin resistance (IR), which was demonstrated to be consistent with the hyperinsulinemic-euglycemic clamp. Few pieces of research have focused on the relationship between METS-IR and diabetes in Chinese. The purpose of this research was to explore the effect of METS-IR on new-onset diabetes in a large multicenter Chinese study.
### Methods
At the baseline of this retrospective longitudinal research, 116855 participators were included in the Chinese cohort study administered from 2010 to 2016. The subjects were stratified by quartiles of METS-IR. To assess the effect of METS-IR on incident diabetes, the Cox regression model was constructed in this study. Stratification analysis and interaction tests were applied to detect the potential effect of METS-IR and incident diabetes among multiple subgroups. To verify whether there was a dose-response relationship between METS-IR and diabetes, a smooth curve fitting was performed. In addition, to further determine the performance of METS -IR in predicting incident diabetes, the receiver operating characteristic curve (ROC) was conducted.
### Results
The average age of the research participators was 44.08 ± 12.93 years, and 62868 ($53.8\%$) were men. METS-IR were significant relationship with new-onset diabetes after adjusting for possible variables (Hazard ratio [HR]: 1.077; $95\%$ confidence interval [CI]: 1.073-1.082, $P \leq 0.0001$), the onset risk for diabetes in Quartile 4 group was 6.261-fold higher than those in Quartile 1 group. Moreover, stratified analyses and interaction tests showed that interaction was detected in the subgroup of age, body mass index, systolic blood pressure, diastolic blood pressure, and fasting plasma glucose, there was no significant interaction between males and females. Furthermore, a dose-response correlation was detected between METS-IR and incident diabetes, the nonlinear relationship was revealed and the inflection point of METS-IR was calculated to be 44.43. When METS-IR≥44.43, compared with METS-IR < 44.43, the trend was gradually saturated, with log-likelihood ratio test $P \leq 0.001.$ Additionally, the area under receiver operating characteristic of the METS-IR in predicting incident diabetes was 0.729, 0.718, and 0.720 at 3, 4, and 5 years, respectively.
### Conclusions
METS-IR was correlated with incident diabetes significantly, and showed a nonlinear relationship. This study also found that METS-IR had good discrimination of diabetes.
## Background
Diabetes mellitus (DM) is a chronic epidemic on an unprecedented scale, which is spiraling out of control [1]. The International Diabetes Federation (IDF) reported that more than one in 10 adults now have diabetes all over the world. It is forecasted that 537 million people were suffered from DM in 2021. The Western Pacific region accounts for more than a third ($38\%$) of the total number of diabetes, with China accounting for a quarter of the total [2, 3]. In recent years, the Chinese Diabetes Society (CDS) has been paying attention to the progression of diabetes in China. The prevalence of diabetes in *China is* still on the rise, reaching $11.2\%$ in 2017 [4, 5]. It is well known that if diabetes is not well managed and treated, this disease will cause damage to multiple organs of the body and leads to complications, such as cardiovascular disease, kidney damage, eye disease, and so on. The direct and indirect costs of diabetes to health services increase continuedly [3, 6]. It is a huge challenge to predict future diabetes incidence.
Insulin resistance (IR) is connected with the development and progression of diabetes significantly (7–9). It reduces insulin efficiency in insulin-responsive tissues (muscle, fat, and liver), which conversely causes a decompensated increase in insulin and hyperinsulinemia, and then leads to chronic metabolic disorders (hyperglycemia, hypertension, hyperlipidemia, etc.) and inflammation (10–14). Therefore, it is requisite to detect IR in the early stages of diabetes, and early prevention in non-diabetes patients with metabolic risk is beneficial to reduce the socioeconomic burden of diabetes and other metabolic diseases (15–18).
Accurate assessment of insulin resistance is usually performed with the Euglycaemic-hyperinsulinaemic clamp (EHC) and Homeostatic model assessment for insulin resistance (HOMA-IR). EHC is still the gold standard method of assessing IR. However, it is mostly utilized in research due to the characteristics of being time-consuming, invasive, and high cost, which inhibit its promotion (19–21). Meanwhile, the practical applicability of HOMA-IR is also limited by invasiveness, complexity, and impracticality, especially in resource-poor areas [21, 22]. To evaluate IR in large epidemiological studies, several simple formulas have been developed based on readily available and inexpensive biochemical indicators. For example, triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), triglyceride glucose index (TγG index), and triglyceride glucose-body mass index(TγG-BMI) were widely employed as a reliable surrogate marker to estimate IR in clinical practice (23–28). These non-insulin-based indexes offer a simpler and cost-effective option for the identification of IR.
Recently, the Metabolic score for insulin resistance (METS-IR), a newly-developed non-insulin-based metabolic score, consists of fasting plasma glucose (FPG), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and body mass index (BMI). It has been demonstrated to be highly consistent with EHC in assessing IR [29].
The relationship between METS-IR and the prevalence of diabetes, however, has received very little investigation, which needs to be further investigated, especially in large epidemiological studies. This research was designed to explore the effect of METS-IR on the incidence of diabetes among a large-scale of Chinese adults.
## Cohort population and study design
Data for this research were collected from a multicenter health check-up program, the Rich Healthcare Group, which was published by Chen et al. at www.Datadryad.org [30]. Data on this website is free and public, the providers give all copyright and ownership rights, and there is no interest involved. The detailed research design has been described in previous studies [30]. Participants in this cohort underwent two or more follow-up visits between 2010 and 2016 in 11 major Chinese cities, and they were at least 20 years old. At each follow-up visit to the health check center, participants completed a detailed questionnaire and had blood samples collected. According to the report of Chen et al., 211,833 subjects ($54.8\%$ males and $45.2\%$ females) were involved in this study [30]. Data with missing values (such as weight, height, gender, and FPG) and with an extreme value of BMI (<15 kg/m2 or >55 kg/m2) were excluded at baseline. In addition, participants with a follow-up interval of fewer than 2 years, those who had been diagnosed with diabetes at baseline, and those who could not determine their diabetes status during follow-up were excluded. There was no need to apply for ethical approval due to the secondary data analysis nature of this study. Previous research received approval from the Rich Healthcare Group review board, and this retrospective study conformed with the Helsinki Declaration.
The dataset contains participants’ medical records, which includes demographic and blood biochemical variables: age, gender, height, weight, systolic and diastolic blood pressure (SBP and DBP), history of smoking, history of drinking, family history of the disease, FPG, TG, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), HDL-C. BMI (kg/m2) is expressed as a weight (kg) divided by height squared (m2). Fasting venous blood samples were measured on an autoanalyzer, and participants need to be fasting for at least 10 hours. The dependent variable of the research was new-onset diabetes, defined as FPG≥7.00 mmol/L and/or self-reported diabetes during follow-up.
To further research, missing values and outliers of TG and HDL-C were excluded($$n = 94$$,978), and the total number of participants was 116,855 participants (62,868 males and 53,987 females) in the end. The arithmetic Formula of METS-IR was Ln [(2*FPG) + TG]*BMI)/(Ln[HDL-C])(1mmol/$L = 18$mg/dl) [29].
## Statistical analysis
Statistical analyses were completed by Empower (R) (www.empowerstats.com) and R-project (version 3.4.3). A two-tailed P value<0.05 indicates statistical significance.
First, all participators were assigned the quartiles of METS-IR. The continuous data under normal distribution were presented in the form of average ± standard deviation, while the skewed distribution was represented by the median (quartile range). One-way analysis and Kruskal-Wallis were utilized for comparing the distribution of normal distribution and skewed distribution between groups. The classified variables were described as numbers (proportions), and comparisons between groups were assessed using the chi-square test.
Subsequently, linear regression analysis was conducted to verify and check the collinearity of variables and report the variance inflation factor (VIF) [31]. Variables with VIF larger than 5 were multicollinearity and could not be included in the Cox regression model. Cox proportional hazards model was carried out to analyze the hazard ratio (HR) and $95\%$ confidence interval (CI) for evaluating METS-IR and the risk of new-onset diabetes. The results of three covariate models were demonstrated on the grounds of the recommendations of the STROBE statement to manipulate for possible confounding bias. Covariates with matched hazard ratio change>$10\%$ can be added as confounders into the model [32]. Crude model: unadjusted, Model I: adjusted for age, gender, smoking status, drinking status, and family history of diabetes, Model II: based on Model I, SBP, DBP, TC, LDL-C, and serum creatinine (SCR) were further adjusted. Further, METS -IR was transformed into a classified variable and trend analysis was calculated for quantifying the stability of the results of regression analysis and observing the nonlinear probability. Moreover, the covariables were converted into the ‘GAM Model’ by the weighted generalized additive model (GAM) to cover the shortage of general linear analysis in the analysis of nonlinearity [33].
Moreover, stratified analysis and interaction testing were applied to analyze the potential effects of METS-IR on incident diabetes in subgroups using multivariable logistic regression models.
The dose-response relationship between METS-IR and incident diabetes was conducted by using a smooth curve fitting and GAM. In the presence of nonlinear correlation, the threshold effect was carried out using a two-piecewise linear regression mode. When the rate between incident diabetes and METS-IR appeared apparent, the inflection point will be calculated automatically by the recursive method.
Furthermore, survival estimates and cumulative diabetes incidence were constructed by the Kaplan-Meier survival analyses, and the log-rank test was used to compute the survival curve functions between METS-IR quartiles.
Additionally, a receiver operating characteristic (ROC) curve was performed to evaluate the performance of METS-IR to predict incident diabetes. Further, ROC was plotted to compare the predictive ability of TG/HDL-C (triglycerides/HDL-c), TγG (Ln[(Glucose*Triglycerides)/2]), TγG-BMI (TyG*BMI) and METS-IR for incident diabetes.
## Baseline characteristics and univariate analysis of study participants
As shown in Table 1, the characteristics of the 116,855 research participators at baseline were described. The total study populations were 62,868 men ($53.8\%$) and 53,987 women ($46.2\%$), and the average age of the research participators was 44.08 ± 12.93 years. At a median follow-up of 3.1± 0.94 years, 2,685 participators ($2.3\%$) had new-onset diabetes. Participants in the higher METS-IR quartile group were older than those in the lower quartile groups (Q1-3). BMI, SBP, DBP, FPG, TC, TG, LDL-C, and SCR increased with the increase of the METS-IR quartile, while HDL-C level decreased (all P values < 0.001). By contrast with participators in quartile 1, subjects in the higher quartiles were more current drinkers and fewer current smokers. No significant differences were found in the Family history of diabetes between the METS-IR quartile group. Univariate linear regression analyses were constructed to examine all significant variables in Table 2. And it showed that age, BMI, SBP, DBP, FPG, TC, TG, LDL-C, METS-IR, and family history of diabetes were positively correlated with new-onset diabetes.
## The multivariate analysis between METS-IR and the risk of new-onset diabetes
Firstly, variable collinearity diagnostics were conducted to calculate the VIF for each covariate. Covariates were deemed to exhibit substantial multicollinearity and were ineligible for inclusion in the multivariate Cox regression model if their VIF was more than 5. The results and details were listed in Table S1. After that, the outcome of the Cox proportional hazards regression analysis was summarized in Table 3. In the Crude model, METS-IR was a positive correlation with incident diabetes (HR= 1.093, $95\%$CI:1.089 to 1.097, $P \leq 0.0001$). Compared with the crude mode, HR($95\%$ CIs) for diabetes incidence was 1.083 (1.078 to 1.087) in Model I. Furthermore, the HR for the incident diabetes was 1.077 ($95\%$ CI: 1.073, 1.082, $P \leq 0.0001$) in Model II. Remained significant even after the continuous covariates were adopted into the GAM model as curves, and the hazard ratio was 1.085 ($95\%$ CI: 1.075 to 1.096, <0.0001), indicating the robustness of the main results. Moreover, when METS-IR was handled as a classified variable into quartiles and using the first quartile as a reference, the trend of incident diabetes increased with METS-IR quartile (P for trend<0.001) in the Crude model. In addition, the risk of incident diabetes was increased with Q4 versus Q1 of METS -IR in Model II (HR 6.261[5.189,7.554], P for trend<0.001). Consistently, there were significantly stronger associations of the METS-IR with incident diabetes.
**Table 3**
| Variable | Crude model (HR,95%CI,P) | Model I (HR,95%CI,P) | Model II (HR,95%CI,P) | GAM (HR,95%CI,P) |
| --- | --- | --- | --- | --- |
| METS-IR | 1.093 (1.089, 1.097) <0.0001 | 1.083 (1.078, 1.087) <0.0001 | 1.077 (1.073, 1.082) <0.0001 | 1.085 (1.075, 1.096) <0.0001 |
| METS-IR (quartile) | METS-IR (quartile) | METS-IR (quartile) | METS-IR (quartile) | METS-IR (quartile) |
| Q1 | Ref. | Ref. | Ref. | Ref. |
| Q2 | 2.011 (1.624, 2.490) <0.0001 | 1.543 (1.245, 1.913) 0.0001 | 1.470 (1.185, 1.823) 0.0005 | 1.255 (0.796, 1.720) 0.3286 |
| Q3 | 5.428 (4.491, 6.561) <0.0001 | 3.542 (2.922, 4.293) <0.0001 | 3.237 (2.667, 3.927) <0.0001 | 2.913 (1.954, 4.341) <0.0001 |
| Q4 | 12.158 (10.150, 14.564) <0.0001 | 7.274 (6.040, 8.760) <0.0001 | 6.261 (5.189, 7.554) <0.0001 | 5.713 (3.884, 8.404) <0.0001 |
| P for trend | 2.351 (2.248, 2.458) <0.0001 | 2.046 (1.951, 2.145) <0.0001 | 1.942 (1.851, 2.038) <0.0001 | 1.942 (1.757, 2.146) <0.0001 |
## Kaplan-Meier analysis of diabetes
The comparison of cumulative diabetes incidence between the quartiles of baseline METS-IR in the Kaplan-Meier curve was shown in Figure 1. Cumulative incidence was the highest in quartile 4 and lowest in quartile1 (log-rank test P values < 0.001). It showed that METS-IR Q4 participants had a greater chance of developing incident diabetes than other groups during the follow-up.
**Figure 1:** *Kaplan–Meier event-free survival curve. Kaplan–Meier analysis of incident of diabetes based on METS-IR quartiles (Log-rank, P < 0.0001). Abbreviation: METS-IR: the metabolic score for insulin resistance.*
## The analyses of the dose-response and threshold effect
A dose-response study using GAM indicated a non-linear connection between METS-IR and incident diabetes (adjusting age, sex, SBP, DBP, TC, LDL-C, SCR, smoking status, drinking status, and family history of diabetes) in Figure 2. We further explored the inflection point of METS-IR was 44.43 (log-likelihood ratio test $P \leq 0.001$, Table 4). When METS-IR < 44.43, the HR was 1.140 ($95\%$ CI: 1.126 to 1.154), however, cubic spline smoothing gradually became saturated (HR: 1.051; $95\%$ CI: 1.043 to 1.058) on the right side.
**Figure 2:** *The non-linear relationship between METS-IR and incident of diabetes. Abbreviation: METS-IR: the metabolic score for insulin resistance.* TABLE_PLACEHOLDER:Table 4
## The results of stratified analyses
In addition, this study conducted a stratified analysis to investigate the effects of potential modifications between METS-IR and incident diabetes, including age, gender, sex, age, BMI, SBP, DBP, FPG, and family history of diabetes. ( Table 5). Age, BMI, SBP, DBP, and FPG interacted with METS-IR and incident diabetes by interaction tests (all P interaction < 0.05). Nevertheless, there was no significant interaction among different stratifications in gender and family history of diabetes. This suggested that the relationship between METS-IR and diabetes mellitus was not affected by gender and family history, and the combination of certain risk factors with METS-IR may enhance its sensitivity.
**Table 5**
| Characteristic | No of participants | HR (95%CI) | P value | P for interaction |
| --- | --- | --- | --- | --- |
| Age(years) | | | | 0.0106 |
| 20 to < 30 | 11204.0 | 1.091 (1.055, 1.128) | <0.0001 | |
| 30 to < 40 | 41985.0 | 1.078 (1.064, 1.092) | <0.0001 | |
| 40 to < 50 | 27171.0 | 1.056 (1.044, 1.067) | <0.0001 | |
| 50 to < 60 | 19569.0 | 1.025 (1.015, 1.035) | <0.0001 | |
| ≥60 | 16926.0 | 1.017 (1.005, 1.030) | 0.0051 | |
| Gender | | | | 0.4759 |
| Male | 62868.0 | 1.030 (1.022, 1.039) | <0.0001 | |
| Female | 53987.0 | 1.034 (1.024, 1.044) | <0.0001 | |
| BMI | | | | 0.0027 |
| < 18.5 | 5991.0 | 1.009 (0.878, 1.160) | 0.8976 | |
| ≥ 18.5, < 24 | 63581.0 | 1.052 (1.039, 1.064) | <0.0001 | |
| ≥ 24, < 28 | 37151.0 | 1.030 (1.018, 1.042) | <0.0001 | |
| ≥ 28 | 10132.0 | 1.019 (1.007, 1.031) | 0.0021 | |
| SBP | | | | <0.0001 |
| <140 | 103990.0 | 1.039 (1.031, 1.046) | <0.0001 | |
| ≥ 140 | 12847.0 | 1.011 (1.001, 1.022) | <0.0001 | |
| DBP | | | | 0.0139 |
| <90 | 106653.0 | 1.035 (1.027, 1.042) | <0.0001 | |
| ≥ 90 | 10184.0 | 1.019 (1.007, 1.032) | <0.0001 | |
| FPG | | | | <0.0001 |
| <100 | 93628.0 | 1.069 (1.057, 1.081) | <0.0001 | |
| ≥100 | 23227.0 | 1.023 (1.015, 1.031) | <0.0001 | |
| Family history of diabetes | | | | 0.2764 |
| No | 114215.0 | 1.032 (1.025, 1.040) | <0.0001 | |
| Yes | 2640.0 | 1.018 (0.993, 1.044) | 0.1646 | |
## The discernibility of METS-IR for diabetes
A time-dependent ROC analysis was performed to assess the predictive efficacy of METS-IR for incident diabetes at various time nodes (Figures 3A–C). The area under the curve (AUC) was 0.729, 0.718, 0.720 at 3, 4, and 5 years, respectively, which revealed a good discriminatory capacity for incident diabetes. In addition, ROC revealed that TG/HDL-C, TγG, TγG-BMI, and METS-IR had AUCs of 0.699, 0.765,0.778, and 0.759, respectively. It seemed that the predictive ability of METS -IR followed by TγG-BMI and was not inferior to TG/HDL-C and TγG (Figure 4). And the cut-off points for the prediction of diabetes with them were shown in Table S2. Therefore, METS-IR can be used to predict incident diabetes during follow-up in Chinese.
**Figure 3:** *Time-dependent receiver operating characteristic (ROC) curves of METE-IR for diabetes at 3 (A), 4 (B) and 5 years (C). METS-IR, the metabolic score for insulin resistance.* **Figure 4:** *The results of receiver operating characteristics curves. Abbreviations: TG/HDL-C, Triglyceride to high-density lipoprotein cholesterol ratio; TγG index, Triglyceride glucose index; TγG-BMI, TyG*BMI; METS-IR, The Metabolic score of insulin resistance.*
## Discussion
The present large cohort Chinese study demonstrated that there was a positive correlation between METS-IR and the onset of diabetes in Chinese. When adjustments were made for covariates, individuals in the top quartile of METS-IR had a 6.261-fold higher risk of developing diabetes than those in the bottom METS-IR quartile. Meanwhile, the association between METS-IR and incidence diabetes was demonstrated to be nonlinear. Furthermore, the result revealed that the cumulative risk of incident diabetes in Chinese adults increased gradually with the increase of METS-IR. Moreover, ROC analyses suggested the METS-IR had significant discriminatory power for new-onset diabetes at 3 years,4 years, and 5 years. These results indicated that METS-IR can be used to predict the onset of diabetes in healthy individuals during follow-up.
Non-insulin-based metabolic indicators used to evaluate IR have developed into an easier and cost-effective alternative method, which is more suitable for epidemiological studies. The previously widely accepted non-insulin-based IR indicators commonly included FPG, parameters of lipids, and indices of obesity, such as TyG, TyG-BMI, and TG/HDL-C (34–36). Likewise, the METS-IR is a simple and economical indicator that combines FPG, lipid profile, and obesity index. It is reported that TyG [23, 24, 37, 38], TyG-BMI [27, 28], and TG/HDL-C [25] were correlated with the risk of diabetes positively. Consistent with present study, previous studies [29, 39, 40] also identified that there was a positive correlation between METS-IR and incident diabetes. Additionally, a prospective cohort study by Chavolla et al. also proved that METS-IR was superior to the TγG index and TG/HDL ratio in diagnostic performance, but there was no significant difference between METS-IR and TγG-BMI index [29]. However, ROC analysis in this research was observed that TG/HDL-C, TγG, TγG-BMI and METS-IR had AUCs of 0.699, 0.765,0.778, and 0.759, respectively, it revealed that the discriminatory power of METS-IR was still superior to TG/HDL ratio, but not TγG. Moreover, Chavolla et al. proved that the effect between METS-IR and diabetes was modulated by age [29]. An epidemiological study conducted by 12,107 Chinese participants and subgroup analyses also consistently confirmed that significant associations remained between gender, age, and FPG level in subgroup analyses [39]. Interestingly, stratified interaction analysis in this study also found differences in the influence of METS-IR and diabetes among age and baseline FPG subgroups, but there was no interaction observed in the subgroup of gender, suggesting that the correlation between METS-IR and diabetes was robust among men and women. Notably, this study also conducted the dose-response analysis between METS-IR and diabetes and showed that the probability of diabetes gradually increased with the increase of METS-IR, which is consistent with results from a cohort of 12,107 rural Chinese participants [39] and a cohort study of 12,290 non-obese Japanese adults [40]. Nonetheless, this study provided additional information that there was a positive correlation between METS-IR and the incidence of DM with a saturation effect, and the inflection point of METS-IR was calculated to be 44.43. When METS-IR ≤44.43, the risk of diabetes increased rapidly with METS-IR, the effect size was 1.140 ($95\%$ CI: 1.126-1.154, $P \leq 0.0001$); while METS-IR>44.43, the tendency gradually saturated compared with the left side of the inflection point (HR=1.051,$95\%$CI: 1.043,1.058 $P \leq 0.0001$). A possible explanation for the conflicting results may be the differences in participant selection and covariables, and further studies are needed to check on the result.
The underlying mechanism of METS-IR associated with diabetes has yet to be elucidated. IR has played its pathological mechanism before the onset of diabetes [41]. The content of reactive oxygen species (ROS) increases with the increase of blood glucose, which may have a toxic reaction on β-cells, leading to pancreatic β-cell dysfunction, and then causing diabetes [42, 43]. During hyperinsulinemia, the efficiency of insulin signaling is inhibited, resulting in reduced glucose uptake from the blood, which increases the risk of diabetes [44]. Consecutively, hyperglycemia can induce excessive peroxide production, which eventually leads to impaired insulin secretion and insulin resistance by promoting multiple oxidative stress pathways [45].*There is* growing evidence about dyslipidemia is a cause of IR [46, 47]. Diabetes is a progressive disease, and a study on animal models of diabetes showed β-cell apoptosis accompanied by long-term hyperglycemia/hyperlipemia (glucolipotoxicity) [48]. Previous literature indicated that fat accumulation is related to IR, which may provoke metabolic disturbances in the liver, and further affect the homeostasis of blood glucose and lipid (49–51). Adipose tissue contributes to metabolic risk and, in addition to the effects of body mass index, is associated with elevated blood glucose and lower HDL-C [52, 53]. Hypertriglyceridemia contributes to a corresponding increase in free fatty acid (FFA) levels, which may impair insulin signaling and induce tissue oxidative stress, giving rise to IR in bone and liver [53, 54]. Simultaneously, lower HDL-C reduces its anti-inflammatory effects and its inhibitory effect on LDL-C oxidation [55]. Besides, IR is affected by many factors, obesity index is also an important factor in IR [56], which can occur even in people with a normal BMI [57]. And BMI has been shown to have a strong association with prediabetes and diabetes in many studies (58–61). Therefore, as one of the important components of the METS-IR model, the obesity index may have a significant impact on the prediction of diabetes by METS-IR. Chavolla and his colleagues found that subjects with high METS-IR index showed increased visceral fat and fasting insulin levels [29]. At the same time, in our prospective study, higher levels of BMI, FPG, TC, TG, and LDL-C were observed at higher baseline METS-IR levels, and persons in higher baseline METS-IR levels had higher cumulative incidence of diabetes, which supported METS-IR can be applied to forecast the prevalence of diabetes.
There were still potential limitations that should be considered in this study. First, the occurrence of diabetes was diagnosed only by FPG and self-report in this study, without the use of the 2-hour oral glucose tolerance test (OGTT) and glycated hemoglobin (HbA1c), which may underestimate the incidence of diabetes in the study. However, due to the operation complexity of OGTT, it is not feasible for large cohorts and similar limitations have been observed in previous large population studies [40]. Second, due to the limitations of the original cohort data, we could not include several potential confounders and biochemical indicators such as diet, exercise, and plasma insulin. In particular, the lack of plasma insulin limited the ability to explore the concordance between METS-IR and HOMA-IR, and patients with diabetes could not be classified in the study because insulin testing is not a routine physical examination, especially in large epidemiological studies. Additionally, there was a lack of data on the dynamic changes of METS-IR during the follow-up survey, so its correlation with diabetes could not be evaluated in this study. Third, the study population was all Chinese, which may limit the extrapolation of such results to other ethnic groups. Despite these limitations, China has a large population of diabetics [3, 4], and the results of this study are still representative.
Although our study had limitations, there were still several advantages. Compared with previous similar studies [29, 39, 40], this study was a large sample size and a multicenter study. Furthermore, the study also conducted a sensitivity analysis by handling METS -IR as a categorical variable and continuous variable to assess the stability of the result.
## Conclusion
This study provides additional evidence supporting that METS-IR was connected with incident diabetes in the cohort of Chinese, and there is a non-linear relationship between METS-IR and diabetes. Meanwhile, METS-IR had a good discriminative ability for incident diabetes. METS-IR may be a reliable alternative method for predicting the risk of diabetes in epidemiological investigations.
## 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 Rich Healthcare Group. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
KF and ZC contributed to the study concept and design, researched and interpreted the data and drafted the manuscript. CH, ZZ and YZ performed the statistical analysis. MX, YT and LF contributed to the discussion. ZC drafted the Manuscript and KF edited the manuscript. All authors read and approved the final 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/fcdhc.2022.1101276/full#supplementary-material
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|
---
title: Patient and Parent Well-Being and Satisfaction With Diabetes Care During a
Comparative Trial of Mobile Self-Monitoring Blood Glucose Technology and Family-Centered
Goal Setting
authors:
- Jillian B. Halper
- Lisa G. Yazel
- Hala El Mikati
- Amy Hatton
- Jennifer Tully
- Xiaochun Li
- Aaron E. Carroll
- Tamara S. Hannon
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012089
doi: 10.3389/fcdhc.2022.769116
license: CC BY 4.0
---
# Patient and Parent Well-Being and Satisfaction With Diabetes Care During a Comparative Trial of Mobile Self-Monitoring Blood Glucose Technology and Family-Centered Goal Setting
## Abstract
Patient engagement in the process of developing a diabetes treatment plan is associated with person-centered care and improved treatment outcomes. The objective of the present study was to evaluate the self-reported patient and parent-centered satisfaction and well-being outcomes associated with the three treatment strategies utilized in a comparative effectiveness trial of technology-enhanced blood glucose monitoring and family-centered goal setting. We evaluated data from 97 adolescent-parent pairs at baseline and 6-months during the randomized intervention. Measures included: Problem Areas in Diabetes (PAID) child and parent scales, pediatric diabetes-related quality of life, sleep quality, and satisfaction with diabetes management. Inclusion criteria were 1) ages 12-18 years, 2) a T1D diagnosis for at least six months and 3) parent/caregiver participation. Longitudinal changes in survey responses were measured at 6 months from baseline. Differences between and within participant groups were evaluated using ANOVA. The average age of youth participants was 14.8 ± 1.6 years with half of the participants being female ($49.5\%$). The predominant ethnicity/race was Non-Hispanic ($89.9\%$) and white ($85.9\%$). We found that youth perceived 1) greater of diabetes-related communication when using a meter capable of transmitting data electronically, 2) increased engagement with diabetes self-management when using family-centered goal setting, and 3) worse sleep quality when using both strategies together (technology-enhanced meter and family-centered goal setting). Throughout the study, scores for self-reported satisfaction with diabetes management were higher in youth than parents. This suggests that patients and parents have different goals and expectations regarding their diabetes care management and care delivery. Our data suggest that youth with diabetes value communication via technology and patient-centered goal setting. Strategies to align youth and parent expectations with the goal of improving satisfaction could be utilized as a strategy to improve partnerships in diabetes care management.
## Introduction
The vast majority of adolescents with type 1 diabetes (T1D) have glycosylated hemoglobin (HbA1c) values above the recommended range, indicating suboptimal glycemic control [1, 2]. It is well-known that adequate frequency of self-monitoring of blood glucose (SMBG), appropriate insulin dosing, and parental involvement in type 1 diabetes management are important to achieve good glycemic control in adolescents [3, 4]. Despite the increasing availability of advanced technologies to improve glycemic control, for adolescents, there are multiple technological and social barriers to adopting these technologies (5–7). Moreover, adolescents are often reluctant to accept recommendations for increasing SMBG or insulin doses when they feel that it increases their diabetes-related distress and burden [8].
Diabetes management is associated with substantial burdens, necessitating ongoing assessment and treatment of mental health and diabetes-related distress during routine diabetes visits [9]. Poor glycemic control is often associated with lack of sufficient SMBG and insulin dosing when there is significant family conflict and increased diabetes-related distress [10]. Previous studies indicate both general and diabetes-specific family conflict are associated with decreased diabetes self-care in adolescents and deteriorations in glycemic control [11, 12], while improved family communication is directly related to improved SMBG and better glycemic control [13]. In addition, shared goals and teamwork between adolescents and parents can decrease diabetes-related family conflict and improve patient outcomes [14].
Shared decision-making is a model of patient-centered care that encourages individuals or families to be actively involved in the medical decisions that impact their health. It is recognized that a personal interest and investment in the diabetes treatment plan from the adolescent with diabetes is associated with engagement with diabetes self-management and improved metabolic control of T1D [9, 15, 16]. We previously created a patient decision aid to utilize with decision coaching to facilitate adolescent patient and parent alignment with goals for diabetes self-management (family-centered goal setting) [17]. We then performed an intervention that combined real-time sharing of SMBG data, electronic messaging, and a clinic-based family-centered goal setting strategy to address patient-centered diabetes care and family-centered goals simultaneously [18]. The objective of previously published study was to compare 3 strategies for improving SMBG and diabetes outcomes in the short-term (6 months). These strategies were: [1] a technology-enhanced blood glucose meter that both shared blood glucose data among patients, their parent, and health care providers, allowing for text messaging; [2] family-centered goal setting; and [3] a combination of [1] and [2]. Utilizing a family-centered goal setting strategy was associated with improved short-term outcomes when introducing new technology for SMBG. However, there is little known about shared decision making with adolescents with diabetes care and whether or not patients and families have an improved experience with healthcare teams who implement this into practice [19].
The objective of the present study was to evaluate the self-reported patient and parent-centered satisfaction and well-being outcomes associated with the three intervention strategies utilized in the published comparative effectiveness trial [18]. We hypothesized that the combination of a technology-enhanced blood glucose meter along with family-centered goal setting would result in higher levels of patient-centered satisfaction with diabetes care.
## Materials and Methods
This study was performed at Indiana University Health, Indianapolis, IN, approved by the Indiana University Institutional Review Board, and all participants signed informed consent/assent prior to enrolling. Inclusion criteria were 1) ages 12-18 years, 2) a T1D diagnosis for at least six months and 3) parent/caregiver participation. Exclusion criteria included diagnoses of other chronic diseases except depression, asthma, and thyroid disease which, if present, were required to be controlled. We did not exclude these conditions because they are commonly present in youth with T1D and when controlled with stable doses of medications would not interfere with the intervention strategies. We excluded individuals currently using a continuous glucose monitoring system at the time of study entry because one of the intervention strategies included a technology-enhanced blood glucose meter and the duplicative systems could contribute to burn-out and/or not using the assigned intervention strategy. The complete methods and primary clinical outcomes of the parent study have been published elsewhere [18]. In short, we identified potential participants from existing pediatric and adolescent diabetes clinics, pre-screened them for inclusion in this study, and invited eligible participants to enroll. After enrollment but prior to being randomized, participants entered a 3-month run-in period of routine diabetes care in an adolescent diabetes clinic to establish baseline characteristics and to accommodate for any enrollment effect on HbA1c prior to implementing the study intervention strategies. A single diabetes care provider (a board-certified pediatric endocrinologist) and a certified diabetes educator/nurse practitioner (CDE, CNP) provided American Diabetes Association endorsed care recommendations during the 3-month run-in period. Standard recommendations were made to perform SMBG at least 4 times per day (fasting, before meals, before bedtime), review SMBG records weekly, and dose insulin as prescribed prior to meals and snacks. Participants were then randomized using block randomization, stratifying by sex, in a 1:1:1 ratio to one of three treatment strategies: meter ($$n = 43$$), goal setting ($$n = 42$$), or meter/goal setting group ($$n = 43$$). This study design allowed for the measurement of longitudinal change in outcomes in individuals by treatment, as patients were serving as their own controls, and between treatment groups.
The sample size estimation for the proposed pilot study was based on change in HbA1c at 6 months from baseline (the mean of 2 measures taken at the onset and the end of run-in). For evaluation of the longitudinal change in outcomes measures in individuals by treatment (patients serve as their own controls): With 90 participants, we had over $90\%$ power to detect a HbA1c difference of 0.5 ± $0.8\%$ from baseline to 6 months among individuals (paired-T tests, α = 0.05). For evaluation of treatment group differences for the 3 treatment arms: With 30 participants per group, we had $80\%$ power to detect a HbA1c difference of 0.5 ± $0.5\%$ (one-way ANOVA, α = 0.05).
## Research Study Visits
After the run-in period, consented patients and parents were randomized to a treatment group during the baseline visit, therefore study activities were based on the allocated group, previously published [18]. Each study visit consisted of meeting with the healthcare providers for routine diabetes care and physical examination, discussion related to the specific intervention strategy, and the completion of study questionnaires.
## Intervention Strategies
HIT-enhanced SMBG strategy - The Telcare System (Concord, MA, https://telcare.com) allowed for real-time SMBG data monitoring for the patient, their parents, and their healthcare provider by automatically transmitting SMBG data to a secure, online, HIPAA-compliant web portal via cellular data networks. Both pre-set and free-text alerts could be sent to any cell phone in real time when SMBG was performed. If randomized into this strategy, participants were encouraged to use the Telcare blood glucose meter throughout the study, regardless of their insulin delivery mode. Regardless if the patient used the intervention strategy meter or another meter, all data were downloaded and reviewed weekly by a CDE and a CNP. Adolescents were consequently either messaged through Telcare system with care recommendations or when possible, directly contacted by phone/text message.
Family-centered goal setting strategy – In addition to meeting with a health educator and a CDE, families randomized into this strategy met with a board-certified pediatric endocrinologist at the randomization visit. Using motivational interviewing, the multidisciplinary team helped the families set goals that were tailored to each family’s desires, while considering the age and stage of maturity based on that age of the participating adolescent. The goal-setting tool utilized was specifically designed for this study by a patient advisory group and is published in detail elsewhere [17]. This tool was used to facilitate identification of diabetes self-management skills or behaviors that both the parent and adolescent agreed were important to work on and a reward system was discussed. Examples of self-management behaviors included specifics regarding self-monitoring of blood glucose, insulin dosing at meals, and adjusting insulin, but these were specific and unique for each parent-adolescent pair depending on their input. Independent from adolescents, parents chose goals that they felt were important and achievable for both themselves and their child. Conversely, adolescents chose goals that they felt were important and achievable for themselves and their parents. Tool responses were exchanged, and a shared decision-making process was utilized to choose mutually agreed-upon goals, tracking systems, and rewards.
For the present study, we focused on the impact of using this process on self-determined quality of life outcomes and diabetes distress.
As an example of a tracking and reward system, parents and adolescents were offered a jar and marbles in two different colors. Written on the jar were the agreed upon goals and rewards. Each time a behavior was demonstrated by either the parent or the adolescent, a marble of the corresponding color could be added to the jar. The adolescent could give the parent points and vice versa. Once an agreed upon number of marbles were added, an agreed-upon reward could be anticipated. This was not a required activity but was offered as an example to give a visual reminder of the goals that they had agreed upon.
Combined strategy – families participated activities for both the HIT-enhanced SMBG and family-center goal setting strategies. This required being instructed on the Telcare System and meeting with the research team to complete all the activities for the family-centered goal setting.
## Data Collection Instruments
Validated questionnaires were administered to youth and parent participants to evaluate patient-centered outcomes measures. All questionnaires were self-administered via a tablet, and data were stored in Research Electronic Data Capture system: REDCap (Vanderbilt University, Nashville, TN, USA), a HIPAA-secure database system approved by the university and healthcare system [20]. The youth participants completed five questionnaires at baseline and six months: 1) The Problem Areas in Diabetes Validated Scale (PAID-Peds) measured burden related to T1D management and related emotional distress [21]; 2) The Pediatric Quality of Life Inventory (PedsQL) version 3.2 was an exploratory measure to assess health-related quality of life and youth participants completed the appropriate PedsQL version for their age, 8-12 years or 13-18 years [22, 23]; 3) The Cleveland Adolescent Sleepiness Questionnaire was collected to assess sleepiness and alertness given the emerging data of higher sleep disturbances in youth with type 1 diabetes [24]; 4) The Adherence in Diabetes Questionnaire measured behaviors regarding T1D management and treatment [25]; and 5) an original patient satisfaction questionnaire was utilized to assess satisfaction with diabetes management during the study at baseline and six months (Supplementary Table 1).
Participating parents completed three questionnaires at baseline and six months: 1) Problems Areas in Diabetes (PAID) measured emotional distress and burden with the adolescent’s diabetes management [26]; 2) The Parental Environment Questionnaire Parent-Child Conflict Scale measured parent-child conflict [27]; and 3) an original parent satisfaction questionnaire (Supplementary Table 1) was utilized to assess satisfaction with their adolescent’s diabetes management during the study. The original satisfaction questionnaires were scored so that a higher score reflects a higher satisfaction level.
## Analysis
We modeled the longitudinal changes in questionnaire responses at six months from baseline. In Statistical Package for Social Sciences version 26 (SPSS v26), we used repeated one-way ANOVA to compare the differences within and between subjects. Linear mixed effects models with random intercepts for subjects were used to model the longitudinal change in questionnaire responses at six months from baseline. The Kenward-Roger approximation was used to test within-subject differences between responses at baseline and 6 months for each intervention strategy group. A likelihood ratio test was used to measure this longitudinal change difference across the three groups. Data were assumed to be missing at random (MAR), and all analyses were conducted in R version 4.0.4 [2021-02-15]. The linear mixed models were fit using the lme4 package.
## Results
One hundred twenty-eight participants enrolled, and 102 families completed the run-in period and were randomized to one of three intervention strategies. Ninety participants completed both the baseline and 6 months study visits. Baseline characteristics of the participants are shown in Table 1. The average age of youth participants was 14.8 ± 1.6 years with half of the participants being female ($49.5\%$). The predominant ethnicity/race was Non-Hispanic ($89.9\%$) and white ($85.9\%$).
**Table 1**
| Unnamed: 0 | Meter (N=35) | Goal Setting (N=34) | Meter/Goal Setting (N=30) | P-value |
| --- | --- | --- | --- | --- |
| Age (years) | | | | 0.6961 |
| Mean (SD)* | 14.7 (1.8) | 14.9 (1.5) | 15.1 (1.6) | |
| Female, n (%) | | | | 0.9912 |
| Female | 17 (48.6%) | 17 (50.0%) | 15 (50.0%) | |
| Male | 18 (51.4%) | 17 (50.0%) | 15 (50.0%) | |
| Race, n (%) | | | | 0.3083 |
| White | 32 (91.4%) | 30 (88.2%) | 23 (76.7%) | |
| Black | 3 (8.6%) | 2 (5.9%) | 4 (13.3%) | |
| Other | 0 (0.0%) | 2 (5.9%) | 3 (10.0%) | |
| Ethnicity, n (%) | | | | 0.4333 |
| Hispanic | 1 (2.9%) | 1 (3.0%) | 3 (11.1%) | |
| Non-Hispanic | 33 (97.1%) | 32 (97.0%) | 24 (88.9%) | |
Baseline and six-month questionnaire responses are shown in Table 2. For child questionnaires, responses were comparable across the groups for all questionnaires at baseline. The scores for PAID-Peds did not change significantly from baseline to 6 months in any group and there were no between group differences in this measure during the study. The PedsQL scores for diabetes symptoms, treatment barriers, treatment adherence, and worry did not change significantly from baseline to 6 months in any group and there were no between group differences in these subscale measures. The PedsQL communication subscale score was increased at 6 months in the HIT-enhanced SMBG (meter-only) group ($$p \leq 0.046$$). The total score on the Cleveland Adolescent Sleepiness Questionnaire changed only in the group assigned to combination therapy ($$p \leq 0.022$$), indicating worse self-reported sleep quality, but there were not between group differences for change in this measure. Adherence in diabetes scores increased in youth participants assigned to the family-centered goal setting treatment strategy ($$p \leq 0.01$$), but there were not between group differences for change in this measure. Satisfaction with the diabetes care plan increased in the HIT-enhanced SMBG (meter-only) group ($$p \leq 0.047$$), but there were not between group differences for change in this measure. For parents responses were comparable across the groups for all questionnaires at baseline except for the Parental Environment questionnaire, which was lower in the combination therapy group at baseline ($$p \leq 0.03$$). There were not between group differences for changes in any measure over time. Throughout the study, patient satisfaction with diabetes care was higher than parent satisfaction (baseline and 6-month differences, $p \leq 0.001$).
**Table 2**
| Unnamed: 0 | Meter | Meter.1 | Meter.2 | Goal Setting | Goal Setting.1 | Goal Setting.2 | Meter/Goal Setting | Meter/Goal Setting.1 | Meter/Goal Setting.2 | Between Groups | Unnamed: 11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Baseline | 6 Months | P-value2 | Baseline | 6 Months | P-value2 | Baseline | 6 Months | P-value2 | P-value1 | |
| Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* | Child Questionnaires* |
| Problem Areas in Diabetes (PAID-Peds) | 15.3 ± 13.2 (34) | 12.6 ± 13.2 (35) | 0.220 | 21.1 ± 17.5 (33) | 15.2 ± 16.3 (28) | 0.054 | 14.2 ± 14.6 (30) | 16.2 ± 15.7 (30) | 0.365 | 0.105 | |
| Pediatric Quality of Life (PedsQL) | | | | | | | | | | | |
| Diabetes Symptoms | 61.1 ± 14.9 (35) | 63.6 ± 18.1 (34) | 0.265 | 56 ± 12.7 (34) | 59.1 ± 8.6 (27) | 0.351 | 57.2 ± 14.7 (30) | 54.3 ± 18.3 (30) | 0.271 | 0.214 | |
| Treatment Barriers | 81.9 ± 16 (35) | 85.4 ± 15.6 (34) | 0.161 | 79.4 ± 17.7 (34) | 79.4 ± 16.8 (27) | 0.767 | 78.3 ± 18 (30) | 83.2 ± 14.6 (30) | 0.090 | 0.322 | |
| Treatment Adherence | 88.9 ± 11.6 (35) | 87.9 ± 11.9 (34) | 0.626 | 83.3 ± 17 (34) | 84.9 ± 13 (27) | 0.966 | 87.7 ± 13.6 (30) | 83.8 ± 15.6 (30) | 0.075 | 0.399 | |
| Worry | 67.7 ± 22.6 (35) | 65.3 ± 24.6 (34) | 0.517 | 51.3 ± 25.1 (34) | 58.5 ± 20.8 (27) | 0.109 | 61.7 ± 21.9 (30) | 60.6 ± 23.7 (30) | 0.800 | 0.220 | |
| Communication | 81.8 ± 17.4 (35) | 87.5 ± 17.2 (34) | 0.046 | 74.2 ± 22.3 (34) | 81.7 ± 18.3 (27) | 0.080 | 84.8 ± 21.1 (30) | 87.1 ± 15 (30) | 0.466 | 0.641 | |
| Cleveland Adolescent Sleepiness Questionnaire | 33.1 ± 10.1 (35) | 33.8 ± 8.9 (34) | 0.664 | 39.7 ± 10.1 (34) | 37.1 ± 10.5 (27) | 0.561 | 36.3 ± 11.1 (30) | 39.9 ± 10.9 (28) | 0.022 | 0.106 | |
| Adherence in Diabetes | 4.0 ± 0.6 (34) | 4.1 ± 0.6 (34) | 0.224 | 3.9 ± 0.5 (33) | 4.2 ± 0.4 (27) | 0.002 | 3.9 ± 0.6 (29) | 4.0 ± 0.7 (30) | 0.407 | 0.182 | |
| Satisfaction | 45.3 ± 8.7 (28) | 48.4 ± 5.7 (30) | 0.047 | 45.9 ± 9.6 (18) | 47.6 ± 5.3 (21) | 0.514 | 50.2 ± 5.8 (17) | 45.4 ± 9.1 (28) | 0.072 | 0.022 | |
| Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* | Parent Questionnaires* |
| Problem Areas in Diabetes – Parent Scale | 31.6 ± 6.6 (35) | 30.8 ± 6.2 (34) | 0.435 | 30.0 ± 6.6 (34) | 29.7 ± 5.5 (27) | 0.897 | 31.6 ± 4.4 (30) | 29.7 ± 5.6 (29) | 0.051 | 0.406 | |
| Environment | 24.7 ± 5.8(35) | 24.0 ± 6.7 (34) | 0.715 | 26.2 ± 7.2(34) | 24.6 ± 7.4(27) | 0.378 | 22.9 ± 3.1(30) | 23.5 ± 3.9(30) | 0.461 | 0.546 | |
| Satisfaction | 34.6 ± 6.5(25) | 33.0 ± 9.0(31) | 0.287 | 37.3 ± 3.5(21) | 36.9 ± 3.5(23) | 0.153 | 36.4 ± 3.8(19) | 33.6 (8.2)(27) | 0.178 | 0.490 | |
## Discussion
In this study, we evaluated patient-and parent-centered outcomes in participants in a comparative effectiveness trial of [1] a blood glucose meter that both shared blood glucose data among patients, their parent, and health care providers, and allowed for text-message communication (HIT-enhanced SMBG strategy); [2] a family-centered goal setting strategy; and [3] a combination of [1] and [2] [18]. In youth participants, we found: 1) increased self-reported diabetes-related communication in the HIT-enhanced SMBG strategy group, 2) increased self-reported engagement with diabetes self-management as measured by The Adherence in Diabetes Questionnaire in the family-centered goal setting group, and, 3) worse self-reported sleep quality in the combination strategy group. Throughout the study, scores for self-reported satisfaction with diabetes management were higher in youth than parents. This suggests that patients and parents have different goals and expectations regarding their diabetes care management and care delivery.
Youth participants who were randomized into the HIT-enhanced SMBG strategy (meter only) perceived an improvement in diabetes-related communication during the 6-month treatment period. The lack of adequate communication between providers and patients with T1D is one of the major reported barriers for care [28]. Some studies have suggested that greater communication with providers can ameliorate perceptions of barriers which can ultimately improve diabetes self-management and outcomes [29]. However, despite the reported improvement in communication in this group, there were no significant objective changes in HbA1c in this group (Supplementary Table 2) [18]. It may be that youth perceived communication was improved due to technological transfer of data to parents/healthcare providers, yet this did not transfer to actions that changed diabetes self-management behaviors.
Youth in the family-centered goal setting group reported increased engagement with diabetes self-management. Despite this perception, there were not significant improvements in either SMBG frequency or HbA1c as reported in our primary outcomes paper (Supplementary Table 2) [18]. This discrepancy between increased perception of engagement with diabetes self-management and no change in HbA1c or frequency of SMBG could be related to the subjectivity of the scale or to the low test-retest reliability of the Adherence in Diabetes Questionnaire, which has not yet been established for this questionnaire [25]. It could also be that youth who verbalize patient-centered goals during treatment self-report better engagement with diabetes self-management because the goals are better aligned with the patient’s reality rather than being assigned to them. Additional study is needed to further develop this patient-centered team approach so that youth-verbalized goals are highlighted and supported. Although those with continuous glucose monitors were excluded from our study, there is likely value in family-centered goal setting in this population and further study would be beneficial.
Although we did not document any significant changes in PAID-Peds questionnaires reported by youth, we noted that our youth scores were low (<40) [30]. This reflects that the participants had low emotional distress and negative emotions, even at baseline. This might have been related to the 3-months run in period which can provide an added level of comfort to the participating families. It also suggests that families and youth who volunteer to participate in diabetes care studies may differ in important ways from the general population of youth with diabetes. Thus, the findings from this study may not be translatable to all populations of youth with diabetes.
Introducing diabetes technology can be associated with diabetes-related distress and sleep-related concerns [31]. The greater the mental and physical burdens of treatment, the more likely they are to interfere with other aspects of health, including sleep. We documented a significant decline in self-reported sleep quality in youth participants in the combination therapy group. Sleep disturbances have been reported to be related to alarm fatigue and fear of hypoglycemia [31]. Although we did not collect quantitative data on sleep outcomes, we speculate that night-time sleep disturbances could be related to the increased time associated with the use of the blood glucose meter that shared data among patients, their parent, and healthcare providers.
Strengths of our study include the randomized-controlled prospective design and 3-month run-in period. The run-in period was designed to blunt the positive effect that participation in a research study, on its own, may have on diabetes self-care and satisfaction with care. In addition, this trial was performed in a real-world clinic-based setting and questionnaires were completed at these appointment times. The limitations include that the study was underpowered for some of the analyses comparing group differences in the primary study. There were analysis limitations based on the questionnaires chosen for this study. The PAID-Peds and Adherence in Diabetes questionnaires are validated for age groups up to 17 years and our inclusion criteria included 18-year-olds. We did not collect all the psychological variables or quality of life data in the parents, which precluded evaluation of psychosocial similarities/differences in parents and their children. Satisfaction data reflected the overall experience with the clinic visit and could not be specifically attributed to the device or data sharing aspects of the study. The Adherence in Diabetes questionnaire was validated in a sample of Danish adolescents and may not have construct validity in US adolescents. There were clinical limitations as well. Due to clinic time and resource constraints, the needed family support may have been insufficient for establishing new care routines. There was no study-related clinical psychologist or social worker to provide ongoing support during the study. Finally, some adolescents may have been in the honeymoon period after diagnosis which may have affected outcomes.
In conclusion, our data do not support our hypothesis that the combination of a technology-enhanced blood glucose meter along with family-centered goal setting would result in higher levels of well-being or patient satisfaction. Our data suggest that recommendations to provide added support for patient-centered strategies in the pediatric/adolescent diabetes clinic may be needed. Youth with diabetes value communication via technology and perceive that communication is improved by technology. Strategies to ensure receipt of this data and interim management based on technology-driven communication should be pursued. Youth with diabetes value partnership and patient-centered goal setting, but parents may be frustrated when they do not get the results that they (parents) expect. Strategies to align youth and parent expectations with the goal of improving satisfaction could be utilized as a strategy to improve partnerships in diabetes care management. Consideration should be taken with regard to self-care strategies including sleep habits. Sleep quality may be decreased with increasing burden of diabetes care. Furthermore, next steps in this research will include implementing a shared decision-making clinical process in a virtual format in preparation for diabetes clinic visits.
## 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 Indiana University Institutional Review Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author Contributions
JH, LY, and TH: responsible for the majority of writing of manuscript. HM and XL: responsible for analysis. AH and JT: responsible for study activities and data management. AC and TH: responsible for study design. All authors contributed to the article and approved the submitted version.
## Funding
Funding for this work provided by: Indiana University; National Center for Advancing Translational Sciences; National Institutes of Health; Agency for Healthcare Research and Quality, Grant/Award number: 5R24HS022434-05.
## 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/fcdhc.2022.769116/full#supplementary-material
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|
---
title: Medication-related burden and associated factors among diabetes mellitus patients
at Felege Hiwot Comprehensive Specialized Hospital in northwest Ethiopia
authors:
- Abaynesh Fentahun Bekalu
- Melaku Kindie Yenit
- Masho Tigabe Tekile
- Mequanent Kassa Birarra
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012090
doi: 10.3389/fcdhc.2022.977216
license: CC BY 4.0
---
# Medication-related burden and associated factors among diabetes mellitus patients at Felege Hiwot Comprehensive Specialized Hospital in northwest Ethiopia
## Abstract
### Background
Evaluating the medicine burden from the patients’ perspective is essential for getting good health outcomes of diabetes mellitus (DM) management. However, data are limited regarding this sensitive area. Thus, the study was aimed to determine the medication-related burden (MRB) and associated factors among DM patients at Felege Hiwot Comprehensive Specialized Hospital (FHCSH) in northwest Ethiopia.
### Methods
A cross-sectional study was conducted on 423 systematically selected DM patients attending the DM clinic of FHCSH from June to August 2020. The medication-related burden was measured by using the Living with Medicines Questionnaire version 3 (LMQ-3). Multiple linear regression was used to identify factors associated with medication-related burden and reported with $95\%$ confidence interval (CI). p-value <0.05 was considered as statistically significant to declare an association.
### Results
The mean LMQ-3 score was 126.52 (± 17.39). The majority of the participants experienced moderate ($58.9\%$, $95\%$ CI: 53.9–63.7) to high ($26.2\%$, $95\%$ CI: 22.5–30.0) degrees of medication burden. Nearly half ($44.9\%$, $95\%$ CI: 39.9–49.7) of the participants were non-adherent to their prescribed medications. VAS score ($B = 12.773$, $$p \leq 0.001$$), ARMS score ($B = 8.505$, $$p \leq 0.001$$), and fasting blood glucose (FBS) on visit ($B = 5.858$, $$p \leq 0.003$$) were significantly associated with high medication-related burden.
### Conclusion
A significant number of patients suffered from high medication-related burden and non-adherence to long-term medicine. Therefore, multidimensional intervention to decrease MRB and to upgrade adherence is required to increase patients’ quality of life.
## Introduction
Diabetes mellitus (DM) is a global public health problem that causes significant morbidity and mortality [1, 2]. Furthermore, DM and its complications are a reason for an increased risk of hospitalization, length of hospital stay, healthcare cost, and reduced health-related quality of life (2–6). In 2019, the estimated global prevalence of DM was $9.3\%$ (463 million people), which is expected to rise to $10.9\%$ (700 million) by 2045. In Ethiopia, according to the International Diabetes Federation 2019 report, the prevalence of DM was $4.3\%$ [7]. A few other studies in Ethiopia also reported that the prevalence of DM ranges from $1.9\%$ to $12.2\%$ (3, 8–12). Based on the literature search, studies regarding the prevalence of DM in the study area were not conducted. However, a single study on medication non-adherence and associated factors among diabetic patients reported that in northwest Ethiopia specifically at Felege Hiwot Comprehensive Specialized Hospital (FHCSH) approximately 2,484 DM patients were registered for follow-up and receiving diabetic care [13].
Medication-related burden (MRB) is a recent concept concerned with the negative experiences resulting from the process of undertaking treatment. It is one aspect of treatment burden, and it includes not only the burden of the medication but also all types of healthcare intervention and patient’s perspective toward MRB (14–16). Medication-related burden can lead to non-adherence and poor clinical outcomes, as well as affecting patient satisfaction, psychological well-being, social functioning, and quality of life. It can lead to poor quality of life as patients spend more of their time, energy, and resources on staying well because they experience burden not only from their illness but also from their ever-expanding healthcare regimens. Burdened patients may struggle with adhering to prescribed medications and treatment care, but the burden may influence adherence to treatment and patients’ health. Patients with multimorbidity and an excessive burden of treatment may not adhere to the prescribed medication (17–19).
Previously conducted studies described that MRB is affected by a variety of sociodemographic, clinical, and treatment-related factors. The significant predictors of high MRB include being male, older age, unemployed, polypharmacy, number of chronic conditions, the severity of the chronic condition, visual analog scale (VAS)-burden scores, needing support, high dosing frequency, paying prescription charge, Adherence to Refills and Medication Scale (ARMS) score, duration of DM, marital status, and presence of hypertension [16, 20, 21]. A study done in Qatar reported that variables such as ARMS score, duration of DM, marital status, employment status, and presence of hypertension were significantly associated with MRB [16].
Patient-reported experience measures and patient-reported outcome measures have a pivotal role in helping patients know how they feel about their own experiences and outcomes of care, including the benefits and risks of treatment (22–24).
Previous studies explained that a high burden of treatment is a reason for increased hospitalization, cognitive impairment, drug interactions, physical side effects, increased healthcare resource utilization, and higher mortality rate. Furthermore, these studies suggest that exploring new interventions to reduce the burden of treatment, ultimately moving toward minimally disruptive medicine, is necessary (22, 23, 25–30). To help patients move toward minimally disruptive medicine, the identification and targeting of risk factors for high medication-related burden is the starting point. Evidence showed that DM is one of the most common chronic illnesses associated with micro- and macrovascular complications that might result in poor clinical outcomes and increased morbidity as well as mortality. Additionally, as DM is associated with a number of comorbidities such as hypertension and dyslipidemia, DM patients might be burdened by medicines (1–4). Despite the growing burden and economic impact of treatment burden in developing countries including Ethiopia, few studies have been conducted globally and most of them were carried out in the developed world. Based on the literature search, studies regarding this sensitive issue are lacking in Ethiopia including studies conducted in this specific country. Therefore, the study was aimed to determine the MRB and associated factors among DM patients at FHCSH in northwest Ethiopia.
## Study design, setting, and period
A cross-sectional study was conducted at FHCSH from June to August 2020, which is found in Bahir Dar City, northwest of Ethiopia. Bahir *Dar is* the capital city of Amhara regional state which is 564 km away from Addis Ababa. The hospital is expected to serve more than seven million people in its catchment area. It has 400 beds and 15 adult outpatient departments, one of which serves as a referral and follow-up clinic for patients with chronic diseases. The DM clinic is situated inside the outpatient department, and a large number of patients attend the follow-up clinic. Based on a previous study, around 2,484 DM patients were registered for follow-up in the clinic [31].
## Population
All adult DM patients attending the outpatient clinic of FHCSH were taken as the source population, whereas adult DM patients attending the outpatient clinic of FHCSH during the study period were considered as the study population. Adults (≥18 years) with a diagnosis of DM for at least 3 months prior to the study, with or without comorbidities, were included. However, DM patients with mental disabilities, any speech impairment, and incomplete charts were excluded.
## Sample size determination and sampling technique
A single population proportion formula [n = (Z α/2)2 p (1 − p)/d 2] was used to calculate the sample size [32]. With $95\%$ confidence level, proportion of medication-related burden $50\%$ because there is no previous study in Ethiopia and relative precision $5\%$, the total sample size was 384. The sample size was corrected for non-response rate ($15\%$) [33], and the final calculated sample size was 442. A systematic random sampling technique was utilized to select the study participants.
## Study variables
The dependent variable was MRB which was measured using Living with Medicines Questionnaire version 3 (LMQ-3), while the independent variables were patients’ sociodemographic variables (age, gender), type of DM, duration of DM, presence of comorbidities, number of comorbidities, adherence to the prescribed medication, number of prescribed medication, and fasting blood glucose (FBS) level.
## Data collection tools
Data were collected by structured questionnaires via face-to-face interviews. The questionnaire has four parts. The first section contains the participants’ sociodemographic characteristics, the second part contains items related to diseases and medication, the third part contains items related to self-reported MRB, and the fourth section contains items related to medication adherence. The LMQ-3 is a 41-item questionnaire where respondents are required to indicate their level of agreement using a five-point Likert-type scale. This tool is comprised of eight domains. The LMQ-3 tool was validated in English [34] and adapted and validated in Arabic [35]. The overall LMQ score was the sum of the scores of all the 41 items in the questionnaire, with scores ranging from 41 to 205 corresponding to the following: extremely high burden, 173–205; high burden, 140–172; moderate burden, 107–139; minimal burden, 74–106; and no burden at all, 41–73. The questionnaire also contained a 10-cm line VAS scale, through which respondents provided a global assessment of the overall burden they experienced (0 “no burden at all” to 10 “extreme burden at all”), with higher scores representing greater perceived burden [16].
A 12-item ARMS rating from 1 (none of the time) to 4 (all of the time) scale ranging from 12 to 48 scores was used to measure adherence. Those patients who scored ≤13 were categorized as adherent and those who scored >13 were non-adherent [20]. This tool was validated in Arabic [16] and the internal consistency was correlated to the Morisky adherence [36]. Other patient-related information was then obtained from medical records using the data collection form that was specifically designed for this study.
## Data quality control
The questionnaire was translated into Amharic language and back-translated to English to check if the translated items retained the same meaning as the original items. Training was given to the data collectors by the investigator and supervisors. A pretest was done by taking $10\%$ of the sample size to assess the understandability, internal consistency, and validity of the questionnaire. The overall LMQ-3 tool had a good internal reliability with a Cronbach’s alpha of 0.90. Each of the eight domains showed good internal consistency with Cronbach’s alpha ranging from 0.72 to 0.98. The internal consistency (Cronbach’s α coefficient) of the overall ARMS score was 0.828.
## Statistical analysis
Data were checked, coded, and cleaned for inconsistencies and missing values and entered into EpiData version 4.6.0.0 (EpiData Association, Odense, Denmark) statistical software and then exported to SPSS version 21 (IBM Corporation, Armonk, NY, USA) for analysis. Descriptive, correlation, comparative, and regression analyses were conducted. Continuous variables were expressed as mean (± SD) when normally distributed or median (IQR) when not normally distributed. Additionally, categorical variables were summarized as frequency (percentage) of the total. The normality of data was assessed by the Shapiro–Wilk test. A Spearman’s and Pearson correlation test were used to assess the relationship between the independent variables and dependent variable. Independent t-tests or one-way analysis of variance (ANOVA) was done to examine the MRB differences among the independent variables. Since the dependent variable MRB was a continuous variable that fulfilled the normality distribution and linearity assumptions, multiple linear regression was used to identify factors associated with MRB. Multicollinearity was assessed using Pearson correlation coefficients. In the linear regression, most of the independent variables were continuous, while a few variables were dummy categorical variables. p-value <0.05 was considered as statistically significant to declare association. In the study, most of the independent variables were continuous (age, duration of DM, VAS score, ARMS score, FBS, number of comorbidities), while a few variables were dummy categorical variables which were categorized as yes/no (using tablet, using injection).
## Sociodemographic characteristics of DM patients
A total of 442 participants were approached and 423 of them agreed to participate in the study giving a response rate of $95.7\%$. Of the participants, more than half of them (234, $55.3\%$) were men. The mean age of the study participants was 40.04 (± 15.67) years. Of the study participants, 322 ($76.1\%$) were urban dwellers and 145 ($34.3\%$) were unable to read and write (Table 1).
**Table 1**
| Variable | Category | Frequencies (N) | Percentages (%) |
| --- | --- | --- | --- |
| Sex | Male | 234 | 55.3 |
| Sex | Female | 189 | 44.7 |
| Age (years) | 18–24 | 68 | 16.1 |
| Age (years) | 25–34 | 121 | 28.6 |
| Age (years) | 35–44 | 83 | 19.6 |
| Age (years) | 45–54 | 59 | 13.9 |
| Age (years) | 55–64 | 52 | 12.3 |
| Age (years) | ≥65 | 40 | 9.5 |
| Residence | Rural | 101 | 23.9 |
| Residence | Urban | 322 | 76.1 |
| Marital status | Married | 224 | 53.3 |
| Marital status | Single | 142 | 33.6 |
| Marital status | Divorced | 21 | 5.0 |
| Marital status | Widowed | 36 | 8.5 |
| Religion | Orthodox | 325 | 76.8 |
| Religion | Muslim | 84 | 19.9 |
| Religion | Protestant | 14 | 3.3 |
| Educational status | Unable to read and write | 145 | 34.3 |
| Educational status | Able to read and write | 35 | 8.3 |
| Educational status | Primary education | 72 | 17.0 |
| Educational status | Secondary education | 43 | 10.2 |
| Educational status | Diploma | 86 | 20.3 |
| Educational status | Degree and above | 42 | 9.9 |
| Occupational status | Housewife | 58 | 13.7 |
| Occupational status | Student | 60 | 14.2 |
| Occupational status | Farmer | 54 | 12.8 |
| Occupational status | Government employee | 136 | 32.2 |
| Occupational status | Private | 99 | 23.4 |
| Occupational status | Other | 16 | 3.8 |
| Cigarette smoking | Yes | 5 | 1.2 |
| Cigarette smoking | No | 418 | 98.8 |
| Alcohol consumption | Yes | 7 | 1.7 |
| Alcohol consumption | No | 416 | 98.3 |
| Lifestyle status | Healthy diet only | 46 | 10.9 |
| Lifestyle status | Exercise only | 238 | 56.3 |
| Lifestyle status | Exercise and healthy diet | 164 | 24.6 |
| Lifestyle status | | 35 | 8.3 |
## Clinical and medication characteristics of DM patients
Among the 423 study participants, 220 ($52.0\%$) had type 1 DM. The median (IQR) duration of DM diagnosis was 4.0 (2.0) years. Comorbidities were present in 89 ($21.0\%$) of the study participants, and the most commonly reported comorbidity was hypertension 65 ($15.4\%$). Most of the participants (365, $86.3\%$) were prescribed with one or two medications. The median (IQR) FBS value was 198 [105] mg/dl, and 345 ($81.6\%$) of the participants had uncontrolled DM (FBS ≥ 126 mg/dl) (Table 2).
