双机器学习框架用于预测年轻台湾男性的长期血糖变化和糖尿病前期风险
Chung-Chi Yang1,2,3,4, Sheng-Tang Wu5,6, Ta-Wei Chu7,8
1Division of Cardiovascular Medicine, Taoyuan Armed Forces General Hospital, Taoyuan 325208, Taiwan.
Diagnostics (Basel, Switzerland)
|October 16, 2025
概括
在年轻男性中早期发现失糖症至关重要. 机器学习模型可以预测长期的葡萄糖变化,并使用基线葡萄糖以外的因素来分类糖尿病前期风险.
科学领域:
- 内分泌学和新陈代谢学
- 计算生物学和生物信息学
- 预防医学 预防医学
背景情况:
- 在年轻成年人中早期发现失糖症 (血糖水平异常) 对于预防长期并发症至关重要,但仍未得到充分研究.
- 这项研究解决了在这个人口群体中对先进查工具的需求.
研究的目的:
- 使用机器学习 (ML) 预测空腹血葡萄糖 (δ-FPG) 的长期变化.
- 为了对年轻男性未来糖尿病前期风险进行分类.
- 为临床应用开发可解释的ML框架.
主要方法:
- 对6247名台湾男性 (18-35岁) 的分析,平均随访时间为5.9年.
- 将ML模型 (随机森林,SGB,XGBoost,弹性网) 与 δ-FPG预测的线性回归进行比较.
- 开发一种XGBoost预糖尿病分类器,使用SMOTE-Tomek用于类不平衡和SHAP值用于可解释性.
主要成果:
- 机器学习模型在预测多个错误指标的δ-FPG方面显著超过了传统的回归.
- 基线禁食血葡萄糖 (FPG基) 是主要预测因素;然而,当FPG基被排除在外时,身体脂肪,白细胞计数,年龄,甲状腺刺激激素,甘油三和LDL胆固醇是关键预测因素.
- 糖尿病前期分类器表现出高灵敏度 (99.53%在特定值) 和可接受的准确性 (0.788),决策曲线分析表明临床效用.
结论:
- 基线FPG是血糖变化最强的指标,但脂肪,炎症,甲状腺功能和脂质配置文件提供了有价值的预测信息.
- 一个可解释的双ML框架提供了切实可行的工具,用于查和风险分层的年轻男性的失糖症.
- 这种方法促进了早期干预和个性化的预防策略.
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