Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population

doi:10.1212/WNL.0000000000012263

Multicenter Study

. 2021 Aug 10;97(6):e619-e628.

doi: 10.1212/WNL.0000000000012263. Epub 2021 May 24.

Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population

Xiangfeng Lu¹, Xiaoge Niu², Chong Shen², Fangchao Liu², Zhongying Liu², Keyong Huang², Laiyuan Wang², Jianxin Li², Dongsheng Hu², Yingxin Zhao², Xueli Yang², Fanghong Lu¹, Xiaoqing Liu², Jie Cao², Shufeng Chen², Hongfan Li², Wuzhuang Tang², Zhanyun Ren², Ling Yu², Xianping Wu², Xigui Wu², Ying Li², Huan Zhang², Jianfeng Huang², Zhibin Hu², Hongbing Shen², Cristen J Willer², Dongfeng Gu²

Affiliations

¹ 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. xiangfenglu@sina.com.
² 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.

PMID: 34031205
PMCID: PMC8424497
DOI: 10.1212/WNL.0000000000012263

Multicenter Study

Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population

Xiangfeng Lu et al. Neurology. 2021.

. 2021 Aug 10;97(6):e619-e628.

doi: 10.1212/WNL.0000000000012263. Epub 2021 May 24.

Authors

Affiliations

¹ 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. xiangfenglu@sina.com.
² 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.

PMID: 34031205
PMCID: PMC8424497
DOI: 10.1212/WNL.0000000000012263

Abstract

Objective: To construct a polygenic risk score (PRS) for stroke and evaluate its utility in risk stratification and primary prevention for stroke.

Methods: 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.

Results: 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%).

Conclusions: Our metaPRS has great potential for risk stratification of stroke and identification of individuals who may benefit more from maintaining ideal CVH.

Classification of evidence: This study provides Class I evidence that metaPRS is predictive of stroke risk.

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Figures

**Figure 1. Study Design and Workflow**
AF = atrial fibrillation; AUC = area under the receiver operating characteristic curve; BMI = body mass index; CAD = coronary artery disease; DBP = diastolic blood pressure; GWAS = genome-wide association study; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; MAP = mean arterial pressure; OR = odds ratio; PP = pulse pressure; PRS = polygenic risk score; SBP = systolic blood pressure; SNP = single nucleotide polymorphism; T2D = type 2 diabetes; TC = total cholesterol; TG = triglycerides; WC = waist circumference.

**Figure 2. Lifetime Risk of Stroke Stratified by Polygenic Risk**
Age-as-time-scale and stratified Cox proportional hazards models with strata defined as cohorts were used to estimate the hazard ratios (HRs) (95% confidence intervals) and cumulative incidence curves of stroke by age 80 years adjusting for sex. PRS = polygenic risk score.

**Figure 3. Lifetime Risk of Stroke According to Polygenic Risk and Family History of Stroke**
Age-as-time-scale and stratified Cox proportional hazards models with strata defined as cohorts were used to estimate the hazard ratios (HRs) (95% confidence intervals) and cumulative incidence curves of stroke by age of 80 years adjusting for sex. PRS = polygenic risk score.

**Figure 4. Absolute Risk Reductions of Stroke From 4 Ideal Cardiometabolic Measurements Across Polygenic Risk Categories**
(A–D) Age-as-time-scale and stratified Cox proportional hazards models with strata defined as cohorts were used to estimate cumulative incidence of stroke by age of 80 years adjusting for sex. *p < 0.007 after Bonferroni correction. ARR = absolute risk reduction.

**Figure 5. Relative and Absolute Risk of Stroke According to Polygenic Risk and Cardiovascular Health (CVH) Profile**
Age-as-time-scale and stratified Cox proportional hazards models with strata defined as cohorts were used to estimate the hazard ratios (HRs) (95% confidence intervals) and cumulative incidence curves of stroke by age of 80 years adjusting for sex. Favorable CVH = 6–7 ideal metrics; intermediate CVH = 4–5 ideal metrics; unfavorable CVH = 0–3 ideal metrics. PRS = polygenic risk score.

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References

1. Zhou M, Wang H, Zeng X, et al. . Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145-1158. - PMC - PubMed
1. Malik R, Chauhan G, Traylor M, et al. . Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50(4):524-537. - PMC - PubMed
1. Montaner J, Ramiro L, Simats A, et al. . Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol. 2020;16(5):247-264. - PubMed
1. Buniello A, MacArthur JAL, Cerezo M, et al. . The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005-D1012. - PMC - PubMed
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[1] Zhou M, Wang H, Zeng X, et al. . Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145-1158. - PMC - PubMed

[2] Zhou M, Wang H, Zeng X, et al. . Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145-1158. - PMC - PubMed

[3] Malik R, Chauhan G, Traylor M, et al. . Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50(4):524-537. - PMC - PubMed

[4] Malik R, Chauhan G, Traylor M, et al. . Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50(4):524-537. - PMC - PubMed

[5] Montaner J, Ramiro L, Simats A, et al. . Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol. 2020;16(5):247-264. - PubMed

[6] Montaner J, Ramiro L, Simats A, et al. . Multilevel omics for the discovery of biomarkers and therapeutic targets for stroke. Nat Rev Neurol. 2020;16(5):247-264. - PubMed

[7] Buniello A, MacArthur JAL, Cerezo M, et al. . The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005-D1012. - PMC - PubMed

[8] Buniello A, MacArthur JAL, Cerezo M, et al. . The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47(D1):D1005-D1012. - PMC - PubMed

[9] Ibrahim-Verbaas CA, Fornage M, Bis JC, et al. . Predicting stroke through genetic risk functions: the CHARGE Risk Score Project. Stroke. 2014;45(2):403-412. - PMC - PubMed

[10] Ibrahim-Verbaas CA, Fornage M, Bis JC, et al. . Predicting stroke through genetic risk functions: the CHARGE Risk Score Project. Stroke. 2014;45(2):403-412. - PMC - PubMed

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Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population

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Development and Validation of a Polygenic Risk Score for Stroke in the Chinese Population

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