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

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...

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Bibliographic Details
Published in:Nature communications Vol. 15; no. 1; pp. 7190 - 18
Main Authors: Gezsi, Andras, Van der Auwera, Sandra, Mäkinen, Hannu, Eszlari, Nora, Hullam, Gabor, Nagy, Tamas, Bonk, Sarah, González-Colom, Rubèn, Gonda, Xenia, Garvert, Linda, Paajanen, Teemu, Gal, Zsofia, Kirchner, Kevin, Millinghoffer, Andras, Schmidt, Carsten O., Bolgar, Bence, Roca, Josep, Cano, Isaac, Kuokkanen, Mikko, Antal, Peter, Juhasz, Gabriella
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 21.08.2024
Nature Publishing Group
Nature Portfolio
Subjects:
45
45/43
631/114/1314
631/208/205/2138
692/308/2056
692/699/476/1414
692/700/478/174
Adult
Aged
Bayes Theorem
Bayesian analysis
Clusters
Comorbidity
Complexity
Depressive Disorder, Major - epidemiology
Depressive Disorder, Major - genetics
Female
Finland - epidemiology
Genetic factors
Genetic Predisposition to Disease
Heritability
Heterogeneity
Humanities and Social Sciences
Humans
Life span
Male
Mathematical models
Mental depression
Middle Aged
multidisciplinary
Multimorbidity
Risk Factors
Science
Science (multidisciplinary)
Spain - epidemiology
United Kingdom - epidemiology
Young Adult
United Kingdom
Finland
Spain
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Description
Summary: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. Major depressive disorder is a heterogeneous condition with varied presentation of symptoms, comorbidities, and related genetic factors. This study aimed to identify clusters of major depressive disorder-related longitudinal multimorbidity trajectories and to characterize the clusters using genetic and risk factor data.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-024-51467-7