Moving toward precision and personalized treatment strategies in psychiatry

Abstract

Precision psychiatry aims to improve routine clinical practice by integrating biological, clinical, and environmental data. Many studies have been performed in different areas of research on major depressive disorder, bipolar disorder, and schizophrenia. Neuroimaging and electroencephalography findings have identified potential circuit-level abnormalities predictive of treatment response. Protein biomarkers, including IL-2, S100B, and NfL, and the kynurenine pathway illustrate the role of immune and metabolic dysregulation. Circadian rhythm disturbances and the gut microbiome have also emerged as critical transdiagnostic contributors to psychiatric symptomatology and outcomes. Moreover, advances in genomic research and polygenic scores support the perspective of personalized risk stratification and medication selection. While challenges remain, such as data replication issues, prediction model accuracy, and scalability, the progress so far achieved underscores the potential of precision psychiatry in improving diagnostic accuracy and treatment effectiveness.

Keywords: biomarkers; bipolar disorder; major depression; personalized medicine; schizophrenia.

Figure 1.

Integrated framework for precision psychiatry across key domains. This conceptual diagram illustrates the multifaceted approach to precision psychiatry, highlighting the six main domains that contribute to personalized treatment strategies. Clinical features (pink), biological markers (blue) including neurophysiology, protein biomarkers, and immunology, metabolic pathways (green), circadian rhythms (orange), and genomics (purple) all converge in an integrative analysis that informs personalized treatment decisions. These treatment recommendations can be categorized into medication selection, treatment strategy, risk prediction, and monitoring plans.