Gu Xiaoqing, Li Qianbing, Wang Xiangfei
Wuhan Sports University, Wuhan, China.
Front Endocrinol (Lausanne). 2025 Jul 4;16:1586828. doi: 10.3389/fendo.2025.1586828. eCollection 2025.
Fertility status is a marker of future health, and female infertility has been shown to be an important medical and social problem. Life's Essential 8 ("LE8") is a comprehensive cardiovascular health assessment proposed by the American Heart Association. The assessment indicators include 4 health behaviors (diet, physical activity, nicotine exposure, and sleep health) and 4 health factors (body mass index, blood lipids, blood glucose, and blood pressure). LE8 and heavy metal exposure have both been shown to be associated with infertility. However, the association between LE8 and heavy metal exposure and female infertility has not been investigated. The aim of this study was to develop a machine learning prediction model for LE8 and heavy metal exposure and the risk of female infertility in the United States.
The National Health and Nutrition Examination Survey ("NHANES") is a nationally representative program conducted by the National Center for Health Statistics to assess the health and nutritional status of the U.S. population. For this study, 873 women between the ages of 20 and 45 were selected from the 2013-2018 NHANES dataset. The association between LE8 and heavy metal exposure and risk of infertility was assessed using logistic regression analysis and six machine learning models (Decision Tree, GBDT, AdaBoost, LGBM, Logistic Regression, Random Forest), and the SHAP algorithm was used to explain the model's decision process.
Of the six machine learning models, the LGBM model has the best predictive performance, with an AUROC of 0.964 on the test set. SHAP analysis showed that LE8, body mass index ("BMI"), diet, Cadmium ("Cd"), Cesium ("Cs"), Molybdenum ("Mo"), Antimony ("Sb"), Tin ("Sn"), education level and pregnancy history were significantly associated with the risk of female infertility. Cd, BMI and LE8 are the variables that contribute most to the prediction of infertility risk. Among them, BMI and LE8 have a negative predictive effect on female infertility in the model, while Cd has a positive contribution to the prediction of female infertility. Further analysis showed that there was a significant interaction between heavy metals and LE8, which may have a synergistic effect on the risk of female infertility.
This study used LE8 and heavy metal exposure to create a machine learning model that predicts the risk of female infertility. The model identified ten key factors. The model demonstrated high predictive accuracy and good clinical interpretability. In the future, LE8 and heavy metal exposure can be used to screen for female infertility early on.
生育状况是未来健康的一个指标，女性不孕症已被证明是一个重要的医学和社会问题。生命基本八项（“LE8”）是美国心脏协会提出的一项全面的心血管健康评估。评估指标包括4种健康行为（饮食、身体活动、尼古丁暴露和睡眠健康）和4种健康因素（体重指数、血脂、血糖和血压）。LE8和重金属暴露都已被证明与不孕症有关。然而，LE8与重金属暴露和女性不孕症之间的关联尚未得到研究。本研究的目的是为美国的LE8、重金属暴露和女性不孕症风险建立一个机器学习预测模型。
美国国家健康与营养检查调查（“NHANES”）是由国家卫生统计中心开展的一项具有全国代表性的项目，旨在评估美国人群的健康和营养状况。在本研究中，从2013 - 2018年NHANES数据集中选取了873名年龄在20至45岁之间的女性。使用逻辑回归分析和六种机器学习模型（决策树、梯度提升决策树、自适应增强算法、轻量级梯度提升机、逻辑回归、随机森林）评估LE8与重金属暴露和不孕症风险之间的关联，并使用SHAP算法解释模型的决策过程。
在六种机器学习模型中，轻量级梯度提升机（LGBM）模型具有最佳预测性能，在测试集上的曲线下面积（AUROC）为0.964。SHAP分析表明，LE8、体重指数（“BMI”）、饮食、镉（“Cd”）、铯（“Cs”）、钼（“Mo”）、锑（“Sb”）、锡（“Sn”）、教育水平和妊娠史与女性不孕症风险显著相关。镉、BMI和LE8是对不孕症风险预测贡献最大的变量。其中，BMI和LE8在模型中对女性不孕症有负向预测作用，而镉对女性不孕症预测有正向贡献。进一步分析表明，重金属与LE8之间存在显著交互作用，这可能对女性不孕症风险产生协同效应。
本研究利用LE8和重金属暴露创建了一个预测女性不孕症风险的机器学习模型。该模型识别出了十个关键因素。该模型显示出高预测准确性和良好的临床可解释性。未来，LE8和重金属暴露可用于早期筛查女性不孕症。
