Multi-omics biomarkers in psychiatric disorders diagnosis and stratification

Abstract

The precise diagnosis and stratification of psychiatric disorders remain formidable challenges in modern medicine, hindered by the absence of objective biomarkers and reliance on subjective clinical criteria. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics, have revolutionized our understanding of complex neuropsychiatric conditions such as schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder. This review critically evaluates the current landscape of multi-omics research in psychiatry, highlighting methodological innovations, integrative strategies, and translational potential for biomarker discovery and clinical implementation. By synthesizing data across diverse molecular layers, multi-omics approaches enable a systems-level view of psychiatric disorders as multifactorial entities shaped by molecular, cellular, environmental, and neurocircuitry interactions. Despite promising advances in diagnostic accuracy and personalized treatment, significant barriers persist, including data heterogeneity, analytical complexity, and the translational gap between molecular signatures and clinical phenotypes. This review systematically explores the contributions of individual omics domains, emerging frameworks for multimodal data integration, the role of systems biology and network-based models, and the impact of large-scale consortia in driving clinical translation.