ARYA Atherosclerosis Journal

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

Cut-off values of obesity indices to predict coronary heart disease incidence by time-dependent receiver operating characteristic curve analysis in 10-year follow-up in study of Yazd Healthy Heart Cohort, Iran

Document Type : Original Article(s)

Authors

1 Center for Healthcare Data Modeling, Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

2 Research Center of Prevention and Epidemiology of Non-Communicable Disease , Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

3 Yazd Cardiovascular Research Center, Center for Healthcare Data Modeling, Department of Biostatistics and Epidemiology, School of Public Health, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

Abstract

BACKGROUND: The current study aimed to determine the optimal cut-off of obesity indices for detecting coronary heart disease (CHD) in 10-year study of Yazd Healthy Heart Cohort (YHHC) in Iran.
METHODS: A total of 2000 individuals aged 20-74 years were enrolled. All participants without cardiovascular disease (CVD) had a full medical check-up at the start of the study. At the start of the study, a variety of obesity indices were assessed and calculated, including body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHpR), waist-to-height ratio (WHtR), A Body Shape Index (ABSI), abdominal volume index (AVI), body adiposity index (BAI), and body roundness index (BRI). Coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), myocardial infarction (MI), Rose Angina Questionnaire (RAQ) (chest pain) greater than 3, and electrocardiographic (ECG) changes in favour of the coronary artery disease (CAD) were considered as the CVD risks. A time-dependent receiver operating characteristic (ROC) curve with right-censored data and naive estimator was used to estimate the time-dependent sensitivity and specificity and the best cut-off of the anthropometric indices for CHD risk.
RESULTS: Overall, 1623 participants (818 men and 805 women) with mean and standard deviation (SD) of weight of 71.21 ± 12.94 kg were included. In a 10-year follow-up, 101 [59 (58.42%) men and 42 (41.58%) women] developed CVD event. WHpR demonstrated the largest area under the time-dependent ROC curve (AUC) of 0.65 and 0.63 as well as 95% confidence interval (CI) of 58.64-72.66 and 50.74-75.55 for men and women, respectively, in predicting CVD. Optimal WHpR cut-off was 0.93 and 0.92, respectively, for men and women.
CONCLUSION: WHpR index was superior to other obesity indices in predicting CHD.

