Department of Mathematics, University of Utah, 155 S 1400 E, JWB 233, Salt Lake City, UT 84112, USA.
Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO 80045, USA.
Sci Adv. 2024 Jan 12;10(2):eadj4490. doi: 10.1126/sciadv.adj4490.
Ovarian aging in women can be described as highly unpredictable within individuals but predictable across large populations. We showed previously that modeling an individual woman's ovarian reserve of primordial follicles using mathematical random walks replicates the natural pattern of growing follicles exiting the reserve. Compiling many simulations yields the observed population distribution of the age at natural menopause (ANM). Here, we have probed how stochastic control of primordial follicle loss might relate to the distribution of the preceding menopausal transition (MT), when women begin to experience menstrual cycle irregularity. We show that identical random walk model conditions produce both the reported MT distribution and the ANM distribution when thresholds are set for growing follicle availability. The MT and ANM are shown to correspond to gaps when primordial follicles fail to grow for 7 and 12 days, respectively. Modeling growing follicle supply is shown to precisely recapitulate epidemiological data and provides quantitative criteria for the MT and ANM in humans.
女性的卵巢衰老在个体内部表现出高度不可预测性，但在大量人群中具有可预测性。我们之前表明，使用数学随机游走模型来模拟个体女性的原始卵泡储备可以复制卵泡从储备中生长出来的自然模式。对许多模拟进行汇总，可以得到自然绝经年龄（ANM）的观察到的人群分布。在这里，我们探究了原始卵泡损失的随机控制如何与绝经前过渡期（MT）的分布相关，当女性开始经历月经周期不规律时，MT 就开始了。我们表明，当为生长卵泡的可用性设置阈值时，相同的随机游走模型条件可以产生报告的 MT 分布和 ANM 分布。MT 和 ANM 分别对应于原始卵泡无法生长 7 天和 12 天的空白期。生长卵泡供应的建模被证明可以精确地再现流行病学数据，并为人类的 MT 和 ANM 提供定量标准。
