Ortiz José A, Lledó B, Luque L, Morales R, Myles S, Pérez María, Guerrero J, Bernabeu A
Molecular Biology, Instituto Bernabeu, Avda. Albufereta 31, 03016, Alicante, Spain.
Reproductive Medicine, Instituto Bernabeu, Albacete, Spain.
J Assist Reprod Genet. 2025 Apr 2. doi: 10.1007/s10815-025-03471-z.
To identify genetic variants associated with an increased likelihood of sub-optimal ovarian response or hyper-response by machine learning.
This retrospective observational study, conducted between March 2018 and April 2022, analyses 495 ovarian stimulations in oocyte donors. Only each donor's first ovarian stimulation was considered. The egg donors were healthy women aged 18 to 35 years. Donor characteristics and ovarian stimulation data were recorded, as well as genotypes of 31 polymorphisms previously identified as modulators of ovarian response. Models to predict the type of ovarian response (sub-optimal, normal, or hyper-response) were performed using 5 different classification machine-learning algorithms. The most important variables were determined by SHAP (Shapley-Additive-exPlanations) values.
Despite being young with good ovarian reserves and using similar stimulation protocols, 15.15% of oocyte donors had a sub-optimal response (4-9 oocytes), while 27.27% showed a hyper-response (over 20 oocytes). The best predictive model was random forest, with an AUC of 0.822. Six significant genetic polymorphisms were identified: three in hormone receptors-oestrogen receptor (ESR2; c.*39G > A, c.984G > A), follicle-stimulating hormone receptor (FSHR; p.Asn680Ser, c.-29G > A), and AMH receptor (AMHR2; c.622-6C > T) and one in growth differentiation factor 9 (GDF9; c.398-39G > C). Four polymorphisms (ESR2, FSHR) were linked to sub-optimal response, while two (AMHR2, GDF9) were associated with hyper-response.
By using a predictive model to asses ovarian response, we identified six genetic polymorphisms associated with ovarian response. Women who carry these genetic variants may be suitable candidates for personalised ovarian stimulation treatments to help prevent inadequate responses.
通过机器学习识别与卵巢反应欠佳或过度反应可能性增加相关的基因变异。
这项回顾性观察研究于2018年3月至2022年4月进行，分析了卵母细胞捐赠者的495次卵巢刺激情况。仅考虑每位捐赠者的首次卵巢刺激。卵子捐赠者为年龄在18至35岁的健康女性。记录了捐赠者特征和卵巢刺激数据，以及先前被确定为卵巢反应调节因子的31种多态性的基因型。使用5种不同的分类机器学习算法构建预测卵巢反应类型（欠佳、正常或过度反应）的模型。通过SHAP（Shapley加性解释）值确定最重要的变量。
尽管卵母细胞捐赠者年轻且卵巢储备良好，并且使用了相似的刺激方案，但15.15%的捐赠者卵巢反应欠佳（4至9个卵母细胞），而27.27%表现为过度反应（超过20个卵母细胞）。最佳预测模型是随机森林，曲线下面积为0.822。鉴定出六个显著的基因多态性：三个在激素受体中——雌激素受体（ESR2；c.*39G＞A，c.984G＞A）、促卵泡激素受体（FSHR；p.Asn680Ser，c.-29G＞A）和抗缪勒管激素受体（AMHR2；c.622-6C＞T），一个在生长分化因子9（GDF9；c.398-39G＞C）中。四个多态性（ESR2、FSHR）与欠佳反应相关，而两个（AMHR2、GDF9）与过度反应相关。
通过使用预测模型评估卵巢反应，我们鉴定出六个与卵巢反应相关的基因多态性。携带这些基因变异的女性可能是个性化卵巢刺激治疗的合适候选者，以帮助预防反应不足。
