Optimal cutoffs of percentage body fat for predicting obesity-related cardiovascular disease risk factors in Korean adults 1 3

Transcription

1 Optimal cutoffs of percentage body fat for predicting obesity-related cardiovascular disease risk factors in Korean adults 1 3 Chul-Hyun Kim, Hye Soon Park, Mira Park, Hyeoijin Kim, and Chan Kim ABSTRACT Background: Obesity is a major health problem. It is associated with cardiovascular disease. The diagnosis of obesity is crucial to treating and preventing obesity-related medical problems. Objective: The objective was to determine optimal percentage body fat cutoffs in Korean adults for predicting obesity-related cardiovascular disease risk factors. Design: We evaluated the body composition and prevalence of obesity-related cardiovascular risk factors, such as hypertension, diabetes mellitus, and dyslipidemia, in 41,088 Korean adults aged y. The optimal percentage body fat cutoffs for Korean adults were determined. Multivariable-adjusted odds ratios (ORs) of overweight and obesity were estimated by logistic regression. Results: The first cutoffs in men and women were 17% and 32% body fat, respectively; the second cutoffs were 21% and 37% body fat, respectively. The percentages of obese men and women were 41.8% and 15.9%, respectively. The adjusted OR of at least one risk factor for overweight or obesity in men was 2.22 (95% CI: 2.07, 2.38) or 4.05 (95% CI: 3.78, 4.33). The adjusted OR for women was 1.95 (95% CI: 1.79, 2.07; P, ) or 3.21 (95% CI: 2.87, 3.57). Conclusions: Only one-fourth of Korean men had a normal body composition, whereas most of the Korean women had a normal body composition. We conclude that susceptibility to cardiovascular disease and its risk factors is higher in Korean men than in Korean women. The cutoffs are useful for providing adequate guidelines for treating and preventing cardiovascular disease. This was the first study to determine cutoffs of percentage body fat for Korean adults. Am J Clin Nutr 2011;94:34 9. INTRODUCTION Obesity causes or exacerbates many health problems, including cardiovascular disease (CVD), type 2 diabetes, and several cancers. It has become one of the major public health problems worldwide (1, 2). To identify individuals who are obese and to evaluate the progress of fat-control interventions, it is necessary to have objective criteria of screening and evaluation for obesity, namely classifying gradations of fatness (3 5). Many methods are used to calculate body fat and predict obesity-related risk factors. Body mass index (BMI) and waist circumference (WC) have been routinely used in most epidemiologic studies because of their simplicity and ease (1 5). However, these measures have not shown a strong correlation with percentage body fat and have an ethnicity-related problem. It has been shown that the BMI established by the World Health Organization for classifying obesity in whites is not applicable to Asian populations (6, 7). Although WC is accepted as the easily obtainable indicator of central adiposity, only a few studies have provided Asian-specific cutoffs (8). High body fat, irrespective of weight, is associated with a high prevalence of CVD and its risk factors (5). Although assessments of body fat are available currently, the optimal percentage body fat cutoffs that reflect obesity-related CVD are obscure. Several studies have explored cutoff values of BMI or WC for Asian children (9 13) or percentage body fat cutoffs for Korean children (14). However, the percentage body fat cutoff for Asians, especially for Korean adults, has not been specified yet. Obesity is a major causative factor for obesity-related disorders such as diabetes, hypertension, and metabolic syndrome. The metabolic syndrome refers to the clustering of CVD risk factors that include diabetes mellitus, obesity, dyslipidemia, and hypertension (16). However, because of unclear pathophysiology and various definitions, metabolic syndrome is still a source of medical controversy (17); therefore, we excluded it from the CVD risk factors in this study. The current study was performed to determine the optimal