**Table 2**
| Variables | Category | Frequencies (N) | Percentages (%) |
| --- | --- | --- | --- |
| Type of DM | Type 1 | 220 | 52.0 |
| Type of DM | Type 2 | 203 | 48.0 |
| Duration of DM | 3 months–4 years | 281 | 66.4 |
| Duration of DM | Above 4 years | 142 | 33.6 |
| Comorbidities | Yes | 89 | 21.0 |
| Comorbidities | No | 334 | 79.0 |
| Type of comorbidities | Hypertension | 65 | 15.4 |
| Type of comorbidities | Heart failure | 12 | 2.8 |
| Type of comorbidities | Dyslipidemia | 6 | 1.4 |
| Type of comorbidities | Asthma | 6 | 1.4 |
| Number of prescribed medications | ≤2 | 365 | 86.3 |
| Number of prescribed medications | >2 | 58 | 13.7 |
| Medication dosage form | Injection | 207 | 48.9 |
| Medication dosage form | Tablet | 201 | 47.5 |
| Medication dosage form | Both injection and tablet | 17 | 4.0 |
| Medication dosage form | Other (capsule, suspension, puff) | 5 | 1.2 |
| DM control status | Uncontrolled | 345 | 81.6 |
| DM control status | Controlled | 78 | 18.4 |
| Payment for prescribed medications | Yes | 368 | 87.0 |
| Payment for prescribed medications | No | 55 | 13.0 |
## Medication-related burden of DM patients
The mean (± SD) LMQ-3 score was 126.52 (17.39). The results showed that the majority ($85.1\%$) of DM patients suffered from a moderate ($58.9\%$, $95\%$ CI: 53.9–63.7) to a high ($26.2\%$, $95\%$ CI: 22.5–30.0) degree of burden. The mean (± SD) VAS score was 3.83 (± 1.07) and the majority of the participants ($60\%$, $95\%$ CI: 55–65.6) were perceived to have a high global burden (Table 3).
**Table 3**
| Variable | Range | Frequency (%) | (95% CI) | Mean (SD) | Median (IQR) |
| --- | --- | --- | --- | --- | --- |
| LMQ overall score | 41–205 | – | – | 126.52 (17.397) | 130 (25) |
| Minimal burden | 74–106 | 63 (14.9) | (11.8–18.7) | – | – |
| Moderate degree of burden | 107–139 | 249 (58.9) | (53.9–63.7) | – | – |
| High burden | 140–172 | 111 (26.2) | (22.5–30.0) | – | – |
| VAS: global burden | 0–10 | – | – | 3.83 (1.07) | 4 (2) |
| VAS: global burden | Up to 4 | 169 (40) | (34.4–45.0) | – | – |
| VAS: global burden | Above 4 | 254 (60) | (55–65.6) | – | – |
Medication-related burden showed a moderately significant positive association with VAS score ($r = 0.561$, $p \leq 0.001$) and ARMS score ($r = 0.518$, $p \leq 0.001$). Additionally, place of residence ($r = 0.109$, $$p \leq 0.026$$), duration of DM diagnosis ($r = 0.151$, $$p \leq 0.002$$), number of comorbidities ($r = 0.157$, $$p \leq 0.001$$), and number of medications ($r = 0.207$, $p \leq 0.001$) had a weak positive association with MRB.
Diabetic patients with FBS ≥126 mg/dl on visit showed high MRB mean score than those with FBS ≤125 mg/dl (129.19 ± 15.9 vs. 114.68 ± 18.82, $p \leq 0.001$). Patients with a high VAS score ≥4 (high global burden) showed high MRB (134.04 ± 12.49 vs. 115.21 ± 17.64, $p \leq 0.001$) than those with a VAS score ≤3 (low burden). Additionally, patients with ARMS score ≥14 (non-adherent) showed high MRB (35.16 ± 1.86 vs. 119.47 ± 18.02, $p \leq 0.001$) than those with ARMS score ≤13 (adherent).
## Factors associated with medication-related burden
Simple and multiple linear regression analyses were done to identify factors associated with MRB (LMQ-3) score. Simple linear regression indicated that age, having college education, being urban, practicing a healthy diet and exercise, duration of DM, absence of comorbidities, number of comorbidities, using two or more medicines, not taking both tablet and injection, FBS level, and ARMS and VAS scores were associated with MRB. However, the multiple linear regression analysis indicated that only VAS score ($B = 12.773$, $$p \leq 0.001$$), ARMS score ($B = 8.505$, $$p \leq 0.001$$), and FBS on visit ($B = 5.858$, $$p \leq 0.003$$) were significantly associated with MRB. For every unit increase in the VAS score (global burden), ARMS score, and FBS on visit, there were 12.773, 8.505, and 5.858 increases in the MRB, respectively. The model analysis showed that the independent variables explained $37\%$ of the variability of the dependent variable MRB (R 2 = 0.37), and the regression model was a good fit for the data (F 16, 404 = 14.88, $p \leq 0.001$) (Table 4).
**Table 4**
| Variables | B | Std. error | Beta | T | p-value | 95% CI |
| --- | --- | --- | --- | --- | --- | --- |
| Age | −0.119 | 0.687 | 0.011 | −0.173 | 0.863 | −1.469 to 1.231 |
| Education | 0.799 | 0.52 | 0.083 | 1.535 | 0.126 | −0.224 to 1.822 |
| Place of residence | −2.33 | 1.809 | −0.057 | −1.288 | 0.199 | −5.887 to 1.227 |
| Lifestyle | −0.308 | 0.792 | −0.017 | 0.389 | 0.698 | −1.865 to 1.249 |
| Duration of DM (years) | −0.051 | 2.018 | −0.001 | −0.025 | 0.98 | −4.02 to 3.92 |
| Presence of comorbidities | −1.653 | 3.986 | −0.039 | −0.415 | 0.679 | −9.489 to 6.183 |
| Total number of comorbidities | 1.042 | 1.997 | 0.033 | 0.522 | 0.602 | −2.884 to 4.967 |
| Presence of hypertension | −3.316 | 3.528 | −0.069 | −0.940 | 0.348 | −10.251 to 3.619 |
| Presence of dyslipidemia | −5.338 | 6.219 | −0.036 | −0.858 | 0.391 | −17.562 to 6.887 |
| Number of medications | −2.146 | 3.463 | −0.042 | −0.62 | 0.536 | −8.953 to 4.661 |
| Tablet | −4.424 | 7.317 | −0.127 | −0.605 | 0.546 | −18.80 to 9.959 |
| Injection | −2.764 | 7.229 | −0.080 | −0.382 | 0.702 | −16.975 to 11.447 |
| Both tablet and injection | −6.980 | 8.306 | −0.079 | −0.840 | 0.401 | −23.308 to 9.349 |
| Fasting blood glucose | 5.858 | 1.958 | 0.131 | 2.992 | 0.003* | 2.009 to 9.707 |
| Overall perception (VAS) | 12.773 | 1.678 | 0.360 | 7.61 | 0.001* | 9.474 to 16.072 |
| ARMS | 8.505 | 1.702 | 0.243 | 4.99 | 0.001* | 5.16 to 11.85 |
| Constant | 132.121 | 40.29 | | 3.287 | 0.001 | 53.234 to 211.645 |
## Adherence to prescribed medication
The adherence to prescribed medication was measured by using the ARMS score. The median (IQR) ARMS score was 13 [3], range 12–48. The result showed that $44.9\%$ ($95\%$ CI: 39.9–49.7) of the study participants were non-adherent to their prescribed medications and $55.1\%$ ($95\%$ CI: 50.3–60.1) were adherent to their prescribed medications (Figure 1).
**Figure 1:** *Adherence to prescribed medication among DM patients attending at Felege Hiwot Comprehensive Specialized Hospital.*
## Discussion
The current findings revealed that the majority of DM patients suffered from a moderate to a high degree of burden. According to the multiple linear regression, high VAS score (perceived global burden), ARMS score, and FBS on visit were significantly associated with high MRB. In the present study, almost half of the DM patients were non-adherent to their prescribed medications.
The results of the present study showed that the majority ($85.1\%$) of DM patients suffered from moderate ($58.9\%$, $95\%$ CI: 53.9–63.7) to high MRB ($26.2\%$, $95\%$ CI: 22.5–30.0), which is similar to the findings of a study in Qatar [minimum ($66.8\%$) to moderate ($24.1\%$)] [16]. However, these values were higher compared to the results of the studies conducted in England [minimum ($33.1\%$) to moderate ($53.6\%$)] [21], New Zealand [moderate (45.1) to high (30.5)] [37], and Kuwait [minimum ($35.4\%$) to moderate ($62.0\%$)] [20]. The possible explanation for the difference could be variations in sociodemographic characteristics, presence and number of comorbidities, number of prescribed medications, duration (years) of DM diagnosis, unavailability of better medical care, poor relationship with the healthcare providers, and inadequate knowledge of patients about their medication in developing nations which might cause higher treatment burden to the current study [15, 38].
The current findings showed that the mean LMQ-3 overall score among patients ≥65 years was higher than those aged <65 years old. This finding was in line with similar studies which reported that overall LMQ scores were related to age [16, 20, 39]. A possible reason might be that age was a risk factor for comorbidities, as the number of comorbidities increases the risk of polypharmacy and the complexity of medication regimens and the passive behavior of the elderly in taking their medications also increases (40–42). In contrast, a study done in England revealed a significantly lower mean LMQ score among the elderly (≥65 years) than adult patients (18–64 years) [21]. The possible explanation for the difference might be variations in educational status between developed and resource-limited settings. Participants who lived in developed nations might have a better attitude and general knowledge about medicine, know about the possible side effects of medicine, and have good communication with health professionals about medicine and the perceived effectiveness of medicine which might lower MRB in elderly participants (43–45).
The MRB was positively associated with uncontrolled DM, which is similar to the study done in Qatar [16]. If blood glucose increases and is not managed properly, the risk of getting acute as well as chronic complications increases, and this might cause acute life-threatening events such as diabetic ketoacidosis and increase the frequency of hospitalization and decrease the patients’ quality of life [5, 46].
In this study, MRB had a statistically significant association with non-adherence to medication. This result agrees with the studies done in Qatar [16] and Kuwait [20]. A possible explanation might be that non-adherence causes a lower serum concentration of medication which affects the effectiveness and efficacy of the medication, and this causes treatment failure which might lead to uncontrolled glycemic level. Finally, it may lead to disease progression associated with acute and chronic complications, enhancing exposure to polypharmacy [19, 47]. Polypharmacy obligates patients to take multiple medications at once and apply many instructions, and it also exposes the patients to possible side effects and adverse drug reactions. In addition, non-adherence leads to a poor quality of life and frequent hospitalization, and this also leads to a high MRB [48].
In the current study, $60\%$ of the patients had a high global perceived burden. The current findings showed a moderate positive relationship with overall LMQ-3 scores (Pearson $r = 0.561$) and a weak positive correlation between VAS score and ARMS score ($r = 0.286$). This implies that the higher the perceived global burden, the lower the medication adherence level.
The study had its limitations. The English version of the LMQ-3 tool was developed and used in a country with a different health system, which has not been used in Ethiopia before which may potentially limit the findings. The study does not include critically ill patients who do not regularly attend the follow-up clinic, and this might limit the representativeness of the findings.
## Conclusion
The vast majority of DM patients suffered from moderate to high degree of burden and non-adherence to long-term medicine. Adherence level, perceived global burden, and FBS were the predictors of MRB in diabetic patients. Therefore, to minimize MRB, multidimensional intervention is required for DM patients with uncontrolled DM, those non-adherent to their prescribed medications, and patients with a high perceived global burden. In addition, the findings of this study would provide data for clinicians on which factors they should target in reducing the medication-related burden of their diabetic patients.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
This study was reviewed and approved by the School of Pharmacy Ethical Review Committee, College of Medicine and Health Sciences, University of Gondar. To ensure confidentiality of data, study subjects were identified using codes, and only authorized persons accessed the collected data. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
AB and MB were involved in the conceptualization and design of the study and data acquisition, analysis, and interpretation and took part in drafting the initial version of the manuscript. MT and MY were responsible for the data acquisition, analysis, and interpretation and for revising and editing the initial 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.
## Abbreviations
ARMS, Adherence to Refills and Medications Scale; DM, diabetes mellitus; FHCSH, Felege Hiwot Comprehensive Specialized Hospital; IQR, interquartile range; MRD, medication-related burden; MRB, perceived medication-related burden; SD, standard deviation; TBQ, Treatment Burden Questionnaire; VAS, visual analog scale; WHO, World Health Organization.
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|
---
title: 'Increased Frequency of Diabetic Ketoacidosis: The Link With COVID-19 Pandemic'
authors:
- Giuseppe d’Annunzio
- Marta Bassi
- Elena Lucia De Rose
- Marilea Lezzi
- Nicola Minuto
- Maria Grazia Calevo
- Alberto Gaiero
- Graziella Fichera
- Riccardo Borea
- Mohamad Maghnie
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012091
doi: 10.3389/fcdhc.2022.846827
license: CC BY 4.0
---
# Increased Frequency of Diabetic Ketoacidosis: The Link With COVID-19 Pandemic
## Abstract
### Aims
Diabetic ketoacidosis is the most severe metabolic derangement due to prolonged insulin deficiency as in type 1 diabetes. Diabetic ketoacidosis, a life-threatening condition, is often diagnosed late. A timely diagnosis is mandatory to prevent its consequences, mainly neurological. The COVID-19 pandemic and lockdown have reduced the availability of medical care and access to hospitals. The aim of our retrospective study was to compare the frequency of ketoacidosis at the diagnosis of type 1 diabetes between the lockdown-post lockdown period and the previous two calendar years, in order to evaluate the impact of the COVID-19 pandemic.
### Patients and Methods
We retrospectively assessed the clinical and metabolic data at the diagnosis of type 1 diabetes in children in the Liguria Region during 3 different time periods: calendar year 2018 (Period A), calendar year 2019 until February 23,2020 (Period B) and from February 24, 2020 onwards to March 31, 2021 (Period C).
### Results
We analyzed 99 patients with newly-diagnosed T1DM from $\frac{01}{01}$/2018 to $\frac{31}{03}$/2021. Briefly, a younger age at diagnosis of T1DM was observed in Period 2 compared to Period 1 ($$p \leq 0.03$$). The frequency of DKA at clinical onset of T1DM was similar in Period A ($32.3\%$) and Period B ($37.5\%$), while it significantly increased in Period C ($61.1\%$) compared to Period B ($37.5\%$) ($$p \leq 0.03$$). PH values were similar in Period A (7.29 ± 0.14) and Period B (7.27 ± 0.17), while they were significantly lower in Period C (7.21 ± 0.17) compared to Period B ($$p \leq 0.04$$).
### Conclusions
An increase in the frequency of diabetic ketoacidosis has been documented in newly diagnosed pediatric patients in the Liguria Region during and after the lockdown period compared to previous calendar years. This increase could have been caused by the delay in diagnosis following the restrictions imposed by the lockdown with consequently reduced access to health care facilities. More information on the risks of ketoacidosis is desirable by means of social and medical awareness campaigns.
## Introduction
In early December 2019, the first patient suffering from a strange form of pneumonia due to a new infectious agent was reported in Hubei Province, China [1]. The disease caused by the Severe Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), recognized as Coronavirus Disease 19 (COVID-19) has spread rapidly across China and worldwide. The World Health Organization defined the COVID-19 outbreak a Public Health Emergency of International Concern on January 30, 2020, and a pandemic was declared on March 11, 2020. On March 9 an epidemic state was declared and a subsequent lockdown restriction was imposed by the government from March 9 to May 3, 2020 [2]. These measures were followed by a reduction in new cases of COVID-19 infection, which intensely modified the lifestyle of healthy and sick people.
In addition to different and conflicting data reported on its clinical severity, the COVID-19 pandemic has represented a serious global concern for patients with chronic conditions, as outpatient activities have been suspended to address emergency situations, with consequent impairment of follow-up programs. Moreover, the restrictions imposed by the lockdown together with the fear of being infected have reduced the number of accesses and the availability of health care services. In addition, the resources and workforce were inevitably concentrated on the COVID-19 pandemic. As a result, attention to diseases other than COVID-19 has decreased, with subsequent delay in the recognition and diagnosis of several serious pediatric diseases, such as type 1 diabetes mellitus [3].
Type 1 diabetes mellitus (T1DM) is an autoimmune disease that occurs in genetically susceptible subjects. Genetic susceptibility alone does not explain the development of the disease, therefore environmental factors play a role as a trigger for the autoimmune response [4, 5]. Although several factors have been studied, a specific one is not yet known [5, 6].
The clinical onset of T1DM is preceded by an asymptomatic period characterized by insulitis responsible for progressive insulin deficiency, with loss of glucose availability [7]. The consequences of insulin deficiency are the rise of counter-regulatory hormones, blood glucose levels and osmolality, resulting in polyuria and polydipsia up to a metabolic decompensation called Diabetic Ketoacidosis (DKA) [8].
DKA is a severe complication of T1DM caused by an insulin deficiency. It includes hyperglycemia affecting osmotic diuresis and volume depletion, electrolytes imbalance, production of ketoacids, and metabolic acidosis. Weight loss, polyuria, and polydipsia are reported. DKA, whose severity depends on delayed recognition, is observed not only at clinical diagnosis of T1DM but also during follow-up, and represents a seriously negative prognostic factor [9, 10].
According to the ISPAD Guidelines, DKA is defined as random plasma glucose >250 mg/dl, pH <7.3, and serum bicarbonate <15 mEq/l [11].
The worldwide incidence of T1DM is variable but has grown more than two to three times in recent decades, particularly in Finland and in the Sardinia region, Italy [12, 13]. Similarly, DKA shows an even higher prevalence than expected, despite the periodic implementation of awareness campaigns [14]. In Italy, DKA accounts for up to $41.2\%$ of new cases of T1DM [12]. DKA can also develop in patients with well-known T1DM; it is referred to as recurrent DKA, and its frequency varies among different countries, reaching up to 8 per 100 person-years [15]. Correct and timely diagnosis of T1DM and also careful follow-up of patients are the cornerstones for DKA prevention, which should be a goal for pediatricians [16, 17]. Several data records report an increased frequency of DKA during the COVID-19 pandemic (18–22). Reduced hospital admissions and increased fear of COVD-19 have been considered the most important factors.
The aim of our cross-sectional study was to compare the frequency of DKA between different time periods, i.e., before and during the lockdown period due to the COVID-19 pandemic.
## Materials and Methods
We retrospectively analyzed clinical and metabolic data of all newly-diagnosed T1DM patients, aged 0.18 years, in the calendar years of 2018 to 2021. Data were collected from the medical records of 3 different pediatric hospitals active in the Liguria Region: the Giannina Gaslini Pediatric Clinic, the hub hosting the Regional Reference Center for Pediatric Diabetes established according to the Regional Law n. 27, 9 August 2013 [23], and 2 spokes: the Pediatric and Neonatology Unit, San Paolo Hospital, Savona and the Maternal Childhood Department, Imperia Hospital.
We enrolled 99 children and adolescents living in the Liguria Region, diagnosed during 3 different time periods: calendar year 2018 (Period A) and calendar year 2019 until February 23,2020 (Period B), i.e., before the spread of the COVID-19 pandemic, and from February 24, 2020 to March 31, 2021 (Period C), i.e., during the pandemic period.
Period A included 31 patients, Period B included 32 patients and Period C included 36 patients. Mean age was 10.3 ± 4.4 years in Period A, 8.2 ± 3.7 years in Period B and 8.8 ± 4.7 years in Period C. Clinical characteristics are reported in Table 1. T1DM was diagnosed according to the ISPAD Guidelines, i.e., random glucose levels >250 mg/dl, in the presence of symptoms such as polyuria, polydipsia, and weight loss up to DKA. Severity of DKA was classified according to the ISPAD Guidelines: random plasma glucose >250 mg/dl, pH <7.3, and serum bicarbonate <15 mEq/l. In particular, 7.1 ≤pH <7.3 defines mild/moderate DKA and pH <7.1 defines severe DKA [11].
**Table 1**
| Time period | 2018 | January 1, 2019 to February 23, 2020 | February 24, 2020 to March 31, 2021 | p-value 2018 vs 2019 | p-value2019 vs 2020 | p-value2018 vs 2020 |
| --- | --- | --- | --- | --- | --- | --- |
| Time period | PERIOD A | PERIOD B | PERIOD C | | | |
| | N = 31 | N = 32 | N = 36 | | | |
| Age at T1DM diagnosis (years) | 10.30 ± 4.44 | 8.24 ± 3.66 | 8.77 ± 4.70 | 0.03 | 0.78 | 0.11 |
| HbA1c (%) | 11.07 ± 2.9 | 11.04 ± 1.9 | 10.8 ± 2 | 0.81 | 0.61 | 0.9 |
| BMI-SDS | −0.20 ± 1.07 | 0.11 ± 1.66 | 0.21 ± 1.17 | 0.69 | 0.57 | 0.25 |
| | N (%) | N (%) | N (%) | | | |
| Celiac disease (tTGAAb) | 2 (9.5) | 5 (20) | 7 (24.1) | 0.43 | 0.75 | 0.27 |
| TgAb | 1 (5.6) | 1 (4.5) | 3 (10.7) | 1.0 | 0.62 | 1.0 |
| TPOAb | 2 (11.1) | 1 (4.5) | 1 (3.6) | 0.58 | 1.0 | 0.55 |
| DKA | 10 (32.3) | 12 (37.5) | 22 (61.1) | 0.79 | 0.09 | 0.03 |
| pH | 7.29 ± 0.14 | 7.27 ± 0.17 | 7.21 ± 0.17 | 0.8 | 0.06 | 0.04 |
| pH* | | | | | | |
| <7 | 2 (6.9) | 3 (9.4) | 7 (20.6) | | | |
| ≥7 e <7.3 | 8 (27.6) | 9 (28.1) | 15 (44.1) | | | |
| ≥7.3 | 19 (65.5) | 20 (62.5) | 12 (35.3) | | | |
The positivity of one or more markers of β-cell autoimmunity, i.e., anti-Glutamic Acid Decarboxylase (GAD), anti insulin (IA), anti Zinc Transporter 8 (ZnT8), anti-Tyrosine Phosphatase 2 (IA2) autoantibodies confirmed the autoimmune etiology of diabetes. Patients with type 2 diabetes, monogenic or other types of diabetes, and other forms of secondary dysglycemia were excluded from the study.
For each T1DM patient we evaluated height with Harpender stadiometer to the nearest 0.1 cm, weight to the nearest 0.1 kg with a calibrated scale, BMI calculated as weight in kilograms divided by the square of height in meters. BMI-SDS was calculated on the reference values of age and gender. We also measured random plasma glucose, HbA1c, fasting C-peptide, total serum IgA levels, liver and kidney function, and serum markers of autoimmune diseases frequently associated to T1DM, i.e., celiac disease by means of anti-transglutaminase IgA (tTGAAb), and thyroid autoimmune disease by means of anti-thyroglobulin (TgAb) and thyroperoxidase (TPOAb) autoantibodies, and serum levels of FT4 and TSH.
Nasal swab for the diagnosis of COVID-19 infection was performed in all patients admitted during and after the lockdown period.
Our study analyzed anonymized and unidentifiable clinical data collected routinely at diabetes clinical onset in the medical records from three hospitals involved, and would not affect care of patients, therefore we consider ethical committee approval to be not mandatory.
## Statistical Analysis
Descriptive statistics were generated for the entire cohort and data were expressed as mean and standard deviation (SD), for continuous variables, and as absolute or relative frequencies for categorical variables. Non-parametric analysis (Mann–Whitney U-test) for continuous variables and the Chi square or Fisher’s exact test for categorical variables were used to measure differences between groups. P-values ≤0.05 were considered statistically significant, and all P-values were based on two tailed tests. Statistical analysis was performed using SPSS for Windows (SPSS Inc., Chicago, IL, USA).
## Results
We analyzed 99 patients with newly-diagnosed T1DM from January 1, 2018 to March 31, 2021. Clinical and metabolic data are reported in Table 1.
DKA was present in $44\%$ of newly-diagnosed patients. In particular we observed 10 cases ($32.3\%$) of DKA in Period A, 12 cases ($37.5\%$) in Period B and 22 cases ($61.1\%$) in Period C. The frequency of DKA at T1DM clinical onset was similar in Period A patients ($32.3\%$) and Period B patients ($37.5\%$), while it significantly increased in Period C patients ($61.1\%$) compared to Period B patients ($37.5\%$) ($$p \leq 0.03$$) (Table 1).
Briefly, a younger age at disease diagnosis was observed in Period B patients compared to Period A patients ($$p \leq 0.03$$) (Table 1). No significant differences were found in age at diagnosis between Period B patients and Period C patients.
Moreover, pH values were similar in Period A patients (7.29 ± 0.14) and in Period B patients (7.27 ± 0.17), while they were significantly lower in Period C patients (7.21 ± 0.17) compared to Period B patients ($$p \leq 0.04$$) (Table 1).
HbA1c levels and BMI-SDS were not different between the 3 calendar periods patients. Similarly, we did not found any difference in the frequency of celiac disease and/or thyroid autoimmune diseases in our patients. Nasal swab for COVID-19 was negative in all tested patients and their caregivers.
During this time period only a girl admitted with mild metabolic decompensation, obesity, and family history for type 2 diabetes mellitus and absence of β-cell autoimmunity markers was diagnosed as type 2 diabetes and was not included in this study.
## Discussion
In our retrospective study we evaluated the different frequencies of DKA at T1DM clinical diagnosis in a population of children and adolescents referred: 1 our Regional Reference Center for Pediatric Diabetes and 2 pediatric hospital in-ward in the Liguria Region. Our findings provide an additional picture of the clinical changes that occurred during the COVID-19 pandemic in Italy regarding T1DM, and confirm the increased frequency DKA in newly-diagnosed pediatric T1DM, as reported by others [18].
DKA still represents the worst clinical onset of T1DM and is an acute and life-threatening complication of the disease. DKA negatively affects the entire course of the disease, being associated with longer hospitalization, more difficult achievement of good metabolic control, reduced frequency and duration of the remission phase, even higher morbidity and mortality rate [9].
Several conditions have been evaluated as predisposing factors for DKA, namely, younger childhood, absence of first-degree relatives with T1DM, low socioeconomic status (meaning less availability of medical care), and poor awareness of diabetes symptoms [17]. The increased severity of and frequency of DKA at T1DM clinical onset reported during and after lockdown period could be the consequence of a substantial decrease in attendance at the Pediatric Emergency Units and general practitioners. The risk of delayed diagnosis of several potentially serious conditions, such as T1DM, has been reported in both the adult and pediatric age groups [24, 25]. A possible explanation for this delay in diagnosis could be the increased attention of healthcare system to the COVD-19 pandemic and related problems, together with the difficulties of travel and reduced access to medical care for children and their families linked to the lockdown itself. It is noteworthy that delayed diagnosis and scarce awareness are the most important factors for DKA severity.
An Italian pediatric multicenter cross-sectional study aimed at evaluating whether the initial phase of COVID-19 pandemic influenced the frequency and severity of DKA recorded data from 68 pediatric diabetes Centers belonging to the Italian Society for Pediatric Endocrinology and Diabetes [18]. The authors reported a $23\%$ reduction in T1DM cases in 2020 compared to 2019. However, among patients diagnosed with DKA, the frequency of severe DKA was higher in 2020 than in 2019 [18]. The authors concluded that the early clinical diagnosis of T1DM was severely affected by the pandemic, and that greater awareness is warranted in case of a “second wave”. It is worth noting that we did not observe a reduction in the number of new T1DM cases in our case series. Another study evaluated the frequency of DKA in 532 pediatric patients with newly-diagnosed T1DM using data from the German Diabetes Prospective Follow-up Registry, which covers more than $90\%$ of pediatric T1DM. DKA was observed in $44.7\%$ of cases and severe DKA in $19.4\%$ of cases, with a significantly higher frequency than in the 2 previous years [19]. The authors hypothesized multifactorial underlying causes, notably the reduction of medical services and the fear of approaching the health care systems, and socioeconomic factors [24, 25].
The incidence of T1DM in 2020 has been reported to follow the upward trend reported between 2011 and 2019, indicating the lack of short-term influence of the COVID-19 pandemic. Despite the stressful situation, social distancing during the lockdown reduced the exposure to common infections, which are a trigger for the development of DKA [20].
The UK Association of Children’s Diabetes Clinicians found a $51\%$ DKA rate at T1DM clinical diagnosis, higher than previously observed, and ascribed to fear of COVID-19, inability to access medical services, and misdiagnosed symptoms as the most important causative factors [21].
A Polish study reported a $12\%$ higher incidence of DKA in pediatric patients diagnosed withT1DM in 2020 compared to 2019, with a significant increase in DKA severity [22].
Type 1 and type 2 diabetes increase the negative impact of COVID-19 infection [26], therefore patients with T1DM deserve attention [27]. To this purpose, recommendations for COVID-19 in children and adolescents with T1DM have been published [28]. Despite the increased frequency of DKA at T1DM clinical onset, no association with other autoimmune diseases has been found [29, 30].
We are aware that our study confirms previous findings already reported, otherwise it strengthens the importance of a global awareness campaign aimed to avoid delayed diagnosis and the subsequent risk of DKA.
Our study has several limitations. First of all, while dealing with a very serious typical issue for pediatric diabetes, it confirms the results already reported. Moreover, the sample size is not so relevant to be considered representative of a population-based study. Further epidemiological studies that also take into account the impact of socioeconomic status on the severity of T1DM are mandatory.
## 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 the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin. Written informed consent was not obtained from the individual(s), or the minor(s)’ legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.
## Author Contributions
Gd’A conceptualized and wrote the paper. MB, ELDR, and ML collected data. NM, AG, GF, and RB followed the patients and collected data. MGC performed statistical analysis. MM revised and approved the manuscript. 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.
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|
---
title: Sex Differences in Depression and Sleep Disturbance as Inter-Related Risk Factors
of Diabetes
authors:
- Clara S. Li
- Rose Porta
- Shefali Chaudhary
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012092
doi: 10.3389/fcdhc.2022.914451
license: CC BY 4.0
---
# Sex Differences in Depression and Sleep Disturbance as Inter-Related Risk Factors of Diabetes
## Abstract
### Objectives
Previous studies identified depression and sleep disturbance as risk factors for diabetes. Sleep disturbance and depression are known to be inter-related. Further, women relative to men are more prone to depression. Here, we investigated how depression and sleep disturbance may jointly influence the risk of diabetes and the effects of sex on these influences.
### Methods
Using the data of 21,229 participants from the 2018 National Health Interview Survey, we performed multivariate logistic regression with diabetes diagnosis as the dependent variable, sex, self-reported frequency of weekly depression and nightly sleep duration, and their interactions with sex as independent variables, and age, race, income, body mass index and physical activity as covariates. We employed Bayesian and Akaike *Information criteria* to identify the best model, evaluated the accuracy of the model in predicting diabetes using receiver operating characteristic analysis, and computed the odds ratios of these risk factors.
### Results
In the two best models, depression frequency and sleep hours interact with sex in determining the diagnosis of diabetes, with higher depression frequency and nightly duration of sleep longer or shorter than 7 to 8 hours associated with higher likelihood of diabetes. The two models both predicted diabetes at an accuracy (area under the receiver operating characteristic curve) of 0.86. Further, these effects were stronger in men than in women at each depression and sleep level.
### Conclusions
Depression and sleep inter-relatedly rather than independently contributes to diabetes. Depression and sleep hours associate with diabetes more significantly in men than in women. The current findings indicate a sex-dependent relationship between depression, sleep disturbance and diabetes risk and add to a growing body of evidence linking mental and physical health.
## 1 Introduction
Over 34 million Americans, or more than 1 in 10, are diagnosed with diabetes, one of the most prevalent chronic diseases in the US, according to the Centers for Disease Control and Prevention, National Diabetes Statistics Report, 2020. Diabetes and related health problems have an enormous impact on US health care spending. Individuals with diabetes face an overall $60\%$ greater risk of early death than non-diabetics, underscoring the importance of identifying risk factors for targeted treatment [1].
In addition to diet and physical activity, sleep and mental health have been investigated as modifiable risk factors of diabetes [2]. Studies examining the relationship between sleep and diabetes associate both short (<6 h) and long (>8 h) sleep duration with increased diabetes risk as strongly as overweight and family history of diabetes [3]. Depression has also been associated with higher risk for diabetes. For instance, a meta-analysis reported that individuals with depression are $37\%$ more likely to develop type 2 diabetes than their depression-free counterparts [4]. However, sleep disturbance is one of the core manifestations of depression (5–7). Individuals with depression can demonstrate insomnia or hypersomnia along with other physical symptoms. Thus, whereas sleep and depression have largely been characterized as independent risk factors for diabetes, it is highly likely that depression and sleep disturbance conduce to diabetes in an inter-related manner.
It is known that women relative to men are more vulnerable to depression, as demonstrated in a meta-analysis [8]. The latter study showed that sex differences in the risk of depression emerge early in the life span with an odds ratio (OR) peaking at adolescence (OR=3.02) and declining afterwards and remaining stable in adulthood (OR=1.71 to 2.02). Thus, about twice as many female experience depression as male adults. Many studies have investigated the genetic [8], neurobiological [9, 10] and socio-psychological [11] mechanisms underlying sex differences in the prevalence of depression. Some have assessed sex differences in the effect of depression on diabetes. For instance, a study showed significant association between depression and diabetes in women (risk ratio or RR=1.06 to 4.19), but not in men (RR=0.43 to 1.10) [12]. Earlier studies have also noted significant association between disturbed sleep patterns and diabetes in women, but not in men [13, 14]. In contrast, a meta-analysis showed higher diabetes-depression association in men (RR=1.24-1.99) than in women (RR=0.95-1.67) [15]. Further, women have more sleep-related complaints, including insomnia, than men [16] and particularly so in the elderly [17]. Thus, it is likely that diabetes may relate to depression and sleep disturbance differently between the sexes [18].
Here, using a large public dataset we investigated how depression and sleep disturbance relate to diabetes and whether the relationships vary between women and men. Based on the literature, we hypothesized that greater severity of depression would be associated with higher likelihood of diabetes and that nightly sleep duration would be associated with diabetes in a U-shaped function, with short and long hours of sleep both associated with higher risk. Further, these relationships would be stronger in females than in males.
## 2.1 Study Design
This was a cross-sectional study using logistic models to assess factors defining diabetes risk.
## 2.2 Data Set and Variables
We used the Integrated Public Use Microdata Series (IPUMS) National Health Interview Survey Data, 2018, of 21,229 participants. The data were collected by random sampling of 35,000 U.S. households and random selection of one adult and one child (if any) from each household for interview. We restricted the sample to those 18 years or older as children and adolescents demonstrate sleep patterns and durations different from those of adults. We intended to generalize the current findings to non-institutionalized adult population of the United States.
The variables in our analytics included a binary response (dependent) variable indicating whether an individual has diabetes (the IPUMS data set did not distinguish between type I or II of diabetes). Independent variables included 1) the average number of self-reported hours of sleep per night over the prior month in four categories: ≤ 5, 6-7, 7-8, and >8 hours; 2) the frequency an individual experiencing depression over the prior year in three categories: never/rarely, monthly, and often (more often than monthly); and 3) sex: males and females. Covariates included age (in years), race (Caucasian, black/African American, Asian, or Native American/Alaska Native), income, body mass index (BMI), and level of physical activity (PA). Household income was categorized into low (annual family income < $35,000), middle (between $35,000 and $75,000), and high (> $75,000). The variable measuring PA was created using the Oncology Nursing *Society formula* for weekly leisure activity score, derived from the Godin Leisure-Time Exercise Questionnaire. The data for ‘light exercise’ were not available and thus substituted with the data on strength-related exercise. Table 1 shows the demographic and clinical characteristics of the sample.
**Table 1**
| Unnamed: 0 | Men (n = 9,860, 46.4%) | Men (n = 9,860, 46.4%).1 | Women (n = 11,369, 53.6%) | Women (n = 11,369, 53.6%).1 |
| --- | --- | --- | --- | --- |
| | Diabetes (+) | Diabetes (-) | Diabetes (+) | Diabetes (-) |
| N (%) | 198 (2.00%) | 9,662 (98.00%) | 209 (1.84%) | 11,160 (98.16%) |
| Age (yr, mean ± SD) | 65.3 ± 11.6 | 49.8 ± 17.9 | 65.4 ± 12.6 | 51.2 ± 18.4 |
| BMI (mean ± SD) | 32.4 ± 8.3 | 28.3 ± 5.6 | 33.1 ± 8.3 | 27.9 ± 6.8 |
| PA, median (range) | 1615 (45-1615) | 796 (29-1615) | 1615 (68-1615) | 1145 (31-1615) |
| Race | Race | Race | Race | Race |
| Caucasian | 153 (1.89%) | 7,939 (98.11%) | 131 (1.43%) | 8,992 (98.56%) |
| Asian | 10 (1.76%) | 558 (98.24%) | 13 (2.16%) | 588 (97.84%) |
| African American | 25 (2.36%) | 1,035 (97.64%) | 59 (3.98%) | 1,423 (96.02%) |
| Native American | 10 (7.14%) | 130 (92.86%) | 6 (3.68%) | 157 (96.32%) |
| Income | Income | Income | Income | Income |
| Low | 105 (3.68%) | 2,744 (96.31%) | 149 (3.61%) | 3,977 (96.39%) |
| Middle | 63 (2.16%) | 2,854 (97.84%) | 36 (1.14%) | 3,120 (98.86%) |
| High | 30 (0.73%) | 4,064 (99.27%) | 24 (0.59%) | 4,063 (99.41%) |
| Depression | Depression | Depression | Depression | Depression |
| Rarely/never | 133 (1.57%) | 8,342 (98.43%) | 138 (1.50%) | 9,073 (98.50%) |
| Monthly | 17 (3.33%) | 493 (96.67%) | 17 (1.96%) | 849 (98.04%) |
| Often | 48 (5.48%) | 827 (94.5%) | 54 (4.18%) | 1,238 (95.82%) |
| Sleep hours/night | Sleep hours/night | Sleep hours/night | Sleep hours/night | Sleep hours/night |
| ≤5 | 31 (3.33%) | 900 (96.67%) | 40 (3.41%) | 1,132 (96.59%) |
| 6-7 | 50 (2.19%) | 2,237 (97.81%) | 49 (1.88%) | 2,554 (98.12%) |
| 7-8 | 76 (1.30%) | 5,784 (98.70%) | 84 (1.27%) | 6,516 (98.73%) |
| >8 | 41 (5.24%) | 741 (94.76%) | 36 (3.62%) | 958 (96.38%) |
## 2.3.1 Multivariate Logistic Regression
We used binomial logistic regression to predict binary outcome (presence/absence) of diabetes using an optimized model [19]. The variables of interest included sex, depression frequency (depression), and nightly hours of sleep (sleep), and the covariates included age, race, household income (income), BMI, and PA.
We first performed a multivariate logistic regression to include all predictors (sex, frequency of depression, and nightly duration of sleep) and covariates (age, race, BMI, PA, and income) in the model. We checked for the statistical significance of the model; further, the three predictors and five covariates showed a significant relationship with diabetes and thus were all included in the model (see Results).
Next, we examined the continuous variables (age, BMI, and PA) for linearity in relation to the logit of the outcome. The results showed that age, BMI and PA were all linearly related to diabetes in logit scale (Figure 1).
**Figure 1:** *Scatter plots with locally weighted scatter plot smoothing (LOWESS) of the relationship between probability of diabetes (in logit scale) and continuous covariates (age, BMI, PA, respectively). All relationships were linear.*
## 2.3.2 Identification of Prediction Model With the Best Fit
The findings of multivariate logistic regression showed that all 3 predictors and 5 covariates showed significant relationships with diabetes (Table 2). The goal of the study was to investigate how depression frequency, sleep duration and sex may interact in the contribution to diabetes. Thus, we employed Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) to determine which of the following models showed the best fit:
**Table 2**
| Variable | Coefficient (SE) | Odds ratio (SE) | p-value |
| --- | --- | --- | --- |
| Sleep (hours/night; 7-8 hours/night as ref.) | Sleep (hours/night; 7-8 hours/night as ref.) | Sleep (hours/night; 7-8 hours/night as ref.) | Sleep (hours/night; 7-8 hours/night as ref.) |
| ≤5 | 0.49 (0.15) | 1.6 (0.25) | 0.001 |
| 6-7 | 0.35 (0.13) | 1.4 (0.19) | 0.008 |
| >8 | 0.49 (0.15) | 1.6 (0.24) | 0.001 |
| Depression frequency (Never/Rarely as ref.) | Depression frequency (Never/Rarely as ref.) | Depression frequency (Never/Rarely as ref.) | Depression frequency (Never/Rarely as ref.) |
| Monthly | 0.59 (0.19) | 1.8 (0.35) | 0.002 |
| Often | 0.84 (0.13) | 2.3 (0.30) | <0.001 |
| Sex (Men as ref.) | -0.47 (0.11) | 0.6 (0.07) | <0.001 |
| Age | 0.05 (0.004) | 1.1 (0.004) | <0.001 |
| BMI | 0.08 (0.006) | 1.1 (0.007) | <0.001 |
| PA | 0.0007 (0.001) | 1.0 (0.0001) | <0.001 |
| Race (Caucasian as ref.) | Race (Caucasian as ref.) | Race (Caucasian as ref.) | Race (Caucasian as ref.) |
| Asian | 0.88 (0.23) | 2.4 (0.55) | <0.001 |
| African American | 0.59 (0.13) | 1.8 (0.24) | <0.001 |
| Native American | 1.18 (0.28) | 3.2 (0.91) | <0.001 |
| Income (Low as ref.) | Income (Low as ref.) | Income (Low as ref.) | Income (Low as ref.) |
| Medium | -0.53 (0.13) | 0.59 (0.07) | <0.001 |
| High | -0.95 (0.16) | 0.37 (0.06) | <0.001 |
Model fit according to BIC and AIC was assessed using Hosmer and Lemeshow’s goodness-of-fit test [20].
## 2.3.3 Consideration of Influential Observations
Logistic regression is highly sensitive to the presence of influential observations (outliers). We assessed potential outliers and their impact on the model. We computed model’s standardized Pearson’s residuals, adjusted for the number of observations that shared the same covariate pattern, to detect potential outliers (i.e., data points with large deviations between observed and fitted values) [21]. We employed *Pregibon delta* beta statistics and Pregibon leverage to evaluate the influence of a data point on estimated coefficients of the model [22]. Pregibon delta beta measures the extent to which inclusion or exclusion of an observation sharing the same covariate pattern changes the beta estimates (coefficient). It is presented in the form of standardized difference in betas, with larger values indicating larger influence on model estimates. Pregibon leverage computes the diagonal elements of the hat matrix adjusted for the number of observations that share the same covariate pattern. Large values indicate covariate patterns farther from the average and a larger effect on the fitted model.
## 2.3.4 Discriminatory Power of the Model
We evaluated the discriminatory power of the best model using Receiver Operating Characteristic (ROC) analysis [23]. We showed the sensitivity and specificity vs. probability cut-off points and the ROC curve in a plot of sensitivity vs. (1 – specificity).
## 3.1 Multivariate Logistic Regression
Table 2 shows the results of multivariate logistic regression. For nightly hours of sleep, we used the category of 7-8 hours as the baseline; for depression frequency, we used “never/rarely” as the baseline; for sex, we used men as the baseline; for race, we used Caucasian as baseline; and, finally, for income, we used “low” income as the baseline. Depression reported at a higher frequency and sleep duration either longer or shorter than 7-8 hours both significantly contributed to the odds of a diagnosis of diabetes.
## 3.2 Model Fit According to BIC and AIC
Although the BIC and AIC values were close across models, the two best models were Model 1 and 2, according to both criteria (Table 3):
**Table 3**
| Logistic models | AIC | BIC |
| --- | --- | --- |
| Model 1* | 3308.09 | 3443.46 |
| Model 2* | 3311.56 | 3454.89 |
| Model 3 | 3314.97 | 3482.2 |
| Model 4 | 3326.73 | 3581.55 |
| Model 5 | 3326.73 | 3581.55 |
| Model 6 | 3326.73 | 3581.15 |
| Model 7 | 3326.73 | 3581.55 |
| Model 8 | 3326.73 | 3581.55 |
Hosmer and Lemeshow’s goodness of fit revealed non-significant difference between predicted and observed frequencies (Model 1: $$p \leq 0.792$$; Model 2: $$p \leq 0.342$$) with Hosmer-Lemeshow χ2 = 4.68 (Model 1) and 9.00 (Model 2), respectively, indicating a good fit for both models.
## 3.3 Influential Observations
We show the scatter plot of standardized Pearson’s residual versus Pregibon leverage in Supplementary Figure S1. The scatter plot combined three diagnostic values and revealed 10 data points to be potential outliers according to all three statistics (residuals >2 AND delta beta >0.01 AND leverage >0.01). We thus repeated logistic regression excluding these 10 data points. As shown in Supplementary Table S1, the exclusion of these subjects did not materially affect the variable coefficients; thus, we retained and presented findings based on the entire sample.
## 3.4 Discriminatory Power of Model 1 and 2
In ROC analysis both model 1 and 2 demonstrated excellent discriminatory power distinguishing the presence vs. absence of diabetes, with an area under curve (AUC) = 0.86 (Figure 2). In the plot of sensitivity and specificity versus probability cut-offs, the sensitivity and specificity curves crossed at a point close to the vertical axis, indicating adequate sensitivity and specificity of the fitted models [23].
**Figure 2:** *Sensitivity vs. specificity plot (left panels) and receiver operating characteristic (ROC) curve (right panels) of (A) model 1 and (B) model 2.*
## 3.5 The Effects of Depression and Sleep on the Probability of Diabetes and Their Interaction With Sex
To visualize the effects of depression frequency and nightly sleep duration on the probability of diabetes, we plotted predictive margins of response (outcome: diabetes) versus predictor in Figures 3A, B. Overall, as revealed by logistic regression (Table 2), more frequent experience of depression was positively related to diabetes risk. On the other hand, nightly sleep hours were associated with diabetes risk in a U function; duration more or less than 7-8 hours was both associated with a higher risk.
**Figure 3:** *Predicted margins of diabetes with 95% confidence intervals with respect to (A) depression frequency, (B) nightly sleep hours, shown separately for males and females, and (C) depression at each level of sleep. Men relative to women showed stronger associations between depression frequency and nightly sleep hours with the probability of diabetes. Further, reduced/increased sleep hours in synergy with higher frequency of depression predicts higher probability of diabetes.*
To examine whether depression frequency and nightly sleep duration may interact to determine diabetes risk, we obtained predictive margins of the interaction effect on diabetes probability using Model 1 (Figure 3C, Supplementary Table S2). For each level of sleep hours, diabetes probability increases with more frequent depression in the same manner. Sleep levels ≤5 hours and >8 hours shared similar, highest probability of diabetes at each level of depression, followed by the probability associated with 6-7 and 7-8 hours of sleep, the latter with the least probability of diabetes.
Further, the effects of depression frequency and nightly hours of sleep interacted with sex in predicting diabetes. The effects of depression and sleep disturbance contributed to diabetes more significantly in men than in women (Table 4). Specifically, at each level of depression, diabetes probability was higher in men than in women: rarely/never depression [coefficient (SE), odds-ratio (SE), p-value in women with men as reference: -0.35 (0.13), 0.70 (0.09), 0.006], monthly depression [-0.86 (0.37), 0.42 (0.15), 0.019], “often” depression [-0.68 (0.21), 0.50 (0.11), 0.001].
**Table 4**
| Variable | Coefficient (SE) | Odds ratio (SE) | p-value |
| --- | --- | --- | --- |
| Model 1: Depression × sex (rarely/never depression with men as ref.) | Model 1: Depression × sex (rarely/never depression with men as ref.) | Model 1: Depression × sex (rarely/never depression with men as ref.) | Model 1: Depression × sex (rarely/never depression with men as ref.) |
| D (rarely/never)s (W) | -0.35 (0.13) | 0.70 (0.09) | 0.006 |
| D (monthly)s (M) | 0.87 (0.28) | 2.39 (0.67) | 0.002 |
| D (monthly)s (W) | 0.01 (0.27) | 1.01 (0.27) | 0.967 |
| D (often)s (M) | 1.02 (0.18) | 2.7 (0.51) | <0.001 |
| D (often)s (W) | 0.34 (0.18) | 1.41 (0.25) | 0.054 |
| Model 2: Sleep × sex (7-8 hours of sleep of men as ref.) | Model 2: Sleep × sex (7-8 hours of sleep of men as ref.) | Model 2: Sleep × sex (7-8 hours of sleep of men as ref.) | Model 2: Sleep × sex (7-8 hours of sleep of men as ref.) |
| S (≤5 h)s (M) | 0.59 (0.23) | 1.82 (0.41) | 0.008 |
| S (≤5 h)s (W) | 0.08 (0.21) | 1.07 (0.23) | 0.720 |
| S (5-6 h)s (M) | 0.51 (0.19) | 1.67 (0.32) | 0.007 |
| S (5-6 h)s (W) | -0.11 (0.19) | 0.89 (0.17) | 0.561 |
| S (7-8 h)s (W) | -0.32 (0.16) | 0.73 (0.12) | 0.053 |
| S (>8 h)s (M) | 0.61 (0.21) | 1.84 (0.38) | 0.004 |
| S (>8 h)s (W) | 0.05 (0.22) | 1.05 (0.23) | 0.811 |
Similarly, at each level of sleep hours, probability of diabetes was greater in men compared to women: sleep < 5 hours [coefficient (SE), odds-ratio (SE), p-value: -0.52 (0.25), 0.59 (0.15), 0.042], 6-7 hours [-0.62 (0.21), 0.53 (0.11), 0.003)], 7-8 hours [-0.32 (0.16), 0.73 (0.12), 0.053], and >8 hours [-0.55 (0.24), 0.57 (0.14), 0.023].
## 4 Discussion
We assessed how sleep duration and depression frequency related to diabetes diagnosis and how these relationships differed between men and women. Both short (< 7 hours/night) and long (> 8 hours/night) sleep durations were associated with a higher probability of a diagnosis of diabetes. Confirming our first hypothesis, this finding aligns with prior studies documenting a U-shaped relationship between sleep duration and diabetes risk [3, 18]. Likewise, experiencing depression daily or weekly was associated with higher probability of a diagnosis of diabetes. Importantly, these relationships were consistent with the symptoms of insomnia and hypersomnia in depression and were stronger in men than in women. Further, the addition of an interaction term of depression × sleep or depression × sleep × sex did not result in better model fit. These findings together suggest an inter-related mechanism associating both depression and sleep disturbance to diabetes. Further, we observed a stronger association in men than in women between these risk factors and diabetes, contrary to our hypothesis. Although the latter findings were largely consistent with the literature, the physiological and psychological mechanisms underlying the sex differences remain to be investigated.
## 4.1 Sleep Disturbance and Depression as Risk Factors of Diabetes
We found that, relative to 7 to 8 hours of nightly sleep, both shorter (< 5 hours) and longer (> 8 hours) sleep were associated with an odds ratio of 1.6 of having a diagnosis of diabetes. A systematic review of 36 studies showed that sleep disturbances significantly impact diabetes risk, with the effects comparable to family history of diabetes and overweight and exceeding the risk associated with physical inactivity [1]. A meta-analysis of 13 independent cohorts with a total of 107,756 male and female participants and 3,586 cases of type 2 diabetes reported that both short and long sleep duration was associated with the likelihood of diabetes, although the underlying mechanisms may be different [3]. There is a relatively strong consensus in the literature that short sleep duration (< 6 hours) is a significant risk factor of diabetes (1, 24–26). Insufficient sleep and poor sleep quality may contribute to diabetes and/or hamper treatment via physiologic mechanisms such as insulin resistance, decreased leptin/increased ghrelin, and tissue inflammation as well as behavioral mechanisms, including elevated food intake, smoking, drinking and sedentary behavior, all disposing individuals to both obesity and diabetes [27]. Indeed, sleep as a crucial survival-related behavior, is regulated through the hypothalamic circuits, and investigators have specifically linked shortened or disturbed sleep to higher cortisol levels, leading to both increased glucose production, decreased glucose utilization, and eventually insulin resistance [24, 28]. Short sleep may also increase levels of the inflammatory marker C-reactive protein, which inhibits physiological function of leptin and contributes to weight gain and impaired glycemic control [3, 28]. Although less conclusive, a few studies have also shown excessively long sleep (>8 hours) as a risk factor for diabetes [4, 18]. In a two-year prospective study individuals whose sleep duration increases ≥2 hours per night are at an enhanced risk of diabetes [29]. Prolonged sleep elevates inflammatory markers similarly to shortened sleep, resulting in dysfunctional regulation of leptin and food intake [18]. As sleep disturbance is associated with poor glycemic control, optimizing sleep may improve overall diabetes management [30].
On the other hand, some have cautioned that the relationship between sleep and diabetes may be accounted for by other confounding or risk factors. Indeed, both shorter and longer than normal sleep has been documented as core symptoms of depression (31–35). Higher rates of depression have been consistently found among those diagnosed with diabetes and vice-versa, indicating a bidirectional etiological relationship [32, 36, 37]. One prospective study of U.S. military members in the Millenium Cohort Study found that, after controlling for baseline covariates, the relationship between depression and diabetes appeared to be insignificant [24]. However, distinct demographics and life experiences of enlisted servicemen might have contributed to these contrasting results. Here, we found that both altered sleep duration and depression were associated with greater odds of a diabetes diagnosis. In particular, the regression models with interaction between sleep and depression did not provide a better fit, suggesting that sleep hours and depression frequency contributed to diabetes risk in a concerted manner. Further, as discussed in detail in the next section, although men relative to women demonstrated a more significant association of both depression and altered sleep hours with the diagnosis of diabetes, both showed a similar pattern of the two risk factors in relation to diabetes. The latter findings suggest an inter-related mechanism by which depression and sleep disturbance contribute to diabetes.
## 4.2 Sex Differences in Sleep Disturbance and Depression as Risk Factors of Diabetes
The effect of sleep on diabetes risk has also been found to vary between sexes. A systematic search of 10 studies found a higher diabetes risk for men sleeping less than 5-6 hours per night, as compared to women [3]. The afore-mentioned study of U.S. military service members reported that, whereas non-depressed women were more likely to be diagnosed with diabetes than non-depressed men, this discrepancy became insignificant when both women and men had depression [24]. Further, Hein et al., reported that among adults with major depression, only male sex was a significant risk factor for diabetes [32]. Thus, the current finding that aberrant sleep duration – whether too short or long – might impact the risk for diabetes in men more significantly than in women is consistent with this literature. On the other hand, because women as compared to men were more likely to experience and report depression [8], we hypothesized a stronger association between depression and diabetes in women than in men. One possibility for the current finding is a ceiling effect of depression masking the relationship between depression and diabetes in women. However, our data did not support this hypothesis, as women did not appear to show more severe depression in the current data set.
The exact mechanism behind this sex-specific association is not clear. But it should be noted that disturbed sleep has been shown to interfere with testosterone production in men with type 2 diabetes. Given the benefits of testosterone on glucose metabolism, deficient sleep might conceivably lead to poorer glycemic control [38]. Further, given the nature of self-report in the current data, the relationship between sex, depression, and diabetes remains to be investigated [37, 39]. Studies with objective measures of glycemic control and sleep quality and duration would be particularly useful in addressing this question.
## 4.3 Limitations and Conclusions
A number of limitations should be considered. First, the data comprised self-reported measures, including sleep duration and subjective experience of depression. Previous studies have reported sex differences in self-reported depressive symptoms, with women tending to report more depressive symptoms than men [40, 41], which may introduce a bias to the current findings. In addition, as depression severity data were not available, we used symptom occurrence rather than severity across the analyses. As no medical measure of the severity of diabetes was available, the status of diabetes was investigated as a categorical variable. Further, the prevalence rates of ~$2\%$ of self-reported diabetes in the current sample is exceedingly low, as compared to the $13\%$ noted for the US populations 18 years of older [42]. The quality of the data set thus presents a major limitation and the current findings would need to be replicated. Second, these were cross-sectional, observational data; thus, we cannot draw any causal inferences from the current findings. Future studies in a controlled and longitudinal setting ought to examine the direction of the relationship between diabetes, sleep, and depression and how the relationships vary between males and females.
To conclude, the study supports a potential sex-related association between depression frequency and nightly sleep duration and diabetes risk and adds to rapidly growing literature linking mental and physical health. Our findings that both short and long sleep and depression elevate the odds of diabetes support an inter-related rather than independent mechanism of depression and sleep disturbance as risk factors of diabetes. This diabetes risk appears to be stronger in men than in women, suggesting the need of more research to understand sex-specific physiological and psychological processes underlying the care and management of those with or at risk of diabetes.
## Data Availability Statement
Publicly available datasets were analyzed in this study. This data can be found here: https://nhis.ipums.org/nhis/.
## 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
CL: study design, data curation, statistical analysis, manuscript writing; RP: study design, data curation, statistical analysis, manuscript writing, SC: statistical analysis, manuscript editing. All authors contributed to the article and approved the submitted version.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
## Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcdhc.2022.914451/full#supplementary-material
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|
---
title: 'Complementarity of Digital Health and Peer Support: “This Is What’s Coming”'
authors:
- Patrick Y. Tang
- Janet Duni
- Malinda M. Peeples
- Sarah D. Kowitt
- Nivedita L. Bhushan
- Rebeccah L. Sokol
- Edwin B. Fisher
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2021
pmcid: PMC10012094
doi: 10.3389/fcdhc.2021.646963
license: CC BY 4.0
---
# Complementarity of Digital Health and Peer Support: “This Is What’s Coming”
## Abstract
### Purpose
This study examined integration of peer support and a Food and Drug Administration-cleared, diabetes management app (DMA) in diabetes self-management support as a scalable model for those with type 2 diabetes mellitus (T2DM).
### Methods
Two lay health Coaches delivered telephone-based self-management support to adults ($$n = 43$$) with T2DM recruited through a primary group practice. Those eligible were offered no-cost access to DMA for the entire 6-month study. Coaches introduced DMA and contacted individuals by phone and text with frequency dependent on participant needs/preferences. DMA supported monitoring of blood glucose, carbohydrate intake, and medication use, as well as messaging personalized to participants’ medication regimens. Clinical data were extracted from DMA, electronic medical records, and Coaches’ records. Structured interviews of 12 participants, 2 Coaches, and 5 project staff were analyzed using deductive pre-identified codes (regarding adoptability, patterns of use, value added, complementarity, and sustainability) utilizing standard procedures for qualitative analysis.
### Results
Of the 43 participants, 38 ($88.4\%$) enrolled in DMA. *In* general, participants used both DMA and lay health coaches, averaging 144.14 DMA entries (structured, e.g., medications, and free form, e.g., “ate at a restaurant” and “stressed”) and 5.86 coach contacts over the 6-month intervention. Correlation between DMA entries and coach contacts ($r = .613$, $p \leq 0.001$) was consistent with complementarity as were participants’ and coaches’ observations that (a) DMA facilitated recognition of patterns and provided reminders and suggestions to achieve self-management plans, whereas (b) coaching provided motivation and addressed challenges that emerged. Mean hemoglobin A1c (A1c) declined from $9.93\%$ to $8.86\%$ ($p \leq 0.001$), with no pattern of coaching or DMA use significantly related to reductions. Staff identified resources to coordinate coach/DMA interventions as a major sustainability challenge.
### Conclusions
DMA and peer support for diabetes management are compatible and complementary. Additional practice integration research is needed for adoption and scale-up.
## Introduction
Integration is important across many dimensions of diabetes care, such as individual self-management behaviors according to the ADCES7 Self-Care Behaviors™ [1] as well as across varied sources of care [2]. This project developed and tested the integration of two sources of support: lay health coaching, idiomatically “soft touch,” and digital health support—“high tech”—for individuals with type 2 diabetes mellitus (T2DM).
Systematic reviews have documented the benefits of peer support for people with diabetes (primarily T2DM) (3–8), as well as many other health concerns [9, 10]. Given effectiveness, the challenge for peer support in diabetes care is to develop models that are feasible, scalable, and cost-effective in real-world settings. At the same time, digital health platforms such as smartphone apps have shown effectiveness in diabetes prevention (11–13) and management (14–17), including a review of reviews that found average reductions of 0.5 points in glycated hemoglobin (A1c), mostly among those with T2DM [18]. Digital health solutions support individuals in their daily self-management activities and provide valuable patient-generated data to providers to enhance shared decision-making regarding treatment decisions [19]. The convenience and low cost of digital health appeal to people that might otherwise face financial, logistical, or communication barriers when participating in face-to-face interventions. The challenges for digital health include engaging and retaining users, and addressing complex needs (12, 20–22).
Evidence from these two areas of study point to the unrealized potential of well-integrated peer support and digital health. A systematic review of web-based strategies in diabetes self-management [20] noted that interventions incorporating peer support were more likely to be effective than those that did not. Furthermore, a meta-analysis found that diabetes mobile phone apps reduced A1c among individuals with type 2 diabetes and the effect size was associated with the amount of feedback from healthcare professionals [23], suggesting that apps can be more effective when there is someone to provide live support. Peer support can provide grounding for digital apps, promoting uptake and continued use by incorporating those tools into routine contacts. Reciprocally, digital health may provide data to prompt and guide the work of peer supporters, while also reducing the burden of routine tasks such as monitoring key behaviors and indicators like blood glucose. Indeed, a 2020 review of reviews and gap analysis examining in-person and technology mediated peer support [8] identified as a gap exploration of “technology-mediated peer support, beyond voice-based telephones … [and including] … technology-mediated peer support modalities (i.e., video conferencing, SMS text message, social media).” This study focused on a diabetes management app (DMA) for T2DM developed by WellDoc, Inc., a collaborator in the present project. The DMA had been cleared by the Food and Drug Administration. Using individuals’ own data (structured and free form, e.g., medications, blood glucose readings, carbohydrates, exercise, or “ate at a restaurant,” “stressed,” and “felt sick”), the DMA responds with adaptive messaging that is personalized to individuals’ medication regimens, and aligned with standards of care of the American Diabetes Association and the Association of Diabetes Care & Education Specialists (ADCES, formerly the American Association of Diabetes Educators) [24, 25]. It also offers messaging and sharing clinical reports with individuals’ providers to enhance patient-provider communications and shared decision-making. Randomized controlled trials of the DMA software demonstrated significant reductions in A1c of 1.9 and 2.0 points in intervention groups [26, 27]. At the time of the present project, the DMA was available by prescription but provided free to participants in the study. It is now also available without prescription.
The present project developed and assessed the offer of peer support and the DMA for self-management education and support for adults with T2DM in a primary care group practice setting. Drawing from the expanding literatures in implementation and dissemination research (28–30), key points of evaluation included (a) patterns of use, including how much participants would utilize each of peer support and DMA, (b) clinical changes and participants’ as well as staff observations regarding the value added by the combination, (c) participant and staff observations as well as patterns of use regarding the complementarity of peer support and DMA, and (d) staff comments regarding the sustainability of the combination within their clinical setting.
Complementarity has many meanings including in common language in which, say, one item of clothing is said to complement, that is, go well with, another. In behavioral economics [31], complementarity refers to a positive association between consumption of two commodities, e.g., bread and butter, in response to price changes for either. As price changes alter the consumption of one, the consumption of the other will change in the same direction. That is, reducing the price of bread leads to increased consumption of not only bread but also butter. Both of these share the sense that things “go together.” *It is* in that sense that we consider complementarity here, that is that use of the DMA and engagement with the Coaches might tend to go together, in contrast to one replacing the other, “substitutability” in behavioral economics terms.
## Research Setting
In 2015, Peers for Progress in the Gillings School of Global Public Health at the University of North Carolina-Chapel Hill (UNC-CH), WellDoc, Inc., and Vanguard Medical Group in New Jersey (Vanguard) joined to pursue this project. Vanguard’s eight primary care sites are recognized patient-centered medical homes with care coordination processes to manage high-risk individuals, electronic medical records (EMRs), disease registries, quality metrics tracking, patient portal, and online appointment scheduling. Vanguard’s clinical staff were involved throughout the project to ensure feasibility. The study was approved by the Institutional Review Board at UNC-CH. Vanguard, WellDoc, and UNC-CH entered into a data use agreement to facilitate data sharing and analysis.
## Formative Evaluation and Intervention Development
UNC-CH researchers conducted a formative evaluation in June–July 2015, following standard health education methods [32], to determine the feasibility of combining peer support and digital health. Telephone interviews were conducted with 4 care coordinators, 1 physician’s assistant, and 4 patients with T2DM at Vanguard [33]. Vanguard patients each received a $30 gift card for their participation in the interviews. They ranged in age from 31 to 73.
As previously described [33], participants noted potential value in a diabetes self-management intervention that employed some combination of DMA and live peer support. Contrary to possible expectations that individuals might object to having their choices and activities guided by an automated or artificial system, no one expressed such objections to using the DMA. Clinical staff saw value in the proposed intervention but expressed concerns about the potential of lay health coaches (Coaches) promoting inaccurate medical information and the lack of coordination with clinical care. The concerns of clinical staff were addressed by employing a certified diabetes educator to assist in training the Coaches, conducting weekly supervisory meetings with the entire study team, and coordinating the activity of Coaches through the nurse care coordinator (JD).
Following the formative evaluation, the study team developed the intervention according to the following principles: [1] the DMA would assist individuals with daily self-management and provide regular encouragement and praise, [2] the Coaches would work with participants to provide social support and assist with problem-solving and overcoming challenges, [3] the Coaches would also encourage participants to utilize a variety of DMA features, [4] participants were free to determine the extent to which they utilized health coaching and the DMA, and [5] data entered by participants into the DMA would prompt timely follow-up from the coaches.
