Unique genetic and risk-factor profiles in multimorbidity clusters of depression-related disease trajectories from a study of 1.2 million subjects

Abstract

The heterogeneity and complexity of symptom presentation, comorbidities and genetic factors pose challenges to the identification of biological mechanisms underlying complex diseases. Current approaches used to identify biological subtypes of major depressive disorder (MDD) mainly focus on clinical characteristics that cannot be linked to specific biological models. Here, we examined multimorbidities to identify MDD subtypes with distinct genetic and environmental factors. We leveraged dynamic Bayesian network approaches to determine a minimal set of multimorbidities relevant to MDD and identified seven clusters of disease-burden trajectories throughout the lifespan among 1.2 million participants from transnational cohorts. The clusters had clear protective- and risk-factor profiles as well as age-specific clinical courses mainly driven by inflammatory processes, and a comprehensive map of heritability and genetic correlations among these clusters was revealed. Our results can guide the development of personalized treatments for MDD based on the unique genetic, clinical and modifiable risk-factor profiles of patients.

Multicentric validation of a Multimorbidity Adjusted Disability Score to stratify depression-related risks using temporal disease maps

“…The findings presented in this study are underpinned by the complementary studies conducted within the TRAJECTOME project 22 that have established a better understanding of the complex multimorbidity landscape associated with MDD across an individual’s lifespan, encompassing both modifiable and genetic risk factors.…”

“…BDMM analysis resulted in an inhomogeneous dynamic Bayesian network, which was utilised to compute temporal PR, ranging from 0 (no association) to 1 (strong association), for MDD in conjunction with sex, socio- economic status, and the set of 86 predetermined consensual diseases 22 . To construct the trajectories, the PR was calculated in four different age ranges: 0-20, 0-40, 0-60, and 0-70 years of age.…”

“…The PR calculated and utilized for MADS computation are reported in the Supplementary material – Appendix 4 . Further details regarding the core analysis conducted in TRAJECTOME can be found in 22 .…”

“…The current observational retrospective multicentric cohort study employed BDMMs to investigate temporal disease maps among MDD and highly prevalent disease conditions 22 in the context of the ERAPERMED EU project TRAJECTOME 23 . The study combined the results of the temporal disease maps identified in TRAJECTOME and the disability weights (DW) 24 documented in the 2019 revision of the Global Burden of Diseases study (GBD), to develop and validate a Multimorbidity Adjusted Disability Score (MADS).…”

Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study

“…On the basis of the temporal disease maps among MDD and highly prevalent disease conditions [ 30 ] generated using Bayesian direct multimorbidity maps (BDMMs) [ 27 , 28 ], a promising method for filtering indirect disease associations in the context of the European Research Area on Personalized Medicine project “Temporal disease map based stratification of depression-related multimorbidities: towards quantitative investigations of patient trajectories and predictions of multi-target drug candidates” (TRAJECTOME) [ 31 ], we combined the probabilities of relevance (PRs) among MDD and its comorbid conditions with the disability weights (DWs) [ 32 ], documented in the 2019 revision of the Global Burden of Disease (GBD) study, to compute the MADS. We used the MADS to generate a risk pyramid and stratify the study population into 5 risk groups using different percentiles of MADS distribution.…”