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Interethnic differences in the accuracy of anthropometric indicators of obesity in screening for high risk of coronary heart disease

Abstract

Background:

Cut points for defining obesity have been derived from mortality data among Whites from Europe and the United States and their accuracy to screen for high risk of coronary heart disease (CHD) in other ethnic groups has been questioned.

Objective:

To compare the accuracy and to define ethnic and gender-specific optimal cut points for body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) when they are used in screening for high risk of CHD in the Latin-American and the US populations.

Methods:

We estimated the accuracy and optimal cut points for BMI, WC and WHR to screen for CHD risk in Latin Americans (n=18 976), non-Hispanic Whites (Whites; n=8956), non-Hispanic Blacks (Blacks; n=5205) and Hispanics (n=5803). High risk of CHD was defined as a 10-year risk 20% (Framingham equation). The area under the receiver operator characteristic curve (AUC) and the misclassification-cost term were used to assess accuracy and to identify optimal cut points.

Results:

WHR had the highest AUC in all ethnic groups (from 0.75 to 0.82) and BMI had the lowest (from 0.50 to 0.59). Optimal cut point for BMI was similar across ethnic/gender groups (27 kg/m2). In women, cut points for WC (94 cm) and WHR (0.91) were consistent by ethnicity. In men, cut points for WC and WHR varied significantly with ethnicity: from 91 cm in Latin Americans to 102 cm in Whites, and from 0.94 in Latin Americans to 0.99 in Hispanics, respectively.

Conclusion:

WHR is the most accurate anthropometric indicator to screen for high risk of CHD, whereas BMI is almost uninformative. The same BMI cut point should be used in all men and women. Unique cut points for WC and WHR should be used in all women, but ethnic-specific cut points seem warranted among men.

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References

  1. Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath Jr CW . Body-mass index and mortality in a prospective cohort of US adults. N Engl J Med 1999; 341: 1097–1105.

    Article  CAS  Google Scholar 

  2. Wilson PW, D'Agostino RB, Sullivan L, Parise H, Kannel WB . Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch Intern Med 2002; 162: 1867–1872.

    Article  Google Scholar 

  3. Lanas F, Avezum A, Bautista LE, Diaz R, Luna M, Islam S et al. Risk factors for acute myocardial infarction in Latin America: the INTERHEART Latin American study. Circulation 2007; 115: 1067–1074.

    Article  Google Scholar 

  4. Bautista LE, Orostegui M, Vera LM, Prada GE, Orozco LC, Herran OF . Prevalence and impact of cardiovascular risk factors in Bucaramanga, Colombia: results from the Countrywide Integrated Noncommunicable Disease Intervention Programme (CINDI/CARMEN) baseline survey. Eur J Cardiovasc Prev Rehabil 2006; 13: 769–775.

    Article  Google Scholar 

  5. Folsom AR, Stevens J, Schreiner PJ, McGovern PG . Body mass index, waist/hip ratio, and coronary heart disease incidence in African Americans and whites. Atherosclerosis Risk in Communities Study Investigators. Am J Epidemiol 1998; 148: 1187–1194.

    Article  CAS  Google Scholar 

  6. Lakka HM, Lakka TA, Tuomilehto J, Salonen JT . Abdominal obesity is associated with increased risk of acute coronary events in men. Eur Heart J 2002; 23: 706–713.

    Article  Google Scholar 

  7. Rimm EB, Stampfer MJ, Giovannucci E, Ascherio A, Spiegelman D, Colditz GA et al. Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men. Am J Epidemiol 1995; 141: 1117–1127.

    Article  CAS  Google Scholar 

  8. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163.

    Article  Google Scholar 

  9. Goh VH, Tain CF, Tong TY, Mok HP, Wong MT . Are BMI and other anthropometric measures appropriate as indices for obesity? A study in an Asian population. J Lipid Res 2004; 45: 1892–1898.

    Article  CAS  Google Scholar 

  10. Molarius A, Seidell JC . Selection of anthropometric indicators for classification of abdominal fatness--a critical review. Int J Obes Relat Metab Disord 1998; 22: 719–727.

    Article  CAS  Google Scholar 

  11. Alberti KG, Zimmet P, Shaw J . Metabolic syndrome—a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006; 23: 469–480.

    Article  CAS  Google Scholar 

  12. Ministerio de Salud de Chile PUCdC. Encuesta Nacional de Salud Chile 2003. Ministerio de Salud: Chile, 2003 (report).

  13. Gómez LF, Samper B, Espinosa G, Mateus JG, Gomez LC . Factores de riesgo Resultados obtenidos en el área demostrativa CARMEN. Bol Epidemiol Distrital 2004; 9: 4–13.

    Google Scholar 

  14. Pichardo R . Estudio de factores de riesgo cardiovascular en la República Dominicana (EFRICARD) 1996–1998. Arch Domin Cardiol 1998; 2: 3.

    Google Scholar 

  15. Ministerio de Salud. Encuesta Nacional de Indicadores Nutricionales, Bioquímicos, Socioeconómicos y Culturales Relacionados con las Enfermedades Crónico Degenerativas, 2006 (Unpublished report).

