Validation of the Korean Genome Epidemiology Study Risk Score to Predict Incident Hypertension in a Large Nationwide Korean Cohort
Background:This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.Methods and Results:Thi...
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| Published in | Circulation Journal Vol. 80; no. 7; pp. 1578 - 1582 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Japan
The Japanese Circulation Society
2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1346-9843 1347-4820 1347-4820 |
| DOI | 10.1253/circj.CJ-15-1334 |
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| Abstract | Background:This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.Methods and Results:This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ2=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343–0.365 and 0.542; 95% CI, 0.523–0.561, respectively).Conclusions:In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578–1582) |
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| AbstractList | This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.
This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ(2)=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343-0.365 and 0.542; 95% CI, 0.523-0.561, respectively).
In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578-1582). This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.BACKGROUNDThis study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ(2)=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343-0.365 and 0.542; 95% CI, 0.523-0.561, respectively).METHODS AND RESULTSThis study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ(2)=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343-0.365 and 0.542; 95% CI, 0.523-0.561, respectively).In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578-1582).CONCLUSIONSIn this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578-1582). Background:This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide sample, and compare its discrimination and calibration with the Framingham and blood pressure (BP)-only models.Methods and Results:This study analyzed 69,918 subjects without HT at baseline from the National Sample Cohort in the National Health Insurance Service database. We compared the Framingham, KoGES, and BP-only models for discrimination using area under the receiver-operating characteristic curves (AROC), calibration using goodness-of-fit tests, and reclassification ability using the continuous net reclassification improvement (NRI) and integrated discrimination improvement. Of 69,918 subjects, 18.6% developed HT during the follow-up. AROC was significantly higher for the KoGES (0.733) than for the Framingham (0.729) or BP-only (0.707) model. Recalibrated Framingham model underestimated HT incidence in all deciles (P<0.001). BP-only model overestimated risk in the lower deciles (P<0.001). KoGES model accurately predicted risk in all except the highest decile (χ2=14.85, P=0.062). The KoGES model led to a significant improvement in risk reclassification compared with the Framingham and BP-only models (NRI, 0.354; 95% confidence interval [CI], 0.343–0.365 and 0.542; 95% CI, 0.523–0.561, respectively).Conclusions:In this validation study, the KoGES model demonstrated better discrimination, calibration, and reclassification ability than either the Framingham or BP-only model. The KoGES model may help identify Korean individuals at high risk for HT. (Circ J 2016; 80: 1578–1582) |
| Author | Lee, Joung-Won Park, Hyun-Young Lim, Nam-Kyoo |
| Author_xml | – sequence: 1 fullname: Lim, Nam-Kyoo organization: Division of Cardiovascular and Rare Disease, Korea National Institute of Health – sequence: 1 fullname: Lee, Joung-Won organization: Division of Cardiovascular and Rare Disease, Korea National Institute of Health – sequence: 1 fullname: Park, Hyun-Young organization: Division of Cardiovascular and Rare Disease, Korea National Institute of Health |
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| CitedBy_id | crossref_primary_10_1038_s41371_021_00645_x crossref_primary_10_1371_journal_pone_0294148 crossref_primary_10_1371_journal_pone_0187240 crossref_primary_10_1038_s41598_024_56170_7 crossref_primary_10_2188_jea_JE20160149 |
