Suppr超能文献

人类卵巢衰老和绝经时间的随机分析。

Stochastic analysis of human ovarian aging and menopause timing.

作者信息

Mondal Anupam, Tcherniak Evelina, Kolomeisky Anatoly B

机构信息

Center for Theoretical Biological Physics, Rice University, Houston, Texas; Department of Chemistry, Rice University, Houston, Texas.

Department of Chemistry, Rice University, Houston, Texas.

出版信息

Biophys J. 2025 Apr 1;124(7):1095-1104. doi: 10.1016/j.bpj.2025.02.004. Epub 2025 Feb 10.

Abstract

Menopause marks a critically important biological event that ends a woman's fertility. It is a result of ovarian aging and depletion of ovarian reserve. Although many aspects of these processes are now well understood, the overall dynamic picture remains unclear. Here, we present a novel theoretical framework to analyze human ovarian aging dynamics and menopause timing. Our method is based on stochastic analysis of underlying processes stimulated by observing follicles sequentially transitioning between different stages during ovulation. This allows us to obtain a fully quantitative description of ovarian aging and menopause timing consistent with available experimental observations. Our model accurately predicts the average age of menopause across geographically diverse human populations. Theoretical analysis suggests a universal relation between the initial follicle reserve, the depletion rates, and the threshold that triggers menopause. In addition, it is found that the distributions of menopause times are quite narrow, and it is proposed that this might be a result of a precise regulation due to the synchronization of transitions between different stages of follicles. Our theoretical approach not only quantitatively explains the dynamics of human ovarian aging and menopause timing but also provides important insights into individual variability in ovarian aging. It can be used as a powerful tool for predicting menopause timing and investigating complex processes of reproductive aging.

摘要

更年期标志着一个极其重要的生物学事件,它结束了女性的生育能力。这是卵巢衰老和卵巢储备耗竭的结果。尽管现在对这些过程的许多方面已经有了很好的理解,但整体动态情况仍不明朗。在此,我们提出一个新的理论框架来分析人类卵巢衰老动态和更年期时间。我们的方法基于对排卵过程中卵泡在不同阶段顺序转变所激发的潜在过程的随机分析。这使我们能够获得与现有实验观察结果一致的卵巢衰老和更年期时间的完全定量描述。我们的模型准确预测了不同地理区域人类群体的平均更年期年龄。理论分析表明初始卵泡储备、耗竭率和触发更年期的阈值之间存在普遍关系。此外,发现更年期时间的分布相当狭窄,并提出这可能是由于卵泡不同阶段转变同步导致的精确调节的结果。我们的理论方法不仅定量解释了人类卵巢衰老和更年期时间的动态,还为卵巢衰老的个体差异提供了重要见解。它可以用作预测更年期时间和研究生殖衰老复杂过程的有力工具。

相似文献

1
Stochastic analysis of human ovarian aging and menopause timing.人类卵巢衰老和绝经时间的随机分析。
Biophys J. 2025 Apr 1;124(7):1095-1104. doi: 10.1016/j.bpj.2025.02.004. Epub 2025 Feb 10.
7
Ovarian hormones and obesity.卵巢激素与肥胖
Hum Reprod Update. 2017 May 1;23(3):300-321. doi: 10.1093/humupd/dmw045.

本文引用的文献

1
Molecular mechanisms of precise timing in cell lysis.细胞裂解中精确计时的分子机制。
Biophys J. 2024 Sep 17;123(18):3090-3099. doi: 10.1016/j.bpj.2024.07.008. Epub 2024 Jul 6.
7
Recapitulating human ovarian aging using random walks.利用随机游走重现人类卵巢衰老。
PeerJ. 2022 Aug 22;10:e13941. doi: 10.7717/peerj.13941. eCollection 2022.
9
The Crazy Ovary.疯狂的卵巢。
Genes (Basel). 2021 Jun 18;12(6):928. doi: 10.3390/genes12060928.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验