percentage body fat cutoffs to predict an increase in obesity-related CVD risk factors in Korean adults. SUBJECTS AND METHODS Validation study This study was conducted from 2002 to One hundred seventy-four healthy adults were recruited through advertisements in the local media. None of the subjects had any known major systemic diseases that would change their body composition abruptly. Each subject provided written informed consent 1 From the Department of Physiology and Biophysics, Antiaging Research Center, School of Medicine, Eulji University, Daejeon, Korea (C-HK); the Department of Family Medicine, ASAN Medical Center, Seoul, Korea (HSP); the Department of Biostatics, School of Medicine, Eulji University, Daejeon, Korea (MP); the Measurement and Evaluation of Physical Education, Korea National Sport University, Seoul, Korea (HK); the Department of Physiology and Biophysics, Antiaging Research Center, School of Medicine, Eulji University, Daejeon, Korea (CK). 2 There was no funding source. 3 Address reprint requests and correspondence to Chan Kim, Department of Physiology and Biophysics, Antiaging Research Center, School of Medicine, Eulji University, #143-5, Yongdu-dong, Chung-Ku, Daejeon , Korea. ckim@eulji.ac.kr. Received August 6, Accepted for publication April 1, First published online April 27, 2011; doi: /ajcn Am J Clin Nutr 2011;94:34 9. Printed in USA. Ó 2011 American Society for Nutrition

2 CUTOFFS OF PERCENTAGE BODY FAT FOR CVD RISK FACTORS 35 in accordance with the guidelines established by the Institutional Review Board of Eulji University School of Medicine. All measurements were conducted on the same day after at least a 4-h fast and voiding. Body composition, including fat mass (FM), soft lean body mass, and body mineral content, were measured by dual-energy X-ray absorptiometry (DXA) (DPX-L with software version 1.3z; Lunar Radiation, Madison, WI) as the reference method. Calibrations were made daily before the DXA scans. An anthropomorphic spine phantom made up of calcium hydroxyapatite embedded in a cm block was scanned for quality control every morning before the subjects were examined. The measured phantom bone mineral density was stable throughout the study period at g/cm 2 (CV: 0.77%). Bottles of ethanol and water were scanned bimonthly for the quality control of fat and soft lean body tissue. The range in measured R values over the study period was (CV: 0.13%) and (CV: 0.11%) for fat and soft lean body mass. A single expert analyzed all DXA scans. FM and fat-free mass (FFM) were estimated by using bioelectrical impedance analysis (BIA) (InBody 3.0; Biospace Co, Seoul, Korea). Precision, reported as the CV, of the BIA measures of interest was 1.54% for FM, 0.74% for FFM, and 0.84% for percentage body fat. Each of 2 electrodes was placed on both palms and both soles, respectively. Before electrode placement, the skin was cleaned with 70% ethanol. The subjects stood on the footplate with bare feet and held both hand electrodes. The between-day precision of this instrument was,5 (2.7%); the within-day precision was always,3 (2.0%). Epidemiologic study The data were collected from the health screen center of Asan Medical Center in Seoul, South Korea, from year 2002 to year The Institutional Review Boards of Eulji University School of Medicine approved this study. In total, 54,573 subjects participated in the study and gave written informed consent. We excluded persons who had any previous systemic disease or were taking drugs that could change body composition abruptly. Systemic diseases included congestive heart failure, impaired renal function, hyper- or hypothyroidism, abnormal liver function, and malignancies. Data for 41,088 subjects (25,537 men and 15,551 women, y-old) were available for final analysis. All clinical profiles and measurements were conducted on the same day after the subjects fasted overnight. Percentage body fat was first taken from BIA (InBody 3.0), which was the same model we had used in the validation study. The data were then transformed into more reliable percentage body fat, ie, DXA-equivalent percentage body fat, by using the