Abstract
Depression is clinically and biologically heterogeneous, mandating classification strategies for personalized medicine. This study explored depression subtypes using metabolomics data from the UK Biobank and validated the subtypes in the Whitehall II cohort. The five-step analysis included: (1) identification of distinct subtypes using non-negative matrix factorization (NMF) and four machine learning algorithms; (2) genome-wide association studies (GWAS) to examine associations across subtypes and controls; (3) comparison of clinical characteristics across subtypes; (4) development of 24 subtype-specific diagnostic models and validation in an independent cohort; and (5) construction and comparison of metabolic networks across subtypes. Cluster analysis of 249 metabolomic indicators in individuals with current depressive episodes (n = 7,945) identified three metabolic subtypes of depression. Subtype 1 was characterized by fatty acid dysregulation, subtype 3 had a hyperlipidemia phenotype, while subtype 2 displayed an intermediate phenotype. Metabolic subtypes were not associated with SNPs. Diagnostic models built using the 249 metabolic indicators yielded the area under the curve (AUC) of 0.644 for the total depression sample and 0.785, 0.817, and 0.942 for subtypes 1, 2, and 3, respectively. Twenty-three additional diagnostic models based on combinations of metabolic indicators improved performance by 12.8-39.6% over a binary classification model. Metabolic networks significantly differed between each subtype and healthy controls but not between the total depressed group and controls. This study defines distinct metabolic subtypes of depression. Future research should combine high-throughput metabolomics with prospectively established depression cohorts and tailored interventions to explore subtype-specific diagnostic and therapeutic biomarkers.