Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany.
Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.
Eur Heart J. 2024 May 27;45(20):1843-1852. doi: 10.1093/eurheartj/ehae048.
It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2.
A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)-referred to as PRS-factor-for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model 'SCORE2 × PRS-factor' was tested by risk reclassification.
In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC.
This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.
目前尚不清楚如何将多基因风险评分(PRS)与心血管疾病(CVD)风险预测的指南推荐工具(如 SCORE2)最佳结合。
在英国生物库(UK Biobank)的参与者中计算了冠心病(CAD)的 PRS。在 PRS 分布的每十分之一内,比较了整个人群与代表临床风险谱的亚组之间 CVD(即 CAD 或中风)的比值比(OR)-称为 PRS 因子。在合并的弗雷明汉/动脉粥样硬化风险社区(ARIC)人群(n = 10757)中进行了复制。通过风险再分类测试了乘法模型“ SCORE2×PRS 因子”的临床适用性。
在临床风险差异很大的亚组中,CVD 的 OR 在每个 PRS 十分位内保持稳定。SCORE2 和 PRS 对 CVD 风险没有显著的交互作用,因此它们可作为乘法因素:SCORE2×PRS 因子=总风险。在 UK Biobank 中,乘法模型将 9.55%(n = 145337)的中等风险(n = 145337)人群转移到高风险组,使该类人群增加了 56.6%。通过 PRS 因子从中等风险重新分类为高风险的个体中,有 8.08%发生了 CVD 事件,是仍处于中等风险的个体(4.08%)的两倍。同样,PRS 因子将Framingham/ARIC 中 8.29%的个体从中度风险转移到高风险。
本研究表明,由临床风险评分确定的绝对 CVD 风险和由 PRS 确定的相对遗传风险提供了独立的信息。这两个组成部分可以形成一个简单的乘法模型,提高指南推荐工具预测 CVD 事件的准确性。