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Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease logoLink to Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
. 2024 Feb 16;13(4):e030233. doi: 10.1161/JAHA.123.030233

Major Depressive Disorder Impacts Peripheral Artery Disease Risk Through Intermediary Risk Factors

Gabrielle Shakt 1,2, Noah L Tsao 1,2, Michael G Levin 1,3, Venexia Walker 2,4, Rachel L Kember 1,5, Derek Klarin 6,7, Phil Tsao 6,8, Benjamin F Voight 1,9,10, Salvatore T Scali 11, Scott M Damrauer 1,2,10,✉
PMCID: PMC11010076  PMID: 38362853

Abstract

Background

Major depressive disorder (MDD) has been identified as a causal risk factor for multiple forms of cardiovascular disease. Although observational evidence has linked MDD to peripheral artery disease (PAD), causal evidence of this relationship is lacking.

Methods and Results

Inverse variance weighted 2‐sample Mendelian randomization was used to test the association the between genetic liability for MDD and genetic liability for PAD. Genetic liability for MDD was associated with increased genetic liability for PAD (odds ratio [OR], 1.17 [95% CI, 1.06–1.29]; P=2.6×10−3). Genetic liability for MDD was also associated with increased genetically determined lifetime smoking (β=0.11 [95% CI, 0.078–0.14]; P=1.2×10−12), decreased alcohol intake (β=−0.078 [95% CI, −0.15 to 0]; P=0.043), and increased body mass index (β=0.10 [95% CI, 0.02–0.19]; P=1.8×10−2), which in turn were associated with genetic liability for PAD (smoking: OR, 2.81 [95% CI, 2.28–3.47], P=9.8×10−22; alcohol: OR, 0.77 [95% CI, 0.66–0.88]; P=1.8×10−4; body mass index: OR, 1.61 [95% CI, 1.52–1.7]; P=1.3×10−57). Controlling for lifetime smoking index, alcohol intake, and body mass index with multivariable Mendelian randomization completely attenuated the association between genetic liability for MDD with genetic liability for PAD.

Conclusions

This work provides evidence for a possible causal association between MDD and PAD that is dependent on intermediate risk factors, adding to the growing body of evidence suggesting that effective management and treatment of cardiovascular diseases may require a composite of physical and mental health interventions.

Keywords: major depressive disorder, Mendelian randomization, peripheral artery disease

Subject Categories: Cardiovascular Disease, Epigenetics

Nonstandard Abbreviations and Acronyms

IVW

inverse variance weighted

MDD

major depressive disorder

MR

Mendelian randomization

Clinical Perspective.

What Is New?

  • Mendelian randomization provides evidence for a potentially causal relationship between major depressive disorder and peripheral artery disease that is dependent on the intermediary risk factors of tobacco smoking and body mass index.

What Are the Clinical Implications?

  • These findings add to the growing body of evidence suggesting that effective management and treatment of cardiovascular diseases may require a composite of physical and mental health interventions.

According to the World Health Organization, major depressive disorder (MDD) is the leading cause of disability in the world affecting >300 million people. Multiple observational studies have linked depression and depressive symptoms to peripheral artery disease (PAD) and PAD outcomes, 1 , 2 suggesting a comorbid nature between the 2 diseases. Estimates of the prevalence rate of depressed individuals with PAD lies between 16% and 35%, and having both PAD and depression increases the risk of death by 24%. 3

Depression has previously been associated with known risk factors of cardiovascular disease (CVD) such as smoking, alcohol consumption, and body mass index (BMI). In a review of 45 epidemiologic studies examining the behavior of people with depressive disorders, individuals with depression, compared with those without, were more likely to smoke and had more difficulty with smoking cessation and abstention. 4 Similarly, many studies have shown a U‐shaped association between BMI and depression, demonstrating higher depression scores in individuals with unhealthy (high or low) BMI compared with healthy individuals. 5

Despite the observational evidence linking MDD to PAD and its risk factors, causal evidence for these associations is lacking; observational studies can be limited by residual confounding, and reverse causation. To attempt to overcome these limitations, we used a genetic approach to causal inference known as Mendelian randomization (MR). MR is a form of instrumental variable analysis in which the exposure is instrumented or proxied by independent genome‐wide significant DNA variants identified by genome‐wide association studies (GWAS). By comparing the relative effects of these genetically predisposed variants on the exposure and the outcome, causal associations can be inferred with limited confounding or reverse causation biases typically present in observational studies.

