OvaRePred (HerTempo): an enhanced ovarian aging clock for personalized reserve assessment, endocrine age modeling, and predicting reproductive milestones across the female lifecycle.

IF 4.6 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM
Frontiers in Endocrinology Pub Date : 2025-09-12 eCollection Date: 2025-01-01 DOI:10.3389/fendo.2025.1658068
Huiyu Xu, Guoshuang Feng, Rui Yang, Yong Han, Hongbin Chi, Rong Li
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引用次数: 0

Abstract

Background: Women display marked variability in ovarian reserve, which is pivotal for fertility and menopausal timing. Traditional criteria, such as Bologna and Poseidon, classify women into broad groups but do not provide individualized predictions for ovarian aging or reproductive milestones. This study aims to refine the AA model (AMH + age) to enhance clinical usability, robustness, and interpretability.

Materials and methods: Single-center retrospective ART cohort (GnRH-antagonist cycles, 2017-2021). Training: 15,241 cycles (2017-2019); Testing: 14,498 cycles (2020-2021). Poor ovarian response (POR) was defined as <5 oocytes. Three logistic-regression specifications were compared: categorical (Model-0), continuous (Model-1), and polynomial (age quadratic, AMH cubic; Model-2). Discrimination (AUC), calibration, and net reclassification improvement (NRI) were evaluated. A two-parameter logistic curve was fitted to age versus predicted POR (used population-level as "predicted DOR") to construct an ovarian-aging trajectory and derive an interpretable "endocrine-age" index. Sensitivity analyses assessed cycle-day AMH variation; a community dataset was used to compare age-stratified AMH distributions.

Results: While all models achieved comparable discrimination (AUC ≈ 0.85), a cubic transformation model (Model-2) demonstrated superior calibration and was selected as the final algorithm. A two-parameter logistic curve allowed translation of ovarian reserve scores into an "endocrine age" and enabled individualized prediction of future milestones, such as diminished reserve with ovarian score of 50 and perimenopause, the lowest ovarian reserve score in our ART population. AMH sampling on different cycle days showed only modest effects from minor fluctuations; only substantial AMH decreases significantly affected prediction accuracy. Age-stratified AMH distributions were similar between ART and community cohorts in women <40, supporting external relevance. The updated OvaRePred (HerTempo) model is cost-effective, scalable, and operationally simple.

Conclusion: OvaRePred (HerTempo) delivers individualized, well-calibrated estimates of ovarian reserve and an interpretable endocrine-age index and future fertility milestone onset. While the tool can inform personalized fertility planning and may have broader public-health utility, the algorithm is trained on ART endpoints. Any projections of future reproductive milestones derived from the population ovarian-aging curve-and the fixed-interval hypothesis that underpins that curve-are hypothesis-generating and require prospective validation, particularly in non-ART cohorts with longitudinal follow-up.

OvaRePred (HerTempo):一种增强的卵巢衰老时钟,用于个性化储备评估、内分泌年龄建模和预测女性生命周期中的生殖里程碑。
背景:女性在卵巢储备方面表现出明显的可变性,这对生育和绝经时间至关重要。传统的标准,如博洛尼亚和波塞冬,将女性分为广泛的群体,但不提供卵巢衰老或生殖里程碑的个性化预测。本研究旨在完善AA模型(AMH +年龄),以提高临床可用性、稳健性和可解释性。材料和方法:单中心回顾性ART队列(gnrh拮抗剂周期,2017-2021)。培训:15,241个周期(2017-2019);测试:14498个周期(2020-2021)。卵巢反应差(POR)定义为:结果:虽然所有模型都达到了相当的判别(AUC≈0.85),但三次变换模型(model -2)具有较好的校准效果,并被选为最终算法。双参数逻辑曲线允许将卵巢储备评分转换为“内分泌年龄”,并能够个性化预测未来的里程碑,例如卵巢储备减少,卵巢评分为50分和围绝经期,这是我们的ART人群中最低的卵巢储备评分。不同周期日的AMH抽样显示,微小波动对其影响不大;只有大量AMH显著降低了受影响的预测精度。结论:OvaRePred (HerTempo)提供了个体化的、校准良好的卵巢储备估计,以及可解释的内分泌年龄指数和未来生育里程碑发作。虽然该工具可以为个性化生育计划提供信息,并可能具有更广泛的公共卫生效用,但该算法是在ART端点上进行训练的。任何从人口卵巢衰老曲线中得出的未来生殖里程碑的预测——以及支撑该曲线的固定间隔假设——都是假设产生的,需要前瞻性验证,特别是在非art队列中进行纵向随访。
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来源期刊
Frontiers in Endocrinology
Frontiers in Endocrinology Medicine-Endocrinology, Diabetes and Metabolism
CiteScore
5.70
自引率
9.60%
发文量
3023
审稿时长
14 weeks
期刊介绍: Frontiers in Endocrinology is a field journal of the "Frontiers in" journal series. In today’s world, endocrinology is becoming increasingly important as it underlies many of the challenges societies face - from obesity and diabetes to reproduction, population control and aging. Endocrinology covers a broad field from basic molecular and cellular communication through to clinical care and some of the most crucial public health issues. The journal, thus, welcomes outstanding contributions in any domain of endocrinology. Frontiers in Endocrinology publishes articles on the most outstanding discoveries across a wide research spectrum of Endocrinology. The mission of Frontiers in Endocrinology is to bring all relevant Endocrinology areas together on a single platform.
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