Fig. 1: Overview of the main methods.

A Rationale and hypothesis of the study. Accumulating evidence suggested that MDD is frequently comorbid not only with other psychiatric disorders but also with several somatic diseases contributing to worse health-related outcomes and decreasing quality of life^68,69,70,71. Thanks to network medicine and system biology approaches, it has been demonstrated that comorbid conditions partially represent common biological mechanisms^72,73,74,75. Furthermore, directly related comorbidities of depression, where the relationships are not mediated by other disorders, represent stronger molecular-level relationships¹⁵ and are time-dependent (i.e., vary with onset age⁷⁶). Finally, a recent comorbidity mapping study of asthma supported that comorbidities are indeed suitable to delineate distinct subgroups of complex multifactorial disorders⁷⁷. B The cohort-specific datasets contain the onset ages of diseases in three-character ICD-10 categories. Data were collected from the participants over various periods, depicted by the length of the grey lines, with disease onsets marked by an ‘x’. Participant trajectories were discretized into cumulative time intervals, as shown at the bottom of the figure. C The structure of the inhomogeneous dynamic Bayesian network used. The boxes correspond to intervals, the nodes in the boxes correspond to diseases, and the solid and dashed edges indicate direct relations between the diseases. This method determined the strongly relevant MDD-related multimorbidities; these nodes are in the Markov boundary of the target variable, indicated by the grey-shaded region and a thick black node border. Genetic and other non-genetic variables also influenced the onset of the diseases (dotted edges). One aim of the study was to identify pleiotropic genetic variants (edges with α) that influence the onset of MDD and its related multimorbidities. These variants confound the direct relationship (edge β) between MDD and its strongly relevant comorbid conditions. D Overview of the study pipeline. We determined MDD-related cross-cohort clusters of all participants in the UKB, CHSS, and THL cohorts by utilizing the temporal trajectories of the participants’ MDD-related multimorbidity burden. The seven identified clusters were then characterized based on disease and non-genetic risk-factor profiles and genetic contributions, and the findings were validated in the two independent cohorts (the FinnGen and SHIP cohorts).