Published: 2017-06-24

Optimal cut-off values for obesity using classification tree in middle-aged adults living Rio de Janeiro city

Wollner Materko, Edil Luis Santos


Background: The goal present study was to identify cut-off points for body mass index (BMI) and waist circumference (WC) to predict values of obesity based body fat percentage (BF%) using classification tree in middle-aged adults living Rio de Janeiro city, Brazil.

Methods: The data was collected in a prospective cohort composed of 886 adults (443 men and 443 women) ranging from 30 to 59 years along two years (2010 - 2011) in Rio de Janeiro City, Brazil. All subjects were submitted to anthropometric evaluation and the gold standard was the percentage of body fat estimated by bioelectrical impedance analysis. The optimal sensitivity was achieved by adjusting BMI and WC cut-off values to predict obesity based on WHO criteria: BF% >25% in men and >35% in women according to the tree classification.

Results: The best cut-off for BMI and WC were 28 kg/m2 and 99 cm, respectively, with a prediction of 99.4% overall tree sensitivity in men. For women, the best cut-off for BMI and WC were 26 kg/m2 and 90 cm, respectively, with a prediction of 90.1% overall tree sensitivity.

Conclusions: The BMI and WC that corresponds to a BF% previously defining obesity is similar to other Western population, but different of the recommended by WHO and NCEP to BMI and WC thresholds, respectively, for defining obesity for both genders.


Body mass index, Classification tree, Obesity, Waist circumference

Full Text:



World Health Organization. Obesity: preventing and managing the global epidemic. World Health Organization: Geneva; 1998:276.

Mangge H, Almer G, Truschnig-Wilders M, Schimidt A, Gasser R, Fuchs D. Inflamation, adiponectin, obesity and cardiovascular risk. Curr Med Chem. 2010;17(36):4511-20.

Dzieciolowska-Baran E, Gawlikowska-Sroka A, Poziomkowaska-Gesicka Teul-Swiniarska I, Sroczynski T. Influence of body mass index on treatment of breathing-related sleep disorders. Eur J Med Res. 2010;4(15 Suppl 2):36-40.

Joost H. Pathogenesis, risk assessment and prevention of type 2 diabetes mellitus. Obes Facts. 2008;1:128-37.

Vrbikova J, Hainer V. Obesity and polycystic ovary syndrome. Obes Facts. 2009;2:26-35.

Aspden RM. Obesity punches above its weight in osteoarthritis. Nat Rev Rheumatol. 2011;7(1):65-8.

Kshatriya S, Liu K, Salah A, Szombathy T, Freeman RH, Reams GP, et al. Obesity hypertension: the regulatory role of leptin. Int J Hypertens. 2011;3:1-8.

Brazil. Ministry of Health. Health Surveillance Secretariat. VIGITEL. Brazil 2012: Surveillance of risk factors and protection for chronic diseases by telephone survey. Brasilia: Ministry of Health, 2013.

Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr. 1999;65:105-14.

Garn SM, Leonard WR, Hawthorne VM. Three limitations of the body mass index. Amer J Clin Nutr 1986;44: 996-7.

Deurenberg P, Deurenberg Yap M, Wang J, Lin FP, Schmidt G. The impact of body builds on the relationship between body mass index and percent body fat. Int J Obes Relat Metab Disord. 1999;23(5):537-42.

Ashton WD, Nanchahal K, Wood DA. Body mass index and metabolic risks factors for coronary heart disease in women. Eur Heart J. 2001;22:46-55.

Gu D, He J, Duan X, Reynolds K, Wu X, Chen J, et al. Body weight and mortality among men and women in China. JAMA. 2006;295(7):776-83.

Kwok S, McElduff P, Ashton DW, Lowe GDO, Wood D, Humphires ES, et al. Indices of obesity and cardiovascular risk factors in British women. Obes Facts 2008;1:190-95.

Razak F, Anand SS, Shannon H, Vuksan V, Davis B, Jacobs R, et al. Defining obesity cut points in a multiethnic population. Circulation. 2007;115:2111-8.

Dalton M, Cameron AJ, Zimmet PZ, Shaw JE, Jolley D, Dunstan DW, et al. Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med. 2003;254(6):555-63.

Perichart-Perera O, Balas-Nakash M, Schiffman-Selechnik E, Barbato-Dosal A, Vadilo-Ortega F. Obesity increases metabolic syndrome risk factors in school-aged children from an urban school in Mexico City. J Am Die Assoc. 2007;107:81-91.

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 Pro¬gram (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143-421.

WHO. WHO STEPwise approach to surveillance (STEPS). Geneva, World Health Organization (WHO); 2008b.

Khaled MA, McCutcheon MJ, Reddy S, Pearman PL, Hunter GR, Weinsier R. Electrial impedance in assessing human body composition: the BIA method. Am J Clin Nutr. 1988;47:789-92.

NIH / USA. Bioeletric impedance analysis in body composition measurement. National Institute of Health Technology Assessment Statement; 1994: 1-35.

World Health Organization. Physical Status: The use and interpretation of anthropometry. Technical Report Series 854, Geneva: World Health Organization; 1995.

Breiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. New York: Chapman & Hall/CRC; 1984.

Mekhasingharak N, Namatra C. Classification and regression tree analysis for predicting visual outcome after open-globe injuries in Siriraj Hospital. J Med Assoc Thai. 2014;97(9):939-46.

Chen YM, Ho SC, Lam SS, Chan SS. Validity of body mass index a waist circumference in the classification of obesity as compared to percent body fat in Chinese middle-aged women. Int J Obes (Lond). 2006;30(6):918-25.

