Untargeted Urinary Metabolomic Profiling Reveals Metabolic Signatures Associated with Anhedonia Severity-Stratified Subgroups in Major Depressive Disorder.

Abstract

Objective: To investigate urinary metabolite differences between subgroups stratified by anhedonia severity in patients with major depressive disorder (MDD) and identify potential biomarkers associated with anhedonia using untargeted metabolomic profiling.

Methods: Fifty-five MDD patients were recruited and stratified by median Snaith-Hamilton Pleasure Scale (SHAPS) total score into Low-SHAPS (n=32) and High-SHAPS (n=23) groups, along with 35 healthy controls (HC). Urinary metabolomic profiling was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differential metabolites were identified using one-way ANOVA for multi-group comparisons and independent-sample t-tests for pairwise comparisons, followed by multivariate statistical analyses and pathway enrichment analysis.

Results: Three-group ANOVA identified 706 nominally differential metabolites (P < 0.05), of which 115 reached P < 0.01. The contrast between Low-SHAPS and High-SHAPS groups showed the most pronounced differences, with 1608 metabolites reaching nominal significance (P < 0.05). OPLS-DA models demonstrated significant discriminative ability across all pairwise comparisons after permutation testing (Low vs High: cross-validated AUC = 0.726, P = 0.010; Low vs HC: cross-validated AUC = 0.886, P = 0.010; High vs HC: cross-validated AUC = 0.901, P = 0.010). Pathway enrichment analysis revealed significant alterations in tryptophan metabolism and phenylalanine metabolism.

Conclusions: MDD patients with differing degrees of anhedonia exhibit widespread urinary metabolic differences. The greater metabolic divergence between High-SHAPS and Low-SHAPS subgroups compared with patient-control comparisons suggests that anhedonia contributes substantially to metabolic heterogeneity in depression. SHAPS-based stratification offers novel metabolomic evidence for precision subtyping and biomarker discovery.