Using this framework, we robustly explored the relationship between MDD and PAD. Using MR approaches we sought to (1) test for evidence of a causal association between MDD and PAD, (2) validate the association of MDD with CVD risk factors, and (3) assess the extent to which traditional CVD risk factors affect the relationship between MDD and PAD.

METHODS

Overall Study Design

Our study consisted of 3 stages of analysis to comprehensively investigate the causal relationship between MDD and PAD (Figure 1). Stage 1 comprised our primary analysis and was a 2‐sample MR experiment to test the association between the genetic liability for MDD and the genetic liability for PAD. Stage 2 tested for potential intermediaries on the pathway linking MDD and PAD using MR to examine prospective associations between MDD and genetically determined levels of common CVD risk factors thought to be influenced by MDD; for those with a significant association, we then tested the association of these risk factors and liability for PAD. Risk factors that had significant associations with MDD and PAD were then carried forward to the next stage. Stage 3 used multivariable MR to test for the independent association between the genetic liability for MDD and the genetic liability for PAD conditioned on the intermediaries identified in stage 2.

Figure 1. Study design.

Figure 1

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Stage 1: 2‐sample Mendelian randomization analysis examining the causal association between the genetic liability to major depressive disorder and the genetic liability to peripheral artery disease using genetic variants associated with major depressive disorder as an instrumental variable. Stage 2: investigating the role of known cardiovascular disease risk factors in the stage 1 analysis by conducting several 2‐sample Mendelian randomization analyses. Stage 3: multivariable Mendelian randomization methods to investigate the relationship between genetic liability to major depressive disorder and genetic liability to peripheral artery disease accounting for cardiovascular disease risk factors.

The study was deemed to be exempt from institutional review board review by the University of Pennsylvania Institutional Review Board. Genomic data were used from published studies that each obtained informed consent from all participants. The data and code that support the findings of this study are available from the corresponding author upon reasonable request.

MR Framework

MR uses genetic variants as instrumental variables to estimate causal effect estimates. For these instrumental variables to produce valid MR results, 3 assumptions must be met:

  1. Relevance: the instrument (genetic variant) is directly associated with the exposure.

  2. Independence: there are no confounders of the association between the instrument (genetic variant) and the outcome.

  3. Exclusion restriction: The instrument (genetic variant) is only associated with the outcome via the exposure.

The relevance assumption was addressed through the use of independent (R 2<0.01) single‐nucleotide polymorphisms associated with the exposure of interest at genome‐wide significance (P<5×10−8) taken from the largest available studies of the exposures of interest. When they were not present (minor allele frequency <0.3) in the outcome data set, high linkage disequilibrium proxies were selected. Palindromic variants were excluded. Instrument strength was assessed with F statistics.

The independence assumption is unlikely to be violated by conventional confounders because DNA variants are fixed at conception and do not vary throughout the lifespan.

The exclusion restriction assumption is most violated by horizontal pleiotropy. Random‐effects inverse‐variance weighted (IVW) meta‐analysis MR, which was used as the primary analysis, allows the instruments to invalid due to pleiotropy as long as the pleiotropy is balanced (ie, not directional). Directional horizontal pleiotropy can be detected as heterogeneity between instruments; this was formally assessed with the Cochran Q statistic as well as visual inspection of scatter plots examining the impact of individual variants on the exposure and outcome traits and leave‐1‐out analyses. Further sensitivity tests included MR‐PRESSO (Mendelian Randomization Pleiotropy Residual Sum and Outlier), which tests for and removes instruments likely to be suffering from directional horizontal pleiotropy, testing the Egger intercept for deviation from 0, another indicator of directional horizontal pleiotropy, and weighted median MR, which requires only 50% of the weight in the analyses come from valid instruments.

Multivariable MR is an extension of the MR method, where the direct causal effects of multiple exposures on an outcome are estimated while conditioning on the other exposures by incorporating the weights for all genetic instruments across all the exposure traits. The primary assumptions of multivariable MR are similar to those of univariable MR.

Data Sources

To maximize power, we leveraged summary statistics from the largest publicly available GWAS for MDD and PAD cardiometabolic risk factors to conduct analyses.

