Multi-omics investigation of metabolic dysregulation in depression: integrating metabolomics, weighted gene co-expression network analysis, and mendelian randomization.

Background: The etiology of depressive disorder, the leading cause of global mental disability, is characterized by systemic metabolic dysregulation. However, the causal metabolites and their mechanistic networks remain elusive.

Methods: We combined untargeted LC/GC-MS metabolomics (N=98 Chinese elderly), weighted gene co-expression network analysis (WGCNA), and two-sample Mendelian randomization (MR) using GWAS data (59,333 depression cases with 434,831 controls) to identify depression-associated metabolites and pathways.

Results: LC/GC-MS analysis identified 1,458 metabolites, with 84 differentially expressed in depression (VIP>1.5, p<0.05). WGCNA revealed a turquoise module enriched in amino acid metabolism (MM>0.7, p<0.05), while MR analysis confirmed 35 causal metabolites, including cysteine-alanine ratio (β=0.18, p=0.003) and serine levels (β=-0.24, p=0.001). Multi-omics integration highlighted glycine/serine/threonine metabolism (Impact = 0.35) and one-carbon folate cycle as core dysregulated pathways. Alterations were characterized by serine deficiency and phosphoserine accumulation, potentially reflecting disturbances in DNA methylation processes. Furthermore, elevated cysteine levels indicated a compensatory response to oxidative stress, and disruptions in purine metabolism pointed to mitochondrial dysfunction, particularly impaired mitochondrial ATP synthesis.

Conclusion: This study establishes a hierarchical metabolic framework for depression, prioritizing single-carbon metabolism and oxidative stress as central therapeutic targets. The findings emphasize methylation dysregulation and mitochondrial dysfunction in elderly depression, offering novel biomarkers for precision intervention.