基于常规血液和生化参数的机器学习模型,用于早期诊断糖尿病病
Wei Yong1, Dan-Dan Peng1, Kai Ye1
1Department of Clinical Laboratory, The Second Affiliated Hospital of Wannan Medical College, Wuhu, China.
Frontiers in endocrinology
|February 13, 2026
概括
机器学习模型有效地使用常规血液检测识别早期糖尿病病 (DKD). 像甘油三糖指数 (TyG) 这样的关键预测指标显示,具有成本效益的查具有前景.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 内分泌学 在内分泌学.
- 数据科学数据科学数据科学
背景情况:
- 糖尿病病 (DKD) 是全球末期病的主要原因.
- 早期的DKD诊断受到当前UACR和eGFR等生物标志物的局限性阻碍.
- 使用常规参数进行DKD早期检测的机器学习 (ML) 应用尚未得到充分探索.
研究的目的:
- 开发和验证用于早期DKD预测的ML模型.
- 为了使用常规的血液和生化参数进行DKD查.
- 为了比较不同ML算法的有效性,以识别早期的DKD.
主要方法:
- 对3114名糖尿病患者的回顾性分析 (EDN1) 和对1496名患者的外部验证 (EDN2).
- 早期的DKD定义为UACR 30-300 mg/g和eGFR ≥60毫升/分钟/1.73m2.2. 在早期的DKD定义为UACR 30-300毫克/g和eGFR ≥60毫升/分钟/1.73m2.2.
- 七个ML算法的比较,特征重要性评估 (SHAP) 和因果关系探索 (孟德尔随机化).
主要成果:
- 在3,114名患者中,有1,333名 (42.8%) 患者出现了早期的DKD.
- 后勤回归显示最佳性能 (AUC=0.689),确定甘油三糖指数 (TyG),性别,肌素,球蛋白和年龄作为顶级预测因素.
- 外部验证突出显示HbA1c,血球蛋白,TyG和中性粒细胞与白蛋白的比率是重要的预测因素.
结论:
- 机器学习模型可以成功地使用常规参数识别早期的DKD.
- 甘油三糖 (TyG) 指数,HbA1c和血球蛋白是早期DKD的关键预测因素.
- 这些ML模型为早期糖尿病病检测提供了潜在的成本有效的查工具.
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