Validating the Chronic Stress Indicator: A Data-Driven Framework for Integrating Physiological and Socio-Behavioral Measures

Chronic stress contributes to cardiovascular disease, diabetes, and mental health disorders through its cumulative physiological toll, or allostatic load (AL). Building on our previously proposed Chronic Stress Indicator (CSI), this study aims to refine, validate, and compare the CSI using a data-driven framework that integrates physiological, socioeconomic, and behavioral factors. Using data from the MIDUS II biomarker project, a nationally representative sample of U.S. adults aged 34–84, we assessed the performance of the refined CSI and traditional AL indices in predicting multiple stress-related outcomes. Advanced statistical techniques, including the Boruta feature selection algorithm and factor analysis, were applied to optimize biomarker selection and weighting. Sensitivity analyses evaluated the robustness and reliability of each construction. Compared with the original CSI, the refined model incorporating socio-behavioral variables and data-driven weighting demonstrated improved predictive performance for short-term stress outcomes, while both traditional and extended models performed well for long-term outcomes. This work advances the measurement of chronic stress by validating and enhancing the CSI, providing a robust tool for identifying at-risk populations and guiding targeted interventions.