  16. Medina-Lezama J, Zea-Diaz H, Morey-Vargas OL, Bolanos-Salazar JF, Munoz-Atahualpa E, Postigo-MacDowall M et al. Prevalence of the metabolic syndrome in Peruvian Andean hispanics: the PREVENCION study. Diabetes Res Clin Pract 2007; 78: 270–281.

    Article  CAS  Google Scholar 

  17. Perez CM, Guzman M, Ortiz AP, Estrella M, Valle Y, Perez N et al. Prevalence of metabolic syndrome in San Juan, Puerto Rico. Ethn Dis 2008; 18: 434–441.

    PubMed  PubMed Central  Google Scholar 

  18. Florez H, Silva E, Fernandez V, Ryder E, Sulbaran T, Campos G et al. Prevalence and risk factors associated with the metabolic syndrome and dyslipidemia in white, black, Amerindian and mixed Hispanics in Zulia State, Venezuela. Diabetes Res Clin Pract 2005; 69: 63–77.

    Article  Google Scholar 

  19. National Center for Health Statistics Centers for Disease Control and Prevention.. Analytic And Reporting Guidelines: The Third National Health and Nutrition Examination Survey,NHANES III (1988–94). National Center for Health Statistics, Centers for Disease Control and Prevention., 1996, http://www.cdc.gov/nchs/data/nhanes/nhanes3/nh3gui.pdf. Accessed on 12/3/2008.

  20. National Center for Health Statistics Centers for Disease Control and Prevention. Analytic And Reporting Guidelines. The National Health and Nutrition Examination Survey (NHANES). National Center for Health Statistics Centers for Disease Control and Prevention, 2006, http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf. (accessed 12/3/2008).

  21. Frohlich ED, Grim C, Labarthe DR, Maxwell MH, Perloff D, Weidman WH . Recommendations for Human Blood Pressure Determination by Sphygmomanometers: Report of a Special Task Force Appointed by the Steering Committee, American Heart Association. Hypertension 1988; 11: 209A–222A.

    Article  Google Scholar 

  22. Korn EL, Graubard BI . Analyses using multiple surveys. In: Korn EL, Graubard BI (eds). Analysis of Health Surveys. Wiley: New York, 1999, pp 159–191.

    Chapter  Google Scholar 

  23. Perloff D, Grim C, Flack J, Frohlich ED, Hill M, McDonald M et al. Human blood pressure determination by sphygmomanometry. Circulation 1993; 88 (Pt 1): 2460–2470.

    Article  CAS  Google Scholar 

  24. National Center for Health Statistics Centers for Disease Control and Prevention. NHANES 1999–2000 Addendum to the NHANES III Analytic Guidelines. National Center for Health Statistics Centers for Disease Control and Prevention 2002, http://www.cdc.gov/nchs/data/nhanes/guidelines1.pdf (accessed 12/3/2008).

  25. Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB . Prediction of coronary heart disease using risk factor categories. Circulation 1998; 97: 1837–1847.

    Article  CAS  Google Scholar 

  26. Wood D, De Backer G, Faergeman O, Graham I, Mancia G, Pyorala K . Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and other Societies on Coronary Prevention. Atherosclerosis 1998; 140: 199–270.

    Article  CAS  Google Scholar 

  27. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III).. Third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002; 106: 3143–3421.

    Article  Google Scholar 

  28. Van Buuren S, Boshuizen HC, Knook DL . Multiple imputation of missing blood pressure covariates in survival analysis. Stat Med 1999; 18: 681–694.

    Article  CAS  Google Scholar 

  29. Rubin D . Multiple Imputation for Non-Response in Surveys. Wiley: New York, NY, 1987.

    Book  Google Scholar 

  30. King G, Honaker J, Joseph A, Scheve K . Analyzing incomplete political science data: an alternative algorithm for multiple imputation. Am Polit Sci Rev 2001; 95: 49–69.

    Google Scholar 

  31. Hanley JA, Hajian-Tilaki KO . Sampling variability of nonparametric estimates of the areas under receiver operating characteristic curves: an update. Acad Radiol 1997; 4: 49–58.

    Article  CAS  Google Scholar 

  32. Pepe MS, Cai T . The analysis of placement values for evaluating discriminatory measures. Biometrics 2004; 60: 528–535.

    Article  Google Scholar 

  33. Dodd LE, Pepe MS . Partial AUC estimation and regression. Biometrics 2003; 59: 614–623.

    Article  Google Scholar 

  34. Greiner M, Pfeiffer D, Smith RD . Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45: 23–41.

    Article  CAS  Google Scholar 

  35. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. WHO: Geneva, 1997.

  36. Bray GA . Overweight is risking fate. Definition, classification, prevalence, and risks. Ann NY Acad Sci 1987; 499: 14–28.

    Article  CAS  Google Scholar 

  37. Dersimonian R, Kacker R . Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 2007; 28: 105–114.

    Article  Google Scholar 

  38. Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005; 366: 1640–1649.

