Korean Circ J.  2018 Aug;48(8):731-740. 10.4070/kcj.2018.0036.

Traditional and Genetic Risk Score and Stroke Risk Prediction in Korea

Affiliations
  • 1Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea. jsunha@yuhs.ac
  • 2Health Insurance Policy Research Institute, National Health Insurance Service, Wonju, Korea.
  • 3Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Seoul, Korea.

Abstract

BACKGROUND AND OBJECTIVES
Whether using both traditional risk factors and genetic variants for stroke as opposed to using either of the 2 alone improves the prediction of stroke risk remains unclear. The purpose of this study was to compare the predictability of stroke risk between models using traditional risk score (TRS) and genetic risk score (GRS).
METHODS
We used a case-cohort study from the Korean Cancer Prevention Study-II (KCPS-II) Biobank (n=156,701). We genotyped 72 single nucleotide polymorphisms (SNPs) identified in genome-wide association study (GWAS) on the KCPS-II sub-cohort members and stroke cases. We calculated GRS by summing the number of risk alleles. Prediction models with or without GRS were evaluated in terms of the area under the receiver operating characteristic curve (AUROC).
RESULTS
Sixteen out of 72 SNPs identified in GWAS showed significant associations with stroke, with an odds ratio greater than 2.0. For participants aged < 40 years, AUROCs for incident stroke were 0.58, 0.65, and 0.67 in models using modifiable TRS only, GRS only, and TRS plus GRS, respectively, showing that GRS only model had better prediction than TRS only. For participants aged ≥40 years, however, TRS only model had better prediction than GRS only model. Favorable levels of traditional risk were associated with significantly lower stroke risks within each genetic risk category.
CONCLUSIONS
TRS and GRS were both independently associated with stroke risk. Using genetic variants in addition to traditional risk factors may be the most accurate way of predicting stroke risk, particularly in relatively younger individuals.

Keyword

Risk factors; Epidemiologic methods; Genetics; Stroke

MeSH Terms

Alleles
Epidemiologic Methods
Genetics
Genome-Wide Association Study
Korea*
Odds Ratio
Polymorphism, Single Nucleotide
Risk Factors
ROC Curve
Stroke*

Figure

  • Figure 1 AUROC for incident stroke according to age groups. AUROC = area under the receiver operating characteristic curve; ROC = receiver operating characteristic.

  • Figure 2 A favorable traditional risk for stroke according to genetic risk category.


Cited by  2 articles

New Prediction Model for Stroke in Korean
Jang-Whan Bae
Korean Circ J. 2018;48(8):741-743.    doi: 10.4070/kcj.2018.0223.

Precision Medicine and Cardiovascular Health: Insights from Mendelian Randomization Analyses
Wes Spiller, Keum Ji Jung, Ji-Young Lee, Sun Ha Jee
Korean Circ J. 2020;50(2):91-111.    doi: 10.4070/kcj.2019.0293.


Reference

1. Statistics Korea. Annual Report on the Causes of Death Statistics 2012. Daejeon: Statistics Korea;2014.
2. Jee SH, Jung KJ, Jeon C, Kimm H, Yun YD, Kim IS. Smoking attributable risk and medical care cost in 2012 in Korea. J Health Inform Stat. 2014; 39:25–41.
3. Jee SH, Park JW, Lee SY, et al. Stroke risk prediction model: a risk profile from the Korean study. Atherosclerosis. 2008; 197:318–325.
Article
4. Williams FM, Carter AM, Hysi PG, et al. Ischemic stroke is associated with the ABO locus: the EuroCLOT study. Ann Neurol. 2013; 73:16–31.
5. Zhuo Y, Yu H, Yang Z, Zee B, Lee J, Kuang L. Prediction factors of recurrent stroke among Chinese adults using retinal vasculature characteristics. J Stroke Cerebrovasc Dis. 2017; 26:679–685.
Article
6. Damen JA, Hooft L, Schuit E, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016; 353:i2416.
Article
7. Morrison AC, Bare LA, Chambless LE, et al. Prediction of coronary heart disease risk using a genetic risk score: the Atherosclerosis Risk in Communities Study. Am J Epidemiol. 2007; 166:28–35.
Article
8. Janssens AC, van Duijn CM. Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet. 2008; 17:R166–R173.
Article
9. Hachiya T, Kamatani Y, Takahashi A, et al. Genetic predisposition to ischemic stroke: a polygenic risk score. Stroke. 2017; 48:253–258.
10. Ibrahim-Verbaas CA, Fornage M, Bis JC, et al. Predicting stroke through genetic risk functions: the CHARGE Risk Score Project. Stroke. 2014; 45:403–412.
11. Gage SH, Davey Smith G, Ware JJ, Flint J, Munafò MR. = E: what GWAS can tell us about the environment. PLoS Genet. 2016; 12:e1005765.
12. Achterberg S, Kappelle LJ, de Bakker PI, Traylor M, Algra A; SMART Study Group and the METASTROKE Consortium. No additional prognostic value of genetic information in the prediction of vascular events after cerebral ischemia of arterial origin: the PROMISe Study. PLoS One. 2015; 10:e0119203.
Article
13. Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature. 2009; 461:747–753.
Article
14. Kofler T, Thériault S, Bossard M, et al. Relationships of measured and genetically determined height with the cardiac conduction system in healthy adults. Circ Arrhythm Electrophysiol. 2017; 10:e004735.
Article
15. Lee SJ, Jee YH, Jung KJ, Hong S, Shin ES, Jee SH. Bilirubin and stroke risk using a mendelian randomization design. Stroke. 2017; 48:1154–1160.
Article
16. Jee YH, Lee SJ, Jung KJ, Jee SH. Alcohol intake and serum glucose levels from the perspective of a mendelian randomization design: the KCPS-II Biobank. PLoS One. 2016; 11:e0162930.
Article
17. Park JK, Kim KS, Kim CB, et al. The accuracy of ICD codes for cerebrovascular diseases in medical insurance claims. Korean J Prev Med. 2000; 33:76–82.
18. Bluher A, Devan WJ, Holliday EG, et al. Heritability of young- and old-onset ischaemic stroke. Eur J Neurol. 2015; 22:1488–1491.
Article
19. Traylor M, Rutten-Jacobs LC, Holliday EG, et al. Differences in common genetic predisposition to ischemic stroke by age and sex. Stroke. 2015; 46:3042–3047.
Article
20. Traylor M, Farrall M, Holliday EG, et al. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE Collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012; 11:951–962.
21. Wang D, Sun Y, Stang P, Berlin JA, Wilcox MA, Li Q. Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling. BMC Proc. 2009; 3:Suppl 7. S109.
Article
22. Kim J, Oh B, Lim JE, Kim MK. No interaction with alcohol consumption, but independent effect of C12orf51 (HECTD4) on type 2 diabetes mellitus in Korean adults aged 40–69 years: the KoGES_Ansan and Ansung Study. PLoS One. 2016; 11:e0149321.
Article
Full Text Links
  • KCJ
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr