将多基因分数整合到指南推的心血管疾病预测中
Ling Li1,2,3, Shichao Pang1, Fabian Starnecker1,2
1Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany.
European heart journal
|March 29, 2024
概括
将多基因风险评分 (PRS) 与SCORE2等临床工具相结合,可以改善心血管疾病 (CVD) 风险预测. 这种乘法模型通过整合遗传和临床风险因素来提高准确性,以获得更好的结果.
科学领域:
- 心血管医学 心血管医学
- 遗传学 遗传学 是一个
- 流行病学 流行病学
背景情况:
- 指南推的心血管疾病 (CVD) 风险预测工具,如SCORE2,有局限性.
- 多基因风险评分 (PRS) 与现有的临床风险评估工具的最佳整合尚不清楚.
研究的目的:
- 评估将多基因风险评分 (PRS) 与指南推的心血管疾病 (CVD) 风险预测工具相结合的最佳方法.
- 评估一个整合SCORE2和PRS的乘法模型的临床实用性,用于CVD风险分层.
主要方法:
- 在英国生物库参与者中计算了冠状动脉疾病 (CAD) PRS (n=432,981) 并在Framingham/ARIC (n=10,757) 验证.
- 在临床风险层中评估了PRS和CVD几率比率 (ORs) 之间的关系.
- 测试了用于风险重新分类和预测改进的乘法模型 (SCORE2 × PRS因子).
主要成果:
- 多基因风险评分 (PRS) 和SCORE2表明对心血管疾病 (CVD) 风险有独立的贡献.
- 一个乘法模型 (SCORE2 × PRS因子) 显著将中等风险的个人重新分类到高风险类别.
- 根据PRS因子进行重新分类,发现与仍然处于中等风险的人相比,心血管疾病发病率高出两倍的个体.
结论:
- 绝对心脏病风险 (临床分数) 和相对遗传风险 (PRS) 提供了补充信息.
- 结合临床风险分数和PRS的简单乘法模型可以提高心血管疾病风险预测工具的精度.
- 这种综合方法可以更好地识别高风险发生心血管疾病的个体.
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