Major Depressive Disorder

Summary statistics for MDD were obtained from the Psychiatric Genomics Consortium analysis, which comprises 135 458 individuals with MDD and 344 901 without MDD from 7 separate European ancestry cohorts. 6 Within the Psychiatric Genomics Consortium, each cohort defined MDD using a study‐specific combination of centrally reviewed methods that included diagnostic interviews, self‐reported symptoms, treatment, and electronic health records. Cohort‐specific analyses were combined with meta‐analysis. The analysis identified 44 genome‐wide significant variants associated with MDD. Heterogeneity between cohorts was robustly explored in the original article through pairwise comparison of genetic correlations and the use of polygenic risk scores to predict cohort specific outcomes using a leave‐1‐out approach and demonstrated to be minimal.

Peripheral Artery Disease

Data for PAD were obtained from a GWAS in the Veterans Affairs Million Veteran Program. 7 The multiancestry study comprises 31 307 and 211 753 individuals with and without clinical PAD, respectively. Despite being multiancestry, most participants were of European ancestry (24 009, 77%). PAD was identified through electronic health record‐based phenotyping based on International Classification of Diseases, Ninth Revision, Clinical Modification, International Classification of Diseases, Tenth Revision, Clinical Modification, and Current Procedural Terminology codes as detailed elsewhere. 7 The analysis identified 19 genome‐wide significant loci associated with PAD that replicated in an independent European‐ancestry sample of 5117 individuals with and 389 291 individuals without PAD from the UK Biobank. The Million Veteran Program PAD GWAS summary statistics were obtained from the National Institutes of Health Database of Genotypes and Phenotypes (dbGaP accession pha004826.1) under application number 33458.

Cardiometabolic Risk Factors

Smoking was instrumented using data from a GWAS of a composite lifetime smoking index, a validated, integrated score that reflects age at initiation, heaviness, duration, and time since cessation, based on self‐report. 8 The GWAS was conducted in 462 690 UK Biobank participants, of whom 249 318 were never smokers (54%), 164 649 were former smokers (36%), and 48 723 (11%) were current smokers. For context, a 1‐SD change in lifetime smoking index is equivalent to an individual smoking 20 cigarettes a day for 15 years and stopping 17 years ago or an individual smoking 60 cigarettes a day for 13 years and stopping 22 years ago. The GWAS identified 126 independent, genome‐wide significant variants associated with lifetime smoking index.

Alcohol intake frequency was instrumented using the OpenGWAS analysis of UK Biobank data. 9 The alcohol intake frequency GWAS (ukb‐b‐5779) comprises 462 346 European ancestry individuals. The GWAS was performed using the UK Biobank filed code 1558 with categorical responses of daily/almost daily (n=101 791), 3 or 4 times a week (n=115 462), once or twice a week (n=129 322), 1 to 3 times a month (n=55 873), special occasions only (n=58 029), and never (n=40 661), analyzed on an ordinal scale in the order given, which encoded less alcohol consumption as a higher value. Because of this, effect estimates from the GWAS were multiplied by a negative one to rescale the summary statistics such that a higher score was associated with more consumption.

BMI was instrumented using data from a GWAS meta‐analysis of BMI comprising 681 275 individuals of European ancestry from the Genetic Investigation of Anthropometric Traits consortium and 456 426 European individuals from the UK Biobank. 10

Statistical Analysis

All analyses were performed in R (version 4.1.0). 11 MR analyses were conducted using TwoSampleMR (version 0.5.6), 12 MR (version 0.7.0), 13 MR‐PRESSO (version 1.0), 14 and followed the guidelines set forth by the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE‐MR) checklist. 15 For continuous exposures, the effect estimate is expressed as the effect of a genetically determined 1‐SD change in the exposure. For binary exposures, the effect estimate is expressed as the effect of a 1 log‐odds change (2.7‐fold) in liability for the exposure. For all analyses, a 2‐sided P<0.05 was considered statistically significant.

RESULTS

Association Between Genetic Liability to MDD and Genetic Liability to PAD

We first tested the association between the genetic liability for MDD and the genetic liability for PAD. Using GWAS summary statistics from the most recent and largest Psychiatric Genomics Consortium GWAS of MDD, 93 independent DNA variants were identified for use as instruments (Table S1). The F statistic of all variants was >10, indicating minimal risk of weak instrument bias. The primary IVW analysis demonstrated that an increased genetic liability for MDD is associated with increased genetic liability for PAD (OR, 1.17 [95% CI, 1.06–1.29]; P=2.64×10−3) (Figure 2). The MR estimates of MDD on PAD were consistent across sensitivity analyses. Additionally, visual examination of the scatter plot (Figure S1) and leave‐1‐out analyses (Figure S2) verified that the effect of genetic liability to MDD on the genetic liability to PAD was not substantially driven by any individual variant. The Egger intercept did not deviate from 0 (Egger intercept=0.005, P=0.49), providing no evidence for directional horizontal pleiotropy.

