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Research Article

Investigating The Causal Effects of Chronic Metabolic Traits on Stroke and The Mediating Role of Depression: Evidence From Mendelian Randomization

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DOI:

10.3791/70295

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June 22nd, 2026

 ,  ,  ,  ,  ,  ,  ,  ,  ,  ,  , 
1Department of Rehabilitation, Second Affiliated Hospital of Jiaxing University, Second Hospital of Jiaxing, 2Department of Emergency, Second Affiliated Hospital of Jiaxing University, Second Hospital of Jiaxing

* These authors contributed equally

In This Article

Summary

This protocol describes an approach to investigate the causal effects of chronic metabolic traits on small-vessel stroke and to assess the mediating role of depression using Mendelian randomization analysis.

Abstract

Chronic metabolic traits such as body mass index (BMI), obesity, and type 2 diabetes (T2D) are known risk factors for cerebrovascular diseases. Depression, often comorbid with metabolic disorders, may mediate these associations. A two-sample Mendelian randomization (MR) analysis was conducted to examine the causal effects of genetically predicted BMI, obesity, and T2D on small-vessel stroke (SVS) and major depressive disorder (MDD). The relationship between depression and SVS was further assessed, and a two-step MR mediation analysis was applied to explore whether depression mediates the associations between chronic metabolic traits and SVS risk. Higher BMI, obesity, and T2D were significantly associated with increased risk of SVS (BMI: OR = 1.25, p = 1.27 × 10-3; Obesity: OR = 1.09, p = 0.032; T2D: OR = 1.17, p = 1.14 × 10-7). All three traits also elevated risk of MDD (BMI: OR = 1.20, p = 3.31 × 10-10; Obesity: OR = 1.06, p = 0.006; T2D: OR = 1.03, p = 0.035). MDD, in turn, increased the risk of SVS (OR = 1.11, p = 0.035). Mediation analyses indicated that depression showed a potential partial mediating effect on the BMI–SVS association (indirect effect = 0.019, p = 0.046), while mediation via obesity or T2D was non-significant. These results highlight the importance of addressing comorbid depression in individuals with metabolic disorders to reduce cerebrovascular risk.

Introduction

Stroke is one of the leading causes of mortality and disability worldwide, with an estimated 12 million new cases annually and more than 100 million people living with its long-term consequences1,2. With the aging global population and the increasing prevalence of lifestyle-related conditions, the burden of stroke is expected to rise further, leading to substantial health and socioeconomic impacts1,2. In addition to well-established risk factors such as hypertension, diabetes, dyslipidemia, smoking, and obesity, emerging evidence has suggested that psychiatric disorders, particularly major depressive disorder (MDD), may also contribute to stroke risk3,4,5. However, the complex interplay between metabolic diseases, depression, and stroke remains poorly understood.

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to reduce confounding and reverse causation, thereby providing a more robust framework for causal inference compared with conventional observational studies6. Unlike observational designs, which are susceptible to residual confounding, measurement error, and reverse causality, MR leverages the random allocation of genetic variants at conception to approximate a natural experiment. In addition, compared with other causal inference approaches such as regression-based mediation models, MR offers greater robustness to unmeasured confounding, particularly when investigating complex, interrelated traits. Large-scale genome-wide association studies (GWAS) have enabled the application of two-sample MR to investigate causal links between complex traits and diseases at the genetic level. Previous MR studies have demonstrated causal effects of metabolic diseases—such as body mass index (BMI), obesity, and type 2 diabetes (T2D)—on stroke7,8,9. Likewise, genetic evidence supports a bidirectional relationship between depression and stroke10. Yet, whether depression mediates the effect of metabolic diseases on stroke has not been systematically assessed.

Several potential biological mechanisms support a mediating role of depression. Chronic metabolic disorders are known to promote systemic inflammation, endothelial dysfunction and dysregulation of the hypothalamic–pituitary–adrenal axis, which may increase vulnerability to depression11. In turn, depression is associated with behavioral risk factors (e.g., smoking, physical inactivity, poor diet), autonomic imbalance, platelet hyperactivity, and increased cortisol secretion, all of which may elevate stroke risk12,13. Observational studies have attempted to address these pathways, but findings remain inconsistent due to confounding, short follow-up, and measurement error14. Consequently, the causal nature of the metabolic disease–depression–stroke pathway remains unclear. Although MR can strengthen causal inference, it cannot fully elucidate detailed biological mechanisms or directly inform clinical interventions, and its validity depends on key assumptions such as the absence of horizontal pleiotropy.