## Recruitment and Training of Lay Health Coaches
Two lay health coaches (Coaches) were recruited through the department of nutrition at a local university. Both had bachelor’s degrees but had not been previously employed in healthcare. The Coaches were employed through UNC-CH with paid, part-time positions (15–20 h per week).
The initial two-day training (18h) was held onsite at Vanguard, covering basic diabetes education, self-management support, effective peer support, communication skills, the DMA, research ethics, coaching protocol, and study documentation. The training was led by a certified diabetes educator and nurse coordinator from Vanguard, a DMA trainer from WellDoc, and trainers from Peers for Progress. The Coaches each received a smartphone on a prepaid plan with a local number to carry out the intervention.
## Participant Recruitment and DMA Onboarding
Vanguard identified 203 potential participants from four of its eight primary care sites according to the following criteria: T2DM, 30–75 years of age, and at least one A1c value ≥ $7.5\%$ in the previous 12 months. Although identified by A1c ≥ $7.5\%$, individuals were contacted in descending order of A1c values in order to recruit a sample of greatest clinical need. During recruitment, Coaches confirmed that participants were fluent in English and had regular access to a web-enabled device (smartphone or computer).
Each potential participant received a mailing containing a signed invitation letter from their primary care provider, HIPAA and consent forms, and a stamped return envelope. Coaches commenced telephone recruitment 2 weeks after the mailings were sent out unless individuals had opted out. Coaches attempted a total of seven contacts, including voicemails and a personalized follow-up letter, before designating individuals unable to be reached. Upon successful contact, Coaches introduced the program, obtained verbal HIPAA authorization and consent, and conducted a health assessment. Participants were asked to return signed HIPAA and consent forms using the stamped return envelopes. Following successful recruitment, Coaches set up a second call to assist with DMA installation and registration with DMA customer care for further technical support.
## Coach Intervention
Beginning with the third call, Coaches began to build rapport with the participants and initiated substantive discussions around self-management. They inquired about the participant’s general health status, answered questions about using the DMA, and assisted participants with entering data into the DMA. Coaches probed emotional status using items from the Diabetes Distress Scale [34, 35] and reviewed individuals’ diabetes self-management, such as their familiarity with A1c and fasting blood glucose measures, self-monitoring blood glucose, medication adherence, diet and exercise, and adherence to recommended clinic visits and examinations. If neither the participant nor DMA data suggested a self-management target, Coaches reviewed the ADCES7 Self-Care Behaviors™ with participants to help set goals. Coaches encouraged the participant to attend diabetes self-management education if they had not already done so, and checked that the participant had a glucometer, testing strips, and prescribed medications.
Frequency of ongoing coaching calls ranged from biweekly to monthly according to participants’ needs and preferences. Each call began with follow-up on matters raised in the previous call as Coaches tailored discussions to individual participants. A protocol guided notification of the nurse coordinator in response to urgent medical issues or emotional distress. The intervention lasted up to 6 months, with individuals participating for 5–6 months depending on latency of enrollment.
The Coaches conducted their telephone calls from the care coordination office at Vanguard. This colocation facilitated back-up and coordination with the clinical team. Additionally, Vanguard, WellDoc, and UNC-CH staff held weekly calls with Coaches to address emergent issues, hot cases, and questions around protocols. This process led to improvements such as streamlined DMA onboarding, better incorporation of diabetes care standards, development of protocols for addressing urgent needs and strategies for motivating participants after the New Year, and guidance on generating discussions around participants’ own DMA data.
## Fidelity of Implementation
Fidelity of implementation was monitored and encouraged in several ways. As noted above, staff (JD, EF, MP, and PT) met weekly with the Coaches to review progress and discuss any adjustments to the protocol as well as emergent clinical concerns. Coaches followed detailed protocols and scripts for initial contact of potential participants including, e.g., number and timing of recruitment calls. As noted in Results, Coaches succeeded in onboarding to the DMA 38 of the 43 participants for whom this was intended ($88.4\%$). Per protocol, however, contacts with the Coaches and DMA entries were left free to vary in order to assess participants’ utilization of these. Project staff monitored coaches’ timely submission of contact notes.
## Evaluation
Evaluation drew from established approaches to implementation and dissemination research including Glasgow’s RE-AIM model, Proctor’s implementation model, and the PRISM model (28–30). It also followed a triangulation design [36] that combined several investigators and interviewers (investigator triangulation) as well as both qualitative and quantitative data (methodological triangulation) [37] to address research questions comprehensively and to validate findings generated by each approach.
## Qualitative Data Collection and Analysis
After the conclusion of the intervention, semi-structured interviews were conducted with 12 participants, 2 Coaches, and 5 project staff from UNC-CH, Vanguard, and WellDoc. Twelve participants, representing different patterns of participation in the program (both coaching and DMA, DMA with little coaching, and coaching with little DMA use), were recruited to participate in these interviews through their Coaches. Vanguard patients each received a $30 gift card for participating in the interviews.
UNC-CH graduate research assistants conducted the interviews by phone or in-person. Interviews probed strengths and challenges of the program, and solicited recommendations for dissemination, scale-up, and tailoring [28, 38]. Interviews lasted 20–70 min, and were audiotaped, transcribed, and imported into Atlas.ti for analysis. Interviews were coded by three graduate research assistants according to deductive pre-identified codes and inductive emergent themes utilizing standard procedures for qualitative analysis [39].
## Quantitative Data and Analysis
Baseline data included age, whether prescribed insulin, most recent A1c, blood pressure, and lipid values from EMR up to 10 months prior to initiation of the study. End-of-treatment values were the last available, up to 1 month following intervention termination. Data from DMA included blood glucose readings, carbohydrate counts, medication adherence, hypoglycemic events, and free text notes/diaries. Coaches’ records included dates of contacts and topics discussed.
Statistical methods used to describe the sample, patterns of use, and value added included descriptive statistics, and t-tests to assess differences between those who participated and those who were eligible but did not participate. Other differences are detailed in the Results, such as participation by sex. Principal components analysis examined the factorial structure among the number of different types of entries to the DMA. Analyses also included correlations among participation variables and between those variables and clinical indicators, and within-group t-tests of changes in clinical indicators.
## Results
Following methodological triangulation’s [36] inclusion of both qualitative and quantitative data [37], the quantitative and qualitative data are integrated in answering each of the research questions. Table 1 includes illustrative quotations from the interviews, organized according to the key sections of the Results: (a) patterns of use, (b) the value added by the coach and DMA support, (c) the complementarity of coach support and DMA, and (d) sustainability within the clinical setting. Each section of the Results draws also from the interview results, including illustrative quotations from Table 1, indicated as from participants—“PQ”; Coaches—“CQ”; or staff—“SQ” and numbered consecutively.
**Table 1**
| Category | Quotation |
| --- | --- |
| Patterns of Use—Adoptability | “I like that it was personal. I liked that it was conversational. Yes, there was some technology involved. I liked that there was someone who could give me correct, intelligent answers, rather than opinion.” (PQ1)“It should be a program that don’t end. It should continue. It should be the oath for the people, well I don’t know if everybody likes it, I know I like it. It should be there for people that want to do it.” (PQ2) |
| Value Added (support from Coach and BlueStar, behavior change) | “It was easy. Like I said, for me it was easy talking to [Coach]. It was easy phone conversations, communication. It wasn’t pushy but it was persistent. I felt comfortable with that. It was not like you’re going in and somebody’s pushing you to do this or pushing you to do that. To me, I guess, he found a level for me to communicate with and that’s why it was a comfortable thing” (PQ3)“He talked to me about so many things. One of the things I tell you that I’m going to be missing his phone call. He was really into it. The advice he gave me I took them and I feel good and the depression started going away once I started doing like he wanted.” (PQ4)“I never really have anybody follow up on me. I was always disappointed with my diabetes doctor because he was very lackadaisical and I changed in this year … With the coaching, Gene called me every once in a while and he reminded me that everyday life you’ve got to keep your eyes open to do the right thing. He was helpful that way.” (PQ5)“Well, before this program I was not doing exercise. That’s one for me. Second of all, I was not testing my sugar like I test them now. That’s another thing. Third, I was smoking and I stopped it where it would make me feel better. I stopped drinking caffeine and now I drink less. I drink more water than anything. Those kind of things, that changed a lot because before me getting into this program I was doing a lot of the other things. Like the smoking, not exercising, the caffeine and all that and now all that is gone.” (PQ6)“I have another patient who she started using a Fitbit during the project. She was able to sync the Fitbit with BlueStar, and really just super jazzed about the program, and being able to track everything that she does. She also prior to the program was not really checking her blood glucose that much, she just really is uncomfortable with the finger prick, so she does check it now, it’s not as often as maybe her provider would like. It’s maybe one or two times a week, but that’s a start, and that’s more than she was before it, so yeah, those specific patient’s really made some drastic improvements I think.” (CQ1) |
| Complementarity | “BlueStar kinda guides you and the coach is a good motivator.” (PQ7)“My conversations with the health coach were tied into BlueStar, but also tied into general wellness. I didn’t use BlueStar for general wellness, but I used my health coach for that.” (PQ8)“The BlueStar it’s okay because there you put numbers and whatever you are day-to-day. The coaching is different. The coaching is a person that is speaking to you. It’s someone that you’re listening to. It’s someone that is giving you advice. Like I said, other than reading. Let’s say like I said, ‘Now, I stopped smoking.’ I cannot put that in the BlueStar. It would tell me you could do this, this, this and that. Instead of the cigarette now you could do this and it would be about the same thing.” (PQ9)“I would characterize the role of BlueStar for my patients as a day-to-day tracker, so something that’s taking place of a paper log book for them, and that’s what they are primarily using to log their blood glucose, and to check off that they took their medications, and in some instances carb count, or calorie count. BlueStar would be more of the day-to-day, and the role of coaching would be to just reiterate how those day-to-day aspects within BlueStar are going, and discuss things that maybe aren’t so day-to-day, like checking in on if they need blood work, or a primary care provider visit, or checking in on their diet, or their exercise routine, which isn’t something that you can really grasp, and get support on through BlueStar I think.” (CQ2)“The only negative thing [about the intervention] was the computer access … I think that was a little cumbersome. If it went to phone, it might have been a little bit easier but I just couldn’t wrap my head around it and it was just kind of complicated. The phone calls and the support, the coaching, and that stuff was very good.” (PQ10) |
| Sustainability, Including Challenges to Sustainability | “I think scaling this up, we would need to add an entirely different staff member to the Vanguard offices, to liaison if we were going to use this same type of setup we have now. I’m not sure that that’s something that Vanguard would do.” (SQ1)Dissemination would need “More care coordinators … That’s always part of the difficulty is that a lot of the clinical people in the facility tend to be overworked and very busy.” (SQ2)“BlueStar has a built in model that has that sustainable. The issue is, does this combination program make it more affordable for the clinics to hire the coach? If you’re comparing it to just a coaching program by itself, without BlueStar and they can do 30 patients versus with BlueStar and they can handle 50 patients. I think that’s a great argument for being more sustainable than a traditional coaching program.” (SQ3)“[I]f the coaches could have had access to the EHR, I think they would have been able to mine more interesting data … I think it would have taken some of the heavy lift off the care coordinators and our medical assistant admin support people to go in and get the latest lab work, and print it for the coaches.” (SQ4)“I don’t look at it as it being bad about the program it’s just the challenges, the biggest challenges for me were some patients were very hard to reach. As a coach, in the beginning, that was harder because I want to try to help them and then they stayed, they elected to enroll so I’m assuming at that point that they, they were kind of like at least past the first stage of change or kind of thinking about changes. I guess the biggest challenge was, for me, and of the program as a whole, was trying to move people who were not even thinking about, not really thinking about changing from that stage to thinking about changing, that was the hardest thing for me.” (CQ3) |
## Recruitment and Sample Characteristics
Of the 203 potential participants identified from EMR data, 140 were contacted for participation, of whom 46 were found to be ineligible. Of the 94 eligible, 47 declined and 47 ($50\%$) consented to participate. This is a common rate of acceptance. For example, recruitment was $51.8\%$ (240 of 463) in a pragmatic trial of the same DMA as used here that was also restricted to those with A1c levels at or above $8\%$ and that included in-person identification by clinicians during regular clinic visits [40].
It should also be noted that individuals were approached for participation starting with those with the highest A1c values. Relative to the baseline A1c of 8.96 in the aforementioned study [40], this resulted in mean baseline A1c of $9.93\%$ in each of the current groups that participated and that declined participation.
The study objective was to examine use of and benefits from the availability of both live coaching and DMA. Availability was operationalized as minimal exposure to each, leaving unconstrained variation in subsequent use. Minimal exposure was defined as two coach contacts in the second of which DMA onboarding was planned. Of the 47 who consented to participate, 43 ($91.5\%$) met this criterion of two coach contacts, among whom 38 ($88.4\%$) enrolled in DMA.
Table 2 compares pre-intervention values for these 43 participants with those of 114 from among the 203 identified from EMR as eligible but excluding the 46 confirmed to be ineligible during initial recruitment. Thus, the 114 to whom the 43 participants are compared includes four who agreed to participate but did not meet the criterion of two coach contacts, 47 who were reached but declined, and 63 who were not reached, some of whom might have been found ineligible had they been reached. There were no appreciable differences between the groups (ps for t-tests ranged from 0.365–0.993).
**Table 2**
| Variable | Participants | Nonparticipants | Total |
| --- | --- | --- | --- |
| Age | 57.12 (9.23) | 55.65 (10.57) | 56.05 (10.22) |
| Age | N = 43 | N = 114 | N = 157 |
| % Female | 48.8% | 39.5% | 42.0% |
| % Female | N = 43 | N = 114 | N = 157 |
| % on Insulin | 37.8% | 30.2% | 35.7% |
| % on Insulin | N = 43 | N = 111 | N = 154 |
| Pre-A1c | 9.93% (1.28) | 9.93% (1.50) | 9.93% (1.44) |
| Pre-A1c | N = 42 | N = 112 | N = 154 |
| Pre-BMI | 33.24 (6.74) | 34.00 (6.55) | 33.78 (6.59) |
| Pre-BMI | N = 42 | N = 103 | N = 145 |
| Pre-Systolic Blood Pressure | 126.56 mm Hg (12.46) | 127.34 mm Hg (11.85) | 127.12 mm Hg (11.99) |
| Pre-Systolic Blood Pressure | N = 43 | N = 109 | N = 152 |
| Pre-Diastolic Blood Pressure | 77.61 mm Hg (9.33) | 78.25 mm Hg (8.68) | 78.07 mm Hg (8.84) |
| Pre-Diastolic Blood Pressure | N = 43 | N = 109 | N = 152 |
| Pre-Low-Density Lipoprotein | 100.67 (27.34) | 98.53 (34.28) | 99.15 (32.35) |
| Pre-Low-Density Lipoprotein | N = 37 | N = 92 | N = 129 |
| Pre-High-Density Lipoprotein | 46.96 (11.98) | 46.38 (14.65) | 46.55 (13.87) |
| Pre-High-Density Lipoprotein | N = 41 | N = 98 | N = 139 |
Participants’ healthcare was covered by commercial insurance or Medicare. From zip codes of participants, estimated per-capita income was $42,126. In the third quarter of 2015 when participants were recruited, the per-capita income in New Jersey was $61,136 [41], $45.1\%$ or almost $20,000 higher than that of participants.
## Patterns of Use
Among the 43 participants, coach contacts and DMA entries are described in Table 3. Coach contacts ranged from 1 to 16 with an average of 5.86. Of the 38 who enrolled in DMA, $29\%$ utilized the DMA with smartphone only, $29\%$ with computer only, and $42\%$ with both. Six participants ($16\%$) entered at least one hypoglycemic value (BG < 70 mg/dl) and 8 ($21.1\%$) entered at least one hyperglycemic value (>300 mg/dl), both of which triggered real-time feedback within the DMA. Fourteen participants ($36.8\%$) transmitted at least one “SMART Visit Report” (integrated DMA data including glucose control and self-management behaviors) to their Vanguard PCP for review at their office visit.
**Table 3**
| Characteristics | Minimum | Maximum | Mean | Std. Deviation |
| --- | --- | --- | --- | --- |
| Coach Contacts | 1.0 | 16.0 | 5.86 | 3.02 |
| Number of blood glucose entries | 0.0 | 952.0 | 70.23 | 161.5 |
| Number of carbohydrate entries | 0.0 | 150.0 | 15.7 | 35.33 |
| Number of medication entries | 0.0 | 360.0 | 44.28 | 78.67 |
| Number of note entries | 0.0 | 154.0 | 14.93 | 37.84 |
| Total BS Entries | 0.0 | 1443.0 | 144.14 | 270.11 |
Ninety-three percent of coach contacts were initiated by the Coaches. Their contact notes provided data on the topics of contacts, including the percentage of contacts that included discussion of each of the ADCES7 Self-Care Behaviors™ [1]. These were healthy eating—$65.6\%$ of contacts, physical activity—$58.2\%$ of contacts, glucose monitoring—$67.7\%$, medication adherence—$69.5\%$, reducing risks—$62.8\%$, problem solving—$65.3\%$, and healthy coping—$63.5\%$ of contacts. Turning to types of coaching support, the percentages of calls in which each of the following types of support were provided were as follows: emotional support—$64.9\%$, encouragement or motivational support—$82.1\%$, problem solving—$42.5\%$, new goal(s) set—$31.2\%$, and review of goal progress—$46.7\%$. Support for PCP visits was included in $24.6\%$ of contacts and miscellaneous topics (economic, legal, social, and health services) in $5.3\%$.
Table 3 also provides the ranges and means of blood glucose, carbohydrate, and medication entries and numbers of notes into DMA. Principal components analysis indicated that they were best characterized as a single factor (Eigen value = 2.792, loadings ranging from.707 to.926). Consequently, these four types of entries were summed to create “total entries” that ranged from 0 to 1,443 with a mean of 144.140 (SD = 270.114).
There were no significant relationships between participant characteristics (age, baseline SBP, DBP, and A1c) and number of either DMA entries or coach contacts (ps range from 0.372 to 0.993). The 13 participants with prescriptions for insulin averaged twice as many total entries into DMA compared to the 30 without insulin prescriptions, 225.23 vs. 110.43, but this was not significant ($$p \leq 0.204$$) because of large variability (SD = 270.11). Similarly, those with insulin prescriptions averaged 6.846 coach contacts versus 5.433 for those without insulin prescriptions, but this also was not significant ($$p \leq 0.161$$). There were no significant differences by sex in DMA entries ($$p \leq 0.789$$) or coach contacts ($$p \leq 0.624$$).
Participants, Coaches, and study staff spoke positively about the program. Participants mentioned the flexibility and user-friendliness of DMA and that there was live coaching from “someone who could give me correct, intelligent answers” (PQ1) and of the value of ongoing support, “It should be a program that don’t end” (PQ2).
## Value Added
Among the 43 participants, pre- and post-A1c values were missing for one participant and post values were missing for an additional four. For these four, post values were conservatively imputed as equal to pre values, resulting in an analytic sample of 42. A1c declined from $9.93\%$ (SD = 1.28), reflecting the selection of participants on the basis of high A1c scores, to $8.86\%$ (SD = 1.84) after the end of the coach intervention ($t = 4.09$, df = 41, $p \leq 0.001$). Among the 37 who both enrolled in DMA and had at least two coach contacts, the change was similar (9.99, SD = 1.32, to $8.80\%$, SD = 1.93, $t = 4.21$, df = 36, $p \leq 0.001$). Using categories from the National Committee for Quality Assurance [42], the percentage with A1c below $9\%$ increased from 27.9 to $55.8\%$ and that below $8\%$ increased from 0 to $33.3\%$.
We divided the 43 participants at/above or below the median for coach contacts (median = 6) and total DMA entries (median = 21). Table 4 shows that for each combination of high or low use of Coach and DMA, A1c values declined a minimum of 0.49 points ($9.69\%$ to $9.20\%$). Although not significant because of small sample sizes, the smallest reduction, 0.49 points, was among those relatively low on both DMA entries and coach contacts.
**Table 4**
| BlueStar Entries and Coach Contacts | Pre-A1c (Std Dev) | Post-A1c (Std Dev) | A1c Change (Std Error) |
| --- | --- | --- | --- |
| Low BlueStar | | | |
| Low Contacts | 9.69% | 9.20% | 0.49 |
| N = 13 | (1.367) | (1.974) | (0.504) |
| Low BlueStar | | | |
| High Contacts | 10.38% | 9.25% | 1.13 |
| N = 8 | (1.166) | (2.063) | (0.597) |
| High BlueStar | | | |
| Low Contacts | 10.78% | 9.23% | 1.55 |
| N = 6 | (1.105) | (2.279) | (0.614) |
| High BlueStar | | | |
| High Contacts | 9.56% | 8.21% | 1.35 |
| N = 15 | (1.213) | (1.393) | (0.436) |
Participants cited the support, guidance, knowledge, and motivation received from their Coach and overall motivation that the program provided. When describing support received from the Coaches, participants described it as “wasn’t pushy but it was persistent” (PQ3), “someone who was there for me when I needed them,” and “friendly, comfortable, personalized” attention. They praised the attention and engagement of the Coaches, “he was really into it” (PQ4), and mentioned how the Coaches made it easy to talk about diabetes, and could help them identify what issues they needed to discuss with a healthcare professional. Participants also spoke about their Coach remaining sensitive to their situations. For instance, participants noted that Coaches were able to adjust recommendations in the face of obstacles such as in the case of a toe complication that made it difficult to walk.
Participants noted the importance of follow-up, “I never really have anybody follow up on me. I was always disappointed with my diabetes doctor because he was very lackadaisical” (PQ5) and expressed disappointment that their healthcare providers did not review their data contained in DMA Smart Visit Reports, thereby underscoring the need to encourage clinicians to utilize DMA generated data.
As also noted in Table 1, participants mentioned specific behavioral changes made through being in the program (PQ6, CQ1). These fell into three categories: improving general health behaviors (e.g., quitting smoking), monitoring data (e.g., blood glucose values), and medication adherence. When explaining these behavioral changes, participants indicated that the DMA and/or the Coach helped them follow their self-management regimen.
## Complementarity
Overall, participants tended to use both DMA and Coaches as indicated by the correlation, $r = .613$ ($p \leq 0.001$) between DMA entries and coach contacts. However, dividing the 43 participants into those above/below the median for DMA entries and coach contacts identified several different patterns. Fourteen favored one or the other but not both of coaches or DMA (8 high coach/low DMA, 6 high DMA/low coach). Complementarity, tending to use or not use both if using or not using either, was indicated by 16 being high on both (including one missing both pre- and post-A1c values and therefore not included in Table 4) and, also, by 13 low on both.
All 38 participants enrolled in DMA had at least some coach contacts because of the introduction of DMA through the Coaches. Among three participants with two or fewer contacts, total DMA entries averaged 6, far below the average across all 38 DMA users of 144.14. Among four who enrolled in DMA but never entered data into it, total coach contacts averaged 3.75 with none reaching the average of 5.86 among the other 34 DMA enrollees. Thus, those with very low engagement in one of Coach or DMA tended to be low in use of the other.
Interviews also indicated complementarity of coaching and DMA: DMA “kinda guides you and the coach is a good motivator” (PQ7), “I didn’t use [DMA] for general wellness, but I used my health coach for that” (PQ8), “it’s okay [DMA] because there you put numbers and whatever you are day-to-day. The coaching is different … It’s someone that you’re listening to” (PQ9), DMA “would be more of the day-to-day, and the role of the coaching would be … discuss things that maybe aren’t so day-to-day” (CQ2). However, as 14 participants favored one of coach or DMA over the other, participants’ comments also reflected preference for one or the other as with “The only negative thing was the computer access … I think that was a little cumbersome … I just couldn’t wrap my head around it … The phone calls and the support, the coaching, and that stuff was very good” (PQ10).
DMA data reports allowed Coaches to identify participants’ self-management challenges and provided a basis to initiate conversations about those issues. Coaches noted that they tailored recommendations to participants based on data from the DMA and their conversations. Participants mentioned how Coaches helped them make sense of trends in their blood glucose and in their medication adherence, as displayed in the DMA. Furthermore, participants reported sharing reports with their Coach and discussing recipes suggested by DMA.
These quantitative and qualitative findings support the complementarity of the DMA and coaching in that use of one tended to vary in parallel with use of the other, that features of one tended to “go well with” features of the other, that they tended to go together. An alternative explanation is that individual differences such as in motivation may lead some people to do more or less of both coach contacts and DMA entries, accounting for their correlation.
## Sustainability
A major challenge for sustainability identified was the importance of the nurse coordinator or other staff to coordinate Coaches and serve as liaison to the clinical team (SQ1,2). Clearly, coaching and DMA are not self-implementing. However, one project staff member noted potential efficiency with the DMA that “has a built-in model that is sustainable.” She noted that, if Coaches could serve, e.g., 30 participants without but 50 with the DMA, “I think that’s a great argument for being more sustainable than a traditional coaching program” (SQ3). Staff also noted that efficiency would be enhanced and the burden on the care coordinator would be reduced if Coaches had direct access to EHR data (SQ4). Additional observations regarding sustainability included (a) potential to enhance integrated, value-based care with less siloing; (b) need for protocols to hire, support, and integrate Coaches into primary care practice; (c) hiring Coaches with more clinical or technical knowledge; and (d) a Coach’s observation that patient motivation remained a challenge, “trying to move people who were … not really thinking about changing from that stage to thinking about changing” (CQ3).
## Discussion
Integration of telephone-based lay health coaching with a DMA in a primary care setting was able to be implemented, accepted by individuals with T2DM, and, in uncontrolled analyses, associated with reductions in A1c. Forty-seven ($50\%$) of 94 eligible individuals agreed to participate in the study, of whom 43 participated at least minimally. Utilization of both live coaching and DMA was frequent and positively associated, suggesting the complementarity of live and automated support—”soft touch” and “high tech.” With all participants initially selected on the basis of elevated A1c and using National Committee for Quality Assurance [42] criteria, the percentage of participants with A1c below $9\%$ increased from 27.9 to $55.8\%$ and below $8\%$ from 0 to $33.3\%$. Additionally, those electing to participate were almost equally divided by gender ($48.8\%$ female).
In post-intervention interviews, participants expressed that they accepted the interface between participant, coach, and DMA. They expected Coaches to have access to their DMA data and use those data as part of the coaching, viewing this as a strength of the intervention. No participant mentioned feeling surveilled or disliking the monitoring by DMA. Instead, several reported liking the accountability from entering blood glucose levels in the DMA and Coaches having access to the data. As one participant put it, “… it was like someone was watching over me or I was answerable to someone. That helped me in putting in the numbers.” These comments mirror those of users of an automated telephone support intervention for diabetes management developed by Oldenburg and his colleagues [43] in Australia: “*It is* good to know ‘someone’ is keeping an eye (on my management).” [ 44] In order to facilitate speedy training and initiation of the intervention, this study recruited lay health coaches that had prior education in promoting healthy lifestyles, behavior change, and managing chronic diseases. However, research on peer support indicates that even nonprofessionals with simple training can be effective in supporting diabetes management [5, 45]. It is important to note that clinical staff perceived the Coaches having completed bachelors’ training and being enrolled in graduate nutrition programs as a strength of the intervention, linked to Coaches’ ability to be quickly deployed and capable in working with clinical staff. Though there are operational considerations that favor the recruitment of Coaches that are younger, have higher levels of formal education, and perceived to be more facile with digital health technologies, future research should explore the feasibility of using different lay health workers across the continuum of peer support [46, 47], from part-time volunteers to state-credentialed Community Health Workers.
The task of incorporating health coaches into routine clinical care may pose challenges to healthcare organizations that do not follow the patient-centered medical home model. In this study, an experienced nurse care coordinator served as bridge and coordinator among participants, Coaches, and the clinical team. This role could be standardized so that nurse care coordinators or other care managers might have the capacity to coordinate several Coaches (48–50).
The complementarity of peer support and digital health reflects a 2017 systematic review of “technology-enabled diabetes self-management” that found that the most effective interventions (a) connected people with diabetes with their healthcare teams using two-way communication, (b) analyzed participant-generated health data, (c) tailored education, and (d) individualized feedback [18]. These features share much with five key functions of peer support, outlined in Table 5, that Peers for Progress has noted as a template for standardization and dissemination [45, 51, 52]. Indeed, Table 5 documents how digital health and live peer support complement the contributions of each to these five key functions. The key functions then provide a template for self-management support through interpersonal or technological modalities or combinations of the two.
**Table 5**
| Key Functions | Peer Support | DMA | Complementarity | Quotation |
| --- | --- | --- | --- | --- |
| Being There | • Intrinsic in relationship• Accentuated by availability, communication skills, empathy, etc. | • Constant availability• Accentuated by sensitivity of algorithms and resources to nuanced needs of individuals | • Peer support can enhance tuning of DMA to individual needs and preferences• DMA can enhance constant and easy availability of support | Peer Support: “Just being there to listen to me when I was having, when it was a bad time. Just kind of talking me through maybe a different way that I can handle it.”“Persistent, not pushy”“Someone who was there for me when I needed them”“Friendly, comfortable, personalized” DMA: “Because I have to record it, it was like someone was watching over me or I was answerable to someone. That helped me in putting in the numbers.” |
| Assistance in Daily Management | • Personalized diabetes self-management education and support• Detailed problem-solving and long-term goal-setting• Model of adequate management | Tools for monitoring, reminders, and tracking progress• Tailored feedback effectively promote healthy behaviors• Resources for healthy lifestyles | • Key messages reinforced by coaches and DMA• Behavioral tips for healthy recipes in DMA provide discussion topics for coaching• DMA handles routine tasks of self-management so coaches can focus on complex issues | Peer Support : “The communication, the help, and just talking through what you have to do to try to get everything in line. Stay with your diet and exercise and take your numbers and try to keep an even keel. That’s the main thing; just try to get your basic life in order. A little more exercise, your eating habits, time of days you eat, things of that sort.” DMA : “The forced regimen of checking my stuff every day twice a day. Taking the meds, that was another forced thing because I had to input it in the system when I was sure I'd taken the medication, right. That's what I was doing” |
| Social and Emotional Support | • Personal, supportive relationshipReadily available, “being there”• Healthy coping, stress management | • “Has my back”, feeling of protection and comfort• Messages of encouragement and reassurance | • Coaches and DMA provide different sources of support• DMA alerts coaches to situations that need follow-up | Peer Support : “It's a job, but they still take their time to talk to you. He doesn't rush. The coach doesn't rush through the conversation. He takes his time to talk to you. It is motivational and is encouraging” DMA: “The BlueStar provided support. Especially with the little ticklers that they sent, like “oh, congratulations! Your glucose is right on target. Keep up the good work” |
| Linkage to Clinical Care and Community Resources | • Encouragement and reminders for clinical care• Prepare patients for clinical visits and follow up after visits• Overcome logistic, socio-economic barriers to care• Live reminders and attention to psychosocial barriers to care | • SMART Visit Reports give insights into participants’ self-management needs• Monitoring provides automated, specific reminders for routine care or care as needed• Geocoded availability of restaurants, other resources | • Coaches and DMA work together to reinforce importance of primary care and specialty referrals• DMA SMART Visit Reports help guide discussions with coaches and providers | Peer Support : “[My health coach] was great. She's a good listener. Also, too she's very encouraging. If you hit a little spot where you're talking about stubborn blood-sugar readings and stuff like that, she'll encourage you to maybe talk to your doctor, your nutritionist, and that kinda stuff. Just not give up.” |
| Ongoing Support | • Available on demand• Quarterly “check-in”; more frequent messaging | • Availability 24/7• Available indefinitely with down or up titration as needed• Continued reimbursement contingent on continued use | • Coaches encourage continued use of DMA | Peer Support: “Basically just to try to help me bring down the targets, where they should be, and pretty much was my intake. He said reduce it. He kinda watched me on a personal level, from my eating habits, to pushing me on the exercising, and just kind of ... It's almost like, you know what you gotta do, and you gotta be pushed to do it, but you gotta get motivated. I think he did a good job of trying to motivate me over the entire study period. He was always there. Sometimes it's not easy to do, because there's fifty million other things you do in the day. You know?” DMA : “The BlueStar, when you take a blood-sugar reading, you can just enter it the information in, you can make notes, there are a lot of things you can do. It's very good that way. Log books sometimes you don't have space to do anything. You've gotta have a pen, you've gotta have a book. I just like having a single item that I can use to do all of this… I've been using it every, basically every day. I'd have to give it a 10.” |
The present observations that participants saw the DMA and live coaching as fitting well together suggest that digital health apps may be considered a form of automated, resource-saving, low-cost support, ready to be paired with live peer support for those with greater need, e.g., suboptimal self-management, complex multi-morbidities, or psychosocial concerns. Extending such thinking beyond the details of this study, population management might titrate app use and live coaching [53]. As an example of this kind or approach, a simple model might include Level One: Doing well (e.g., A1c < $7\%$, no psychosocial concerns)—Routine availability of app; Level Two: Little clinical or psychosocial concern (e.g., A1c < $7.5\%$, no pronounced psychosocial concerns)—Routine encouragement of app through primary care visits; Level Three: Moderate clinical concern (e.g., A1c > $7.5\%$)—Limited coach contacts to support self-management and encourage use of app; and Level Four: Substantial clinical concern (e.g., A1c > $8\%$ and/or appreciable psychosocial distress)—Live coaching for 6 months, renewable as needed, with incorporation of app as acceptable or helpful. Although they surely do not provide adequate data for concluding that such a model would be effective, the present study results suggest that this type of titration of interventions may be realistic and worthy of future research.
Digital health is “what’s coming” across a range of functions and services. With mobile health apps available not only for wellness support but also for most common conditions, there are solutions available to support each step of the journey of those with diabetes on devices that people already own. Experts have noted the remarkable market-driven growth of mobile health apps for diabetes [54, 55]. People with diabetes, clinicians, investors, and tech developers all seem to recognize the promise of digital health in diabetes care, and regulators are encouraging growth in this field by lowering barriers to accessing new technologies [56].
The issue is not if but how to deliver digital health to individuals and integrate it with clinical care. Health coaches may be a valuable strategy in rolling out digital health to patient populations. From the perspective of digital health, an important contribution of health coaching is how it promoted the uptake and continued use of DMA. From the perspective of the health coaches, DMA provided a focal point for participant monitoring and generating discussion, which helped to shape participant encounters and pinpoint key areas of improvement for each individual participant. In addition to more rigorous, controlled tests of efficacy and effectiveness, follow-up studies might examine how digital health solutions impact the ways in which Coaches deliver diabetes support, such as by comparing how Coaches adjust their counseling strategies when assisted by digital health solutions versus when using traditional strategies. In addition, future developments might involve adding in-person coaching, in either individual or group formats.
## Limitations
In this study, participants were encouraged to engage with service offerings as they wished, similar to the way in which comprehensive chronic disease programs might be implemented under real-world conditions. One limitation of the study is the selection bias for participants who are interested in digital health and have the means and skills to use such tools.
The intervention was planned to meet the needs of those having trouble managing their diabetes (mean baseline A1c = $9.93\%$) and the sample had, on average, incomes estimated at $31.1\%$ below the statewide average. However, the intervention was not tailored to any specific racial or ethnic group, perhaps limiting generality to some groups with disproportionate diabetes burden. At the time of this study, the DMA was only available in English, which excluded individuals not fluent in English.
Other limitations of the project include the relatively small sample size and the lack of controls. Additionally, participant data were drawn from EMR records as part of routine care instead of being gathered as part of an independent collection of evaluation data.
## Conclusions
Contributing to the growing research around digital health for diabetes management, this study shows that integrated health coaching/DMA is feasible and acceptable for individuals with diabetes, coaches, and clinicians. The findings suggest that these two approaches complement each other, meriting additional studies to test integrated models at scale and address implementation challenges for clinical and organizational adoption.
## Data Availability Statement
The datasets presented in this article are not readily available because of the modest size of the sample, and because the data consist largely of clinical data, we would anticipate problems in assuring confidentiality of participants’ data and, so, have decided not to make the data available. Questions about details of the data may be directed to PT ([email protected]) or EF ([email protected]). Requests to access the datasets should be directed to PT ([email protected]) and EF ([email protected]).
## Ethics Statement
The studies involving human participants were reviewed and approved by the Institutional Review Board at the University of North Carolina at Chapel Hill. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
PT, JD, MP, and EF led the development, implementation, and evaluation of the project and the preparation of the manuscript. SK, NB, and RS contributed to planning the implementation and evaluation of the project, and contributed to conducting evaluation including data management, data analysis, and coding and interpretation of interviews. All authors contributed to the article and approved the submitted version.
## Funding
Funding for this project was provided by a Gillings Innovation Laboratory award funded by the 2007 Gillings Gift to UNC-Chapel Hill’s Gillings School of Global Public Health. The funding source had no role in the study design; data collection; administration of the interventions; analysis, interpretation, reporting of data; or decision to submit the findings for publication.
## Conflict of Interest
Subsequent to completion of this study, EF and PT became part of a contract with WellDoc that provided BlueStar® Diabetes for the present study. The contract was for development of programs in areas other than diabetes. MP is an employee of WellDoc that provided the BlueStar® Diabetes for the study. She collaborated throughout planning, implementation, and preparation of this manuscript, and specifically contributed to the description of BlueStar® Diabetes and product user data.
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: Intralesional Infiltrations of Cell-Free Filtrates Derived from Human Diabetic
Tissues Delay the Healing Process and Recreate Diabetes Histopathological Changes
in Healthy Rats
authors:
- Jorge Berlanga-Acosta
- Maday Fernández-Mayola
- Yssel Mendoza-Marí
- Ariana García-Ojalvo
- Raymond J. Playford
- Gerardo Guillen-Nieto
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2021
pmcid: PMC10012095
doi: 10.3389/fcdhc.2021.617741
license: CC BY 4.0
---
# Intralesional Infiltrations of Cell-Free Filtrates Derived from Human Diabetic Tissues Delay the Healing Process and Recreate Diabetes Histopathological Changes in Healthy Rats
## Abstract
Lower limb ulcers in type-2 diabetic patients are a frequent complication that tributes to amputation and reduces survival. We hypothesized that diabetic healing impairment and other histopathologic hallmarks are mediated by a T2DM-induced tissue priming/metabolic memory that can be transferred from humans to healthy recipient animals and consequently reproduce diabetic donor’s phenotypes. We examined the effect of human T2DM tissue homogenates injected into non-diabetic rat excisional wounds. Fresh granulation tissue, popliteal artery, and peroneal nerve of patients with T2DM were obtained following amputation. Post-mammoplasty granulation and post-traumatic amputation-tissue of normal subjects acted as controls. The homogenates were intralesionally injected for 6–7 days into rats’ excisional thickness wounds. Infiltration with the different homogenates caused impaired wound closure, inflammation, nerve degeneration, and arterial thickening (all $P \leq 0.01$ vs relevant control) resembling histopathology of diabetic donor tissues. Control materials caused marginal inflammation only. Infiltration with glycated bovine albumin provoked inflammation and wound healing delay but did not induce arterial thickening. The reproduction of human diabetic traits in healthy recipient animals through a tissue homogenate support the notion on the existence of tissue metabolic memory-associated and transmissible factors, involved in the pathogenesis of diabetic complications. These may have futuristic clinical implications for medical interventions.
## Highlights
The tissue healing response is progressively undermined as a diabetic complication, leading to ulceration, amputation, and mortality. Metabolic memory is invoked as a molecular driver behind the perpetuation of diabetic histopathological hallmarks and clinical complications. This study describes unprecedented evidences indicating that the diabetic impaired healing, as the archetypical peripheral angiopathy and neuropathy, may be faithfully reproduced in healthy animals, through the administration of a cells-free filtrate from diabetic donors’ tissues, with no inter-species barriers. This suggests the existence of transmissible signaling factors, beyond glucotoxicity, involved in the pathogenesis of diabetic damages. These factors seem to impose their message in a short temporary window which incites to reconsider classic concepts of diabetes pathology. The identification of these “drivers” will introduce novel preventive and therapeutic avenues for diabetes.
## Introduction
Diabetes mellitus (DM) is a heterogeneous group of chronic metabolic conditions of pandemic magnitude, characterized by elevated blood glucose levels, resulting from the inability to produce insulin, resistance to insulin action, or both [1].
A torpid healing process along with chronic ulceration and ulcer recurrence constitute a single pathogenic unit and a frequent diabetic complication, which have rendered alarming figures of lower extremity amputations along the history [2]. Chronic wounds in general and diabetic foot ulcers (DFU) remain as a persistent medical problem, a scientific challenge, and a socio-economic burden [3]. The epidemiological association between an excessive mortality rate with the onset of the diabetic foot syndrome has been documented [2]. Accordingly, preventing diabetics’ ulceration, improving their healing response, and prolonging ulcer remission time represent years of survival for this population [4].
Regardless of the pathogenic differences between the two main clinical forms of diabetes, they both share systemic vascular and nerves damages [5]. The histopathological hallmarks of diabetic skin angiopathy include perivascular inflammation and collagenization, arterial wall thickening; media layer fibrohyaline degeneration, luminal obliteration, and ultimately vascular rarefaction [6, 7]. Peripheral diabetic neuropathy is predominantly characterized by different degrees of nerve fascicles edema, Wallerian degeneration, and myelin fragmentation [8, 9].
The molecular mechanisms underlying diabetic torpid healing and wound chronicity remain elusive. Compelling findings support the existence of a diabetic metabolic memory as a main driver for the perpetuation of multi-organ complications (10–12), including the torpid wound healing response [13, 14].
Recent evidences attest the substantial pathogenic contribution of epigenetic forces for metabolic memory expression, penetrance, and particularly for its generational transmission (15–18). Different evidences converge to indicate that “vascular glycemic memory” [19, 20], and other diabetic complications-related histological hallmarks are promoted by underlying abnormal epigenetic programs (21–23). The fact that various epigenetic mechanisms mediate the intra and transgenerational intra-species inheritance of diabetic-related traits [24, 25], render explanation for our earlier observation that diabetic granulation tissue “inherits,” and morphologically recreates in a period of days [26] the structural changes of chronic evolution which distinctively characterize diabetic skin histology (27–29).
By a series of sequential experiments, this study describes unprecedented evidences indicating that the diabetic impaired healing phenotype, as abnormal histological hallmarks in vessels and nerves, may be reproduced in healthy recipient animals through the administration of different diabetic human donor-derived tissue homogenates. These findings suggest that the molecular “operators” underlying diabetic tissue phenotypes are capable of inter-species transmission that may impose their signature, and ultimately promote host’s tissue remodeling in a diabetic donor’s similar fashion.
## Ethics and Consents
Use of human tissue was approved by relevant local and national regulatory authorities. Subjects gave verbal and informed written consent for the use of material that had been excised for clinical reasons. Diabetic donor materials (granulation tissue and amputated lower limb nerve and artery) were obtained from the National Institute of Angiology and Vascular Surgery (Diabetic Angiopathy Service). The brachial artery from a healthy donor was obtained at the Frank Pais National Orthopaedic Hospital, whereas the control granulation tissue from a healthy donor was collected at the Hermanos Ameijeiras Hospital (Plastic and Reconstructive Surgery Service); all in the city of Havana, Cuba. All animal experiments were approved by local animal ethics committees (Animal Welfare Committee of the Center for Genetic Engineering and Biotechnology, Havana, Cuba). Prevention of pain and distress in our experimental animals meets humane and methodological requisites, since animal suffering impairs the healing process [30, 31].
## Collection of Human Samples
Granulation tissue, popliteal artery, and peroneal nerve fragments from a 67 years old male Type-2DM patient afflicted by critical limb ischemia were obtained by dissection immediately after lower limb amputation. This patient underwent major lower extremity amputation due to the critical limb ischemia and the ensued unbearable pain at rest. The lateral side of the distal portion of the right limb was mostly covered by a drought ischemic plaque with no clinical phlogistic changes suggestive of infection. The patient underwent a prophylactic antibiotic scheme 72 h before amputation (flucloxacillin/vancomycin/metronidazole). Recorded Rx reports ruled out signs of osteomyelitis and/or soft tissue infection. Accordingly, this patient was selected as the potential donor because in addition to be clinically non-infected, he was not under the intralesional use of rh-EGF (Heberprot-P), or bone marrow stem cell administration nor any other interventional protocol. Histology of the popliteal artery showed typical changes of peripheral vascular disease comprising intimal proliferation and media thickening with calcified plaques and fatty streaks whereas peroneal nerve tissue demonstrated typical Wallerian degeneration, myelin retraction, and fascicular cavitation. Control granulation tissue was obtained from a 42-year-old healthy female donor undergoing second intent healing by suture dehiscence following cosmetic mammoplasty. Fresh brachial artery tissue was collected from a male healthy donor (45 years old), following surgical amputation due to traffic accident and appeared histologically normal. Immediately following resection/amputation in the surgical room, collected materials were washed with sterile ice-cold normal saline to remove fibrin, blood, and debris. Multiple popliteal artery fragments including deep adjacent soft tissues were $10\%$ buffered formalin fixed for histological analysis and characterization. Other matched fragments were cryopreserved in liquid nitrogen until processing. The target artery and adjacent soft tissue samples were examined on the bases of broadly accepted morphological parameters for arterial vascular pathology [32].
## Cells-Free Filtrate (CFF) Preparation
Collected tissue was allowed to thaw, weighed and approx. 100 mg of wet tissue placed in 2 ml vial containing 1 ml of normal saline, homogenized using a Tissue Lyser II for 3 min at 30 revolutions per second. Samples were then centrifuged at 10,000 rpm for 10 min at 4°C, sterilized by filtration through 0.2 µm nitrocellulose filters (Sartorius Lab Instruments), aliquoted into sterile Eppendorf vials and stored at −70°C. Prior to use, total protein, glucose concentrations, and cytokine content of the samples were determined using the Bicinchoninic Acid Protein Assay Kit (Sigma-Aldrich, USA) and standard commercial kits for glucose, malondialdehyde (MDA), IL-1β, and IL-6 (all from Abcam, USA). A high sensitivity and specificity ELISA system to measure AGE concentrations was purchased from Wuxi Donglin Sci & Tech Development Co, LTD., China. According to the manufacturer, this kit is endowed with a detection range from less than 33.8 to 8,000 ng/ml (A3-South, 100 # Shuigoutou, Renminxi Rd, Wuxi, Jiangsu, 214031, PRC). Manufacturer’s instructions were followed for the determinations.
## Production of Glycated BSA
Glycated BSA was prepared according to published methods [33]. The BSA molecular mass increase and glycation sites were investigated by ESI-MS/MS as previously described to identify AGE formation [34]. Sites of glycation using ESI-MS/MS showed glycation of lysine residues 14, 206, 223, 234, 415, 473, 476, 526, 546 (gamm.biocomp.cigb.edu.cu/data/servicios/BSA glucosilada).
## Induction of Skin Wound in Rats, Infiltration With Test Solutions, and Subsequent Analyses
Adult male Sprague Dawley rats ($$n = 8$$ per group) were individually housed for 10 days acclimatization in steel grid-bottomed cages (to prevent contamination of wounds with bedding material) and allowed access to standard rodent chow and water ad libitum throughout the study. Following acclimatization and under anesthesia [ketamine (80 mg/kg)/xylazine $2\%$ (10 mg/kg) cocktail] each rat underwent two dorsal, symmetrical, retro-scapular full-thickness wounds including panniuclus carnosum with a 6 mm diameter disposable biotomes (Acu-Punch, Acuderm Inc., USA) as described [35]. Immediately following induction of wounds, animals received local infiltration of test product (100 µg protein in 500 µl saline, or saline alone) into the wound and on a once daily basis until end of experiment. For the initial experiment (granulation tissue injection), animals were sacrificed on day 7 under terminal anesthesia. For experiments 2–4, the protocol was modified to euthanize animals on day 6 to ensure that no wounds were completely closed at the end of the test period, should any homogenate may cause increased healing. Wound size on days 0, 3, 5, and 6 was determined by tracing wound margins on transparent polypropylene films, digitized, and two-dimensional digital planimetric calculation of wound closure determined. Results on day 0 (28 mm2) were defined as $100\%$ [36, 37]. At the end of the study, animals were killed under terminal anesthesia, the entire wound area with intact surrounding skin excised, fixed in $10\%$ buffered formalin, paraffin-embedded, and sections stained using H&E. Images were captured using a BX43 Olympus microscope, coupled to a digital camera and central command unit (Olympus Dp-21), and processed using ImageJ software (ImageJ 1.48v, NIH, Bethesda, MD, USA) [38]. All histological assessments were performed by two independent subjects and an external pathologist under blinded conditions. Inflammatory infiltrate scoring (scored from 0 to 8), and fibro-vascular reaction (based on the degree of collagen bundle formation) were quantitated according to published methods [37, 39]. The degree of vascular wall remodeling was quantitated from arterial wall-to-lumen ratios following described procedures [40, 41] and percentage of damaged nerve fibers and degree of Wallerian degeneration determined as described [42, 43].
## Histochemistry and Immunohistochemistry
Tissue sections of human origin and the matched recipient rats were stained with Congo red, and Mallory’s trichrome techniques for amyloid and collagen deposits identification as described [44]. Other sections were mounted on poly-l-lysine coated slides (DAKO, Carpinteria, CA, USA) in order to reduce inter-tissue/experimental variations during immunohistochemistry studies. The slides corresponding to three wounds per sub-group (except for protocol 3) were dewaxed and rehydrated through graded washes of ethanol. Rehydrated slides were exposed to antigen retrieval solution for 20 min at 80°C and washed with 0.05 M tris-buffered saline (pH 7.6) for 5 min. Endogenous peroxidase was blocked, and subsequently the tissue sections were exposed to pre-heated antigen retrieval solution high pH (DAKO) for 10 min. Following equilibration to room temperature non-specific binding blocking solution was used for 20 min. The sections were then incubated for 40 min with antibodies directed to: [1] TNF-α (Ab6671), [2] AGE (Ab23722), [3] RAGE (Ab228861), [4] e-NOS with a “gain-of-function” phosphorylation in Ser-1177 (Ab75369), and [5] an active isoform of NFκβ (p65) phosphorylated in serine 529 (Ab97726), according to manufacturer’s specification. The immunolabeling reaction was developed as described for the Mouse and Rabbit Specific HRP/DAB (ABC) Detection IHC kit (Ab64264) and semiquantitatively graded as described [45]. As internal immunoreaction reference, a granulation tissue fragment of the donor’s diabetic-ischemic ulcer was used. Previous studies from our group had immunohistologically characterized this type of tissue [46]. Non-specific tissue labeling internal controls included the omission/replacement of the primary antibody by the background reducing antibody diluent (Ab6424), and normal rabbit serum (Boster Biological Technology, Pleasanton CA, USA, catalog # AR1010).
## Study 1. Effect of human diabetic ischemic ulcer granulation tissue on rat skin wound healing and histology.
Rationale: To determine if the healing response was impaired and histological changes occurred in rat wounds when injected with T2DM ischemic chronic ulcer-granulation tissue CFF, as compared to human healthy granulation tissue control.
Three groups of rats ($$n = 8$$ per group with two wounds per rat, giving 16 wounds per condition) received wound infiltration of:
## Study 2. Effect of human diabetic arterial tissue CFF on rat skin wound healing and histology.
Rationale: To examine if arterial tissue distant from ulcerated areas of T2DM patient caused similar healing impairment and angiopathy changes to those seen in experiment 1.
Three groups of rats ($$n = 8$$ per group with two wounds per rat, giving 16 wounds per condition) received wound infiltration of:
## Study 3. Effect of T2DM human nerve CFF on rat skin wound healing and histology.
Rationale: To examine if nerve tissue distant from ulcerated areas of T2DM patient caused similar histopathological changes to those described for experiments 1 and 2, including nerve fascicles degeneration.
Two groups of rats ($$n = 8$$ per group with two wounds per rat, giving 16 wounds per condition) received wound infiltration of: Rats received wound infiltration of
## Study 4. Relevance of protein glycation on rat wound healing and histology.
Rationale: To examine if injection with a glycated protein (BSA) caused healing impairment and similar histopathological changes to those described in experiments 1–3 when diabetic tissues CFFs were used.
Three groups of rats ($$n = 8$$ per group with two wounds per rat, giving 16 wounds per condition) received wound infiltration of:
## Statistical Analyses
Statistical analyses were performed using GraphPad Prism software 6.01 (La Jolla, CA, USA). For analyses comprising more than two groups, the Kruskal-Wallis test was performed followed by Dunn’s multiple comparisons test. Comparisons between two groups were analyzed using the Mann Whitney test. p-value <0.05 was considered statistically significant. All results are expressed as mean ± SD.
## Study 1. Effect of Human Diabetic Ischemic Ulcer Granulation Tissue on Rat Wound Healing Response and Local Histology
Diabetic ischemic ulcer granulation tissue exhibited the largest concentrations of MDA, IL-6, and AGEs as compared to the relevant healthy donor-control granulation tissue. As shown in Table 1, diabetic homogenate levels of MDA and IL-6 levels largely exceeded the rest of the samples.
**Table 1**
| Samples | MDA/mg protein (nmol/mg) | IL-6/mg protein (pg/mg) | IL-1β/mg protein (pg/mg) | AGE/mg protein (ng/mg) |
| --- | --- | --- | --- | --- |
| Diabetic ischemic ulcer granulation tissue | 1.363 | 44.84 | 32.40 | 2.966 |
| Healthy donor-granulation tissue | 0.836 | 0.64 | 14.13 | 0.26 |
| Artery T2DM | 0.275 | 16.67 | 50.58 | 2.615 |
| Artery-healthy donor | 0.601 | 10.43 | 16.97 | 0.676 |
| Nerve T2DM | 0.192 | 0.39 | 26.02 | 0.631 |
| Glycated BSA | – | – | – | 683.8 |
Infiltration with human healthy donor granulation tissue homogenate did not delay wound healing, and did not cause morphological changes when compared against the effect of saline injections (Table 2). Histology of the initial human granulation tissue from healthy donor (Figure 1A) and the corresponding rat wounds injected with either saline or healthy granulation tissue (Figure 1B), all showed a mild infiltration with lymphocytes, normal caliber vessels, and well organized/mature collagen fibers. In contrast, infiltration with granulation tissue from the T2DM subject resulted in delayed wound healing, a marked inflammatory infiltrate mainly based on lymphocytes, and a constellation of abnormal vascular histological changes that mirrored those of the human donor T2DM granulation tissue. The diabetic donor’s granulation tissue angiogenic response was mostly abnormal and incomplete. According to the predominant pathological changes we describe it as: [1] precocious vascular walls thickening since early angiogenic sprouting stage (Figure 1C), [2] arteriolar walls thickening with luminal narrowing on the bases of media layer expansion and invasion into the intima with layers fusion (Figure 1D), [3] exaggerated media-concentric collagenization (Figure 1E), [4] endothelial cells collar hyperplasia, or with a bulky aspect, or onset of a fusiform, fibroblastic-like phenotype that project into the lumen (Figure 1F), [5] abnormal or incomplete venular organization (Figure 1G).
The recipient rats treated with the diabetic granulation tissue-derived homogenate reproduced in their granulation tissue most of the above described abnormalities. Accordingly, this heterogeneous group of changes includes: [1] vascular walls thickening since early stages (Figure 1H), [2] arteriolar walls thickening with luminal narrowing (Figure 1I), [3] exaggerated periadventitial concentric collagenization (Figure 1J), [4] endothelial collar hyperplasia with bulky aspect of some cells (Figure 1K), [5] abnormal or incomplete venular organization (Figure 1L). Again, none of the above described changes were detected in the rats treated with the healthy donor/normal artery recipient rats (Figure 1B).
In order to further characterize these unprecedented findings we conducted histochemical and immunohistochemical reactions, in which the diabetic donor granulation tissue and the matched recipient rats’ were compared, concurrently having the healthy arterial donor recipient rats’ as controls. The formerly described arteriolar fibrogenesis in both the diabetic donor (Figure 2A) and the matched recipient rats (Figure 2B) proved to be positive to Mallory trichrome reaction. Furthermore, similar to the diabetic donor tissue sample (Figure 2D), the vascular walls of the recipient animals’ granulation tissue were positive to Congo red reaction (Figure 2E). The control rats treated with the healthy donor homogenate were positive to collagen in a physiological pattern and convincingly negative to Congo red, neglecting any amyloid material vascular accumulation (Figures 2C, F).
**Figure 2:** *Panels (A–C). Mallory trichrome reaction for collagen identification. Intense positive reaction to Mallory staining corresponding to a vascular structure in the ischemic ulcer granulation tissue of the diabetic donor (A). Collagen (blue stain) involves the walls and encroaches into the lumen leading to complete obliteration. Periadventitial collagen fibers are also identifiable. Rats treated with the ischemic ulcer granulation tissue- (B), also showed a collagen deposit disorder leading to luminal encroachment of Mallory stained bundles. No perivascular, intramural, or intraluminal collagen accumulation was detected in the granulation tissue samples of the rats receiving the homogenate of the healthy donor/normal granulation tissue (C). Congo red, suggesting a possible amyloid material accumulation in the endothelial collar of vessels and adjacent area is shown in panels D and E for the diabetic ulcer granulation tissue donor, and the recipient rats, respectively. No positive reaction to Congo red (F) was detected in the samples derived from healthy donor/normal granulation tissue –treated rats.*
The human diabetic tissue sample exhibited intense AGE reactivity which was mostly accumulated in the granulation tissue extracellular matrix in proximity to vascular structures (Figure 3A). Correspondingly, AGE reactivity was also identified in the endothelial collar as in granulation tissue matrix of the diabetic material recipient rats (Figure 3B). No AGE accumulation was evidenced in the granulation matrix of control rats receiving the human healthy donor granulation tissue homogenate (Figure 3C). Intense RAGE expression was observed in vascular walls and adjacent cells in both the diabetic donor and the cognate recipient rats’ granulation tissue (Figures 3D, E); whereas this marker was not detected in the control rats (Figure 3F). TNF-α expression was intensely detected in granulation tissue infiltrating cells and in vascular structures cells of both the diabetic donor and the recipient rats’ tissue (Figures 3G, H respectively). Very limited expression was identified however in round cells scattered across the granulation tissue field of control rats treated with the healthy donor material (Figure 3I). Probing the pathologic donor (Figure 3J) and the recipient rats (Figure 3K) samples with an antibody generated against an active isoform of NF-κB, showed a p65 intense expression in round, possibly inflammatory cells, as in other cells integrated to the arteriolar walls. In contrast, a pale and very well-circumscribed p65 signal was found in a limited number of cells in the healthy donor recipient control animals (Figure 3L). In correspondence to the described vessels structural damages, the e-NOS expression appeared deficitary in the diabetic donor tissue (Figure 3M), as in the matched recipient rats. Interestingly, e-NOS expression intensity and distribution seemed to parallel the vascular damages (Figure 3N). This is substantially contrasting with the e-NOS pattern of expression in the vessels walls of the healthy donor recipient rats (Figure 3O). Finally, formal histological scoring based on wall-to-lumen ratio confirmed the findings of arterial thickening and luminal obliteration, which was associated to nerve damage, and inflammatory scores in the T2DM treated wounds (Table 2).
**Figure 3:** *Immunohistological characterization of rats’ granulation tissue to the administration of T2DM or healthy-normal tissue cells-free filtrates. Panels from (A–O) corresponding to protocol 1. Diabetic ischemic ulcer granulation tissue CFF administration. Panels (A–C), correspond to AGE expression in diabetic granulation tissue donor sample, the matched recipient rats, and rats treated with healthy donor granulation tissue, respectively. (A) Intense labeling of AGE accumulation in the granulation tissue extracellular matrix as in the walls of a pathologic arteriole. (B) AGE is also largely accumulated in the granulation tissue and microvascular structures, including the endothelial layer of rats receiving the diabetic granulation tissue homogenate. (C) No AGE expression is detected in the animals treated with the healthy donor granulation tissue from a cosmetic mammoplasty. TNF-α expression is shown in panels (D–F). As shown by panel (D), this cytokine is intensely expressed by different granulation tissue structures, including the vascular collar. The rats treated with the diabetic material likewise show TNF-α expression in the endothelial collar. A thickened arteriole, along with other adjacent micro arterioles is shown in panel (E). Rats treated with the healthy donor’s granulation tissue homogenate also express TNF-α in granulation tissue cells but in a well-delimited pattern and with far less intensity, panel (F). RAGE is abundantly expressed by granulation tissue infiltrating cells within the diabetic donor granulation tissue as by few cells embedded in the vascular collar—panel (G). A similar pattern and intensity of expression are observed in the diabetic material recipient rats. As shown in panel (H), cells of the microarterioles collar are clearly positive to RAGE expression. RAGE is not expressed by the rats treated with the healthy donor’s granulation tissue homogenate – panel (I). The descried inflammatory profile is in line with the findings obtained with an antibody targeting an active NF-κβ p65. For both, the granulation tissue sample of the diabetic donor panel (J) and for the recipient rats panel (K), p65 is detected mostly in the nucleus of a multitude of cells, including the vascular wall and the endothelial layer. In contrast, p65 is faintly expressed by some cells and incipient microvascular structures within the granulation tissue of control rats, treated with the healthy donor homogenate —panel (L). e-NOS expression is almost absent in the vascular structures of the diabetic donor ischemic granulation tissue—panel (M). This derangement is also detected in the granulation tissue vessels of the diabetic material recipient rats. As shown in panel (N), few vascular structures (encircled) express e-NOS, and importantly, this expression seems to decline when the morphology of the venules is abnormal. Panel (O) demonstrates the intense expression of e-NOS by the walls of morphologically normal vessels in rats treated with the healthy donor granulation tissue homogenate. Panels (P–S) corresponding to protocol 2. Diabetic PAD arterial tissue CFF administration. As shown in panels (P, Q), the administration of the diabetic arterial homogenate triggered an inflammatory response given by a neat expression of TNF-α and RAGE, respectively, in the granulation tissue of otherwise normal recipient rats. The former was mostly circumscribed to vascular structures. Panel (P) shows a nest of de novo formed microarterioles (arrow) in which TNF-α is detected. RAGE shows a broader expression in cells infiltrating the granulation tissue panel (Q). In sharp contrast, TNF-α is limitedly and marginally expressed by some cells within the microscopic field, whereas RAGE was not expressed by the components of the granulation tissue of the rats treated with the healthy donor material (panels R, S, respectively). Panels (T–Z) corresponding to protocol 4. Effect of glycated BSA administration. As illustrated in panels (T–U), and in contrast to the effect of native BSA, the administration of glycated BSA promoted the accumulation of AGE in the extracellular matrix, as in most microvascular structures including the endothelial lining panel (T). Accordingly, TNF-α was detected in a broad constellation of cells, microvessels walls, as in a structure similar to a nerve fascicle (Panel U, square). RAGE is also immunodetected in infiltrating cells as in the endothelial collar of vascular structures panel (V). Native BSA treated rats did not show to accumulate AGE in any structure/cell of the granulation tissue panel (X). As described formerly, TNF-α expression is marginal and limited to some cells and incipient microvascular structures panel (Y). Native BSA did not show to induce RAGE expression panel (Z). All microphotographs magnification was x 40. Mayer’s hematoxylin counterstain.*
## Study 2. Effect of Human Diabetic Arterial Tissue Homogenate on Rat Wound Healing Response and Local Histology
In sharp contrast to the values detected in the homogenate derived from the healthy donor artery, the homogenate obtained from the ischemic diabetic counterpart contained the highest levels of both IL-1β and IL-6 with considerable AGEs accumulation (Table 1).
The rate of wound healing was similar in saline-treated or normal arterial tissue-treated animals, but significantly delayed in wounds that received arterial tissue homogenate from the ischemic T2DM donor patient (Table 2). Histology of rat wounds injected with the normal arterial tissue CFF showed a healthy granulation tissue appearance with normal vessels and no arteriolar remodeling (Figure 4A). However, wounds treated with T2DM arterial homogenate had appearances similar to that found when diabetic ulcer granulation tissue was used (Study 1). Changes included abnormal and incomplete angiogenesis, endothelial collar hypertrophy, and media layer sector infiltration by cells and concentric collagenization (Figure 4B).
**Figure 4:** *Panels (A, B) correspond to protocol 2. Granulation tissue of rats treated with arterial tissue homogenate. (A) Image representative of the histological response of the granulation tissue of rats treated with healthy donor/normal artery in which tissue and vascular architecture is normal. The arrows indicate to an arteriole with normal walls and patent luminal aspect. Abundant number of fusiform, fibroblasts-like cells in a woven collagen matrix is observed. H/E. Magnification ×20. (B) Representative image of the vascular response in rats treated with the diabetic artery homogenate. The circle targets an artery with a remarkable wall thickening at the media layer sector with hypercellularity in which fusiform cells are overrepresented. A conglomerate of round, basophilic mononuclear cells is also observable about the lower right corner. H/E. Magnification ×40.*
Formal histological scoring confirmed these findings of increased arterial thickening, nerve damage, and inflammatory scores, all being significantly increased (Table 2). Immunostaining showed that as compared to normal/healthy artery-derived homogenate, the wounds treated with the diabetic arterial homogenate exhibited intense expression of both TNF-α and RAGE. TNF-α appeared to be far more circumscribed to vascular structures (Figure 3P) whereas RAGE immunolabeling was found in a large number of cells within the granulation tissue extracellular matrix (Figure 3Q). Arterial healthy donor-treated rats showed a pale TNF-α expression by some cells infiltrating the granulation tissue filed (Figure 3R) inasmuch RAGE did not appear to be expressed (Figure 3S).