Figure 2. Genetic liability to major depressive disorder is associated with genetic liability to peripheral artery disease.

Figure 2

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Two‐sample Mendelian randomization was performed using a genetic instrument containing 93 independent single‐nucleotide polymorphisms associated with major depressive disorder to examine the causal effect of genetic liability to major depressive disorder on the genetic liability to PAD. Inverse‐variance weighting was used as the primary analysis and was further examined with multiple sensitivity analyses. The number of single‐nucleotide polymorphisms in the instrument, ORs per 1‐SD change in the exposure, 95% CIs, and P values are displayed. ORs represent the effect of a 1 log‐odds (2.7‐fold) increase in the liability for the exposure. IVW indicates inverse variance weighted; MDD, major depressive disorder; OR, odds ratio; PAD, peripheral artery disease; and SNP, single‐nucleotide polymorphism.

Association Between Genetic Liability to MDD and Genetically Determined Cardiometabolic Factors

We next tested the association between genetic liability for MDD with genetically determined lifetime smoking index, alcohol intake frequency, and BMI to identify potential intermediaries on the pathway linking MDD and PAD. The genetic instruments contained 87, 87, and 40, variants, for lifetime smoking index (Table S2), alcohol intake frequency (Table S3), and BMI (Table S4), respectively, and F statistics were all >10. IVW MR demonstrated significant associations between the genetic liability for MDD and genetically determined lifetime smoking index (β=0.11 [95% CI, 0.08–0.14]; P=1.15×10−12), alcohol intake frequency (β=−0.08 [95% CI; −0.15 to 0]; P=4.3×10−2), and BMI (β=0.10 [95% CI, 0.02–0.19]; P=1.76×10−2) (Figure 3A). Sensitivity analyses supported these associations and can be found in Figures S3 through S11.

Figure 3. Assessment of potential intermediaries on the pathway connecting major depressive disorder and PAD.

Figure 3

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Forest plot displaying estimates of the association between the genetic liability for major depressive disorder and genetically inferred cardiovascular disease risk factors (A) and genetically inferred cardiovascular disease risk factors and genetic liability for PAD (B). Results are from the primary analysis using inverse variance weighted 2‐sample Mendelian randomization to evaluate the relationship between known cardiovascular disease risk factors and our exposure and outcome. The number of single‐nucleotide polymorphisms in the instrument, OR, 95% CIs, and P values are displayed for Mendelian randomization estimates. A, The effect estimate (β) represents the effect of a 1 log‐odds (2.7‐fold) increase in the liability for the exposure on the outcomes, as expressed in SDs. B, The effect estimate (OR) represents the effect of a 1 log‐odds (2.7‐fold) increase in the liability for the exposure on the outcome. BMI indicates body mass index; IVW, inverse variance weighted; MDD, major depressive disorder; OR, odds ratio; PAD, peripheral artery disease; and SNP, single‐nucleotide polymorphism.

Association Between Genetically Determined Cardiometabolic Factors and Genetic Liability for PAD

We then tested the association between genetically determined lifetime smoking index, alcohol intake frequency, and BMI with genetic liability for PAD. Instruments contained 142, 107, and 928, variants, for lifetime smoking index (Table S5), alcohol intake frequency (Table S6), and BMI (Table S7), respectively, and F statistics were all >10. Genetically determined lifetime smoking index (IVW: odds ratio [OR], 2.81 [95% CI, 2.28–3.47]; P=9.8×10−22), alcohol intake frequency (IVW: OR, 0.77 [95% CI, 0.66–0.88]; P=1.8×10−4), and BMI (IVW: OR, 1.61 [95% CI, 1.52–1.70]; P=1.3×10−57) were all significantly associated with genetic liability to PAD (Figure 3B). Sensitivity analyses supported these associations and can be found in Figures S12 through S20.

