Urbut Sarah M, Cho So Mi Jemma, Paruchuri Kaavya, Truong Buu, Haidermota Sara, Peloso Gina M, Hornsby Whitney E, Philippakis Anthony, Fahed Akl C, Natarajan Pradeep
Division of Cardiology (S.M.U., K.P., B.T., A.C.F., P.N.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston.
Center for Genomic Medicine (S.M.U., S.M.J.C., K.P., B.T., S.H., W.E.H., A.C.F., P.N.), Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston.
Circ Genom Precis Med. 2025 Feb;18(1):e004681. doi: 10.1161/CIRCGEN.124.004681. Epub 2025 Jan 24.
Earlier identification of high coronary artery disease (CAD) risk individuals may enable more effective prevention strategies. However, existing 10-year risk frameworks are ineffective at earlier identification. We sought to understand how the variable importance of genomic and clinical factors across life stages may significantly improve lifelong CAD event prediction.
A longitudinal study was performed using data from 2 cohort studies: the FOS (Framingham Offspring Study) with 3588 participants aged 19 to 57 years and the UKB (UK Biobank) with 327 837 participants aged 40 years to 70 years. A total of 134 765 and 3 831 734 person-time years were observed in FOS and UKB, respectively. Hazard ratios for CAD were calculated for polygenic risk score (PRS) and clinical risk factors at each age of enrollment. The relative importance of PRS and pooled cohort equations in predicting CAD events was also evaluated by age groups.
The importance of CAD PRS diminished over the life course, with a hazard ratio of 3.58 (95% CI, 1.39-9.19) at the age of 19 years in FOS and a hazard ratio of 1.51 (95% CI, 1.48-1.54) by the age of 70 years in UKB. Clinical risk factors exhibited similar age-dependent trends. PRS significantly outperformed pooled cohort equations in identifying subsequent CAD events in the 40- to 45-year age group, with 3.2-fold more appropriately identified events. Overall, adding PRS improved the area under the receiving operating curve of the pooled cohort equations by an average of +5.1% (95% CI, 4.9%-5.2%) across all age groups; among individuals <55 years, PRS augmented the area under the receiver operater curve (ROC) of the pooled cohort equations by 6.5% (95% CI, 5.5%-7.5%; <0.001).
Genomic and clinical risk factors for CAD display time-varying importance across the lifespan. The study underscores the added value of CAD PRS, particularly among individuals younger than 55 years, for enhancing early risk prediction and prevention strategies. All results are available at https://surbut.github.io/dynamicHRpaper/index.html.
更早识别出高冠状动脉疾病（CAD）风险个体可能会使预防策略更有效。然而，现有的10年风险框架在早期识别方面效果不佳。我们试图了解基因组和临床因素在不同生命阶段的可变重要性如何显著改善对CAD事件的终身预测。
使用来自2个队列研究的数据进行了一项纵向研究：弗雷明汉后代研究（FOS），有3588名年龄在19至57岁的参与者；以及英国生物银行（UKB），有327837名年龄在40至70岁的参与者。在FOS和UKB中分别观察到总计134765和3831734人年。计算了每个入组年龄时多基因风险评分（PRS）和临床风险因素的CAD风险比。还按年龄组评估了PRS和合并队列方程在预测CAD事件中的相对重要性。
CAD PRS的重要性在生命过程中逐渐降低，在FOS中19岁时风险比为3.58（95%CI，1.39 - 9.19），在UKB中70岁时风险比为1.51（95%CI，1.48 - 1.54）。临床风险因素呈现出类似的年龄依赖性趋势。在40至45岁年龄组中，PRS在识别后续CAD事件方面显著优于合并队列方程，正确识别的事件多出3.2倍。总体而言，添加PRS使合并队列方程的接受者操作曲线下面积在所有年龄组中平均提高了+5.1%（95%CI，4.9% - 5.2%）；在年龄小于55岁的个体中，PRS使合并队列方程的接受者操作曲线（ROC）下面积增加了6.5%（95%CI，5.5% - 7.5%；P<0.001）。
CAD的基因组和临床风险因素在整个生命周期中显示出随时间变化的重要性。该研究强调了CAD PRS的附加价值，特别是在55岁以下个体中，对于加强早期风险预测和预防策略具有重要意义。所有结果可在https://surbut.github.io/dynamicHRpaper/index.html获取。
