PDF(Pubmed)
Abstract：
UNASSIGNED: Coronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare.
UNASSIGNED: We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression.
UNASSIGNED: We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.
UNASSIGNED: We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression.
UNASSIGNED: We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.
摘要：
■冠状动脉疾病(CAD)是一种高度可遗传的多因素疾病。许多全基因组关联研究（GWAS）促进了多基因风险评分（PRS）的构建，以预测CAD的未来发病率。然而,仅在欧洲人口中。此外，识别具有全因死亡风险升高的CAD患者是二级预防的关键挑战，这将在很大程度上有助于减轻公共医疗保健的负担。
■我们招募了1,776名中国CAD患者，并进行了长达11年的医学随访。使用修剪和阈值法计算CAD的PRS及其14个危险因素。通过Cox回归计算它们与全因死亡的相关性。
■我们发现CAD的PRS及其七个危险因素，即心肌梗塞，缺血性卒中,心绞痛,心力衰竭,低密度脂蛋白胆固醇，总胆固醇和C反应蛋白，与死亡显著相关(P≤0.05)，而体重指数的PRS表现出中等相关性（P<0.1）。使用5倍交叉验证的Elastic-netCox回归将这9个PRS模型整合到一个meta评分中，metaPRS,在对不同死亡风险的患者进行分层方面表现良好（P<0.0001）。将metaPRS与临床危险因素相结合，进一步增加了辨别能力和4％的敏感性。由对CAD的遗传易感性及其危险因素产生的metaPRS可以很好地根据CAD患者的死亡风险对其进行分层。整合metaPRS和临床危险因素可能有助于确定预后不良风险较高的患者。
■我们招募了1,776名中国CAD患者，并进行了长达11年的医学随访。使用修剪和阈值法计算CAD的PRS及其14个危险因素。通过Cox回归计算它们与全因死亡的相关性。
■我们发现CAD的PRS及其七个危险因素，即心肌梗塞，缺血性卒中,心绞痛,心力衰竭,低密度脂蛋白胆固醇，总胆固醇和C反应蛋白，与死亡显著相关(P≤0.05)，而体重指数的PRS表现出中等相关性（P<0.1）。使用5倍交叉验证的Elastic-netCox回归将这9个PRS模型整合到一个meta评分中，metaPRS,在对不同死亡风险的患者进行分层方面表现良好（P<0.0001）。将metaPRS与临床危险因素相结合，进一步增加了辨别能力和4％的敏感性。由对CAD的遗传易感性及其危险因素产生的metaPRS可以很好地根据CAD患者的死亡风险对其进行分层。整合metaPRS和临床危险因素可能有助于确定预后不良风险较高的患者。
