Physiological dysregulation arising from chronic stress is a key mechanism linking psychosocial factors to long-term health outcomes, yet early identification typically relies on invasive or resource-intensive measurements. This study evaluates whether high-dimensional psychometric survey data can support scalable, non-invasive screening for latent physiological dysregulation. Using longitudinal data from the Midlife in the United States (MIDUS) Waves 2 and 3, we develop a screening-oriented modeling framework that separates longitudinal risk estimation from deployable screening model construction. Physiological targets are defined across inflammatory, metabolic, and neuroendocrine domains using three canonical allostatic load formulations. A teacher–ranking–pruning–student pipeline combines stable feature ranking, parsimony-driven dimensionality reduction, and knowledge distillation. Predictor dimensionality is reduced by more than an order of magnitude without loss of screening performance. Distilled student models consistently outperform linear, tree-based, and direct neural baselines, achieving area under the receiver operating characteristic curve values up to approximately 0.78 and substantial precision–recall lift over baseline prevalence. Longitudinal information is exploited during model development but not required at inference, enabling deployment using psychometric data alone. These findings demonstrate the feasibility of non-invasive screening for latent physiological dysregulation and provide a generalizable framework for translating longitudinal cohort data into deployable population health tools.
Alin Alecu (Fri,) studied this question.