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| References_xml | – reference: 23. Burchard EG, Ziv E, Coyle N, Gomez SL, Tang H, Karter AJ, et al. The importance of race and ethnic background in biomedical research and clinical practice. N Engl J Med 2003; 348: 1170–1175. – reference: 2. Chen J. Epidemiology of hypertension and chronic kidney disease in China. Curr Opin Nephrol Hypertens 2010; 19: 278–282. – reference: 1. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380: 2224–2260. – reference: 5. World health Organization. A global brief on hypertension: Silent killer, global public health crisis. Geneva: WHO, 2013. – reference: 14. Otsuka T, Kachi Y, Takada H, Kato K, Kodani E, Ibuki C, et al. Development of a risk prediction model for incident hypertension in a working-age Japanese male population. Hypertens Res 2015; 38: 419–425. – reference: 8. The Trials of Hypertension Prevention Collaborative Research Group. Effects of weight loss and sodium reduction intervention on blood pressure and hypertension incidence in overweight people with high-normal blood pressure: The Trials of Hypertension Prevention, phase II. Arch Intern Med 1997; 157: 657–667. – reference: 17. Weiner DE, Tighiouart H, Elsayed EF, Griffith JL, Salem DN, Levey AS, et al. The Framingham predictive instrument in chronic kidney disease. J Am Coll Cardiol 2007; 50: 217–224. – reference: 29. Shai I, Jiang R, Manson JE, Stampfer MJ, Willett WC, Colditz GA, et al. Ethnicity, obesity, and risk of type 2 diabetes in women: A 20-year follow-up study. Diabetes Care 2006; 29: 1585–1590. – reference: 33. Mitchell BD, Kammerer CM, Blangero J, Mahaney MC, Rainwater DL, Dyke B, et al. Genetic and environmental contributions to cardiovascular risk factors in Mexican Americans: The San Antonio Family Heart Study. Circulation 1996; 94: 2159–2170. – reference: 31. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship. Obes Rev 2002; 3: 141–146. – reference: 32. Schulman KA, Berlin JA, Harless W, Kerner JF, Sistrunk S, Gersh BJ, et al. The effect of race and sex on physicians’ recommendations for cardiac catheterization. N Engl J Med 1999; 340: 618–626. – reference: 11. Kivimaki M, Batty GD, Singh-Manoux A, Ferrie JE, Tabak AG, Jokela M, et al. Validating the Framingham Hypertension Risk Score: Results from the Whitehall II study. Hypertension 2009; 54: 496–501. – reference: 21. 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: 965–980. – reference: 12. Chien KL, Hsu HC, Su TC, Chang WT, Sung FC, Chen MF, et al. Prediction models for the risk of new-onset hypertension in ethnic Chinese in Taiwan. J Hum Hypertens 2011; 25: 294–303. – reference: 28. Ohira T, Iso H. Cardiovascular disease epidemiology in Asia: An overview. Circ J 2013; 77: 1646–1652. – reference: 16. D’Agostino RB Sr, Grundy S, Sullivan LM, Wilson P; CHD Risk Prediction Group. Validation of the Framingham coronary heart disease prediction scores: Results of a multiple ethnic groups investigation. JAMA 2001; 286: 180–187. – reference: 25. Cowie CC, Harris MI, Silverman RE, Johnson EW, Rust KF. Effect of multiple risk factors on differences between blacks and whites in the prevalence of non-insulin-dependent diabetes mellitus in the United States. Am J Epidemiol 1993; 137: 719–732. – reference: 4. Kario K, Ogawa H, Okumura K, Okura T, Saito S, Ueno T, et al. SYMPLICITY HTN-Japan: First randomized controlled trial of catheter-based renal denervation in Asian patients. 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| Snippet | Background:This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large... This study aimed to validate the Korean Genome Epidemiology Study (KoGES) risk score to predict the 4-year risk of hypertension (HT) in a large nationwide... |
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| SubjectTerms | Adult Blood Pressure - genetics Cohort Studies Databases, Genetic Female Follow-Up Studies Framingham risk score Genome, Human Humans Hypertension Hypertension - epidemiology Hypertension - genetics KoGES Male Middle Aged Models, Genetic Republic of Korea - epidemiology Risk Factors Risk score Validation |
| Title | Validation of the Korean Genome Epidemiology Study Risk Score to Predict Incident Hypertension in a Large Nationwide Korean Cohort |
| URI | https://www.jstage.jst.go.jp/article/circj/80/7/80_CJ-15-1334/_article/-char/en https://www.ncbi.nlm.nih.gov/pubmed/27238835 https://www.proquest.com/docview/1800403463 |
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