Immunometabolic Pathways: Investigating Mediators of Major Depressive Disorder and Atherosclerotic Cardiovascular Disease Comorbidity

Background: Major depressive disorder (MDD) and cardiovascular diseases (CVDs) often co-occur whereby comorbidity results in poorer clinical outcomes, presumably due to shared immunometabolic pathways. Identifying shared biomarkers for MDD-CVD comorbidity may provide targets for prevention or treatment. Methods: Using data from the NESDA (Netherlands Study of Depression and Anxiety) (n = 2256, 66.3% female, mean age 41.86 ± 13.08 years at baseline), validated with the UK Biobank (UKB) data (n = 35,668, 56.14% female, mean age 63.95 ± 7.74 years), this study aimed to identify 1) biomarkers, closely associated with current MDD, and 2) longitudinal pathways linking MDD and atherosclerotic CVD. Plasma metabolites (nuclear magnetic resonance) and inflammatory markers were used as exposures within a machine learning framework. Influential biomarkers were integrated into a temporal network analysis linking MDD to subsequent CVDs, exploring longitudinal pathways through causal discovery, validated by sensitivity analysis and centrality assessment. External validation included mediation and regression analysis adjusting for covariates. Results: Network analysis identified stable direct paths from MDD to CVDs via tumor necrosis factor α (TNF-α), tyrosine, and fatty acids and indirect paths via acetate, high-density lipoprotein (HDL) diameter, interleukin 6, AGP, high-sensitivity C-reactive protein, and low-density lipoprotein triglycerides. Among these, acetate, tyrosine, AGP (α₁-acid glycoprotein), and HDL diameter potentially mediated the MDD-CVD connection, given that these were identified as key nodes within the network. UKB validation confirmed HDL diameter (β = 0.004) and AGP (β = 0.003) as significant depression-CVD mediators (both p < .001), after adjusting for age, sex, deprivation index, alcohol consumption, smoking status, physical activity, and body mass index. Conclusions: These analyses identified biomarkers shared in MDD and CVDs and may drive comorbid pathology risk.