Modeling cardiovascular risk across the menopause transition: a synthetic cohort study

The menopause transition is associated with adverse cardiometabolic changes, but annualized modeling of menopause-related cardiovascular risk remains limited. We developed a synthetic cohort framework to simulate menopause transition, risk-factor trajectories, menopausal hormone therapy (MHT) use, and atherosclerotic cardiovascular disease (ASCVD) risk over time.

I generated a synthetic cohort of 100,000 women aged 40-79 years without prior ASCVD. Age at natural menopause was assigned using a literature-informed mixture distribution, and menopausal status was defined from age and menopause timing. Systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, and body mass index were simulated jointly using correlated distributions; smoking, diabetes, antihypertensive treatment, and MHT use were assigned probabilistically. Ten-year ASCVD risk was estimated annually using the published female Pooled Cohort Equation structure. Annual updates included aging, transition to postmenopause, risk-factor changes, diabetes onset, treatment changes, and competing non-cardiovascular disease (non-CVD) mortality.

At baseline, the mean age was 55.0 ± 7.6 years, and 33.4% of women were premenopausal. Mean predicted 10-year ASCVD risk was 1.18% in premenopausal women and 4.35% in postmenopausal women; the proportion above the 7.5% high-risk threshold was 0.20% and 13.73%, respectively. Over 10 years, the model generated 4,891 first ASCVD events (4.89%) and 11,046 non-CVD deaths (11.05%). ASCVD incidence was 1.56% among premenopausal women at baseline and 6.56% among those postmenopausal at baseline.

This synthetic cohort provides a transparent framework for simulating menopause-related cardiovascular risk trajectories and prevention scenarios.