Topological Data Analysis Communities Reveal Gene-Environment-Brain Subtypes of Major Depression in UK Biobank and...

Major depressive disorder (MDD) exhibits substantial clinical heterogeneity, which complicates prognosis and treatment. Characterizing MDD subtypes could enhance personalized approaches. We developed a topological data analysis (TDA) framework with graph-based community detection to identify patient subgroups using multimodal data.

We implemented a TDA pipeline in participants with MDD from the UK Biobank, with genetic-environmental (G-E, 20,715 subjects) and genetic-environmental-neuroimaging (G-E-I, 3044 subjects) data. We systematically compared genetic, environmental, and neuroimaging features, alone and combined, to stratify individuals with MDD across 18 health-related outcomes. For each outcome's best-performing feature set, a novel feature-ranking approach identified features driving graph construction and community-based outcome differentiation. Cross-cohort validation through selective, heterogeneous replication utilized 2 independent datasets: the GSRD (European Group for the Study of Resistant Depression) (G-E data, 1017 subjects) and Hospital San Raffaele (HSR) (G-E and imaging data, 71-87 subjects).

The G-E combination demonstrated superior stratification performance for 13 outcomes, including treatment-resistant depression (TRD), symptom subtypes, and suicidal phenotypes. Community profiling revealed distinct patterns: trauma-stress exposures were linked to TRD and episode severity, whereas substance-behavioral profiles were associated with anxious symptoms. Environmental factors primarily determined most health-related outcomes, whereas neuroimaging features best discriminated medical comorbidities. Partial replication was observed for G-E sets in the GSRD (self-harm behavior, anxious features) and preliminary imaging-based replication in the HSR (vascular problems), with limited statistical power for imaging analyses. Environmental stress-related top-ranked features were consistent across cohorts.

TDA successfully identified relevant MDD subgroups with domain-specific multimodal contributions. These findings underscore the value of multimodal integration for comprehensive health-related outcome stratification, with modalities contributing selectively to specific outcome domains. TDA-based community detection is a promising framework for MDD stratification and precision medicine.