Identification of an obesity index for predicting metabolic syndrome by gender: the rural Chinese cohort study
Tóm tắt
To compare the accuracy of different obesity indexes, including waist circumference (WC), weight-to-height ratio (WHtR), body mass index (BMI), and lipid accumulation product (LAP), in predicting metabolic syndrome (MetS) and to estimate the optimal cutoffs of these indexes in a rural Chinese adult population. This prospective cohort involved 8468 participants who were followed up for 6 years. MetS was defined by the International Diabetes Federation, American Heart Association, and National Heart, Lung, and Blood Institute criteria. The power of the 4 indexes for predicting MetS was estimated by receiver operating characteristic (ROC) curve analysis and optimal cutoffs were determined by the maximum of Youden’s index. As compared with WHtR, BMI, and LAP, WC had the largest area under the ROC curve (AUC) for predicting MetS after adjusting for age, smoking, drinking, physical activity, and education level. The AUCs (95% CIs) for WC, WHtR, BMI, and LAP for men and women were 0.862 (0.851–0.873) and 0.806 (0.794–0.817), 0.832 (0.820–0.843) and 0.789 (0.777–0.801), 0.824 (0.812–0.835) and 0.790 (0.778–0.802), and 0.798 (0.785–0.810) and 0.771 (0.759–0.784), respectively. The optimal cutoffs of WC for men and women were 83.30 and 76.80 cm. Those of WHtR, BMI, and LAP were approximately 0.51 and 0.50, 23.90 and 23.00 kg/m2, and 19.23 and 20.48 cm.mmol/L, respectively. WC as a preferred index over WHtR, BMI, and LAP for predicting MetS in rural Chinese adults of both genders; the optimal cutoffs for men and women were 83.30 and 76.80 cm.
Tài liệu tham khảo
Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365(9468):1415–28.
Grundy SM. Metabolic syndrome pandemic. Arterioscler Thromb Vasc Biol. 2008;28(4):629–36.
Grundy SM. Metabolic syndrome: connecting and reconciling cardiovascular and diabetes worlds. J Am Coll Cardiol. 2006;47(6):1093–100.
Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, Rinfret S, Schiffrin EL, Eisenberg MJ. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.
Wu SH, Liu Z, Ho SC. Metabolic syndrome and all-cause mortality: a meta-analysis of prospective cohort studies. Eur J Epidemiol. 2010;25(6):375–84.
O'Neill S, O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2015;16(1):1–12.
Cai H, Huang J, Xu G, Yang Z, Liu M, Mi Y, Liu W, Wang H, Qian D. Prevalence and determinants of metabolic syndrome among women in Chinese rural areas. PLoS One. 2012;7(5):e36936.
Song P, Yu J, Chang X, Wang M, An L: Prevalence and Correlates of Metabolic Syndrome in Chinese Children: The China Health and Nutrition Survey. Nutrients 2017, 9(1).
Mozumdar A, Liguori G. Persistent increase of prevalence of metabolic syndrome among U.S. adults: NHANES III to NHANES. Diabetes care 2011. 1999-2006;34(1):216–9.
Xi B, He D, Hu Y, Zhou D. Prevalence of metabolic syndrome and its influencing factors among the Chinese adults: the China health and nutrition survey in 2009. Prev Med. 2013;57(6):867–71.
Han TS, Williams K, Sattar N, Hunt KJ, Lean MEJ, Haffner SM. Analysis of obesity and Hyperinsulinemia in the development of metabolic syndrome: San Antonio heart study. Obes Res. 2002;10(9):923–31.
Yong-Woo Park M. PhD, Shankuan Zhu, MD, PhD, Latha Palaniappan, MD, Stanley, Heshka, PhD, Mercedes R. Carnethon, PhD, and Steven B. Heymsfield, MD: the metabolic syndrome: prevalence and associated risk factor findings in the US population from the third National Health and nutrition examination survey, 1988-1994. Arch Intern Med. 2003;24:427–36.
Agredo-Zuniga RA, Aguilar-de Plata C, Suarez-Ortegon MF. Waist:height ratio, waist circumference and metabolic syndrome abnormalities in Colombian schooled adolescents: a multivariate analysis considering located adiposity. Br J Nutr. 2015;114(5):700–5.
Beydoun MA, Kuczmarski MT, Wang Y, Mason MA, Evans MK, Zonderman AB. Receiver-operating characteristics of adiposity for metabolic syndrome: the healthy aging in neighborhoods of diversity across the life span (HANDLS) study. Public Health Nutr. 2011;14(1):77–92.
Sakurai M, Takamura T, Miura K, Kaneko S, Nakagawa H. BMI may be better than waist circumference for defining metabolic syndrome in Japanese women. Diabetes Care. 2008;31(3):e12.
Rodea-Montero ER, Evia-Viscarra ML, Apolinar-Jimenez E. Waist-to-height ratio is a better anthropometric index than waist circumference and BMI in predicting metabolic syndrome among obese Mexican adolescents. Int J Endocrinol. 2014;2014:195407.
Nascimento-Ferreira MV, Rendo-Urteaga T, Vilanova-Campelo RC, Carvalho HB, da Paz OG, Paes Landim MB, Torres-Leal FL. The lipid accumulation product is a powerful tool to predict metabolic syndrome in undiagnosed Brazilian adults. Clin Nutr. 2017;36(6):1693–700.
