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

TL;DR

This study introduces a joint modeling approach that uses both the mean and variability of sex hormones to better predict body fat changes in peri- and post-menopausal women, highlighting the importance of biomarker variability.

Contribution

The paper develops a novel joint model estimating subject-level means and variances of biomarkers to improve health outcome predictions in epidemiology.

Findings

Larger hormone variability linked to greater fat mass change.

The proposed method outperforms existing approaches in accuracy.

Biomarker variability provides additional predictive value.

Abstract

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 and follicle-stimulating hormone 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. In this paper, we develop a joint model that estimates subject-level means and variances of longitudinal biomarkers to predict 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. Analyses of women's health data reveal larger variability of E2 or larger variability of FSH were associated with higher levels of fat mass change and higher levels of lean mass change across the menopausal transition.