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9 reviewsDepression is strongly associated with a range of subsequent diseases. To elucidate key mechanistic pathways for targetedinterventions, this study aimed to determine the main disease networks associated with depression as well as their underlyinggenetic determinants. We developed a novel three-dimensional network approach which refines disease association verification byincorporating regularized partial correlations, and facilitates robust identification and visualization of disease clusters (i.e., groups ofdepression-associated diseases with high within-group connectivity) through both non-temporal (illustrating by x-axis and y-axis)1234567890();,:and temporal (by z-axis) dimensions. We applied this approach to a matched cohort of 54,284 middle aged patients diagnosed withdepression and their 496,005 age- and sex-matched unexposed individuals from the Swedish national registers and validated ourfindings in a cohort from the UK Biobank. Additionally, we conducted genetic analyses, including polygenic risk score (PRS) andgenome-wide association studies (GWAS), using genetic data from 10,754 depression patients in the UK Biobank. Our analysis ofthe Swedish cohort identified nine reliable disease clusters consisting of 85 component diseases associated with depression, ofwhich six clusters with 30 diseases were successfully validated using the UK Biobank cohort. These were clusters characterized bycentral 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 todepression and the susceptibility for subsequent disease clusters, while GWAS identified eight genome-wide significant loci in fourof the clusters. Overall, our novel three-dimensional disease network approach identified six robust disease clusters after depressionacross