Yang F, Lv JH, Lei SF, Chen XD, Liu MY, Jian WX, Xu H, Tan LJ, Deng FY, Yang YJ, et al. Receiver-operating characteristic analyses of body mass index, waist circumference and waist-to-hip ratio for obesity: screening in young adults in central south of China. Clin Nutr. 2006;25(6):1030–9.
Guo SX, Zhang XH, Zhang JY, He J, Yan YZ, Ma JL, Ma RL, Guo H, Mu LT, Li SG, et al. Visceral adiposity and anthropometric indicators as screening tools of metabolic syndrome among low income rural adults in Xinjiang. Sci Rep. 2016;6:36091.
Zhang ZQ, Deng J, He LP, Ling WH, Su YX, Chen YM. Comparison of various anthropometric and body fat indices in identifying cardiometabolic disturbances in Chinese men and women. PLoS One. 2013;8(8):e70893.
Zeng Q, He Y, Dong S, Zhao X, Chen Z, Song Z, Chang G, Yang F, Wang Y. Optimal cut-off values of BMI, waist circumference and waist:height ratio for defining obesity in Chinese adults. Br J Nutr. 2014;112(10):1735–44.
Zhang XH, Zhang M, He J, Yan YZ, Ma JL, Wang K, Ma RL, Guo H, Mu LT, Ding YS, et al. Comparison of anthropometric and Atherogenic indices as screening tools of metabolic syndrome in the Kazakh adult population in Xinjiang. Int J Environ Res Public Health. 2016;13(4):428.
Kahn HS. The "lipid accumulation product" performs better than the body mass index for recognizing cardiovascular risk: a population-based comparison. BMC Cardiovasc Disord. 2005;5:26.
Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, Fruchart JC, James WP, Loria CM, Smith SC Jr, et al. 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. 2009;120(16):1640–5.
Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, Sowers MR. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med. 2008;168(15):1617–24.
Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, Pratt M, Ekelund U, Yngve A, Sallis JF, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.
Ulla Uusitalo AC, Liisa Palonen, Milorad Toša Zikic, et al.: Geographical variation in the major risk factors of coronary heart disease in men and women aged 35–64 years. Wld hlth statist quan 1988, 41:115–140.
Doak CM, Hoffman DJ, Norris SA, Campos Ponce M, Polman K, Griffiths PL. Is body mass index an appropriate proxy for body fat in children? Global Food Security. 2013;2(2):65–71.
Wang CJ, Li YQ, Wang L, Li LL, Guo YR, Zhang LY, Zhang MX, Bie RH. Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents. PLoS One. 2012;7(8):e43834.
Perloff D, Grim C, Flack J, Frohlich ED, Hill M, Mcdonald M, Morgenstern BZ. Human blood-pressure determination by Sphygmomanometry. Circulation. 1993;88(5):2460–70.
Bairaktari E, Hatzidimou K, Tzallas C, Vini M, Katsaraki A, Tselepis A, Elisaf M, Tsolas O. Estimation of LDL cholesterol based on the Friedewald formula and on apo B levels. Clin Biochem. 2000;33(7):549–55.
Liu Y, Tong G, Tong W, Lu L, Qin X. Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic risk factors in Chinese subjects? BMC Public Health. 2011;11:35.
de Oliveira CC, Roriz AK, Ramos LB, Gomes Neto M. Indicators of adiposity predictors of metabolic syndrome in the elderly. Annals of nutrition & metabolism. 2017;70(1):9–15.
Yu J, Tao Y, Tao Y, Yang S, Yu Y, Li B, Jin L. 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.
Motamed N, Razmjou S, Hemmasi G, Maadi M, Zamani F. Lipid accumulation product and metabolic syndrome: a population-based study in northern Iran, Amol. J Endocrinol Investig. 2016;39(4):375–82.
Ko KP, Oh DK, Min H, Kim CS, Park JK, Kim Y, Kim SS. Prospective study of optimal obesity index cutoffs for predicting development of multiple metabolic risk factors: the Korean genome and epidemiology study. J Epidemiol. 2012;22(5):433–9.
Saito I. Epidemiological evidence of type 2 diabetes mellitus, metabolic syndrome, and cardiovascular disease in Japan. Circulation journal : official journal of the Japanese Circulation Society. 2012;76(5):1066–73.
Shao J, Yu L, Shen X, Li D, Wang K. Waist-to-height ratio, an optimal predictor for obesity and metabolic syndrome in Chinese adults. J Nutr Health Aging. 2010;14(9):782–5.
Janes H, Pepe MS. Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am J Epidemiol. 2008;168(1):89–97.
Chiang JK, Koo M. Lipid accumulation product: a simple and accurate index for predicting metabolic syndrome in Taiwanese people aged 50 and over. BMC Cardiovasc Disord. 2012;12:78.
Tellechea ML, Aranguren F, Martinez-Larrad MT, Serrano-Rios M, Taverna MJ, Frechtel GD. Ability of lipid accumulation product to identify metabolic syndrome in healthy men from Buenos Aires. Diabetes Care. 2009;32(7):e85.
Taverna MJ, Martinez-Larrad MT, Frechtel GD, Serrano-Rios M. Lipid accumulation product: a powerful marker of metabolic syndrome in healthy population. Eur J Endocrinol. 2011;164(4):559–67.
Yuji M. Metabolic syndrome-definition and diagnostic criteria in Japan. J Atheroscler Thromb. 2005;12(6):301.
Yuji M. Metabolic syndrome-definition and diagnostic criteria in Japan. J Jpn Soc Intern Med. 2005;94:188–203.