Abstract
Purpose
To determine optimal body mass index (BMI) cut-points for the identification of cardiometabolic risk in black South African adults.
Methods
We performed a cross-sectional study of a weighted sample of healthy black South Africans aged 25–65 years (721 men, 1386 women) from the North West and Free State Provinces. Demographic, lifestyle and anthropometric measures were taken, and blood pressure, fasting serum triglycerides, high-density lipoprotein (HDL) cholesterol and blood glucose were measured. We defined elevated cardiometabolic risk as having three or more risk factors according to international metabolic syndrome criteria. Receiver operating characteristic curves were applied to identify an optimal BMI cut-point for men and women.
Results
BMI had good diagnostic performance to identify clustering of three or more risk factors, as well as individual risk factors: low HDL-cholesterol, elevated fasting glucose and triglycerides, with areas under the curve >.6, but not for high blood pressure. Optimal BMI cut-points averaged 22 kg/m2 for men and 28 kg/m2 for women, respectively, with better sensitivity in men (44.0–71.9 %), and in women (60.6–69.8 %), compared to a BMI of 30 kg/m2 (17–19.1, 53–61.4 %, respectively). Men and women with a BMI >22 and >28 kg/m2, respectively, had significantly increased probability of elevated cardiometabolic risk after adjustment for age, alcohol use and smoking.
Conclusion
In black South African men, a BMI cut-point of 22 kg/m2 identifies those at cardiometabolic risk, whereas a BMI of 30 kg/m2 underestimates risk. In women, a cut-point of 28 kg/m2, approaching the WHO obesity cut-point, identifies those at risk.
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References
Razak F, Corsi DJ, Subramanian SV (2013) Change in the body mass index distribution for women: analysis of surveys from 37 low- and middle-income countries. PLoS Med 10:e1001367. doi:10.1371/journal.pmed.1001367
Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH (1999) The disease burden associated with overweight and obesity. JAMA 282:1523–1529
WHO (1998) Obesity. Preventing and managing the global epidemic. In: Report of a WHO consultation on obesity. World Health Organization, Geneva, p 1–276
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr, International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society; International Association for the Study of Obesity (2009) Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120:1640–1645. doi:10.1161/CIRCULATIONAHA.109.192644
Berrington de Gonzalez A, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ, Moore SC, Tobias GS, Anton-Culver H, Freeman LB, Beeson WL, Clipp SL, English DR, Folsom AR, Freedman DM, Giles G, Hakansson N, Henderson KD, Hoffman-Bolton J, Hoppin JA, Koenig KL, Lee IM, Linet MS, Park Y, Pocobelli G, Schatzkin A, Sesso HD, Weiderpass E, Willcox BJ, Wolk A, Zeleniuch-Jacquotte A, Willett WC, Thun MJ (2010) Body-mass index and mortality among 1.46 million white adults. New Engl J Med 363:2211–2219. doi:10.1056/NEJMoa1000367
Abbasi F, Blasey C, Reaven GM (2013) Cardiometabolic risk factors and obesity: does it matter whether BMI or waist circumference is the index of obesity? Am J Clin Nutr 98:637–640. doi:10.3945/ajcn.112.047506
Deurenberg-Yap M, Deurenberg P (2003) Is a re-evaluation of WHO body mass index cut-off values needed? The case of Asians in Singapore. Nutr Rev 61:S80–S87
Razak F, Anand S, Shannon H, Vuksan V, Davis B, Jacobs R, Teo K, McQueen M, Yusuf S (2007) Defining obesity cut points in a multiethnic population. Circulation 115:2111–2118
Odegaard AO, Pereira MA, Koh WP, Gross MD, Duval S, Yu MC, Yuan JM (2010) BMI, all-cause and cause-specific mortality in Chinese Singaporean men and women: the Singapore Chinese health study. PLoS One 5:e14000. doi:10.1371/journal.pone.0014000
Bodicoat DH, Gray LJ, Henson J, Webb D, Guru A, Misra A, Gupta R, Vikram N, Sattar N, Davies MJ, Khunti K (2014) Body mass index and waist circumference cut-points in multi-ethnic populations from the UK and India: the ADDITION-Leicester, Jaipur heart watch and New Delhi cross-sectional studies. PLoS One 9:e90813. doi:10.1371/journal.pone.0090813