Vikram NK, Pandey RM, Misra A, Sharma R, Devi JR, Khanna N. Non-obese (body mass index <25 kg/m2) Asian Indians with normal waist circumference have high cardiovascular risk. Nutrition. 2003;19(6):503-9.

Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y. Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Cli Nutr. 2000;72(3):694-701.

Luke A, Durazo-Arvizzu R, Rotimi C, Prewitt E, Forrester T, Wilks R, et al. Relation between BMI and body fat in black population samples from Nigeria, Jamaica and the United States. Am J Epidemiol. 2000;145:620-8.

Kagawa M, Uenishi K, Kuroiwa C, Mori M, Binns CW. Is the BMI cut-off level for Japanese females for obesity set too high? A consideration from a body composition perspective. Asia Pac J Clin Nutr. 2006;15(4):502-7.

Deurenberg P, Yap M, Van Staveren WA. Body mass index and percent body fat: a meta-analysis among different ethnic groups. Int J Obes. 1998;22(12):1164-71.

Fernandez-Real JM, Vayreda M, Casamitjana R, Saez M, Ricart W. Body mass index (BMI) and percent fat mass. A BMI >27.5 kg/m2 could be indicative of obesity in the Spanish population. Med Clin (Barc). 2001;117(18):681-4.

Craig P, Colagiuri S, Hussain Z, Palu T. Identifying cut-points in anthropometric indexes for predicting previously undiagnosed diabetes and cardiovascular risk factors in the Tongan population. Obes Res Clin Pract. 2007;1(1):17-25.

Piers LS, Rowley KG, Soares MJ, O´Dea K. Relation of adiposity and body fat distribution to body mass index in Australians of Aboriginal and European ancestry. Eur J Clin Nutr. 2003;57:956-63.

Deurenberg P, Yap M, Van Staveren WA. Body Mass Index and Percent Body Fat: A meta-analysis among different ethnic groups. Int J Obes. 1998;22(12):1164-71.

James WP, Chunming C, Inoue S. Appropriate Asian body mass indices? Obes Rev. 2002;3(3):139.

International Obesity Task Force (on behalf of the Steering Committee). The Asia-Pacif perspective: redefining obesity and its treatment. Western Pacif Region. Sydney, Australia: Heath Communications Australia Pty Limited; 2002.

Bouguerra R, Alberti H, Smida H, Salem LB, Rayana CB, El Atti J, et al. Waist circumference cut-off points for identification of abdominal obesity among the tunisian adult population. Diabetes Obes Metab. 2007;9(6):859-68.

Mansour AA, Al-Jazairi MI. Cut-off values for anthropometric variables that confer increased risk of type 2 diabetes mellitus and hypertension in Iraq. Arch Med Res. 2007;38(2):253-8.

Pua YH, Ong PH. Anthropometric indices as screening tools for cardiovascular risk factors in Singaporean women. Asia Pac J Clin Nutr. 2005:14(1):74-9.

Ito H, Nakasuga K, Ohshima A, Maruyama T, Kaji Y, Haranda M, et al. Detection of cardiovascular risk factors by indices of obesity obtained from anthropometry and dual-energy X-ray absorptiometry in Japanese individuals. Int J Obes Relat Metab Disord. 2003;27(2):232-7.

Hsu HS, Liu CS, Pi-Sunyer FX, Lin CH, Li CL, Li CC, et al. The associations of different measurements of obesity with cardiovascular risk factors in Chinese. Eur J Clin Invest. 2011;41(4):393-404.

Zaher ZM, Zambari R, Pheng CS, Muruga V, Ng B, Appannah G, et al. Optimal cut-off levels to define obesity: body mass index and waist circumference, and their relationship to cardiovascular disease, dyslipidaemia, hypertension and diabetes in Malaysia. Asia Pac J Clin Nutr. 2009;18(2):209-16.

Temcharoen P, Kaewboonruang P, Pradipasen M, Srisorachart S. The optimal cut-off points of body mass index which reflect the risk factors of cardiovascular disease in the urban Thai male population. J Med Assoc Thai. 2009;92(7):S68-74.

Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, Liu IL, et al. Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes. 2002;26:1232-8.

Berber A, Gomez-Santos R, Fanghanel G, Sanchez-Reyes L. Anthropometric indexes in the prediction of type 2 diabetes mellitus, hypertension and dyslipidaemia in a Mexican population. Int J Obes. 2001;25(12):1794-99.

Gregory OC, Carvalán C, Ramirez-Zea M, Martorell R, Stein AD. Detection of cardio-metabolic risk by BMI and waist circumference among a population of Guatemalan adults. Public Health Nutr. 2007;11(10):1037-45.

Larsson B, Svardsudd K, Welin L, Björntorp P, Tibblin G. Abdominal adipose tissue distribution, obesity, and risk of cardiovascular disease and death: 13 year follow up of participants in the study of men born in 1913. Br Med J. 1984;288(6428):1401-4.

Lean ME, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ. 1995;311:158-61.

Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. American Heart Associa¬tion; National Heart, Lung, and Blood Institute. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112:2735-52.

Hara K, Matsushita Y, Horikoshi M, Yoshiike N, Yokoya¬ma T, Tanaka H, et al. A proposal for the cut-off point of waist circumference for the diagnosis of metabolic syndrome in the Japanese population. Diabetes Care. 2006;29(5):1123-4.

Graham I, Atar D, Borch-Johnsen K, Boysen G, Burell G, Cifkova R, et al. ESC Committee for Practice Guidelines. European guidelines on cardiovascular disease prevention in clinical practice: executive summary. Atherosclerosis. 2007;194:1-45.

Hanley JA, Mcneil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiol. 1982;143(1):29-36.