548. DECODING THE BIOMOLECULAR LANDSCAPE OF PSYCHIATRIC DISORDERS FOR IMPROVING PATIENT STRATIFICATION

Abstract Background Precision psychiatry seeks to enhance diagnostic accuracy and treatment efficacy by integrating biological, clinical, and environmental data. Psychiatric disorders such as major depressive disorder, bipolar disorder, and schizophrenia exhibit high heterogeneity, complicating effective treatment selection. Recent advancements in proteomics, circadian biology, and gut-brain axis research offer novel biomarkers that may improve patient stratification, yet challenges in reproducibility and clinical translation persist. Aims & Objectives This presentation will examine biomolecular markers associated with psychiatric disorders and their role in patient stratification. We focus on protein biomarkers (e.g., IL-2, S100B, NfL), kynurenine pathway metabolites, circadian disruptions, and gut-brain interactions to establish their relevance in personalized psychiatric care. Method We discuss biomolecular data from multiple sources, including proteomic profiles, circadian parameters, and gut microbiome interactions. This approach allows for the identification of transdiagnostic and disorder-specific biomarkers that contribute to patient stratification. Results Findings suggest that protein biomarkers like S100B and NfL correlate with neuroinflammation and neuronal injury, while alterations in the kynurenine pathway are associated with mood regulation and treatment outcomes. Circadian dysregulation, particularly in melatonin rhythms and chronotype variations, emerges as a significant factor influencing mood disorder susceptibility and therapeutic response. Additionally, gut microbiota alterations have been linked to psychiatric symptoms, highlighting the gut-brain axis as a critical component of disease pathophysiology. Integration of these biomarkers enhances the ability to stratify patients into biologically distinct subgroups. Discussion & Conclusions Recent advances in biomolecular research, including key biomarkers and the gut-brain axis, have the potential to advance precision psychiatry. Precision psychiatry holds promise in moving beyond symptom-based classifications towards biologically informed patient stratification. Combining biomarker data with computational modeling and machine learning approaches may refine predictive algorithms for treatment selection. However, challenges remain, including data harmonization, biomarker validation, and ethical considerations in personalized medicine. Addressing these issues will be crucial in advancing precision psychiatry from research to clinical implementation, ultimately improving patient outcomes.