validation study based regression equations. Blood pressure was measured in the right upper arm by a standard mercury sphygmomanometer with an appropriately sized cuff. Hypertension was defined as a systolic blood pressure 140 mm Hg, a diastolic blood pressure 90 mm Hg, or treatment of hypertension (18). A 12-h fasting blood sample was collected from each subject and prepared for diagnosis of dyslipidemia and diabetes mellitus. Total cholesterol (TC) and triglyceride concentrations were measured by using enzymatic methods. HDL cholesterol was measured after precipitation of apolipoprotein B containing lipoproteins with sodium phosphotungstate and magnesium chloride. LDL-cholesterol concentrations were calculated by using the Friedewald formula. Fasting serum glucose (FSG) was measured by using the glucose oxidase method. Glycated hemoglobin (Hb A 1C ) was measured by liquid chromatography. Dyslipidemia was defined as TC 240 mg/dl and/or HDL cholesterol,40 mg/dl and/or triglyceride 150 mg/dl or treatment of dyslipidemia (19, 20). Diabetes mellitus was defined as FSG 126 mg/dl and Hb A 1C 6.5% or treatment of the disease (20, 21). An individual was considered a patient when they had 1 of3cvdriskfactors,suchashypertension, dyslipidemia, and diabetes mellitus. Statistical analyses Data are summarized as means 6 SDs or percentages according to variable characteristics. The statistical significance of differences in subject characteristics was analyzed by using the independent t test. The correlation between BIA- and DXA-based percentage body fat was estimated by using Pearson s correlations. Simple linear regression analysis was performed to obtain regression equations for converting percentage body fat (derived from BIA) to DXA-equivalent percentage body fat. A minimum P value approach (22) was conducted for cutoff points of percentage body fat, which optimally differentiated normal from risk groups. The 2 2 contingency tables with the CVD outcome and dichotomized percentage body fat were created. The chi-square test statistics were calculated, varying the dichotomizing point of percentage body fat in the interval of 1% body fat. The point of percentage body fat that had the maximum chi-square statistic was selected as the cutoff that best differentiated between outcome risk groups. Multivariate binary logistic regression analysis was used for odds ratios (ORs) in overweight and obese subjects. Covariates for ORs included age, smoking history (nonsmoker; ex-smoker; 9, 10 19, 20 cigarettes/d), alcohol drinking (never, occasionally, often), physical activity (regular exercise, none-exercise), economic levels of household income ($1000, $ , $ , $ , or $4000/mo), and education levels (9 y, 9 12 y, y, bachelor, graduate school, or more). SAS version 6.12 (SAS Institute, Cary, NC) was used for the statistical analysis. P values,0.05 were considered to be statistically significant. RESULTS Validation study We performed the validation study using DXA as the reference method to get more reliable percentage body fat values. The characteristics of the study population are shown in Table 1.The correlation coefficients between the BIA and DXA data for men and women were and for percentage body fat and and for FFM, respectively (all P, 0.001). The total errors of estimation of percentage body fat in men and women, respectively, were 2.1% and 2.3% (rated as excellent, 2.5% body fat)andthoseofffminmenandwomenwere1.6and1.2kg (rated as ideal,,2.5 kg for men and,1.5 kg for women) by using Lohman s criteria (23). The regression equations for converting BIA-derived percentage body fat values to DXA-equivalent percentage body fat values are as follows: DXA-equivalent percentage body fat for men = (1.03 BIA % body fat) 1.83 (R 2 = and SEE = 2.24% body fat;

