CSenescence-associated-secretory phenotype proteins and inflammatory pathways are involved in psychosis: A...

Biofluid proteins are promising transdiagnostic markers for delineating disease heterogeneity in psychosis. This study examined proteomic profiles in a transdiagnostic early psychosis sample to test the hypothesis that psychosis is associated with proteomic signatures of premature aging. We analyzed proteomic data from 85 individuals with early psychosis and 35 healthy controls from the Human Connectome Project for Early Psychosis (HCP-EP), using the Olink platform across four validated panels (Cardiometabolic, Inflammation, Cardiovascular II, and III). Our dual analytic strategy included: (1) calculating an index based on senescence-associated secretory phenotype (SASP) proteins to assess cellular aging, and (2) analyzing all 374 proteins using Gene Set Enrichment Analysis to identify whether predefined biological pathways are collectively enriched. We compared the SASP index and enriched pathways between groups and examined associations with sociodemographic, physical health, cognitive, psychological well-being, psychosocial functioning, symptom, and medication data. The SASP index was significantly elevated in the psychosis group. Pathway analyses revealed enrichment of two inflammation-related pathways in psychosis after multiple comparison correction: Cellular Response to Tumor Necrosis Factor and Monocyte Chemotaxis. The higher SASP index and the enriched inflammatory pathways were associated with increased body mass index (BMI) and reduced psychological well-being. Our findings are consistent with prior research linking psychosis to premature aging and an increased inflammation response. They also underscore the value of examining multiple biomarkers and biological pathways simultaneously and highlight the importance of conceptualizing psychosis as a whole-body condition. However, given the sample size, results should be considered preliminary. Larger, longitudinal studies are needed to track proteomic changes over time and assess the predictive value of these biomarkers for identifying vulnerable individuals and clinical outcomes.