Stochastic analysis sheds light on aging and the timing of menopause

Stochastic analysis sheds light on aging and the timing of menopause

Menopause, driven by aging and depletion of the ovarian reserve, marks the end of a woman's fertility, and while many aspects of these processes are well understood, the overall dynamics remain unclear. A new study by researchers at Rice University published in Biophysical JournalFebruary 10 introduces a novel approach to unraveling the complex patterns of ovarian aging using stochastic analysis, a mathematical approach that examines systems by evaluating all potential outcomes using random probability.

Led by Anatoly Kolomisky, professor of chemistry and chemical and biomolecular engineering, the research team developed a theoretical framework that quantitatively predicts the timing of menopause. By analyzing the transition of ovarian follicles at different stages, the research model explains why menopause occurs and sheds light on individual variability and population differences. These findings could improve fertility planning, influence health care decisions related to hormonal therapies, and improve our understanding of age-related health risks associated with ovarian aging.

By viewing menopause as a sequential process involving random transitions of follicles, we can better understand individual variability and population-wide trends in the timing of menopause. “

Anatoly Kolomisky, Professor of Chemistry and Chemical and Biomolecular Engineering, Rice University

A new theoretical model reveals the secret of menopause

The research team hypothesized that ovarian aging follows a stochastic sequential process influenced by follicles changing over multiple developmental stages. Unlike previous studies that focused primarily on hormonal and genetic influences, this study used explicit analytical calculations supported by extensive computer simulations.

The approach allowed researchers to model the gradual depletion of ovarian follicular reserves and provide a detailed quantitative framework consistent with medical data from diverse populations.

“By applying stochastic analysis, we can go beyond broad observations and develop precise, predictive insights into the timing and variability of menopause,” Kolomisky said.

Key findings discover the timing of menopause

The researchers discovered a universal relationship between three critical factors: the initial follicular reserve, the rate of ovarian depletion and the threshold that triggers menopause. Their model also found that menopause occurs in a surprisingly narrow age group, a phenomenon that had not yet been fully explained.

“One of the most unexpected findings was the synchronization of follicular transitions, which may regulate the timing of menopause,” Kolomisky said. “This suggests that underlying biochemical processes ensure a relatively consistent age of menopause despite individual variation.”

Anupam Mondal, a postdoctoral researcher at the Center for Theoretical Biological Physics, and student Evelina Tcherniak from the Department of Biomolecular Engineering co-authored the study, which was supported by the Welch Foundation and the Center for Theoretical Biological Physics.

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