In this study, two hypotheses were investigated using univariable, multivariable, and two-step two-sample MR analyses. First, the causal associations between metabolic traits (BMI, obesity, and T2D) and the risk of small-vessel stroke (SVS) were examined. Second, the potential mediating role of depression in these associations was assessed, and the proportion mediated was quantified. This analytical framework is particularly suitable for disentangling mediation effects among correlated metabolic and psychiatric traits, as it decomposes total effects into direct and indirect components while minimizing confounding. By leveraging large-scale GWAS summary statistics, this study provides novel genetic evidence to clarify the interplay between metabolic disorders, depression, and SVS. These findings may help to identify biological pathways underlying stroke risk and inform integrated prevention strategies targeting both metabolic health and mental health.

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Protocol

Univariable, multivariable, and two-step two-sample MR mediation analyses were conducted to investigate whether genetically predicted chronic metabolic diseases were causally associated with SVS risk and to assess the proportion of mediation by MDD. The flow chart of MR analyses in this study is shown in Figure 1. Detailed characteristics of all GWAS datasets used in this study are summarized in Supplementary Table 1. GWAS summary statistics were obtained from a publicly available database (IEU OpenGWAS: https://gwas.mrcieu.ac.uk/) using a Mendelian randomization analysis software platform (see Table of Materials) via the extract_instruments() and extract_outcome_data() functions. Exposures included BMI15 (ebi-a-GCST90013974), obesity16 (ieu-a-90), and type 2 diabetes (T2D; ebi-a-GCST006867). The mediator was major depressive disorder (MDD; ieu-a-1188)18, and the outcome was SVS (ebi-a-GCST006909). All GWAS datasets included individuals of predominantly European ancestry to minimize population stratification bias.

GWAS data of chronic metabolic traits

Chronic metabolic diseases were examined as exposures, including BMI, obesity, and type 2 diabetes. GWAS summary statistics for BMI15 (ebi-a-GCST90013974) and obesity16 (ieu-a-90) were obtained from the MRC-IEU and GIANT consortium (European ancestry, up to 407,609 individuals for BMI and 32,858 cases/65,839 controls for obesity). Type 2 diabetes17 data (ebi-a-GCST006867) were derived from the DIAGRAM consortium (European ancestry, 62,892 cases/596,424 controls). These GWAS datasets are publicly available and primarily based on the UK Biobank and other large-scale cohorts.

GWAS data for depression

For the outcome of depression, the latest GWAS summary statistics from the Psychiatric Genomics Consortium (PGC) (ieu-a-1188)18 were used, including 59,851 cases and 113,154 controls of European ancestry. MDD diagnosis in the included cohorts was based on structured clinical interviews or validated self-reports according to DSM-III/IV or ICD-9/10 diagnostic criteria. Individuals with a history of bipolar disorder or schizophrenia were excluded.

GWAS data for SVS

Summary statistics for stroke were obtained from the MRC-IEU consortium (ebi-a-GCST006909)19, which included up to 5,386 cases and 192,662 controls of predominantly European ancestry. SVS was defined based on physician diagnosis and/or clinical records. This dataset has been widely used in MR studies investigating vascular risk factors and is publicly available.

