Simulation-based model of menopause: hormonal changes and cardiovascular risk in a digital twin view.

OBJECTIVE

Menopause is associated with profound hormonal changes, including declines in estradiol and progesterone and increases in follicle stimulating hormone (FSH) and luteinizing hormone (LH), which contribute to elevated cardiovascular disease (CVD) risk. Digital twin frameworks offer a novel approach to simulate these complex dynamics.

METHOD: This study developed a semi-mechanistic digital twin model of menopause using 1000 simulated women (50% age ≥55 years, 50% age <55 years) followed over 90 days. Hormonal dynamics were modeled as cyclical in non-menopausal women and stable in postmenopausal women. Cardiovascular risk was assessed with linear mixed-effects models and generalized estimating equations, adjusting for age, body mass index and smoking.

RESULTS

The model reproduced expected patterns, with estradiol and progesterone peaks at ovulation and luteal phases in non-menopausal women, and stable, low levels in postmenopausal women, alongside elevated gonadotropins. After adjustment, menopause remained significantly associated with hormonal changes and CVD risk (p < 0.001). CVD-related differences were more pronounced in non-menopausal women, where estradiol peaks and luteal progesterone amplitudes were attenuated.

CONCLUSION

This proof of concept highlights the potential of digital twin models to capture menopause-related hormonal dynamics and their cardiovascular implications. Future work should integrate real-world data, perimenopausal variability and ethical governance to enhance clinical translation.