Disease clusters and their genetic determinants following a diagnosis of depression: analyses based on a novel...

Depression is strongly associated with a range of subsequent diseases. To elucidate key mechanistic pathways for targeted interventions, this study aimed to determine the main disease networks associated with depression as well as their underlying genetic determinants. We developed a novel three-dimensional network approach which refines disease association verification by incorporating regularized partial correlations, and facilitates robust identification and visualization of disease clusters (i.e., groups of depression-associated diseases with high within-group connectivity) through both non-temporal (illustrating by x-axis and y-axis) and temporal (by z-axis) dimensions. We applied this approach to a matched cohort of 54,284 middle aged patients diagnosed with depression and their 496,005 age- and sex-matched unexposed individuals from the Swedish national registers and validated our findings in a cohort from the UK Biobank. Additionally, we conducted genetic analyses, including polygenic risk score (PRS) and genome-wide association studies (GWAS), using genetic data from 10,754 depression patients in the UK Biobank. Our analysis of the Swedish cohort identified nine reliable disease clusters consisting of 85 component diseases associated with depression, of which six clusters with 30 diseases were successfully validated using the UK Biobank cohort. These were clusters characterized by central nervous system (CNS) diseases, respiratory system diseases, cardiovascular and metabolic diseases, gastrointestinal diseases, musculoskeletal diseases, and mental disorders. PRS analysis revealed a dose-response relationship between genetic liability to depression and the susceptibility for subsequent disease clusters, while GWAS identified eight genome-wide significant loci in four of the clusters. Overall, our novel three-dimensional disease network approach identified six robust disease clusters after depression across two large cohorts, each with shared and cluster-specific genetic underpinnings. These findings warrant further research on genetic-based risk prediction and the development of therapeutic interventions aimed at health improvement for patients with depression.