在英国生物银行评估一种基于机器学习的冠状动脉疾病代谢标志物
Kyle Gibson1, Iain S Forrest2, Ben O Petrazzini3
1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Atherosclerosis
|January 12, 2025
概括
冠状动脉疾病 (CAD) 的新代谢风险评分有效预测动脉样硬化,并发症和死亡率. 这种使用代谢数据的机器学习方法为评估心血管风险提供了强大的工具.
科学领域:
- 心血管疾病研究研究
- 机器学习在医学中的应用
- 代谢学 代谢学 代谢学
背景情况:
- 对于冠状动脉疾病 (CAD) 现有的in silico评分使用临床数据,但尚未完全利用代谢学概况.
- 代谢物生物标志物分析提供了增强的可移植性和客观性,用于疾病预测中的机器学习.
研究的目的:
- 为了评估从机器学习模型中获得的定量CAD风险评分,该模型是通过代谢数据进行训练的.
- 评估这种代谢性CAD风险评分 (M-CAD) 与动脉样硬化,CAD后果和死亡率指标的关联.
主要方法:
- 开发了一个CAD预测机器学习模型,使用来自93,642名英国生物库参与者的代谢数据.
- 评估模型的概率作为CAD (M-CAD) 的定量代谢风险得分.
- 检查了M-CAD与动脉硬度指数,射出分数,CAD后果和死亡率的关系.
主要成果:
- 该模型为CAD预测实现了0.712的ROC曲线下的面积.
- 增加的M-CAD与较高的动脉硬度和较低的射出分数相关.
- 较高的M-CAD四分位数/十分位数显示肌动脉梗塞,全因死亡率,心血管疾病相关死亡率和CAD相关死亡率逐步增加.
结论:
- 基于代谢的机器学习模型可以生成定量CAD风险评分.
- 这种M-CAD得分与动脉样硬化负担,CAD并发症和死亡风险有显著联系.
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