School of Medicine Publications and Presentations
Document Type
Article
Publication Date
8-21-2024
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.
Recommended Citation
Gezsi, A., Van der Auwera, S., Mäkinen, H., Eszlari, N., Hullam, G., Nagy, T., ... & Juhasz, G. (2024). Unique genetic and risk-factor profiles in clusters of major depressive disorder-related multimorbidity trajectories. Nature Communications, 15(1), 7190. https://doi.org/10.1038/s41467-024-51467-7
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Nature Communications
DOI
https://doi.org/10.1038/s41467-024-51467-7
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics
Comments
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