## Study 3. Effect of T2DM Human Nerve Homogenate on Rat Wound Healing Response and Local Histology
Fascicle fragments of the peroneal nerve of the amputated diabetic donor exhibited histopathological evidences of Wallerian degeneration, with intense degree of myelin retraction, lysis, and fascicular cavitation (not shown). Of note, diabetic nerve tissue sample showed the lowest concentration levels of MDA, IL-6, and AGE similar to those detected for healthy donor artery (Table 1). Analogous to the effect induced by the diabetic artery homogenate, only $33\%$ of the injured area appeared to be closed in the wounds treated with the diabetic nerve-derived homogenate (Table 2). Furthermore, this homogenate also promoted arterial wall thickening, mostly supported by a dramatic hypercellularity and collagenization in the media layer sector (Figure 5A). Wallerian degeneration in a severe degree was also promoted by the diabetic nerve material infiltration (Figure 5B), likewise reproducing the observations accounted with the two previous diabetic tissue samples.
**Figure 5:** *Panels (A, B) correspond to protocol 3. Granulation tissue of rats treated with diabetic peripheral nerve tissue homogenate. (A) Representative image of the vascular response of rats treated with a diabetic nervous tissue homogenate. As described for the diabetic artery response—here again, there is a clear arteriolar wall thickening at the media layer sector with hypercellularity in which some fusiform-like cells are concentrically aligned at the periphery. H/E. Magnification ×20. (B) The arrow points to the section of a nerve fascicle in which degenerative changes including demyelination, contraction, and cavitation are associated to the administration of the diabetic nerve tissue homogenate. H/E. Magnification ×40.*
## Study 4. Relevance of Protein Glycation on Wound Healing Histology
The AGEs levels determined in the glycated BSA samples were 683.8 ng/mg of protein, which was 230 times higher than the highest value determined for the diabetic tissue samples (Table 1). This highly glycated BSA significantly impaired wound closure rate as compared to saline or normal BSA-treated animals (Table 2). Compared to animals receiving saline, those that had received native BSA showed normal histology with no differences in granulation tissue structure, maturation, histological patterns of angiogenesis, inflammatory infiltrate intensity, and nerve fascicles integrity (Table 2; Figure 6A). Glycated BSA, however, caused significant increase of inflammatory infiltrate and nerve fascicle degeneration consisting in myelin retraction, lysis, and cavitation (Table 2). Noteworthy, from the qualitative analysis and the morphometric measurements, arteriolar morphology was not altered by glycated BSA. Wall-to-lumen ratio data were very similar among the three groups in the experiment (Table 2). Thus, glycated BSA administration did not result in arteriolar thickening (Figure 6B). Further immunohistological examinations of these samples showed that glycated BSA promoted a well-delimited AGE accumulation in the granulation tissue-emerging microvasculature (Figure 3T), which was coincident with the increase of TNF-α (Figure 3U) and RAGE expression (Figure 3V) in granulation tissue structures and infiltrating cells. Of note, granulation tissue samples derived from the rats treated with native BSA did not show AGE accumulation (Figure 3X). Again, TNF-α was limitedly expressed by scar area infiltrating cells and microvascular structures (Figure 3Y). Finally, RAGE expression was not detected (Figure 3Z).
**Figure 6:** *Panels (A, B) correspond to protocol 4. Granulation tissue of rats treated with highly glycated BSA. (A) Image representative of the normal histology of a nerve fascicle and adjacent microvascular, and matrix structure of animals treated with non-glycated, native BSA. It is noticeable the perfect myelin preservation within the endoneurium. H/E. Magnification ×40. (B) Highly glycated BSA damaged the nerve fascicles which exhibit severe myelin loss with the subsequent aspect of cavitation (arrow). Of note, however, is the observation that the abnormal hyperglycated environment did not promote any type of arteriolar wall remodeling (square). H/E. Magnification ×40.*
## Discussion
Despite the limitations of this work (absence of a control healthy donor-derived peripheral nerve, limited number of samples, and incomplete mechanistic insights—including lack of studies addressing the VEGF axis), it demonstrates for the first time that the torpid healing phenotype and cutaneous histopathological hallmarks of diabetes can be experimentally reproduced in a laboratory animal species through the injection of CFF obtained from human diabetic tissue samples.
Given the fact that the first experiment of passive transference of diabetic wound granulation tissue homogenate to healthy rats’ wounds, translated in an unexpected phenotypical recreation of the donor’s arteriolar wall thickening and nerve damages; subsequent experiments examined the potential impact of diabetic artery, and nerve tissues homogenates. Accordingly, this experimental series with diabetic tissues homogenates steadily rendered four major reproducible findings: [1] wound closure delay, [2] arteriolar wall thickening: endothelial encroachment/perivascular hypercellularity and collagenization, [3] nerve fascicle degeneration, and [4] intense inflammation; which were never detected when granulation or arterial tissue homogenates from healthy donors were administered. The histological findings presented here faithfully represent the results of early preliminary pilot experiments, which were supporting for this experimental protocol in terms of dose and time frame of administration. It is worth mentioning that the immunohistochemical characterization studies allow to propose that “diabetic memory transference” was not limited to morphological traits, as it appeared to include diabetic donor’s intrinsic functional alterations like the nuclear expression of p65 phosphorylated in serine 529, and the deficient profile of a hyperactive isoform of e-NOS in rats treated with the diabetic granulation tissue homogenate. The drivers behind this “inherited dysfunctionalities” from the human diabetic donor, to rats’ wound neovessels remain to be identified. Nevertheless, the overexpression of phosphorylated p65 in the diabetic material recipient animals is not attributable to a canonical host versus graft activation as it was only marginally expressed in those rats receiving the healthy human donor granulation tissue. It is also noteworthy that T2DM tissue caused positive Congo red staining in the endothelial collar of the vessels in the granulation tissue of recipient rats. Whether this finding represents passive transference of toxic amyloid, a similar reactant material, or a proximal signaling factor promoting an acute vascular amyloid accumulation remains unknown. This histochemical marker was not identified in the rats’ samples treated with the healthy donor/normal granulation tissue.
The production of a standardized skin wound in rodents is well established for examining factors that enhance or delay the rate of skin healing [35]. The initial studies (granulation tissue experiment) allowed 7 days for wound recovery but was modified to 6 days for subsequent studies to ensure that wounds were not completely closed at harvesting should a test material increase, rather decrease healing. Previous studies examining the effects of diabetes on wound repair used animals that are diabetic through genetic abnormalities [47, 48] or through administration of compounds such as streptozotocin [49, 50]. In contrast, our hypothesis was not reliant on manipulating glucose control in the test animals and we therefore used non-diabetic animals. Thus, our demonstrations on the reproduction of this DM-ulcer phenotype were generated on the background of a normal rat with healthy microvascular and peripheral nervous systems.
We considered tissue needed to be collected and processed immediately in the operating room to prevent deterioration. This limited the number of samples that could be obtained and as we were unable to acquire a sample of a “normal” nerve, effects of T2DM nerve tissue on wound healing were compared against a saline control. All tissue homogenates were microfiltered (0.2 µm) to ensure sterility. This does not exclude that microbial by-products were present in the T2DM ulcer homogenate, but not in the control, or T2DM artery or nerve samples, as they were collected under sterile conditions in the operating room. As similar results were seen using all three T2DM tissue sites, bacterial products are unlikely to be cause of the changes observed. Furthermore, the patient was not in a septic status, and the major amputation was decided due to critical limb ischemia (classified as Rutherford’s category 5), intractable ischemic pain, and not by bone or soft tissue invasive infection. Reviewing of the image study reports allowed us to rule out infection in bone and/or deep soft tissues of the target limb.
Our finding that normal human tissues did not cause the pathological abnormalities seen using T2DM tissue also demonstrates that T2DM-type histological and morphological changes were not due to generic introduction of human xenogeneic material, but triggered by aberrant molecular signalers present within the T2DM tissues. This suggests that in the tissue lysate soluble messengers are contained, acting as driving forces toward the torpid wound healing and an abnormal vascular organization.
The membrane RAGE interacts with a diverse range of endogenous ligands generically termed AGEs. AGE formation is markedly increased in serum and tissues, including atherosclerotic plaques of DM patients [51], and in keeping with this, the cell free homogenates derived from the T2DM granulation and arterial tissues had higher MDA, pro-inflammatory cytokines, and AGEs content compared to their relevant controls. Given that there is evidence on the pathogenic role of the AGE/RAGE signaling pathway in diabetes vascular disease (52–54), and having observed increased RAGE and TNF-α expression in rat wounds injected with T2DM tissues in each experimental protocol; we investigated if AGE/RAGE interactions were the common pathway to explain our results. Thus we tested glycated BSA as an exemplar AGE product in our rat wound model [55]. Although glycated BSA delayed healing, increased inflammation, induced nerve injury, and RAGE expression, it did not result in arteriolar thickening despite the high concentration of AGEs (683.8 ng/mg) attained in our glycation process. In line with this, the diabetic nerve tissue homogenate proved to promote arterial wall hypercellularity and thickening, despite containing the lowest concentrations of AGEs. These evidences suggest that factor(s) other than the toxic impact of highly glycated proteins and inflammatory program-associated signalers within the homogenate, are responsible for the observed vascular wall remodeling. Primarily, a direct potential glucotoxic effect was ruled out because glucose concentrations in the homogenates were all below 5 mmol/L. Previous studies had shown that delayed wound healing in diabetes is not caused by local high-glucose concentration itself [56]. Nevertheless, future studies will examine the presence of intermediate glycation products, and of advanced lipoxidation end-products in the donor tissues homogenates, given their atherogenic nature and their potential contribution to the pathologic vascular remodeling observed in the recipient rats [57, 58].
The factor(s) involved in the phenomenon described here may not be a single molecule, but a combination of signaling agents such as present within cell exosomes. Exosomes that comprise a constellation of subcellular fragments appear to play important roles in cell-to-cell communication, horizontal gene transfer, immune modulation, and participate in the development of diabetes and its associated complications [59]. In support of this notion, recent studies suggest that exosomes may play an important role in the pathophysiology of degenerative conditions such as retinal degeneration [60]. Further work is required to examine these ideas. The futuristic implications of these evidences anticipate additional studies in search for evidences reproducibility, as this protocol was based on the tissues collected from a single diabetic donor.
Current reviews of the pathogenesis of diabetic foot ulceration describe the contributions of neuropathy, microangiopathy, and impaired metabolism [61]. Our studies demonstrated an additional contribution of metabolic memory/tissue priming “drivers” present both within the ulcer and in distant, internal tissues as arteries and nerves. Although we have not identified the exact causative factor within the T2DM tissue responsible for the transmitted changes, our studies have several clinical implications. A. Microvascular changes, assumed to be slowly progressive in T2DM patients, can be rapidly transmittable into a non-diabetic rat model, suggesting pathological vascular changes may occur much quicker than considered currently. B. The interspecies transmission of a chronic granulation tissue phenotype, even when the recipient animal is not diabetic and has a normal vascular bed, shows donor T2DM ulcers contain local tissue signaling factors that may be involved in ulcer initiation, extension, perpetuation, and relapse. C. Delayed healing with associated tissue pathological changes was transmissible using T2DM arterial and nerve tissue, distant from the actual ulcer area, suggesting metabolic memory/tissue priming is present in T2DM tissues distant from ulcerated areas D. Generalized pro-ulcerogenic tissue priming, independent of vascular insufficiency, may help explain why even mild degrees of trauma can precipitate serious ulceration in T2DM patients.
Taken together, our studies suggest that, in addition to the well-established risk factors of vascular insufficiency, neuropathy, and trauma, tissue priming/metabolic memory may be involved in the pathogenesis, failure to heal, and frequent recurrence of skin ulceration seen in T2DM patients. Furthermore, the drivers of these tissue priming/metabolic memory-derived events can be transferred from diabetic humans to normal healthy animals, excluding the existence of inter-species barriers and imposing their diabetic histologic archetypical damages program.
## Data Availability Statement
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.
## Ethics Statement
The animal study was reviewed and approved by Dr. Jorge Castro-Velazco, president, Animal Welfare Board, CIGB, Animal Facility.
## Author Contributions
JB-A contributed to study original idea and design; acquisition of data; analysis and interpretation of data; drafting of the manuscript; and studies supervision. MF-M contributed to study concept and design; acquisition and data curation; analysis and interpretation of data. YM-M contributed to study design; acquisition of data; analysis and interpretation of data. AG-O contributed to study design; acquisition of data; analysis and interpretation of data; drafting of the manuscript. GG-N contributed to study design, analysis and interpretation of data. RP contributed to analysis and interpretation of data and drafting of the manuscript. All authors contributed to the article and approved the submitted version.
## Funding
BioCubaFarma/IBM 3051-280 Research Project Account.
## 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.
## Abbreviations
AGE, advanced glycation end products; BSA, bovine serum albumin; DM, diabetes mellitus; CFF, cells-free filtrate; IL-1β, interleukin 1β; IL-6, interleukin 6; MDA, malondialdehyde; RAGE, receptor for AGE; T2DM, type 2 diabetes mellitus; TNF-α, tumor necrosis factor-a; e-NOS, endothelial nitric oxide synthase; NF-κB, nuclear factor kappa-light-chain-enhancer of activated B cells; rh-EGF, recombinant human Epidermal Growth Factor.
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|
---
title: Worsening glycemic control in youth with type 2 diabetes during COVID-19
authors:
- Sonum Bharill
- Tyger Lin
- Alexander Arking
- Elizabeth A. Brown
- Margaret West
- Kelly Busin
- Sheela N. Magge
- Risa M. Wolf
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012097
doi: 10.3389/fcdhc.2022.968113
license: CC BY 4.0
---
# Worsening glycemic control in youth with type 2 diabetes during COVID-19
## Abstract
### Introduction
The COVID-19 pandemic has disproportionately affected minority and lower socioeconomic populations, who also have higher rates of type 2 diabetes (T2D). The impact of virtual school, decreased activity level, and worsening food insecurity on pediatric T2D is unknown. The goal of this study was to evaluate weight trends and glycemic control in youth with existing T2D during the COVID-19 pandemic.
### Methods
A retrospective study of youth <21 years of age diagnosed with T2D prior to March 11, 2020 was conducted at an academic pediatric diabetes center to compare glycemic control, weight, and BMI in the year prior to the COVID-19 pandemic (March 2019-2020) to during COVID-19 (March 2020-2021). Paired t-tests and linear mixed effects models were used to analyze changes during this period.
### Results
A total of 63 youth with T2D were included (median age 15.0 (IQR 14-16) years, $59\%$ female, $74.6\%$ black, $14.3\%$ Hispanic, $77.8\%$ with Medicaid insurance). Median duration of diabetes was 0.8 (IQR 0.2-2.0) years. There was no difference in weight or BMI from the pre-COVID-19 period compared to during COVID-19 (Weight: 101.5 v 102.9 kg, $$p \leq 0.18$$; BMI: 36.0 v 36.1 kg/m2, $$p \leq 0.72$$). Hemoglobin A1c significantly increased during COVID-19 ($7.6\%$ vs $8.6\%$, $$p \leq 0.0002$$)
### Conclusion
While hemoglobin A1c increased significantly in youth with T2D during the COVID-19 pandemic, there was no significant change in weight or BMI possibly due to glucosuria associated with hyperglycemia. Youth with T2D are at high risk for diabetes complications, and the worsening glycemic control in this population highlights the need to prioritize close follow-up and disease management to prevent further metabolic decompensation.
## Introduction
The COVID-19 pandemic resulted in a worldwide lockdown, during which time most children and adolescents attended school virtually [1]. Rates of overweight and obesity among children and adolescents during this time have increased significantly worldwide, owing to an increase in higher calorie processed foods, decreased activity level, increasing food insecurity and increased rates of stress and anxiety due to the pandemic [2, 3]. Moreover, the COVID-19 pandemic has disproportionately affected minority and lower socioeconomic status populations [4], who have higher rates of obesity, type 2 diabetes (T2D), and associated complications [5, 6].
The obesity epidemic over the last 20 years has led to an increase in youth onset T2D, most notably in non-Hispanic black, Asian/Pacific Islanders and American Indians (5–9). Almost half of new-onset diabetes presenting in adolescents is attributed to type 2 diabetes [10, 11], and youth onset type 2 diabetes is often characterized by a more rapidly progressive decline in B-cell function compared to adults [12]. Studies have also demonstrated a high rate of diabetes-related complications in the majority of youth onset type 2 diabetes that is present by young adulthood [13]. Management of pediatric T2D is aided by adherence to schedules and parental support [14], and negatively affected by depression, sedentary behavior, and a diet rich in processed foods [15]. These are all factors which have been severely disrupted during the COVID-19 pandemic.
The impact of virtual school, decreased activity level, and worsening food insecurity on pediatric T2D has not been thoroughly analyzed, but there is concern for worsening pediatric obesity, thereby exacerbating T2D and worsening glycemic control. A recent study demonstrated an increased incidence of diabetic ketoacidosis (DKA) at initial T2D presentation during the COVID-19 pandemic [16], however there is little data regarding changes in glycemic control among adolescents with known T2D during the COVID-19 pandemic. Among youth with known type 1 diabetes (T1D), studies have shown an increase in DKA with maintenance of overall glycemic control during the COVID-19 pandemic [17, 18]. Clinical experience during the COVID-19 pandemic suggested an increase in overweight and obesity with worsening glycemic control in patients with T2D diagnosed prior to the COVID-19 pandemic. This study sought to evaluate weight trends and glycemic control in youth with known T2D during the COVID-19 pandemic compared to the prior year.
## Participants
A retrospective chart review of youth with T2D seen at an urban academic pediatric diabetes center was conducted to compare glycemic control in the year prior to and the first year of the COVID-19 pandemic. Patients younger than 21 years old, diagnosed with T2D anytime prior to March 11, 2020 and had an endocrine visit in the pre-COVID-19 period with confirmed negative diabetes-associated antibodies (glutamic acid decarboxylase-65, insulin auto-antibody, islet antigen-2 antibody, and/or islet cell antibody) were included in the study. Patients with steroid- or medication-induced diabetes, prediabetes, and post-transplant diabetes were excluded. We compared parameters from visit(s) during the year prior to the COVID-19 pandemic (from March 11, 2019 to March 10, 2020) to those from visit(s) during the COVID-19 period (March 11, 2020 to March 10, 2021). Participants served as their own historical controls. Because this analysis focuses on the changes from the pre-COVID-19 to the COVID-19 period, patients with no follow up in the COVID-19 period were excluded from the analysis ($$n = 3$$). Per the American Diabetes Association guidelines for 2019 and 2020, the goal HbA1c was <$7.0\%$. This study was approved by the institutional review board at the Johns Hopkins Hospital in adherence to the Declaration of Helsinki, with a waiver of consent.
## Data source and variables
Clinical diabetes-related variables were manually extracted from the electronic medical record (EMR). Age at diagnosis, duration of diabetes, weight, body mass index (BMI), and hemoglobin A1c (HbA1c) values from endocrine clinic visits were recorded, as well as modality (in-person or virtual) of visit. In the COVID-19 period when visits may have been via telemedicine and weight, BMI, or HbA1c measurements may not have been available from an endocrine clinic visit, these measures were taken from a non-endocrine visit in proximity to the endocrine visit. Hospital admissions and emergency department (ED) visits per patient during the pre-COVID and COVID periods were tallied from the EMR and Care Everywhere, allowing access to medical records from outside of the primary institution. Where available, we gathered data on virtual or in-person school, and whether parents were at home with the child.
## Statistical analysis
Variables were assessed for normal distribution using the Shapiro-Wilk test. Descriptive statistics were summarized using mean and the standard deviation for continuous normal variables, median and interquartile range for non-normally distributed, and percentage for categorical variables. The Wilcoxon signed rank test was used to compare the number of ED, hospital, endocrine, and missed visits in the two periods. A paired t-test was used to compare the mean weight, BMI, BMI Z-score and HbA1c in the two periods for participants with follow up in the pre and COVID periods. The 3 patients that did not have follow up measures in the COVID-period and were thus excluded, did not differ significantly at baseline in a two-sample T test from those who did have follow up in HbA1c, BMI, BMI z-score or weight. Regression analysis was conducted using a multivariable mixed effects model with a randomly varying intercept. Changes in in trajectory of weight, BMI, BMI Z-score, and HbA1c were assessed during the pre-COVID-19 year to the year with COVID-19 restrictions using a linear spline model with a knot at March 11, 2020. Descriptive variables including gender, age, race, ethnicity, duration of diagnosis, insurance type, if a parent was home in the COVID-19 period, and if the subject was in virtual school were tested for significance in the models as covariates and for their interaction with the slope before and after the knot. SAS version 15.2 was used in all analysis.
## Participants and baseline clinical characteristics
At total of 63 youth with T2D diagnosed prior to the start of the COVID-19 “stay-at-home” order on March 11th, 2020 were included in this analysis. As shown in Table 1, median age of study participants was 15.0 (IQR 14-16) years, $59\%$ were female, $74.6\%$ were African American, and $77.8\%$ had public insurance. At the start of the study time frame, the median duration of diabetes was 0.8 (IQR 0.2-2.0) years, and participants’ median baseline HbA1c was 7.2 (IQR 6.1-9.6) %. Baseline mean weight was 102.7 ± 30.5 kilograms and median baseline BMI was 35.0 (IQR 29.7-43.8) kg/m2. During the pre-COVID year, there were on average 2.2 measures for each of the variables (weight, BMI, BMI z-score, HbA1c), with a range of 1-4 measures per participant. During the COVID year, there were on average 1.4 with a range of 0-4 measures per participant for weight, BMI, BMI z-score, and a range of 0-3 measurements for HbA1c. Pre-COVID, the median number of endocrine visits attended was 2, with a median of one missed endocrine visit. Median number of hospital admissions in the pre-COVID period among study participants was 0 (range 0-6), and average number of ED visits was 0 (range 0-6). In unadjusted analysis, a lower baseline BMI Z-score was significantly associated with female sex ($$p \leq 0.011$$) and longer duration of diabetes diagnosis ($$p \leq 0.0007$$).
**Table 1**
| Variable | Value |
| --- | --- |
| Age in years, median (Q1-Q3) | 15.0 (14–16) |
| Diagnosis duration in years, median (Q1-Q3) | 0.8 (0.2-2.0) |
| Age in years at diagnosis, median (Q1-Q3) | 14 (12–15) |
| Gender, n (%) | Gender, n (%) |
| Female | 37 (59%) |
| Race and ethnicity, n (%) | Race and ethnicity, n (%) |
| Non-Hispanic Black | 47 (74.6%) |
| Non-Hispanic White | 6 (9.5%) |
| Hispanic | 9 (14.3%) |
| Other | 1 (1.6%) |
| Insurance, n (%) | Insurance, n (%) |
| Private | 14 (22.2%) |
| Public | 49 (77.8%) |
| Weight in Kg, mean (SD) | 102.7 (30.5) |
| BMI, median (IQR) | 35 (29.7-43.8) |
| BMI Z-score, median (IQR) | 2.3 (1.9-2.7) |
| Hemoglobin A1c %, median (IQR) | 7.2 (6.1-9.6) |
## Changes in clinical characteristics during COVID-19
During the pre-COVID-19 period, the mean HbA1c was $7.6\%$, which increased to a mean HbA1c of $8.6\%$ during the COVID-19 period ($$p \leq 0.0002$$), (Table 2; Figure 1A). As shown in Figure 1 B., a linear mixed effect spline model with the knot on March 11th, 2020, showed an increase in the slope of HbA1c during the COVID-19 period compared to the pre-COVID period. Compared to the pre-COVID period, where the mean change in HbA1c over time was relatively flat ($0.01\%$ per 30-day month), during the COVID period, the mean increase in HbA1c over time was $0.13\%$ per 30-day month ($$p \leq 0.0397$$). There was no significant change in the mean or trajectory of weight, BMI, or BMI z-score. These findings held true when adjusted for sex, age, race, ethnicity, duration of T2D diagnosis, insurance type, and virtual school attendance.
There was no change in the mean number of endocrine visits, missed endocrine visits, ED visits or hospitalizations from the pre-COVID-19 to COVID-19 period. However, in the pre-COVID-19 period, all diabetes clinic visits were in-person, while in the COVID-19 period, $62.8\%$ of visits were via tele-medicine. Of note, there were 3 patients who did not have in-person follow up during the COVID-19 period and therefore had missing vitals or laboratory testing. These patients were compared to those that had follow-up data available, and no statistically significant differences were identified in baseline characteristics.
## Discussion
This study showed that youth with known T2D experienced worsening glycemic control during the COVID-19 pandemic. Although a pandemic related increase in obesity has been documented worldwide, we did not see a significant change in BMI, BMI z-score, or weight in this study population. This may be due to a detrimental change in body composition from a lack of physical activity, leading to decreased lean mass with an increase in fat mass. Chronic glucosuria caused by suboptimal glycemic control mitigating weight gain may have also played a role in the lack of significant change in BMI, BMI z-score or weight. Others have theorized that parents working from home resulted in more home-cooked meals and an increase in consumption of fruits and vegetables, which may have also led to reduced weight gain [19]. However, data from the United States and internationally suggest that an increase in sedentary behaviors and high calorie snacking in combination with more screen time in response to stress have resulted in weight gain among children and adolescents (19–23). Our findings are similar to a small study from Malaysia of 30 adolescents with T2D who had a significant increase in HbA1c during the COVID-19 pandemic, along with a significant decrease in BMI and weight parameters that they theorized was due to worsened glycemic control and catabolic state [24]. However, these results may not be generalizable to our population in the United States. In the United States, T2D disproportionately affects minority and low-income youth [5, 6], which was reflected in this study’s participants of whom $74.6\%$ were African American, and $77.8\%$ had public insurance. Further, this same population has also been significantly impacted by the COVID-19 pandemic [4, 25] and its consequences, including increasing food insecurity [26] and disparities in the success of virtual school [27]. The transition from in-person to virtual school led to an increase in sedentary behavior [28], along with an expected increase in screen time and a decrease in physical activity [29, 30]. Moreover, the change from in person to video visits ($62.8\%$ of endocrine visits during the COVID-19 period) also likely contributed to the worsening of HbA1c during the COVID-19 pandemic. Experience from prior natural disasters, such as hurricane Katrina in Louisiana and the Hull flood in the UK, suggests that management of chronic diseases, including diabetes, is negatively affected even months to years after these catastrophes [31, 32]. Indeed, in this study, HbA1c increased significantly in this patient population, a trend that may persist and needs to be addressed by the medical community.
T2D in youth has been shown to be distinct from T2D in adults with poorer outcomes, rapid β cell decline, insulin resistance and weight gain [33]. After new diagnosis of T2D many patients are able to discontinue insulin therapy [34]; however, there is a high rate of treatment failure requiring therapy intensification with restarting insulin or initiation of other diabetes medication [35]. While the American Diabetes Association (ADA) 2021 guidelines recommend intensive lifestyle programs with increased physical activity and healthy eating habits to promote weight loss, adherence is suboptimal. While making recommended and sustainable modifications in diet and physical activity has always been difficult for patients with T2D [36], the COVID-19 pandemic has made implementing positive lifestyle changes even more challenging. Recent data suggests that in youth with T2D, reduction in BMI correlates with improvement in HbA1c, an outcome that is more likely to be achieved with medication other than insulin therapy [37]. Chang et al. also found no difference in HbA1c between patients prescribed basal insulin and those prescribed basal plus prandial and correctional insulin, suggesting that providers should consider simplifying insulin regimen. DPP4 inhibitors and SGLT-2 inhibitors, which are being studied in the pediatric population, may expand treatment options, and provide much needed alternatives to insulin’s lipogenic effect. Given the high rate of diabetes associated complications in youth onset T2D, it is important to maintain close patient follow-up and routine monitoring for complications per ADA guidelines [38, 39].
Adolescents with T2D are also at increased risk of depression and anxiety [40]. This mental health burden can lead to increased insulin resistance and worsened glycemic control due to less exercise, worse diet, and less medication adherence [41]. Some have suggested that children may have benefitted from the increase in time spent with family during the COVID-19 pandemic [42]. However, it is likely that fear of COVID-19 exposure to self and loved ones, social isolation, financial strain, and cessation of school-based support systems contributed to worsening of anxiety and depression among youth with T2D [21]. This highlights the importance of the ADA recommendation for yearly depression screening, with appropriate referral to mental health resources, which has become increasingly relevant during the COVID-19 pandemic.
This is among the first reports examining the impact of the COVID-19 pandemic on youth with existing type 2 diabetes. While the patient demographics are diverse, this study is limited by the small study sample from a single institution. This study cohort was predominantly minority youth; thus, results may not be generalizable to other populations. The study cohort includes some participants ($$n = 13$$) who were recently diagnosed with T2D in the year prior to March 10th 2020, which could potentially affect results given they have fewer data points in the pre-COVID period. Also, given the natural progression of T2D they may have improvement in glycemic control shortly after diagnosis, potentially leading to an underestimation of worsening of glycemic control during the study period. As a retrospective study, data regarding school attendance, virtual vs in-person school, parental presence in the home during the COVID-19 period and COVID-19 positivity were not captured consistently in the EMR. Although hospital admission and ED data was collected from the EMR and Care Everywhere, it is possible that participants presented to a hospital system not connected to our EMR and thus ED/hospital admission data may be an underestimate. Because of multiple comparisons made within this study, its findings should be replicated. Larger, prospective, multicenter studies across the United States are recommended to determine the full impact of the COVID-19 pandemic on this high-risk pediatric population with T2D.
In summary, youth with type 2 diabetes experienced a worsening of glycemic control during the COVID-19 pandemic. Providers should work closely with patients to optimize diabetes treatment regimens, while also providing additional resources to address the impact of COVID-19, in an effort to improve glycemic control and prevent long-term diabetes related 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 author.
## Ethics statement
This study was approved by the institutional review board at the Johns Hopkins Hospital in adherence to the Declaration of Helsinki, with a waiver of consent.
## Author contributions
RW and SM conceived of the study. TL, AA, SB, MW, KB, extracted the data. EB analyzed the data. SB and RW wrote the manuscript. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
## 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: 'Clinical risk factors and social needs of 30-day readmission among patients
with diabetes: A retrospective study of the Deep South'
authors:
- Cassidi C. McDaniel
- Chiahung Chou
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012098
doi: 10.3389/fcdhc.2022.1050579
license: CC BY 4.0
---
# Clinical risk factors and social needs of 30-day readmission among patients with diabetes: A retrospective study of the Deep South
## Abstract
### Introduction
Evidence is needed for 30-day readmission risk factors (clinical factors and social needs) among patients with diabetes in the Deep South. To address this need, our objectives were to identify risk factors associated with 30-day readmissions among this population and determine the added predictive value of considering social needs.
### Methods
This retrospective cohort study utilized electronic health records from an urban health system in the Southeastern U.S. The unit of analysis was index hospitalization with a 30-day washout period. The index hospitalizations were preceded by a 6-month pre-index period to capture risk factors (including social needs), and hospitalizations were followed 30 days post-discharge to evaluate all-cause readmissions (1=readmission; 0=no readmission). We performed unadjusted (chi-square and student’s t-test, where applicable) and adjusted analyses (multiple logistic regression) to predict 30-day readmissions.
### Results
A total of 26,332 adults were retained in the study population. Eligible patients contributed a total of 42,126 index hospitalizations, and the readmission rate was $15.21\%$. Risk factors associated with 30-day readmissions included demographics (e.g., age, race/ethnicity, insurance), characteristics of hospitalizations (e.g., admission type, discharge status, length of stay), labs and vitals (e.g., highest and lowest blood glucose measurements, systolic and diastolic blood pressure), co-existing chronic conditions, and preadmission antihyperglycemic medication use. In univariate analyses of social needs, activities of daily living ($p \leq 0.001$), alcohol use ($p \leq 0.001$), substance use ($$p \leq 0.002$$), smoking/tobacco use ($p \leq 0.001$), employment status ($p \leq 0.001$), housing stability ($p \leq 0.001$), and social support ($$p \leq 0.043$$) were significantly associated with readmission status. In the sensitivity analysis, former alcohol use was significantly associated with higher odds of readmission compared to no alcohol use [aOR ($95\%$ CI): 1.121 (1.008-1.247)].
### Conclusions
Clinical assessment of readmission risk in the Deep South should consider patients’ demographics, characteristics of hospitalizations, labs, vitals, co-existing chronic conditions, preadmission antihyperglycemic medication use, and social need (i.e., former alcohol use). Factors associated with readmission risk can help pharmacists and other healthcare providers identify high-risk patient groups for all-cause 30-day readmissions during transitions of care. Further research is needed about the influence of social needs on readmissions among populations with diabetes to understand the potential clinical utility of incorporating social needs into clinical services.
## Introduction
According to the International Diabetes Federation as of 2021, diabetes affects 536.6 million people across the world [1]. Diabetes is most prevalent in high-income countries (HIC) at $11.1\%$ compared to middle-income ($10.8\%$) and low-income ($5.5\%$) countries [1]. Problematic increases in the prevalence of diabetes are expected across the world in the coming decades, and predictions reveal that low- and middle-income countries (LMIC) will account for $94\%$ of the increased prevalence by 2045 [1]. The World Health Organization’s Global Report on Diabetes highlights the burden of diabetes due to complications, such as vision impairment, kidney problems, cardiovascular disease, and lower extremity amputations [2]. Additional burdens resulting from complications of diabetes or other co-existing chronic conditions might include hospitalizations and subsequent readmissions, which are the focus of this report.
Populations with diabetes are at risk of experiencing burdensome hospitalizations. Diabetes and related complications have been estimated to be the fifth leading reason for hospital admissions in the U. S. [3]. The process of patients receiving hospital care, being discharged from hospital care, and returning to home care is known as a ‘transition of care’ [4]. Transitions of care have been referred to as “vulnerable exchange points that contribute to unnecessarily high rates of health services use and health care spending” that “expose chronically ill people to lapses in quality and safety” [4]. This point is particularly true for populations with diabetes, who are highly susceptible to readmissions within the next 30 days after hospital discharge [5, 6]. This 30-day period is a commonly used target indicator for risk standardization by the Centers for Medicare and Medicaid Services (CMS) [7]. Patients with diabetes face significantly higher all-cause 30-day readmission rates than those without diabetes ($24.3\%$ versus $17.7\%$, respectively) [8] and longer hospital stays [9]. These frequent and longer duration hospitalizations constitute a sizeable economic burden globally for patients with diabetes [2]. Reducing 30-day readmission rates is essential to decreasing medical expenditures [10]. Overall, readmissions among populations with diabetes are an important public health issue in diabetes care.
In efforts to inform evidence for reducing readmissions, prior research studied risk factors associated with readmissions for patients with diabetes, but evidence gaps remain. In 2020, a systematic review and meta-analysis pooled findings across 18 studies to estimate the influence of risk factors for 30-day readmissions among populations with diabetes [11]. The risk was higher based on male gender, older age, non-White race, Medicare and Medicaid insurance coverage, the presence of comorbidities, longer length of stay, and use of insulin [11]. In accordance with findings from this systematic review, literature on risk factors has focused mainly on patient demographics and clinical data elements from electronic health records (EHRs) to predict readmissions for populations with diabetes (9, 11–16). Also, the EHRs used in these investigations mostly covered populations in the Northeast region of the U.S., so the generalizability to people living in the Deep *South is* questionable. Additionally, looking outside the scope of demographics and healthcare alone is needed given the recognized impact of non-healthcare factors, like social determinants of health and behaviors, on diabetes care, management, and outcomes [17].
Social and behavioral factors are now widely recognized to influence health outcomes. It is essential to consider these factors (which we will refer to as ‘social needs’) during the investigation of readmissions when these patient-reported measures are captured through data integration in EHRs [18]. CMS defines social needs as “individual-level, adverse social conditions that can negatively impact a person’s health or health care” [19]. Social needs, such as homelessness, substance use, and challenges affording basic needs (e.g., food, clothing, utilities) or healthcare, have been linked with preventable 30-day readmissions [20]. Further, a recent national investigation revealed an increased risk of readmission with a higher number of unmet social needs; the readmission rate more than doubled between having no social needs ($11.5\%$) versus one social need ($27.0\%$) [21]. These prior investigations of social needs were among the general adult population (19–21), so the impact of social needs on readmission for people with diabetes remains unclear.
Patients with diabetes living in the Deep South have not been a population of focus for the literature studying readmission risk or transitions of care, despite the disproportionately higher diabetes prevalence in this area [22]. Thus, our study seeks to expand and generalize prior research on readmission risk factors for patients with diabetes to the Deep South. The novelty of our approach includes expanding the risk factors considered to include social needs, along with differentiation of readmission status by diabetes type (type 1 versus type 2 diabetes). Our objectives were to identify risk factors associated with all-cause 30-day readmissions among patients with diabetes in the Deep South and determine whether social needs added value in predicting all-cause 30-day readmissions. Findings of risk factors associated with readmissions will apply to the unique needs of patients with diabetes in the Deep South. Our definition of the Deep South includes states such as Alabama, Georgia, Louisiana, Mississippi, South Carolina, and Tennessee [23, 24], and findings will be represented by a population of patients with diabetes in Alabama.
## Study design and data source
This retrospective cohort study used EHRs from an urban health system in the Southeastern U.S. from January 1, 2016 through October 1, 2020. Data from the EHRs were generated through routine clinical practice, and the data were not collected for research purposes. The EHRs were de-identified and extracted to be used for secondary research in this study. Because the pre-existing, de-identified EHRs were used, this research did not involve any interaction with patients. The study protocol was reviewed and approved by the Auburn University Institutional Review Board for the Protection of Human Subjects in Research (IRB) under the exempt review application process.
## Study population
Patients eligible for inclusion were adults (≥18 years old at the time of hospital admission) diagnosed with diabetes (type 1 or type 2) before an inpatient hospitalization. Diagnosis of diabetes was identified through at least one diabetes diagnosis code before the index hospitalization (ICD-9-CM or ICD-10-CM from the Chronic Conditions Warehouse [25] or SNOMED code). People without a diabetes diagnosis were included and assumed to have diabetes if a prescription for diabetes medication was ordered during the 6-month period before hospitalization [13, 15]. Patients with diagnosis codes for gestational diabetes were excluded. The cohort flow diagram (Figure 1) depicts how the original sample of adults with diabetes and ≥ one inpatient encounter was selected from the EHRs. From this original sample, we made further restrictions to identify eligible index hospitalizations following the eligibility criteria outlined in Outcomes section below. When identifying the eligible study population, we did not consider patients’ socioeconomic status within the sampling strategy because data for socioeconomics were not available in the EHRs.
**Figure 1:** *Flow diagram for cohort identification.*
## Outcomes
The primary outcome was all-cause 30-day readmission, which was operationalized dichotomously as 1=readmission versus 0=no readmission. We focused on all-cause 30-day readmissions, as opposed to diabetes-related readmissions, to reflect the overall healthcare experiences during readmissions for patients with diabetes. We used index hospitalization as the unit of analysis so that each person could have multiple index hospitalizations. We followed each index hospitalization for 30 days after discharge to evaluate readmission status. Subsequent hospitalizations that were recorded during the same or overlapping time periods (e.g., the same discharge date) were counted as the same hospitalization to avoid duplicate counting of hospitalizations [13, 26].
We required that index hospitalizations be preceded by a 6-month pre-index period to capture baseline risk factors [12]. We also required that index hospitalizations have a 30-day washout period (with no documented inpatient hospitalization) before the admission date to ensure the hospitalization was an initial admission and not a readmission itself [12]. We excluded index hospitalizations that were pregnancy-related [following prior literature of pregnancy-related diagnosis codes [27]] or had a length of stay longer than one year [26]. We also excluded hospitalizations with missing discharge dates or hospitalizations discharged to hospital transfer or unknown/missing discharge status [13, 15]. Hospitalizations discharged to hospice were also excluded to remove patients receiving ‘end-of-life care’ due to the variability of readmissions after discharge to hospice [28]. Lastly, we excluded hospitalizations with documented death during hospitalization [13, 15] or death within 30 days post-discharge (when readmission did not precede the death date).
## Risk factors
The selection and inclusion of specific risk factors were driven by prior literature that identified associations with 30-day readmissions among patients with diabetes. The research conducted previously indicates that various factors may impact readmission risk in diabetes, such as demographics, labs, medication use, healthcare utilization, other chronic conditions, etc. ( 9, 11–15, 26). We incorporated findings of readmission risk factors from the foundational works of Rubin et al. through validation of the Diabetes Early Readmission Risk Indicator (DERRI™) [14, 15] and Karunakaran et al. through expansion of the DERRI™ to include additional pre- and post-discharge risk factors [13]. However, we were limited to including risk factors available through data elements in the EHRs. Details about the measurement of all risk factors can be found in the Supplemental Table (see Supplementary Material).
Prior literature guided our operationalization of risk factors (9, 11–15, 26). We differentiated between type 1 and type 2 diabetes based on diagnosis codes any time before the index hospitalization. Diabetes type was coded as unknown when no diagnosis code was present, but a prescription for a diabetes medication was filled during the 6-month pre-index period. Preadmission prescriptions for diabetes medications were classified into the following medication classes: insulin, metformin, sulfonylureas, thiazolidinediones (TZD), dipeptidyl peptidase 4 (DPP-4) inhibitors, sodium-glucose cotransporter-2 (SGLT2) inhibitors, glucagon-like peptide-1 (GLP-1) receptor agonists, and others (meglitinides, α-glucosidase inhibitors, amylin analog, cycloset, bile acid sequestrants). Prior research found extreme blood glucose measures [i.e., high (>180 mg/dL) and low (<70 mg/dL)] to be associated with readmissions [13], so we retained the highest and lowest blood glucose value during the index hospitalization to represent proxy variables for hyperglycemia and hypoglycemia, respectively. For labs (HbA1c, albumin, creatinine, hematocrit, white blood cell count, potassium, sodium), body mass index (BMI), and blood pressure, we retained the record nearest to the index hospitalization admission date [15]. HbA1c was capped at $3.5\%$ and $18.5\%$ in the EHRs. Implausible values for other labs and blood pressure were excluded following cut-offs recommended by Estiri et al. [ 29], except for creatinine [<0.1 mg/dL or >15 mg/dL was used [30]] and hematocrit (observations outside of six standard deviations were removed as an alternative approach used by Estiri). We also excluded implausible values for BMI [<10 kg/m2 or >100 kg/m2 [30]]. Vitals, including BMI, systolic blood pressure, and diastolic blood pressure, were converted to categorical variables based on clinical cut-points from the National Heart, Lung, and Blood Institute [31] and the American College of Cardiology/American Heart Association [32]. We captured comorbidities using the Charlson Comorbidity Index (CCI) [33].
Risk factors for the index hospitalization were included, such as length of stay, admission type (i.e., emergency, elective, urgent, trauma), and discharge status (i.e., home, home health, skilled nursing facility, against medical advice, other). Discharge statuses of ‘other’ included discharges to court/law enforcement, custodial care or support, federal facilities, psychiatric facilities, short-term facilities, and other facilities. We captured macrovascular and microvascular complications using ICD-9-CM codes from Karunakaran et al. [ 13], and we translated these to ICD-10-CM codes using the following resources: icd9data.com, icd10data.com, and Glasheen et al. [ 33]. Social needs were available through structured data obtained from patients’ self-reported measures in the EHRs; the social needs were descriptive measures, not validated questionnaires. We followed the definition and conceptualization from CMS to include the following 20 social needs [19]: activities of daily living, feeling unsafe at home, alcohol use, substance use, smoking/tobacco use, e-cigs/vaping device use, household characteristics (i.e., abuse, alcohol abuse, substance abuse, smoking), employment, work activity level, education, financial security, housing stability, living situation, social support, feeling stressed, stressors, and physical activity. We retained the social needs response from the record nearest to the index hospitalization admission date. Some social needs were not included in the analyses due to high rates of missingness in the data source. For instance, food security was not included because responses were missing for >$99\%$ of index hospitalization records.
## Statistical analysis
We stratified index hospitalizations by all-cause 30-day readmission status. For univariate analyses, chi-square and t-tests (where applicable) compared risk factors for index hospitalizations with versus without 30-day readmission. Risk factors associated with 30-day readmission status at the $p \leq 0.10$ level were included in the adjusted analyses [13]. For adjusted analyses, we used logistic regression with generalized estimating equations (GEE) to account for the correlation within individuals because one individual could have multiple index hospitalizations (13–15). We employed a multivariable logistic regression model with GEE for the adjusted analysis and reported adjusted odds ratios (aOR) and $95\%$ confidence intervals ($95\%$ CIs) for risk factors to demonstrate their relationship with 30-day readmission status. We did not include macrovascular complications in the adjusted regression model because this information was already captured in the CCI calculation, while microvascular complications were retained in the model.
We used chi-square tests to compare social needs for index hospitalizations with versus without 30-day readmission. We evaluated whether social needs added value in predicting all-cause 30-day readmissions by comparing a multivariable logistic regression model without social needs to the model with social needs. After controlling for all covariates in the baseline regression model, a significant association between the newly added variables (i.e., social needs) was considered to add predictive value [34]. We used SAS, version 9.4 (SAS Institute, Cary, NC) for data analyses.
## Handling of missing data
We treated missing EHRs data to be missing at random based on the possibility that the documentation of clinical records or reporting of social needs could be related to patients’ healthcare utilization. We imputed missing data using multiple imputations by chained equations (MICE), also known as fully conditional specification, which has been recommended as a valid method to handle missing EHR data [35]. During the imputation of missing data, we followed recommendations from Wells et al. to perform imputation while considering variables representing healthcare use (prior hospitalization, follow-up appointment post-discharge), diabetes severity (HbA1c), comorbidities (CCI score), socio-economic status (insurance type), and the outcome (30-day readmission) [35]. The levels of missingness were highest for social needs variables (reported in Table 2), so we performed a sensitivity analysis to remove social needs with high levels of missingness (≥$70\%$) from the regression model.
## Results
Among the original sample of 34,073 adults with diabetes and one or more inpatient hospitalization, 26,332 adults were retained after applying eligibility criteria (Figure 1). Of the 42,126 index hospitalizations, 6407 ($15.21\%$) were followed by all-cause 30-day readmissions. Among those readmitted, the average time to readmission was approximately 13 days (Mean (SD)=13.34 (8.41), Median=12.00, Min=1, Max=30).
## Characteristics of study population
Personal characteristics, including race/ethnicity, marital status, and insurance coverage, were associated with readmission status ($p \leq 0.001$), while age and gender were not significantly associated with readmission status. The year of discharge was not significant in univariate analysis ($$p \leq 0.862$$). The distribution of other risk factors with readmission status can be found in Table 1.
**Table 1**
| Characteristics/Risk factors | Readmission statusN=26,332Hospitalizations (Hosp.)=42,126 | Readmission statusN=26,332Hospitalizations (Hosp.)=42,126.1 | Missing % | p-value |
| --- | --- | --- | --- | --- |
| | ReadmissionHosp. = 6407 | No readmissionHosp. = 35,719 | | |
| Age, Mean (SD) | 59.98 (14.52) | 60.33 (14.57) | 0 | 0.080 |
| Race/Ethnicity, N (%) | | | 0 | <0.001 |
| Non-Hispanic White | 3281 (51.21) | 18,263 (51.13) | 0 | <0.001 |
| Non-Hispanic Black | 2727 (42.56) | 14,751 (41.30) | 0 | <0.001 |
| Non-Hispanic Asian | 143 (2.23) | 741 (2.07) | 0 | <0.001 |
| Hispanic | 89 (1.39) | 545 (1.53) | 0 | <0.001 |
| Other/unknown | 167 (2.61) | 1419 (3.97) | 0 | <0.001 |
| Gender, N (%) | | | 0 | 0.504 |
| Female | 3139 (48.99) | 17,662 (49.45) | 0 | 0.504 |
| Male | 3268 (51.01) | 18,057 (50.55) | 0 | 0.504 |
| Marital status, N (%) | | | <1 | <0.001 |
| Married/Life partner | 2691 (42.09) | 15,804 (44.72) | <1 | <0.001 |
| Single | 1866 (29.18) | 10,121 (28.64) | <1 | <0.001 |
| Widowed | 812 (12.70) | 4423 (12.52) | <1 | <0.001 |
| Divorced | 859 (13.43) | 4197 (11.88) | <1 | <0.001 |
| Separated | 166 (2.60) | 792 (2.24) | <1 | <0.001 |
| Insurance, N (%) | | | 0 | <0.001 |
| Medicare | 3970 (61.96) | 20,866 (58.42) | 0 | <0.001 |
| Private | 1063 (16.59) | 7215 (20.20) | 0 | <0.001 |
| Medicaid | 859 (13.41) | 3815 (10.68) | 0 | <0.001 |
| Self-pay | 235 (3.67) | 1788 (5.01) | 0 | <0.001 |
| Other/unknown | 134 (2.09) | 1011 (2.83) | 0 | <0.001 |
| VA/Tricare | 81 (1.26) | 629 (1.76) | 0 | <0.001 |
| Indigent/charity care | 65 (1.01) | 395 (1.11) | 0 | <0.001 |
| Diabetes type, N (%) | | | 0 | <0.001 |
| Type 2 | 5939 (92.70) | 33,600 (94.07) | 0 | <0.001 |
| Type 1 | 366 (5.71) | 1850 (5.18) | 0 | <0.001 |
| Unknown | 102 (1.59) | 269 (0.75) | 0 | <0.001 |
| Admission type, N (%) | | | <0.1 | <0.001 |
| Emergency | 4304 (67.18) | 21,875 (61.27) | <0.1 | <0.001 |
| Elective | 1040 (16.23) | 8220 (23.02) | <0.1 | <0.001 |
| Urgent | 974 (15.20) | 4599 (12.88) | <0.1 | <0.001 |
| Trauma | 89 (1.39) | 1007 (2.82) | <0.1 | <0.001 |
| Discharge status, N (%) | | | 0 | <0.001 |
| Home | 3448 (53.82) | 22,186 (62.11) | 0 | <0.001 |
| Home health | 2068 (32.28) | 9593 (26.86) | 0 | <0.001 |
| Skilled nursing facility | 732 (11.43) | 3153 (8.83) | 0 | <0.001 |
| Against medical advice | 57 (0.89) | 245 (0.69) | 0 | <0.001 |
| Other | 102 (1.59) | 542 (1.52) | 0 | <0.001 |
| Body mass index, N (%) | | | 11 | <0.001 |
| Underweight (<18.5 kg/m2) | 185 (3.18) | 705 (2.23) | 11 | <0.001 |
| Healthy weight (18.5-24.9 kg/m2) | 1333 (22.93) | 6043 (19.15) | 11 | <0.001 |
| Overweight (25.0-29.9 kg/m2) | 1581 (27.20) | 8630 (27.34) | 11 | <0.001 |
| Obese (≥30 kg/m2) | 2714 (46.69) | 16,183 (51.28) | 11 | <0.001 |
| Systolic blood pressure, N (%) | | | 0 | <0.001 |
| Normal/elevated (<130 mmHg) | 2968 (46.32) | 14,953 (41.86) | 0 | <0.001 |
| Stage 1 HTN (130-139 mmHg) | 896 (13.98) | 5291 (14.81) | 0 | <0.001 |
| Stage 2 HTN (≥140 mmHg) | 2543 (39.69) | 15,475 (43.32) | 0 | <0.001 |
| Diastolic blood pressure, N (%) | | | 0 | <0.001 |
| Normal (<80 mmHg) | 4257 (66.44) | 22,863 (64.01) | 0 | <0.001 |
| Stage 1 HTN (80-89 mmHg) | 1138 (17.76) | 7255 (20.31) | 0 | <0.001 |
| Stage 2 HTN (≥90 mmHg) | 1012 (15.80) | 5601 (15.68) | 0 | <0.001 |
| HbA1c (%), Mean (SD) | 7.74 (2.37) | 7.84 (2.42) | 27 | 0.009 |
| Albumin (gm/dL), Mean (SD) | 3.48 (0.64) | 3.64 (0.60) | 19 | <0.001 |
| Creatinine (mg/dL), Mean (SD) | 2.05 (2.32) | 1.75 (2.02) | <1 | <0.001 |
| Highest blood glucose (mg/dL), Mean (SD) | 283.80 (133.1) | 270.30 (127.6) | <1 | <0.001 |
| Lowest blood glucose (mg/dL), Mean (SD) | 94.14 (34.81) | 100.70 (36.43) | <1 | <0.001 |
| Hematocrit (%), Mean (SD) | 33.91 (6.88) | 35.63 (6.64) | <1 | <0.001 |
| White blood cell count (103/cmm), Mean (SD) | 9.98 (10.02) | 9.74 (6.19) | <1 | 0.062 |
| Potassium (mMol/L), Mean (SD) | 4.19 (0.67) | 4.16 (0.63) | <1 | 0.006 |
| Sodium (mMol/L), Mean (SD) | 136.30 (4.43) | 136.60 (4.16) | <1 | <0.001 |
| Length of stay (days), Mean (SD) | 8.16 (10.53) | 6.42 (9.34) | 0 | <0.001 |
| Charlson Comorbidity Index, Mean (SD) | 6.95 (4.03) | 5.38 (3.71) | 0 | <0.001 |
| Macrovascular complications, N (%) | | | 0 | <0.001 |
| 0 | 2025 (31.61) | 13,733 (38.45) | 0 | <0.001 |
| 1 | 1678 (26.19) | 10,121 (28.34) | 0 | <0.001 |
| 2 | 1775 (27.70) | 8307 (23.26) | 0 | <0.001 |
| 3 | 814 (12.70) | 3177 (8.89) | 0 | <0.001 |
| 4 | 115 (1.79) | 381 (1.07) | 0 | <0.001 |
| Microvascular complications, N (%) | | | 0 | <0.001 |
| 0 | 3037 (47.40) | 20,455 (57.27) | 0 | <0.001 |
| 1 | 2241 (34.98) | 10,828 (30.31) | 0 | <0.001 |
| 2 | 870 (13.58) | 3521 (9.86) | 0 | <0.001 |
| 3 | 259 (4.04) | 915 (2.56) | 0 | <0.001 |
| Anemia diagnosis, N (%) | | | 0 | <0.001 |
| No | 2476 (38.65) | 19,055 (53.35) | 0 | <0.001 |
| Yes | 3931 (61.35) | 16,664 (46.65) | 0 | <0.001 |
| Preadmission insulin use, N (%) | | | 0 | <0.001 |
| No | 3208 (50.07) | 22,238 (62.26) | 0 | <0.001 |
| Yes | 3199 (49.93) | 13,481 (37.74) | 0 | <0.001 |
| Preadmission metformin use, N (%) | | | 0 | <0.001 |
| No | 5551 (86.64) | 29,867 (83.62) | 0 | <0.001 |
| Yes | 856 (13.36) | 5852 (16.38) | 0 | <0.001 |
| Preadmission sulfonylurea use, N (%) | | | 0 | 0.210 |
| No | 5973 (93.23) | 33,143 (92.79) | 0 | 0.210 |
| Yes | 434 (6.77) | 2576 (7.21) | 0 | 0.210 |
| Preadmission GLP-1 use, N (%) | | | 0 | 0.020 |
| No | 6284 (98.08) | 34,863 (97.60) | 0 | 0.020 |
| Yes | 123 (1.92) | 856 (2.40) | 0 | 0.020 |
| Preadmission DPP-4 use, N (%) | | | 0 | 0.653 |
| No | 6236 (97.33) | 34,730 (97.23) | 0 | 0.653 |
| Yes | 171 (2.67) | 989 (2.77) | 0 | 0.653 |
| Preadmission SGLT2 use, N (%) | | | 0 | 0.060 |
| No | 6337 (98.91) | 35,224 (98.61) | 0 | 0.060 |
| Yes | 70 (1.09) | 495 (1.39) | 0 | 0.060 |
| Preadmission TZD use, N (%) | | | 0 | 0.011 |
| No | 6373 (99.47) | 35,421 (99.17) | 0 | 0.011 |
| Yes | 34 (0.53) | 298 (0.83) | 0 | 0.011 |
| Preadmission other diabetes medications*, N (%) | | | 0 | 0.101 |
| No | 6365 (99.34) | 35,542 (99.50) | 0 | 0.101 |
| Yes | 42 (0.66) | 177 (0.50) | 0 | 0.101 |
| Prior admission within 90 days of index hospital admission, N (%) | | | 0 | <0.001 |
| No | 5712 (89.15) | 34,026 (95.26) | 0 | <0.001 |
| Yes | 695 (10.85) | 1693 (4.74) | 0 | <0.001 |
| Discharge status of most recent hospital stay within last year, N (%) | | | 0 | <0.001 |
| No hospitalization within past year | 4638 (72.39) | 30,449 (85.25) | 0 | <0.001 |
| Home | 925 (14.44) | 2847 (7.97) | 0 | <0.001 |
| Home health | 540 (8.43) | 1483 (4.15) | 0 | <0.001 |
| Skilled nursing facility | 136 (2.12) | 364 (1.02) | 0 | <0.001 |
| Against medical advice | 20 (0.31) | 42 (0.12) | 0 | <0.001 |
| Other | 148 (2.31) | 534 (1.50) | 0 | <0.001 |
| Follow-up appointment after discharge, N (%) | | | 0 | 0.001 |
| Yes | 4144 (64.68) | 22,331 (62.52) | 0 | 0.001 |
| No | 2263 (35.32) | 13,388 (37.48) | 0 | 0.001 |
| Discharge year, N (%) | | | 0 | 0.862 |
| 2016 | 671 (10.47) | 3782 (10.59) | 0 | 0.862 |
| 2017 | 1522 (23.76) | 8322 (23.30) | 0 | 0.862 |
| 2018 | 1571 (24.52) | 8751 (24.50) | 0 | 0.862 |
| 2019 | 1593 (24.86) | 9075 (25.41) | 0 | 0.862 |
| 2020 | 1050 (16.39) | 5789 (16.21) | 0 | 0.862 |
## Distribution of social needs by readmission status
The distributions of social needs by readmission status are presented in Table 2. For activities of daily living, readmission was associated with higher distributions of needing some help or being dependent on others compared to no readmission ($p \leq 0.001$; see Table 2 for frequencies). Readmission was associated with higher distributions of former alcohol use ($p \leq 0.001$), former substance use ($$p \leq 0.002$$), and former smoking/tobacco use ($p \leq 0.001$) compared to no readmission. Readmission was associated with higher distributions of disabled employment status and lower distributions of employed status compared to no readmission ($p \leq 0.001$). Unstable housing was more frequent among the readmission group than the no-readmission group ($p \leq 0.001$), and social support was significantly associated with readmission status ($$p \leq 0.043$$). Social needs demonstrating an insignificant relationship with readmission status included feeling unsafe at home, household characteristics (i.e., abuse, alcohol abuse, substance abuse, smoking), using vaping devices, work activity level, education level, financial security, living situation, feeling stressed, types of stressors, and participating in physical activity (p≥0.05 for all).
**Table 2**
| Social needs | Readmission statusN=26,332Hospitalizations (Hosp.)=42,126 | Readmission statusN=26,332Hospitalizations (Hosp.)=42,126.1 | Missing % | p-value |
| --- | --- | --- | --- | --- |
| | ReadmissionHosp. = 6407 Hosp. (%) | No readmissionHosp. = 35,719 Hosp. (%) | | |
| Activities of daily living | | | 80.0 | <0.001 |
| Independent | 1038 (72.13) | 5548 (77.10) | 80.0 | <0.001 |
| Needs some help | 317 (22.03) | 1345 (18.69) | 80.0 | <0.001 |
| Dependent | 84 (5.84) | 303 (4.21) | 80.0 | <0.001 |
| Feels unsafe at home | | | 70.0 | 0.340 |
| No | 2005 (96.81) | 10,278 (96.39) | 70.0 | 0.340 |
| Yes | 66 (3.19) | 385 (3.61) | 70.0 | 0.340 |
| Household abuse | | | 45.0 | 0.633 |
| No | 3586 (99.17) | 19,464 (99.09) | 45.0 | 0.633 |
| Yes | 30 (0.83) | 179 (0.91) | 45.0 | 0.633 |
| Alcohol use | | | 44.0 | <0.001 |
| Current | 628 (17.41) | 4496 (22.50) | 44.0 | <0.001 |
| Former | 586 (16.24) | 2502 (12.52) | 44.0 | <0.001 |
| | 2280 (63.19) | 12,329 (61.71) | 44.0 | <0.001 |
| Within the past year | 114 (3.16) | 651 (3.26) | 44.0 | <0.001 |
| Substance use | | | 45.0 | 0.002 |
| Current | 193 (5.41) | 1143 (5.80) | 45.0 | 0.002 |
| Former | 255 (7.15) | 1123 (5.70) | 45.0 | 0.002 |
| | 3078 (86.34) | 17,275 (87.68) | 45.0 | 0.002 |
| Within the past year | 39 (1.09) | 162 (0.82) | 45.0 | 0.002 |
| Smoking/tobacco use | | | 40.0 | <0.001 |
| Current smoker | 743 (19.02) | 4260 (19.79) | 40.0 | <0.001 |
| Former smoker | 1507 (38.58) | 7582 (35.22) | 40.0 | <0.001 |
| Never smoked | 1656 (42.40) | 9685 (44.99) | 40.0 | <0.001 |
| E-cigs/vaping device use | | | 90.0 | 0.542 |
| No | 649 (96.43) | 3543 (96.88) | 90.0 | 0.542 |
| Yes | 24 (3.57) | 114 (3.12) | 90.0 | 0.542 |
| Household alcohol abuse | | | 85.0 | 0.955 |
| No | 1019 (96.31) | 4909 (96.35) | 85.0 | 0.955 |
| Yes | 39 (3.69) | 186 (3.65) | 85.0 | 0.955 |
| Household substance abuse | | | 86.0 | 0.688 |
| No | 1007 (95.90) | 4834 (95.63) | 86.0 | 0.688 |
| Yes | 43 (4.10) | 221 (4.37) | 86.0 | 0.688 |
| Household smoking | | | 86.0 | 0.379 |
| No | 771 (73.92) | 3782 (75.22) | 86.0 | 0.379 |
| Yes | 272 (26.08) | 1246 (24.78) | 86.0 | 0.379 |
| Employment status | | | 63.0 | <0.001 |
| Retired | 853 (35.36) | 4805 (36.46) | 63.0 | <0.001 |
| Disabled | 890 (36.90) | 3927 (29.80) | 63.0 | <0.001 |
| Employed | 333 (13.81) | 2515 (19.08) | 63.0 | <0.001 |
| Unemployed | 292 (12.11) | 1639 (12.44) | 63.0 | <0.001 |
| Others (part time or student) | 44 (1.82) | 292 (2.22) | 63.0 | <0.001 |
| Work activity level | | | 96.0 | 0.702 |
| Desk/office | 90 (33.83) | 452 (30.79) | 96.0 | 0.702 |
| Heavy physical work | 38 (14.29) | 218 (14.85) | 96.0 | 0.702 |
| Moderate physical work | 74 (27.82) | 403 (27.45) | 96.0 | 0.702 |
| Occasional physical work | 64 (24.06) | 395 (26.91) | 96.0 | 0.702 |
| Education level | | | 93.0 | 0.681 |
| High school or less than high school | 220 (46.03) | 1282 (48.88) | 93.0 | 0.681 |
| Some college | 157 (32.85) | 815 (31.07) | 93.0 | 0.681 |
| University degree | 74 (15.48) | 396 (15.10) | 93.0 | 0.681 |
| Postgraduate | 27 (5.65) | 130 (4.96) | 93.0 | 0.681 |
| Financial security | | | 88.0 | 0.766 |
| No | 675 (79.98) | 3340 (80.42) | 88.0 | 0.766 |
| Yes | 169 (20.02) | 813 (19.58) | 88.0 | 0.766 |
| Housing stability | | | 54.0 | <0.001 |
| Home | 2712 (91.19) | 15,487 (93.35) | 54.0 | <0.001 |
| Unstable housing | 36 (1.21) | 143 (0.86) | 54.0 | <0.001 |
| Homeless | 40 (1.34) | 230 (1.39) | 54.0 | <0.001 |
| Others | 186 (6.25) | 730 (4.40) | 54.0 | <0.001 |
| Living situation | | | 49.0 | 0.864 |
| Lives with someone | 2364 (74.60) | 13,704 (74.45) | 49.0 | 0.864 |
| Lives alone | 805 (25.40) | 4702 (25.55) | 49.0 | 0.864 |
| Social support | | | 86.0 | 0.043 |
| Yes | 907 (89.45) | 4254 (87.14) | 86.0 | 0.043 |
| No | 107 (10.55) | 628 (12.86) | 86.0 | 0.043 |
| Feels stressed | | | 92.0 | 0.480 |
| No | 410 (71.18) | 1990 (69.70) | 92.0 | 0.480 |
| Yes | 166 (28.82) | 865 (30.30) | 92.0 | 0.480 |
| Stressors | | | 98.0 | 0.083 |
| Health-related stressor | 89 (61.38) | 464 (59.72) | 98.0 | 0.083 |
| Finances | 33 (22.76) | 134 (17.25) | 98.0 | 0.083 |
| Social stressor | 23 (15.86) | 179 (23.04) | 98.0 | 0.083 |
| Physical activity | | | 70.0 | 0.417 |
| No | 1213 (58.07) | 6029 (57.10) | 70.0 | 0.417 |
| Yes | 876 (41.93) | 4529 (42.90) | 70.0 | 0.417 |
## Likelihood of readmission from regression models
The adjusted odds for the multivariable logistic regression models without and with social needs are presented in Table 3. Here, we summarize results from the multivariable logistic regression model without social needs. Increasing age was associated with a lower likelihood of readmission [aOR ($95\%$ CI): 0.995 (0.993-0.998)]. Patients of non-Hispanic Black or other/unknown race/ethnicity had lower odds of being readmitted than patients of non-Hispanic White race/ethnicity [aOR ($95\%$ CI): 0.888 (0.833-0.946) and 0.715 (0.604-0.846), respectively]. Patients with Medicare or Medicaid were significantly more likely to be readmitted than those covered by private insurance [aOR ($95\%$ CI): 1.133 (1.043-1.232) and 1.315 (1.182-1.464), respectively]. While there was no significant difference in readmission between type 1 and type 2 diabetes, patients with unknown diabetes had higher odds of readmission than patients with type 2 diabetes [aOR ($95\%$ CI): 1.550 (1.212-1.981)].
