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心血管疾病特异性虚弱指数的开发和评估:基于机器学习的英国生物库分析

Jiatang Xu^1,2, Zhensheng Hu^2,3, Kai Huang^2,4

¹Department of Cardiovascular Surgery, The First Affiliated Hospital of Sun Yat-sen University, Sun Yat-sen University, No. 58 Zhongshan Second Road, Guangzhou 510030, China.

European journal of preventive cardiology

|August 22, 2025

概括

使用机器学习开发了一种新的心血管疾病特异性虚弱指数 (FICVD). FICVD改善了心血管疾病风险预测和分层,尤其是在与遗传风险因素相结合时.

科学领域:

心脏病学
老年学
生物统计学
遗传学

背景情况:

对心血管疾病 (CVD) 风险的脆弱性评估缺乏特异性.
优化对心脏病风险的脆弱性评估策略对于早期干预至关重要.
机器学习算法为增强风险预测模型提供了新的方法.

研究的目的:

通过机器学习开发和验证心血管疾病特异性脆弱性指数 (FICVD).
评估FICVD的预测性能与传统的CVD脆弱性指数 (FI) 相比.
评估FICVD和多基因风险评分 (PRS) 对心脏病风险分层的综合实用性.

主要方法:

使用来自英国生物库的366,622名参与者的前队列.
使用弹性净回归来从与健康相关的项目构建FICVD.
产生多基因风险评分 (PRS) 来评估心血管疾病的遗传易感性.

主要成果:

与传统的FI相比,FICVD显示出较高的CVD风险危险比率.
在预测10年CVD时,FICVD曲线下的面积明显高于FI (0. 649比0. 605).
结合高FICVD和高遗传风险,发现个体患心血管疾病的风险增加了2.84倍.

结论:

开发的FICVD在预测和分层心血管疾病风险方面表现优异.
将FICVD与遗传易感性结合起来,可以更好地识别患有心血管疾病的高风险个体.
FICVD提供了一种创新的方法来优化个性化心脏病风险评估,并指导早期干预策略.

关键词:

心血管疾病脆弱性遗传易感性机器学习风险分层

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