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Gene–environment–brain topology reveals clinical subtypes of depression in UK Biobank

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Název: Gene–environment–brain topology reveals clinical subtypes of depression in UK Biobank
Autoři: Emma Tassi, Alessandro Pigoni, Nunzio Turtulici, Federica Colombo, Lidia Fortaner-Uyà, Anna Maria Bianchi, Francesco Benedetti, Chiara Fabbri, Benedetta Vai, Paolo Brambilla, Eleonora Maggioni
Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-20 (2025)
Informace o vydavateli: Nature Portfolio, 2025.
Rok vydání: 2025
Sbírka: LCC:Medicine
LCC:Science
Témata: Major depressive disorder, Genetic, Environment, Brain imaging, Topological data analysis, Medicine, Science
Popis: Abstract Major depressive disorder (MDD) is a leading cause of disability worldwide, affecting over 300 million people and posing a significant burden on healthcare systems. The heterogeneity of MDD can be attributed to diverse etiologic mechanisms. Characterizing MDD subtypes with distinct clinical manifestations could improve patient care through targeted personalized interventions. Topological Data Analysis (TDA) has emerged as a promising tool for identifying homogeneous subgroups of diverse medical conditions and key disease markers. Our study applied TDA to data from a UK Biobank MDD subcohort comprising 3052 samples, leveraging genetic, environmental, and neuroimaging data to assess their differential capability in predicting clinical outcomes in MDD. TDA graphs were built from unimodal and multimodal feature sets and quantitatively compared based on their capability to predict depression severity, physical comorbidities, and treatment response outcomes. Our findings showed a key role of the environment in determining the severity of depressive symptoms. Comorbid medical conditions of MDD were best predicted by brain imaging characteristics, while brain functional patterns resulted in the best predictors of the treatment response profiles. Our results suggest that considering genetic, environmental, and brain characteristics is essential to characterize the heterogeneity of MDD, providing avenues for the definition of robust markers of health outcomes in MDD.
Druh dokumentu: article
Popis souboru: electronic resource
Jazyk: English
ISSN: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-025-19624-0
Přístupová URL adresa: https://doaj.org/article/57d6f28b613f454e9304cf6a9bbae839
Přístupové číslo: edsdoj.57d6f28b613f454e9304cf6a9bbae839
Databáze: Directory of Open Access Journals
Popis
Abstrakt:Abstract Major depressive disorder (MDD) is a leading cause of disability worldwide, affecting over 300 million people and posing a significant burden on healthcare systems. The heterogeneity of MDD can be attributed to diverse etiologic mechanisms. Characterizing MDD subtypes with distinct clinical manifestations could improve patient care through targeted personalized interventions. Topological Data Analysis (TDA) has emerged as a promising tool for identifying homogeneous subgroups of diverse medical conditions and key disease markers. Our study applied TDA to data from a UK Biobank MDD subcohort comprising 3052 samples, leveraging genetic, environmental, and neuroimaging data to assess their differential capability in predicting clinical outcomes in MDD. TDA graphs were built from unimodal and multimodal feature sets and quantitatively compared based on their capability to predict depression severity, physical comorbidities, and treatment response outcomes. Our findings showed a key role of the environment in determining the severity of depressive symptoms. Comorbid medical conditions of MDD were best predicted by brain imaging characteristics, while brain functional patterns resulted in the best predictors of the treatment response profiles. Our results suggest that considering genetic, environmental, and brain characteristics is essential to characterize the heterogeneity of MDD, providing avenues for the definition of robust markers of health outcomes in MDD.
ISSN:20452322
DOI:10.1038/s41598-025-19624-0