    Article  Google Scholar 

  39. Canoy D, Boekholdt SM, Wareham N, Luben R, Welch A, Bingham S et al. Body fat distribution and risk of coronary heart disease in men and women in the European prospective investigation into cancer and nutrition in Norfolk cohort: a population-based prospective study. Circulation 2007; 116: 2933–2943.

    Article  Google Scholar 

  40. Okosun IS, Chandra KM, Choi S, Christman J, Dever GE, Prewitt TE . Hypertension and type 2 diabetes comorbidity in adults in the United States: risk of overall and regional adiposity. Obes Res 2001; 9: 1–9.

    Article  CAS  Google Scholar 

  41. Okosun IS, Tedders SH, Choi S, Dever GE . Abdominal adiposity values associated with established body mass indexes in white, black and Hispanic Americans. A study from the Third National Health and Nutrition Examination Survey. Int J Obes Relat Metab Disord 2000; 24: 1279–1285.

    Article  CAS  Google Scholar 

  42. Sanchez-Castillo CP, Velazquez-Monroy O, Berber A, Lara-Esqueda A, Tapia-Conyer R, James WP . Anthropometric cutoff points for predicting chronic diseases in the Mexican National Health Survey 2000. Obes Res 2003; 11: 442–451.

    Article  Google Scholar 

  43. Pitanga FJ, Lessa I . [Anthropometric indexes of obesity as an instrument of screening for high coronary risk in adults in the city of Salvador—Bahia]. Arq Bras Cardiol 2005; 85: 26–31.

    Article  Google Scholar 

  44. Zhu S, Wang Z, Heshka S, Heo M, Faith MS, Heymsfield SB . Waist circumference and obesity-associated risk factors among whites in the third National Health and Nutrition Examination Survey: clinical action thresholds. Am J Clin Nutr 2002; 76: 743–749.

    Article  CAS  Google Scholar 

  45. Wang J, Thornton JC, Bari S, Williamson B, Gallagher D, Heymsfield SB et al. Comparisons of waist circumferences measured at 4 sites. Am J Clin Nutr 2003; 77: 379–384.

    Article  Google Scholar 

  46. Hoffman DJ, Wang Z, Gallagher D, Heymsfield SB . Comparison of visceral adipose tissue mass in adult African Americans and whites. Obes Res 2005; 13: 66–74.

    Article  Google Scholar 

  47. D'Agostino Sr RB, Grundy S, Sullivan LM, Wilson P . Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 2001; 286: 180–187.

    Article  Google Scholar 

  48. Fustinoni O, Biller J . Ethnicity and stroke: beware of the fallacies. Stroke 2000; 31: 1013–1015.

    Article  CAS  Google Scholar 

  49. Kaplan JB, Bennett T . Use of race and ethnicity in biomedical publication. JAMA 2003; 289: 2709–2716.

    Article  Google Scholar 

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Acknowledgements

We thank Dr Paula Margozzini (Pontificia Universidad Católica de Chile); Claudia González (Ministerio de Salud de Chile); Dr Luis A Santa María (Instituto Nacional de Salud, Perú); Dr Manuel Guzmán and Dr Lillian Haddock (University of Puerto Rico, School of Medicine, Puerto Rico); and Professors José Villasmil and Mairelis Nuváez (Universidad del Zulia). The ‘Encuesta Nacional de Salud 2003’ was funded by the Ministerio de Salud de Chile. The ‘Encuesta Nacional de Indicadores Nutricionales, Bioquímicos, Socioeconómicos y Culturales Relacionados con las Enfermedades Crónico Degenerativas’ was funded by the Instituto Nacional de Salud, Lima, Perú. The study of the ‘Prevalence of Metabolic Syndrome and its Individual Components in the adult population of the San Juan Metropolitan Area in Puerto Rico’ was funded by an unrestricted grant from Merck Sharp & Dohme Corporation with additional support from the National Institutes of Health/National Center for Research Resources (NCRR/NIH) grant awards G12RR03051 and P20RR011126. CARMEN-Bucaramanga was funded by the Secretaría de Salud Municipal de Bucaramanga and the Secretaría Departamental de Salud de Santander. The ‘Prueba de Validación de la Encuesta Nacional de Factores de Riesgo’ was funded by the Ministerio de Salud de la República Argentina and the Gobierno de la Provincia de Tierra del Fuego, Antártida e Islas del Atlántico Sur, Argentina. The ‘Encuesta Nacional de Factores de Riesgo’ was funded by the Ministerio de Salud de la República Argentina. The ‘Estudio CARMEN Santa Fe’ was funded by the Secretaría Distrital de Salud de Bogotá. The Zulia Coronary Heart Disease Risk Factor Study was funded by the Fondo Nacional de Ciencia, Tecnología e Innovación (FONACIT) and the Fundación Venezolana de Hipertensión Arterial (FUNDAHIPERTENSION).

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Correspondence to L E Bautista.

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Supplementary Information accompanies the paper on International Journal of Obesity website (http://www.nature.com/ijo)

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Herrera, V., Casas, J., Miranda, J. et al. Interethnic differences in the accuracy of anthropometric indicators of obesity in screening for high risk of coronary heart disease. Int J Obes 33, 568–576 (2009). https://doi.org/10.1038/ijo.2009.35

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