**Table 3**
| Risk factors | Multivariable logistic regression model without social needsaOR (95% CI)N=26,332Hospitalizations=42,126 | Multivariable logistic regression model with social needsaOR (95% CI)N=26,332Hospitalizations =42,126 |
| --- | --- | --- |
| Age | 0.995 (0.993-0.998)* | 0.996 (0.992-0.999)* |
| Race/Ethnicity | Race/Ethnicity | Race/Ethnicity |
| Non-Hispanic White | (ref.) | (ref.) |
| Non-Hispanic Black | 0.888 (0.833-0.946)* | 0.875 (0.808-0.948)* |
| Non-Hispanic Asian | 0.913 (0.755-1.105) | 0.872 (0.714-1.065) |
| Hispanic | 0.894 (0.705-1.133) | 0.869 (0.662-1.141) |
| Other/unknown | 0.715 (0.604-0.846)* | 0.700 (0.584-0.840)* |
| Marital status | Marital status | Marital status |
| Married/Life partner | (ref.) | (ref.) |
| Single | 0.985 (0.913-1.062) | 0.982 (0.897-1.075) |
| Widowed | 0.999 (0.911-1.096) | 1.037 (0.930-1.157) |
| Divorced | 1.075 (0.984-1.174) | 1.067 (0.960-1.186) |
| Separated | 1.096 (0.916-1.312) | 1.144 (0.937-1.397) |
| Insurance | Insurance | Insurance |
| Medicare | 1.133 (1.043-1.232)* | 1.098 (0.957-1.261) |
| Private | (ref.) | (ref.) |
| Medicaid | 1.315 (1.182-1.464)* | 1.225 (1.054-1.425)* |
| Self-pay | 0.960 (0.818-1.126) | 0.933 (0.765-1.138) |
| Other/unknown | 0.940 (0.771-1.146) | 0.943 (0.747-1.191) |
| VA/Tricare | 0.958 (0.749-1.226) | 1.193 (0.694-2.052) |
| Indigent/charity care | 1.074 (0.813-1.419) | 1.200 (0.832-1.729) |
| Diabetes type | Diabetes type | Diabetes type |
| Type 2 | (ref.) | (ref.) |
| Type 1 | 0.925 (0.810-1.056) | 0.958 (0.813-1.128) |
| Unknown | 1.550 (1.212-1.981)* | 1.485 (1.078-2.046)* |
| Admission type | Admission type | Admission type |
| Emergency | (ref.) | (ref.) |
| Elective | 0.791 (0.729-0.858)* | 0.785 (0.714-0.863)* |
| Urgent | 1.027 (0.947-1.114) | 1.013 (0.926-1.107) |
| Trauma | 0.553 (0.441-0.693)* | 0.569 (0.450-0.720)* |
| Discharge status | Discharge status | Discharge status |
| Home | (ref.) | (ref.) |
| Home health | 1.201 (1.126-1.282)* | 1.198 (1.111-1.291)* |
| Skilled nursing facility | 1.201 (1.086-1.329)* | 1.158 (0.998-1.342) |
| Against medical advice | 1.297 (0.956-1.758) | 1.338 (0.953-1.878) |
| Other | 0.818 (0.652-1.026) | 0.717 (0.530-0.970)* |
| Body mass index | Body mass index | Body mass index |
| Underweight (<18.5 kg/m2) | 1.116 (0.937-1.330) | 1.094 (0.882-1.357) |
| Healthy weight (18.5-24.9 kg/m2) | 1.083 (1.002-1.170)* | 1.067 (0.976-1.166) |
| Overweight (25.0-29.9 kg/m2) | 1.029 (0.960-1.103) | 1.036 (0.963-1.113) |
| Obese (≥30 kg/m2) | (ref.) | (ref.) |
| Systolic blood pressure | Systolic blood pressure | Systolic blood pressure |
| Normal/elevated (<130 mmHg) | (ref.) | (ref.) |
| Stage 1 HTN (130-139 mmHg) | 0.955 (0.878-1.038) | 0.959 (0.874-1.052) |
| Stage 2 HTN (≥140 mmHg) | 0.896 (0.840-0.955)* | 0.888 (0.828-0.954)* |
| Diastolic blood pressure | Diastolic blood pressure | Diastolic blood pressure |
| Normal (<80 mmHg) | (ref.) | (ref.) |
| Stage 1 HTN (80-89 mmHg) | 0.923 (0.857-0.995)* | 0.954 (0.862-1.056) |
| Stage 2 HTN (≥90 mmHg) | 1.092 (1.005-1.188)* | 1.116 (1.012-1.231)* |
| HbA1c (%) | 0.985 (0.967-1.004) | 0.989 (0.970-1.009) |
| Albumin (gm/dL) | 0.867 (0.823-0.913)* | 0.882 (0.826-0.941)* |
| Creatinine (mg/dL) | 0.996 (0.981-1.011) | 0.997 (0.978-1.016) |
| Highest blood glucose (mg/dL) | 1.001 (1.000-1.001)* | 1.000 (1.000-1.001)* |
| Lowest blood glucose (mg/dL) | 0.999 (0.998-1.000)* | 0.999 (0.998-1.000)* |
| Hematocrit (%) | 0.985 (0.980-0.990)* | 0.984 (0.979-0.989)* |
| White blood cell count (103/cmm) | 1.004 (1.001-1.007)* | 1.003 (1.000-1.007) |
| Potassium (mMol/L) | 1.013 (0.970-1.058) | 1.016 (0.966-1.069) |
| Sodium (mMol/L) | 0.994 (0.987-1.001) | 0.994 (0.986-1.002) |
| Length of stay (days) | 1.006 (1.003-1.009)* | 1.006 (1.003-1.009)* |
| Charlson Comorbidity Index | 1.056 (1.047-1.064)* | 1.052 (1.043-1.062)* |
| Microvascular complications | Microvascular complications | Microvascular complications |
| 0 | (ref.) | (ref.) |
| 1 | 1.040 (0.973-1.111) | 1.022 (0.947-1.104) |
| 2 | 1.031 (0.936-1.136) | 1.036 (0.934-1.150) |
| 3 | 1.109 (0.945-1.302) | 1.211 (0.978-1.499) |
| Anemia diagnosis | Anemia diagnosis | Anemia diagnosis |
| No | (ref.) | (ref.) |
| Yes | 1.125 (1.052-1.204)* | 1.125 (1.044-1.213)* |
| Preadmission insulin use | Preadmission insulin use | Preadmission insulin use |
| No | (ref.) | (ref.) |
| Yes | 1.192 (1.118-1.270)* | 1.219 (1.130-1.315)* |
| Preadmission metformin use | Preadmission metformin use | Preadmission metformin use |
| No | (ref.) | (ref.) |
| Yes | 0.921 (0.847-1.001) | 0.924 (0.845-1.010) |
| Preadmission GLP-1 use | Preadmission GLP-1 use | Preadmission GLP-1 use |
| No | (ref.) | (ref.) |
| Yes | 0.923 (0.756-1.126) | 1.045 (0.805-1.356) |
| Preadmission SGLT2 use | Preadmission SGLT2 use | Preadmission SGLT2 use |
| No | (ref.) | (ref.) |
| Yes | 1.054 (0.812-1.370) | 1.189 (0.857-1.648) |
| Preadmission TZD use | Preadmission TZD use | Preadmission TZD use |
| No | (ref.) | (ref.) |
| Yes | 0.728 (0.506-1.048) | 0.755 (0.504-1.131) |
| Prior admission within 90 days of index hospital admission | Prior admission within 90 days of index hospital admission | Prior admission within 90 days of index hospital admission |
| No | (ref.) | (ref.) |
| Yes | 1.149 (1.023-1.289)* | 1.169 (1.032-1.323)* |
| Discharge status of most recent hospital stay within last year | Discharge status of most recent hospital stay within last year | Discharge status of most recent hospital stay within last year |
| No hospitalization within past year | (ref.) | (ref.) |
| Home | 1.504 (1.367-1.655)* | 1.448 (1.302-1.611)* |
| Home health | 1.438 (1.276-1.620)* | 1.381 (1.200-1.589)* |
| Skilled nursing facility | 1.353 (1.090-1.680)* | 1.166 (0.847-1.604) |
| Against medical advice | 2.206 (1.256-3.847)* | 2.025 (1.121-3.660)* |
| Other | 1.119 (0.918-1.364) | 1.036 (0.827-1.298) |
| Follow-up appointment after discharge | Follow-up appointment after discharge | Follow-up appointment after discharge |
| Yes | (ref.) | (ref.) |
| No | 0.927 (0.873-0.985)* | 0.934 (0.871-1.002) |
| Activities of daily living | Activities of daily living | Activities of daily living |
| Independent | - | (ref.) |
| Needs some help | - | 0.986 (0.851-1.143) |
| Dependent | - | 1.141 (0.854-1.523) |
| Alcohol use | Alcohol use | Alcohol use |
| Current | - | 0.947 (0.839-1.069) |
| Former | - | 1.114 (0.995-1.247) |
| | - | (ref.) |
| Within the past year | - | 1.048 (0.814-1.351) |
| Substance use | Substance use | Substance use |
| Current | - | 1.013 (0.806-1.273) |
| Former | - | 1.047 (0.886-1.236) |
| | - | (ref.) |
| Within the past year | - | 1.054 (0.726-1.531) |
| Smoking/tobacco use | Smoking/tobacco use | Smoking/tobacco use |
| Current smoker | - | 1.022 (0.903-1.156) |
| Former smoker | - | 1.027 (0.935-1.129) |
| Never smoked | - | (ref.) |
| Employment status | Employment status | Employment status |
| Retired | - | 1.045 (0.860-1.269) |
| Disabled | - | 1.100 (0.917-1.319) |
| Employed | - | (ref.) |
| Unemployed | - | 1.006 (0.783-1.292) |
| Others (part time or student) | - | 1.048 (0.725-1.513) |
| Housing stability | Housing stability | Housing stability |
| Home | - | (ref.) |
| Unstable housing | - | 1.147 (0.777-1.694) |
| Homeless | - | 1.019 (0.773-1.345) |
| Others | - | 1.046 (0.793-1.380) |
| Social support | Social support | Social support |
| Yes | - | (ref.) |
| No | - | 0.970 (0.722-1.305) |
| Stressors | Stressors | Stressors |
| Health-related stressor | - | (ref.) |
| Finances | - | 1.132 (0.674-1.901) |
| Social stressor | - | 0.685 (0.383-1.227) |
Index hospitalizations classified as elective or trauma admissions had lower odds of being readmitted than those with emergency-related admissions [aOR ($95\%$ CI): 0.791 (0.729-0.858) and 0.553 (0.441-0.693), respectively]. Index hospitalizations discharged to home health or skilled nursing facilities had higher odds of being readmitted than those discharged to home [aOR ($95\%$ CI): 1.201 (1.126-1.282) and 1.201 (1.086-1.329), respectively]. The healthy BMI category was associated with higher odds of readmission compared to obese BMI [aOR ($95\%$ CI): 1.083 (1.002-1.170)]. Stage 1 hypertension (diastolic blood pressure 80-90 mmHg) was associated with lower odds of readmission compared to normal diastolic blood pressure [aOR ($95\%$ CI): 0.923 (0.857-0.995)], but stage 2 hypertension (diastolic blood pressure ≥90 mmHg) was associated with higher odds of readmission [aOR ($95\%$ CI): 1.092 (1.005-1.188)].
HbA1c was not significantly associated with readmission status. For blood glucose measurements during hospitalization, higher values for the highest blood glucose were associated with a higher likelihood of readmission, and increasing values for the lowest blood glucose were associated with a lower likelihood of readmission. Other labs, including albumin [aOR ($95\%$ CI): 0.867 (0.823-0.913)], hematocrit [aOR ($95\%$ CI): 0.985 (0.980-0.990)], and white blood cell count [aOR ($95\%$ CI): 1.004 (1.001-1.007)], were significantly associated with readmission status. The odds of readmission were increased with a longer length of stay [aOR ($95\%$ CI): 1.006 (1.003-1.009)] and a higher CCI [aOR ($95\%$ CI): 1.056 (1.047-1.064)].
An anemia diagnosis increased the odds of readmission compared to no diagnosis [aOR ($95\%$ CI): 1.125 (1.052-1.204)]. Preadmission insulin use increased the odds of readmission [aOR ($95\%$ CI): 1.192 (1.118-1.270)], but other diabetes medication classes were not associated with readmission status. A prior hospital stay within the past 90 days increased the odds of readmission [aOR ($95\%$ CI): 1.149 (1.023-1.289)]. The discharge status of the most recent hospitalization within the last year was a significant predictor of readmission. Discharges to home [aOR ($95\%$ CI): 1.504 (1.367-1.655)], home health [aOR ($95\%$ CI): 1.438 (1.276-1.620)], skilled nursing facility [aOR ($95\%$ CI): 1.353 (1.090-1.680)], or against medical advice [aOR ($95\%$ CI): 2.206 (1.256-3.847)] were associated with increased odds of readmission compared to those with no prior hospitalizations within the last year. Lastly, no follow-up appointment after discharge was associated with lower odds of readmission [aOR ($95\%$ CI): 0.927 (0.873-0.985)].
## Added predictive value of social needs
We briefly highlight the different results for the regression model with social needs here. The risk factors no longer significantly associated with readmission status were *Medicare versus* private insurance, discharge status to a skilled nursing facility, healthy BMI category, diastolic blood pressure 80-89 mmHg, white blood cell count, discharge status of most recent hospital stay to a skilled nursing facility, and follow-up appointment after discharge. After controlling for clinical risk factors in the baseline model and social needs, index hospital discharge status to other vs. home was significantly associated with a lower likelihood of readmission [aOR ($95\%$ CI): 0.717 (0.530-0.970)]. Here, no social needs were significantly associated with readmission status (p≥0.05; see Table 3 for aORs for social needs). In the sensitivity analysis, we excluded social needs with high levels of missingness (≥$70\%$) from the regression model (i.e., activities of daily living, social support, and types of stressors). Results were similar to the two reported regression models in Table 3, except for the result for former versus no alcohol use. In the sensitivity analysis, former alcohol use was significantly associated with higher odds of readmission compared to no alcohol use [aOR ($95\%$ CI): 1.121 (1.008-1.247)].
## Discussion
We identified various factors associated with 30-day readmissions among patients with diabetes in the Deep South. These factors included demographics (i.e., age, race/ethnicity, insurance status, unknown diabetes type), characteristics of hospitalizations (i.e., admission type, discharge status, length of stay, prior hospitalizations, discharge status of the most recent hospital stay within the last year), labs and vitals (i.e., albumin, hematocrit, highest and lowest blood glucose measurements, blood pressure), co-existing chronic conditions (i.e., CCI score and anemia diagnosis), preadmission medication use (i.e., insulin use), and social need (i.e., former alcohol use). These risk factors can support the readmission risk assessment for patients with diabetes in the Deep South. Factors associated with readmission risk can help identify high-risk patient groups for all-cause 30-day readmissions during pharmacy clinical services.
Our work in studying readmission risk among patients with diabetes in the Deep South expands upon the foundational work by Rubin et al. and Karunakaran et al. in creating, validating, and extending the DERRI™ (13–15). Our findings apply these prior works to the Deep South population. The majority of risk factors we found to be associated with readmission risk are supported by similar findings from Karunakaran et al., but some key differences were found for risk factors, such as age, gender, employment status, creatinine, having a follow-up appointment after discharge, etc. [ 13]. Looking at pooled results across studies from a systematic review, our findings for readmission risk being associated with insurance type, comorbidities, insulin use, and length of stay align closely with prior literature [11]. However, our findings for gender, race, and age contrasted with their results, demonstrating key differences in findings from the Deep South population compared to other U.S. populations [11].
Our study adds value and new information to the transitions of care literature in its comprehensive assessment of factors influencing readmissions among people with diabetes and its expansion to include social needs. Even though social factors have long been recognized to influence health outcomes [18], limited studies investigating readmissions have considered social needs among non-disease-specific populations [20, 21]. In contrast, a recent study by Pinheiro et al. recognized the cumulative effect of social needs in increasing patients’ risk for heart failure-related hospitalizations [36]. Our study applied a similar approach to Pinheiro et al. [ 36] by applying their methods to the diabetes context investigating the influence of social needs on readmissions. We identified various social needs associated with readmission risk in unadjusted analyses, and we found former alcohol use to be associated with an increased risk of readmission in the sensitivity analysis. Thus, alcohol use was found to be “an independent predictor” [34] of 30-day readmissions among patients with diabetes in the Deep South. Prior literature among other populations also found alcohol use/abuse to increase readmission risk [37] or to have no significant effect [38]. We are limited in interpreting our finding of the potential relationship between alcohol use and readmission because all people reporting alcohol use were grouped together into “current” or “former” alcohol use categories. Thus, we were not able to determine the amount of alcohol consumption or to differentiate between alcohol use versus abuse, which could influence the relationship with readmissions.
The recognized importance and consideration of social needs in health outcomes and clinical care also bring forth expected challenges. Our experience through this study demonstrates the challenges from incomplete data capture of social needs within EHRs. The missingness of data for social needs varied by concept, ranging from a low of $40\%$ for smoking/tobacco use to a high of $98\%$ for type of stressors. Due to the lack of complete data, we imputed missing data using multiple imputation. Imputation of social needs with high missingness likely limited our ability to learn anything about the associations we were interested in testing due to high variance. Thus, the high levels of missingness for social needs may have influenced the nonsignificant findings between social needs and readmission status in the adjusted regression model. Still, our findings of significant relationships between social needs and readmission status in either unadjusted or the sensitivity analysis support a call for further research. Further research with more complete social needs data is needed to understand the influence of social needs on readmissions among populations with diabetes. Recent work in linking EHRs with social factors available through U.S. Census data brings a potential solution to the challenge of capturing social needs in clinical data [39]. Further work is also needed to understand the potential clinical utility of incorporating social needs into clinical services. For instance, pharmacy clinical services could serve an integral role in collecting social needs data from patients given pharmacists’ more routine interactions with patients compared to other healthcare settings.
One major strength of this study and its findings is its focus on patients with diabetes in the Deep South. Our focus on the Deep South fills the existing research gap for readmissions among the diabetes population in this area, where the prevalence of diabetes surpasses the national average [22]. Prior literature in this realm has studied many other populations with diabetes, such as insured populations (commercial, Medicare, or Medicaid), clinical data from the general U.S. population, or EHRs from health systems in the Northeast (9, 11–15, 40). In the present study, we demonstrated the multitude of risk factors contributing to readmission risk among patients with diabetes in the Deep South. Our findings can support decision-making around factors influencing patients’ readmission risks in the Deep South, including expanding healthcare decision-making to consider individuals’ social needs [41]. Our findings can also provide evidence for future intervention studies for populations with diabetes. Different clinical interventions, including pharmacy clinical services [42], have been efficacious in reducing hospital readmission rates among patients with diabetes [5, 43]. Community health workers could serve a vital role in coordinating social needs through community-based interventions to further reduce readmissions [44]. Further, incorporating social needs into a case management intervention has proven beneficial in reducing inpatient admissions [45].
Another key strength of this study was investigating readmission risk by diabetes type. We found no significant difference in readmission risk for patients diagnosed with type 1 versus type 2 diabetes. Through a recent systematic review, Soh et al. identified a gap in the literature studying readmission risk by diabetes type because most studies lump patients with type 1 and type 2 diabetes together [11]. To address this gap, we included diabetes type as a risk factor in studying readmission risk. Our findings demonstrate that diabetes type may not significantly affect readmission risk among patients in the Deep South. However, patients with unknown diabetes type had significantly higher odds of 30-day readmission than patients with type 2 diabetes. Patients were classified as having an unknown diabetes type because they were prescribed a diabetes medication before the hospitalization but did not have any diabetes diagnosis codes. The higher readmission risk for patients with unknown diabetes in the Deep *South is* an interesting finding that calls for further investigation. Our finding is supported by prior research showing a higher likelihood of readmission when diabetes is not coded in the medical record, further highlighting the importance of diabetes even when patients may be hospitalized for other reasons [40].
In conclusion, we identified the factors that impacted the risk of 30-day readmissions among patients with diabetes in the Deep South. Clinical assessment of readmission risk in the Deep South should consider patients’ demographics, characteristics of hospitalizations, labs, vitals, co-existing chronic conditions, preadmission antihyperglycemic medication use, and social needs. Factors associated with readmission risk can help pharmacists and other healthcare providers identify high-risk patient groups for all-cause 30-day readmissions during transitions of care. Further research is needed about the influence of social needs on readmissions among populations with diabetes to understand the potential clinical utility of incorporating social needs into clinical services.
## Limitations
We cautiously report that factors are associated with increased readmission risk due to the secondary data analysis of EHRs. Because our data source was EHRs, we were limited to capturing readmissions within our health system, and we may have missed readmissions occurring in another health system. Although we made efforts to control for confounding variables in the adjusted analysis, there is still potential for residual confounding. We were also limited in analyzing factors available in the EHRs. Other factors that might be expected to influence readmissions and diabetes care, such as detailed information from physician notes about discharge planning, self-care behaviors, psychosocial factors (e.g., diabetes distress) [46], or other social needs [38], were not available in the limited dataset. The missingness of social needs data is a major limitation of this study, and the population of patients reporting social needs could represent a biased sample. Clinically important variables, such as cholesterol and procedures, were not available in the data source. We also did not have access to 9-digit zip codes, which prevented us from calculating the patients’ living distance from the hospital that has been continually documented as an important predictor for readmission in patients with diabetes (13–15). We acknowledge that this data was collected for clinical practice purposes rather than research purposes, so the reliability of data elements, such as outpatient prescription records, could be a limitation. However, previous research among patients with diabetes has shown that prescription orders documented in EHRs can be used to represent prescriptions filled and dispensed [47]. Lastly, there is a potential for misclassification bias in identifying patients with diabetes. Patients taking antihyperglycemic medications for other conditions (e.g., metformin for prediabetes or polycystic ovary syndrome) could have been incorrectly classified as having diabetes. However, this was not expected to have major effects on findings because less than $0.9\%$ of patients took antihyperglycemic medications but did not have a diabetes diagnosis code.
## Data availability statement
The data analyzed in this study is subject to the following licenses/restrictions: The dataset used in this study is confidential and cannot be shared.
## Ethics statement
The studies involving human participants were reviewed and approved by Auburn University Institutional Review Board for the Protection of Human Subjects in Research (IRB). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
CM and CC contributed to conceptualization, methodology, data acquisition, investigation, project administration, funding acquisition, and reviewing/editing the manuscript. CM performed software programming, data curation, formal analysis, and writing the original manuscript draft. CC supervised the research. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number TL1TR003106. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. McDaniel was supported by the American Foundation for Pharmaceutical Education (AFPE), and she is currently supported by the PhRMA Foundation under the Pre-Doctoral Fellowship in Health Outcomes Research. Chou is currently supported by the PhRMA Collaborative Actions to Reach Equity (CAREs) grant program.
## Acknowledgments
Preliminary results from this work were presented in abstract form at the Association for Clinical and Translational Science Annual Meeting in April 2021 and the American Association of Colleges of Pharmacy Annual Meeting in July 2022.
## 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/fcdhc.2022.1050579/full#supplementary-material
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|
---
title: 'SMART-ly Managing Type 1 Diabetes - Modifying Glucose Metabolism With an Online
Mind-Body Intervention: A Feasibility and Pilot Study'
authors:
- James E. Stahl
- Hima R. Ammana
- Leigh Kwak
- Richard J. Comi
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012099
doi: 10.3389/fcdhc.2022.802461
license: CC BY 4.0
---
# SMART-ly Managing Type 1 Diabetes - Modifying Glucose Metabolism With an Online Mind-Body Intervention: A Feasibility and Pilot Study
## Abstract
### Objective
Managing type 1 diabetes is stressful. Stress physiology influences glucose metabolism. Continuous glucose monitors allow us to track glucose variability in the real-world environment. Managing stress and cultivating resiliency should improve diabetes management and reduce glucose variability.
### Research Design and Methods
The study was designed as a randomized prospective cohort pre-post study with wait time control. Participants were adult type 1 diabetes patients who used a continuous glucose monitor and recruited from an academic endocrinology practice. The intervention was the Stress Management and Resiliency Training (SMART) program conducted over 8 sessions over web-based video conference software. The main outcome measures were Glucose variability, the Diabetes Self-Management questionnaire (DSMQ),Short-Form Six-Dimension (SF-6D), and the Connor-Davidson Resiliency (CD-RSIC) instrument.
### Results
There was statistically significant improvement in participants DSMQ and CD RISC scores though the SF-6D did not change. Participants under age 50 years-old showed a statistically significant reduction in average glucose ($$p \leq .03$$) and Glucose Management Index (GMI) ($$p \leq .02$$). Participants also had reduced percentage of time high and increased time in range though this did not reach statistical significance. The participants found doing the intervention online acceptable if not always ideal.
### Conclusions
An 8-session stress management and resiliency training program reduced diabetes related stress and improved resiliency and reduced average blood glucose and GMI in those under 50 years-old.
### Clinical Trial Registration
ClinicalTrials.gov, identifier NCT04944264.
## Introduction
Managing type 1 diabetes is stressful. Diabetes is a chronic illness that relies on self-care. Decisions about diet, exercise, and dose of medication must be made multiple times a day. In Type 1 diabetes, the patient provides all or nearly all the insulin required to control their metabolism, placing greater emphasis on correct medication decisions than in type 2 diabetes. Even patients with sensor directed insulin pumps must make the decisions about meal dosing several times a day. Patients with Type 1 diabetes must balance these self-care requirements with the usual stressors of daily life. Several studies have documented decreased quality of life for people with Type 1 diabetes when compared to those without diabetes. Diabetes specific measures of quality of life show direct associations of worsening life quality with worsening control or presence of diabetes-related complications (1–3).
Stress is a mind-body phenomenon. Stress creates a cascade of effects touching on every system in the body, including the cardiovascular, neurologic, and metabolic systems. The stress response activates the hypothalamic-pituitary-adrenal (HPA) axis resulting in the release of cortico-releasing hormone (CRH) and subsequently adrenocorticotropic hormone (ACTH) from the pituitary which in turn drives the release of stress hormones, such as glucocorticoids. Glucocorticoids stimulate (glycogenolysis) in the liver, sympathetic nervous system mediated vasoconstriction, proteolysis and lipolysis and suppress innate immunity, reproductive function, and bone and muscle growth as well as changes in mood, e.g., depression. This response is useful in the short term but pathogenic if prolonged. For patients with both type 1 and type 2 diabetes, the stress surrounding the management of their disease, diabetic distress [4, 5], can create a viscous cycle when trying to manage their blood sugar. Prior studies have indicate that training to reduce stress can have a positive impact on both quality of life and the degree of metabolic control of patients with diabetes [6].
New tools are changing the landscape of diabetes care. Continuous glucose monitors provide real time feedback to the patient, informing their medication, diet and exercise decisions [7]. They also provide new parameters to assess diabetes control – the Glucose Management Indicator (GMI) which provides an estimate of average control similar to the familiar hemoglobin A1c (HbA1c), Time in Range (TIR) which provides the percentage of time spent within certain glucose concentrations (usually 100-180) and estimates of variation – coefficient of variation (CV) or standard deviation (SD),both contribute to cardiovascular risk. Although only the HbA1c has been directly tied to risk of microvascular complications, it is likely that these other parameters particularly those related to variability in control are related of microvascular risk [8, 9].
The Stress Management and Resiliency Training (SMART) training program (developed by the Benson-Henry Institute for Mind Body Medicine at Massachusetts General Hospital) is a comprehensive well-validated successful stress management program designed to reduce stress and increase resiliency in response to stress [10].
However, it has not been specifically examined in Type 1 diabetes. The use of new sensor technology makes it possible to look in greater detail at the impact of stress management on diabetes glucose control. Finally, due to the restrictions of the novel coronavirus SARS-CoV2 (COVID-19) pandemic, we delivered the SMART program via an internet platform which allows much greater potential access for patients. Therefore, we devised a study to look at the impact of the SMART stress management program in patients with Type 1 diabetes for impact on quality of life, glucose control parameters recorded by sensors and delivered on an online video conference platform. We hypothesized that the online course would deliver similar impacts on quality of life as has been seen in the past from on-site courses and applications of the SMART program. We also hypothesize that the intervention would reduce glucose variability as well as average glucose and time in range.
## Participant Recruitment
Recruitment occurred through the Dartmouth-Hitchcock Medical Center (DHMC) endocrinology clinic and the endocrinologists working there. Candidates were included if they had type 1 diabetes and used a continuous glucose monitor. Candidates were excluded if they were < 21 years old and could not give informed consent. To allow for controlled analysis, on presentation at each site, participants were randomly assigned to one of two cohorts: 1) immediate start (A) and 2) delayed start (B). The immediate arm began at the next available class. The delayed start group began 4 weeks later. During their wait, this group was offered usual care.
## Description of Intervention
The Stress Management and Resiliency Training (SMART) program (bensonhenryinstitute.org) is well validated comprehensive stress management program. It is designed to cultivate both the early recognition of stress in the mind and body, develop skills to mitigate stress and evoke the relaxation response and cultivate resiliency. It is an 8-session program, typically run in a live group setting, taking advantage of the opportunity to cultivate social support. It can also be run for individuals.
Mind-Body Medicine takes as a core principle that the mind and body are a unity. Psychosocial stress creates cellular stress and in turn mitochondrial oxidative stress [11]. Stress causes a cascade of phenomena that result in among other things gluconeogenesis [12, 13], hence part of the reasoning for this class of intervention. The program specifically incorporates elements of training in the relaxation response, mindfulness, cognitive behavioral training, social support and prosocial behavior, positive psychology, belief and conscious expectation, exercise, diet, and sleep. The SMART program uses a top-down approach [14], training the prefrontal cortex to downregulate among other things the stress response in the amygdala which in turn creates a positive cascade of events mediated through the hormonal, cardiovascular and nervous systems to encourage healing and optimal function (15–17).
In this study’s case it was delivered via a videoconferencing platform. This was done both as a means of testing delivering this service in a rural setting where patient might be geographically distant or isolated and to accommodate the need for social distancing during the COVID-19 pandemic.
## Study Design
This pilot was designed as a prospective cohort pre-post intervention study with participants randomized to an immediate start or wait time control. The study was approved by the Committee for the Protection of Human Subjects at Dartmouth Hitchcock Medical Center and Dartmouth College. All participants provided written informed consent.
## Hypothesis
We hypothesized that the course on videoconferencing platform would deliver similar effects on quality of life as has been seen in the past from on-site courses, and that the intervention would reduce glucose variability and improve resiliency.
## Outcome Measures
Demographic data was collected during enrollment. The outcome measures collected were the mean glucose, glucose standard deviation (SD), the Glucose Management Index (GMI), systolic blood pressure (sBP), diastolic blood pressure (dBP), glycated hemoglobin (HbA1c), the Short-Form Six-Dimension (SF-6D), the Diabetes Self-Management Questionnaire (DSMQ), the Connor-Davidson Resilience Scale (CD-RISC).
## Continuous Glucose Monitor Related Outcomes
The GMI (Glucose Management Index) approximates the laboratory HbA1C level expected based on average glucose measured using continuous glucose monitoring (CGM) values. Average glucose is derived from at least 12 days of CGM data. The GMI may be similar to, higher than or lower than the laboratory HbA1C. The glucose standard deviation is a measure of the variability of the glucose measured by the CGM.
## SF-6D
The SF-6D is a preference-based measure of health with that uses six-dimensions to classify health status: physical functioning, role functioning, social functioning, pain and discomfort, mental health and vitality [18, 19]. It is derived from the SF-36 Health Survey, a widely used generic health profile developed in the US. Participants select one of the levels (ranging from 4 to 6 levels depending on the dimension) which best describes their current health status. The scoring algorithm of preference-based values in different levels (SF-6D) was mapped to single composite score. This algorithm was derived from the work at the University of Sheffield. The authors have registered in the University of Sheffield website.
## The DSMQ
The Diabetes Self-Management Questionnaire (DSMQ) is a well validated measure to assess diabetes self-care activities [20]. Diabetes self-care activities in turn are highly correlated to diabetic distress and glycemic control (21–23). The scale has 4 main domains: medication adherence, glucose monitoring, physical activity and healthcare system contact related to glycemic control, e.g., a clinical interaction related to medication management.
The DSMQ consists of 16 items formulated as behavioral descriptions from the person’s point of view. For example, respondents rate the extent to which each description applies to them on a four-point Likert scale (3 –’applies to me very much’ to 0 –’does not apply to me’), referring to the previous eight weeks. Higher scores indicate more desirable self-management behavior. The 9 negatively framed items require reverse scoring.
## The CD-RISC
CD-RISC [24] comprises 25 statements covering 17 domains relevant to stress and resiliency as experienced by the participant over the past month. These include adaptability, self-efficacy, sense of control, purpose, focus, social support, humor, agency, optimism, and others. The response scale has a 5-point range: 0 (not true at all), 1 (rarely true), 2 (sometimes true), 3 (often true), and 4 (true nearly all the time). Scores are added up to a maximum score of 100. The higher the score the higher resilience.
## Qualitative questions
At study completion, participants were surveyed with open-ended questions asking what they found to be barriers and facilitators in participating in the study and what did they value or not value about participating in the study.
## Timing of Evaluations
Participants completed the evaluations at three points throughout the course of the study: at $T = 0$ (study start); $T = 1$ (one month after starting classes); $T = 3$ (one month following completion of 8 weeks of classes). The wait-time participants began their evaluations and instruction one month after the immediate-start group.
## Statistical Analyses
Descriptive analysis of continuous variables included median and interquartile range (IQR), or mean and standard deviation (SD) as appropriate. Categorical variables were reported as counts and percentages. Baseline characteristics were compared between the two groups using Chi-square test or Fisher’s exact test where appropriate for categorical variables and t-test or ANOVA for continuous variables for all enrolled participants. Linear regression analysis was used analyze and explore the effect of independent variables on the outcome measures.
Computations were performed using (JMP15, SAS Institute Inc., Cary, NC). Statistical significance was defined as $p \leq 0.05$ based on a two-sided hypothesis test with no adjustments made for multiple comparisons.
Sample size is an a priori best guess estimate of the number of participants needed to detect a hypothesized difference. As this was a pilot study, testing for both effect and feasibility in a complex changing environment this was not a relevant requirement.
## Results
A total of 34 participants were contacted and enrolled. Five dropped out before the study started. The stated reasons being extent of time between recruitment and study start and personal scheduling and logistical concerns. Twenty-seven began the study, 3 dropped out because of due to family and logistical reasons (see Figure 1). The median age for the group on enrollment was 61 and included more women ($77\%$) than men ($23\%$). The immediate start and wait time control groups were statistically indistinguishable from each other (Table 1).
**Figure 1:** *Recruitment.* TABLE_PLACEHOLDER:Table 1 Because our main physiologic outcome measures were based on CGM data we needed to determine their measurement variability. This was in case we had to control for this in our analysis. Our patients used 3 different types of CGM which did indeed have significant measurement variability. However, this difference between types of CGM remained consistent within CGM class, across all sample times and across the CGM provided metrics. Almost a third ($27\%$) of study patients were on insulin pumps and had been for at least 1 year, though that was not an inclusion or exclusion criterium.
After controlling for CGM, glucose variability as measured by standard deviation statistically significantly reduced ($P \leq .05$), even in this relatively small sample. The other measures had parameters in the desirable direction but did not achieve statistical significance during the period the participants were being measure in the study. Systolic and diastolic blood pressures declined for the under 50 group but did not reach statistical significance and remained stable for the older group. HgbA1c, in the context of the CGM, unfortunately was not consistently collected for the groups and was too sparse for analysis.
On subset analysis (Table 2) the main items found were that patients who were 50 years-old and younger ($$n = 5$$) had drops in mean blood glucose of 10 pts 161 to 151 $$p \leq .03$$ and GMI dropping from 7.2 to 6.9 $$p \leq .02$$ the rest of the data showed decreased percent times high and increased time in range (TIR), but these later did not achieve statistical significance in this small sample.
**Table 2**
| Unnamed: 0 | Average BG | Average BG.1 | GMI | GMI.1 | Percent high | Percent high.1 | Time in range | Time in range.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Time | Age | Age | Age | Age | Age | Age | Age | Age |
| | <50 | >50 | <50 | >50 | <50 | >50 | <50 | >50 |
| | 5 | 19 | 5 | 19 | 5 | 19 | 5 | 19 |
| T = 0 | 160.8 | 153.6 | 7.16% | 6.98% | 30.9% | 28.5% | 64.0% | 67.0% |
| 1 mo. | 156.1 | 154.2 | 7.04% | 6.97% | 28.3% | 28.2% | 68.5% | 65.0% |
| 2 mo. | 151.4 | 154.8 | 6.92% | 6.95% | 25.8% | 27.8% | 73.0% | 70.0% |
| P | 0.03 | 0.9 | 0.02 | 0.85 | .2 | .9 | .8 | .8 |
Qualitatively these changes were accompanied by significant improvements in DSMQ and CD-RISC. The SF6D scores remained statistically unchanged. ( See Table 3) On subset analysis, the improvement in psychologic resiliency and the reduction in stress was driven most by improvements in the domains of humor, purpose and sense of control, and clarity of focus.
**Table 3**
| Measure | Sample time | n | Mean | Median | Min | Max | IQR | p difference |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DSMQ | T0 | 27.0 | 34.6 | 34.0 | 26.0 | 48.0 | 13.0 | |
| | T3 | 17.0 | 40.1 | 37.0 | 32.0 | 46.0 | 7.75 | 0.006 |
| SF-6D | T0 | 20.0 | | 0.95 | 0.92 | 1.0 | 0.02 | |
| | T3 | 17.0 | | 0.95 | 0.93 | 0.99 | 0.03 | ns |
| CD-RISC | T0 | 27.0 | 50.3 | 59.0 | 0.0 | 98.0 | 76.0 | |
| | T3 | 17.0 | 68.7 | 68.0 | 59.5 | 95.0 | 35.5 | 0.036 |
## Qualitative Feedback
We asked 4 qualitative open-ended questions regarding participants’ experience with the intervention and one general comment opportunity.
Did you experience anything that made participating in the project easier or help you participate in the study? The answers broke down into 3 broad categories in order of frequency: 1) having the program online was helpful when travel or logistics were difficult 2) the handouts were helpful 3) the teacher’s compassion and humor
Did you experience any barriers or difficulties in participating with in the study? These answers broke down into the following in order of frequency: 1) Personal logistics, scheduling, and family time 2) Pre-class handouts were sometimes delayed 3) Homework sometimes felt burdensome
4) Group conversation online were sometimes difficult 5) Internet connection 6) Task assigned were sometimes not specific enough What did you value about participating in the study? These answers broke down into the following in order of frequency:
1) Lessons learned about stress management were very valuable 2) Camaraderie with others dealing with same issues 3) Developing new tools and skills What did you not value about participating in the study? These answers broke down into the following in order of frequency:
1) Being online versus being in person 2) Homework sometimes burdensome 3) Multiple emails from research team 4) Online Handouts
5) Personal logistics, scheduling, and family time 6) Insufficient disease specific counseling General comments, broke down into the following in order of frequency: 1) Very helpful and gratitude for participating
2) Teachers’ skill, compassion, and humor 3) Camaraderie with peers 4) Stress over keeping up with homework 5) Preference for in person classes
## Discussion and Conclusions
The primary findings from this pilot study were two-fold. Though this study was limited in sample size and duration, the first finding was that participation in the 8 session SMART program [15] achieved measurable improvement in relevant clinical parameters for type 1 diabetes patients, specifically reducing their glucose variability in the group as a whole and reducing both average glucose and GMI in those under 50 years-old. This was true in even well controlled type 1 patients with both CGM, and insulin pumps examined in this pilot. This might indicate that younger patients may be more physiologically or psychologically flexible and responsive than their older peers. This difference may also be related to the duration the participants have lived with diabetes. The intervention also seemed to significantly reduce the stress surrounding managing diabetes and in improving their resiliency. Second, it was demonstrated that this could be achieved using an on-line version of the program. Third, improvements in psychologic resiliency stress reduction seemed to most attributable to improvements in the domains of humor, purpose and sense of control, and clarity of focus.
From a physiologic perspective, our experience shows that this intervention could be added to the armamentarium for treating diabetes, potentially make a large difference. This makes a great deal of sense in that diabetes physiology is directly influenced by stress physiology, and how it changes energy metabolism at the tissue, cellular and intra-cellular levels [12, 13, 16, 25]. This seemed particularly true for the younger participants. By extension, this set of tools should also influence and improve the care of other diseases with direct stress-related metabolic changes and neuro-endo-cardiovascular feedback derangements [26, 27] such as heart disease, e.g., hypertension [28, 29], congestive heart failure [30] and pulmonary disease, e.g., asthma, chronic obstructive pulmonary disease [31].
Demonstrating the feasibility of using an online platform opens the door to much greater accessibility to these tools. Though this version of the SMART intervention was developed in response to the constraints caused by the COVID-19 pandemic, it proves the principle that the core lessons of the program are extensible to other platforms. It was interesting to see that there was an intimacy and bonding that occurred during the intervention that we had only expected with in-person groups before. The pandemic may have accelerated this process and the acceptability of these tools. However, we think it more likely that this may have been due to the structured journey the patients took during the course, where they shared personal feelings and insights in a guided fashion, perhaps much more than they would have in an ad hoc less focused on-line gathering. That said there were definite strengths and weakness to the online platform. On the one hand, it allowed a more geographically diverse group to gather and be formed than might have been possible otherwise. On the other hand, the participants did notice limitations in the kind and strength of their interactions that they didn’t necessarily find satisfactory. This may in part the different nature of the conversational floor and etiquette required in online interactions that is less natural for those who are internet immigrants versus internet natives [32, 33].
Overall, the project has demonstrated that it is indeed feasible to measurably modify diabetes physiology through a mind-body intervention and to do so in a way that may improve access to those with limited geographic access such as those in rural communities.
## Challenges
There were several challenges that were faced in this study. First, was that the program was run during the COVID19 pandemic and during a period of great political tumult. Both of these stress inducing external factors could have limited the amount of overall stress reduction the patients experienced. Second, another potential confounder was participants’ internet connectivity and facility with technology which was expressed in some of the qualitative feedback. Finally, one should always take care when analyzing a project depending on skilled operators. Though the core program has been manualized and study, the skill of the teacher could confound the programs generalizability.
## Implications for Future Research
The findings from the study suggest the need for larger scale randomized clinical trials powered to explore the effects of this intervention on a larger scale and further explore the barriers and facilitators of a stress management program delivered in online for which should be highly accessible to a broad population
## Strengths and Limitations
A strength of the study was the ability to take advantage of CGM and the internet.
A limitation of the study was the dropout rate which might bias the results. Ambient stress was also quite high at the time of the study – COVID-19 pandemic, presidential election, shifting most work and social activities online
## 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 Committee for the Protection of Human Subjects - Dartmouth-Hitchcock Medical Center. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
JS was responsible for study inception and design, recruitment of study sites, intervention, data interpretation and manuscript preparation. HA was responsible for recruitment of study sites and manuscript preparation. LK was responsible for recruitment of study sites and manuscript preparation. RC was responsible for study inception and design, recruitment of study sites and manuscript preparation. All authors contributed to the article and approved the submitted version.
## Funding
The project was supported with funding from the Hitchcock Foundation at Dartmouth Hitchcock Medical Center.
## 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: Prevalence and grade of diabetic peripheral neuropathy among known diabetic
patients in rural Uganda
authors:
- Dalton Kambale Munyambalu
- Idania Hildago
- Yves Tibamwenda Bafwa
- Charles Abonga Lagoro
- Franck Katembo Sikakulya
- Bienfait Mumbere Vahwere
- Ephraim Dafiewhare
- Lazaro Martinez
- Fardous Abeya Charles
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2023
pmcid: PMC10012102
doi: 10.3389/fcdhc.2022.1001872
license: CC BY 4.0
---
# Prevalence and grade of diabetic peripheral neuropathy among known diabetic patients in rural Uganda
## Abstract
### Background
Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus (DM). Approximately $50\%$ of diabetic patients are estimated to develop DPN, depending on disease duration and diabetic control. Early diagnosis of DPN will avoid complications, including non-traumatic lower limb amputation, which is considered the most debilitating complication, as well as significant psychological, social, and economical problems. There is a paucity of literature on DPN from rural Uganda. This study aimed to deliver the prevalence and grade of DPN among DM patients in rural Uganda.
### Methods
A cross-sectional study that recruited 319 known DM patients was conducted in an outpatient clinic and a diabetic clinic at Kampala International University-Teaching Hospital (KIU-TH), Bushenyi, Uganda, between December 2019 and March 2020. Questionnaires were used to obtain clinical and sociodemographic data, a neurological examination was carried out to assess the DPN, and a blood sample was collected from each participant (for random/fasting blood glucose and glycosylated hemoglobin analyses). Data were analyzed using Stata version 15.0.
### Results
The sample size was 319 participants. The mean age of study participants was 59.4 ± 14.6 years and there were 197 ($61.8\%$) females. The prevalence of DPN was $65.8\%$ ($\frac{210}{319}$) ($95\%$ CI $60.4\%$ to $70.9\%$), and $44.8\%$ of participants had mild DPN, $42.4\%$ had moderate DPN, and $12.8\%$ had severe DPN.
### Conclusion
The prevalence of DPN at KIU-TH was higher among DM patients and its stage might have a negative impact on the progression of Diabetes Mellitus. Therefore, clinicians should consider neurological examination as a routine during assessment of all DM patients especially in rural areas where resources and facilities are often limited so that complications related to *Diabetic mellitus* will be prevented.
## Introduction
Diabetes mellitus (DM) globally spread and affects people of all ages and races [1]. The International Diabetes Federation (IDF) reported, in 2021, that 537 million adults aged 20–79 years are currently living with DM, which represents $10.5\%$ of the world’s population in this age group, with Africa being the part of the world where more than half the people with DM are undiagnosed [2]. About $80\%$ of diabetic patients live in low- and middle-income countries and almost 4 million people die of diabetes and its complications, with half of these people below the age of 60 years (1–3). The incidence of diabetic complications is expected to increase, and about 6.7 million adults are estimated to have died as a result of DM or its complications in 2021 [1, 2]. DM affects a wide variety of neurological complications, which may involve the peripheral or autonomic nervous system, or both, mostly impairing the quality of life of patients, with impact on morbidity and mortality outcomes [4].
Diabetic peripheral neuropathy (DPN) corresponds to a type of nerve damage that typically affects the feet and legs, and sometimes affects the hands and arms in diabetic patients [5]. DPN is the commonest diabetic neuropathy and must be diagnosed after the exclusion of other causes of polyneuropathy. Distal symmetric sensorimotor polyneuropathy is the most common type of DPN [5, 6]. However, some patients might be asymptomatic. The condition seems to be irreversible and remains the most common chronic complication of DM (4–6). The real prevalence of DPN is not known and reports vary from $10\%$ to $90\%$ in diabetic patients, depending on the criteria and methods used to define neuropathy, and it has been reported that neurological complications occur equally in both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) patients [6]. The regional prevalence of DPN is $12.9\%$ in North America and $3.2\%$ generally in Africa [7]. Approximately $50\%$ of patients with diabetes are estimated to develop DPN, depending on disease duration and diabetic control [8].
Early diagnosis of DPN will avoid complications, the most debilitating of which is non-traumatic lower limb amputation, resulting in significant psychological and socioeconomic problems for the patient due to depression, societal stigmatization, and job loss due to the loss of a limb (6–8). In Africa there is widespread poverty and inadequate health insurance, which impedes the acquisition of efficient prostheses that would offset some of these problems. Even when overt gangrene has not yet complicated a foot ulcer, treatment of an ulcer drains the financial resources of the patient [9].
Few studies have been carried out in urban Uganda, but less is known in rural areas where patients are coming to seek help for their health in the advanced stages of the disease and with a high risk of complications related to DM [10]. Therefore, the purpose of this study was to highlight the prevalence of DPN and its grade among DM patients in a rural southwestern Ugandan health facility.
## Study design
This was a cross-sectional study, conducted from 13 December 2019 to 25 March 2020.
## Study site
The study was carried out at Kampala International University Teaching Hospital (KIU-TH) in the Department of Internal Medicine, located in Bushenyi District, in rural southwest Uganda.
## Study participants
Participants included all adult known DM patients (aged 18 years and above) who attended the medical outpatient patient department (MOPD), DM clinic, general outpatient department (GOPD), and private outpatient department (POPD) during the duration of the study, and who consented to participate. We excluded any patients with a mental disorder, patients who were unable to withstand an interview, patients who changed their consent, all pregnant women, newly diagnosed DM patients, and very sick DM patients admitted in the medical ward.
Participants were consecutively recruited. The sample size was of 319 patients using Leslie’s formula [1965], as shown in Equation 1: N = desired sample size for population greater than 10,000.
Z2 = standard normal deviation, assuming a $95\%$ CI $Z = 1.96.$
P = proportion in the population estimated to have DPN in Uganda (Mulago Hospital, Kampala) = $29.4\%$ according to Kisozi et al. [ 10].
## Data collection
Data were collected using a paper-based investigator-administered questionnaire that was designed in simple English and translated in local language for those who were unable to understand English.
Patients were given information about the study, and then written consent was sought and signed. Demographics (i.e., age, sex, address, marital status, education status), history of chronic illness, such as hypertension, kidney disease, and HIV infection status, and social habits, such as the use of alcohol and volume, smoking cigarette status and number of sticks per day, and adherence to medication were taken.
The body mass index (BMI) was calculated from a ratio of the patients’ weight in kilograms to the square value of the height in meters (kg/m2). Normal BMI was defined as< 24.9 kg/m2, overweight as 25 to 29.9 kg/m2, and obesity as ≥ 30 kg/m2 (WHO, 2000). Blood pressure was measured by using a manual sphygmomanometer, with appropriate cuff sizes for the patient arms being used. High blood pressure was defined as having a systolic blood pressure ≥ l40 mmHg or diastolic pressure ≥ 90 mmHg (European Society of Cardiology/European Society of Hypertension, 2018).
The physical/neurological examination was done. Pressure sensation was assessed using a 10-g monofilament (i.e., the Semmes–Weinstein monofilament test) at four of the ten standard sites of the sole of the feet (plantar base of the big toe, second and fifth toes, and at the heel), avoiding areas with callosity. Vibration sense was elicited using a 128-Hz turning fork at the big toe. Achilles deep tendon reflex was tested by using a standard patellar hammer.
Using a sterile disposable syringe and needle, 4 ml of blood was withdrawn from the anterior cubital fossa of each patient after cleaning the skin with a swab soaked in $70\%$ alcohol. The blood sample was placed in a EDTA (ethylenediaminetetraacetic acid) purple container for random blood sugar (RBS)/fasting blood sugar (FBS) and glycosylated hemoglobin (HbA1c) analyses. Furthermore, RBS/FBS was screened using a Control D glucometer machine made in India [2018] by Haiden Technology with the manufacturer’s glucose sticks. The level of HbA1c was screened using an Ichroma II Machine [2017] and the appropriated reagents for measuring HbA1c. Each study participant received a printed copy of their RBS/FBS and HbA1c results.
The Neuropathy Disability Score (NDS) was used in assessing the grade of DPN for each patient. The NDS system is a tool of neuropathy evaluation score ranging from 0 to 10, which can also be used for assessment of severity of peripheral neuropathy by considering four parameters: vibration sense by using a 128-Hz tuning fork (0 = present, 1 = reduced/absent for each foot), temperature sensation by using a cold tuning fork (0 = present, 1 = reduced/absent for each foot), pin-prick sensation by a monofilament test (0 = present, 1 = reduced/absent for each foot), and ankle reflex/Achilles tendon reflex by using a patellar hammer (0 = normal, 1 = present with reinforcement, 2 = absent per side). Absence of neuropathy (normal) was considered when the score was from 0 up to 2. The grade of DPN disability was graded as follows: mild (scores: 3–5), moderate (scores: 6–8), and severe (scores: 9–10). The NDS is validated and is found to be $65\%$ sensitive and $91\%$ specific for diagnosing diabetic neuropathy [11]. We used a 10-g monofilament test, patellar hammer, 128-Hz tuning fork/Hartman C 128 for the assessment of DPN, as described above.
## Data analysis
Data were captured in paper forms and entered into Epi Info™ 7.2, Microsoft Excel version 2010 and exported into Stata 15.0 for analysis. Data were processed accordingly and summarized using means for continuous variables or proportions for categorical variables.
For determining the prevalence of DPN at KIU-TH, we summarized data as frequencies and percentages, and $95\%$ CIs were obtained for prevalence as an estimation measure.
## Ethics approval and consent to participate
The study was conducted after approval of Kampala International University-Research Ethics Committee (KIU-REC) under reference UG-REC-$\frac{023}{201939.}$
Written informed consent was obtained. Confidentiality for all the patients involved in the study was assured. People diagnosed with DPN were referred to the appropriate medical personnel.
## Results
Overall, 338 participants arrived at the MOPD, DM clinic, GOPD, and POPD at KIU-TH. Five participants were excluded from the study because three were newly diagnosed as having DM and two were very sick. A total of 333 participants met the inclusion criteria, and among them five declined to consent, and nine declined the examination and the blood sample collection. Finally 319 study participants consented, filled the study questionnaire, were examined, and blood samples taken during the study period and analyzed.
## Characteristics of the study participants
In Table 1 below, majority ($61.8\%$) of the participants were females, most ($83.7\%$) of them were married and residing in a rural area ($85.3\%$), with a mean age of 59.4 ± 14.6 years, and were agricultural workers by occupation ($77.7\%$). In addition, most participants were T2DM ($95.3\%$) on oral hypoglycemic agents ($63.6\%$), overweight ($55.8\%$) with a mean BMI of 26.26 ± 3.48 kg/m2, and poor glycemic control ($53.9\%$), with a DM duration of less than 10 years ($65.5\%$), i.e., with a mean duration of 7.33 ± 6.40 years. A few study participants were taking alcohol or had an Audit Score of 1 ($10\%$), with a history of smoking ($13.5\%$) and hypertension ($50.2\%$).
**Table 1**
| Baseline characteristic | N = 319 |
| --- | --- |
| Age (years), mean (± SD) | 59.4 (± 14.6) |
| Female, n (%) | 197 (61.8) |
| Rural residence, n (%) | 272 (85.3) |
| Married, n (%) | 267 (83.7) |
| Education level, n (%) | Education level, n (%) |
| Primary | 129 (40.4) |
| Secondary | 35 (10) |
| | 137 (42.9) |
| Occupation, n (%) | Occupation, n (%) |
| Agricultural workers | 248 (77.7) |
| Private business | 22 (6.9) |
| Professional | 22 (6.9) |
| Alcohol (Audit Score), n (%) | Alcohol (Audit Score), n (%) |
| Audit 1 | 35 (10) |
| Audit 2 | 31 (9.7) |
| Smoking, n (%) | 43 (13.5) |
| DM duration, mean (± SD) | 7.33 (± 6.40) |
| < 10 years | |
| ≥ 10 years | 209 (65.5) |
| Types of DM, n (%) | 110 (34.5) |
| T1DM | |
| T2DM | 15 (4.7) |
| Diabetic therapy, n (%) | 304 (95.3) |
| Oral hypoglycemic agents | 203 (63.6) |
| Insulin | 41 (12.9) |
| Both oral hypoglycemic agents + insulin | 57 (17.9) |
| Not on diabetic therapy | 3 (5.6) |
| BMI (kg/m2), mean (± SD) | 26.26 (± 3.48) |
| BMI categories (kg/m2), n (%) | BMI categories (kg/m2), n (%) |
| Normal (18.5–24.9) | 103 (32.3) |
| Overweight (25.0–29.9) | 178 (55.8) |
| Obese (≥ 30) | 38 (11.9) |
| HbA1c per cent, mean (± SD) | 7.61 (± 2.47) |
| HbA1c per cent, n (%) | HbA1c per cent, n (%) |
| Good glycemic control (< 7.0) | 147 (46.1) |
| Poor glycemic control (≥ 7.0) | 172 (53.9) |
| Fasting glucose (mmol/l), mean (± SD) | 10.35 (± 5.16) |
| Systolic blood pressure (mmHg), mean ( ± SD) | 138.4 (± 19.77) |
| Diastolic blood pressure (mmHg), mean (± SD) | 85.8 (± 12.95) |
| Hypertension, n (%) | 160 (50.2) |
| HIV, n (%) | 29 (9.1) |
The aim of this study was to determine the prevalence and grade of DPN among known DM patients in a rural setting of Uganda (KIU-TH, Ishaka in the Bushenyi District of southwestern Uganda). In this current study, most of the participants were female, agricultural workers, and from rural residency, with a mean age of 59.4 ± 14.6 years.
Garoushi et al. in a meta-analysis study conducted in the USA, the UK, France, Belgium, and South Africa [2018], and Morkid et al. in Bangladesh [2010], found that advanced age was significant with the occurrence of DPN [12]. DPN develops progressively over months to years, and by the time the aging process is taking place there is a decrease in peripheral nerves function, mostly in lower extremities, with physical disabilities, gait disturbance, and falls.
## Prevalence of DPN
The prevalence of DPN among adult diabetic patients attending Kampala International University-Teaching Hospital was $65.8\%$ ($\frac{210}{319}$), ($95\%$ CI 60.4-70.9) by using the Neuropathy Disability Score (NDS) (Figure 1).
**Figure 1:** *Prevalence of DPN among known DM patients attending KIU-TH.*
The overall prevalence of peripheral neuropathy among adult diabetic patients attending KIU-TH was $65.8\%$ ($95\%$ CI $60.4\%$ to $70.9\%$).
This study’s result is similar to the global prevalence of DPN [1]. This could be explained by the fact that the global prevalence considers all the population by using a standard score (NDS) for assessing DPN. In a study carried out in India, the prevalence of DPN is lower than of this study [13]. This disparity is due to the age of the participants, in which the previous study enrolled patients from age 30 years and above and they considered all the neurological complications in diabetic patients.
The prevalence of DPN in our study is similar to a study carried out in Morocco [14]. The reason for the similarity could be because of the, almost, same social conditions as African countries and because methods used for the assessment of DPN were the same. This current study found a lower prevalence than a study carried out in Nigeria by Salawu et al. [ 15], because our study included all types of diabetes, whereas the other study considered only patients with T2DM.
The prevalence of DPN in this study was higher than those in studies carried out in Cameroon and Kampala [4, 10]. In our study, the prevalence is higher probably because of the sample size, and the tools and criteria used for diagnosing DPN. In addition, the above study that was carried out in Kampala was conducted among newly diagnosed DM patients only.
## Grade of DPN among known DM patients attending KIU-TH.
In our study, 210 ($65.8\%$) participants had DPN, $44.8\%$ had mild DPN, $42.4\%$ had moderate DPN, and $12.8\%$ had severe DPN.
## Grade of DPN
In our study, 210 ($65.8\%$) participants had DPN, $44.8\%$ ($\frac{94}{210}$) had mild DPN, $42.4\%$ ($\frac{89}{210}$) had moderate DPN, and $12.8\%$ ($\frac{27}{210}$) had severe DPN.
The study of Kazemi et al. in Iran [16] found that most of their participants had mild DPN and a few of them developed severe DPN, which corresponds to our findings as well, whereby the method used for the assessment of DPN were similar (NDS), with the same study design by considering a large number of DM patients. However, in Kampala, Kisozi et al. [ 10] got a larger number of patients with moderate DPN. The discrepancy could have been explained by the fact that in our study we used different tools for assessing DPN in the context of a rural setting and few of our participants had foot ulcers as a predictor of advanced DPN.
Vogt et al., in their study carried out in Tanzania [17], detected that severe DPN represented almost one-quarter of patients, which is different from our findings, because from their research, they compared only assessment tools for DPN without considering patient findings based on symptoms and signs. The NDS is an important tool for assessing DPN and constitutes a good predictor for risk of ulceration. Knowing DPN patients based on this score might help us to prevent patients from foot ulceration, diabetic foot, and other related complications that have a poor prognosis [18].
## Strengths and limitations
This is the first study concerning DPN in the southwest of Uganda and the sample size used was large to make a clear conclusion about our findings. Furthermore, the study did not use other diagnostic specific tests of DPN, such us corneal confocal microscopy and electrophysiological studies (e.g., nerve conduction studies, electromyography).
## Conclusion
The prevalence of DPN among known diabetic patients attending KIU-TH was higher, classified respectively in mild, moderate, and severe DPN, according to the number among DM patients. This might have a negative impact as DM is progressing. Therefore, for better prevention of other chronic complications, clinicians should consider neurological examination as routine during regular assessment of all DM patients, especially in rural areas where resources and facilities are often limited.
## 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 Kampala International University-Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
DM conceived, designed the study, participated in the data collection, analysis, and drafted the manuscript. FA and YB analyzed the data and performed statistical tests. IH prepared and analyzed the collected blood samples. FS, BM, LM, ED, and CL assisted in the study conception/design and critically reviewed the manuscript. All authors approved the manuscript for publication.
## Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: 'Elevated Serum Sialic Acid Levels May be Associated With Diabetes Retinopathy:
A Cross-Sectional Study in Ghana'
authors:
- William K. B. A Owiredu
- Christian Obirikorang
- Alberta Boye Agoe
- Emmanuel Acheampong
- Enoch Odame Anto
- Seth D. Amanquah
- Hope Agbodzakey
- Evans Asamoah Adu
- Hubert Owusu
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012103
doi: 10.3389/fcdhc.2022.871051
license: CC BY 4.0
---
# Elevated Serum Sialic Acid Levels May be Associated With Diabetes Retinopathy: A Cross-Sectional Study in Ghana
## Abstract
This study determined the association between serum sialic acid (SSA) and metabolic risk factors in Ghanaian Type 2 diabetes (T2DM) with and without micro vascular complications. This cross-sectional study recruited 150 T2DM out-patients visiting the diabetic Clinic at the Tema General Hospital, Ghana. Fasting blood samples were collected and analyzed for Total Cholesterol (TC), Triglyceride (TG), Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), Fasting Plasma Glucose (FPG), Glycated Haemoglobin (HbA1c), SSA and C-Reactive Protein. SSA levels were significantly higher in diabetics with retinopathy (210.12 ± 85.09mg/dl) compared with those with nephropathy and those without complication (p-value= 0.005). Body adiposity index (BAI) (r= -0.419, p-value = 0.037) and Triglyceride (r= -0.576, p-value = 0.003), had a moderate negative correlation with SSA levels. In a One-Way Analysis of Covariance (Adjusted for TG and BAI), SSA could distinguish between diabetics with retinopathy and those without complications (p-value = 0.004) but not nephropathy (p-value = 0.099). Within group linear regression analysis showed that *Elevated serum* sialic acid was found in type 2 diabetic patients with retinopathic micro-vascular complications. Therefore, estimation of sialic acid levels may help with the early prediction and prevention of microvascular complications occurring due to diabetes, thereby decreasing the mortality and morbidity.
## Introduction
Type 2 diabetes is a chronic metabolic disorder characterized by persistent hyperglycemia [1]. It has been associated with increased risk of cardiovascular comorbidities and microvascular complications including nephropathy, retinopathy and neuropathy [2]. Diabetes-related nephropathy and retinopathy are common causes of chronic kidney diseases and non-congenital blindness worldwide [3]. However, knowledge of this among Ghanaians remain extremely low with only $17.7\%$ and $5.4\%$ of diabetics having knowledge about such microvascular complications as retinopathy and nephropathy respectively [4]. With a diabetes prevalence of $6\%$, higher than the continental average of $4.7\%$ [5, 6], diabetes and its complications present an enormous socioeconomic and health burden for people in developing countries [7]. Therefore, with the development and severity of diabetic complications being dependent on the duration of the disease and how early it is detected and managed [8], there’s then the need to identify early markers, which will not only monitor disease progression and prognosis soon after diagnosis, but also predict the onset of micro-vascular complications.
Sialic acid, also referred to as N-acetyl neuraminic acid, comprises the terminal component of oligosaccharide chains of several glycoproteins and glycolipids [9]. Serum sialic acid concentration is a marker of the acute phase response and constitutes the terminal component of many acute phase proteins including α1-acid glycoprotein, haptoglobin, fibrinogen, transferrin and complement [10]. Levels of serum sialic acid are increased in several pathologic conditions such as inflammation and malignancy [11]. A cytokine-induced acute phase response has been hypothesized to play an integral role in the pathophysiology of Type 2 diabetes mellitus (T2DM) [12]. Elevated levels of sialic acid predict individual features of metabolic syndrome such as hypertension and dyslipidaemia independently of body mass index (BMI) [13]. This study was therefore focused on determining the association between sialic acid and known metabolic risk factors of T2DM in microvascular complications.
## Study Design/Site
This cross-sectional study was conducted at the Diabetic Clinic, Eye Clinic and the Chemical Pathology unit of the Tema General Hospital. Tema General *Hospital is* the largest public health institution in the Tema *Metropolitan area* in Ghana. This area has a total projected population of 403,943. The hospital serves as a main referral centre within the Tema Metropolis. Its catchment area covers the whole metropolis including satellite towns and villages that extends as far as Sakumono, Lashibi and Nungua. It has twelve wards with a 294-bed capacity and also provides 24-hour Specialist and General Service to both in-patients and out-patients.