Keywords

References
  1. World Health Organization. Noncommunicable diseases country profiles 2018. Geneva, Switzerland: World Health Organization; 2018.
  2. Bastien M, Poirier P, Lemieux I, Despres JP. Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog Cardiovasc Dis 2014; 56(4): 369-81.
  3. Gregg EW, Shaw JE. Global health effects of overweight and obesity. N Engl J Med 2017; 377(1): 80-1.
  4. Eknoyan G. Adolphe Quetelet (1796-1874)-the average man and indices of obesity. Nephrol Dial Transplant 2008; 23(1): 47-51.
  5. Coutinho T, Goel K, Correa de SD, Carter RE, Hodge DO, Kragelund C, et al. Combining body mass index with measures of central obesity in the assessment of mortality in subjects with coronary disease: Role of "normal weight central obesity". J Am Coll Cardiol 2013; 61(5): 553-60.
  6. 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(9497): 1640-9.
  7. Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J. Body mass index, waist circumference and waist: hip ratio as predictors of cardiovascular risk--a review of the literature. Eur J Clin Nutr 2010; 64(1): 16-22.
  8. 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(4): 743-9.
  9. Guerrero-Romero F, Rodriguez-Moran M. Abdominal volume index. An anthropometry-based index for estimation of obesity is strongly related to impaired glucose tolerance and type 2 diabetes mellitus. Arch Med Res 2003; 34(5): 428-32.
  10. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev 2010; 23(2): 247-69.
  11. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: Systematic review and meta-analysis. Obes Rev 2012; 13(3): 275-86.
  12. Moore SC. Waist versus weight: Which matters more for mortality? Am J Clin Nutr 2009; 89(4): 1003-4.
  13. Krakauer NY, Krakauer JC. A new body shape index predicts mortality hazard independently of body mass index. PLoS One 2012; 7(7): e39504.
  14. Haghighatdoost F, Sarrafzadegan N, Mohammadifard N, Asgary S, Boshtam M, Azadbakht L. Assessing body shape index as a risk predictor for cardiovascular diseases and metabolic syndrome among Iranian adults. Nutrition 2014; 30(6): 636-44.
  15. Maessen MF, Eijsvogels TM, Verheggen RJ, Hopman MT, Verbeek AL, de Vegt F. Entering a new era of body indices: the feasibility of a body shape index and body roundness index to identify cardiovascular health status. PLoS One 2014; 9(9): e107212.
  16. Ji M, Zhang S, An R. Effectiveness of A Body Shape Index (ABSI) in predicting chronic diseases and mortality: A systematic review and meta-analysis. Obes Rev 2018; 19(5): 737-59.
  17. Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG, et al. A better index of body adiposity. Obesity (Silver Spring) 2011; 19(5): 1083-9.
  18. Wang F, Chen Y, Chang Y, Sun G, Sun Y. New anthropometric indices or old ones: which perform better in estimating cardiovascular risks in Chinese adults. BMC Cardiovasc Disord 2018; 18(1): 14.
  19. Rao G, Powell-Wiley TM, Ancheta I, Hairston K, Kirley K, Lear SA, et al. Identification of obesity and cardiovascular risk in ethnically and racially diverse populations: A scientific statement from the American Heart Association. Circulation 2015; 132(5): 457-72.
  20. Pepe MS. The statistical evaluation of medical tests for classification and prediction. New York, NY: Oxford University Press; 2003.
  21. Kamarudin AN, Cox T, Kolamunnage-Dona R. Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med Res Methodol 2017; 17(1): 53.
  22. Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000; 56(2): 337-44.
  23. Silveira EA, da Silva Filho RR, Spexoto MCB, Haghighatdoost F, Sarrafzadegan N, de Oliveira C. The role of sarcopenic obesity in cancer and cardiovascular disease: A synthesis of the evidence on pathophysiological aspects and clinical implications. Int J Mol Sci 2021; 22(9): 4339.
  24. Aparecida SE, Vaseghi G, de Carvalho Santos AS, Kliemann N, Masoudkabir F, Noll M, et al. Visceral obesity and its shared role in cancer and cardiovascular disease: A scoping review of the pathophysiology and pharmacological treatments. Int J Mol Sci 2020; 21(23): 9042.
  25. Namayandeh Sm, Abbas R. Longitudinal study of blood pressure during 8 years; patterns and correlates: Yazd Healthy Heart Project. J Hypertens 2015; 5(1): 1000215.
  26. Chen HY, Chiu YL, Chuang YF, Hsu SP, Pai MF, Yang JY, et al. Visceral adiposity index and risks of cardiovascular events and mortality in prevalent hemodialysis patients. Cardiovasc Diabetol 2014; 13: 136.
  27. Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics 2005; 61(1): 92-105.
  28. R 3.5.3 for Windows [Online]. [cited 2018 Jul 2]; Available from: URL: https://cran.r-project.org/bin/windows/base/old/3.5.1
  29. Foucher Y. ROCt v0.9.5 [Online]. [cited 2017 Feb 19]; Available from: URL: https://www.rdocumentation.org/packages/ROCt/versions/0.9.5
  30. Perkins NJ, Schisterman EF. The inconsistency of "optimal" cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol 2006; 163(7): 670-5.
  31. Nicklas BJ, Penninx BW, Cesari M, Kritchevsky SB, Newman AB, Kanaya AM, et al. Association of visceral adipose tissue with incident myocardial infarction in older men and women: The Health, Aging and Body Composition Study. Am J Epidemiol 2004; 160(8): 741-9.
  32. Liu J, Tse LA, Liu Z, Rangarajan S, Hu B, Yin L, et al. Predictive values of anthropometric measurements for cardiometabolic risk factors and cardiovascular diseases among 44 048 chinese. J Am Heart Assoc 2019; 8(16): e010870.
  33. Hadaegh F, Zabetian A, Sarbakhsh P, Khalili D, James WP, Azizi F. Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7.6 years follow-up in an Iranian population. Int J Obes (Lond) 2009; 33(12): 1437-45.
  34. Dong X, Liu Y, Yang J, Sun Y, Chen L. Efficiency of anthropometric indicators of obesity for identifying cardiovascular risk factors in a Chinese population. Postgrad Med J 2011; 87(1026): 251-6.
  35. Arjmand G, Shidfar F, Molavi Nojoomi M, Amirfarhangi A. Anthropometric indices and their relationship with coronary artery diseases. Health Scope 2015; 4(3): e25120.
  36. Gelber RP, Gaziano JM, Orav EJ, Manson JE, Buring JE, Kurth T. Measures of obesity and cardiovascular risk among men and women. J Am Coll Cardiol 2008; 52(8): 605-15.
  37. Garg VP, Vedanthan R, Islami F, Pourshams A, Poutschi H, Khademi H, et al. Heart disease is associated with anthropometric indices and change in body size perception over the life course: The Golestan Cohort Study. Glob Heart 2015; 10(4): 245-54.
  38. Delavari A, Forouzanfar MH, Alikhani S, Sharifian A, Kelishadi R. First nationwide study of the prevalence of the metabolic syndrome and optimal cutoff points of waist circumference in the Middle East: the national survey of risk factors for noncommunicable diseases of Iran. Diabetes Care 2009; 32(6): 1092-7.
  39. Esteghamati A, Khalilzadeh O, Rashidi A, Meysamie A, Haghazali M, Asgari F, et al. Association between physical activity and insulin resistance in Iranian adults: National Surveillance of Risk Factors of Non-Communicable Diseases (SuRFNCD-2007). Prev Med 2009; 49(5): 402-6.
  40. Lear SA, James PT, Ko GT, Kumanyika S. Appropriateness of waist circumference and waist-to-hip ratio cutoffs for different ethnic groups. Eur J Clin Nutr 2010; 64(1): 42-61.
  41. Darbandi M, Pasdar Y, Moradi S, Mohamed HJJ, Hamzeh B, Salimi Y. Discriminatory capacity of anthropometric indices for cardiovascular disease in adults: A systematic review and meta-analysis. Prev Chronic Dis 2020; 17: E131.
  42. Tabary M, Cheraghian B, Mohammadi Z, Rahimi Z, Naderian MR, Danehchin L, et al. Association of anthropometric indices with cardiovascular disease risk factors among adults: a study in Iran. Eur J Cardiovasc Nurs 2021; 20(4): 358-66.
  43. Lam BC, Koh GC, Chen C, Wong MT, Fallows SJ. Comparison of body mass index (BMI), body adiposity index (BAI), waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) as predictors of cardiovascular disease risk factors in an adult population in Singapore. PLoS One 2015; 10(4): e0122985.
  44. Yu J, Tao Y, Tao Y, Yang S, Yu Y, Li B, et al. Optimal cut-off of obesity indices to predict cardiovascular disease risk factors and metabolic syndrome among adults in Northeast China. BMC Public Health 2016; 16(1): 1079.
ARYA Atherosclerosis Journal
Volume 18, Issue 3
March 2022
Pages 1-10
Files
Share
How to cite
Statistics