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基于炎症相关指标的2型糖尿病机器学习预测模型的识别和验证
Ming-Hui Xia1,2, Jia-Xin Wu1,2, Ben Niu1,2,3
1The Affiliated Children's Hospital, School of Public Health, Suzhou Medical College, Soochow University, Jiangsu, P.R. China.
Population health management
|December 10, 2025
概括
这项研究开发了一种可解释的机器学习模型,使用炎症标志物和遗传因素预测新发型2型糖尿病 (T2D) 风险. 该模型通过突出显示BMI和C反应蛋白等关键预测因素,有助于早期T2D预防.
科学领域:
- 代谢障碍 代谢障碍 代谢障碍
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 2型糖尿病 (T2D) 是一种广泛的代谢障碍,具有重大的全球健康和经济影响.
- 炎症指标与发展新发型T2D的风险之间的联系需要进一步调查.
研究的目的:
- 通过使用与炎症相关的指标,识别和验证可解释的T2D风险预测模型.
- 评估遗传易感性和炎症标志物对T2D发病率的联合影响.
主要方法:
- 分析了220,937名在基线没有糖尿病的参与者的英国生物库数据.
- 使用六个机器学习算法开发预测模型,通过LASSO回归进行特征选择.
- 使用夏普利添加式扩展 (SHAP) 解释最佳模型,并使用考克斯回归评估综合遗传风险评分 (GRS) 和炎症因素.
主要成果:
- 极端梯度增强模型实现了高预测性能 (测试组中的AUC=0.838).
- 确定的主要预测因素包括体重指数,胆固醇,年龄,氨酸转移酶,高密度脂蛋白和预后营养指数.
- 炎症标志物如C反应蛋白和白细胞计数与未来T2D风险有很强的相关性,并整合GRS改进了模型预测.
结论:
- 开发了一种可解释的机器学习模型,用于预测新发型T2D风险,强调炎症和遗传学的作用.
- 这些发现为早期T2D预防和干预策略提供了有价值的工具.
- 这项研究提供了关于炎症和糖尿病发展之间的复杂关系的见解.
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