Metabolic and Multi-Omics Biomarkers in Schizophrenia: Translational Advances and Clinical Challenges

DOI

https://www.doi.org/10.59256/ijrtmr.20260602065

Published In

Volume 6, Issue 2 (2026)

Published Online

March-April 2026

Pages

458-464

Article Type

Review Article

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Schizophrenia is increasingly recognised as a systemic disorder characterised by disrupted metabolic, inflammatory, and neurobiological processes rather than solely neurotransmitter imbalance. This review synthesises current evidence on metabolic dysregulation and biomarker development in schizophrenia, with emphasis on mitochondrial dysfunction, oxidative stress, lipid abnormalities, amino acid alterations, and impaired energy metabolism. Emerging findings from metabolomics, lipidomics, proteomics, and multi-omics integration demonstrate consistent disturbances in brain and peripheral systems, including altered glutamate, kynurenine, and arginine pathways, reduced glutathione, and mitochondrial quality control deficits. Peripheral biomarkers such as lactate, triglycerides, serine, and brain-derived neurotrophic factor show potential diagnostic, prognostic, and treatment-response relevance, although none are yet clinically validated. Evidence also highlights the role of gut microbiota–brain interactions and immune–metabolic crosstalk in disease pathophysiology. Advances in machine learning and integrative multi-omics approaches have improved classification accuracy and support biologically informed patient stratification. However, significant challenges remain, including methodological heterogeneity, small sample sizes, lack of longitudinal validation, and limited clinical translation. Overall, schizophrenia appears to involve interconnected metabolic and neuroimmune disturbances across central and peripheral systems. Future research focusing on large-scale, longitudinal, and multi-omics integration is essential to identify reliable biomarkers and enable precision-based diagnosis, prognosis, and treatment strategies in schizophrenia.