3 36 KIM ET AL TABLE 1 Anthropometric and metabolic variables in Korean adults 1 Men Women Total Validation study n Age (y) Weight (kg) y Height (cm) y BMI (kg/m 2 ) Body fat (%) y Epidemiologic study n 25,537 15,551 41,088 Age (y) Height (cm) y Weight (kg) y BMI (kg/m 2 ) y Body fat (%) y Body fat mass (kg) y SBP (mm Hg) y DBP (mm Hg) y FBS (mg/dl) y Hb A 1C (%) y TC (mg/dl) y TG (mg/dl) y LDL-C (mg/dl) y HDL-C (mg/dl) y Hypertension [n (%)] 6535 (25.6) 2990 (19.2) y 9,525 (23.2) Diabetes mellitus [n (%)] 2869 (11.2) 1234 (7.9) y 4,103 (10.0) Dyslipidemia [n (%)] 13,003 (50.9) 4433 (28.5) y 17,436 (42.4) 1 risk factor [n (%)] 16,050 (62.8) 6179 (39.7) y 22,229 (54.1) 2 risk factors [n (%)] 5269 (20.6) 2013 (12.9) y 7282 (17.7) Three risk factors [n (%)] 1088 (4.3) 465 (3.0) y 1553 (3.8) BMI [n (%)] 23 kg/m 2 18,569 (72.7) 7733 (49.7) y 26,302 (64.0) 25 kg/m 2 11,028 (43.2) 4155 (26.7) y (37.0) 30 kg/m (2.9) 400 (2.6) 1133 (2.8) 1 SBP, systolic blood pressure; DBP, diastolic blood pressure; FBS, 12-h fasting blood sugar; Hb A 1C, glycated hemoglobin; TC, total cholesterol; TG, triglyceride; LDL-C, LDL cholesterol; HDL-C, HDL cholesterol. y Significantly different from men, P, (independent t test and the chi-square test). 2 Mean 6 SD (all such values). P, 0.001) and for women = (1.11 BIA % body fat) 1.34 (R 2 = and SEE = 2.26% body fat; P, 0.001). Epidemiologic study We estimated %BF from BIA data and then transformed the data using equations obtained from the validation study. The anthropometric indexes, body composition, and prevalence of CVD risk factors of the subjects are presented in Table 1. Most obesityrelated variables and CVD risk factors were higher in men than in women, except for percentage body fat, FM, and HDL cholesterol. The prevalences with at least one risk factor for Korean men and women were 62.8% and 39.7%, respectively (Table 1). Strong and graded associations of monotonically increasing percentage body fat with prevalence of CVD risk factors were observed (Figure 1). The increasing prevalence of risk factors in men was started as low as 10% body fat points, whereas the prevalence in women was not increased at,25% body fat. The prevalence of at least one risk factor in Korean men who had 15 20% body fat was 58.8%, and this value corresponded with that for Korean women, who had 35 40% body fat. The prevalence of at least one risk factor in Korean men who had 20 25% body fat was 72.6%, which was equivalent to that for Korean women, who had.40% body fat (Figure 1). The cutoffs of percentage body fat for overweight and obesity were analyzed by using the minimum P value approach. The maximum chi-square statistics for overweight were (P, ) at 17% body fat in men and (P, ) at 32% body fat in women (Figure 2A). After the subjects who had a body fat percentage lower than the first cutoffs were eliminated (ie, 17% body fat for men and 32% body fat for women), the data were reanalyzed to get the second cutoffs. The maximum chi-square values for obese subjects were (P, ) at 21% body fat in men and (P, 0.001) at 37% body fat in women (Figure 2B). We defined overweight as the range between the first and the second cutoffs, and we defined obese as percentage body fat higher than the second cutoff. For men, 74.1% and 41.8% had a percentage body fat value greater than the first and second cutoffs, respectively; for women, 46.2% and 15.9% had a percentage body fat value greater than the first and second cutoffs, respectively (Table 2). The risks of CVD associated with adiposity are shown in Table 3. Compared with the low-risk group (men:,17% body fat; women:,32% body fat), the adjusted ORs of all disorders

4 CUTOFFS OF PERCENTAGE BODY FAT FOR CVD RISK FACTORS 37 FIGURE 1. The relation between percentage body fat and prevalence of cardiovascular disease risk factors in Korean men (A) and women (B). DM, diabetes mellitus; HT, hypertension. were gradually increased with adiposity in both men and women. In men, the adjusted ORs of at least one risk factor were 2.22 (95% CI: 2.07, 2.38; P, 0.001) in the overweight group (17 21% body fat) and 4.05 (95% CI: 3.78, 4.33; P, 0.001) in the obese group ( 21% body fat). In women, the adjusted ORs of at least one risk factor were 1.95 (95% CI: 1.79, 2.12; P, 0.001) in the overweight group (32 37% body fat) and 3.21 (95% CI: 2.87, 3.57; P, 0.001) in the obese group (37% body fat). DISCUSSION Although BMI is the most commonly used measure in obesityassociated epidemiologic studies