可解释的机器学习识别了与2型糖尿病患者膜过率相关的代谢物
Tian-Feng An1, Zhi-Peng Zhang1, Jun-Tang Xue2
1Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China.
Frontiers in endocrinology
|June 25, 2024
概括
新的生物标志物,如氨酸和氨酸可以帮助检测脏疾病在2型糖尿病 (T2D) 患者. 机器学习模型准确地识别了这些标记,以更好地评估功能.
科学领域:
- 代谢学 代谢学 代谢学
- 生物标志物发现发现
- 医疗保健中的机器学习
背景情况:
- 2型糖尿病 (T2D) 经常与脏疾病同时发生,这对公众健康构成重大挑战.
- 目前用于评估功能的方法,如基于血清肌氨酸的估计球过率 (eGFR),在T2D患者中可能不可靠.
- 需要新的生物标志物来准确评估T2D中的功能.
研究的目的:
- 通过可解释的机器学习框架,在T2D患者中识别与膜过率 (GFR) 相关的血代谢特征.
- 利用已识别的代谢生物标志物,开发和验证T2D中功能预测模型.
主要方法:
- 利用了一个发展队列 (1626名T2D患者) 和一个外部验证队列 (716名T2D患者).
- 采用直角局部最小平方差分分析 (OPLS-DA) 进行代谢物特征选.
- 机器学习模型的比较:逻辑回归 (LR),支持向量机 (SVM),随机森林 (RF) 和极端梯度增强 (XGBoost).
- 应用沙普利增量解释 (SHAP) 来实现模型的可解释性.
主要成果:
- 较高水平的谷氨基甲尼丁 (C5DC),甲尼丁 (C10),素和其他甲尼丁与T2D患者的GFR降低显著相关.
- XGBoost模型表现出卓越的性能,实现了0.90的AUROC (内部验证) 和0.970 (外部验证).
- SHAP分析确定了C5DC,素,甘油三,年龄和T2D持续时间的特定值,与降低的GFR相关.
结论:
- 血中素和一组甲胺是检测T2D患者降低GFR的有希望的生物标志物.
- 这些代谢标志物显示出评估功能独立于传统风险因素的潜力.
- 机器学习,特别是XGBoost,有效地整合了新陈代谢数据,以便在T2D中准确预测GFR.
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