Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Influence of blood pressure polygenic risk scores and environmental factors on coronary artery disease in the Korean Genome and Epidemiology Study

Abstract

The present study aimed to investigate the association of blood pressure polygenic risk scores (BP PRSs) with coronary artery disease (CAD) in a Korean population and the interaction effects between PRSs and environmental factors on CAD. Data were derived from the Cardiovascular Disease Association Study (CAVAS; N = 5100) and the Health Examinee Study (HEXA; N = 58,623) within the Korean Genome and Epidemiology Study. PRSs for systolic and diastolic BP were calculated with the weighted allele sum of >200 single-nucleotide polymorphisms. Multivariable logistic regression models were used. BP PRSs were strongly associated with systolic BP (SBP), diastolic BP (DBP), and hypertension in both CAVAS and HEXA (p < 0.0001). PRSSBP was significantly associated with CAD in CAVAS, while PRSSBP and PRSDBP were significantly associated with CAD in HEXA. There was an interaction effect between the BP PRSs and environmental factors on CAD. The odds ratios (ORs) for CAD were 1.036 (95% confidence interval [CI], 1.016–1.055) for obesity, 1.028 (95% CI, 1.011–1.045) for abdominal obesity, 1.030 (95% CI, 1.009–1.050) for triglyceride, 1.024 (95% CI, 1.008–1.041) for high-density lipoprotein cholesterol, and 1.039 for smoking (95% CI, 1.003–1.077) in CAVAS. There was no significant interaction in HEXA, except between PRSDBP and triglyceride (OR, 1.012; 95% CI, 1.001–1.024). BP PRS was associated with an increased risk of hypertension and CAD. The interactions among PRSs and environmental risk factors increased the risk of CAD. Multi-component interventions to lower BP in the population via healthy behaviors are needed to prevent CAD regardless of genetic predisposition.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The effect of polygenic risk scores at high versus low risk levels of environmental factors for coronary artery disease.

Similar content being viewed by others

Data availability

Raw data are available from the National Biobank of Korea (https://nih.go.kr/biobank/cmm/main/engMainPage.do).

References

  1. Padmanabhan S, Dominiczak AF. Genomics of hypertension: the road to precision medicine. Nat Rev Cardiol. 2021;18:235–50.

    Article  CAS  PubMed  Google Scholar 

  2. Flint AC, Conell C, Ren X, Banki NM, Chan SL, Rao VA, et al. Effect of systolic and diastolic blood pressure on cardiovascular outcomes. N Engl J Med. 2019;381:243–51.

    Article  PubMed  Google Scholar 

  3. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392:1736–88.

    Article  Google Scholar 

  4. Said MA, van de Vegte YJ, Zafar MM, van der Ende MY, Raja GK, Verweij N, et al. Contributions of interactions between lifestyle and genetics on coronary artery disease risk. Curr Cardiol Rep. 2019;21:89.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Cardiometabolic Risk Working Group: Executive Committee. Cardiometabolic risk in Canada: a detailed analysis and position paper by the cardiometabolic risk working group. Can J Cardiol. 2011;27:e1–33.

    Article  Google Scholar 

  6. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1412–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Publisher correction: genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018;50:1755.

    Article  CAS  PubMed  Google Scholar 

  8. Lim JE, Kim HO, Rhee SY, Kim MK, Kim YJ, Oh B. Gene-environment interactions related to blood pressure traits in two community-based Korean cohorts. Genet Epidemiol. 2019;43:402–13.

    Article  PubMed  Google Scholar 

  9. Lim NK, Lee JY, Lee JY, Park HY, Cho MC. The role of genetic risk score in predicting the risk of hypertension in the Korean population: Korean Genome and Epidemiology Study. PLoS ONE. 2015;10:e0131603.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Krogager ML, Skals RK, Appel EVR, Schnurr TM, Engelbrechtsen L, Have CT, et al. Hypertension genetic risk score is associated with burden of coronary heart disease among patients referred for coronary angiography. PLoS ONE. 2018;13:e0208645.

    Article  Google Scholar 

  11. Havulinna AS, Kettunen J, Ukkola O, Osmond C, Eriksson JG, Kesäniemi YA, et al. A blood pressure genetic risk score is a significant predictor of incident cardiovascular events in 32,669 individuals. Hypertension. 2013;61:987–94.

    Article  CAS  PubMed  Google Scholar 

  12. Parcha V, Pampana A, Shetty NS, Irvin MR, Natarajan P, Lin HJ, et al. Association of a multiancestry genome-wide blood pressure polygenic risk score with adverse cardiovascular events. Circ Genom Precis Med. 2022;15:e003946.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Vaura F, Kauko A, Suvila K, Havulinna AS, Mars N, Salomaa V, et al. Polygenic risk scores predict hypertension onset and cardiovascular risk. Hypertension. 2021;77:1119–27.