Statistical analysis

All analyses were conducted in R (version 4.1.2) using a Mendelian randomization analysis software platform (see Table of Materials). Two-sample MR was conducted using single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which must satisfy three core assumptions: relevance (associated with the exposure), independence (not associated with confounders), and exclusion restriction (affecting the outcome only through the exposure)20,21,22. SNPs were selected based on genome-wide significance (p < 5 × 10-5), linkage disequilibrium pruning (r2 < 0.001 within a 10,000 kB window), and F-statistics >10 to avoid weak instrument bias20,22. LD clumping was performed using the European reference panel from the 1000 Genomes Project23. Palindromic and ambiguous SNPs were excluded during both data extraction and harmonization procedures. During outcome data extraction via the extract_outcome_data() function, palindromic SNPs were initially retained using standard extraction functions, and allele frequency information was used to infer strand alignment when possible. SNPs with minor allele frequency (MAF) below a predefined threshold (e.g., MAF < 0.3) were retained, as strand orientation could be reliably inferred, whereas those with intermediate allele frequencies were considered ambiguous. During the harmonization step through the harmonise_data() function, palindromic SNPs with ambiguous strand orientation—particularly those with intermediate allele frequencies—were excluded to ensure consistent alignment of effect alleles across exposure, mediator, and outcome datasets. SNP harmonization was conducted via the harmonise_data() function to align effect alleles across datasets. During this process, allele strands were aligned to ensure consistency, and SNPs with incompatible alleles or strand ambiguity were excluded. After filtering, approximately 600–800 SNPs for BMI, 70–90 SNPs for obesity, and 326 SNPs for T2D were retained for downstream analyses (see Table 1 and Table 2).

For univariable MR, the primary method used was inverse-variance weighted(IVW), which estimates the causal effect by combining SNP-specific ratio estimates using inverse-variance weighting under the assumption of no horizontal pleiotropy20. Pleiotropy was assessed using the MR-Egger intercept test implemented via the mr_pleiotropy() function, with P < 0.05 indicating potential horizontal pleiotropy. Heterogeneity was evaluated using Cochran’s Q test, implemented through the mr_heterogeneity() function. If heterogeneity was detected (P < 0.05), a random-effects IVW model was applied; otherwise, a fixed-effects IVW model was used. For multivariable MR analyses, SNPs were combined across exposures and mediators. Effect alleles were harmonized using the harmonise_data() function across exposure, mediator, and outcome datasets, and multivariable IVW analysis was performed as the primary method.

To account for multiple comparisons, false discovery rate (FDR) correction was applied using the Benjamini–Hochberg method via the p.adjust() function in R across the main Mendelian randomization analyses. Specifically, adjustment was performed for the total number of primary causal tests, including the effects of metabolic traits on SVS (n = 3), metabolic traits on depression (n = 3), and depression on SVS (n = 1), resulting in a total of seven tests. Adjusted P-values (FDR q-values) were calculated, and statistical significance was interpreted considering both nominal P-values and FDR-corrected values. Results that remained significant after FDR correction were considered robust, while those with nominal significance only were interpreted cautiously.

Mediation analysis

For mediation analyses, a two-step MR framework was applied to estimate the indirect effects of chronic metabolic traits on SVS through depression. This framework was implemented by performing two sequential MR analyses using GWAS summary statistics. In this framework, E denotes the exposure (BMI, obesity, or T2D), M denotes the mediator (depression), and Y denotes the outcome (SVS). First, the causal effect of each chronic metabolic trait (BMI, obesity, and T2D) on mediator (β (EStatic equilibrium, ΣFx=0, force diagram, educational physics concept, vector analysis, balanceM)) was estimated. Second, the causal effect of mediator on outcome (β (MStatic equilibrium, ΣFx=0, force diagram, educational physics concept, vector analysis, balanceY)) was evaluated. The indirect effect (mediation effect) was calculated by multiplying these two estimates:

Causal pathway equation β(E→M)×β(M→Y) in statistical analysis, path analysis method.

and the proportion mediated was derived by dividing the indirect effect by the total effect

(Proportion mediated = Mediation analysis formula, β indirect over β(E→Y), equation representing effect paths in statistics.).

The standard error (SE) of the indirect effect was estimated using the delta method, assuming independence between the two effect estimates:

SE indirect = Mediation analysis formula with standard error terms; statistical method for causal pathway analysis.

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Results

Causal effects of chronic metabolic traits on SVS

In the univariable MR analyses, genetically predicted higher BMI, obesity, and T2D were all positively associated with an increased risk of SVS. The associations were consistent across sensitivity analyses, with no evidence of substantial horizontal pleiotropy from the MR-Egger intercept test. Genetically predicted higher BMI was significantly associated with an increased risk of SVS, showing 25.0% higher odds (OR = 1.250, 95% ...