because of its simplicity, it is also well recognized that BMI has many limitations. First, BMI cannot reflect body composition, and it requires different standards for different ethnicities (9, 24, 25). More cross-ethnic population studies on body composition are needed because the relation between percentage body fat and BMI depends on ethnicity and on age and sex (26). Second, many studies have controversially reported the association between BMI and mortality, especially in the Asian population. Whereas the relation in whites in Europe and the United States have usually been U-shaped (27 31), the relation in Asians has been U- or J-shaped (32, 33). Another major problem with BMI is that the typical SE of estimation of percentage body fat from BMI exceeds 5% (34, 35). This means that if an individual is estimated to have 25% body fat by BMI, the actual value is between 20% and 30% body fat. In addition, BMI is less useful in individual body-composition assessments, and it may result in an individual being classified as obese when he or she is actually not. Therefore, percentage body fat has been recommended for use in epidemiologic research on adiposity rather than BMI and other anthropometric measures (34, 35). WC has been recognized as a useful tool for assessing central obesity. The International Diabetes Federation has defined central obesity for different ethnic populations based on WC measurements obtained from epidemiologic data from various ethnic populations (34). However, the values for Asians had some limitations in this study. The cutoffs for Japanese and Chinese were determined by using data from only one Japanese study (12) and one Singaporean study (10). There were only 1193 (775 men and 418 women aged y) subjects in the Japanese study, and the data were collected from an obesity clinic. In the Singaporean study, 36% of the subjects sampled were not Chinese. In addition, the results of these 2 studies were opposite. The cutoffs for the Japanese men and women were 85 and 90 cm, whereas those FIGURE 2. Chi-square values at each cutoff of percentage body fat by at least one risk factor in Korean men and women. The first cutoffs (A) are chisquare values for all subjects by the 2 2 contingency tables with at least one risk factor and dichotomized percentage body fat, varying the dichotomizing point of percentage body fat in the interval of 1% body fat for the first cutoff. The second cutoffs (B) are chi-square values derived after elimination of subjects with values lower than the first cutoff.

5 38 KIM ET AL TABLE 2 Percentage of men and women in the different percentage body fat groups 1 Percentage body fat Frequency % CP Men (n = 25,537), Women (n = 15,551), Classification of percentage body fat normal: M, 25.9%; F, 53.8%; overweight: M, 32.3%; F, 30.3%; obese: M, 41.8%; F, 15.9%. CP, cumulative percentage. for the Chinese men and women were 90 and 80 cm. Therefore, well-designed WC studies are absolutely necessary to resolve this problem. With technologic advancements, BIA has been widely used in epidemiologic studies for adiposity. BIA has many advantages over BMI in determining fatness, because BIA is reproducible, portable, and easily performed and is much more reliable than BMI (34, 36, 37). We performed a cross-validation study using DXA as a reference method, because BIA data must be crosscalibrated in a representative population sample. After confirming the accuracy and applicability of the BIA data to DXA-derived percentage body fat, we obtained the prevalence and determined the first (17% compared with 32%) and second (21% compared with 37%) percentage body fat cutoffs for Korean men and women by using the minimum P value approach. Only 25.9% of Korean men had normal body composition (,17% body fat), whereas about half of Korean women (53.8%) TABLE 3 Adjusted odds ratios of obesity categories for the cardiovascular disease risk factors 1 had a value lower than the first cutoff for percentage body fat (,32%). These results indicate that men have much lower percentage body fat cutoffs and a higher prevalence of obesity than do women. We also identified