使用可解释机器学习揭示压力-高血糖比率和心脏代谢多病症风险之间的关系
Shouxin Wei1, Sijia Yu2, Chuan Qian3
1Department of Gastrointestinal Surgery, Suining Central Hospital, Suining, China. weishouxin@sns120.cn.
European journal of medical research
|January 20, 2026
概括
压力-高血糖比 (SHR) 显示了与心脏代谢多病症 (CMM) 风险的U形联系. 较高的SHR水平可能表明CMM风险增加,这表明SHRR可能会增加.
科学领域:
- 心血管研究研究心血管研究
- 代谢性疾病流行病学
- 生物标记分析 生物标记分析
背景情况:
- 心脏代谢多病症 (CMM) 是一个重大的全球健康挑战.
- 压力-高血糖比 (SHR) 是一种具有预后影响的新生物标志物,但其在CMM中的作用尚不清楚.
研究的目的:
- 调查SHR和CMM风险之间的关联.
- 评估SHR在CMM风险评估中的临床实用性.
主要方法:
- 对NHANES数据 (12,279名参与者) 的横截面分析.
- 权重后勤回归和机器学习 (梯度增强机器) 用于CMM预测.
- 使用CHARLS数据进行外部验证;针对BMI和WC角色进行中介分析.
主要成果:
- 在SHR和CMM风险之间存在U形关系.
- 较高的SHR水平与显著增加的CMM风险有关 (OR=116.890高于0.841).
- 机器学习模型 (AUC=0.880) 将年龄,SHR和WC确定为关键预测因素;BMI和WC调解了SHR-CMM链接.
结论:
- SHR与CMM风险呈现出非线性,U形的关联,突出了其早期诊断潜力.
- 基于机器学习的预测模型可以增强个性化的CMM风险评估.
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