Predicting Future Mental Disorders Based on Plasma Proteins and Polygenic Risk Score

Traditional psychiatric diagnosis relies on subjective symptom assessment, lacking objective biomarkers that hinder early detection and personalized treatment. Plasma proteins and polygenic risk score (PRS), as potential predictive tools, hold promise for advancing early diagnosis of mental disorders. This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses (depression, schizophrenia, and post-traumatic stress disorder (PTSD)). Using participant data from the UK Biobank-Pharma Proteomics Project, we screen protein associations with mental disorders through least absolute shrinkage and selection operator (LASSO) analysis and construct a Cox regression risk prediction model by integrating the PRS. Additionally, we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves. Our findings reveal distinct predictive patterns across disorders. For depression, integrating plasma proteins with PRS significantly improves prediction beyond the clinical model (C-index=0.632 2). For schizophrenia, adding plasma proteins enhances predictive performance, whereas PRS provides no significant improvement. For PTSD, neither plasma proteins nor PRS add substantial predictive value beyond clinical variables. Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups (depression: HR=2.34, P < 0.001; schizophrenia: HR=5.47, P < 0.001; PTSD: HR=3.02, P < 0.001). Although it shows good performance in short-term prediction, its long-term prediction ability has decreased, and it needs to be further optimized in the future. This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.