From the Key Laboratory of Cardiovascular Epidemiology and Department of Epidemiology (Xiangfeng Lu, X.N., Fangchao Liu, Z.L., K.H., L.W., J.L., J.C., S.C., H.L., Xigui Wu, Y.L., J.H., D.G.), State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing; Department of Epidemiology and Biostatistics (C.S., Z.H., H.S.), Center for Global Health, School of Public Health, Nanjing Medical University; Department of Biostatistics and Epidemiology (D.H.), School of Public Health, Shenzhen University Health Science Center, Guangdong; Cardio-Cerebrovascular Control and Research Center (Y.Z., Fanghong Lu), Institute of Basic Medicine, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan; Tianjin Key Laboratory of Environment, Nutrition and Public Health (X.Y.), Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin; Division of Epidemiology (Xiaoqing Liu), Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou; Department of Neurology (W.T., Z.R.), Affiliated Yixing People's Hospital of Jiangsu University, People's Hospital of Yixing City, Yixing; Department of Cardiology (L.Y.), Fujian Provincial Hospital, Fuzhou; Center for Chronic and Noncommunicable Disease Control and Prevention (Xianping Wu), Sichuan Center for Disease Control and Prevention, Chengdu; Center for Genetic Epidemiology and Genomics (H.Z.), School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancer (H.S.), Chinese Academy of Medical Sciences (2019RU038); and Department of Internal Medicine, Division of Cardiovascular Medicine (C.J.W.), and Department of Human Genetics (C.J.W.), University of Michigan, Ann Arbor.
Neurology. 2021 Aug 10;97(6):e619-e628. doi: 10.1212/WNL.0000000000012263. Epub 2021 May 24.
To construct a polygenic risk score (PRS) for stroke and evaluate its utility in risk stratification and primary prevention for stroke.
Using a meta-analytic approach and large genome-wide association results for stroke and stroke-related traits in East Asians, we generated a combined PRS (metaPRS) by incorporating 534 genetic variants in a training set of 2,872 patients with stroke and 2,494 controls. We then validated its association with incident stroke using Cox regression models in large Chinese population-based prospective cohorts comprising 41,006 individuals.
During a total of 367,750 person-years (mean follow-up 9.0 years), 1,227 participants developed stroke before age 80 years. Individuals with high polygenic risk had an about 2-fold higher risk of incident stroke compared with those with low polygenic risk (hazard ratio [HR] 1.99, 95% confidence interval [CI] 1.66-2.38), with the lifetime risk of stroke being 25.2% (95% CI 22.5%-27.7%) and 13.6% (95% CI 11.6%-15.5%), respectively. Individuals with both high polygenic risk and family history displayed lifetime risk as high as 41.1% (95% CI 31.4%-49.5%). Individuals with high polygenic risk achieved greater benefits in terms of absolute risk reductions from adherence to ideal fasting blood glucose and total cholesterol than those with low polygenic risk. Maintaining favorable cardiovascular health (CVH) profile could substantially mitigate the increased risk conferred by high polygenic risk to the level of low polygenic risk (from 34.6% to 13.2%).
Our metaPRS has great potential for risk stratification of stroke and identification of individuals who may benefit more from maintaining ideal CVH.
This study provides Class I evidence that metaPRS is predictive of stroke risk.
构建中风的多基因风险评分(PRS),并评估其在中风风险分层和一级预防中的效用。
我们采用荟萃分析方法和东亚人群中风和中风相关特征的大型全基因组关联研究结果,通过纳入训练集中 2872 例中风患者和 2494 例对照中的 534 个遗传变异,生成一个综合 PRS(metaPRS)。然后,我们使用 Cox 回归模型在中国大型基于人群的前瞻性队列中对其与中风发病的相关性进行验证,该队列包含 41006 名个体。
在总共 367750 人年(平均随访 9.0 年)中,有 1227 名参与者在 80 岁前发生中风。与低多基因风险者相比,高多基因风险者发生中风的风险约高 2 倍(风险比 [HR] 1.99,95%置信区间 [CI] 1.66-2.38),终生中风风险分别为 25.2%(95%CI 22.5%-27.7%)和 13.6%(95%CI 11.6%-15.5%)。同时具有高多基因风险和家族史的个体终生风险高达 41.1%(95%CI 31.4%-49.5%)。与低多基因风险者相比,高多基因风险者通过遵循理想空腹血糖和总胆固醇水平可获得更大的绝对风险降低获益。保持良好的心血管健康(CVH)评分可显著降低高多基因风险所带来的风险,使其达到低多基因风险的水平(从 34.6%降至 13.2%)。
我们的 metaPRS 对中风风险分层和识别可能从维持理想 CVH 中获益更多的个体具有很大的潜力。
本研究提供了 I 级证据,表明 metaPRS 可预测中风风险。