Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores: evidence from the China Kadoorie Biobank.
10.1097/CM9.0000000000002694
- Author:
Songchun YANG
1
;
Dong SUN
1
;
Zhijia SUN
1
;
Canqing YU
1
;
Yu GUO
2
;
Jiahui SI
1
;
Dianjianyi SUN
1
;
Yuanjie PANG
1
;
Pei PEI
3
;
Ling YANG
4
;
Iona Y MILLWOOD
4
;
Robin G WALTERS
4
;
Yiping CHEN
4
;
Huaidong DU
4
;
Zengchang PANG
5
;
Dan SCHMIDT
6
;
Rebecca STEVENS
6
;
Robert CLARKE
6
;
Junshi CHEN
7
;
Zhengming CHEN
6
;
Jun LV
1
;
Liming LI
1
Author Information
1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
2. Fuwai Hospital Chinese Academy of Medical Sciences, Beijing 100730, China.
3. Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China.
4. Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK.
5. Qingdao Center of Disease Control and Prevention, Qingdao, Shandong 266033, China.
6. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK.
7. China National Center for Food Safety Risk Assessment, Beijing 100738, China.
- Collective Name:China Kadoorie Biobank Collaborative Group
- Publication Type:Journal Article
- MeSH:
Male;
Humans;
Female;
Coronary Artery Disease/genetics*;
Biological Specimen Banks;
East Asian People;
Risk Assessment/methods*;
Genetic Predisposition to Disease/genetics*;
Risk Factors;
Genome-Wide Association Study
- From:
Chinese Medical Journal
2023;136(20):2476-2483
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population.
METHODS:Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately.
RESULTS:In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model.
CONCLUSIONS:In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.
