# Metabolomics biomarkers for precision psychiatry

## TL;DR

Metabolomics can reveal biological patterns in mental disorders, offering a path to personalized psychiatric treatments by identifying metabolic dysregulation.

## Contribution

This mini-review summarizes key metabolomic findings in psychiatric disorders and outlines strategies to overcome current challenges in the field.

## Key findings

- Schizophrenia shows disruptions in arginine, proline, and energy metabolism.
- Bipolar disorder involves amino acid and kynurenine pathway dysregulation with phase-specific signatures.
- Major depressive disorder features widespread amino acid and bioenergetic changes, including treatment-responsive markers.

## Abstract

Mental disorders remain diagnosed primarily through symptom-based classification systems that overlook biological heterogeneity, preventing the identification of mechanistically distinct patient subgroups and precluding pathophysiology-guided treatment selection. Metabolomics offers a promising pathway towards precision psychiatry by capturing dynamic biochemical readouts at the functional endpoint of the omics cascade, integrating genetic, environmental, and pharmacological influences on cellular metabolism. Over the past 15 years, untargeted and targeted metabolomics studies using nuclear magnetic resonance spectroscopy and mass spectrometry have identified consistent patterns of metabolic dysregulation across psychiatric disorders, particularly involving amino acid metabolism, lipid signaling, energy homeostasis, and oxidative stress pathways. Schizophrenia presents disruptions in arginine and proline metabolism, glutathione metabolism, and energy-related processes. Bipolar disorder shows perturbations in branched-chain and aromatic amino acids, kynurenine pathway, and tricarboxylic acid cycle dysfunction with phase-specific metabolic signatures. Major depressive disorder exhibits widespread alterations in amino acid turnover, bioenergetic processes, membrane lipid homeostasis, and glutamate-GABA cycling, with treatment-responsive metabolic changes. Despite these advances, substantial challenges remain: heterogeneous findings with disorder overlap, limited replication cohorts, predominance of cross-sectional designs, confounding by medication and lifestyle factors, pre-analytical variability, and high-dimensional data complexity. Future research requires harmonized multi-site protocols, longitudinal validation studies, multi-platform analytical approaches, integration with genomics, proteomics, and digital phenotyping, and implementation of artificial intelligence frameworks to enhance phenotype discrimination and predictive accuracy. In this mini-review, we provide an overview of current methodologies, major findings, strengths, challenges, and emerging directions in psychiatric metabolomics, with the goal of facilitating the translation of metabolomic insights into clinically applicable, personalized psychiatric treatment.