开发,测试和比较基于新生活方式的预测模型,以预测冠状动脉心脏病风险
Qiaoxin Shi1,2, Haeyoon Jang1, Mengyao Wang1
1School of Public Health, The University of Hong Kong Li Ka Shing Faculty of Medicine, Pokfulam, Hong Kong SAR, China.
Scientific reports
|October 21, 2025
概括
使用生活方式行为和多基因风险评分 (PRS) 预测冠心病 (CHD) 风险的新模型显示出有希望. 这些基于生活方式的模型 (LBM) 提供了适度的准确性,与传统方法相比,可以通过整合PRS来改进.
科学领域:
- 心血管疾病流行病学
- 遗传学和个性化医学
- 数字健康和可穿戴技术
背景情况:
- 标准冠心病 (CHD) 风险预测模型通常依赖于具有挑战性的实验室标记物.
- 需要取得进展,以纳入可访问的生活方式行为和遗传信息,以改善风险评估.
- 现有的模型,如聚合队列方程 (PCE) 和SCORE2,需要增强,以获得更广泛的适用性.
研究的目的:
- 开发和验证使用生活方式行为和多基因风险评分 (PRS) 的新型心脏病风险预测模型.
- 将这些新模型的性能与已建立的方程 (PCE和SCORE2) 进行比较.
- 评估将PRS整合到基于生活方式的模型中,以预测CHD风险的附加值.
主要方法:
- 利用了来自291,151名英国白人英国人的英国生物库数据.
- 开发了一种基于生活方式的模型 (LBM),包括年龄,性别,BMI,饮食,吸烟和可穿戴的身体活动.
- 基于300个遗传变异的CHD计算加权PRS,并应用Cox回归来预测风险.
主要成果:
- 在LBM,PCE,和SCORE2显示了可比的歧视 (C指数~0.71).
- 纳入PRS改善了LBM,PCE和SCORE2的C指数 (高达0.733).
- LBM表现出良好的校准,在添加PRS后略有改善,并显示出4.30%的净重新分类改进.
结论:
- 结合生活方式和人体测量数据的非实验室的LBM提供了适度的CHD风险预测准确度.
- 添加PRS可以提高基于生活方式的模型的预测性能.
- 需要进一步的外部验证来确认开发的LBM的概括性.
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