## Study Population
The convenience sampling technique was used to recruit diabetic subjects scheduled for appointment at the diabetic outpatient’s clinic of the Tema General hospital during the study period. Participants included in the study comprised those who have been diagnosed of T2DM and were 40 years and above. The participants have had the condition for more than a year and were on diet with oral hypoglycaemic drugs. Pregnant women and participants with chronic inflammation from other infection were excluded. Out of the total of one hundred and fifty [150] T2DM patients recruited for the study, forty-one [41] were clinically diagnosed of diabetic nephropathy, twenty-seven [27] had been clinically diagnosed of retinopathy and eighty-two [82] had no complications (Figure 1). Pre-validated standard questionnaires were used to obtain socio-demographic and clinical information from the participants.
**Figure 1:** *Flow diagram of the participant selection process.*
## Anthropometric Measurement
Body weight and height of study participants were measured using a standard physician’s scale and a stadiometer. Waist and hip circumferences were obtained with tape measure. Body mass index (BMI) was calculated as body weight (in kilograms) divided by the square of height (in meters). Waist-hip-ratio (WHR) and was calculated by dividing the waist circumference by the hip circumference. The body adiposity index (BAI) was calculated according to the formula as described by Bergman et al. [ 14], and visceral adiposity index (VAI) was calculated by the formula as described by Amato & Giordano [15].
## Blood Pressure Measurement
Blood Pressure (BP) was recorded after subjects had relaxed for at least 5 minutes. Measurements were taken with the subject being in the seated position using an automated BP monitor (Omron HEM-5001, Kyoto, Japan) placed on the subject’s right arm. Measurement was done twice within an interval of five minutes, and the average reading was recorded.
## Sample Collection and Processing
Venous blood samples (4 mls) were taken from subjects after 8-12-hour overnight fast. Two millilitres (2ml) each was placed in a Sodium fluoride and serum separator tubes. The samples in the Sodium fluoride tubes were centrifuged at 1,000g for 5 minutes and was used for the analysis of plasma glucose. Samples collected into the serum separator tubes were centrifuged at 1,000g for 15 minutes at room temperature after 30 minute-standing. Serum was separated into plain sample containers and frozen at -20°C for a period of up to one month until analysed.
## Biochemical Assay
Serum glucose, HbA1c, Total Cholesterol, Triglyceride and HDL-cholesterol were determined using BT 3000 Chemistry Auto Analyzer and reagent kits. The LDL cholesterol was derived Friedewald’s formula.
## Sialic Acid Assay
The double-antibody sandwich enzyme-linked immunosorbent one-step process assay (ELISA) was used to assay the level of lipid –bound sialic acid (LSA) in the samples. The standard, test sample and HRP-labelled LSA antibodies were added to enzyme pre-coated wells. These were incubated at 37°C for 60 minutes, washed to remove uncombined enzyme after which chromogen solutions were added. A colour change from blue to yellow after the acid reaction indicated a positive sample and absorbance measured at 450nm wavelength.
## Ozotex-C-Reactive Protein Determination
C-reactive protein was determined using the latex agglutination method. Serum samples were serially diluted and one drop of each diluted serum sample was placed in a glass slide circle. The content of each slide was mixed separately and spread with the mixing sticks provided in the kit. Agglutination within two minutes is a positive test and indicated the presence of CRP in the test specimen. The highest dilution that showed clear cut agglutination within 2 minutes indicated the CRP titre and the approximate concentration was obtained by multiplying titre by the sensitivity of the test.
Where $S = 0.6$mg/dl.
## Ethical Consideration
The research protocol was reviewed and approved by the Committee for Human Research, Publications and Ethics (CHRPE) of the School of Medicine and Dentistry, KNUST (Ref no. CHPRE/AP/$\frac{205}{16}$) and the management of the Tema General Hospital. The study was conducted according to the guidelines of the Declaration of Helsinki.
The objectives and benefits of the study were explained to the diabetic patients at the time of initial data collection, and verbal and written consent were obtained from them. Respondents were assured that the information gathered was to be used strictly for research and academic purpose only. In addition, respondents were given the freedom to opt out any time they think they cannot continue with the study
## Statistical Analysis
Results were expressed as mean ± S.D. except where otherwise stated. Statistical analysis was performed using SPSS version 20.0 (SPSS Inc.) and GraphPad prism 5 for Windows. Normal distribution and homogeneity of the variances were tested using Kolmogorov-Smirnov and Levène tests, respectively. Student t-test was used to compare the significance of the difference in the mean values of any two groups and chi-square analysis was used to compare frequency between the two groups. One-way Analysis of variance/covariance and post-hoc with Bonferroni corrections were used evaluate the differences in mean SSA levels between the groups. Correlations between parameters were analysed using the Pearson r test for variables with normal distribution. Linear regression analysis was performed to evaluate the relationship between SSA and metabolic analytes within groups. $P \leq 0.05$ was considered statistically significant.
## Results
Diabetic nephropathy and retinopathy were more prevalent in the female diabetics ($68.3\%$, $66.7\%$ respectively) than the male diabetics ($31.7\%$, $33.3\%$ respectively). However, while most risk factor parameters such as fasting blood glucose, HbA1c, blood pressure (SBP/DBP), and serum inflammatory markers did not show any statistically significant difference between the sexes, female diabetics also reported significantly lower levels of HDL-C ($$P \leq 0.018$$), and significantly higher levels of BMI, VAI and BAI ($$P \leq 0.021$$, 0.001, and 0.007 respectively) (Table 1).
**Table 1**
| Variable | Total (n=150) | Male (n = 58) | Female (n = 92) | P-value |
| --- | --- | --- | --- | --- |
| Age (Mean ± SD) | 58.90 ± 12.43 | 59.17 ± 13.72 | 58.73 ± 11.63 | 0.823 |
| Age group n (%) | | | | 0.197 |
| <30 | 3 (2.0) | 3 (100.0) | 0 (0.0) | |
| 30-39 | 8 (5.3) | 2 (25.0) | 6 (75.0) | |
| 40-49 | 25 (16.7) | 8 (32.0) | 17 (68.0) | |
| 50-59 | 38 (25.3) | 11 (28.9) | 27 (71.1) | |
| 60-69 | 44 (29.3) | 20 (45.5) | 24 (54.5) | |
| 70-79 | 27 (18.0) | 12 (44.4) | 15 (55.6) | |
| ≥ 80 | 5 (3.3) | 2 (40.0) | 3 (60.0) | |
| WC (cm) | | 91.59 ± 12.77 | 92.97 ± 13.62 | 0.539 |
| WHR | 0.54 ± 0.12 | 0.90 ± 0.08 | 0.91 ± 0.07 | 0.611 |
| Adiposity indices | Adiposity indices | Adiposity indices | Adiposity indices | Adiposity indices |
| VAI | 1.72 ± 2.44 | 1.35 ± 0.09 | 1.96 ± 0.32 | 0.132 |
| BAI | 32.57 ± 7.93 | 29.89 ± 7.34 | 34.31 ± 7.82 | 0.001 |
| BMI n (Kg/m2) | | 26.98 ± 5.20 | 29.54 ± 5.88 | 0.007 |
| BMI n (%) | | | | 0.021 |
| Underweight | 3 (100) | 1 (33.3) | 2 (66.7) | |
| Normal | 40 (100) | 23 (57.5) | 17 (42.5) | |
| Overweight | 53 (100) | 20 (37.7) | 33 (62.3) | |
| Obese | 54 (100) | 14 (25.9) | 40 (74.1) | |
| Disease complication n (%) | | | | 0.348 |
| | 72 (100) | 36 (43.9) | 46 (56.1) | |
| Nephropathy | 41 (100) | 13 (31.7) | 28 (68.3) | |
| Retinopathy | 27 (100) | 9 (33.3) | 18 (66.7) | |
| FBG (mmol/l) | 9.36 ± 3.82 | 9.39 ± 4.45 | 9.33 ± 3.40 | 0.929 |
| HBA1c (%) | 7.46 ± 1.38 | 7.21 ± 1.32 | 7.61 ± 1.40 | 0.078 |
| Blood Pressure (mmHg) | Blood Pressure (mmHg) | Blood Pressure (mmHg) | Blood Pressure (mmHg) | Blood Pressure (mmHg) |
| SBP | 128.10 ± 17.08 | 127.50 ± 16.99 | 128.48 ± 17.22 | 0.734 |
| DBP | 81.67 ± 8.44 | 81.57 ± 8.67 | 81.74 ± 8.33 | 0.905 |
| Inflammatory parameters | Inflammatory parameters | Inflammatory parameters | Inflammatory parameters | Inflammatory parameters |
| SSA (mg/dL) | 196.70 ± 87.21 | 192.00 ± 96.81 | 199.62 ± 80.99 | 0.604 |
| CRP | 0.12 ± 0.37 | 0.09 ± 0.04 | 0.13 ± 0.04 | 0.545 |
| Lipid profile | Lipid profile | Lipid profile | Lipid profile | Lipid profile |
| TC (mmol/L) | 5.18 ± 1.31 | 4.93 ± 1.20 | 5.21 ± 1.30 | 0.191 |
| TG (mmol/L) | 1.20 ± 0.50 | 1.23 ± 0.65 | 1.17 ± 0.37 | 0.479 |
| HDL-C (mmol/L) | 1.38 ± 0.40 | 1.28 ± 0.34 | 1.44 ± 0.43 | 0.018 |
| LDL-C (mmol/L) | 3.56 ± 1.30 | 3.48 ± 1.26 | 3.61 ± 1.33 | 0.558 |
SSA levels were elevated among all the diabetic study participants regardless of whether one was having any microvascular complications or not (Table 2). However, serum sialic acid was significantly elevated among diabetics with retinopathy compared with those with nephropathy, and without complications (p-value = 0.005). HbA1c levels differed among the three groups (p-value = 0.005; significantly lower among those with nephropathy but similar between retinopathy and without complications). Blood pressure showed a significant difference across the various groups. Waist circumference, waist-to-hip ratio, adiposity indices, CRP and BMI showed no statistically significant difference on comparison among the groups.
**Table 2**
| Variables | Disease Complication | Disease Complication.1 | Disease Complication.2 | p-value |
| --- | --- | --- | --- | --- |
| Variables | | Nephropathy | Retinopathy | |
| Variables | (n = 82) | (n = 41) | (n = 27) | |
| Age (Mean ± SD) | 57.22 ± 13.06 | 61.46 ± 11.53 | 60.11 ± 11.40 | 0.175 |
| FBG (mmol/l) | 9.25 ± 3.57 | 9.39 ± 4.12 | 9.62 ± 4.24 | 0.909 |
| HbA1c (%) | 7.60 ± 1.34 | 6.90 ± 0.97*^ | 7.89 ± 1.78 | 0.005 |
| Blood Pressure (mmHg) | Blood Pressure (mmHg) | Blood Pressure (mmHg) | Blood Pressure (mmHg) | Blood Pressure (mmHg) |
| SBP | 126.89 ± 14.37 | 133.90 ± 23.01 | 122.96 ± 11.37# | 0.022 |
| DBP | 83.11 ± 6.22 | 81.22 ± 11.22 | 78.00 ± 8.52# | 0.021 |
| Inflammatory parameters | Inflammatory parameters | Inflammatory parameters | Inflammatory parameters | Inflammatory parameters |
| SSA (mg/dL) | 172.22 ± 35.88 | 183.27 ± 39.1 | 210.12 ± 85.09#^ | 0.005 |
| CRP | 0.16 ± 0.05 | 0.09 ± 0.03 | 0.02 ± 0.02 | 0.197 |
| Lipid profile | Lipid profile | Lipid profile | Lipid profile | Lipid profile |
| TC (mmol/L) | 5.05 ± 1.19 | 5.07 ± 1.46 | 5.31 ± 1.17 | 0.642 |
| TG (mmol/L) | 1.22 ± 0.57 | 1.15 ± 0.36 | 1.20 ± 0.42 | 0.799 |
| HDL-C (mmol/L) | 1.38 ± 0.41 | 1.28 ± 0.40 | 1.50 ± 0.35 | 0.076 |
| LDL-C (mmol/L) | 3.47 ± 1.26 | 3.75 ± 1.46 | 3.52 ± 1.16 | 0.527 |
| WC (cm) | 92.56 ± 15.46 | 90.59 ± 10.34 | 94.87 ± 9.42 | 0.428 |
| WHR | 0.90 ± 0.07 | 0.89 ± 0.08 | 0.92 ± 0.05 | 0.311 |
| Adiposity indices | Adiposity indices | Adiposity indices | Adiposity indices | Adiposity indices |
| VAI | 1.52 ± 0.81 | 2.27 ± 0.48 | 1.53 ± 0.75 | 0.251 |
| BAI | 32.64 ± 9.77 | 32.01 ± 5.20 | 33.37 ± 4.30 | 0.788 |
| BMI n (Kg/m2) | 28.90 ± 6.33 | 28.14 ± 4.89 | 28.09 ± 5.02 | 0.708 |
When adjusted for age, increasing SSA levels shows a significantly moderate association with decreasing levels of triglycerides (r= -5.74, p-value = 0.003) among diabetics with retinopathy. Also, BAI showed moderate negative correlation with SSA (r= -0.419, p-value= 0.037) (Table 3). In an ANCOVA analysis adjusting for BAI and TG levels (Figure 2), mean SSA levels significantly differed between diabetics with retinopathy compared with those without complications (p-value =0.004).
A multiple linear regression analysis (stepwise) revealed that a one-point increase in Triglyceride level was associated with a significant decrease in SSA levels (-113.82, $95\%$CI: -181.91 to -45.74). Thus, variations in TG levels explains $33.2\%$ of variations in SSA levels among diabetic patients with retinopathy, as shown by the R2 value in Table 4. However, these significant changes were not observed among diabetic patients without complications and those with nephropathy (p-value >0.05).
**Table 4**
| Case/Control | Model | R | Adjusted R Square | SEE | Change Statistics | Change Statistics.1 | Change Statistics.2 | Change Statistics.3 | Change Statistics.4 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Case/Control | Model | R | Adjusted R Square | SEE | R2change | F change | df1 | df2 | P-value |
| Without Microvascular Complication | a | .053a | -0.012 | 36.49804 | 0.003 | 0.186 | 1 | 67 | 0.667 |
| Without Microvascular Complication | b | .060b | -0.027 | 36.7586 | 0.001 | 0.054 | 1 | 66 | 0.818 |
| Without Microvascular Complication | c | .148c | -0.023 | 36.69853 | 0.018 | 1.216 | 1 | 65 | 0.274 |
| Nephropathy | a | .242a | 0.033 | 38.49679 | 0.059 | 2.243 | 1 | 36 | 0.143 |
| Nephropathy | b | .358b | 0.078 | 37.58126 | 0.069 | 2.775 | 1 | 35 | 0.105 |
| Nephropathy | c | .358c | 0.051 | 38.127 | 0.000 | 0.005 | 1 | 34 | 0.944 |
| Retinopathy | a | .576a | 0.304 | 71.005 | 0.332 | 11.905 | 1 | 24 | 0.002 |
| Retinopathy | b | .660b | 0.387 | 66.646 | 0.104 | 4.242 | 1 | 23 | 0.051 |
| Retinopathy | c | .660c | 0.359 | 68.144 | 0.000 | 0.000 | 1 | 22 | 0.999 |
## Discussion
Retinopathy and nephropathy are the major micro-vascular complications that lead to blindness and end-stage renal disease in diabetics [16]. Many studies have indicated that diabetic complications are mainly due to the chronic hyperglycaemia that exerts its health effects through several mechanisms such as hypertension, dyslipidaemia, platelet activation, and altered endothelial metabolism [17]. This study was therefore conducted to determine the association between sialic acid and known metabolic risk factors in Type 2 Diabetic patients with and without microvascular complications. Among the total number of patients recruited in this study, 68 presented with micro-vascular complications of which $39.7\%$ had developed retinopathy and $60.3\%$ had developed nephropathy. However, $54.7\%$ of the diabetic patients did not have any microvascular complications.
There was an increasing trend in concentrations of serum sialic acid (SSA) levels among the diabetics in this study. Patients who had developed retinopathy had the highest level of SSA, followed by those who had developed nephropathy with the least levels seen in those without any microvascular complications. These concentrations of SSA in this study were higher than concentrations from diabetics in several other studies [18, 19] which found significantly elevated sialic acid levels in patients compared to their controls. These differences may be due to differences in methods used to measure SSA, the health status of the participants as well as individual differences in the SSA levels. In T2DM in general, the circulating sialic acid concentration is elevated in comparison with nondiabetic subjects [3]. The vascular endothelium is enriched with sialic acid moieties which are released into circulation when there is extensive microvascular damage in T2DM. A cytokine-induced acute phase response exacerbated by the diabetic process has also been implicated to cause elevations in levels of SSA [20]. This finding thus confirms that sialic acid may prove to be a useful marker in patients with T2DM particularly those with complications.
Diabetic patients with retinopathy had significantly higher SSA compared to those without any complications. This finding agrees with the study by Merat al [21]., and Crook et al. [ 22] but inconsistent with the study by Deepa et al. [ 23] who found no significant difference in serum sialic acid levels in diabetics with proliferative and non-proliferative retinopathy, non-retinopathic diabetics and non-diabetic patients. Again, unlike Prajna et al. [ 24] who observed significantly higher SSA among diabetics with nephropathy compared to patients without any complications, our results showed no significant difference between these category of diabetics. Khan et al. [ 25] in their study found significantly higher serum SSA levels in patients with retinopathy, nephropathy and coronary artery disease compared to diabetics who did not have complications. In this study, difference in SSA levels between patients who had developed retinopathy and those who had nephropathy did not reach statistical significance. Generally, sialic acid is bound to acute phase proteins with negligible free sialic acid in circulation. Thus, to explain further, the associations observed in this study, a study that evaluate free and bound SSA levels will be needed.
How, the likely explanation to these associations is that acute phase response and tissue injury caused by diabetic vascular complications is pronounced among patients with retinopathy. Sialic acid levels in plasma may be influenced by several factors including variations in the sialylation of apolipoproteins before their secretion into plasma; variations in the amount of sialic acid-containing apolipoproteins on lipoprotein in plasma; and modifications of the SSA on lipoprotein constituents following their secretion in plasma Crook et al. [ 22].
Hyperglycaemia is a significant stressor that has also been shown to cause chronic inflammation [12, 26]. Elevated glucose levels could promote inflammation by increased oxidative stress [27], although the relationship between inflammation markers and glycaemic control is not been fully understood. SSA showed no significant relationship with HbA1c or FPG. This result is consistent with the findings of Lindberg et al. [ 28] who indicated that hyperglycaemia may be unlikely to have a major effect on the acute phase response in T2DM. Others studies [19, 24] have however found a significantly positive correlation between sialic acid, HbA1c and FPG.
Hypertension significantly impact the incidence and progression of cardiovascular events and microvascular complications [29]. SBP was significantly higher in diabetic patients who had developed nephropathy than those with retinopathy in this current study. Aside CVDs, hypertension particularly magnifies risk of nephropathy which occurs in about $40\%$ of diabetic patients [29]. Population-based studies have shown that CVD mortality was 7.5 times greater among persons with T2DM and its risk was associated with elevated sialic acid levels [30, 31]. Crook et al. [ 22] found significant association between total sialic acid and both SBP and DBP in their study although the relationship between the lipid-associated sialic acid levels and systolic pressure did not reach significance in T2DM. These findings are consistent with this study, where no significant association was observed between blood pressure and sialic acid.
Furthermore, there was no statistically significant difference in lipid parameters among the three categories of diabetic patients except for triglyceride levels, which showed a significant negative correlation with SSA levels among diabetics with retinopathy. Reports from other studies (32–34) have however indicated a significant correlation between sialic acid and TG, TC and LDL-C. Inconsistent with our findings, Crook et al. found no significant relationship between sialic acid and TG in diabetics with retinopathy [22].
Production of inflammatory mediators by visceral adipose tissue induces the release of acute-phase reactants in hepatocytes and endothelial cells [35]. Elevated CRP levels have been associated with abdominal adiposity in some studies (36–40). Waist circumference, waist-to-hip ratio as well as BAI and VAI did not vary among the diabetic patients. However, while serum sialic acid showed a significantly inverse relationship with BAI among participants with retinopathy, it rather showed a non-significant positive correlation among those with nephropathy. In patients without any microvascular complications, SSA showed a significantly positive correlation with BAI.
The are some limitations which are vital when interpreting the findings of this study. First, the small sample size has a significant impact on the power, interpretation of the results and inconsistent findings with other studies. Second, the cross-sectional study design of the current study did not allow generalisation of our result in the general population, thus the utility of sialic acid estimation in early prediction and prevention of microvascular complication in diabetes. However, there were considerable number of studies that supported our study findings. Thus, a prospective cohort study with larger sample size, considering the measurement of bound and free sialic acid. in addition, to the routine analytes will be useful to evaluate this associations with precision.
## Conclusion
Elevated serum sialic acid was associated with the presence nephropathic and retinopathic micro-vascular complications in type 2 diabetic patients. There was also direct association of HbA1c with elevation of SSA and CRP.
## 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 Committee of Human Research, Publications and Ethics, School of Medicine and Dentistry, KNUST, Kumasi. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
WO, AB, and CO designed the study. Research data collection and laboratory analysis was performed by AB and HA. The data analysis and interpretation were performed by HA, EA, EOA, and EAA. SA, HA, and AB wrote the manuscript. WO, CO, SA, EOA, and EA reviewed the manuscript. All authors read and approved the final manuscript.
## Funding
This study did not receive funding from private, government or not-for-profit organization and was fully funded by authors.
## Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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|
---
title: A Survey on the Current Status of Ophthalmological Consultations in Patients
With Diabetes Undergoing Maintenance Hemodialysis and the Effectiveness of Education
on Consultation Behavior –Experience of a Single Hemodialysis Clinic in Japan
authors:
- Moritsugu Kimura
- Masao Toyoda
- Nobumichi Saito
- Makiko Abe
- Eri Kato
- Akemi Sugihara
- Naoto Ishida
- Masafumi Fukagawa
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012105
doi: 10.3389/fcdhc.2021.827718
license: CC BY 4.0
---
# A Survey on the Current Status of Ophthalmological Consultations in Patients With Diabetes Undergoing Maintenance Hemodialysis and the Effectiveness of Education on Consultation Behavior –Experience of a Single Hemodialysis Clinic in Japan
## Abstract
### Introduction
It is extremely important for patients with diabetes undergoing maintenance hemodialysis (MHD) to receive regular ophthalmologic examinations. However, even in the field of MHD in Japan, where there are many hemodialysis patients and the survival rate is said to be one of the highest in the world, we often see patients with diabetes who do not receive regular ophthalmologic examinations. In this study, we surveyed the status of ophthalmology consultations and the use of diabetic eye notebook (DEN) among hemodialysis patients with diabetes at hemodialysis clinics to confirm the current situation, with the aim of confirming the effectiveness of education on consultation behavior by medical care staff.
### Materials and Methods
This study included 38 diabetic hemodialysis patients attending one MHD clinic in Japan for one year from March 2018 to March 2019. In the first fact-finding survey in March 2018, hemodialysis care unit nurses (HCUNs) in the hemodialysis unit asked the diabetic hemodialysis patients whether they had consulted an ophthalmologist and used the DEN. Based on the results, the HCUNs recommended that hemodialysis patients with complications of diabetes be educated about the usefulness of regular ophthalmologic examinations, even during MHD, and that they use the DEN. This was followed by a second fact-finding survey in March 2019 to reconfirm ophthalmology consultations and DEN use.
### Results
Regarding the presence of ophthalmology consultations, 22 of 38 ($58\%$) patients had regular ophthalmology consultations in March 2018, and 27 of 38 ($71\%$) patients had consultations in the following year after receiving information from an HCUN. Only 1 of 22 patients ($5\%$) who consulted the ophthalmologist in March 2018 used a DEN, but 19 of 27 patients ($70\%$) used it the following year.
### Conclusion
In the future, the development and utilization of a new DEN that includes more detailed patient information, and the spread of self-care guidance to patients by multidisciplinary health care professionals, will increase the consultation rate of MHD patients in Japan and reduce the incidence and progression of ocular diseases in MHD patients.
## Introduction
End-stage renal disease (ESRD) has become an emerging health problem worldwide. The eye shares striking developmental, structural and genetic pathways with the kidney, suggesting that kidney disease and ocular disease may be closely related [1].
In particular, retinopathy affects the progression of nephropathy in patients with diabetic nephropathy, and it is said that early detection and management of patients with retinopathy is important to reduce the risk of death in patients with diabetic nephropathy (2–4). Patients with ESRD are at risk for developing ocular disease. This risk is associated with comorbidities that are common in ESRD patients, as well as to the unique effects of hemodialysis and the uremic state, which can lead to changes in the conjunctivae, cornea, retina, and macula. The most common ophthalmological complaints in ESRD patients include redness, irritation of the eyes, which may be associated with elevation of the product of the serum calcium and phosphorus concentrations, the so-called calcium-phosphorus product or Ca × P. In patients with chronically elevated calcium-phosphate products, band keratopathy may result. Other ophthalmological symptoms include retinal hemorrhage, ischemic optic neuropathy, ophthalmological infection, elevated intraocular pressure, retinal detachment, and macular edema. Prompt recognition that these conditions may threaten a patient’s vision is required [5, 6].
Moreover, ESRD patients with diabetes mellitus undergoing hemodialysis have a higher incidence of ocular diseases, including diabetic retinopathy (DR), exudative retinal detachment, which can cause a major cause of decreasing visual acuity and blindness (7–11). Therefore, it is very important for patients with diabetes undergoing MHD to have regular ophthalmologic examinations. However, even in the field of MHD in Japan, which has a large number of hemodialysis patients and where the survival rate is said to be among of the highest in the world [12], we often see patients with diabetes who do not receive regular ophthalmologic examinations [12, 13]. In addition, collaboration between physicians and ophthalmologists is said to be important in the treatment of diabetes in Japan. Figures 1A, B shows a diabetic eye notebook (DEN), the DEN was published by the Japanese Society of Ophthalmic Diabetology in 2002 as one of the solutions to this problem, patients are recommended to use this diary to consult ophthalmologist regularly [14]. This study aimed to reduce the incidence and progression of ocular diseases in MHD patients by improving the ophthalmology consultation rate of MHD patients with diabetes, and we surveyed the status of ophthalmology consultations and the use of the DEN among patients with diabetes undergoing MHD at single hemodialysis clinic in order to confirm the current situation and discuss the need for medical collaboration between hemodialysis clinics and ophthalmology clinics and the educational activities for medical staff.
**Figure 1:** *(A) The cover of the diabetic eye notebook (DEN). (B) The detailed contents of the diabetic eye notebook (DEN).*
## Materials and Methods
The subjects were 38 patients (males: $$n = 25$$, females: $$n = 13$$) among 46 diabetic MHD patients who attended the Sechi Clinic (Isehara City, Kanagawa Prefecture) in March 2018, who continued to attend the clinic until March 2019, excluding patients who were transferred or died. In the first fact-finding survey in March 2018, hemodialysis care unit nurses (HCUNs) asked patients with diabetes undergoing MHD whether they were consulting the ophthalmological clinic and using a DEN. Based on the results, HCUNs verbally explained the three major complications of diabetes to MHD patients with diabetes, educated them on the usefulness of regular ophthalmologic consultations even during MHD because of the risk of blindness, and recommended the use of DEN. The third edition of the DEN was used, and the ophthalmologist’s name, date of consultation, date of next consultation, corrected vision, etc. were recorded by the ophthalmologist. In addition, if there were any diseases, the status, changes, and treatment details were also recorded in “consultation notes” (Figure 1B). In the second fact-finding survey in March 2019, as in the first survey, the HCUNs asked MHD patients if they were consulting ophthalmologists and if they were using their DEN. From the medical records of 38 subjects, we also confirmed the history of ophthalmological consultation and the diagnosis of ocular diseases when they were referred to Seichi Clinic for MHD. This is a descriptive study of MHD patients who were followed up for one year after HCUNs awareness campaign.
## Results
The mean age of the 38 patients was 68.7 years and the mean duration of hemodialysis was 7.2 years. Diabetes mellitus was treated with diet alone in 6 cases. The others received insulin therapy or GLP-1 receptor agonist therapy, or some oral hypoglycemic agents (Table 1). Regarding the diagnoses of ocular disease listed on the referral letters of the 38 patients who started MHD at the Seichi Clinic, 1 patient had no ocular disease, in 5 patients, the ocular disease status was unknown, and 32 patients had some kind of ocular disease. Of the 32 patients with confirmed ocular disease, 11 did not consult an ophthalmologist. The most common ocular diseases were DR in 19 patients, cataract in 17 patients, others in 5 patients (Table 2). Regarding the presence of ophthalmology consultations, 22 of 38 ($58\%$) patients had regular ophthalmology consultations in March 2018, and 27 out of 38 ($71\%$) patients had consultations in the following year after the awareness campaign (Figure 2). Only 1 of 22 patients ($5\%$) who consulted the ophthalmologist in March 2018 used the DEN, but 19 of 27 patients ($70\%$) used it the following year (Figure 3).
## Discussion
In this study, HCUNs explained the necessity of ophthalmologic examinations to patients at a MHD clinic, which resulted in a $10\%$ increase in the ophthalmological examination rate from approximately $60\%$ to approximately $70\%$ after one year, and the utilization rate of DEN increased significantly from $5\%$ to $70\%$. The reason why the HCUNs were able to achieve these results after only one year of educational activities may be that the effects of the collaboration between the physician, HCUNs and ophthalmologist before the introduction of MHD remained. However, considering the fact that $30\%$ of patients had still not consulted ophthalmological clinics and that $30\%$ of patients who did consult a clinic did not use the DEN. This may be due to the following problems: 1) dialysis physicians do not cooperate with ophthalmologists or physicians before the introduction of hemodialysis, and 2) self-care education for hemodialysis patients is not as complete as it was before the introduction of dialysis.
## 1) Dialysis Physicians Do Not Cooperate With Ophthalmologists or Physicians Before the Introduction of Hemodialysis
The fact that 11 of the 32 patients who had consulted an ophthalmologist before starting hemodialysis and who had been diagnosed with some type of ocular disease did not consult an ophthalmologist (Table 2) suggests that the physician and ophthalmologist may have collaborated prior to the start of dialysis, but that the dialysis physician may have stopped working with the ophthalmologist once dialysis started. In Japan, the importance of collaboration between physicians and ophthalmologists from the early stages of diabetes has been reported for some time [15, 16], and in 2002, the Japanese Diabetic Eye Society issued the DEN, which recommends regular consults to ophthalmologists, as a means of collaboration [14]. On the other hand, there are no reports on collaboration between dialysis doctors and ophthalmologists. However, considering the fact that dialysis patients are at high risk for the development of various ocular diseases [5, 6], it is important for dialysis physicians along with HCUNs to collaborate more closely with ophthalmologists using collaboration tools such as the DEN, and more case reports are expected. In addition, while the DEN is an effective handbook for collaboration with ophthalmologists, it does not have a column for important patient information such as weight, blood glucose, lipid profile, or liver/renal function, etc., even though there is a column for HbA1c levels. This information can only be provided in the “Consultation notes” section (Figure 1B). In addition, in the current 4th edition, the “Consultation notes” section has been deleted, making it difficult to describe additional information. In order for ophthalmologists, physicians, dialysis physicians and HCUNs to better collaborate on patient information in the future, it may be necessary to develop a new DEN that can describe this information.
In other countries, it is said that a partnership between primary care physicians and ophthalmologists is the only way to save many people at risk of diabetic retinopathy. Furthermore, in Japan, when diabetic nephropathy transitions to end-stage renal failure, the doctor in charge is often changed from a diabetologist to a nephrologist or a dialysis specialist [15]. Under these circumstances, the use of a handbook such as DEN as a tool for understanding the clinical course of eye disease and for continuing cooperation with ophthalmologists is expected to help maintain the quality of life of many diabetic dialysis patients in Japan and abroad.
## 2) Self-Care Education for Hemodialysis Patients Is Not as Complete as It Was Before the Introduction of Hemodialysis. Importance of Education and Team Care for Patients With Diabetes Undergoing MHD
Japan has very few kidney transplants and many MHD patients in comparison to the prevalence of ESRD [12]. In addition, the survival rate of MHD patients is one of the highest in the world [13]. Japanese clinical practice patterns differ from those of other countries in many ways, including the reasons for the longevity of MHD patients, as reported in the DOPPS (Dialysis Outcomes and Practice Patterns Study) study [17, 18].
This is reflected by the fact that the rate of ophthalmologic examinations and the use of DEN significantly increased only one year after the initiation of the awareness campaign by the HCUNs; however, the results of the first survey confirmed the fact that there has been a lack of awareness-raising activities and patient self-care education for ophthalmologic examinations. In recent years, “patient involvement in healthcare” has been attracting international attention as a way to achieve safe, high-quality healthcare. Patient involvement in healthcare means that patients and their families collaborate with medical professionals to improve the quality and safety of medical care, and the modes of participation are said to include a wide range of areas, not only at the level of the individual patient (e.g., decisions about treatment choices and self-care) but also in relation to hospital management and other areas (19–22). In this focus on the importance of patient involvement in healthcare, the importance of self-care education for diabetes has long been reported in Japan [23, 24], and steps have been taken to allow reimbursement for patient education and guidance to prevent dialysis. However, the importance of self-care education after the initiation of hemodialysis has not been emphasized as much. Through this survey, it was confirmed patients with diabetes require thorough self-care education before and after the initiation of hemodialysis in order to enhance patient involvement in healthcare. The hemodialysis unit team in *Japan is* usually composed of several healthcare professionals (e.g., nurses, clinical engineers, dieticians, and doctors) [25]. If all of these medical professionals involved in dialysis understand the existence of DEN and can provide the same self-care guidance to patients as they did before the introduction of dialysis, it is estimated that more MHD patients will consult ophthalmological clinics.
## Conclusion
This study shows that the rate of ophthalmological consultation and the rate of DEN use can be increased by the awareness campaign by HCUNs. In the future, the development and utilization of a new DEN that includes more detailed patient information, and the spread of self-care guidance to patients by multidisciplinary health care professionals, will increase the consultation rate of MHD patients in Japan and reduce the incidence and progression of ocular diseases in MHD patients.
It is difficult to demonstrate the effectiveness of these results in countries with different healthcare systems from Japan. Longer-term studies involving other countries and other facilities are needed. However, the findings suggest that it is not detrimental for patients with diabetes in any country to have healthcare providers who are involved in their care to encourage and educate patients to seek ophthalmological care, and to use services such as the DEN. Based on the results of this study, we hope that more medical professionals around the world will become aware of patient involvement in healthcare and educate patients about ophthalmologic examinations for diabetic hemodialysis patients using tools such as the DEN, so that the quality of life of patients can be maintained and improved as much as possible.
## 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 Toyu Medical Research Ethics Review Committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Informed consent was received from patients for the submissionof this manuscript to an academic journal.
## Author Contributions
MK, MT, AS, NI, and MF contributed to conception and design of the study. NS, MA, and EK organized the database. MK, MT, and NS performed the statistical analysis. MK and MT wrote the manuscript. All authors contributed to manuscript 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
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: Observational Study of Glycemic Impact of Anticipatory and Early-Race Athletic
Competition Stress in Type 1 Diabetes
authors:
- Nicole Hobbs
- Rachel Brandt
- Sadaf Maghsoudipour
- Mert Sevil
- Mudassir Rashid
- Laurie Quinn
- Ali Cinar
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012106
doi: 10.3389/fcdhc.2022.816316
license: CC BY 4.0
---
# Observational Study of Glycemic Impact of Anticipatory and Early-Race Athletic Competition Stress in Type 1 Diabetes
## Abstract
Athletic competitions and the associated psychological stress are a challenge for people with type 1 diabetes (T1D). This study aims to understand the influence of anticipatory and early race competition stress on blood glucose concentrations and to identify personality, demographic, or behavioral traits indicative in the scope of the impact. Ten recreational athletes with T1D competed in an athletic competition and an exercise-intensity matched non-competition “training” session for comparison. The two hours prior to exercise and the first 30 minutes of exercise were compared between the paired exercise sessions to assess the influence of anticipatory and early race stress. The effectiveness index, average CGM glucose, and the ingested carbohydrate to injected insulin ratio were compared between the paired sessions through regression. In 9 of 12 races studied, an elevated CGM for the race over the individual training session was observed. The rate of change of the CGM during the first 30 minutes of exercise notably differed between the race and training ($$p \leq 0.02$$) with a less rapid decline in CGM occurring during the race for 11 of 12 paired sessions and an increasing CGM trend during the race for 7 of the 12 sessions with the rate of change (mean ± standard deviation) as 1.36 ± 6.07 and -2.59 ± 2.68 mg/dL per 5 minutes for the race and training, respectively. Individuals with longer durations of diabetes often decreased their carbohydrate-to-insulin ratio on race day, taking more insulin, than on the training day while the reverse was noted for those newly diagnosed (r = -0.52, $$p \leq 0.05$$). The presence of athletic competition stress can impact glycemia. With an increasing duration of diabetes, the athletes may be expecting elevated competition glucose concentrations and take preventive measures.
## 1 Introduction
In the management of type 1 diabetes (T1D), exercise can represent a significant challenge as the glycemic response to exercise is influenced by the exercise intensity and duration, the composition and timing of prior meals or snacks, the amount of circulating insulin, and the location of insulin delivery [1]. Individuals with T1D need to adjust their insulin administration and carbohydrate consumption to accommodate these factors. Experts in exercise physiology and endocrinology have developed guidelines to help people with T1D to exercise and compete in athletic competitions safely (1–3), and these broad recommendations are a foundation for the treatment plan that needs to be adjusted based upon the current physiological, psychological and metabolic state of the individual. This challenge is amplified on competition days, as the added stress can cause drastically different glucose responses than on training days.
The existing research on athletic competitions with people with T1D has focused on demonstrating the relative safety of participation and to acknowledge that a subpopulation of competitive athletes with T1D exists ranging from recreational [4, 5] to professional athletes [3]. For example, T1D Olympic swimmer Gary Hall, Jr. reported that his blood glucose can spike from 100 mg/dL to 300 mg/dL in the 21 seconds of a 50 meter race [6]. This spike in blood glucose concentrations (BGC) likely occurred due to a combination of increased hepatic glycogenolysis associated with high-intensity physical activity and the hormone responses, such as epinephrine, glucagon, growth hormone and cortisol, associated with the stress of competition [7]. Competition anxiety has been highly studied in terms of performance and the personal psycho-social factors that influence its presence [8], but its influence on BGC in people with T1D has not been previously studied.
The anxiety response is highly dependent on an individual’s personality traits. Obtaining feedback on each individual’s general perceptions of anxiety can provide insight into the role that anxiety may play in competitive sports. Anxiety can be assessed through surveys or measurement of relevant physiological variables [9, 10]. While it is widely accepted that athletes with T1D should expect competition anxiety to influence their blood glucose dynamics, the degree to which these changes occur and the relationship of these changes to the amount of anxiety has not been well studied. A better understanding of this response will increase safety for athletes with T1D and may improve their athletic performances.
The goal of this research study was two-fold. In a group of adults with T1D, we observed the influence of anticipatory and early race athletic competition stress on BGC and identified the personality, demographic, or behavioral traits that impacted this stress. This was the first study to consider the influence of athletic competition stress through a comparison of the athletic competition and a non-competitive intensity-matched exercise session. The results of this study may impact the advice given to recreational athletes with T1D to optimize their BGC to increase safety and help them to achieve their best athletic performances.
## 2.1 Experimental Setup and Inclusion Criteria
Ten individuals (aged 18-60) with a diagnosis of T1D for greater than 6 months and planned athletic competition within the study period were recruited for participation in this study. These individuals were required to have completed a similar athletic competition within the last two years and must have been following the same diabetes therapy at that time without a severe hypo- or hyperglycemic event requiring assistance from a medical professional. Subjects were excluded from the study for the following reasons: metabolic instability as evidenced by hospitalizations for diabetes or other diabetes-related complications (e.g., diabetic ketoacidosis and hypoglycemic seizures) within the preceding three months; severe macrovascular disease, as evidenced by severe peripheral artery disease (PAD; e.g., tissue ischemia with/at risk for gangrene and amputation); history of myocardial infarction, heart failure, thromboembolic disease, or unstable angina; uncontrolled hypertension; severe microvascular disease as evidenced by history of vision-threatening proliferative or non-proliferative retinal disease; kidney disease; any uncontrolled non-musculoskeletal condition that would limit the subject’s ability to participate in the exercise program (e.g., chronic obstructive airways disease); musculoskeletal conditions such as neurological or orthopedic conditions affecting lower limb strength and mobility (e.g., stroke; insensitive foot); pregnancy; and documented medical condition or physical impairment that is judged by the health care practitioner to contraindicate exercise. This study was approved by the Illinois Institute of Technology Institutional Review Board.
## 2.2 Details of Procedures
This was an observational descriptive study with the subjects following their standard physical activity routines at home and participating in an athletic competition of their choosing (running race ranging from 5K to marathon distance). For 12 hours prior to the competition, the subjects kept detailed diaries about their meals, snacks, insulin doses, and physical activity (type, time, duration, intensity). A continuous glucose monitor (CGM) (Dexcom G6, San Diego, CA) recorded the glycemic responses. To measure physiological variables of interest, a wristband (Empatica E4, Milan, Italy) [11] was worn for the competition if the subject was willing. The wristband has a photoplethysmography (PPG) sensor that generates heart rate and heart rate variability, an infrared thermopile to read peripheral skin temperature, an electrodermal activity sensor, and a 3-dimensional accelerometer.
The subjects completed a second exercise session which closely mimicked their competition exercise session in terms of intensity. In this non-competition session, the subjects completed 30 minutes of running at their competition pace. For 12 hours prior to this non-competitive exercise session, the subjects were asked to consume the same meals or snacks, insulin dosing if applicable, feasible and would not impair health, and any routines they have for preparation for the competition. The subjects kept detailed diaries about their meals, snacks, insulin doses, and physical activity (type, time, duration, intensity). The wristband and CGM were provided for physiological signal and glucose measurements.
A brief health history was performed, hemoglobin A1C (A1CNow+; Bayer, Metrika, Sunnyvale, CA) was obtained, and subjects completed the State-Trait Anxiety Inventory (STAI) [9] and the Sport Competition Anxiety Test (SCAT) [8] to assess anxiety-proneness in general and in relation to athletic competitions. Trait anxiety is a personality trait representative of relatively stable individual differences in anxiety-proneness. Thus, a person’s tendency to perceive a situation as stressful, dangerous, or threatening is related to their trait anxiety. The participants also completed the Hypoglycemia Fear Survey II (HFS) [12] to assess the influence of fear of hypoglycemia on diabetes management behaviors surrounding the competitive and non-competitive exercise sessions.
Ten people with type 1 diabetes completed the study. Two individual participated in the study twice for a total of 12 races and 12 intensity-matched non-competition exercise sessions studied.
## 2.3 Data Analysis
The difference between the competition and non-competition exercise session in CGM glucose concentrations and the slope of the CGM was assessed with student’s t-tests. The slope was determined through simple linear regression. The percentage of time above, below, and inside the target glucose range is assessed.
Regression models were developed to assess the influence of the athletic competition stress on (A) the ratio of ingested carbohydrates to injected insulin (ICII) calculated as the specific amount of insulin administered, above the basal infusion rate, relative to the reported amount of carbohydrates consumed, (B) an “effectiveness index” which quantifies the variability in glucose concentration after accommodating for the expected effect of administered insulin and carbohydrates consumed [13, 14], (C) the average CGM glucose (CGM) in the anticipatory period, (D) the average CGM in the first 30 minutes of exercise, (E) the slope of the CGM in the anticipatory period, and (F) the slope of the CGM in the first 30 minutes of exercise. Additional details regarding the calculation of these metrics can be found in the supplementary material section 2. These regression models predicted the difference in these metrics between the competition and non-competition exercise within each participant. The within-individual variation is of interest since a higher interpersonal variability is expected. The data included for modeling included the anticipatory stress period and the early exercise period defined as three hours prior to the exercise session through 30 minutes of exercise. The proposed models included inputs corresponding to the STAI trait-anxiety score, SCAT score, HFS, Age, Duration of Diabetes, HbA1c, and BMI.
Projection to latent structures regression (PLS), also called partial least squares, was applied to assess the interactions between the normalized input variables using the SIMPLS algorithm [15]. In PLS, a latent variable is a linear combination of input variables where the weight vectors to calculate the latent variables are called loading vectors. The latent variables are oriented such that they best explain the variance in the input variables and the variance in the response variable while explaining the maximal possible variance between the input and response variables.
The significant inputs of the PLS models were used in multiple linear regression (MLR) models and assessed through the coefficient of determination, R 2, the adjusted R 2, and the predictive R 2. The predictive R 2 is a method which applies leave one out cross validation to provide a metric assessing the model fit to data that were removed from the set of data used to estimate the model. This metric is calculated as the sum of squares of the residuals of the withheld data points. A predictive R 2 value greater than 0.5 indicates a model with strong predictive ability [16].
As a secondary assessment of variables that were not significant in the regression models, Pearson’s correlation coefficient was employed to assess correlation between variables of interest with reported p-values corresponding to the student’s t-distribution.
## 3 Results
Ten subjects completed the study and two of them completed 2 competitive races each for a total of 12 races and 12 intensity-matched non-competitive exercise sessions studied. The subjects were recreational athletes with race paces ranging from 6:30 minutes per mile (4 minutes per kilometer for subject participating in a 5K race) to 11 minutes per mile (6 minutes 50 seconds per kilometer for subject participating in a marathon). These subjects represented a wide range of general trait anxiety as measured by the Trait subscale of State Trait Anxiety Inventory as demonstrated by a score of $70\%$ [29, 89] (median [interquartile range]) percentile rank where the $50\%$ would represent the average score for age and gender matched adults [9]. These subjects comprised an average trait anxiety group relative to those individuals participating in athletic competitions as measured by the Sport Competition Anxiety test (SCAT) with scores of 22 [19, 24] where a score below 17 indicates low trait anxiety and a score greater than 24 indicates high trait anxiety in competitive athletes [8]. These subjects scored slightly higher on the Hypoglycemia Fear Survey II - Behavior (HFS-B) section (21 [18, 24]) and slightly lower than average on the Hypoglycemia Fear Survey II - Worry (HFS-W) section (19 [16, 24]) than the general T1D adult population with scores of 17.9 ± 9.3 and 22.3 ± 14.4, respectively [12]. The demographic information is included in Table 1.
**Table 1**
| Demographic Information | Demographic Information.1 |
| --- | --- |
| Gender | 6 Male/4 Female |
| Age (years) | 32 [25, 38] |
| Duration of Diabetes (years) | 13 [3, 24] |
| BMI (kg/m2) | 23.1 [21.2, 26.3] |
| HbA1c (%) | 6.1 [6.0, 6.5] |
| Diabetes Treatment | 5 Pump/5 MDI |
| Personal CGM Use | All |
| Race Distance | 2 x 5k, 1 x 7k, 1 x 10k, 1 x 10-mile, 7 x Marathon |
The exercise period was defined as the first 30 minutes of exercise. In 9 of 12 races studied, the average CGM glucose during the exercise period was elevated during the race compared to the individual training session; however, this increase was not statistically significant ($$p \leq 0.28$$). Alternatively, in only 4 of 12 sessions the average glucose concentration was higher in the anticipatory period (3 hours prior to exercise) on the day of the race compared to the day of the non-competition exercise session. The elevation in glucose concentration observed on the day of the race is primarily occurring in the period of study. The rate of change of the CGM during the first 30 minutes of exercise notably differed between the race and training ($$p \leq 0.02$$) with a less rapid decline in CGM occurring during the race for 11 of 12 paired sessions and an increasing CGM trend during the race for 7 of the 12 sessions with the rate of change (mean ± standard deviation) as 1.36 ± 6.07 and -2.59 ± 2.68 mg/dL per 5minutes for the race and training, respectively. The median and interquartile range relative to the start of exercise for CGM glucose concentration and rate of change in CGM glucose concentration for the competition and non-competition sessions are shown in Figure 1, 2, respectively.
**Figure 1:** *Median and Interquartile range for CGM glucose concentration for the competition and non-competition exercise sessions.* **Figure 2:** *Median and Interquartile range for the rate of change of CGM glucose concentration for the competition and non-competition exercise sessions.*
Overall, the percentage of time in the target glycemic range (70-180 mg/dL) was higher on the day of the race compared to the training session in the anticipatory period as shown in Table 2, but this increase was not statistically significant ($$p \leq 0.93$$). The difference was smaller for the exercise period, but again tighter glycemic control was observed on the day of the race. Notably, an increase in hyperglycemia (>180 mg/dL) was observed in the first 30 minutes of the race while an increase in time in euglycemia was observed on the training day. A few individuals experienced more severe hyperglycemia on the day of the race. The average glucose concentrations were similar in the anticipatory period, yet the average glucose concentrations in the first 30 minutes of exercise differed more greatly with a statistically insignificant increase observed on race day ($$p \leq 0.28$$).
**Table 2**
| CGM Range by Event | CGM Range by Event.1 | <551 | 55 – 70 | 70 – 180 | 180 – 250 | >250 | Average |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Race | Anticipatory (3 hours) | 0 [0,0] | 0 [0,0] | 96.1 [64.2,100] | 3.9 [0,24.5] | 0 [0,0] | 143.1 [127.4,162.0] |
| Race | Exercise (30 min) | 0 [0,0] | 0 [0,0] | 93.1 [37.9,100] | 0 [0,20.7] | 0 [0,1.7] | 167.5 [138.0,196.0] |
| Training | Anticipatory (3 hours) | 0 [0,0] | 0 [0,0] | 83.1 [23.4,100] | 9.1 [0,58.6] | 0 [0,3.6] | 156.9 [125.3,203.5] |
| Training | Exercise (30 min) | 0 [0,0] | 0 [0,0] | 100 [12.1,100] | 0 [0,24.1] | 0 [0,36.2] | 129.6 [100.6,223.2] |
PLS regression explained a large percentage of the variance of several of the analyzed output response variables with the models for (A) Difference in ICII Ratio ($90.0\%$), (C) Difference in Average CGM - Anticipatory ($86.5\%$)), (D) Difference in Average CGM - Exercise ($85.0\%$), and (E) Difference in Slope of CGM - Anticipatory ($84.5\%$) as shown in Figure 3 and Supplementary Table S3. The PLS models captured less of the variance observed in (B) Difference in Effectiveness Index ($38.2\%$) and (F) Difference in Slope of CGM - Exercise ($50.4\%$) as shown in Figure 3 and Supplementary Table S3. The latent variables capture the systemic variation in the data set, with each latent variable successively capturing variations that are not encoded in the preceding more dominant latent variables, thus yielding latent variables that do not capture redundant information and succinctly describe the variations in the data set. Figure 3 illustrates the contributions of the input variables to each latent variable of the PLS models and the total variance of the response variable explained by each component.
**Figure 3:** *Contribution of PLS Loadings per Input Variable. The total percent variance of the response variable explained by each latent variable is indicated by total bar height. The contribution of each input variable to the corresponding latent variable is indicated by area of shading and in order of appearance with larger loadings on the top. Each subplot represents a PLS model for a unique response variable with (A) the difference in ICII, (B) the difference in Effectiveness Index, (C) the difference in average CGM in the anticipatory period (D) the difference in average CGM in the exercise period, (E) the difference in the slope of the CGM in the anticipatory period, and (F) the difference in the slope of the CGM in the exercise period.*
The behavioral variable of the difference in ICII between the race and training session was highly explained by the included demographic data (Figure 3A). The variables with the most significant relationship to the difference in the ingested carbohydrate to injected insulin ratio between the training and competition sessions include HbA1c and age (Figure 4A). In the corresponding MLR model, Table 3A, both age and HbA1c are significant variables and the model explains a high degree of variance in the observed data with R 2=0.86, adjusted R 2=0.83, and $p \leq 0.001$ and the relationship is likely to be representative of new data points due to the high predictive R 2 as shown in Table 4A.
**Figure 4:** *Variable importance in explaining variations observed in the response variables. Each subplot represents a PLS model for a unique response variable with (A) the difference in ICII, (B) the difference in Effectiveness Index, (C) the difference in average CGM in the anticipatory period (D) the difference in average CGM in the exercise period, (E) the difference in the slope of the CGM in the anticipatory period, and (F) the difference in the slope of the CGM in the exercise period.* TABLE_PLACEHOLDER:Table 3 TABLE_PLACEHOLDER:Table 4 The ICII was also among the most impactful factors in all models of related to glycemia (Figures 4C-F). The variables with the most significant relationship to the difference in effectiveness index include the age, BMI, and duration of diabetes (Figure 4B); although, the total percentage of variance in effective index that was explained by this model was relatively low (Figure 3B) and the corresponding MLR model performed poorly in all model fitting criteria assessed (Table 4B). The calculated effectiveness index was for the anticipatory period prior to the race was large and negative, indicating a higher glucose concentration than was estimated for that time. The difference in effectiveness index indicated a higher difference on race day than on training, which may indicate a trend towards a higher insulin requirement prior to the race ($$p \leq 0.005$$). This elevation in required insulin on the day of the race may be influenced by BMI, relative glycemic control (HbA1c), duration of diabetes, and the observation of the impact may be more notable with an increase in the difference in injected insulin relative to carbohydrates consumed between the race and training sessions.
The variables related to the difference in the average CGM for both the anticipatory period and the exercise period include age, BMI, duration of diabetes, and ICII (Figures 4C, D). The MLR models for the difference in average CGM described a high degree of variance in the training data set, but performed poorly in the leave-one-out cross validation of the predictive R 2 (Tables 4C, D).
The difference in the slope of the CGM in the anticipatory period was related to the STAI trait score, the duration of diabetes, HbA1c, and ICII (Figure 4E). The MLR model for the difference in the CGM slope in the anticipatory period found the duration of diabetes, the individual personality trait for anxiety-proneness as measured by the STAI-trait survey, and the ICII to be significant variables (Table 3E). This model explained a high degree of variance in the data set (R 2=0.85 and adjusted R 2=0.77) and would likely describe the variance observed in a broader population of recreational athletes with T1D due to the high predictive R 2=0.62 (Table 4E). In the exercise period, the slope of the CGM had the most significant relationship to the ICII and HbA1c (Figure 4F); however, the corresponding MLR model performed poorly in all model fitting criteria assessed (Table 4F).
Individuals with longer duration of diabetes were more likely to increase their insulin carbohydrate ratio on the competitive race day than on the non-competitive training day while the reverse was true for those with a shorter duration of diabetes with T1D (r = -0.57, $$p \leq 0.05$$). While there are some significant factors in the difference in the carbohydrate to insulin ratio between the race and training sessions, the absolute amount of carbohydrates consumed prior to the exercise session is not significantly related to the factors under study. In an assessment of carbohydrates consumed during the exercise sessions, it was observed that the athletes competing shorter races (5k or 10k) did not consume any carbohydrates during the exercise sessions. The marathon runners consumed 17-48 g carbohydrates per hour of the race with two participants consuming carbohydrates only when nearing hypoglycemia and the other 4 participants consuming carbohydrates on a pre-determined schedule. In the 6 marathon participants, the additional carbohydrate supplementation began 30 minutes or later into the race.
The Empatica E4 wristband was worn by 4 individuals with successful data collection in both the training and athletic competition sessions. There was no significant difference in estimated energy expenditure [17, 18] between the two exercise sessions ($$p \leq 0.97$$, confidence interval: [-1.82, 1.79]). The difference in galvanic skin response (GSR) between the race and training sessions was highly negatively correlated with the difference in the effectiveness index estimate (r = -0.83, $$p \leq 0.10$$). The larger increase in GSR in the race over training corresponded with a larger negative effectiveness index (higher insulin requirement) on race day though the relation was not statistically significant. The heart rate difference between exercise sessions was correlated with the difference in the average CGM ($r = 0.89$, $$p \leq 0.08$$), though again not statistically significant.
## 4 Discussion
Elevations in blood glucose concentration and reductions in insulin sensitivity prior to and during an athletic competition relative to exercise in training at the same intensity have been observed in several recreational athletes with T1D. Competition stress may lead to an elevated glucose trend when compared to a training exercise session at the same intensity. This elevation is primarily due to an increasing glucose trend observed in the period of time prior to the competition when the individual is making treatment decisions for the upcoming race and may be experiencing anticipatory stress. This increasing CGM slope prior to the competition may be influenced by the individual’s anxiety-proneness as measured by the STAI-trait survey, duration of diabetes, diabetes management as measured by HbA1c, and the insulin dosing behavior as measured by the ICII (Table 3E).
With an increasing duration of diabetes, the individual may be expecting this increase in glucose concentration with the athletic competition and take preventative measures to avoid race-day hyperglycemia such as injecting more insulin with any carbohydrates consumed pre-race. Despite these corrective actions, the average CGM was higher in the first 30 minutes of exercise on the competitive race day compared to the non-competitive session in many of the subjects. The increase in ICII may also relate to a desire for euglycemia with a plan to consume mid-race nutrition to maximize performance during an endurance event. The exercise management in T1D consensus statement and the guidelines for competitive athletes with T1D [1, 2] recommend consumption of 60 to 90g of carbohydrate per hour to maximize performance in events of duration similar to a marathon. Our marathon participants in this study consumed between 17-48 g of carbohydrates per hour of the race. Additional counseling may be required to encourage athletes to consume more carbohydrates to maximize performance while ensuring confidence in the higher insulin administration required to maintain euglycemia. While the time period compared between the competition and non-competition exercise sessions are of the same duration in this analysis, the total duration of exercise in the competition exercise sessions are frequently longer (i.e. the participants completing a marathon distance did not complete this duration of activity in the non-competition session). This may have influenced the individual treatment strategies regarding ICII.
Fear of hypoglycemia may also alter the ICII as participants may have aimed for higher CGM profiles in training or on race day. As many of the more newly-diagnosed individuals utilized a higher ICII, taking less insulin on race day, which led to higher BGC, additional education for these individuals may be recommended to ensure a similar level of confidence in taking the required larger insulin dose prior to exercise on the day of an athletic competition. As practiced by more experienced racers with T1D, increasing or maintaining the insulin dose compared to the dose on the non-competition day was more appropriate. A concern regarding hypoglycemia or the need to cease running while treating a hypoglycemic event is reasonable as it may be detrimental towards meeting the individual goals set for the athletic competition. A deeper assessment between the behaviors of the athletes with T1D and concerns regarding hypoglycemia is of interest.
The carbohydrate-insulin ratio is a static value determined by the person with diabetes and the doctor regarding the amount of insulin to give for an amount of carbohydrates at a particular time of the day. The ingested-carbohydrate-to-injected-insulin (ICII) ratio was calculated as the specific amount of insulin administered, above the basal infusion rate, relative to the reported amount of carbohydrates consumed. This metric aimed to capture the individual treatment strategies for the competition and non-competition exercise sessions. The participants in this study are experienced in managing their diabetes while exercising, and in all cases the ICII was larger than the reported carbohydrate-insulin ratio, a relative reduction in insulin dose for the exercise period, as is recommended [1].
All the participants in this study had experience running races with T1D and had a personal CGM with a prescription from their medical care team. Our study team did not provide any training regarding diabetes management, or the exercise guidelines outlined in the consensus statements. The participants in this study appeared to prioritize avoiding hypoglycemia with the trade-off of spending a greater percentage of time in mild hyperglycemia on both the day of the athletic competition and the non-competition exercise session compared with the recommended guideline of >$70\%$ between 70-180mg/dL and <$25\%$ for glucose levels greater than 180mg/dL [2]. The complete avoidance of hypoglycemia during exercise, $0\%$ of time <70mg/dL for all subjects, indicates skillful use of the CGM and the CGM trends in combination with timely supplementation of carbohydrates when required. Due to the desire to not cease exercise as is recommended during a hypoglycemic event [19], this study participants appeared to be highly proactive in preventing hypoglycemia with additional carbohydrate consumption prior to the start of the exercise session if the CGM trend indicated a hypoglycemia risk in their opinion. This is likely common in this population that would be hesitant to stop exercising when working towards a competition goal.
The degree to which each person cared about their performance in a race and the corresponding degree of athletic competition stress experienced has not been quantified. We aimed to focus solely on competitions with meaningful, personal value, but that interpretation will be dependent on the individual. Despite that limitation and our small sample size (10 participants completing 12 competitions and 12 exercise-intensity matched non-competition exercise sessions), trends in the impact of competition stress have been observed. Due to the small sample size, the data was not separated into a training and testing set. The predictive R 2 may allow for an assessment of overfitting to the available data. The models for the difference in the ICII ratio and the difference in the slope of the CGM in the anticipatory period both yield high predictive R 2 values. The trends observed in this study should be viewed as a preliminary observation and further research with a larger sample is necessary to confirm the findings of this study.
Competition stress frequently leads to an elevated glucose trend when compared to a training session at the same intensity. The elevation in glycemia is impacted by the individual behavior related to ingested carbohydrate to injected insulin ratio between the race and training session. With an increasing duration of diabetes, the individual may be expecting this phenomenon and take preventive measures such as administering more insulin with any carbohydrates consumed pre-race. Additional education for the newly-diagnosed individuals may be valuable for reducing hyperglycemia on the day of the athletic competition to help athletes reach their peak potential. The effectiveness index, which accommodates both the behavioral impact of the ingested carbohydrate to injected insulin ratio and the impact on CGM, indicated a general trend towards higher insulin requirement during the athletic competition with the trend related to several demographic traits.
## 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 Illinois Institute of Technology Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
NH developed the study protocol, conducted the research study, and conducted the primary data analysis. RB assisted in development of the study protocol and conducting the research study. She also provided feedback on the analysis and interpretation of the results. SM contributed to the data analysis. MS assisted in conducting the research study and participated in the data analysis. MR participated in the data analysis and interpretation of the results. LQ assisted in the development of the study protocol and provided data analysis and interpretation of the results. AC assisted in the development of the study protocol and provided data analysis and interpretation of the results. All authors contributed to the article and approved the submitted version.
## Funding
This work is sponsored by NIH NIDDK under grants 1DP3DK101075 and F31DK116524, and JDRF under grant 2-SRA-2017-506-M-B made possible through collaboration between the JDRF and The Leona M. and Harry B. Helmsley Charitable Trust.
## 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/fcdhc.2022.816316/full#supplementary-material
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|
---
title: 'BrazIliaN Type 1 & 2 DiabetEs Disease Registry (BINDER): longitudinal, real-world
study of diabetes mellitus control in Brazil'
authors:
- Bianca de Almeida-Pititto
- Freddy G. Eliaschewitz
- Mauricio A. de Paula
- Graziela C. Ferreira
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012111
doi: 10.3389/fcdhc.2022.934629
license: CC BY 4.0
---
# BrazIliaN Type 1 & 2 DiabetEs Disease Registry (BINDER): longitudinal, real-world study of diabetes mellitus control in Brazil
## Abstract
### Introduction
This study aimed at assessing the patterns of care and glycemic control of patients with diabetes (DM) in real life during a follow-up of 2 years in the public and private health sectors in Brazil.
### Methods
BINDER was an observational study of patients >18 years old, with type-1 (T1DM) and type-2 DM (T2DM), followed at 250 sites from 40 cities across the five regions of Brazil. The results for the 1,266 participants who were followed for 2 years are presented.
### Main results
Most patients were Caucasians ($75\%$), male ($56.7\%$) and from the private health sector ($71\%$). Of the 1,266 patients who entered the analysis, 104 ($8.2\%$) had T1DM and 1162 ($91.8\%$) had T2DM. Patients followed in the private sector represented $48\%$ of the patients with T1DM and $73\%$ of those with T2DM. For T1DM, in addition to insulins (NPH in $24\%$, regular in $11\%$, long-acting analogues in $58\%$, fast-acting analogues in $53\%$, and others in $12\%$), the patients received biguanide ($20\%$), SGLT2-I ($4\%$), and GLP-1Ra (<$1\%$). After 2 years, $13\%$ of T1DM patients were using biguanide, $9\%$ SGLT2-I, $1\%$ GLP-1Ra, and $1\%$ pioglitazone; the use of NPH and regular insulins decreased to $13\%$ and $8\%$, respectively, while $72\%$ were receiving long-acting insulin analogues, and $78\%$ fast-acting insulin analogues. Treatment for T2DM consisted of biguanide ($77\%$), sulfonylureas ($33\%$), DPP4 inhibitors ($24\%$), SGLT2-I ($13\%$), GLP-1Ra ($2.5\%$), and insulin ($27\%$), with percentages not changing during follow-up. Regarding glucose control, mean HbA1c at baseline and after 2 years of follow-up was 8.2 (1.6)% and 7.5 (1.6)% for T1DM, and 8.4 (1.9)% and 7.2 (1.3)% for T2DM, respectively. After 2 years, HbA1c<$7\%$ was reached in $25\%$ of T1DM and $55\%$ of T2DM patients from private institutions and in $20.5\%$ of T1DM and $47\%$ of T2DM from public institutions.
### Conclusion
Most patients did not reach the HbA1c target in private or public health systems. At the 2-year follow-up, there were no significant improvements in HbA1c in either T1DM or T2DM, which suggests an important clinical inertia.
## Introduction
According to the International Diabetes Federation, 463 million people are currently living with diabetes (DM) worldwide [1, 2]. In 2019, it was estimated that there were about 16.8 million people aged from 20 to 79 years with DM in Brazil, with a projected increase of $55\%$ by the year 2045 [1, 2]. Type 2 diabetes (T2DM) comprises approximately $90\%$ of all DM diagnoses [3]. Estimates related to the number of existing cases of type 1 diabetes (T1DM) in children and adolescents from 0 to 14 years show that Brazil occupies the third place in the global panorama, with 55,500 cases, behind India [95,600] and the United States [94,200] [1].