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Discussion

In this MR study, the causal associations between major chronic metabolic traits (BMI, obesity, and T2D), depression, and SVS were systematically investigated. The study highlights three key findings. First, genetically predicted higher BMI, obesity, and T2D were causally associated with an increased risk of SVS. Second, these metabolic traits were also associated with an elevated risk of depression, while depression itself showed a causal relationship with SVS. Third, mediation analyses suggested that depression partly ...

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Disclosures

The authors declare that they have no competing interests.

Acknowledgements

This work was supported by the Science and Technology Program of Jiaxing (2023AD11020, 2023AD11019, 2022AY30029, 2023AD31033) and the Medical and Health Science and Technology Program of Zhejiang (2024KY1689).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
R software (version 4.1.2)The R Foundationhttps://www.r-project.org/Statistical computing environment
TwoSampleMR package (version 0.6.22)IEU OpenGWAShttps://mrcieu.github.io/TwoSampleMR/index.htmlMendelian randomization analysis software platform

References

  1. GBD 2021 Stroke Risk Factor Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2021: A systematic analysis for the global burden of disease study 2021. Lancet Neurol. 23 (10), 973-1003 (2024).
  2. Wang, C. H., Xin, Z. K. Causal association between 25-hydroxyvitamin d status and cataract development: A two-sample mendelian randomization study. World J Clin Cases. 12 (16), 2789-2795 (2024).
  3. Cai, H., et al. Major depression and small vessel stroke: A mendelian randomization analysis. J Neurol. 266 (11), 2859-2866 (2019).
  4. Harshfield, E. L., et al. Association between depressive symptoms and incident cardiovascular diseases. JAMA. 324 (23), 2396-2405 (2020).
  5. Penninx, B. W., Milaneschi, Y., Lamers, F., Vogelzangs, N. Understanding the somatic consequences of depression: Biological mechanisms and the role of depression symptom profile. BMC Med. 11, 129(2013).
  6. Davey Smith, G., Hemani, G. Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 23 (R1), R89-R98 (2014).
  7. Georgakis, M. K., et al. Diabetes mellitus, glycemic traits, and cerebrovascular disease: A mendelian randomization study. Neurology. 96 (13), e1732-e1742 (2021).
  8. Larsson, S. C., Bäck, M., Rees, J. M. B., Mason, A. M., Burgess, S. Body mass index and body composition in relation to 14 cardiovascular conditions in uk biobank: A mendelian randomization study. Eur Heart J. 41 (2), 221-226 (2020).
  9. Marini, S., et al. Mendelian randomization study of obesity and cerebrovascular disease. Ann Neurol. 87 (4), 516-524 (2020).
  10. Li, G. H., et al. Evaluation of bi-directional causal association between depression and cardiovascular diseases: A mendelian randomization study. Psychol Med. 52 (9), 1765-1776 (2022).
  11. Calagua-Bedoya, E. A., Rajasekaran, V., De Witte, L., Perez-Rodriguez, M. M. The role of inflammation in depression and beyond: A primer for clinicians. Curr Psychiatry Rep. 26 (10), 514-529 (2024).
  12. Hare, D. L., Toukhsati, S. R., Johansson, P., Jaarsma, T. Depression and cardiovascular disease: A clinical review. Eur Heart J. 35 (21), 1365-1372 (2014).
  13. Krittanawong, C., et al. Association of depression and cardiovascular disease. Am J Med. 136 (9), 881-895 (2023).
  14. Osborne, M. T., et al. Disentangling the links between psychosocial stress and cardiovascular disease. Circ Cardiovasc Imaging. 13 (8), e010931(2020).
  15. Mbatchou, J., et al. Computationally efficient whole-genome regression for quantitative and binary traits. Nat Genet. 53 (7), 1097-1103 (2021).
  16. Berndt, S. I., et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nat Genet. 45 (5), 501-512 (2013).
  17. Xue, A., et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun. 9 (1), 2941(2018).