Gordon-Larsen P, Adair LS, Meigs JB, Mayer-Davis E, Herring A, Yan SK, Zhang B, Du S, Popkin BM (2013) Discordant risk: overweight and cardiometabolic risk in Chinese adults. Obesity 21:E166–E174. doi:10.1002/oby.2040910.1038/oby.2012.152
Javed F, Aziz EF, Sabharwal MS, Nadkarni GN, Khan SA, Cordova JP, Benjo AM, Gallagher D, Herzog E, Messerli FH, Pi-Sunyer FX (2011) Association of BMI and cardiovascular risk stratification in the elderly African-American females. Obesity 19:1182–1186. doi:10.1038/oby.2010.307
Katzmarzyk PT, Bray GA, Greenway FL, Johnson WD, Newton RL Jr, Ravussin E, Ryan DH, Bouchard C (2011) Ethnic-specific BMI and waist circumference thresholds. Obesity 19:1272–1278. doi:10.1038/oby.2010.319
Teo K, Chow CK, Vaz M, Rangarajan S, Yusuf S, Group PI-W (2009) The Prospective Urban Rural Epidemiology (PURE) study: examining the impact of societal influences on chronic noncommunicable diseases in low-, middle-, and high-income countries. Am Heart J 158(1–7):e1. doi:10.1016/j.ahj.2009.04.019
Yusuf S, Islam S, Chow CK, Rangarajan S, Dagenais G, Diaz R, Gupta R, Kelishadi R, Iqbal R, Avezum A, Kruger A, Kutty R, Lanas F, Lisheng L, Wei L, Lopez-Jaramillo P, Oguz A, Rahman O, Swidan H, Yusoff K, Zatonski W, Rosengren A, Teo KK, Prospective Urban Rural Epidemiology Study I (2011) Use of secondary prevention drugs for cardiovascular disease in the community in high-income, middle-income, and low-income countries (the PURE Study): a prospective epidemiological survey. Lancet 378:1231–1243. doi:10.1016/S0140-6736(11)61215-4
Van Zyl S, Van der Merwe LJ, Walsh CM, Groenewald AJ, Van Rooyen FC (2012) Risk-factor profiles for chronic diseases of lifestyle and metabolic syndrome in an urban and rural setting in South Africa. Afr J Prim Health Care Fam Med. doi:10.4102/phcfm.v4i1.346
Statistics South Africa (2007) Community survey 2007. Statistical release P0301
ISAK (2006) International standards for anthropometric assessment. The International Society for the Advancement of Kinanthropometry, Potchefstroom
Alberti KG, Zimmet P, Shaw J (2006) Metabolic syndrome—a new world-wide definition. A consensus statement from the international diabetes federation. Diabet Med 23:469–480. doi:10.1111/j.1464-5491.2006.01858.x
Shisana O, Labadarios D, Rehle T, Simbayi L, Zuma K, Dhansay A, Reddy P, Parker W, Hoosain E, Naidoo P, Hongoro C, Mchiza Z, Steyn N, Dwane N, Makoane M, Maluleke T, Ramlagan S, Zungu N, Evans M, Jacobs L, Faber M, Team S (2013) South African national health and nutrition examination survey (SANHANES-1). In: Human Sciences Research Council, Cape Town, p 401
Peer N, Steyn K, Levitt N (2015) Differential obesity indices identify the metabolic syndrome in Black men and women in Cape Town: the CRIBSA study. J Public Health. doi:10.1093/pubmed/fdu115
Motala AA, Esterhuizen T, Pirie FJ, Omar MA (2011) The prevalence of metabolic syndrome and determination of the optimal waist circumference cutoff points in a rural South african community. Diabet Care 34:1032–1037. doi:10.2337/dc10-1921
Ware LJ, Rennie KL, Kruger HS, Kruger IM, Greeff M, Fourie CM, Huisman HW, Scheepers JD, Uys AS, Kruger R, Van Rooyen JM, Schutte R, Schutte AE (2014) Evaluation of waist-to-height ratio to predict 5 year cardiometabolic risk in sub-Saharan African adults. Nutr Metab Cardiovasc Dis 24:900–907. doi:10.1016/j.numecd.2014.02.005
Crowther NJ, Norris SA (2012) The current waist circumference cut point used for the diagnosis of metabolic syndrome in sub-Saharan African women is not appropriate. PLoS One 7:e48883. doi:10.1371/journal.pone.0048883
Matsha TE, Hassan MS, Hon GM, Soita DJ, Kengne AP, Erasmus RT (2013) Derivation and validation of a waist circumference optimal cutoff for diagnosing metabolic syndrome in a South African mixed ancestry population. Int J Cardiol 168:2954–2955. doi:10.1016/j.ijcard.2013.03.150
Deurenberg-Yap M, Yian T, Kai C, Deurenberg P, van Staveren WA (1999) Manifestation of cardiovascular risk factors at low levels of body mass index and waist-to-hip ratio in Singaporean Chinese. Asia Pac J Clin Nutr 8:177–183
Steyn NP, McHiza ZJ (2014) Obesity and the nutrition transition in Sub-Saharan Africa. Ann N Y Acad Sci 1311:88–101. doi:10.1111/nyas.12433
Toselli S, Gualdi-Russo E, Boulos DN, Anwar WA, Lakhoua C, Jaouadi I, Khyatti M, Hemminki K (2014) Prevalence of overweight and obesity in adults from North Africa. Eur J Pub Health 24(Suppl 1):31–39. doi:10.1093/eurpub/cku103