Association Between Genetic Liability to MDD and Genetic Liability to PAD Accounting for Potential Influence of Cardiometabolic Factors

Finally, we used multivariable MR to assess the association of the genetic liability for MDD and the genetic liability for PAD accounting for the potential influence of cardiometabolic risk factors. The effect of genetic liability for MDD on PAD was robust to controlling for genetic liability to alcohol intake frequency (OR, 1.14 [95% CI, 1.03–1.27]; P=0.014) and attenuated such that it did not exclude the null when controlling for genetically determined smoking status (OR, 1.04 [95% CI, 0.916–1.18]; P=0.53) or BMI (OR, 1.18 [95% CI, 1.00–1.39]; P=0.058). When controlling for all 3 intermediaries, there was not a detectable effect of MDD on PAD (OR, 0.94 [95% CI, 0.77–1.16]; P=0.58) (Figure 4).

Figure 4. Multivariable Mendelian randomization estimates of the association between the genetic liability for major depressive disorder and the genetic liability for PAD controlling for intermediary risk factors.

Figure 4

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Multivariable Mendelian randomization was performed to consider the effect of cardiovascular risk factors in the primary Mendelian randomization analysis. The association of genetic liability to major depressive disorder with genetic liability to PAD is not independent of established cardiometabolic risk factors (smoking, alcohol, and BMI), which is expressed as the effect of a genetically determined 1‐SD change in the exposure. The number of single‐nucleotide polymorphisms in the instrument, ORs per 1‐SD change in the exposure, 95% CIs, and P values are displayed. The odds ratio represents the effect of a 1 log‐odds (2.7‐fold) increase in the liability for the exposure on the outcome. BMI indicates body mass index; IVW, inverse variance weighted; MDD, major depressive disorder; OR, odds ratio; PAD, peripheral artery disease; and SNP, single‐nucleotide polymorphism.

DISCUSSION

In this study, we used MR to test for a potentially causal relationship between MDD and PAD. Our analyses demonstrated a significant association between the genetic liability for MDD and increased genetic liability for PAD, supporting a potential causal association. This association was completely attenuated when controlling for genetically determined lifetime smoking BMI, suggesting these factors may lie on the causal pathway between MDD and PAD.

Our findings are in keeping with the observational literature demonstrating an increased risk of PAD among individuals suffering from major depression and help clarify the conflicting role of intermediary cardiovascular risk factors reported in observational studies. An electronic health record‐based study of nearly 2 million individuals from the United Kingdom who were free from disease at baseline demonstrated that those with depression at baseline (hazard ratio [HR], 1.24 [95% CI, 1.18–1.30]), and separately those with incident depression (HR, 1.30 [95% CI, 1.14–1.48]), were at increased risk of incident PAD even after controlling for traditional cardiovascular risk factors. 16 Similar findings have been reported from prospective cohorts with respect to an association between MDD and PAD, yet differ with respect to the role of cardiovascular risk factors. Among individuals with baseline coronary artery disease enrolled in the Heart and Soul study, concomitant depression was associated with increased rates or prevalent PAD (OR, 1.79 [95% CI, 1.06–3.04]; P=0.03) or incident PAD events (HR, 2.09 [95% CI, 1.09–4.00]; P=0.03) on age‐ and sex‐adjusted analyses but not on multivariable models accounting for CVD risk factors, comorbidities, medications, inflammation, and health behaviors. 2 Our findings are similar to those reported from the Heart and Soul cohort in that we failed to detect an association between MDD and PAD when accounting for potential confounders and intermediary risk factors. As such, our results lend support to the concept that MDD acts through these intermediary risk factors to influence PAD risk.

The potential causal relationship of MDD with CVD, and its dependence on intermediary risk factors, is not restricted to PAD. Similar results have been demonstrated using for other atherosclerotic cardiovascular diseases using MR. 17 demonstrated evidence of the association between genetic liability for MDD and coronary artery disease (OR, 1.14 [95% CI, 1.06–1.24]; P=1.0×10 −3) and myocardial infarction (OR, 1.21 [95% CI, 1.11–1.33]; P=4.8×10–5) with similar sizes of effect as we reported for PAD. Also, in keeping with our results, these analyses demonstrated limited evidence for an independent causal effect of MDD on either coronary disease or myocardial infarction when adjusting for smoking. Separately, 18 reported essentially identical effects of the genetic liability for MDD and genetic liability for coronary artery disease (OR, 1.099 [95% CI, 1.031–1.170]; P=0.004) and myocardial infarction (OR, 1.146 [95% CI, 1.070–1.228]; P=1.05×10−4). 17 , 19 Interestingly, despite generally overlapping risk factors, MR studies have been unable to find consistent evidence to support a causal association between MDD and stroke or atrial fibrillation, 20 , 21 suggesting some component of CVD‐subtype specific biology.