increasing CVD risks according to percentage body fat increments. These findings suggest that the prevalence of each risk factor increases as the amount of fatness increases, which is consistent with the findings of others (35, 39). Although the prevalence of CVD risk factors in Korean women increased from.25% body fat, the prevalence in men increased from 10% body fat. The prevalence of at least one risk factor in Korean men with 20% body fat was similar to that in Korean women with 40% body fat. These results suggest that even a low degree of percentage body fat could influence Korean men rather than women and that men would be much more vulnerable to CVD and its risk factors. The high prevalence of overweight and obesity was consistent with significantly high risks (ORs) for CVD risk factors: 2.22 and 1.95 times the risks in overweight groups and 4.05 and 3.21 times the risks in obese groups compared with the normal percentage body fat for Korean men and women. These high risks may result in adverse health outcomes and impaired quality of life in the Korean population. Certainly, these problems would induce an increased incidence of circulatory diseases, which is the secondhighest cause of death in both men and women in Korea (39). The important issue that Korean men have a higher rate of circulatory death and prevalence of CVD and its risk factors has been addressed (39). In 2005, the Korean Statistical Information Service reported that the mortality rates of CVD, including stroke, ischemic heart disease, and other heart diseases, were 47.4% and 17.0% for men and women aged y, respectively (39). The Korea National Health and Nutrition Survey observed that the prevalence of CVD risk factors was higher in men than in women aged y: 22.7% compared with 13.4% for hypertension, 51.2% compared with 27.9% for low HDL cholesterol, 28.4% compared with 16.2% for hypertriglyceridemia, and 16.6% compared with 8.1% for diabetes (39). Again, in agreement with our findings from the current study, these findings support that Korean men are more susceptible to CVD and its risk factors than are Korean women. The current study had some limitations. The lack of WC data was a limitation, because it is important in the study of the metabolic syndrome and central obesity, even though the metabolic syndrome is still controversial due to unclear pathophysiology. Furthermore, the relation of percentage body fat to risks of Men (n = 25,537) Women (n = 15,551) Normal (,17.0%BF) Overweight ( %BF) Obese (21.0%BF) Normal (,32.0%BF) Overweight ( %BF) Obese (37.0%BF) Hypertension y (1.44, 1.72) 2.52 y (2.33, 2.74) y (1.52, 1.86) 2.74 y (2.44, 3.08) Diabetes mellitus y (1.18, 1.51) 2.00 y (1.78, 2.24) y (1.25, 1.69) 2.13 y (1.84, 2.50) Dyslipidemia y (2.04, 2.34) 3.52 y (3.29, 3.76) y (1.74, 2.07) 2.52 y (2.27, 2.80) 1 risk factor y (2.07, 2.38) 4.05 y (3.78, 4.33) y (1.79, 2.12) 3.21 y (2.87, 3.57) 2 risk factors y (1.68, 2.06) 3.37 y (3.07, 3.70) y (1.80, 2.32) 3.37 y (2.94, 3.87) Three risk factors y (1.30, 2.00) 2.80 y (2.30, 3.41) y (1.15, 1.90) 2.36 y (1.84, 3.04) 1 All values are odds ratios; 95% CIs in parentheses. %BF, percentage body fat. Multivariate binary logistic regression analysis was used to derive odds ratios with adjustment for the confounding variables in overweight and obese subjects. y Significantly different from normal, P,

6 CUTOFFS OF PERCENTAGE BODY FAT FOR CVD RISK FACTORS 39 CVD was determined by using a cross-sectional approach, which meant that causality was not examined. Future studies using a prospective design should be conducted to evaluate the validity of these percentage body fat cutoffs. In many Asian countries, the prevalence of obesity has increased many times over the past few decades, and these countries have also experienced obesity-related disorders such as diabetes, hypertension, and CVD. It is important to determine the meaningful cutoff points of adiposity, which can result in adequate guidelines for the treatment and prevention of these disorders. To our knowledge, this was the first study that attempted to determine cutoffs of percentage body fat in Korean