Using Subject-Level Variability to Predict Time-Varying Outcomes: Investigating the Association between Hormone Variability and BMD Trajectories over the Menopausal Transition

TL;DR

This study introduces a joint modeling approach to analyze how hormone variability, especially FSH, predicts bone mineral density changes during menopause, revealing that hormone variability and mean levels have distinct impacts on bone health trajectories.

Contribution

The paper presents a novel joint model linking hormone residual variances to bone health, highlighting the importance of hormone variability in predicting BMD changes during menopause.

Findings

Higher FSH variability predicts BMD decline before menopause.

Higher mean E2 is linked to slower BMD decrease during menopause.

Naive two-stage methods often underestimate uncertainty in variance estimates.

Abstract

Women are at increased risk of bone loss during the menopausal transition; in fact, nearly 50\% of women's lifetime bone loss occurs during this time. The longitudinal relationships between estradiol (E2) and follicle-stimulating hormone (FSH), two hormones that change have characteristic changes during the menopausal transition, and bone health outcomes are complex. However, in addition to level and rate of change in E2 and FSH, variability in these hormones across the menopausal transition may be an important predictor of bone health, but this question has yet to be well explored. We introduce a joint model that characterizes individual mean estradiol (E2) trajectories and the individual residual variances and links these variances to bone health trajectories. In our application, we found that higher FSH variability was associated with declines in bone mineral density (BMD) before menopause, but this association was moderated over time after the menopausal transition. Additionally, higher mean E2, but not E2 variability, was associated with slower decreases in during the menopausal transition. We also include a simulation study that shows that naive two-stage methods often fail to propagate uncertainty in the individual-level variance estimates, resulting in estimation bias and invalid interval coverage.