Longitudinal associations between inflammatory biomarkers, polygenic risk scores and cardiometabolic outcomes in major depressive and anxiety disorders: Findings from the Netherlands Study of Depression and Anxiety (NESDA).

Abstract

Cardiometabolic dysfunctions are prevalent in individuals with major depressive (MDD) and/or anxiety disorders, yet the longitudinal relationships between inflammation, polygenic susceptibility, and cardiometabolic outcomes remain incompletely understood. We analyzed longitudinal data from 2,716 participants retrieved from the Netherlands Study of Depression and Anxiety (NESDA) across three measurements over six years. Longitudinal associations between inflammatory biomarkers (CRP, IL-6, and TNF-α), polygenic risk scores (PRS), and ten cardiometabolic outcomes were assessed using linear mixed-effects models. Between-group differences were tested via current diagnostic subgroup interaction terms while adjusting for common covariates. Population-level associations were evaluated in the full sample, and PRS-related variance explained was quantified using changes in marginal R2. No interactions between inflammatory biomarkers or PRSes and current MDD and/or anxiety diagnosis survived FDR correction, although several nominally significant interactions were observed. In the full sample, higher CRP, IL-6, and TNF-α levels were consistently associated with higher BMI, waist circumference, and triglyceride levels, and lower HDL cholesterol, while CRP and IL-6 were additionally associated with blood pressure and glucose-related outcomes. PRSCRP was associated with multiple cardiometabolic outcomes, explaining an additional 0.16 % of the variance in systolic blood pressure to 0.72 % in BMI. All eight cardiometabolic PRSes showed positive associations with their corresponding outcomes and explained up to 12.88 % of the variance in LDL cholesterol. Overall, inflammatory biomarkers and polygenic susceptibility to inflammatory and cardiometabolic traits are associated with cardiometabolic outcomes. These findings may help inform future mechanistic and causal studies aimed at improving cardiometabolic risk assessment in people with mental health disorders.