代谢炎症生物标志物与Helicobacter pylori感染之间的关联:一种可解释的机器学习预测方法
Yue Zhang1, Ruifeng Duan1, Xin Chen1
1Department of Gastroenterology and Digestive Endoscopy Center, The Second Hospital of Jilin University, Changchun, China.
Frontiers in nutrition
|December 5, 2025
概括
代谢标志物如甘油三糖 (TyG) 指数和TyG/HDL-C比率与Helicobacter pylori (HP) 感染有关. XGBoost机器学习模型显示了HP风险的可靠预测性能.
科学领域:
- 代谢健康 代谢健康
- 传染病流行病学 传染病流行病学
- 机器学习在医疗保健中的应用
背景情况:
- 调查了代谢-炎症标志物和Helicobacter pylori (HP) 感染之间的联系.
- 专注于甘油三糖 (TyG) 指数,TyG/HDL-C比率以及全身炎症生物标志物.
研究的目的:
- 评估代谢-炎症标志物与HP感染之间的关联.
- 为了比较可解释的机器学习模型的性能,以预测HP感染风险.
主要方法:
- 分析了来自NHANES参与者和中国队列的数据.
- 使用多变量回归来评估标志物-HP关联.
- 通过使用AUC,准确度,灵敏度,特异性,精度,F1得分和卡帕统计数据,比较了11个机器学习模型.
- 通过SHAP值和决策曲线分析评估模型的解释性.
主要成果:
- 在NHANES队列中,TyG指数和TyG/HDL-C比率显示出与HP感染的独立关联.
- 在中国队列中,TyG/HDL-C比率仍然显著.
- 在XGBoost和MLP模型中,在培训和验证之间表现一致 (AUC为0.77),表明了良好的概括性.
- 随机森林和高斯过程模型过度适合,AUC从0.97降至0.75.
结论:
- TyG指数和TyG/HDL-C比率与HP感染有关.
- XGBoost为HP感染风险预测提供了一个稳定和可泛化的机器学习框架.
- 像XGBoost这样的可解释AI模型可以提高预测感染风险的临床相关性.
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