Biological psychiatry · 2026
Topological Data Analysis Communities Reveal Gene-Environment-Brain Subtypes of Major Depression in UK Biobank and Multisite Cohorts.
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Tassi Emma, Pigoni Alessandro, Colombo Federica, Fortaner-Uyà Lidia, Colombo Cristina, Bianchi Anna Maria, Benedetti Francesco, Fabbri Chiara, Serretti Alessandro, GSRD Network, Vai Benedetta, Brambilla Paolo, Maggioni Eleonora
Аннотация
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.