  18. Wray, N. R., et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 50 (5), 668-681 (2018).
  19. Malik, R., et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 50 (4), 524-537 (2018).
  20. Burgess, S., Butterworth, A., Thompson, S. G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 37 (7), 658-665 (2013).
  21. Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N., Davey Smith, G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med. 27 (8), 1133-1163 (2008).
  22. Pierce, B. L., Ahsan, H., Vanderweele, T. J. Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 40 (3), 740-752 (2011).
  23. Auton, A., et al. A global reference for human genetic variation. Nature. 526 (7571), 68-74 (2015).
  24. Bradley, S. A., et al. Role of diabetes in stroke: Recent advances in pathophysiology and clinical management. Diabetes Metab Res Rev. 38 (2), e3495(2022).
  25. Lau, L. H., Lew, J., Borschmann, K., Thijs, V., Ekinci, E. I. Prevalence of diabetes and its effects on stroke outcomes: A meta-analysis and literature review. J Diabetes Investig. 10 (3), 780-792 (2019).
  26. Lee, S. H., Jung, J. M., Park, M. H. Obesity paradox and stroke outcomes according to stroke subtype: A propensity score-matched analysis. Int J Obes (Lond). 47 (8), 669-676 (2023).
  27. Ye, J., et al. Association between the weight-adjusted waist index and stroke: A cross-sectional study. BMC Public Health. 23 (1), 1689(2023).
  28. Chehaibi, K., Hrira, M. Y., Trabelsi, I., Escolà-Gil, J. C., Slimane, M. N. Gene variant and level of il-1β in ischemic stroke patients with and without type 2 diabetes mellitus. J Mol Neurosci. 57 (3), 404-409 (2015).
  29. Chen, R., Yan, J., Liu, P., Wang, Z., Wang, C. Plasminogen activator inhibitor links obesity and thrombotic cerebrovascular diseases: The roles of pai-1 and obesity on stroke. Metab Brain Dis. 32 (3), 667-673 (2017).
  30. Letra, L., Sena, C. Cerebrovascular disease: Consequences of obesity-induced endothelial dysfunction. Adv Neurobiol. 19, 163-189 (2017).
  31. Campayo, A., Gómez-Biel, C. H., Lobo, A. Diabetes and depression. Curr Psychiatry Rep. 13 (1), 26-30 (2011).
  32. Milaneschi, Y., Simmons, W. K., Van Rossum, E. F. C., Penninx, B. W. Depression and obesity: Evidence of shared biological mechanisms. Mol Psychiatry. 24 (1), 18-33 (2019).
  33. Roy, T., Lloyd, C. E. Epidemiology of depression and diabetes: A systematic review. J Affect Disord. 142, S8-S21 (2012).
  34. Li, P., et al. Pramipexole improves depression-like behavior in diabetes mellitus with depression rats by inhibiting nlrp3 inflammasome-mediated neuroinflammation and preventing impaired neuroplasticity. J Affect Disord. 356, 586-596 (2024).
  35. Miller, A. H., Raison, C. L. The role of inflammation in depression: From evolutionary imperative to modern treatment target. Nat Rev Immunol. 16 (1), 22-34 (2016).
  36. Pan, A., Sun, Q., Okereke, O. I., Rexrode, K. M., Hu, F. B. Depression and risk of stroke morbidity and mortality: A meta-analysis and systematic review. JAMA. 306 (11), 1241-1249 (2011).
  37. Penninx, B. W. Depression and cardiovascular disease: Epidemiological evidence on their linking mechanisms. Neurosci Biobehav Rev. 74 (Pt B), 277-286 (2017).
  38. Whooley, M. A., Wong, J. M. Depression and cardiovascular disorders. Annu Rev Clin Psychol. 9, 327-354 (2013).
  39. Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., Thompson, S. G. Using published data in mendelian randomization: A blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 30 (7), 543-552 (2015).
  40. Holmes, M. V., Ala-Korpela, M., Smith, G. D. Mendelian randomization in cardiometabolic disease: Challenges in evaluating causality. Nat Rev Cardiol. 14 (10), 577-590 (2017).

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Small Vessel StrokeMajor Depressive DisorderType 2 DiabetesBody Mass IndexObesity RiskDepression MediationCerebrovascular Disease
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