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

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.