    Article  CAS  PubMed  Google Scholar 

  14. Wan EYF, Fung WT, Schooling CM, Yeung SLA, Kwok MK, Yu YET, et al. Blood pressure and risk of cardiovascular disease in UK Biobank: a Mendelian Randomization Study. Hypertension. 2021;77:367–75.

    Article  CAS  PubMed  Google Scholar 

  15. Hüls A, Ickstadt K, Schikowski T, Krämer U. Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression. BMC Genet. 2017;18:55.

    Article  PubMed  PubMed Central  Google Scholar 

  16. San-Cristobal R, de Toro-Martín J, Vohl MC. Appraisal of gene-environment interactions in GWAS for evidence-based precision nutrition implementation. Curr Nutr Rep. 2022;11:563–73.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N Engl J Med. 2016;375:2349–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Said MA, Verweij N, van der Harst P. Associations of combined genetic and lifestyle risks with incident cardiovascular disease and diabetes in the UK biobank study. JAMA Cardiol. 2018;3:693–702.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kim Y, Han BG, KoGES group. Cohort profile: the Korean Genome and Epidemiology Study (KoGES) Consortium. Int J Epidemiol. 2017;46:1350.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Moon S, Kim YJ, Han S, Hwang MY, Shin DM, Park MY, et al. The Korea biobank array: design and identification of coding variants associated with blood biochemical traits. Sci Rep. 2019;9:1382.

    Article  PubMed  PubMed Central  ADS  Google Scholar 

  21. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Tyrrell J, Wood AR, Ames RM, Yaghootkar H, Beaumont RN, Jones SE, et al. Gene-obesogenic environment interactions in the UK Biobank study. Int J Epidemiol. 2017;46:559–75.

    PubMed  PubMed Central  Google Scholar 

  23. Hosmer DW, Hosmer T, Le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat Med. 1997;16:965–80.

    Article  CAS  PubMed  Google Scholar 

  24. Cheng JL, Wang AL, Wan J. Association between the M235T polymorphism of the AGT gene and cytokines in patients with hypertension. Exp Ther Med. 2012;3:509–12.

    Article  CAS  PubMed  Google Scholar 

  25. Liu DX, Zhang YQ, Hu B, Zhang J, Zhao Q. Association of AT1R polymorphism with hypertension risk: an update meta-analysis based on 28,952 subjects. J Renin Angiotensin Aldosterone Syst. 2015;6:898–909.

    Article  Google Scholar 

  26. Franklin SS, Larson MG, Khan SA, Wong ND, Leip EP, Kannel WB, et al. Does the relation of blood pressure to coronary heart disease risk change with aging? The Framingham Heart Study. Circulation. 2001;103:1245–9.

    Article  CAS  PubMed  Google Scholar 

  27. Smith JA, Ware EB, Middha P, Beacher L, Kardia SLR. Current applications of genetic risk scores to cardiovascular outcomes and subclinical phenotypes. Curr Epidemiol Rep. 2015;2:180–90.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kim H, Kim S, Han S, Rane PP, Fox KM, Qian Y, et al. Prevalence and incidence of atherosclerotic cardiovascular disease and its risk factors in Korea: a nationwide population-based study. BMC Public Health. 2019;19:1112.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was conducted with bioresources from the National Biobank of Korea, the Korean Disease Control and Prevention Agency, Republic of Korea (KBN-2021-002).

Funding

This work was supported by a National Research Foundation of Korea grant funded by the Korean Government (No. 2020R1A2C1014449).

Author information

Authors and Affiliations

Authors

Contributions

KW and EYL designed this study. KW and JEL contributed to the data processing. KW contributed to the statistical analysis and table preparation. All authors contributed to the manuscript writing and approved the final version of the manuscript.

Corresponding author

Correspondence to Eun Young Lee.

Ethics declarations

Competing interests

The authors declare no competing interest.

Ethics approval and consent to participate

Before data collection, the Korean Genome and Epidemiology study protocol was approved by the Institutional Review Board of the Korea National Institute of Health. Written informed consent was obtained from all participants. In addition, the present study was approved by the Institutional Review Board at Catholic Kkottongnae University.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Woo, K., Lim, J.E. & Lee, E.Y. Influence of blood pressure polygenic risk scores and environmental factors on coronary artery disease in the Korean Genome and Epidemiology Study. J Hum Hypertens 38, 221–227 (2024). https://doi.org/10.1038/s41371-023-00878-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41371-023-00878-y

Search

Quick links