Chronic non-communicable diseases (NCDs) are responsible for nearly two thirds of deaths in Brazil, $5.3\%$ of which due to DM [4]. In addition, DM is known to be an important risk factor for chronic cardiovascular disease (CVD), which accounts for $31.3\%$ of deaths in our country [5].
Over the last decades, age-standardized rates have shown a tendency to reduced mortality caused by CVD and DM in Brazil [6, 7], in agreement with the aging of the population and the extension of life with the disease. The considerable burden of these diseases was highlighted in the Project on the Global Burden of Disease in Brazil (Burden of disease in Brazil, 1990–2016), in which DM was identified to be responsible for $4.7\%$ of disability-adjusted life-years (DALY) in total and $6.1\%$ of DALY originated by NCDs [8].
One of the great current challenges is, therefore, to deal with this increase in morbidity, which requires controlling the disease and preventing complications. These data are even more worrisome when considering the number of affected people in Brazil. Brazilian data on the prevalence of DM representative of the population of nine capitals date from the 1980s [9]. At that time, it was estimated that approximately $7.6\%$ of the Brazilian population aged between 30 and 69 years had DM, with both genders being equally affected, and with the prevalence of the disease increasing with age and body fat. A more recent estimate of the prevalence of self-reported DM in Brazil was performed by the Surveillance System of Risk and Protective Factors for Chronic Diseases by Telephone Survey (VIGITEL, Vigilância de Fatores de Risco por Inquérito Telefônico), implemented in 27 state capitals since 2006 [10]. In the VIGITEL 2018, $8.1\%$ of women and $7.1\%$ of men ≥18 years old in Brazil reported having DM; the numbers increased with age, reaching $23.1\%$ in individuals over 64 years of age, and decreased with higher the level of education, affecting $15.2\%$ of the participants with from 0 to 8 years of schooling and $3.7\%$ in the group with higher education [10].
The high prevalence of DM exerts a negative impact on health not only due to mortality, but also through complications and disabilities resulting from the prolonged time living with the disease and poor metabolic control. In addition to the health-related effects, diabetes is associated with an unwanted economic impact on both individual and society levels. Studies show that associated costs increase according to the duration of DM and the presence of micro- and macrovascular complications [11, 12]. Inadequate glycemic control can aggravate these medical conditions and has been reported in studies including patients with T1DM and T2DM treated in the Brazilian Public Unified Health System (SUS, Sistema Único de Saúde) [11, 13, 14]. Data related to the management of diabetes in the private sector in Brazil are still scarce.
To understand this scenario, there is a lack of data on the prevalence of chronic complications and comorbidities, including cardiovascular risk factors, associated with DM in the Brazilian population. In this regard, public and private health services represent opportunities to access professional care and different medications, providing information to guide better strategies for secondary and tertiary prevention of DM. The disease burden of DM is a relevant concern that requires secondary and tertiary prevention strategies. To develop these actions, it is necessary to understand the epidemiological and current management landscape of patients with diabetes in Brazil.
The BrazIliaN Type 1 & 2 DiabetEs Disease Registry (BINDER) study was an observational study, with both a cross-sectional and a longitudinal phase, designed to assess the demographic and clinical characteristics, patterns of care and glycemic control of patients with DM in real life during a follow-up of 2 years in the public and private health sectors in Brazil. In this paper, we present the results of the longitudinal analysis which included the patients followed for 2 years.
## Study design and population
This was a observational study of individuals with DM followed for 2 years in the BINDER study. BINDER included patients with T1DM and T2DM followed by 250 physicians from different public and private healthcare services, geographically distributed in 40 cities across the five regions of Brazil. The study had both cross-sectional and longitudinal phases (for a total duration of 2 years). Five waves of data collection were performed; for each wave, information from the last 6 months was obtained. To be enrolled in the study, patients had to be 18 years or older, have T1DM or T2DM, and had to have attended at least one medical visit at the study site in the 6 months prior to study entry. Pregnancy, gestational diabetes and other types of DM except T1DM or T2DM were excluded.
Each medical specialist (endocrinologists, cardiologists, or general practitioners) was responsible for recruiting about ten patients. To minimize patient selection bias, investigators were instructed to recruit patients in a retrospective consecutive manner starting from the patients that were last seen in the service according to medical charts. The initial sample of the study comprised 2,488 patients who entered the first wave of data collection (baseline visit). In the longitudinal phase, four subsequent follow-up visits were planned to occur every 6 months until the completion of the 2-year follow-up period. In this paper, we present the results obtained for the 1,266 participants who completed the final visit scheduled to occur after 2 years of follow-up and comprised the population of the longitudinal analysis.
Participating study centers were selected by the Associação Brasileira de Organizações Representativas de Pesquisa Clínica according to a proprietary database. A total of 250 sites/medical specialists of 40 Brazilian cities of the five country regions were chosen: 124 in the Southeast; 48 in the Northeast; 38 in the South Region; 30 in the Central-West Region; and 10 in the North Region.
The participant physicians collected data from patient medical charts covering the medical appointments that occurred from 07-Apr-2016, the date of study initiation, to 13-Dec-2019, the date of the final visit for the study.
The study was conducted after the approval by the ethics committee of the Universidade Federal de São Paulo (São Paulo, Brazil), and the study was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization guidelines for Good Clinical Practice. Informed consent was obtained from all patients.
## Data collection, variables and evaluation criteria
Data were collected from medical charts using an electronic CRF (e-CRF), and data management was performed according to the Data Validation Plan with data review processes in order to clarify data issues.
Variables of interest in the cross-sectional (baseline) phase were age, gender, ethnicity, educational level, body mass index (BMI), age at diagnosis, DM duration (time since diagnosis), abdominal circumference, blood pressure and laboratory results, risk factors for CVD, comorbidities, DM complications, glycemic control, medical specialties involved in patient care, and type of treatment. For the subsequent waves and longitudinal phase, collected data included glycemic control (HbA1c), weight, BMI, use of insulins and other medications, number of medications, and comorbidities and complications.
The achievement of individual HbA1c target (<$7.0\%$ or defined individual target) in patients with T1DM and T2DM at the study baseline (cross-sectional phase) and after 2 years of follow-up was the primary objective of the study and was described by the proportion of patients who reached the target in the overall study population and per DM type. The proportion was complemented by the respective $95\%$ confidence interval (CI). Secondary objectives included the description of patients regarding their demographic and clinical characteristics, presence of comorbidities, complications, patterns of treatment and hospitalizations at baseline and during the follow-up period.
As this is a disease registry, non-interventional study, no data were collected beyond those required for routine clinical practice. However, Adverse Drug Reactions to any Sanofi product that occurred during the course of the study was to be reported to the Sponsor within 24 hours from the moment the investigator was notified about the case, in compliance with pharmacovigilance practice.
## Statistical considerations and analysis
Statistical analysis was based on pooled data from all patients. Given the observational nature of the study, the statistical analysis was mainly descriptive, using appropriate summary statistics according to the type of variable. Descriptive statistics as number of non-missing data, range (minimum and maximum values), mean, standard deviation (SD), median and interquartile range (IQR) were calculated for summarizing numerical variables. Frequencies and proportions were calculated for summarizing categorical variables. There was no data imputation for missing/not available data in the calculations. The number of participants with available information for each variable are displayed in the tables, when considered relevant.
For the longitudinal phase, statistical analysis was based on pooled data of all patients who had available data at baseline and also at the end of follow-up, after 2 years. Descriptive analyses were performed according to the DM type and health care system (private and public sectors).
For the cross-sectional phase, nearly 2,500 patients were planned to be enrolled. Considering a planned sample size of 2,500 patients for the cross-sectional phase and assuming that T2DM comprise $90\%$ of DM cases, the study expected to recruit about 2,250 patients with T2DM and 250 patients with T1DM. The sample size of 2,250 T2DM patients would ensure $95\%$ CIs with a maximum width of $2.1\%$ below and above point estimate. On the other hand, with a sample size of 250 T1DM patients, the maximum expected width was $6.2\%$ below and above the point estimate.
Sample size calculation was performed based on published data from population studies conducted in Brazil that estimated the proportion of patients with HbA1c values within the target. Considering an expected proportion of $27\%$ of patients with T2DM within the HbA1c target [3], the sample of 2,250 patients with T2DM would allow assessing this proportion with $95\%$ CIs with a maximum width of $1.8\%$ below and above the point estimate; and for an expected proportion of $10\%$ of T1DM patients within the HbA1c target [14], the sample of 250 patients with T1DM would allow assessing this proportion with $95\%$ CIs with a maximum width of $3.7\%$ below and above the point estimate.
## Results
Baseline characteristics and comorbidities of the subset of patients who entered the longitudinal analysis were similar to those of the patients comprising the total study sample (data not shown). The baseline sample comprised $91.9\%$ of patients with T2DM, the mean age was 63 years, and $52.2\%$ were from the Southeast Region, while the sample at the end of the follow-up period had $91.8\%$ of patients with T2DM, mean age of 62 years, and $51.8\%$ from the Southeast Region.
## Patient characteristics
The study sample for the longitudinal analysis comprised a total of 1,266 patients of the BINDER study who had completed the 2 years of follow-up with data collection in all five waves. As shown in Table 1, $56.7\%$ of patients were male, $74.7\%$ were Caucasian, and $33.5\%$ had a college or higher degree of education. One hundred and four patients had T1DM ($8.2\%$), and 1162 ($91.8\%$) had T2DM. At the time of the initial study visit, the mean age of T1DM and T2DM patients were 35.0 and 63.7 years, respectively; patients aged 18 to 30 years comprised $38.5\%$ of the T1DM group and $0.5\%$ of the T2DM patients. T1DM patients were under treatment for a longer time (mean treatment duration: 15.8years for T1DM vs 9.8 years for T2DM), although the mean time since DM diagnosis was similar between T1DM (16.5 years) and T2DM (17.8 years). Of the assessed patients, $48\%$ of those with T1DM and $73.2\%$ of those with T2DM were followed in the private health sector (Table 1). A family history of DM was reported by $12.2\%$ and $25.3\%$ of patients with T1DM and T2DM, respectively.
**Table 1**
| Characteristic | All Patients n=1266 | T1DM n=104 | T2DM n=1162 |
| --- | --- | --- | --- |
| Gender, n (%) | n=1263 | n=104 | n=1159 |
| Male | 716 (56.7%) | 65 (62.5%) | 651 (56.2%) |
| Female | 547 (43.3%) | 39 (37.5%) | 508 (43.8%) |
| Age, years | n=1262 | n=104 | n=1158 |
| Mean ± SD | 61.3 ± 14.3 | 35.0 ± 12.0 | 63.7 ± 11.9 |
| Range | 18 – 93 | 18 – 74 | 19 – 93 |
| Age, years | n=1262 | n=104 | n=1158 |
| 18 to 30 years | 46 (3.6%) | 40 (38.5%) | 6 (0.5%) |
| 31 to 50 years | 193 (15.3%) | 50 (48.1%) | 143 (12.3%) |
| 51 to 70 years | 680 (53.9%) | 13 (12.5%) | 667 (57.6%) |
| >70 years | 343 (27.2%) | 1 (1.0%) | 342 (29.5%) |
| Ethnicity, n (%) | n=997 | n=90 | n=907 |
| Caucasian | 745 (74.7%) | 67 (74.4%) | 678 (74.8%) |
| Brown/Latin/Mixed | 117 (11.7%) | 19 (21.1%) | 98 (10.8%) |
| Black | 107 (10.7%) | 4 (4.4%) | 103 (11.4%) |
| Asian/Indigenous/Yellow | 28 (2.8%) | 0 (0%) | 28 (3.1%) |
| Country region | n=1266 | n=104 | n=1162 |
| Southeast | 656 (51.8%) | 34 (32.7%) | 622 (53.5%) |
| South | 208 (16.4%) | 17 (16.3%) | 191 (16.4%) |
| Northeast | 146 (11.5%) | 34 (32.7%) | 112 (9.6%) |
| Central-West | 181 (14.3%) | 19 (18.3%) | 162 (13.9%) |
| North | 75 (5.9%) | 0 (0.0%) | 75 (6.5%) |
| Formal education | 804 | 80 | 724 |
| College or higher | 269 (33.5%) | 36 (45.0%) | 233 (32.2%) |
| High school | 291 (36.2%) | 31 (38.8%) | 260 (35.9%) |
| Elementary school | 227 (28.2%) | 13 (16.2%) | 214 (29.6%) |
| Illiterate | 17 (2.1%) | 0 | 17 (2.3%) |
| Medical specialty | n=1266 | n=104 | n=1162 |
| Endocrinologist | 620 (49%) | 81 (77.9%) | 539 (46.4%) |
| General Practitioner | 241 (19%) | 16 (15.4%) | 225 (19.4%) |
| Cardiologist | 405 (32%) | 7 (6.7%) | 398 (34.3%) |
| Health-care sector | n=1208 | n=100 | n=1108 |
| Private | 859 (71.1%) | 48 (48.0%) | 811 (73.2%) |
| Public | 349 (28.9%) | 52 (52.0%) | 297 (26.8%) |
| Family history of diabetes | 297 (24.4%) | 11 (12.2%) | 286 (25.3%) |
| Age at first diagnosis, years | n=1104 | n=101 | n=1003 |
| Range | 1 to 88 | 3 to 70 | 1 to 88 |
| Mean ± SD | 49.9 ± 16 | 19 ± 11.6 | 53 ± 12.7 |
| Median (IQR) | 52 (1 – 88) | 17 (12 – 24) | 54 (45 – 61) |
| Time since first diagnosis, years | n=1266 | n=104 | n=1162 |
| Range | 0 to 92 | 0 to 45 | 0 to 92 |
| Mean ± SD | 17.7 ± 20.2 | 16.5 ± 10.4 | 17.8 ± 20.9 |
| Median (IQR) | 10 (5 – 20) | 15 (8 – 23.5) | 10 (4 – 20) |
| Treatment duration, years | n=1014 | n=92 | n=922 |
| Range | 0 to 70 | 2 to 45 | 0 to 70 |
| Mean ± SD | 10.3 ± 8.4 | 15.8 ± 9.7 | 9.8 ± 8.1 |
| Median (IQR) | 8 (4 – 15) | 14 (8 – 23) | 7 (4 – 14) |
Of the 104 patients with T1DM, $77.9\%$ were followed by endocrinologists, $15.4\%$ by general practitioners, and $6.7\%$ by cardiologists, while among those with T2DM, $46.4\%$ were followed by endocrinologists, $19.4\%$ by general practitioners, and $34.2\%$ by cardiologists.
Overall, the number of medical appointments for DM management per year ranged from 1 to 21. The median (IQR) number of consultations per year were 2 [1-4] and 1 [1-3] for T1DM and T2DM, respectively.
## Comorbidities and complications associated with DM
Considering the information collected from baseline until the end of the 2-year follow-up, 1,219 ($96.3\%$) patients presented at least one comorbidity or complication associated with DM (Table 2). Patients with T1DM presented a lower prevalence of hypertension ($31.1\%$ vs $82.4\%$), dyslipidemia ($48.9\%$ vs $77.9\%$), overweight/obesity ($22.2\%$ vs $40.7\%$) and smoking habit ($2.2\%$ vs $8.8\%$) and a higheprevalence of hypothyroidism ($33.3\%$ vs $15.0\%$) than those with T2DM. Similar frequencies of sedentarism, elevated uric acid and sleep apnea were observed between groups.
**Table 2**
| Unnamed: 0 | All Patients n=1266 | T1DM n=104 | T2DM n=1162 |
| --- | --- | --- | --- |
| Medical conditions related to diabetes* | Medical conditions related to diabetes* | Medical conditions related to diabetes* | Medical conditions related to diabetes* |
| | 47 (3.7%) | 14 (13.5%) | 33 (2.8%) |
| Any (at least one) | 1219 (96.3%) | 90 (86.5%) | 1129 (97.2%) |
| Comorbidities | Comorbidities | Comorbidities | Comorbidities |
| Hypertension | 958 (78.6%) | 28 (31.1%) | 930 (82.4%) |
| Dyslipidemia | 923 (75.7%) | 44 (48.9%) | 879 (77.9%) |
| Obesity/overweight | 479 (39.3%) | 20 (22.2%) | 459 (40.7%) |
| Sedentary life | 379 (31.1%) | 25 (27.8%) | 354 (31.4%) |
| Hypothyroidism | 199 (16.3%) | 30 (33.3%) | 169 (15.0%) |
| Smoking | 101 (8.3%) | 2 (2.2%) | 99 (8.8%) |
| Elevated uric acid | 53 (4.3%) | 3 (3.3%) | 50 (4.4%) |
| Sleep apnea | 36 (3.0%) | 3 (3.3%) | 33 (2.9%) |
| Complications | Complications | Complications | Complications |
| Cardiovascular disease | 269 (22.1%) | 3 (3.3%) | 266 (23.6%) |
| History of infarction | 152 (12.5%) | 4 (4.4%) | 148 (13.1%) |
| Other coronary diseases | 167 (13.7%) | 5 (5.6%) | 162 (14.3%) |
| Retinopathy | 161 (13.2%) | 35 (38.9%) | 126 (11.2%) |
| Neuropathic pain | 132 (10.8%) | 22 (24.4%) | 110 (9.7%) |
| Renal complications/renal failure | 122 (10%) | 18 (20.0%) | 104 (9.2%) |
| Microalbuminuria | 128 (10.5%) | 19 (21.1%) | 109 (9.7%) |
| Stroke history | 71 (5.8%) | 0 | 71 (6.3%) |
| Diabetic foot | 31 (2.5%) | 8 (8.9%) | 23 (2.0%) |
| Impotence | 32 (2.6%) | 1 (1.1%) | 31 (2.7%) |
| Unstable angina | 17 (1.4%) | 2 (2.2%) | 15 (1.3%) |
| Blindness | 14 (1.1%) | 3 (3.3%) | 11 (1.0%) |
| Amputation of limbs | 12 (1.0%) | 1 (1.1%) | 11 (1.0%) |
Regarding chronic complications, microvascular complications (retinopathy, blindness, microalbuminuria, and renal disease), diabetic foot and neuropathy were more prevalent in patients with T1DM, while macrovascular complications (CVD, history of infarction, other coronary diseases, and stoke history) and erectile dysfunction were more frequently reported in the group of T2DM. The prevalence of lower-limb amputation did not differ between groups.
Dyslipidemia treatment since the baseline assessment reached the frequency of $97\%$ in both groups (not shown).
Regarding the medications used for treatment of DM at the final visit, as shown in Table 3, after 2 years $100\%$ of T1DM patients were using insulin, together with oral medications such as biguanide ($13.0\%$), iSGLT2 ($8.7\%$), and pioglitazone ($1.1\%$), or with non-insulin injectable medications such as GLP1 ($1.1\%$). Patients with T2DM used oral medications at a higher frequency: biguanide ($70.4\%$), sulfonylurea ($31.4\%$), DPP4 inhibitor ($26.8\%$), SGLT2 inhibitor ($21.9\%$) and pioglitazone ($4.4\%$); regarding injectables, $2.0\%$ were in use of GLP1 and $27.3\%$ were in use of a type of insulin.
**Table 3**
| Unnamed: 0 | T1DM | T1DM.1 | T2DM | T2DM.1 |
| --- | --- | --- | --- | --- |
| DM treatment | Baseline | Final | Baseline | Final |
| | n=104 | n=92 | n=1115 | n=990 |
| Biguanide (Metformin) | 21 (20.2%) | 12 (13.0%) | 862 (77.3%) | 697 (70.4%) |
| Sulfonylurea | 1 (1.0%) | 0 (0%) | 371 (33.3%) | 311 (31.4%) |
| DPP4 Inhibitor | 0 | 0 (0%) | 270 (24.2%) | 265 (26.8%) |
| SGLT2 Inhibitor | 4 (3.8%) | 8 (8.7%) | 145 (13%) | 217 (21.9%) |
| GLP-1 | 0 | 1 (1.1%) | 28 (2.5%) | 20 (2.0%) |
| TZD (Pioglitazone) | 0 | 1 (1.1%) | 0 | 44 (4.4%) |
| Apha-Glucosidade Inhibitor (Acarbose) | 0 | 0 (0%) | 0 | 3 (0.3%) |
| Glinides | 0 | 0 (0%) | 0 | 1 (0.1%) |
| Insulin | 103 (99.0%) | 92 (100%) | 302 (27.1%) | 270 (27.3%) |
| NPH | 25 (24%) | 12 (13.0%) | 191 (17.1%) | 172 (17.4%) |
| Long-acting insulin analogues | 60 (57.7%) | 66 (71.7%) | 90 (8.1%) | 81 (8.2%) |
| Fast-acting insulin analogues | 55 (52.9%) | 72 (78.3%) | 38 (3.4%) | 40 (4.0%) |
| Regular | 11 (10.6%) | 7 (7.6%) | 44 (3.9%) | 47 (4.7%) |
| Premixed | 1 (1%) | 5 (5.4%) | 18 (1.6%) | 13 (1.3%) |
| Other insulins | 12 (11.5%) | 0 (0%) | 3 (0.3%) | 2 (0.2%) |
| Combined | 0 | 1 (1.1%) | 0 | 17 (1.7%) |
It is interesting to note that 28 ($26.9\%$) T1DM patients and 280 ($24.3\%$) T2DM patients reported having discontinued the medication in some moment during the study (Table 4). The main reasons mentioned for drug withdrawal were lack of efficacy ($28.6\%$), hypoglycemia risk ($17.9\%$), and difficulty in dose titration ($17.9\%$) for T1DM patients; while for patients with T2DM these were lack of efficacy ($27.5\%$), hypoglycemia risk ($18.2\%$), and cost ($16.1\%$).
**Table 4**
| Unnamed: 0 | T1DM | T2DM |
| --- | --- | --- |
| Patients using any drug | n=104 | n=1153 |
| Patients with any withdrawal reported, n (%) | 28 (26.9%) | 280 (24.3%) |
| Reason for discontinuation, n (%) | N=28 | N=280 |
| Lack of efficacy | 8 (28.6%) | 77 (27.5%) |
| Hypoglycemia risk | 5 (17.9%) | 51 (18.2%) |
| Difficulty in dose titration | 5 (17.9%) | 16 (5.7%) |
| Patient request | 2 (7.1%) | 37 (13.2%) |
| Cost | 0 (0.0%) | 45 (16.1%) |
| Adverse events | 1 (3.6%) | 25 (8.9%) |
| Route of administration | 1 (3.6%) | 9 (3.2%) |
| Drug interaction | 1 (3.6%) | 8 (2.9%) |
| Other reason | 13 (46.4%) | 111 (39.6%) |
## Glycemic control
Figure 1A shows the percentage of patients within the target HbA1c <$7\%$ at baseline and at the last follow-up visit, according to the type of DM and health service. At the baseline assessment, 22 ($25.3\%$; $95\%$ CI, $16.2\%$ to $34.4\%$) T1DM patients had HbA1c levels <$7\%$ and 23 ($29.5\%$; $95\%$ CI, $19.4\%$ to $39.6\%$) were within the individual goal. After 2 years of follow-up, the percentages of patients with HbA1c <$7\%$ and HbA1c within the individual target were $22.5\%$ ($$n = 18$$; $95\%$ CI, $13.4\%$ to $31.6\%$) and $27.8\%$ ($$n = 20$$; $95\%$ CI, $17.4\%$ to $38.1\%$), respectively. For T2DM patients, the proportion of patients within the goal of HbA1c <$7\%$ changed from $45.5\%$ ($$n = 375$$; $95\%$ CI, $42.1\%$ to $48.9\%$) at baseline to $51.0\%$ ($$n = 369$$; $95\%$ CI, $47.4\%$ to $54.7\%$) after 2 years follow-up, while the proportion of patients within the individual HbA1c target changed from $47.1\%$ ($$n = 364$$; $95\%$ CI, $43.6\%$ to $50.6\%$) to $54.4\%$ ($$n = 356$$; $95\%$ CI, $50.6\%$ to $58.2\%$).
**Figure 1:** *HbA1c. (A) Percentage of patients achieving the target of HbA1c <7%, according to the DM type and health care sector. (B) Mean ( ± SD) HbA1c (%) per visit, according to the DM type.*
The analysis according to health-care system showed that after 2 years, the target of HbA1c <$7\%$ was reached in $25.5\%$ ($95\%$ CI, $10.9\%$ to $39.1\%$) of T1DM and in $55.3\%$ ($95\%$ CI, $51.1\%$ to $59.5\%$) of T2DM patients being followed in the private sector and in $20.5\%$ ($95\%$ CI, $8.5\%$ to $32.4\%$) of T1DM and $40.6\%$ ($95\%$ CI, $33.4\%$ to $47.7\%$) of T2DM treated in the public sector. For patients with T1DM, no difference in the proportion of patients within the goal was observed between patients followed in the public and in the private sectors. For patients with T2DM, results suggest a better glycemic control in the private sector at baseline as well as after 2 years of follow-up.
Figure 1B shows the mean (± SD) HbA1c in each of the five waves of data collection, according to the type of DM. The mean ± SD of HbA1c at baseline and after 2 years of follow-up was, respectively, 8.2 (1.6)% and 8.4 (1.9)% among T1DM patients, and 7.5 (1.6)% and 7.2 (1.3)% among those with T2DM.
## Weight control
Table 5 shows the changes in BMI from baseline to the final visit. In both groups, the mean change in BMI was low (0 ± 2.4 Kg/m2 in T1DM, and -0.3 ± 2.2 Kg/m2 in T2DM). After 2 years, most patients had maintained their weight and only a minority of patients had a decrease in the BMI category.
**Table 5**
| Unnamed: 0 | All patients | T1DM | T2DM |
| --- | --- | --- | --- |
| Change in BMI from baseline to the final visit, in Kg/m2 | n=749 | n=84 | n=665 |
| Mean ± SD | -0.2 ± 2.3 | 0 ± 2.4 | -0.3 ± 2.2 |
| Median (IQR) | 0 (-1 – 0.7) | 0.3 (-0.8 – 1) | 0 (-1.1 – 0.7) |
| BMI category (Kg/m²) in the final visit in relation to baseline, n (%) | n=749 | n=84 | n=665 |
| No change from baseline to the final visit | 612 (81.7%) | 71 (84.5%) | 541 (81.3%) |
| Increase in the BMI category from baseline to the final visit | 56 (7.5%) | 9 (10.7%) | 47 (7.1%) |
| ≤24.9 to ≥25–29.9 Kg/m² | 22 (2.9%) | 6 (7.1%) | 16 (2.4%) |
| ≤24.9 to ≥30–39 Kg/m² | 2 (0.3%) | 0 | 2 (0.3%) |
| 25–29.9 to ≥30–39 Kg/m² | 23 (3.1%) | 3 (3.6%) | 20 (3.0%) |
| ≥30–39 to >39.9 Kg/m² | 9 (1.2%) | 0 | 9 (1.4%) |
| Decrease in BMI category from baseline to the final visit | 81 (10.8%) | 4 (4.8%) | 77 (11.6%) |
| 25–29.9 Kg/m² to ≤24.9 Kg/m² | 34 (4.5%) | 2 (2.4%) | 32 (4.8%) |
| 30–39.9 Kg/m² to 25–29.9 Kg/m² | 36 (4.8%) | 2 (2.4%) | 34 (5.1%) |
| 30–39.9 Kg/m² to ≤24.9 Kg/m² | 2 (0.3%) | 0 | 2 (0.3%) |
| >39.9 Kg/m² to 30–39.9 Kg/m² | 7 (0.9%) | 0 | 7 (1.1%) |
| >39.9 Kg/m² to 25–29.9 Kg/m² | 1 (0.1%) | 0 | 1 (0.2%) |
| >39.9 Kg/m² to Up to 24.9 Kg/m² | 1 (0.1%) | 0 | 1 (0.2%) |
## Discussion
The BINDER study represented an important opportunity to observe in a real-word scenario the patterns of disease management, glycemic control, DM-associated complications and morbidities of patients with T1DM and T2DM for a period of 2 years of follow-up in the public and private health sectors in Brazil.
In relation to the sociodemographic profile of participants and distribution of care, the sample population was not intended to represent the Brazilian population; as a result, the study sample comprised $74.7\%$ of Caucasians and $33.5\%$ of patients having a high level of education, which are above nationwide proportions. However, it is important to emphasize that, from the point of view of the distribution of DM types, we found a distribution similar to the one reported in large epidemiological studies, with T2DM accounting for about $90\%$ of patients [1]. In the current study, of the 1,266 patients followed for 2 years, $73.2\%$ of T2DM and $48\%$ of T1DM patients were seen in the private health sector, thus offering an opportunity to assess the profile of morbidities and glycemic control in this kind of health-care service. Of note, patients with T1DM were more commonly seen in the public healthcare system than T2DM patients, which may be indicative of a greater preparation and availability of drugs for the management of T1DM in specialized public services, such as tertiary services and centers linked to Universities. In addition, we also observed that patients with T1DM were seen in the vast majority of cases ($77.9\%$) by endocrinologists, while the management of patients with T2DM was more distributed between medical specialties, with nearly $50\%$ of cases being seen by endocrinologists, followed by general practitioners ($19.4\%$) and cardiologists ($34.2\%$). This result contrasts with a previous study in which it was found that in the public service, nearly $80\%$ of patients with T2DM were followed by a general practitioner [10].
Nearly $97\%$ of patients presented at least one associated morbidity, with this percentage being higher among patients with T2DM than in those with T1DM ($97.2\%$ vs. $86.5\%$). Of note, T2DM patients had a mean age higher than 60 years at the baseline assessment. This high prevalence of associated morbidities is in agreement with estimates reported in health surveys and epidemiological studies conducted in the elderly population in Brazil. Results from the Brazilian National Health Survey (PNS) of 2013 showed that the proportion of individuals aged 60 years or older with at least one NCD was $76\%$ in the overall population [15]. Data from The Brazilian Longitudinal Study of Aging (ELSI-Brazil), a large-scale, nationally representative study of 9,412 participants aged 50 or older evidenced that $67.8\%$ of these individuals presented ≥2 NCD and $47.1\%$ ≥3 NCD, with an increase in the number of morbidities according to age [16, 17]. It is important to consider that, in addition to being elderly; these patients with DM have an average of 15 years since diagnosis.
Patients with T1DM had a mean age of 35 years. When the morbidities most commonly related to T1DM are considered, we observe a higher prevalence of microvascular complications directly linked to DM, such as retinopathy, kidney disease and neuropathy, in addition to hypothyroidism. These findings are also in line with the literature that shows a higher presence of other autoimmune diseases, such as hypothyroidism [18]. Regarding the morbidities associated with DM, the frequencies observed among patients with T1DM in the present study are similar to the prevalence found in a study with over 50,000 patients with T1DM in Europe and the United States [19]. In this epidemiological study, prevalence of 14 to $25\%$ of hypertension, 28 to $51\%$ of dyslipidemia, 51 to $69\%$ of overweight, and 20 to $33\%$ of obesity were reported for patients with T1DM aged between 26 and 50 years old [19].
Regarding chronic complications, a higher prevalence of microvascular complications (retinopathy, blindness, microalbuminuria and kidney disease), diabetic foot and neuropathy was observed in the group of patients with T1DM, while macrovascular diseases (CVD, coronary disease, and cerebrovascular disease) and report of impotence were more frequent in those with T2DM. However, prevalence of lower limb amputation did not differ between groups. There is a paucity of studies that report the frequency of chronic complications in people with DM in Brazil. In a national, multicenter study that evaluated chronic complications in T2DM based on data from 2008 [8], the frequencies reported contrast with the ones found in the present study. Costa et al. reported a prevalence of diabetic foot of $1.1\%$, neuropathy of $27.7\%$, retinopathy of $42.4\%$, blindness of $2.9\%$, amputation of $4.7\%$, while in the current study these frequencies in T2DM were $2.0\%$, $9.7\%$, $11.2\%$, $1.0\%$, $1.0\%$, respectively. The current study, although not representative of the Brazilian population, offers an opportunity to describe the frequencies of micro- and macrovascular complications in a different scenario where most of T2DM were seen in private health care services.
Treatment for dyslipidemia reached a percentage of $97\%$ in both T1DM and T2DM patients. This rate is surprisingly high when compared with the results of other studies. The ARATEUS study evaluated the medical charts of 662 patients with T2DM and observed that in the first 2 years of follow-up, only $29\%$ of patients were in use of statins for the management of the dyslipidemia [20].
As expected, pharmacological treatment of T1DM consisted of insulin use in $100\%$ of cases, accompanied by the utilization of certain classes of oral medication, among which biguanide was the most commonly used drug, having been available for the treatment of DM for a long time. ISGLT2 have been gaining space in the prescription of therapy for T1DM in this sample, where about $50\%$ of patients were followed up in the private health system. Among patients with T2DM, a higher percentage of use of more recent drugs that are still not available in the public healthcare system (SUS, Sistema Único de Saúde) was observed during the study. This includes drugs such as iDPP4 ($26.8\%$) and iSGLT2 ($21.9\%$), which were being used in a frequency similar to that of sulfonylureas ($31.4\%$). GLP1 analogues were being used by only $2.0\%$ of patients with T2DM. This scenario must be interpreted in the light of the knowledge that $75\%$ of the T2DM patients in the study sample were monitored in the private health system. It is interesting to note that $27.3\%$ of patients with DM2 used insulin in this sample.
Regarding the glycemic target, the present study found that, among patients with T1DM, $25.3\%$ had HbA1c <$7\%$ at the baseline visit and $22.5\%$ at the end of the 2-year follow-up, and among the cases with T2DM, these prevalence rates were $45.5\%$ and $51.0\%$, respectively. Population-based studies conducted in Brazil and involving the assessment of glycemic control in patients treated at the public health system showed that $26\%$ of patients with T2DM were within the HbA1c <$7\%$ target [13], and $11.6\%$ of adults and $23.2\%$ of children and adolescents with T1DM reached this goal of HbA1c [14].
A multicenter study conducted in Latin America collected data on patients seen in the private health-care system, including 878 patients from Brazil, and found a result similar to the results presented here, with $40\%$ of patients having HbA1c <$7\%$ [21]. In a recent robust study evaluating patients with T1DM in the United States, Austria and Germany, in specialized DM care centers, a mean (± SD) of HbA1c of 8.1 ± $1.6\%$ was observed in European centers and 8.6 ± $1.8\%$ in American centers, with a percentage of patients who reached the HbA1c target <$7\%$ of $39\%$ and $21\%$, respectively [19].
Importantly, the proportion of patients who did not achieve the individualized target in the present study were also alarmingly high ($79\%$ for T1DM and $53.3\%$ for T2DM). The low proportion of patients with adequate glycemic control among both T1DM and T2DM patients is even more worrisome, when it is observed that there were no significant improvements in the mean HbA1c levels nor in the frequency of patients within the HbA1c target in both public and private sectors during the 2-year follow-up. This finding might indicate the existence of clinical inertia, that is, that situation in daily clinical practice in which the medical specialist is unaware or does not feel confident about the clinical condition of the patient and, as a result, tends to not adopt any correction of the therapeutic management in the face of unsatisfactory glycemic control. Clinical inertia is due to at least three factors: overestimation of the care provided, use of unfounded reasons to avoid intensification of therapy, and the lack of a well-trained interdisciplinary team to help the patient achieve the desired therapeutic goals [22]. Better and faster results in glycemic control can only be achieved safely with educational strategies, structured self-monitoring of blood glucose and adequate pharmacological therapy in most cases [23].
Alongside glycemic treatment, adequate weight maintenance is another important factor. Obesity was observed in $22.2\%$ and $40.7\%$ of patients with T1DM and T2DM, respectively. During the follow-up, no significant weight loss was observed for both DM groups. Emphasizing the importance of weight loss for glycemic control in T2DM, a recently published study demonstrated reduced blood glucose and improved secretion and sensitivity to insulin in patients with DM and obesity undergoing weight loss, either through diet or surgery (gastric bypass). Moreover, after weight loss, there was a reduction in the dose of the antidiabetic medications by approximately $75\%$ in both groups [24]. In the Look AHEAD study, the behavioral management of obesity associated with lifestyle interventions (diet and physical activity) was superior to the DM education program in terms of weight loss and glycemic control in patients with DM and overweight. Furthermore, there were reductions in hospitalizations, medication use and health-related costs in the first group [25].
The current study has some limitations that should be considered when interpreting and extrapolating the data. The main limitation is that the sample population analyzed here is not representative of the Brazilian population. However, this lack of representativeness was expected at a certain level as the study was designed to assess the management and glycemic control of individuals with DM followed in clinical research centers that were selected and invited to participate from a pre-established list, not randomly chosen. Despite this, participating centers were located in cities of varied sizes and from different regions of Brazil, with some of them being part of the public health system (SUS). Although there was loss of follow-up during the study, the participants initially selected did not differ from those who remained in the study for the longitudinal analysis. This observation does not guarantee that participants who had missed follow-up visits had better or worse glycemic control during the follow-up. Despite these considerations, the results of glycemic control presented here are similar to the findings of other studies conducted in the Brazilian population with DM (11–14). Another limitation is the fact that the study sample is comprised of patients with higher level of education with the majority coming from private health-care facilities, which does not represent the Brazilian population. Regarding this, it is well-known that a higher prevalence of DM is observed among the population with a lower level of education (Vigitel, 2019) and that other social determinants, such as socioeconomic status and access to health services, are associated with worse glycemic control [26]. In this scenario, the population profile of this study could also be seen as an opportunity for investigation, as studies involving the private health sector and individuals with higher education are scarce. Importantly, our results showed a poor glycemic control and features compatible with clinical inertia, even in a sample with this profile.
## Conclusion
Our study shows that after a 2-year period of follow-up, compared with baseline there were no relevant changes in the percentage of patients who achieved the goal of HbA1c <$7\%$, in either T1DM or T2DM, in public or private health-care systems. These results might indicate that, besides the relevance of non-pharmacological treatment of diabetes, there is important therapeutic inertia that also needs to be addressed.
## 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 study was conducted after the approval by the ethics committee of the Universidade Federal de São Paulo (São Paulo, Brazil), and the study was conducted in accordance with the Declaration of Helsinki and the International Conference on Harmonization guidelines for Good Clinical Practice. Informed consent was obtained from all patients. CAAE: 52622115.9.000.5505. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
BA-P: Data interpretation; Writing - original draft; Writing - review & editing. FE: Investigation; Visualization; Roles/Writing - original draft; Writing - review & editing. MP: Data curation; Methodology; Project administration; Supervision; Validation; Roles/Writing - original draft; Writing - review & editing. GF: Conceptualization; Data curation; Project administration; Resources; Supervision; Validation; Roles/Writing - original draft; Writing - review & editing. All authors contributed to the article and approve the submitted version.
## Funding
This study was supported by Sanofi. Editorial support in the preparation of this publication was provided by DENDRIX and paid for by Sanofi. 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.
## Acknowledgments
We thank Felipe Lauand for conceptualization and data aquisition, Marina Szacher who was responsible for project administration, resources and supervision, Ana Truzzi for formal statistical analysis and conceptualization, and Maura Gonzaga for formal statistical analysis.
## Conflict of interest
FE has received financial support for clinical research from Sanofi, Lilly, Novo Nordisk, Amgen, Abbvie, and Bayer, and served as a speaker for Lilly, Sanofi, AstraZeneca, Novo Nordisk and Bayer. MP and GF are employees of Sanofi.
The remaining 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.
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|
---
title: Diabetes Mellitus as a Risk Factor for Trigger Finger –a Longitudinal Cohort
Study Over More Than 20 Years
authors:
- Jin Persson Löfgren
- Malin Zimmerman
- Lars B. Dahlin
- Peter M. Nilsson
- Mattias Rydberg
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2021
pmcid: PMC10012113
doi: 10.3389/fcdhc.2021.708721
license: CC BY 4.0
---
# Diabetes Mellitus as a Risk Factor for Trigger Finger –a Longitudinal Cohort Study Over More Than 20 Years
## Abstract
### Background and Aim
Trigger finger (TF) or stenosing tenosynovitis has been associated with diabetes mellitus (DM), although today’s knowledge is mostly based on cross-sectional and case-control studies. Thus, the aim of the present population-based cohort study over more than 20 years was to investigate DM as a risk factor for TF.
### Methods
Data from Malmö Diet and Cancer Study (MDCS), including 30,446 individuals, were analysed with regards to baseline DM and known or potential confounders. Information regarding TF diagnosis until study end date of Dec 31st, 2018, was retrieved from the Swedish National Patient Register (NPR) using ICD-codes. Survival probability was investigated in Kaplan-Meier plots. Cox proportional hazard regression model was used to evaluate DM as risk factor for TF, adjusted for several confounders and presented as Hazard Ratio (HR) with $95\%$ confidence intervals (CI).
### Results
At baseline, $4.6\%$ (1,$\frac{393}{30}$,357) participants had DM. In total, $3.2\%$ ($\frac{974}{30}$,357) participants were diagnosed with TF during the study period. Kaplan-Meier plot showed that the probability for incident TF was significantly higher in participants with baseline DM compared with individuals without baseline DM. Adjusted HR for DM as risk factor for TF was 2.0 ($95\%$ CI: 1.5-2.6, $p \leq 0.001$).
### Conclusion
This longitudinal study showed that DM is an important risk factor for developing TF. When adjusting for sex, age, BMI, manual work, statin use, smoking and alcohol consumption, DM remained the main risk factor for TF.
## Introduction
Diabetes Mellitus (DM) is one of modern time’s most challenging long-term public health challenges, both in terms of individual suffering and health care economics. The number of patients diagnosed with diabetes mellitus type 1 (T1D) and type 2 (T2D) is rapidly increasing [1]. Complications caused by DM include a wide variety of disorders, where cardiovascular complications, nephropathy, neuropathy, and retinopathy are the most studied. Less studied is “the diabetic hand”, which includes trigger finger (TF), Dupuytren’s disease with contracture of the finger joints (DC), limited joint mobility (LJM), carpal tunnel syndrome (CTS) and ulnar nerve entrapment (UNE) [2]. Individuals with DM are also more likely to suffer from bilateral involvement and multiple disorders [3].
Trigger finger (TF), also known as stenosing tenosynovitis, tenovaginitis or digitus saltans, is a condition where the flexor tendon is obstructed in its tendon sheath at the first annular (A1) pulley. This results in a locking phenomenon, and the affected finger can only be extended with additional force or passive manipulation, which can be painful. The thumb and the ring finger are most affected, followed by the long finger (4–6). There is no known cause of TF, and its pathogenesis is not completely defined [7]. Treatment options include intra- or extra synovial corticosteroid injection and percutaneous or open surgical release of the A1 pulley (7–9).
The prevalence of TF is approximately 1-$2\%$ in the general population [10, 11]. Women are affected twice as often as men and prevalence peaks in ages 50 to 59 years [10]. Prevalence rates and the risk for TF in individuals with DM are not conclusive, ranging from $1.5\%$ to $20\%$ depending on the group studied (5, 11–13). Our aim of the present population-based cohort study over more than 20 years, was to investigate the impact of DM as a risk factor for TF, using the large Malmö Diet and Cancer study (MDCS) cohort in southern Sweden.
## Study Design
In the Malmö Diet and Cancer Study (MDCS) cohort, participants with DM at baseline were identified. Incident TF diagnosis during the study period was identified using the Swedish National Patient Register (NPR). The outcome, using the statistical method described below, was the probability for incident TF in participants with baseline DM compared with individuals without baseline DM. In this study, we did not use data collected during and/or at the end of the study period, therefore exposure during the study period was not included. Potential confounders included in this study are age, sex, BMI, manual work, statin use, smoking habits, and alcohol consumption. Potential sources of bias include selection, detection and reporting bias and are described in more detail in section 4.1 Strengths and Limitations.
## Study Population
The present data was retrieved from the Malmö Diet and Cost Study (MDCS) cohort. The initial objective with MDCS was to study the association between diet and development of cancer [14]. Participants in the MDCS were recruited during 1991-1996 in Malmö, a city in southern Sweden of approximately 250 000 inhabitants [15]. Participants were 44-74 years old when recruited; $60\%$ being women. Participants provided information regarding their work life, socio-economic situation, heredity, lifestyle, diet, and medical history in a questionnaire, a 7-day food diary, and a 45-60 min diet history interview. Blood samples as well as blood pressure, height and weight, lean body mass and body fat mass, were collected at baseline.
## Baseline Definitions
Age was defined as the participant’s age at enrollment in the MDCS. Body mass index (BMI) was calculated from data collected at enrollment and expressed in kg/m2. Prevalent DM was defined based on the participant’s questionnaire, if medical history stated a DM diagnosis or the use of antidiabetic medication, or if the participant had fasting plasma glucose concentration ≥ 7.0 mmol/L. Information regarding baseline DM was also retrieved from several other registries, previously described in detail [16]. Manual work was based on free text answer in the participant’s questionnaire and classified using the Nordic standard occupational classification [17], which has been previously described for the MDCS cohort [15]. Statin use included simvastatin, pravastatin and fluvastatin and was based on the participant’s questionnaire and the 7-day food diary. Smoking habits were collected from the participant’s questionnaire. Current smokers were defined as regular or occasional smokers. Previous smokers were split into regular and occasional smokers using the ratio from the current smoker group above, and number of cigarettes per day was assumed using the mean value of regular and occasional smokers. Smoking habits is presented as pack years; number of cigarettes smoked per day divided by 20 multiplied by numbers of years smoked. Alcohol consumption was based on the participant’s questionnaire and was converted into g/day.
## End Point Definition
End point was either incident TF diagnosis, death, emigration, or end of study Dec 31st, 2018. Information regarding prevalent and incident TF diagnosis was retrieved from the Swedish National Patient Register (NPR). International Statistical Classification of Diseases and Related Health Problems (ICD) version 8, 9 or 10 codes 731.02, 727X, 727A and M653 were used as diagnosis codes for TF. Diagnosis was made by hospital-based physicians, mainly by orthopaedic and hand surgeons, whereas TF diagnoses from primary health care were not included in NPR.
## Statistical Methods
Participants with a prevalent TF diagnosis at baseline or with missing information regarding start date and/or BMI were excluded from further analysis. Age was normally distributed and presented as mean with standard deviations (SD). When comparing age in the group free from incident TF with the group with incident TF, the independent t-test was used. BMI, pack years for smokers and previous smokers, and alcohol consumption were not normally distributed and are presented as median with interquartile range [IQR]. For these parameters, the Mann-Whitney U test was used when comparing the incident free group with the incident TF group. For categorical variables, i.e., sex, prevalent DM, manual work and statin use, proportion (%) was used, and the Chi-squared test was used for group comparisons.
Data was analysed and presented as survival probability and hazard probability, using the Kaplan-Meier and Cox proportional hazard (PH) regression methods [18]. A log-rank test was used to compare survival probability for participants with prevalent baseline DM with those without prevalent baseline DM. However, the Kaplan-Meier method gives no estimate of the actual impact of DM, and there is no possibility to assess the impact of confounders. Thus, Cox proportional hazard (PH) regression model was also used in the survival analysis [19], where hazard ratio (HR) was reported with a $95\%$ confidence interval (CI). The included confounders were sex, age, BMI, manual work, statin use, alcohol consumption and smoking habits. Sex and age were selected to adjust for differences in the compared groups. BMI is a potential risk factor for TF [20, 21] as well as statin use [22, 23]. HR was firstly assessed for each covariate in separate univariate Cox PH regression models. Then, several multivariate Cox PH regression models were used to investigate how the covariates would affect the HR for incident TF in relation to prevalent baseline DM. The assumption of proportional hazard was assessed by log-log plots and visual assessment of Kaplan-Meier curves and no violation was found.
All statistical analyses were performed using IBM SPSS Statistics version 26 (SPSS Inc., Chicago, IL, USA) and $p \leq 0.05$ was considered significant.
## Ethics Approval Statement
For both this study and the original study, the ethical application was approved by the Regional Ethical Review Board in Lund, Sweden (DNR: LU51-90; 2009-633; 2019-01439) and carried out in accordance with the World Medical Association’s Declaration of Helsinki.
## Results
The total number of participants in the MDCS was 30,446. Participants, where start date and/or BMI were missing, were not included in further analysis ($$n = 58$$). The same was applied for participants who were already diagnosed with TF ($$n = 31$$) when they were recruited to the MDCS (Figure 1).
**Figure 1:** *Derivation of the study cohort from Malmö Diet and Cancer study. Flow chart showing exclusion criteria and data availability for individuals included in the multivariate analysis. TF, Trigger finger; BMI, body mass index; DM, diabetes mellitus.*
## Baseline Characteristics
Mean age for all individuals was 57.5 (SD 7.6) years and $40\%$ (12,$\frac{085}{30}$,357) were male. BMI was median 25.8 [5.0] kg/m2. At baseline, there were 1,$\frac{393}{30}$,357 ($4.6\%$) participants with prevalent DM.
In the MDCS, information regarding statin use was available for $67\%$ (20,$\frac{445}{30}$,357) and $3.2\%$ ($\frac{655}{20}$,445) used statins. Information regarding manual work was available for $93\%$ (20,$\frac{266}{30}$,357) of the participants and $38\%$ (10,$\frac{631}{20}$,266) were classified as manual workers, Data regarding smoking habits defined as pack years was available for $90\%$ (27,$\frac{450}{30}$,357) of the cohort, and smoking participants had smoked for a median of 6.8 [18.4] pack years. Data on alcohol consumption was available in $93\%$ (28,$\frac{160}{30}$,357), and median alcohol consumption was 7.2 [13.7] g/day.
## End-Point Data
In total, 263 individuals left the study before end date of Dec 31st, 2018, due to emigration, and 12,609 individuals passed away during the study period. Median follow-up time for individuals without prevalent DM at baseline was 23.3 [7.1] years. For individuals with prevalent DM at baseline, median follow-up time was 18.3 [12.6] years.
## Results for Trigger Finger
In total, 974 individuals were diagnosed with TF during the study period. Individuals with incident TF were younger, more likely to be female, had higher BMI and more often had DM. There were no differences in proportion with manual work, statin use, alcohol consumption and smoking habits between the two groups (Table 1).
**Table 1**
| Characteristics | All individuals (n = 30,357) | Without TF (n = 29,383) | Incident TF (n = 974) | P-value* |
| --- | --- | --- | --- | --- |
| Age, years (SD) | 57.5 (7.6) | 57.6 (7.6) | 55.6 (7.4) | <0.001 |
| Male sex (%) | 12,085 (40) | 11,788 (40) | 297 (31) | <0.001 |
| BMI, kg/m2 [IQR] | 25.8 [5.0] | 25.8 [5.0] | 26.2 [5.0] | 0.001 |
| Prevalent DM (%) | 1,393 (4.6) | 1,317 (4.5) | 76 (7.8) | <0.001 |
| | (n=28,266) | (n=27,339) | (n=927) | |
| Manual work (%) | 10,631 (38) | 10,294 (38) | 337 (36) | 0.42 |
| | (n=20,445) | (n=19,723) | (n=722) | |
| Statin use (%) | 655 (3.2) | 628 (3.2) | 27 (3.7) | 0.41 |
| | (n=28,160) | (n=27,247) | (n=913) | |
| Alcohol consumption, g/day [IQR] | 10.7 [13.7] | 10.8 [13.8] | 10.3 [12.8] | 0.85 |
| | (n=27,450) | (n=26,543) | (n=907) | |
| Smoking habits, pack years | 11.0 [18.38] | 11.0 [18.4] | 10.3 [18.0] | 0.59 |
Kaplan-Meier survival probability plots showed that the cumulative probability of TF incidence was higher for individuals with baseline DM compared with individuals without baseline DM (log-rank test $p \leq 0.0001$) (Figure 2).
**Figure 2:** *Kaplan-Meier plots for trigger finger, with and without diabetes mellitus at baseline. Log-rank test for difference in survival probability for TF with or without baseline DM is p < 0.001. The difference between the curves is proportional at all times. Individuals are censored if they leave the cohort before the end date due to emigration or death. TF, Trigger finger; DM, diabetes mellitus.*
Baseline DM resulted in increased HR in the univariate Cox PH regression model (HR 2.27; $95\%$ CI: 1.8-2.87; $p \leq 0.001$). Female sex and higher BMI also resulted in an increased HR (HR 1.36; $95\%$ CI; 1.19-1.56; $p \leq 0.001$ and HR 1.03; $95\%$ CI 1.02-1.05; $p \leq 0.001$). Higher age resulted in a decrease in HR (HR 0.98; $95\%$ CI: 0.98-0.99; $p \leq 0.001$). Manual work, statin use, smoking habits and alcohol consumption did not affect HR. In the first multivariate Cox PH regression model, with DM as the covariate, HR remained significantly increased when separately adjusting for sex, age, manual work, BMI, statin use, alcohol consumption and smoking habits (Table 2). When simultaneously adjusting for all confounders, HR remained significantly (HR 2.00; $95\%$ CI: 1.51-2.63; $p \leq 0.001$).
**Table 2**
| Univariate analysis without confounders | Univariate analysis without confounders.1 | Univariate analysis without confounders.2 | Univariate analysis without confounders.3 | Univariate analysis without confounders.4 |
| --- | --- | --- | --- | --- |
| Covariate | Confounder | HR | 95% CI | P-value |
| DM | – | 2.271 | 1.797-2.871 | <0.001 |
| Sex | – | 1.359 | 1.185-1.558 | <0.001 |
| Age | – | 0.984 | 0.975-0.992 | <0.001 |
| BMI | – | 1.032 | 1.016-1.047 | <0.001 |
| Manual work | – | 1.013 | 0.886-1.158 | 0.855 |
| Statin use | – | 1.399 | 0.953-2.056 | 0.087 |
| Alcohol consumption | – | 0.996 | 0.991-1.002 | 0.215 |
| Smoking habits | – | 1.002 | 0.997-1.007 | 0.348 |
| Multivariate analysis with one confounder | Multivariate analysis with one confounder | Multivariate analysis with one confounder | Multivariate analysis with one confounder | Multivariate analysis with one confounder |
| Covariate | Confounder | HR | 95% CI | P-value |
| DM | Sex | 2.363 | 1.869-2.989 | <0.001 |
| DM | Age | 2.387 | 1.886-3.021 | <0.001 |
| DM | BMI | 2.128 | 1.677-2.699 | <0.001 |
| DM | Manual work | 2.283 | 1.791-2.909 | <0.001 |
| DM | Statin use | 2.130 | 1.643-2.761 | <0.001 |
| DM | Alcohol consumption | 2.219 | 1.733-2.842 | <0.001 |
| DM | Smoking habits | 2.249 | 1.759-2.875 | <0.001 |
| Multivariate analysis with two confounders | Multivariate analysis with two confounders | Multivariate analysis with two confounders | Multivariate analysis with two confounders | Multivariate analysis with two confounders |
| Covariate | Confounders | HR | 95% CI | P-value |
| DM | Sex and age | 2.468 | 1.949-3.125 | <0.001 |
| Multivariate analysis with all confounders | Multivariate analysis with all confounders | Multivariate analysis with all confounders | Multivariate analysis with all confounders | Multivariate analysis with all confounders |
| Covariate | Confounders | HR | 95% CI | P-value |
| DM | Sex, age, BMI, manual work, statin use, alcohol consumption and smoking habits | 1.995 | 1.511-2.633 | <0.001 |
## Discussion
The present observational, longitudinal study showed that DM is an important risk factor for developing TF. When sex, age, BMI, manual work, statin use, smoking and alcohol consumption were added as confounders in the multivariate model, DM remained the main risk factor for TF. Our findings add to the previous knowledge where DM has been associated with a higher incidence of TF [5, 11, 13]. This further strengthens the need for including recurring, systematic hand examinations in modern diabetes care.
DM is associated with several mechanisms which could explain the increased risk of developing TF. The flexor tendon and A1 pulley can both be affected by diabetes complications. Formation of advanced glycation end products (AGEs) is the result of hyperglycaemia induced non-enzymatic reaction between glucose and proteins [24, 25]. AGEs create pathological collagen cross-links, and are accumulated in tissue with slow turn over, such as tendons [25]. The result is a thicker, stiffer, and tougher tendon (23–26).
Furthermore, dysregulation of inflammatory mediators in tendinopathy has been shown in T2D, together with increased apoptosis and formation of fibrous tissue [27]. Increased levels of growth factors, disturbed signalling pathways and disrupted interactions in extracellular matrix (ECM) are involved in both types of DM, which might contribute to the development of several hand disorders in the diabetic hand, including TF (26–29).
Individuals with T1D are typically younger at onset than individuals with T2D, and hand complications in individuals with T1D decrease with better glycaemic control [30, 31]. A potential long period of insulin resistance, hyperinsulinemia, and dyslipidaemia before a T2D diagnosis, might also contribute to the development of hand disorders [32, 33]. It is difficult to assess any differences between T1D and T2D in hand disorders, as many studies lack this information, which is a limitation also for this study. However, studies including only people with T1D or T2D, or reporting their results clearly separated for types of DM, have shown that duration is associated with development of hand disorders in both types of diabetes (34–38).
Besides DM as a risk factor for TF, we also investigated several other known and potential TF risk factors, including age, female sex, BMI, manual work, statin use, and any inflammatory response caused by smoking and/or alcohol consumption. The purpose was to find any other strong associations which should be considered in the multivariate analysis in order to define the impact of DM as a risk factor. We found an increased risk for TF associated with female sex and increased age. Oestrogen and progesterone have previously been shown to be involved in tendon metabolism and healing [32]. In this study, with $60\%$ female participants in the MDCS, sex was thus an important confounder when assessing the impact of DM as a risk factor. Age is a known risk factor for TF and could partly be explained with the accumulation of AGEs as a result of normal aging [39]. We also observed a small increased HR with BMI as the only covariate. As people with prevalent T2D at baseline show a higher mean BMI, the increased risk could at least partly be explained by the increased risk from DM. However, there could also be a link between higher BMI with potential insulin resistance, hyperlipidaemia, and tendinopathy [40, 41]. The role of manual work in the development of TF is controversial, as evidence of job exposure might lead to compensation claims. While biomechanical stressors are associated with TF, age, gender, and medical history are also risk factors [42]. Tendinopathy is a known side effect from statin use and was therefore considered a confounder in this study [22, 23]. We did not find any correlation with manual work or statin use in this study.
As shown in the present study, there is evidence that DM could be a considerable risk factor for TF. We have chosen to use the term risk factor instead of risk marker, even though we cannot provide true causal evidence according to Bradford Hill’s criteria [43]. As described above, previous studies have suggested several ways in which DM affects the tendon and ECM, indicating that there could be a causal relationship. We have also adjusted for other potential causal confounders, which further motivate using the term risk factor.
DM is also a risk factor for development of several other hand disorders that are included in the diabetic hand. Recent studies, including unpublished material, have shown that DM is a risk factor for CTS, UNE, and DC, using the same cohort data as this study [44]. However, the cause for this increased risk in people with DM has yet to been proven, where hand disorder prevalence, disease development and severity are most heterogeneous. Further research is needed to determine and explain a causal link between DM and hand disorders, preferably including data regarding type of DM, duration, and glycaemic control.
## Strengths and Limitations
This study has limitations, where lack of information regarding type of DM and DM duration may be the most important ones. Data was collected in the MDCS in the beginning of the 1990s, often described DM as insulin-dependent or non-insulin-dependent. This was practice then and can be seen in other studies as well. In this study, if the participant had a DM diagnosis when recruited to the MDCS, this was defined as baseline DM and duration was not known. Associations with glycaemic control has been shown in other studies [12, 34, 36]; unfortunately, this information was not available for all participants included in this study.
Potential bias in this study include detection, selection and reporting bias. Participants in the MDCS with baseline DM would be included in diabetes treatment programs, which might result in TF detection bias, resulting in a higher detection rate for TF as the participants with DM regularly see health care professionals. On the other hand, there was no selection bias when recruiting participants to MDCS cohort, as hand diagnosis were not included in the original scope of the MDCS. Selection bias in the MDCS cohort have been previously described, where participants had a lower mortality compared with controls [45]. It is unlikely that this would affect the TF ratio in comparison with the control group. Reporting bias can probably be neglected as TF diagnosis were reported to the NPR without any correlation to the MDCS.
Regarding the statistical method, it is unclear how the large number of censored participants in the Cox PH regression model affects the results. In the statistical model, confounders were chosen based on previous knowledge regarding risk factors for TF together with data availability in the MDCS. Consequently, confounders might be missing due to lack of knowledge and/or data availability.
This study also has several strengths. The MDCS included data from more than 30,000 participants. Participants ages and the follow-up period were relevant for TF, where prevalence increases with age [4]. Relevant confounders, such as age, sex, BMI, manual work, statin use, smoking habits, alcohol consumption, were included in the Cox PH regression model.
## Conclusions
In conclusion, the present study showed that DM is an important risk factor for developing TF. When sex, age, BMI, manual work, statin use, smoking and alcohol consumption were added as confounders, DM remained the main risk factor for TF.
## Data Availability Statement
Public access to the data is restricted by the Swedish authorities (Public access to Information and Secrecy Act (https://www.government.se/information-material/$\frac{2009}{09}$/public-access-to-information-and-secrecy-act/)). Data used in this study can be made available for researchers after special review that includes approval of the research project by both an Ethics committee and the authorities’ data safety committees. Requests to access the datasets should be directed to (https://www.malmo-kohorter.lu.se/malmo-cohorts).
## Ethics Statement
The studies involving human participants were reviewed and approved by Regional Ethical Review Board in Lund, Sweden (DNR: LU51-90; 2009-633; 2019-01439). The patients/participants provided their written informed consent to participate in this study.
## Author Contributions
All authors contributed to the study design. Data analysis was performed by JPL as well as manuscript drafts. All authors agree to be accountable for the content of the work. All authors contributed to the article and approved the submitted version.
## Funding
Regarding the Malmö Diet and Cancer study, this was funded by grants from the Swedish Cancer Society, the Swedish Medical Research Council, AFA insurance, the Albert Påhlsson and Gunnar Nilsson Foundations and the Malmö city council. 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. The present study was funded by Skåne University Hospital and local founds at Lund University,the Swedish Diabetes Foundations, the Swedish Research Council (grant number 2021-01942), the Regional Agreement on Medical Training and Clinical Research (ALF) between Region Skåne and Lund University and finally the Stig and Ragna Gorthons foundation.
## 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: 'Barriers to and enablers of type 2 diabetes screening among women with prior
gestational diabetes: A qualitative study applying the Theoretical Domains Framework'
authors:
- Amelia J. Lake
- Amelia Williams
- Adriana C. H. Neven
- Jacqueline A. Boyle
- James A. Dunbar
- Christel Hendrieckx
- Melinda Morrison
- Sharleen L. O’Reilly
- Helena Teede
- Jane Speight
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2023
pmcid: PMC10012118
doi: 10.3389/fcdhc.2023.1086186
license: CC BY 4.0
---
# Barriers to and enablers of type 2 diabetes screening among women with prior gestational diabetes: A qualitative study applying the Theoretical Domains Framework
## Abstract
### Introduction
Women with previous gestational diabetes mellitus (GDM) are at increased risk of type 2 diabetes (T2D). Guidelines recommend postnatal diabetes screening (oral glucose tolerance test or HbA1c) typically 6-12 weeks after birth, with screening maintained at regular intervals thereafter. Despite this, around half of women are not screened, representing a critical missed opportunity for early identification of prediabetes or type 2 diabetes. While policy and practice-level recommendations are comprehensive, those at the personal-level primarily focus on increasing screening knowledge and risk perception, potentially missing other influential behavioral determinants. We aimed to identify modifiable, personal-level factors impacting postpartum type 2 diabetes screening among Australian women with prior gestational diabetes and recommend intervention functions and behavior change techniques to underpin intervention content.
### Research design and methods
Semi-structured interviews with participants recruited via Australia’s National Gestational Diabetes Register, using a guide based on the Theoretical Domains Framework (TDF). Using an inductive-deductive approach, we coded data to TDF domains. We used established criteria to identify ‘important’ domains which we then mapped to the Capability, Opportunity, Motivation–Behavior (COM-B) model.
### Results
Nineteen women participated: 34 ± 4 years, 19 ± 4 months postpartum, $63\%$ Australian-born, $90\%$ metropolitan, $58\%$ screened for T2D according to guidelines. Eight TDF domains were identified: ‘knowledge’, ‘memory, attention, and decision-making processes’, ‘environmental context and resources’, ‘social influences’, ‘emotion’, ‘beliefs about consequences’, ‘social role and identity’, and ‘beliefs about capabilities’. Study strengths include a methodologically rigorous design; limitations include low recruitment and homogenous sample.
### Conclusions
This study identified numerous modifiable barriers and enablers to postpartum T2D screening for women with prior GDM. By mapping to the COM-B, we identified intervention functions and behavior change techniques to underpin intervention content. These findings provide a valuable evidence base for developing messaging and interventions that target the behavioral determinants most likely to optimize T2D screening uptake among women with prior GDM.
## Introduction
Gestational diabetes mellitus (GDM) affects approximately $13\%$ of births worldwide, and is acknowledged as “the fastest growing type of diabetes in Australia” [1, 2]. Women with prior GDM have an eight-fold increased lifetime risk of type 2 diabetes (T2D) compared to women with normoglycemic pregnancies [3]. Early and regular postpartum T2D screening is essential as undiagnosed and persistent elevated blood glucose levels (hyperglycaemia) increases risk for adverse health outcomes [4]. Australian guidelines recommend T2D screening (currently via an Oral Glucose Tolerance Test or OGTT), 6-12 weeks postpartum and, every 1-3 years thereafter [5]. Despite the benefits, only 43-$58\%$ of women in Australia complete the OGTT [6], reflecting screening rates worldwide [7, 8].
