Manikpurage Hasanga D, Ricard Jasmin, Houessou Ursula, Bourgault Jérôme, Gagnon Eloi, Gobeil Émilie, Girard Arnaud, Li Zhonglin, Eslami Aida, Mathieu Patrick, Bossé Yohan, Arsenault Benoit J, Thériault Sébastien
Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Québec, (QC), Canada.
Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec - Université Laval, Québec, (QC), Canada; Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec, (QC), Canada.
Atherosclerosis. 2025 Feb;401:119083. doi: 10.1016/j.atherosclerosis.2024.119083. Epub 2024 Dec 5.
Estimating the genetic risk of coronary artery disease (CAD) is now possible by aggregating data from genome-wide association studies (GWAS) into polygenic risk scores (PRS). Combining multiple PRS for specific circulating blood lipids could improve risk prediction. Here, we sought to evaluate the performance of PRS derived from CAD and blood lipids GWAS to predict the incidence of CAD.
This study included individuals aged between 40 and 69 from UK Biobank. We conducted GWAS for blood lipids measured by nuclear magnetic resonance in individuals without lipid-lowering treatments (n = 73,915). Summary statistics were used to derive PRS in the remaining participants (n = 318,051). A PRS was derived using the CARDIoGRAMplusC4D GWAS. Hazard ratios (HR) for CAD (n = 9017 out of 301,576; median follow-up: 12.6 years) were calculated per standard deviation increase in each PRS. Models' discrimination capacity and goodness-of-fit were evaluated.
Out of 30 PRS, 27 were significantly associated with the incidence of CAD (p < 0.0017). The optimal combination of PRS included PRS for CAD, VLDL-C, total cholesterol and triglycerides. Discriminative capacities were significantly increased in the model including PRS and clinical risk factors (CRF) (C-statistic = 0.778 [0.773-0.782]) compared to the model with CRF only (C-statistic = 0.755 [0.751-0.760], difference = 0.022 [0.020-0.025]). Although the C-statistic remained similar when independent lipids PRS were added to the model with PRS and CRF (C-statistic = 0.778 [0.773-0.783]), the goodness-of-fit was significantly increased (chi-square test statistic = 20.18, p = 1.56e-04).
Although independently associated with CAD incidence, blood lipids PRS provide modest improvement in the predictive performance when added to PRS.
通过将全基因组关联研究（GWAS）数据汇总为多基因风险评分（PRS），现在可以估计冠状动脉疾病（CAD）的遗传风险。结合针对特定循环血脂的多个PRS可改善风险预测。在此，我们试图评估源自CAD和血脂GWAS的PRS预测CAD发病率的性能。
本研究纳入了英国生物银行中年龄在40至69岁之间的个体。我们对未接受降脂治疗的个体（n = 73,915）通过核磁共振测量的血脂进行了GWAS。汇总统计数据用于在其余参与者（n = 318,051）中得出PRS。使用CARDIoGRAMplusC4D GWAS得出一个PRS。计算每个PRS每增加一个标准差时CAD（301,576人中n = 9017；中位随访时间：12.6年）的风险比（HR）。评估模型的辨别能力和拟合优度。
在30个PRS中，27个与CAD发病率显著相关（p < 0.0017）。PRS的最佳组合包括CAD、极低密度脂蛋白胆固醇（VLDL-C）、总胆固醇和甘油三酯的PRS。与仅包含临床风险因素（CRF）的模型相比，包含PRS和临床风险因素（CRF）的模型的辨别能力显著提高（C统计量 = 0.778 [0.773 - 0.782]）（仅CRF模型的C统计量 = 0.755 [0.751 - 0.760]，差异 = 0.022 [0.020 - 0.025]）。尽管当将独立的血脂PRS添加到包含PRS和CRF的模型中时C统计量保持相似（C统计量 = 0.778 [0.773 - 0.783]），但拟合优度显著提高（卡方检验统计量 = 20.18，p = 1.56e - 04）。
尽管血脂PRS与CAD发病率独立相关，但添加到PRS中时，其在预测性能方面的改善有限。
