Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories

Andras Gezsi, Sandra Van der Auwera, Hannu Mäkinen, Nora Eszlari, Gabor Hullam, Tamas Nagy, Sarah Bonk, Rubèn González-Colom, Xenia Gonda, Linda Garvert, Teemu Paajanen, Zsofia Gal, Kevin Kirchner, Andras Millinghoffer, Carsten O. Schmidt, Bence Bolgar, Josep Roca, Isaac Cano, Mikko Kuokkanen, Peter AntalGabriella Juhasz

Research output: Contribution to journalArticleScientificpeer-review

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 non-genetic 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 cohorts in the UK, Finland, and Spain. 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 non-genetic risk-factor profiles of patients.

Original languageEnglish
Article number7190
JournalNature Communications
Volume15
Issue number1
Number of pages18
ISSN2041-1723
DOIs
Publication statusPublished - Dec 2024
MoE publication typeA1 Journal article-refereed

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

Fields of Science

  • 116 Chemical sciences

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