Estimating survival in data-driven phenotypes of mental health symptoms and peripheral biomarkers: A prospective study

Abstract

Background: Chronic psychological stress has widespread implications, including heightened mortality risk, mental and physical health conditions, and socioeconomic consequences. Stratified precision psychiatry shows promise in mitigating these effects by leveraging clinical heterogeneity to personalize interventions. However, little attention has been given to patient self-report. Methods: We addressed this by combining stress-related self-report measures with peripheral biomarkers in a latent profile analysis and survival model. The latent profile models were estimated in a representative U.S. cohort (n = 1255; mean age = 57 years; 57% female) and cross-validated in Tokyo, Japan (n = 377; mean age = 55 years; 56% female). Results: We identified three distinct groups: “Good Mental Health”, “Poor Mental Health”, and “High Inflammation”. Compared to the “Good Mental Health” group, the “High Inflammation” and “Poor Mental Health” groups had an increased risk of mortality, but did not differ in mortality risk from each other. Conclusions: This study emphasizes the role of patient self-report in stratified psychiatry.

Keywords

• Psychosocial stress
• Inflammation
• Survival
• Latent profile analysis
• Mental health