Lebel A, Kestens Y, Clary C, Bisset S, Subramanian SV (2014) Geographic variability in the association between socioeconomic status and BMI in the USA and Canada. PLoS One 9:e99158. doi:10.1371/journal.pone.0099158
Gallus S, Lugo A, Murisic B, Bosetti C, Boffetta P, La Vecchia C (2015) Overweight and obesity in 16 European countries. Eur J Nutr 54:679–689. doi:10.1007/s00394-014-0746-4
Sumner AE, Zhou J, Doumatey A, Imoisili OE, Amoah A, Acheampong J, Oli J, Johnson T, Adebamowo C, Rotimi CN (2010) Low HDL-cholesterol with normal triglyceride levels is the most common lipid pattern in West Africans and African Americans with metabolic syndrome: implications for cardiovascular disease prevention. CVD Prev Control 5:75–80. doi:10.1016/j.cvdpc.2010.07.003
Sumner AE, Vega GL, Genovese DJ, Finley KB, Bergman RN, Boston RC (2005) Normal triglyceride levels despite insulin resistance in African Americans: role of lipoprotein lipase. Metabolism 54:902–909. doi:10.1016/j.metabol.2005.03.001
Schutte AE, Schutte R, Huisman HW, van Rooyen JM, Fourie CM, Malan NT, Malan L, Mels CM, Smith W, Moss SJ, Towers GW, Kruger HS, Wentzel-Viljoen E, Vorster HH, Kruger A (2012) Are behavioural risk factors to be blamed for the conversion from optimal blood pressure to hypertensive status in black South Africans? A 5-year prospective study. Int J Epidemiol 41:1114–1123. doi:10.1093/ije/dys106
Zatu MC, van Rooyen JM, du Loots T, Wentzel-Viljoen E, Greeff M, Schutte AE (2014) Self-reported alcohol intake is a better estimate of 5-year change in blood pressure than biochemical markers in low resource settings: the PURE study. J Hypertens 32:749–755. doi:10.1097/HJH.0000000000000093
De Lorenzo A, Deurenberg P, Pietrantuono M, Di Daniele N, Cervelli V, Andreoli A (2003) How fat is obese? Acta Diabetol 40:S254–S257
Acknowledgments
H.S.K. conceived the study and was responsible for quality control of anthropometric data and interpretation of the results. A.E.S. was responsible for quality control and interpretation of the blood pressure data. A.K. was involved in the study design and data collection of the PURE-North West data. C.M.W. was responsible for the study design and collection of the Free State data. K.L.R. advised on all statistical analyses and interpretation of the results. All authors were involved in writing the paper and final approval of the submitted version. Dr. Suria Ellis of the Statistical Consultation Service at North-West University performed the sample weighting analysis. The authors would like to thank all supporting staff and the participants of the PURE and AHA-FS studies and in particular: PURE-South Africa: The PURE-NWP-SA research team, field workers and office staff in the Africa Unit for Transdisciplinary Health Research (AUTHeR), Faculty of Health Sciences, North-West University, Potchefstroom, South Africa. PURE International: Dr. S. Yusuf and the PURE project office staff at the Population Health Research Institute (PHRI), Hamilton Health Sciences and McMaster University, Ontario, Canada.
Funders
The study received funding from South African Medical Research Council, South Africa-Netherlands Research Programme on Alternatives in Development, South African National Research Foundation (North-West and Free State studies), North-West University and Population Health Research Institute, Canada. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors, and therefore, the National Research Foundation does not accept any liability in regard thereto.
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The North West and Free State studies were approved by the Ethics Committees of the North-West University and the University of the Free State. Research has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Volunteers gave written informed consent prior to their inclusion in the study.
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Kruger, H.S., Schutte, A.E., Walsh, C.M. et al. Body mass index cut-points to identify cardiometabolic risk in black South Africans. Eur J Nutr 56, 193–202 (2017). https://doi.org/10.1007/s00394-015-1069-9
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DOI: https://doi.org/10.1007/s00394-015-1069-9