Neuroanatomical dimensions in major depression linked to cognition, adverse life events, self-harm, metabolomics and genetics

Journal article

Background

Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone.

Methods

Using the semi-supervised machine learning algorithm, Heterogeneity through Discriminative Analysis (HYDRA), we had identified two neuroanatomical dimensions in deeply phenotyped (i.e., comprehensively assessed across neuroimaging, clinical, and behavioural domains), medication-free participants with MDD from the COORDINATE-MDD consortium. In the present study, we apply this pre-trained HYDRA model to the UK Biobank (UKB) to validate these dimensions in a large general population and a subsample with current depressive symptoms.

Results

Dimension 2 (D2), compared to Dimension 1 (D1), is characterized by reduced grey and white matter volumes and limited treatment response to antidepressant and placebo medications. Out-of-sample validation in the UKB general population (n = 37,235) confirms these neuroanatomical features and reveals D2 associations with cognitive impairments, adverse life events, self-harm and suicide attempts, a pro-atherogenic lipid profile, and genetic links to neurodegenerative traits. Similar profiles are observed in the UKB subsample with current depressive symptoms (n = 1455).

Conclusions

D1 and D2 represent distinct neurobiological mechanisms underlying MDD. The validation in a general population-based cohort and in a cohort sample with depressive symptoms delineates mechanisms underlying heterogeneity in MDD.

Authors: Xiao, W; Woodham, RD; Cui, Y; Wen, J; Antoniades, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: communications medicine
• Publication date: 2025-11-15
• Acceptance date: 2025-10-23
• DOI: 10.1038/s43856-025-01219-5
• EISSN: 2730-664X
• ISSN: 2730-664X
• Language: English
• Keywords: Depression (economics); Genome-wide association study; Adverse effect; Metabolomics; Genomics; Disease