A recent American Heart Association scientific statement has drawn attention to the highly comorbid nature of major depression and PAD and to the importance of applying the biopsychosocial model to understanding PAD. 21 As many as 1 in 5 individuals presenting with PAD have comorbid depression, which is associated with worse limb outcomes and higher all‐cause mortality. 22 , 23 , 24 Furthermore, individuals with comorbid depression have longer lengths of stay following interventions, increased hospital costs, and higher rates of discharge to rehabilitation or nursing facilities. 25 Our results suggest that both addressing underlying depression and mental health, as well as addressing risk factors that are exacerbated by depression, offer opportunities to reduce the risk of PAD and improve patient outcomes.

Limitations

Importantly, our analyses are subject to limitations, and the results should be interpreted accordingly. First, although MR can be used to address some of the shortfalls of observation studies and facilitate causal inference, it requires its own set of assumptions that are difficult to prove. As such, MR can provide evidence of but not prove causality. Second, to maximize power we used data from the largest available GWAS of MDD and PAD. For MDD, this analysis contained data from 23andMe, which poses strict data access limitations on the results, making available only the top 10 000 genetic associations. Because full summary statistics are not available, it was not possible to perform bidirectional MR and to satisfy the assumptions necessary to perform multivariable MR for mediation; as such, we could not formally calculate mediation statistics. Finally, to maximize power for PAD we used a set of multipopulation summary statistics that may have introduced heterogeneity into the analyses. Based on the majority of the participants in the GWAS being genetically similar to European reference populations and emerging evidence that the effects of causal variants are constant across ancestries, this bias is likely minimal. 26

CONCLUSIONS

This work provides evidence that MDD's genetic liability resides on a putatively causal pathway to PAD through the intermediate causal risk factors of smoking and BMI. These findings add to the growing body of evidence suggesting that effective management and treatment of CVD may require a composite of physical and mental health interventions.

Sources of Funding

S.M.D. was supported by the US Department of Veterans Affairs Clinical Science Research and Development Award IK2‐CX001780. This publication does not represent the views of the Department of Veterans Affairs or the United States government. M.G.L. was supported by the Institute for Translational Medicine and Therapeutics of the Perelman School of Medicine at the University of Pennsylvania, National Institutes of Health/National Heart, Lung, and Blood Institute National Research Service Award postdoctoral fellowship (T32HL007843), Measey Foundation, and Doris Duke Foundation (grant 2023‐0224). V.W. is supported by the Medical Research Council Integrative Epidemiology Unit at the University of Bristol [MC_UU_00032/03] and COVID‐19 Longitudinal Health and Wellbeing National Core Study, which is funded by the Medical Research Council (MC_PC_20059) and National Institute for Health and Care Research (COV‐LT‐0009). R.L.K. is supported by National Institute on Alcohol Abuse and Alcoholism (funding number K01‐AA028292). D.K. is supported by the Department of Veterans Affairs (IK2BX005759‐01), American Heart Association (23SCEFIA1153369), and Baszucki Research Initiative provided to Stanford Vascular Surgery. P.T. is supported by grants from the Veterans Affairs Office of Research and Development (BX‐003362‐01), National Institutes of Health (1R01‐HL101388; 1R01‐HL122939), and California Tobacco‐Related Disease Research Program of the University of California (T29IR0636). B.F.V. acknowledges support from the National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (DK126194).

Disclosures

S.M.D. receives research support from RenalytixAI and Novo Nordisk, outside the scope of the current research. D.K. is a scientific advisor and reports consulting fees from Bitterroot Bio, Inc., unrelated to the present work. S.S. is on the scientific advisory board with no consulting fees for Humacyte, Inc. The remaining authors have no disclosures to report.

Supporting information

Tables S1–S7

JAH3-13-e030233-s001.xlsx (130.2KB, xlsx)

Figures S1–S20

JAH3-13-e030233-s002.pdf (2.1MB, pdf)

This article was sent to Mahasin S. Mujahid, PhD, MS, FAHA, Associate Editor, for review by expert referees, editorial decision, and final disposition.

For Sources of Funding and Disclosures, see page 7.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Tables S1–S7

JAH3-13-e030233-s001.xlsx (130.2KB, xlsx)

Figures S1–S20

JAH3-13-e030233-s002.pdf (2.1MB, pdf)

Articles from Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease are provided here courtesy of Wiley

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