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BACKGROUND: Depression is a major psychiatric illness with a global burden, poor treatment response, and limited biological markers, leaving diagnosis reliant on behavioral rather than biological methods. While there is an increasing number of individual metabolomic studies investigating depression, comprehensive meta-analyses remain lacking. METHODS: In this study, we conducted three meta-analyses integrating 11 publicly available blood mass spectrometry (MS) datasets with 1050 samples (386 depression patients, 664 controls) and 1111 unique metabolites. Robust Rank Aggregation (RRA), Uniform Manifold Approximation and Projection (UMAP)-based clustering, and Partial Least Squares Discriminant Analysis (PLS-DA) were used as complementary integrative approaches. RESULTS: We identified 42 altered metabolites, with consistent elevations in glutamate, aspartate, 2-hydroxybutyrate, and ketone bodies, and reductions in glycine, glutamine, taurine, branched-chain amino acids, citrate, and urea-cycle intermediates. RRA replicated strong disruptions in glutamatergic, tryptophan, aromatic amino acid, and branched-chain amino acid metabolism. Pathway enrichment indicated coordinated alterations in amino acid, nitrogen, and nucleotide metabolism, and neurotransmitter signaling. CONCLUSION: Together, these results suggest that dysregulation in neurotransmitter synthesis and signal transduction, impaired excitatory-inhibitory cycles in the brain, altered energy metabolism, and nitrogen and nucleotide metabolism contribute to the development of depression. This metabolomic signature offers a potential access point to the development of novel therapeutic and diagnostic approaches.
Panina et al. (Fri,) studied this question.