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Publications

Journal Article Ahead of print

PubMed: 42431439 DOI: 10.1016/j.biopsych.2026.06.030

Topological data analysis communities reveal gene-environment-brain subtypes of major depression in UK Biobank and multi-site cohorts.

Biol Psychiatry

Authors: Emma Tassi, Alessandro Pigoni, Federica Colombo, Lidia Fortaner-Uyà, Cristina Colombo, Anna Maria Bianchi, Francesco Benedetti, Chiara Fabbri, Alessandro Serretti, Benedetta Vai, Paolo Brambilla, Eleonora Maggioni

Affiliations

  • Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
  • Division of Neuroscience, Unit of Psychiatry and Clinical Psychobiology, IRCCS Ospedale San Raffaele, Milan, Italy.
  • Division of Neuroscience, Unit of Psychiatry and Clinical Psychobiology, IRCCS Ospedale San Raffaele, Milan, Italy; University Vita-Salute San Raffaele, Milano, Italy.
  • Mood Disorder Unit, IRCCS Ospedale San Raffaele, Milan, Italy; University Vita-Salute San Raffaele, Milano, Italy.
  • Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
  • Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy.
  • Department of Medicine and Surgery, Kore University of Enna, Enna, Italy; Oasi Research Institute-IRCCS, Troina, Italy.
  • Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.

Abstract

BACKGROUND

Major depressive disorder (MDD) exhibits substantial clinical heterogeneity complicating 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 MDD UK Biobank participants with gene-environment (G-E, N=20,715) and gene-environment-neuroimaging (G-E-I, N=3,044) data. We systematically compared genetic, environmental, and neuroimaging features, alone and combined, to stratify MDD individuals 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 two independent datasets: GSRD (G-E data, N=1,017) and HSR (G-E and imaging data, N=71-87).

RESULTS

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 linked to TRD and episode severity, while substance-behavioral profiles associated with anxious symptoms. Environmental factors primarily determined most health-related outcomes, whereas neuroimaging features best discriminate medical comorbidities. Partial replication was observed for gene-environment sets in GSRD (self-harm behavior, anxious features) and preliminary imaging-based replication in 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.

MeSH

Depression, Genes, Neuroimaging, Stress, Physiological, Brain, Multiple Chronic Conditions, Therapeutics, Environment, Data Analysis, Psychiatry