开发一种基于机器学习的模型,用于预测代谢功能障碍相关的脂肪性肝病 (MASLD) 患者的糖尿病风险
Sijie Yang1, Ruiqing Mo2, Wenqiang Wang2
1People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China yangsijiemdj@sina.com.
BMJ open
|January 28, 2026
概括
机器学习模型可以预测代谢功能障碍相关的脂肪性肝病 (MASLD) 患者的糖尿病风险. 尿白蛋白和脂质积累产品等关键因素突显了对MASLD患者早期干预的需要.
科学领域:
- 肝病学 肝病学是一种肝病学.
- 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:
- 与代谢功能障碍相关的脂肪性肝病 (MASLD) 是一个越来越令人担忧的疾病,通常与糖尿病 (DM) 一起存在.
- 在MASLD患者中早期发现糖尿病风险对于预防并发症至关重要.
- 心血管和脏并发症在MASLD人群中经常与DM有关.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测诊断为MASLD的个体的糖尿病风险.
- 在MASLD队列中确定糖尿病发展的重要预测因素.
- 利用一个具有全国代表性的数据集来进行可靠的模型开发和验证.
主要方法:
- 分析了2007年至2018年间6310名MASLD参与者的数据.
- 使用特征选择技术,包括随机森林,LASSO和SVM-RFE.
- 开发了九个ML模型,通过准确性,AUC和F1得分等指标评估性能; SHAP用于可解释性.
主要成果:
- 确定了八个关键预测变量:年龄,尿白蛋白 (Ualb),总胆固醇 (TC),脂质积累产物 (LAP),尿蛋白,白细胞计数,尿酸和内脏肥胖指数.
- 光梯度增强机 (LightGBM) 模型在九个算法中显示出优异的预测性能.
- SHAP分析发现Ualb,年龄,TC和LAP是糖尿病风险最有影响力的预测因素.
结论:
- 一个基于ML的模型有效地识别了患DM高风险的MASLD个体.
- 光GBM算法为MASLD的糖尿病风险预测提供了高准确性和可解释性.
- 尿中的白蛋白和LAP是早期糖尿病风险评估的关键标志物,指导个性化干预.
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