Modelling Menopause Age with Emphasis on the Relationship Between Age, Non-Growing Follicles and Ovarian Depletion

This study presents a mathematical model for predicting the age of menopause by examining the dynamic interactions between non-growing follicles (NGFs), hormonal regulation, and ovarian reserve depletion. Central to the model are key parameters (k₁ to k₉), which represent rates of follicle growth, atresia, hormonal production, and ovarian reserve depletion. The model incorporates variables such as follicle-stimulating hormone, luteinizing hormone, and ovarian reserve, linking them to age-dependent changes in NGFs. Sensitivity analysis reveals the significant impact of parameters k₁, k₂, and k₉, which accelerate or delay menopause depending on their values. Simulation results align with observed biological data, showing NGFs decline from approximately 700,000 at birth to 1,000 at menopause, typically around age 50–51. Hormonal feedback loops, such as rising FSH levels compensating for declining NGFs, are critical in accelerating ovarian reserve depletion. The findings highlight the interplay of genetic, environmental, and hormonal factors in determining menopause timing. Applications of the model include predictive medicine, fertility planning, and personalized treatments such as hormone replacement therapy. The model marks a significant advancement in understanding menopause dynamics, its predictive capabilities and examines interventions for women at risk of early menopause. This reports the highlight the complex relationship between NGFs, hormonal regulation, and ovarian health, offering a valuable framework for future studies and clinical applications.