Metabolomics and Psychological and Mental Illness

Psychological and mental disorders are characterized by complex etiologies, marked clinical heterogeneity, and difficulties in early diagnosis. Traditional symptom-based diagnostic approaches are often limited in their ability to fully reveal the underlying biological mechanisms of these conditions. Metabolomics, by systematically detecting small-molecule metabolites in biological samples such as blood, urine, and cerebrospinal fluid, can dynamically reflect metabolic alterations associated with disease states, thereby providing an important tool for mechanistic investigation, biomarker discovery, and precision intervention in psychological and mental disorders. This chapter reviews recent progress in the application of metabolomics to depression, schizophrenia, and bipolar disorder. Current evidence indicates that depression is mainly associated with abnormalities in the tryptophan–serotonin pathway, glutamate–GABA balance, mitochondrial energy metabolism, and lipid metabolism; metabolic alterations in schizophrenia are primarily concentrated in neurotransmitter-related pathways, amino acid metabolism, membrane lipid homeostasis, and glucose and energy metabolism; while bipolar disorder is closely linked to potential biomarkers such as glycine and methylmalonic acid, as well as disturbances in amino acid, energy, and carbohydrate metabolism. These findings suggest that psychological and mental disorders are not only characterized by neurotransmitter dysregulation but are also accompanied by broader systemic metabolic disturbances. Metabolomics therefore holds considerable promise for early identification, disease stratification, differential diagnosis, treatment response prediction, and individualized therapy. It may also be used to monitor drug-related metabolic side effects and provide guidance for nutritional interventions and precision pharmacotherapy. However, current studies still face several challenges, including limited sample sizes, variability across analytical platforms, substantial interference from medication and lifestyle factors, and insufficient reproducibility of findings. In the future, the integration of high-throughput detection technologies, multi-omics approaches, and artificial intelligence-based analytical methods is expected to further promote the transition of psychological and mental disorder research from symptom-based description toward mechanism-oriented classification and precision management.