National and international studies have highlighted that policy, practice, and personal-level factors contribute to low screening uptake. Common policy-level factors include limitations related to public healthcare and lack of consensus on screening guidelines [9]. Practice-level factors include healthcare silos, lack of focus on ongoing risk in consultations, and lack of reminder systems (9–11). Systematic reviews of qualitative studies have identified common personal-level screening barriers, including competing demands, lack of practical social support, challenges related to the screening procedure, lack of knowledge and absence of advice from health professionals (12–14). Similar findings are reflected in an Australian context (15–21).
Recommendations to increase screening uptake have primarily focused on initiatives at policy and practice-levels including information provision and reminders (12–14). The limitations of such approaches have been acknowledged (6, 22–24). A key element of best practice personal-level intervention development is explicit use of theoretical approaches to both identify behavioral determinants and targeting them via intervention content underpinned by behavior change techniques [25, 26]. To date, there has been a paucity of such research. Thus, in-depth exploration of personal-level behavioral factors influencing women’s screening is warranted, to develop recommendations for persuasive messaging grounded in health behavior change theory.
The Theoretical Domains Framework (TDF) is an example of a theoretical approach to identifying determinants of a defined behaviour. The TDF comprises 14 behavior change domains derived from 33 behavior change theories [27]. The TDF has been used to identify determinants of health behaviors, including among women with GDM [28].
The TDF maps to the multi-layered Behavior Change Wheel (BCW, Figure 1) [29]. The BCW is an eight-step intervention development framework grounded in behavioural science. The framework has been used widely, including to address barriers to diabetes self-management [30]. Adjacent to the TDF layer, and at the core of the BCW, is the Capability, Opportunity, Motivation-Behavior (COM-B) model which posits that behavior is an interaction between Capability (physical and psychological), Opportunity (physical and social) and Motivation (reflective and automatic processes). The COM-B and TDF are encircled by nine intervention functions: persuasion, incentivization, environmental restructuring, education, coercion, enablement, modelling, training, and restrictions. The nine functions and 14 TDF domains are linked to established behavior change techniques, defined as “active components of an intervention designed to change behavior” [29, 31].
**Figure 1:** *Behavior Change Wheel (reproduced with permission) (29).*
While the BCW and COM-B/TDF components have demonstrated utility in identifying factors impacting health lifestyle behaviors [32, 33], to date, no studies have utilised it specifically to explore, in-depth, determinants of uptake of T2D screening among women with previous GDM. The aims of this study were to i) apply the BCW theoretical approach to identify modifiable barriers to, and enablers of, T2D screening among Australian women with prior GDM and ii) recommend intervention functions and behavior change techniques to underpin personal-level intervention messaging to optimise postpartum T2D screening uptake.
## Study design and ethics
We conducted in-depth, semi-structured interviews. Ethics approval was provided by Deakin University Human Research Ethics Committee (HEAG-H 09_2020). This study is reported according to COnsolidated criteria for REporting Qualitative research (COREQ) and Standards for Reporting Qualitative Research (SRQR, Supplemental Materials S1-S2) [34, 35].
## Reflexivity
Two female researchers, both experienced in (health) psychology and qualitative research, engaged with study participants: AL is a postdoctoral research fellow, and AW is a postgraduate research assistant. They had no relationship with participants outside the study and no involvement in their clinical care. To enhance reflexivity, the interviewer (AL) completed a reflective journal with each interview, exploring perceptions, assumptions, and subjectivities.
## Participant recruitment
All participants were registered on the National Diabetes Services Scheme (NDSS) National Gestational Diabetes Register (NGDR). The NDSS is an initiative of the Australian Government administered by Diabetes Australia. The NDSS coordinated invitations to 953 NGDR registrants via email (July 2020; reminder September). Eligibility criteria included: age 18-50 years, consent to being contacted for research purposes, 12-24 months postpartum, and English speaking. Participants registered interest via email and were telephoned by the researchers to discuss the study and schedule an interview.
## Interview guide development
We developed a semi-structured interview guide based on the TDF domains (Supplemental Materials S3). The guide was pilot tested with a volunteer who had prior GDM, and minor modifications were made. In response to the COVID-19 pandemic, a question was added to invite opinions on home-based alternatives to the 6–12-week OGTT.
## Procedure
All participants received a cover letter, plain language statement and a consent form, which they completed prior to interview (Supplemental Materials S4). Interviews were conducted by AL, via Zoom or telephone, from July to October 2020. Following recommendations for theory-based qualitative research, we set an a priori minimum sample target of $$n = 10$.$ At 15 interviews, we considered no new information was forthcoming [36], and conducted four additional interviews as confirmation. Each participant received an AUD$30 e-voucher and was invited to review the transcript of their interview.
## Data handling and analysis
All interviews were audio-recorded, de-identified and transcribed professionally. Transcripts and participant details were stored digitally in password-protected files. We analysed data using NVivo (released in March 2020) [37].
## Coding framework
We developed a coding framework using an inductive-deductive model to avoid ‘rigid operationalisation’ of the TDF [38]. Using an inductive approach, two researchers (AL, AW) generated theme labels for similar clusters of data. We developed theme definitions in consultation with co-authors. For the deductive element, we categorised themes into domains using a TDF-based coding manual, which contained clear statements about how the inductively generated themes would be categorised within the TDF (Supplemental Materials S5). We updated the coding framework and manual iteratively during data collection, practising reflexivity throughout [39].
## Data coding and analysis
After data familiarisation, two researchers (AL, AW) jointly coded one transcript. Using the manual, we coded all text relevant to the target behavior into its corresponding TDF domain, and again as a barrier or enabler. Following this, both researchers coded two ($10\%$) transcripts independently. Inter-rater reliability was calculated using the NVivo coding comparison function (Kappa coefficient=0.73, $99\%$ agreement). Coding differences were discussed and considered within the broader contextual meaning. Following this, one researcher (AW) coded remaining transcripts with queries addressed in consultation with co-authors. Participant responses about preferences for resource and reminders formats, and home-based alternatives to the postpartum OGTT were excluded from the main analysis but are available in Supplemental Materials (S6).
## Identification of ‘important’ TDF domains
We used three established criteria to identify TDF domains of ‘high importance’: frequency (total number of codes to a TDF domain), presence of conflicting beliefs/themes, and evidence of themes likely to influence behavior [39]. Responses to ‘important’ TDF domains are reported below, responses to remaining TDF domains are reported in Supplemental Materials (S7).
## Identification of intervention functions and behavior change techniques
Using the BCW (Figure 1), we developed a conceptual model linking qualitative data (synthesised into the TDF domains of ‘high importance’ and mapped to COM-B elements) to intervention functions [29]. We then used established procedure and taxonomies to make recommendations on behavior change techniques to underpin intervention content [29, 31].
## Participant characteristics
Twenty-one women ($2\%$) registered interest; 19 were interviewed, two were uncontactable. Participants were (mean+SD): 34 ± 4 years, 19 ± 4 months post-partum. Most had completed OGTT within guidelines ($58\%$), were Australian born ($63\%$) and resided in metropolitan areas ($90\%$), Table 1. Average interview duration was 41 minutes (range: 26-59). All participants reviewed and approved their transcripts; no changes were requested.
**Table 1**
| Demographic characteristic | N |
| --- | --- |
| Age (years) | 34.2 ± 3.9 |
| Education levela | Education levela |
| Year 12/VCE/Certificate/Diploma | 7 (38.9) |
| Degree | 7 (38.9) |
| Postgraduate | 4 (22.2) |
| Employment status | Employment status |
| Home duties | 5 (26.3) |
| Employed part-time | 11 (57.9) |
| Employed full-time | 3 (15.8) |
| Place of residence | Place of residence |
| Metropolitan | 17 (89.5) |
| Rural | 2 (10.5) |
| Country of birth | Country of birth |
| Australia | 12 (63.2) |
| China | 2 (10.5) |
| Otherb | 5 (26.5) |
| Language spoken at home | Language spoken at home |
| English | 18 (94.7) |
| Mandarin | 1 (5.3) |
| Family history of type 2 diabetes | 10 (52.6) |
| Months since pregnancy affected by gestational diabetes | 18.5 ± 3.7 |
| Uptake of screening for type 2 diabetes | Uptake of screening for type 2 diabetes |
| OGTT completed at some point | 16 (84.2) |
| OGTT completed within guidelines (i.e., 6-12 weeks postpartum) | 11 (57.9) |
| Annual screen for type 2 diabetes completed | 4 (21.1) |
## Important TDF domains
Themes were coded to 13 of the 14 TDF domains. Eight domains were represented in at least two of the three importance criteria and assessed as ‘high’ importance (Table 2). The eight important TDF domains are described below and in Table 3.
## Knowledge
All participants were aware of the association between GDM and T2D and understood that the purpose of postpartum screening was “…to make sure it [GDM] disappeared” (ID11). Women who understood that GDM increases the risk of developing T2D generally assigned greater importance to screening: “I’m higher risk … I have gone and done the follow-up testing” (ID10). Some noted that knowledge of the OGTT procedure and requirements acquired at GDM diagnosis was helpful as “…you know what to expect” (ID05).
Women’s knowledge of ongoing screening requirements was low: “It hadn’t registered that it was a yearly thing” (ID04) and “It’s just one blood test you have to take, is it?” ( ID12). Relatedly, lack of knowledge of immediate and ongoing T2D risk translated into confusion about the rationale for regular follow-up: “I thought once I had the – the six week one, I’d be in the all-clear” (ID14).
## Memory, attention & decision processes
Women described the impact of cognitive overload associated with the demands of managing life with a baby: “I don’t think I actively decided against it [OGTT], it was just … a bit too hard” (ID01). Women also had difficulty maintaining attention to ongoing risk and screening requirements: “I put it out of my mind for a little bit … and then I forgot” (ID11). Relatedly, the asymptomatic nature of T2D meant that it was “easy to … forget when you have no symptoms” (ID01). Conversely, screening reminders were unanimously valued “my trigger to get it done” (ID03) and prompted attention to ongoing risk: “this is something you should still check-up on” (ID13).
## Environmental context & resources
Competing demands. Women often described how personal circumstances reduced opportunity to schedule “A time around their [newborn] chaotic routine” (ID08). This was just one of multiple competing demands the women faced: “I don’t feel like I’ve got any time ever to do things that I need to do” (ID13).
Screening requirements and environment. Women expressed that the requirements, timing, and duration of screening were impractical: “…you have to sit around for at least two hours, and it was just not very possible with the newborn” (ID01). For some, seeking alternate care for their newborn was undesirable: “… it’s not very practical … for a mum to leave the baby for that long” (ID03). Lack of child and nursing-friendly facilities and extended waiting times exacerbated these challenges: “…there’s just nowhere nice to sit and breastfeed and it was dusty” (ID08). Unique to the context of the COVID-19 pandemic were concerns about physical safety during the screening visit: “I don’t want to sit in a hospital around sick people for three hours … and especially now with all this Coronavirus, I definitely wouldn’t do it” (ID02).
Education and resources. Antenatal education was a key enabler to understanding T2D risk and importance of postpartum screening: “…in the first education session, they drilled it into us just to make sure you go” (ID04). For some, print-based resources reinforced learnings. Conversely, some noted reduction of “information after having the baby” (ID10), while others perceived that screening information was buried within an excess of “…piles of paperwork” (ID15).
## Social influences
Social support. Availability of practical support for the logistics of screening reduced the strain of competing demands: “I have a very supportive husband … he made sure that he was helpful with the other kids so that I could get to the appointment” (ID10). Family and friends were a powerful influence on women’s screening decisions: “… if they’re concerned about me or are trying to support me in trying to do something for my own health then I certainly respect that and listen to them” (ID16).
Communication with health professionals. Health professionals were regarded as a trusted and valuable source of information: “…they know your health status … if your GP calls you and tells you to do something, you generally do it” (ID14). Continuity of care reinforced the value placed on advice: “I already had that trusting relationship with them, and they knew my history … I was comfortable in her expertise in the condition” (ID14). While most women were informed about their T2D risk, few were advised about ongoing screening requirements: “She [GP] didn’t tell me” (ID02).
Social comparison. Knowing that other women attended screening was an enabler. For example, one participant identified closely with an online blogger who had been diagnosed with T2D after GDM, and whose narrative reinforced the importance of screening: “I had it in the back of my mind that she had developed it … I’m sure that probably had a subconscious role to play” (ID03).
## Emotion
Fear/anxiety. Some participants fear of T2D diagnosis impacted screening behavior: “I was worried that I would have it” (ID04). Often, this arose from perceiving T2D management as analogous to the “…regimented living style” (ID06) experienced during GDM. While fear was a clear barrier, a sense of relief and “peace of mind” (ID01) encouraged women “to keep getting the checks done” (ID21). Conversely, for a minority, an appropriate level of concern motivated screening attendance: “I was nervous that I might still have it … I wanted to get it done” (ID16). Some participants feared “needles” (ID12) or had misconceptions about potential harm: “…I felt the sugar flushed in my blood … you always worry about whether your body will react properly” (ID09).
Postpartum abandonment. Participants described disappointment about the rapid reduction of support after birth which greatly contrasted with experiences during pregnancy: “…the support that we received was fantastic … the minute the baby was born it, sort of, stopped” (ID16). Some described postpartum care as highly infant-focused: “I did leave that session thinking, ‘Great. My baby’s happy and healthy.’ But, for me it was, like, two questions and done” (ID04). Participants noted the need for ongoing support to prioritise their health: “reinforcing you are as important as your baby” (ID12).
## Beliefs about consequences
Perceived necessity. Believing that T2D screening is important for future health was a powerful enabler: “for my own body’s sake, it was the right thing to do” (ID04). Conversely, lack of symptoms and “feeling okay” (ID06) reduced perceived necessity: “I don’t feel any different from before I was pregnant, so it’s not of real urgency for me” (ID02). Perceiving GDM as transient meant screening was “less of a priority” (ID01) for some. Annual screening was sometimes perceived as unnecessary if the women had received a negative OGTT result “…it’ll be fine” (ID08) or were planning another pregnancy: “… they’ll screen me when I get pregnant again” (ID13).
Consequences of screening. Screening benefits typically outweighed costs. Knowing one’s health status was an enabler for some: “It’s important for women to be aware of what’s going on in their bodies, so the screening is really helpful” (ID10), but not all: “I’m not prepared to know” (ID06). For some, the appointment provided opportunity for personal time in the early postpartum period: “being able to just sit and have some time out to read a book was actually pleasant” (ID21). Most described minimal negative consequences: “It wasn’t really anything too onerous, except for a couple of hours to sit around and wait, which didn’t bother me” (ID04).
Anticipated outcome. For some, the possibility of undiagnosed diabetes was a motivator: “…it could be worse if I wasn’t tested regularly” (ID05). For others, anticipating that they may already have T2D was a barrier: “…it could be too late” (ID06). Expectation of a negative OGTT result was an enabler: “I knew it was going to be a normal test, so I wasn’t anxious” (ID01).
## Social professional role and identity
Social identity and motherhood role expectations were both barriers and enablers. Some women described that they “prioritise [their child’s] health over [their] own health” (ID12). For others, being a mother motivated them to screen: “…to be a good mum … you need to make sure that you’re also looking after yourself” (ID21).
## Beliefs about capabilities
Perceived competence. For some, compensatory strategies such as continuing to self-monitor blood glucose or engaging in healthy habits reduced the perceived necessity of screening: “…I haven’t had a test yet, but I’ve pricked myself a few times and it’s always been normal” (ID02). A minority believed that they would recognize symptoms of T2D: “I can read my body pretty well … if everything felt fine I probably wouldn’t [screen]” (ID13).
Perceived ability to manage T2D risk. Believing that T2D can be prevented, delayed, or managed influenced willingness to screen. High self-efficacy (i.e., belief in capability to manage risk or T2D) appeared to foster a sense of agency and reduce apprehension related to screening: “I feel like it’s within my control to a certain extent” (ID05). Conversely, one participant reported that low confidence in making diet and lifestyle change led to avoidance of screening: “I haven’t learned a different way of how to deal with stress aside from eating” (ID06).
## Intervention functions and behavior change techniques to promote uptake of postpartum diabetes screening
We identified five intervention functions (education, environmental restructuring, enablement, persuasion, modelling) and 12 behavior change techniques to address the eight ‘important’ TDF domains [29]. We illustrate the linkages from evidence (synthesised into TDF domains) to intervention components (functions and behavior change techniques) in Figure 2.
**Figure 2:** *TDF domains/COM-B components linked to intervention functions and behavior change techniques.*
## Discussion
Using a methodology grounded in behavior change theory, we identified multiple modifiable determinants of T2D screening uptake among Australian women with prior GDM. Eight ‘high’ importance TDF domains, mapped onto the COM-B model, provide an evidence base for future messaging development and intervention targets.
Participants had high awareness of T2D risk and the rationale for screening, consistent with prior Australian studies [16, 18, 19, 21]. Linkages between TDF domains provide context for this finding. Knowledge was acquired via health professional advice (‘social influences’) and education/resources (‘environmental context & resources’). Despite this knowledge, some participants reported a missed or delayed OGTT, emphasising the need for messaging that targets additional psychosocial factors.
Knowledge related to ongoing T2D screening and risk appears to be lacking, as evidenced by confusion about frequency, test type and rationale for periodic screening. This was likely influenced by the temporary suspension of NGDR annual reminders in response to changes to guidelines [40] for postpartum testing during the COVID-19 pandemic. However, uptake in *Australia is* typically low [6]. Our findings suggest that this reflects cognitive overload due to fatigue and parenting demands, and a disproportionate emphasis on the onerous 6-12-week OGTT, whereas future follow-up is the simpler fasting glucose or HbA1c (4, 12–14). Two distinguishing screening enablers were maternal identity and social-emotional support. These novel findings suggest that addressing stereotypical norms and beliefs about maternal role and providing emotional support may be effective in motivating screening uptake.
## Comparison with previous research
Previous research has recommended the use of system-based or local reminders, as forgetting is a common barrier to screening [12]. This was not identified in the present study. While our findings contrast with prior Australian qualitative research [15, 18], they may reflect system-based improvements in the NGDR and high registration rate [6]. Prior research has indicated that NGDR reminders are not effective in promoting screening uptake [6, 22]. Our research confirms that women value reminders and there is scope to incorporate messages targeting the psychosocial barriers and enablers identified in this study.
Competing demands and screening-related challenges were key personal-level barriers in this and other studies [14]. Prior recommendations suggest altering the physical environment to be more ‘baby friendly’ [12]. Women’s expressed concerns regarding physical safety in the context of the COVID-19 pandemic challenge this recommendation; supported by findings from a recent Danish study [41]. Instead, clinician support and encouraging women to seek practical social support (a key enabler in overcoming situational and contextual barriers) may be more pragmatic [26].
Concordant with prior research, women often prioritised their child’s needs over their own health [14]. Reduced healthcare information and support postpartum (‘postpartum abandonment’) was disempowering and served to confirm women’s perceptions of their own health being less important, an issue identified in a recent scoping review [26].
Fear of T2D diagnosis was the most frequently reported motivational barrier, and often related to extrapolation of prior experience of GDM management to future diabetes management. Fear is a widely identified emotion in health behavior change research, including for postpartum diabetes screening [14]. While appropriate concern is a necessary component of risk perception, fear is unlikely to promote behavior change, particularly in the absence of self-efficacy and response efficacy (i.e., a person’s belief that changing their behavior will reduce risk) [42]. The present findings align with this behavioral pathway. For example, key enablers were understanding personal risk and perceiving screening as an important health behavior that may mitigate potential adverse health consequences (high response efficacy). Conversely, barriers included low, or distal representations of personal risk and lack of perceived benefit, or necessity, of screening (low response efficacy). Similar findings, reported elsewhere, suggest that addressing beliefs and perceived personal risk is essential in increasing T2D screening uptake (12–14).
Relatedly, a major screening barrier for some women was perceived competence in managing their own health, mostly among those who had delayed or not attended screening. Misplaced beliefs in their own capability to recognize T2D symptoms reduced the priority women assigned to formal screening [14]. Previous research noted use of compensatory strategies (e.g. self-testing) in-lieu of formal screening [14]. Finally, while home-based alternatives to formal OGTT may be attractive and viable options in future [43], further research is needed to establish their feasibility and effectiveness, given the concerns raised by the women in this study. Collectively, these findings indicate the need for education to address misconceptions about the ability to recognize T2D symptoms, and the rationale for timely, formal screening and ongoing surveillance.
These findings suggest the potentially important role of self-efficacy in screening uptake. Low self-efficacy, regarding personal capability to prevent or manage T2D, negatively impacted willingness to screen, which has been noted in only two other Australian studies [15, 21]. Uniquely, our study also shows the positive influence of self-efficacy, which has not been previously reported. This novel finding aligns with research proposing the mediating relationship between fear and self-efficacy and suggests the potential value of increasing self-efficacy through empowerment-based messaging to increase screening uptake [42].
## Policy
Prior recommendations to increase screening uptake typically targeted knowledge [12, 13]. While an important prerequisite, our findings suggest that ‘Knowledge’ is already broadly addressed by existing educational policies in an Australian context. However, screening prompts and reminders provided by Australia’s National Gestational Diabetes Register could be improved by review to ensure that the messaging both addresses the determinants identified in this study and is underpinned by the recommended behaviour change techniques. Most of the modifiable determinants of T2D screening uptake relate to the Opportunity and Motivation elements of the COM-B model. Therefore, screening uptake is most likely to be improved by timely [44], person-centred messaging that addresses opportunistic barriers (e.g., competing demands and elements of the screening procedure), motivational factors (e.g., emotions, beliefs, self-efficacy, maternal identity).
A further consideration for policymakers is the current reliance on the 6–12-week OGTT, which is a barrier to screening and could be replaced with a simpler fasting glucose, HbA1c or home-based alternatives. This would require further research to establish their feasibility and effectiveness. Finally, the women’s concerns about postpartum abandonment suggest that greater consideration needs to be given to ensuring appropriate handovers from tertiary to primary care, and continued support during this time.
## Clinical practice
We have identified behavior change techniques to underpin psychoeducational messaging (Figure 2). These techniques can be extrapolated for use in the clinical context. For example, clinicians could promote screening uptake through providing accurate and timely information about T2D risk, the rationale and positive health consequences of screening. It is important that clinicians use a gain-framed approach to emphasize the potential positive outcomes of screening such as reassurance if screening is negative and the ability to access timely and effective treatments if screening is positive. Clinicians are well placed to arrange or address practical or emotional support to overcome the unique barriers that the woman may be experiencing. Further, clinicians can frame screening as an important role modelling behavior which is of direct benefit to the health and wellbeing of the family [44]. Specific communication points for clinicians are reported in a recent systematic review published by our research team [14].
## Strengths and limitations
This study was limited by the homogenous sample: most participants reported having attended the postpartum OGTT (some overdue). Participation rate was low, and recruitment coincided with the onset of the COVID-19 pandemic, suggesting that those who participated were highly engaged. All participants were fluent English speakers, and most lived in metropolitan areas and had strong social networks. Further, the generalizability to Indigenous women and culturally and linguistically diverse populations is limited by the predominantly Anglo-Australian participants. Recognising the inherent limitations of representativeness [45], our findings provide valuable insight into enabling factors and strategies to overcome screening barriers.
A key strength is the study design, underscored by explicit use of theoretical approach and intervention development frameworks [25]. Data collection was optimised by piloting the interview schedule and including follow-up prompts to promote in-depth exploration [39]. Several strategies were implemented to enhance the trustworthiness of the data, including participant checking, reflective journals, audit trails and involvement of multiple researchers at each stage of the analysis [34, 35].
## Future research
Next steps involve developing discrete, person-centered psycho-educational messages for implementation within the national register and other delivery approaches. We have proposed behavior change techniques to underpin the messaging (Figure 2) although others shown to be associated with significant improvements in diabetes self-management (e.g., goal setting) [46] should also be considered.
Finally, further research is needed to establish the unique behavioral determinants of screening among women from culturally diverse and minority backgrounds. Indigenous, Chinese, and non-English speaking women are at greater risk of T2D than women of White European ethnicity. Given that the former experience unique barriers to T2D prevention and screening [3, 47, 48] this needs to be a priority area for future research [48].
## Conclusions
This study identified numerous modifiable barriers and enablers to postpartum T2D screening for women with prior GDM. By mapping to the BCW, we identified intervention functions and behavior change techniques to underpin intervention content. These findings provide a valuable evidence base for developing messaging and interventions that target the behavioral determinants most likely to optimise T2D screening uptake among women with prior GDM.
## Resource identification initiative
NVivo (RRID: SCR_014802).
## Data availability statement
Additional detail about the datasets analyzed for this study can be found in supplemental materials. The data generated and analyzed during the current study are not publicly available due to restrictions on sharing qualitative data. Requests to access the datasets should be directed to Dr Amelia J Lake (E: [email protected]).
## Ethics statement
The studies involving human participants were reviewed and approved by Deakin University Human Research Ethics Committee (HEAG-H 09_2020). The participants provided their written informed consent to take part in this study.
## Author contributions
AL designed the interview guide and TDF-based coding manual in consultation from co-authors. AL conducted all interviews. AL and AW coded, analysed and interpreted data and prepared the manuscript with input, review, and approval from all co-authors. 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 views expressed in the submitted article are our own and not an official position of the institutions or funding body.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcdhc.2023.1086186/full#supplementary-material
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|
---
title: 'Sequelae of Hospitalization for Diabetic Foot Ulcers at LASUTH Ikeja Lagos:
A Prospective Observational Study'
authors:
- Olufunmilayo Olubusola Adeleye
- Adetutu Oluwatosin Williams
- Akin Olusola Dada
- Ejiofor T. Ugwu
- Anthonia Okeoghene Ogbera
- Olujimi Olanrewaju Sodipo
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012119
doi: 10.3389/fcdhc.2022.889264
license: CC BY 4.0
---
# Sequelae of Hospitalization for Diabetic Foot Ulcers at LASUTH Ikeja Lagos: A Prospective Observational Study
## Abstract
### Abstract
Diabetic foot ulcers (DFUs) remain important sequelae of diabetes (DM) which cause debilitating effects on the sufferer. The evolution of some aspects of epidemiology and the current clinical impact of DFUs was examined.
### Methods
A single-center prospective observational study. Study subjects were consecutively recruited.
### Results
Total medical admissions during the study period were 2288, 350 were DM related, out of these 112 were admitted for DFU. $32\%$ of total DM admissions were for DFU. The mean age of the study subjects is 58 ± 11.0 range is from 35 years to 87 years. Males were slightly predominant ($51.8\%$). Most of them were actively employed ($92\%$), and the majority were in the 55 to 64 years age category. Most of them had not been diabetic for longer than 8 years ($61\%$). The mean duration of DM is 8.32±7.27 years. The mean duration of ulcer at presentation was 72.0±138.13 days. The majority of the patients ($80.3\%$) presented with severe (grades 3 to 5) ulcers, Wagner grade four was the most predominant. Regarding clinical outcome, 24 ($24.7\%$) had an amputation, 3 of which were minor. The factor that was associated with amputation was concomitant heart failure – OR 6.00 CI 0.589-61.07, 0.498-4.856. Death occurred in 16 ($18.4\%$). The factors associated with mortality were severe anemia OR 2.00 CI 0.65 – 6.113, severe renal impairment requiring dialysis OR 3.93 CI 0.232-66.5, concomitant stroke OR 8.42 CI 0.71-99.6, and peripheral arterial disease- OR 18.33 CI 2.27 -147 p-value- 0.006.
### Conclusion
The hallmark of DFU in this report is late presentation, it accounted for a significant proportion of the total medical admissions, although the case fatality of DFU reduced from previous reports from the center, mortality, and amputation rates are still unacceptably high. Concomittant heart failure was a factor of amputation. Mortality was associated with severe anemia, renal impairment and peripheral arterial disease.
## Introduction
Diabetic foot ulcers (DFUs) are common and potentially debilitating complications suffered by diabetic patients. The global prevalence of DFUs was recently estimated to be $6.3\%$ [1]. There is a significant burden imposed by DFUs on those affected and health systems globally [2]. DFUs account for one of the most frequent causes of hospitalization in diabetic(DM) patients [3]. Majority of these DFU admissions are due to neuropathy, peripheral vascular disease (PVD) and infections [4]. Outcomes of DFUs are complex and not only influenced by comorbidities but also by the presence of diabetes-related chronic complications affecting other organs [5, 6]. The most significant outcome of DFU apart from the death of the affected patient is amputation with its attendant long-term morbidity and predisposition to mortality. The outcome of foot ulcers also varies depending on the parameters considered as well as the health systems involved, for instance in Thailand, major amputations were performed in only $4.2\%$ and $1.1\%$ of mortality recorded for cases admitted for DFU [7]. In Africa, DFUs result in infections, foot gangrene, prolonged hospitalization, and ultimately amputation or death [8]. In a meta-analysis by Rigato et al [9], the overall prevalence of foot ulcers in Africa was $13\%$ and was noted to have increased over time. In 2001, $15\%$ of patients across Africa hospitalized for DFU had a major amputation and the overall mortality was $14\%$ [9]. Similarly in Nigeria, the outcome of hospitalization for DFU from a recent multicenter evaluation reported overall mortality of $21\%$ and amputation of $35.4\%$ [10, 11]. It is important to report regional figures of DFU-related outcomes in a bid to not only audit clinical care provision, but also influence the equitable allocation of health financial and manpower resource. An earlier report from the current study center by Ogbera in 2007 observed that DFUs and hyperglycemic emergencies were common causes of death among hospitalized diabetic patients, the case fatality rate of DFUs was $28\%$ [12]. This study aims to identify the current status of outcomes of patients hospitalized for DFUs, document factors associated with amputation and mortality, and detect any changes in statistics related to DFUs compared with the earlier reports from the center.
## Methods
This is a cross-sectional, prospective observational study. The study was conducted at the Lagos State University Teaching Hospital (LASUTH). Consecutive patients hospitalized for DFUs at the medical wards, who gave verbal consent, were enrolled for 13 months. Those enrolled had type 2 DM. ( only one type 1 Diabetic was admitted for DFU during the period).
Demographic, clinical, and laboratory data were recorded. Information sought included age, sex, diabetes duration, type of diabetes, visual impairment, smoking status, hospital or facility of usual care for diabetes before hospitalization(primary, secondary, tertiary, or alternative medicine) if currently employed.
Ulcer characteristics and risk factors for ulceration sought include; onset, whether traumatic or spontaneous, if walking barefoot/unshod, foot care education, history of amputation, ulcer site, Wagner grade, evidence of infection, presence of foot deformity, Neuropathy(Peripheral neuropathy was examined with Semmes Weinstein 10g monofilament or reduced vibration sense to 128hz tuning fork), and PVD.
Comorbid conditions sought include; hypertension, hyperglycemia, hypoglycemia, anemia, stroke, heart failure, shock, blood transfusion, and renal impairment with or without the requirement of renal replacement in form of hemodialysis.
Laboratory investigations documented include; glycated hemoglobin(HbA1c), white cell count, hematocrit, proteinuria, blood culture, Doppler ultrasound scan of lower limb vessels, and x-ray.
Management outcomes documented include ulcer healing and duration of same, amputation and extent of same, duration of hospitalization, whether discharged or demised during admission, and condition of ulcer after 3 months of follow-up.
Definition of study variables: Diabetic foot infections are defined according to the definition of the international working group on diabetes foot [13]. Ulcers were graded with the Wagner grading of foot lesions [14]. Ischemia was assessed clinically by palpation of the dorsalis pedis and posterior tibial arteries and Doppler ultrasound assessment of vascular flow. Inability to palpate the arteries or less than $50\%$ narrowing was classified as PVD. Hypertension refers to blood pressure (BP) of $\frac{140}{90}$mmHg and above or the use of blood pressure-lowering medications for the treatment of the same. Shock is systolic BP less than 90mmhg or diastolic BP less than 60mmhg. Anemia is hematocrit less than $40\%$ in men and $36\%$ in women. Hyperglycemic crisis in this study is a blood glucose level above 450mg/dl.
All study subjects were managed according to the hospital protocol in a multidisciplinary care setting.
Insulin was the only antidiabetic agent administered. The study subjects were followed up for 3 months after discharge or until death.
Data were analyzed using Statistical Package for Social Science version 23. Frequency and percentages were obtained for categorical variables. Comparison of proportions and association between categorical variables was done using chi-square and logistic regression. A p-value of less than 0.05 was considered significant.
Ethical clearance was obtained from the hospital’s research and ethics committee.
## Results
A total of 2,288 patients were admitted to the medical wards during the study period. Out of these, 350 were for DM-related causes. 112 patients with DFU were hospitalized and recruited (Figure 1). DFUs formed $32\%$ of all DM-related admissions. Out of this number 11 patients left against medical advice during treatment. Some patients had missing data due to the inability to afford some of the investigations.
**Figure 1:** *Pie chart showing proportion of admissions.*
The mean age of the study subjects is 58 ± 11.0 ranging from 35 years to 87 years. Males were slightly predominant ($51.8\%$). Most of them were actively employed ($92\%$), and the majority were in the 55 to 64 years age category. Most of them had not been diabetic for longer than 8 years ($61\%$). The mean duration of DM is 8.32 ± 7.27 years.
A majority ($67\%$) had never received foot care education. Many of them had sought treatment from alternative practitioners and prayer houses before presenting to the hospital. The mean duration of ulcer at presentation was 72.0 ± 138.13 days. Table 1 shows the demographic pattern, ulcer characteristics, and clinical parameters.
**Table 1**
| Unnamed: 0 | Frequency(Percent) |
| --- | --- |
| Age category | Age category |
| 35 - 44 | 9 (8.0) |
| 45 - 54 | 31 (27.7) |
| 55 - 64 | 40 (35.7) |
| 65 - 74 | 25 (22.3) |
| >=75 | 7 (6.3) |
| Males | 58 (51.8) |
| History of smoking | History of smoking |
| Current | 1 (0.9) |
| Ex smoker | 15 (13.4) |
| Never | 96 (85.7) |
| Duration of DM<8yrs | 68 (60.7) |
| No foot education | 75 (67.0) |
| Spontaneous ulcer | 61 (54.5) |
| No foot deformity | 79 (70.5) |
| Visual impairment | 25 (22.3) |
| Neuropathy | 85 (78.0) |
| PAD | 61 (54.5) |
| Walks Unshod | 61 (54.5) |
| Hx of hypertension | 53 (47.3) |
| Shock | 2 (1.8) |
| Anaemia | 58 (51.8) |
| hyperglycemia | 26 (23.2) |
| hypoglycemia | 23 (20.5) |
| Stroke | 6 (5.4) |
| Heart failure | 4 (3.6) |
| Renal impairment | 26 (23.2) |
| Required dialysis | 2 (1.8) |
| Required Blood transf | 44 (39.3) |
| Ulcer healed during admission | 11 (9.8) |
The majority of the patients ($80.3\%$) presented with severe (grades 3 to 5) ulcers, Wagner grade four was the most predominant.
Neuropathy is a prominent causative factor as $78\%$ had clinical evidence of neuropathy. PAD was observed in $54\%$ of the subjects.
Glycemic control before hospitalization was poor as evidenced by the markedly deranged mean HbA1c of $9.9\%$.
Regarding clinical outcome, 24 ($24.7\%$) had an amputation, 3 of which were minor (Figure 2). The factors that were associated with amputation were, heart failure – OR 6.00 CI 0.589-61.07, PAD OR 1.200 CI 0.445-3.239., a requirement of blood transfusion OR 1.625 CI 0.607 -4.35, and renal impairment OR1.556 CI 0.498-4.856. Although these did not reach statistical significance (Table 2). Death occurred in 16 ($18.4\%$) during hospitalization although the actual cause of death was not elucidated in this study Figure 3. The factors associated with mortality were the requirement of transfusion OR 2.00 CI 0.65 – 6.113, requiring dialysis OR 3.93 CI 0.232-66.5, concomitant stroke OR 8.42 CI 0.71-99.6, and PAD- OR 18.33 CI 2.27 -147 with a p-value of 0.006. Presentation to the hospital in hyperglycemic crisis was not a factor in mortality (Table 3).
**Figure 2:** *Frequency of Amputation.* TABLE_PLACEHOLDER:Table 2 **Figure 3:** *Outcome of hospitalization.* TABLE_PLACEHOLDER:Table 3
## Discussion
Diabetic foot ulcers are a complication of DM with a far-reaching global impact in terms of cost, morbidity, and mortality. The increasing prevalence of type 2 DM in Africa and Nigeria implies that DM-related complications will also be on the upward trend. This study seeks to report the evolution of the outcome of hospitalization for DFU at one of the leading referral centers in the city of Lagos in the South West of Nigeria. Lagos is the most populous city in Nigeria and the second-most populous city in Africa [15]. In the report by Ogbera in 2007 [12], at the same center, DFUs accounted for the second most common cause of death in the population studied($30\%$) with a case fatality of $25\%$. DFUs accounted for 32 percent of all DM-related admissions in this study. Mortality from DFUs was $18.4\%$ and a case fatality rate of $14\%$ indicated an improvement in outcome, although this value is still unacceptably high. The short duration of this study poses a challenge to ascertaining reasons for the modest improvement in outcome. However, the fact that the majority of the study population presented late to the hospital makes patient-related reasons a conjecture. A possible explanation may be better availability of diagnostic facilities and enhanced synergy among the multidisciplinary team involved with the management of these patients. In addition, the earlier study was conducted when LASUTH was newly elevated from a secondary to a tertiary health facility. Furthermore, the earlier study did not elaborate on the clinical and laboratory characteristics of the DFUs study population.
In the current report, there were more males hospitalized for DFU which agrees with previous studies (9, 16–19). Males may be involved with jobs and activities that are more physical placing them at higher risk of foot ulceration.
It is noteworthy that the majority of the study subjects had been diabetic for 8 years or less. Similarly, Ekpebegh and Eregie et al [20, 21] reported findings of a relatively shorter duration of DM in those hospitalized for DFU. This observation may be due to a higher number of patients walking barefoot or unshod which places them at higher risk of the earlier development of foot complications. Furthermore, being economically disadvantaged has been shown to cause poor medication adherence with consequent poor glycemic control that will predispose to the early development of complications. In contrast to this observation, in a report from Manchester, UK, the mean duration of DM in a cohort of 194 patients studied was 15.4 ± 9.9 years [22]. Similarly, the report from Thailand reported a mean duration of DM of 12.2 years among patients hospitalized for DFU [7].
Another notable observation in this study is the late presentation to the hospital by the majority of the patients. The mean duration of the ulcer at presentation was two and a half months. Late presentation is defined as an ulcer presenting to the hospital after three weeks and is reported to be the hallmark of DFUs in some studies from Africa and other resource-poor countries [23, 24]. Late presentation may be attributed to ignorance of the possible sequelae of prolonged foot ulceration, poor access to healthcare, seeking alternative remedies, and poverty. The majority of the subjects in this study pay out of pocket for healthcare services.
Although the etiology of DFUs is multifactorial, the major contributory factors are neuropathy and PVD. Other previously identified associated factors include foot deformity and poor glycemic control [25]. In this study 78 percent of the study subjects had clinical evidence of neuropathy and 54.5 percent had evidence of PVD.
Twenty–five out of those who remained on admission had an amputation and 3 of these were minor giving an amputation rate of $24.7\%$. A recent study from the Republic of Benin, a close West African Country to Nigeria reported a prevalence of amputation of $33.2\%$ among patients hospitalized for DFU [26]. Interestingly; a study from Abuja Nigeria reported a drop in the prevalence of amputation to $10\%$ after commencement of multidisciplinary care of patients with DFU at the center [27]. The factors associated with amputation in this study were, PVD, heart failure, and neuropathy. The odds of amputation in patients who had concomitant heart failure are 6 times those who did not. Other studies have reported the association of PVD and neuropathy with amputation [2]. Heart failure will further worsen the peripherally reduced perfusion from PVD and reduce the chances of healing of a foot ulcer. Other reported associations with amputation include increasing age, longer duration of DM, proteinuria, and elevated creatinine levels (28–31) Mortality recorded in this study was $18.4\%$. The factors associated with mortality were severe anemia requiring blood transfusion, renal impairment requiring hemodialysis, hypertension, stroke, and PVD in ascending order of impact. The hospitalized DFU patient in this study who had a concomitant stroke had 8 times the odds of death and those with PVD had 18 times the odds of death. Boyko et al. reported the increased mortality and association with PVD evidenced by lower ABPI in the patient with DFU [32].
## Limitations
The major limitation of this study was its observational nature, thus data from patients who could not afford some investigations were missing. Moreover, those who left against medical advice could not be followed up implying that the mortality could have been higher than what is currently reported. Although all patients were managed as per protocol, some variability in individual clinical care received cannot be ruled out. The small sample size could also prevent the generalization of the outcome.
## Conclusions
The prevalence of foot ulcers is comparable to what has been reported across Africa. It is still marked by a late presentation to the hospital. The amputation rate is still unacceptably high. Case fatality of DFU however reduced from what was reported from this center in a previous study. The reason for this improvement would need to be further elucidated and confirmed with a longitudinal study. Patients admitted for DFU with concomitant comorbidities require prompt and vigorous attention to prevent mortality. Preventative measures such as advocacy at community levels, provision of foot care education, and setting up clinics dedicated to foot care may serve to reduce the prevalence and incidence of diabetic foot ulcers.
## 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 Lagos State university Teaching Hospital Ethical committee. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author Contributions
OA Conceptualized the study, did data interpretation and developed the manuscript. EU conceptualized and designed the protocol. AW, AD, and OS took part in data collection and reviewed the manuscript for intellectual content. AO reviewed the manuscript. All authors 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: How has the COVID-19 Pandemic Affected Diabetes Self-Management in People With
Diabetes? - A One-Year Follow-Up Study
authors:
- Kasper Olesen
- Lene Eide Joensen
- Kristoffer Panduro Madsen
- Ingrid Willaing
journal: Frontiers in Clinical Diabetes and Healthcare
year: 2022
pmcid: PMC10012120
doi: 10.3389/fcdhc.2022.867025
license: CC BY 4.0
---
# How has the COVID-19 Pandemic Affected Diabetes Self-Management in People With Diabetes? - A One-Year Follow-Up Study
## Abstract
### Background and Aim
In Denmark, the COVID-19 pandemic resulted in two lockdowns, one from March to May 2020 and another from December 2020 to April 2021, which had severe impact on everyday life. The aim of this study was to explore changes in diabetes self-management behaviors during the pandemic and to examine how specific population characteristics were associated with changes in diabetes management.
### Methods and Participants
In a cohort study from March 2020 to April 2021, two online questionnaires were collected from a total of 760 people with diabetes. Descriptive statistics were used to assess the proportion of participants experiencing improvements, deterioration, and status quo in diabetes self-management during the pandemic. Using logistic regressions, baseline characteristics were explored as potential predictors of change.
### Results
Approximately half of the participants reported that they experienced lower physical activity in April 2021 compared to before the pandemic, approximately one fifth reported diabetes self-management to be more difficult than prior to the pandemic, and one fifth reported eating more unhealthily than before the pandemic. Some participants reported higher frequency of high blood glucose levels ($28\%$), low blood glucose levels ($13\%$) and more frequent blood glucose variability ($33\%$) compared to before. Easier diabetes self-management was reported by relatively few participants, however, $15\%$ reported eating more healthily, and $20\%$ reported being more physically active. We were largely unable to identify predictors of change in exercise activities. The few baseline characteristics identified as predictors of difficulties in diabetes self-management and adverse blood glucose levels due to the pandemic were sub-optimal psychological health, including high diabetes distress levels.
### Conclusion
Findings indicate that many people with diabetes changed diabetes self-management behaviors during the pandemic, mostly in a negative direction. Particularly high diabetes distress levels in the beginning of the pandemic was a predictor of both positive and negative change in diabetes self-management, indicating that people with high diabetes distress levels could potentially benefit from increased support in diabetes care during a period of crisis.
## Introduction
In early 2020, the COVID-19 disease spread worldwide and was declared a global pandemic by the World Health Organization (WHO) on March 11 [1]. The consequences of COVID-19 infection were largely unknown at the time, but it was quickly established that the disease could cause severe respiratory infections and potentially be fatal [2]. Mass-media coverage reported high mortality levels in some regions of developed countries, and societies across the globe quickly implemented measures to prevent spread of the disease. Among other challenges, it was suggested that the pandemic and its accompanying societal changes could result in significant consequences for access to high quality health care. Moreover, it was reported that people with diabetes were at increased risk of severe disease if infected with COVID-19 [3].
In Denmark, all schools and childcare services were closed on March 12, 2020, employees in the public sector with noncritical functions were ordered to work from home and gatherings of more than ten people were prohibited, resulting in many people not going to work on a daily basis and temporary closure of non-critical public facilities such as cinemas, restaurants, and sport facilities [4]. During the following months, restrictions were gradually lifted; however, some restrictions were reintroduced the following winter due to an increased incidence of COVID-19 in the population [4]. From December 2020 to April 2021, a second lockdown was enforced due to renewed widespread disease in the population.
For people with diabetes, complex diabetes self-management is necessary to avoid adverse acute and long-term consequences of the illness and is, for many people, an integrated part of daily life [5, 6]. A primary goal in the management of diabetes is to always obtain near-normal blood sugar levels for which exercise and a healthy diet are among the integral components [5, 6]. A variety of factors influencing diabetes self-management has been identified including age, gender, educational attainment, diabetes type and duration, and psychological health. However, consistency is lacking across studies and most of the variance in diabetes self-management remains unexplained [7]. This indicates that factors influencing diabetes self-management are not fully understood and may vary across time and setting.
Research on the impact of the COVID-19 pandemic and diabetes management has suggested that many people experienced difficulties in managing their diabetes due to the pandemic. Thus, deterioration in healthy lifestyle, i.e., less exercise and unhealthier diet have been observed in various populations (8–13), and according to one study particularly in women [9]. A single study found improvements in diabetes self-management in a population characterized by young participants with high educational level [14], whereas a recent qualitative study found improvements in diabetes self-management [15]. A recent meta-analysis analyzed the body of literature on COVID-19 and blood glucose levels in people with type 1 and type 2 diabetes and concluded that the studies were heterogenous in their findings with many studies being inconclusive [16]. Some studies, however, reported higher blood glucose levels either in combination with weight gain [17, 18] or independent of weight gain [19]. While previous studies have suggested that changes in diabetes self-management due to the pandemic have been negative overall, it is largely unknown what characterizes people with diabetes who have experienced either improved or deteriorated diabetes self-management behaviors during the pandemic. Research has established that people with diabetes distress experience difficulties in managing diabetes (20–22). The potential psychosocial burden of the pandemic and disruption in everyday life activities by societal restriction may have constituted a new challenge for people with diabetes in maintaining their diabetes management (20–22). Whilst the restrictions for many may have been perceived as a burden, they could simultaneously offer opportunities for improved diabetes self-management [15]. Knowledge about characteristics that facilitated or reduced optimal diabetes care during the pandemic may give the opportunity to move away from “one-size-fits-all solutions” and in times of crisis to focus resources at those with the highest risk of being negatively affected by such disruption.
For these reasons, the aim of this study was to explore changes in self-reported ability to effectively manage diabetes, including changes in diet, exercise, and blood glucose levels from the beginning and one year into the pandemic. Another aim was to examine how selected population characteristics were associated with positive as well as negative changes in diabetes self-management during the pandemic.
## Study Design
In March 2020, a cohort was established by inviting 2,430 adults (aged >18 years) with diabetes to participate in an online questionnaire-based survey, hereafter referred to as the ‘baseline questionnaire’. In a follow-up questionnaire one year after the initiation of the survey, responders from the initial survey were invited to complete a new questionnaire, referred to hereafter as the ‘follow-up questionnaire’, providing information about their experienced changes in diabetes management behaviors during the pandemic and their everyday life during the last year of the COVID-19 pandemic. Participants were recruited from two user panels situated at, respectively, Steno Diabetes Center Copenhagen and the Danish Diabetes Association. Together, the panels represent people with type 1 or type 2 diabetes from across Denmark.
Questionnaires were sent to potential participants’ e-mail addresses, and informed consent was obtained digitally from all participants. Upon sign-up to each of the user panels, participants had agreed to be contacted for research purposes. For this analysis, we exclusively utilized data from participants responding to both the baseline and the follow-up questionnaire. Among these participants, characteristics from the baseline questionnaire was compared to behavior changes reported in the follow-up questionnaire, approximately one year after the baseline survey. Moreover, descriptive data from both baseline and follow-up were presented as well. Recruitment strategy and baseline characteristics have been reported in detail elsewhere [23].
## Included Measures
Information about age, gender, education, employment status, cohabitation status, type of diabetes, diabetes duration, diabetes complications, and health status was obtained from the baseline questionnaire [23]. Likewise, psychosocial data on diabetes distress (2-item Diabetes Distress Scale (DDS-2)) [24], quality of life [25] and worries related to diabetes and the pandemic [23] were collected from the baseline questionnaire. Diabetes distress and quality of life were measured on continuous scales; worries about diabetes were measured by a set of dichotomous variables. Baseline data and measures have been described in detail elsewhere [23]. Variables measuring perceived impact of the COVID-19 pandemic on diabetes self-management in the follow-up questionnaire were developed by Fisher and colleagues [13] and translated into Danish for this study. Wordings and meanings of the adopted questions were tested among people similar to the participants. After minor adjustments, the interviewees did not indicate further problems related to comprehensibility and unambiguity of the questions. Impact of the pandemic on difficulties in diabetes management was assessed by the question, “Compared to before the coronavirus pandemic, how has your diabetes management changed?” It was assessed by a 7-point scale from “significantly easier”, “moderately easier”, “slightly easier”, “no change”, “slightly harder”, “moderately harder”, and “significantly harder,” with the opportunity to indicate “no change” using the middle option [13]. A similar item asked about changes in the amount of exercise, using a 7-point scale from “much less” to “much more” [13]. Furthermore, a new item was developed measuring changes in diet ranging from “much healthier” to “much unhealthier”. Perceived impact of the coronavirus pandemic on blood glucose levels was assessed using 7-point scales asking the respondent to indicate if they experienced major changes regarding frequency of: high blood glucose, low blood glucose and larger glucose variability with response options ranging from “a lot more frequent” to “a lot less frequent” [13]. Medication taking was measured with a similar item but with only five response options: “a lot more regularly”, “a little more regularly”, “no change”, “a little less regularly”, and a lot less regularly. Exact wording of the outcome variables and their response options is available in Supplementary Material.
## Statistical Analysis
Population characteristics and perceived changes in diabetes management behaviors were explored using descriptive statistics. The data were inappropriate for cumulative logit models due to violation of the proportional odds assumption. Instead, baseline predictors of change in diabetes management were analyzed using nominal logistic regressions with independent variables measured at baseline and categorized outcome variables measured at follow up. The 7-point scales used as outcomes were reversed if needed to let low values consistently represent desirable status and collapsed into three levels 1–3 (positive change), 4 (unchanged), 5–7 (negative change). The “unchanged” category was used as reference.
All estimates were adjusted for age, gender, education, employment status, diabetes type, diabetes duration, diabetes-specific complications, and co-morbidities. The models were not adjusted for psychosocial variables, i.e., DDS-2, quality of life and worries due to similarities between the concepts and risk of over-adjustment. Thus, impact of psychosocial factors was estimated in independent models, each with a single psychosocial factor added. All analyses were carried out using SAS Enterprise Guide 7.1. The level of significance was $p \leq 0.05.$
## Results
Out of 1,396 individuals who replied to the baseline questionnaire, 760 ($54\%$) also responded to the follow-up questionnaire.
Table 1 shows characteristics of the study population at baseline. Most participants were older than 65 years, men ($58\%$), and had completed a higher education (<5 years) ($58\%$). Almost two thirds had type 2 diabetes, had long diabetes duration ($50\%$ > 15 years) and almost one out of five had one or more complications to diabetes. More than half ($55\%$) had at least one additional chronic disease, whereas one out of ten reported at least one mental disorder. Most participants ($71\%$) reported high quality of life, whereas ($78\%$) scored less than two on the DDS-2 indicating little or no diabetes distress. Almost two thirds were worried about being overly affected due to their diabetes if infected with COVID-19, and one third was worried about not being able to manage their diabetes properly if infected by COVID-19.
**Table 1**
| Characteristic | Frequency (%) |
| --- | --- |
| Gender | Gender |
| Male | 439 (57.8) |
| Female | 321 (42.2) |
| Age (years) | Age (years) |
| Years (P25, Median, P75) | (57, 66, 72) |
| Educational attainment | Educational attainment |
| Primary | 44 (5.8) |
| Secondary | 243 (32.0) |
| Higher education (<5 years) | 441 (58.0) |
| Higher education (>= 5 years) | 32 (4.2) |
| Employment | Employment |
| Employed | 251 (33.0) |
| Retired | 453 (59.6) |
| Other | 56 (7.4) |
| Diabetes type | Diabetes type |
| Type 1 | 262 (34.5) |
| Type 2 | 498 (65.5) |
| Diabetes duration | Diabetes duration |
| Years (P25, Median, P75) | (8, 15, 26) |
| Complications to diabetes | Complications to diabetes |
| 0 complications | 611 (80.4) |
| 1+ complications | 149 (19.6) |
| Chronic diseases (other than diabetes) | Chronic diseases (other than diabetes) |
| 0 chronic diseases | 341 (44.9) |
| 1+ chronic diseases | 419 (55.1) |
| Mental disorder | Mental disorder |
| | 673 (88.6) |
| 1+ mental disorder | 87 (11.4) |
| Quality of Life* | Quality of Life* |
| Suboptimal quality of life | 224 (29.5) |
| High quality of life | 536 (70.5) |
| Diabetes distress* | Diabetes distress* |
| Little to no DDS (score <2) | 589 (77.5) |
| Moderate to high DDS (score >=2) | 171 (22.5) |
| Overly affected due to diabetes if infected | Overly affected due to diabetes if infected |
| Not worried | 293 (38.6) |
| Worried | 467 (61.5) |
| Unable to manage diabetes if infected | Unable to manage diabetes if infected |
| Not worried | 521 (68.6) |
| Worried | 239 (31.4) |
Table 2 shows the distributions of perceived changes in difficulties in diabetes self-management, medication taking, diet, physical activity, high blood glucose levels, low blood glucose levels and blood glucose variability measured one year after baseline. Approximately three quarters of the participants reported unchanged difficulties in managing diabetes, almost none ($3\%$) reported that diabetes management was easier during the pandemic whereas the remaining participants ($21\%$) reported diabetes management to be harder. Almost all participants ($93\%$) reported unchanged medication taking. Most of the participants experienced unchanged diet ($62\%$) and blood glucose levels (65-, 62-, $79\%$) whereas a minority ($29\%$) experienced unchanged physical activity. Among people who experienced changes, difficulties in managing diabetes were more frequent in all categories – particularly regarding high and low blood glucose levels and blood glucose variability, where less than $8\%$ experienced improvements in one or more of the measures. In all, 13- to $33\%$ experienced more frequent low and high blood glucose levels and blood glucose variability.
**Table 2**
| Events | Proportion (%) responses in categories | Proportion (%) responses in categories.1 | Proportion (%) responses in categories.2 | Proportion (%) responses in categories.3 | Proportion (%) responses in categories.4 | Proportion (%) responses in categories.5 | Proportion (%) responses in categories.6 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Diabetes management | Much Easier | Somewhat Easier | Easier | Unchanged | Slightly Harder | Somewhat Harder | Much Harder |
| Type 1 diabetes | 0.4 | 1.2 | 3.1 | 71.8 | 20.2 | 3.1 | 0.4 |
| Type 2 diabetes | 0.8 | 0.4 | 1.0 | 76.3 | 15.3 | 4.6 | 1.6 |
| All | 0.7 | 0.7 | 1.7 | 74.7 | 17.0 | 3.1 | 1.2 |
| Medication taking | | Much more regularly | Slightly more regularly | Unchanged | Slightly less regularly | Much less regularly | |
| Type 1 diabetes | | | 3.1 | 92.8 | 3.1 | 1.2 | |
| Type 2 diabetes | | 0.2 | 0.6 | 92.8 | 2.1 | 4.2 | |
| All | | 0.1 | 1.5 | 92.8 | 2.5 | 3.2 | |
| Diet | Much Healthier | Somewhat Healthier | Healthier | Unchanged | Slightly Unhealthier | Somewhat Unhealthier | Much Unhealthier |
| Type 1 diabetes | 0.0 | 2.7 | 7.3 | 67.2 | 18.3 | 4.2 | 0.4 |
| Type 2 diabetes | 2.2 | 3.4 | 11.9 | 59.8 | 17.3 | 5.2 | 0.2 |
| All | 1.5 | 3.2 | 10.3 | 62.4 | 17.6 | 4.9 | 0.3 |
| Physical activity | Much More | Somewhat More | Slightly More | Unchanged | Slightly Less | Somewhat Less | Much Less |
| Type 1 diabetes | 1.2 | 4.6 | 13.0 | 30.5 | 26.3 | 16.4 | 8.0 |
| Type 2 diabetes | 1.2 | 7.6 | 12.5 | 28.3 | 20.3 | 21.9 | 8.2 |
| All | 1.2 | 6.6 | 12.6 | 29.1 | 22.4 | 20.0 | 8.2 |
| Blood sugar levels | Much Less | Somewhat Less | Slightly Less | Unchanged | Slightly More | Somewhat More | Much More |
| High blood sugars | | | | | | | |
| Type 1 diabetes | 0.8 | 2.3 | 3.8 | 60.3 | 23.7 | 9.2 | 0.0 |
| Type 2 diabetes | 1.4 | 1.4 | 4.2 | 67.3 | 20.1 | 4.8 | 0.8 |
| All | 1.2 | 1.7 | 4.1 | 64.9 | 21.3 | 6.3 | 0.5 |
| Blood sugar variability | | | | | | | |
| Type 1 diabetes | 0.4 | 0.8 | 3.8 | 54.6 | 27.9 | 10.7 | 1.9 |
| Type 2 diabetes | 1.0 | 1.2 | 2.0 | 66.5 | 22.9 | 5.8 | 0.6 |
| All | 0.8 | 1.1 | 2.6 | 62.4 | 24.6 | 7.5 | 1.1 |
| Low blood sugars | | | | | | | |
| Type 1 diabetes | 1.2 | 1.9 | 4.6 | 66.8 | 22.1 | 3.1 | 0.4 |
| Type 2 diabetes | 3.4 | 1.0 | 3.6 | 84.7 | 6.2 | 1.0 | 0.0 |
| All | 2.6 | 1.3 | 4.0 | 78.6 | 11.7 | 1.7 | 0.1 |
Tables 3A, 3B show regression coefficients predicting whether the pandemic had made it, respectively, easier (3A) or harder (3B) to manage diabetes according to participant reporting one year after baseline.
Shown in Table 3A, participants with less frequent high blood glucose levels, less frequent blood glucose variability and less frequent low blood glucose levels were characterized by higher levels of diabetes distress compared to participants experiencing no change in blood glucose levels. Participants who reported healthier eating were more likely to have a mental disorder and to have type 2 diabetes. Additionally, participants who experienced less frequent low blood glucose levels had lower quality of life and were more often worried about their diabetes management during the pandemic compared to participants experiencing no change in blood glucose levels. Due to the low number of participants reporting easier diabetes management ($$n = 34$$), more regularly medication taking ($$n = 12$$) and less regularly medication taking ($$n = 45$$) medication taking, the study did not offer the opportunity to explore predictors of these kind of changes.
As shown in Table 3B, experiences of more frequent high blood glucose levels and more frequent blood glucose variability were associated with lower quality of life, higher diabetes distress and worries about being overly affected by the COVID-19 in case of infection. Experiences of more frequent low blood glucose levels were associated with higher diabetes distress and being worried about management of diabetes during the pandemic. Participants who reported eating more unhealthily were more likely to be female, have type 2 diabetes and have lower quality of life.
## Discussion
The onset of the COVID-19 pandemic from March 2020 had significant impact on everyday life among people with diabetes as well as on their diabetes self-management the following year. In this study of 760 individuals with type 1 or type 2 diabetes, several changes in diabetes self-management were observed. However, most participants reported unchanged diabetes self-management across all investigated aspects, except for physical activity for which many reported less activity. Negative impact of COVID-19 was dominating compared to positive impact in almost all investigated aspects of diabetes self-management, including impact on difficulties in diabetes management, eating unhealthily and doing less physical activity. Negative changes were also reported in terms of more frequent sub-optimal blood glucose levels. Almost one out of four participants reported diabetes management to be harder during the pandemic; very few reported it to be easier. This means that despite home-based working and cancelled social activities with better opportunities for planning and performing diabetes self-management, only very few participants considered the situation advantageous for diabetes management. In line with this, most studies find that diabetes management has been increasingly difficult and less effective due to the pandemic (8–11, 26, 27) whereas a single study on young, highly educated people demonstrated improved diabetes management [14]. Likewise most studies report negative impact on specific self-management behaviors, i.e., less exercise and more unhealthy diet during the pandemic across various populations (12, 18, 28–32). Regarding changes in blood glucose levels during the pandemic, previous findings are mixed and improvements have been found particularly in studies of people with type 1 diabetes [15, 33]. Our finding that less physical activity was the most frequently reported behavior change is not surprising, as the physical activity level was likely to be affected when organized sports activities were shut down during the pandemic. Furthermore, for people who were working from home, transport to work and physical activity as a part of the workday was not “automatically” happening. The results suggest that the lack of exercise due to COVID-19 restrictions was not compensated for by the participants by introducing new forms of appropriate exercise. This finding is in line with a systematic review of exercise among older adults who, during the lockdown, performed less exercise or presented with more sedentary behavior. Most of the identified studies of behavior change during the lockdown presented the same findings [12, 34]. Likewise, maintaining an unhealthier diet was also reported in some studies whereas none reported improved diet because of the lockdown [33]. Lower level of physical activity in the study population combined with overall difficulties in managing diabetes is likely to have influenced blood glucose levels [34] which may explain the increased frequency of high and low blood glucose levels as well as variability in blood glucose levels during the pandemic.
Overall, our analyses showed that sub-optimal psychosocial health was associated with negative impact on diabetes self-management behaviors and frequency of adverse blood glucose levels. Particularly diabetes distress at baseline was associated with changes in diabetes self-management behaviors during the pandemic. Importantly, higher diabetes distress was simultaneously associated with lowered and increased frequency of adverse blood glucose levels. Research has shown associations between psychosocial health and diabetes self-management. For example, research before the pandemic had already established strong evidence for associations between diabetes distress and diabetes self-management [20, 35]. Improved diabetes self-management among some participants with high diabetes distress may be a consequence of successful support from health care professionals and/or relatives combined with room for improvements in diabetes self-management behaviors in this sub-population of people with diabetes distress. This suggests that people with diabetes distress constitute a heterogenous group with different needs for support. We were largely unable to identify characteristics of people with changed level of physical activity indicating that people who changed level of physical activity was a heterogenous group with no common characteristics. People in jobs, among whom many were sent home, were not significantly more prone to changes in diabetes self-management during the lockdown.
A key strength of the present study is the individual level linkage between survey responses in the early stages of the pandemic and follow up responses one year later. This provided the opportunity to include factors measured at baseline in the first week of the first lockdown – free of influence from future events - to the changes reported at follow up. Another strength of the study is the high number of participants with type 1 and type 2 diabetes included in the study population. The inclusion of validated measures in the questionnaire ensured high internal validity of the self-reported predictors.
Limitations include that due to relatively few participants reporting improvements in diabetes self-management behaviors, the study lacked statistical power to sufficiently explore predictors of improved diabetes management during the pandemic. Mechanisms towards improved diabetes management during pandemics as well as other societal crises should be explored with different study designs. As with any self-report method, recall bias may have influenced the data and findings of the study, particularly when asking to events more than a year back in time in the follow-up questionnaire and accuracy of the responses may be reduced. Therefore, we did not ask participants to give exact measures of changes in their activities, but merely requested participants to report trends, e.g., much less, somewhat less, slightly less, unchanged. The exact proportion of people reporting change should be interpreted with caution. Diabetes self-management behaviors are, however, an integral part of everyday life for people with diabetes, and it is likely that significant behavior changes will be remembered. The beginning of the pandemic and national lockdown was a watershed moment for many Danes, which makes the everyday life and events of the time easier to recall. From our data we are unable to establish to which extent the identified predictors of change in diabetes management are exclusively due to the pandemic or whether they would have occurred regardless of the pandemic.
Due to the study design, certain groups have been highly represented in the study population. In particular people with type 2 diabetes and people who have passed statutory retirement age are highly represented. Thus, the study data does not offer opportunity for in depth analyses of people in specific life-situations, such as being responsible for small children and a full-time job while managing diabetes.
In conclusion, this study confirmed that many people with diabetes experienced difficulties in managing diabetes as usual during the pandemic, although the majority did not experience any difference. An important predictor for both deterioration and improvement in diabetes management was diabetes distress. Thus, diabetes management should be of concern during times of crisis and particular attention should be devoted to people with high levels of diabetes distress, who will be at increased risk of experiencing difficulties in managing diabetes.
## 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 study was reviewed and approved by the Danish Data Protection Agency. Participants provided written informed consent prior to participating in the study.
## Author Contributions
KO, LJ, KM and IW designed the study. KM prepared data for analysis and KO carried out the statistical analyses. Interpretation of the findings was discussed among all authors. KO drafted the manuscript and all authors contributed to the final version of the manuscript.
## Acknowledgments
We wish to give thanks to the respondents for their participation in the study, and to Lone Holm and Andrea Aaen Petersen (Steno Diabetes Center Copenhagen) and Kasper Arnskov Nielsen (Danish Diabetes Association) for their indispensable efforts in participant recruitment. Finally, we would like to thank the research team at the University of Copenhagen behind ‘Standing together – at a distance’ whom the survey was developed in close collaboration with.
## 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/fcdhc.2022.867025/full#supplementary-material
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