Variance as a predictor of health outcomes: Subject-level trajectories and variability of sex hormones to predict body fat changes in peri- and postmenopausal women

Longitudinal biomarker data and cross-sectional outcomes are routinely collected in modern epidemiology studies, often with the goal of informing tailored early intervention decisions. For example, hormones, such as estradiol (E2) and follicle-stimulating hormone (FSH), may predict changes in womens' health during the midlife. Most existing methods focus on constructing predictors from mean marker trajectories. However, subject-level biomarker variability may also provide critical information about disease risks and health outcomes. Current literature does not provide statistical models to investigate such relationships with valid uncertainty quantification. In this paper we develop a fully Bayesian joint model that estimates subject-level means, variances, and covariances of multiple longitudinal biomarkers and uses these as predictors to evaluate their respective associations with a cross-sectional health outcome. Simulations demonstrate excellent recovery of true model parameters. The proposed method provides less biased and more efficient estimates, relative to alternative approaches that either ignore subject-level differences in variances or perform two-stage estimation where estimated marker variances are treated as observed. Empowered by the model, analyses of women's health data reveal, for the first time, that larger variability of E2 was associated with slower increases in waist circumference across the menopausal transition.