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.