A mechanistic digital twin of ovarian aging integrating follicular dynamics, mitochondrial decline, and lifestyle perturbations.

Ovarian aging determines reproductive lifespan, yet the mechanisms driving inter-individual variability remain incompletely understood. We developed a mechanistic digital twin to investigate how follicular dynamics, mitochondrial energetics, and lifestyle-related oxidative stress jointly shape hormonal trajectories and menopause timing. A system of stochastic differential equations modeled primordial follicle depletion, granulosa cell growth, AMH secretion, mitochondrial decline, and the accumulation of reactive oxygen species (ROS). Smoking, obesity, and circadian disruption modulated oxidative and inflammatory pathways. We simulated 1000 digital twins from ages 18-25 until menopause (AMH <0.1 ng/mL). Monthly outputs included AMH, gonadotropins, estradiol, progesterone, mitochondrial function, ROS, and lifestyle exposures. Parameter uncertainty and key drivers were assessed using posterior sampling and Sobol sensitivity indices. Simulations reproduced hallmark features of ovarian aging, including exponential AMH decline with realistic values (6.4 ng/mL at 25 years, 1.5 at 35, 0.7 at 40), widening inter-individual divergence after age 40, loss of endocrine cyclicity, and a right-skewed menopause age distribution (median ≈47 years). Mitochondrial function declined progressively while ROS accumulated, forming a tightly coupled aging axis. Lifestyle perturbations produced differentiated effects consistent with epidemiological evidence. Smoking exerted the strongest impact, advancing AMH depletion by approximately 1.5-2 years. Obesity induced chronic low-grade inflammation with modest mitochondrial impairment. Circadian disruption destabilized steroid hormone rhythms and advanced AMH decline by approximately 1 year. This mechanistic digital twin captures realistic endocrine aging patterns and quantifies how modifiable lifestyle factors accelerate ovarian aging through mitochondrial and oxidative pathways, enabling an individualized prediction of reproductive aging.