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

Andras Gezsi, Sandra 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 Antal and Gabriella Juhasz ()
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Andras Gezsi: Budapest University of Technology and Economics
Sandra Auwera: University Medicine Greifswald
Hannu Mäkinen: Finnish Institute for Health and Welfare
Nora Eszlari: Semmelweis University
Gabor Hullam: Budapest University of Technology and Economics
Tamas Nagy: Budapest University of Technology and Economics
Sarah Bonk: University Medicine Greifswald
Rubèn González-Colom: Universitat de Barcelona
Xenia Gonda: Semmelweis University
Linda Garvert: University Medicine Greifswald
Teemu Paajanen: Finnish Institute for Health and Welfare
Zsofia Gal: Semmelweis University
Kevin Kirchner: University Medicine Greifswald
Andras Millinghoffer: Abiomics Europe Ltd.
Carsten O. Schmidt: University Medicine Greifswald
Bence Bolgar: Budapest University of Technology and Economics
Josep Roca: Universitat de Barcelona
Isaac Cano: Universitat de Barcelona
Mikko Kuokkanen: Finnish Institute for Health and Welfare
Peter Antal: Budapest University of Technology and Economics
Gabriella Juhasz: Semmelweis University

Nature Communications, 2024, vol. 15, issue 1, 1-18

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-51467-7

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