adults. We confirmed a significant increase in obesity-related CVD risk factors in this population after these cutoffs were determined. The authors responsibilities were as follows CH-K, HSP, and CK: study concept and design; CH-K, HSP, and CK: acquisition of data; CH-K, MP, HK, and CK: analysis and interpretation of the data; CH-K and HK: drafting of the manuscript; and CH-K and CK: critical revision of the manuscript and study supervision. None of the authors declared a conflict of interest. REFERENCES 1. Kopelman PG. Obesity as a medical problem. Nature 2000;404: Haslam DW, James WP. Obesity. Lancet 2005;366: Hossain P, Kawar B, Nahas ME. Obesity and diabetes in the developing world a growing challenge. N Engl J Med 2007;356: Kuczmarski RJ, Flegal KM. Criteria for definition of overweight in transition: background and recommendations for the United States. Am J Clin Nutr 2000;72: WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363: World Health Organization. Obesity: preventing and managing the global epidemic. Report on a WHO Consultation on Obesity. World Health Organ Tech Rep Ser 2000; World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Consultation. World Health Organ Tech Rep Ser 1995; Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Consensus Group. The metabolic syndrome a new worldwide definition. Lancet 2005;366: Deurenberg P. Universal cut-off BMI points for obesity are not appropriate. Br J Nutr 2001;85: Tan CE, Ma S, Wai D, Chew SK, Tai ES. Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care 2004;27: Snehalatha C, Viswanathan V, Ramachandran A. Cutoff values for normal anthropometric variables in asian Indian adults. Diabetes Care 2003;26: Examination Committee of Criteria for Obesity Disease in Japan, Japan Society for the Study of Obesity. New criteria for obesity disease in Japan. Circ J 2002;66: Park YM, Kwon HS, Lim SY, et al. Optimal waist circumference cutoff value reflecting insulin resistance as a diagnostic criterion of metabolic syndrome in a nondiabetic Korean population aged 40 years and over: the Chngju Metabolic Disease Cohort (CMC) Study. Yonsei Med J 2010;51: Lee K, Lee S, Kim SY, Kim SJ, Kim YJ. Percent body fat cutoff vales for classifying overweight and obesity recommended by the International Obesity Task Force (IOTF) in Korean children. Asia Pac J Clin Nutr 2007;16: Ellis KJ, Bell SJ, Chertow GM, et al. Bioelectrical impedance methods in clinical research: a follow-up to the NIH Technology Assessment Conference. Nutrition 1999;15: Grundy SM. Metabolic syndrome pandemic. Arterioscler Thromb Vasc Biol 2008;28: Duvnjak L, Duvnjak M. The metabolic syndrome an ongoing story. J Physiol Pharmacol 2009;60: Lenfant C, Chonanian AV, Jones DW, et al. Seventh reports of the Joint National Committee on the Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7): resetting the hypertension sails. Hypertension 2003;41: Expert Panel on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol in Adults (Adults Treatment Panel III). JAMA 2001;285: Ito H, Nakasuga K, Ohshima A, et al. Detection of cardiovascular risk factors by indices of obesity obtained from anthropometry and dual-energy X-ray absorptionmetry in Japanese individuals. Int J Obes Relat Metab Disord 2003;27: Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 2003;26:S Mazumdar M, Classman JR. Categorizing a prognostic variable: review of methods, code for easy implementation and applications to decision-making about cancer treatments. Stat Med 2000;19: Lohman TG. Advances in body composition assessment. Champaign, IL: Human Kinetics Publisher, Deurenberg P, Deurenberg-Yap M, Guricci S. Asians are different from Caucassians from each other in their body mass index/body fat per cent relationship. Obes Rev 2002;3: Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes Relat Metab Disord 2000;24: Deurenberg P, Deurenberg-Yap M. Validity of body composition methods across ethnic population groups. Acta Diabetol 2003;40:S Prospective Studies Collaboration, Whitlock G, Lewington S, et al. Body-mass index and cause-specific mortality in adults: collaborative analysis of 57 prospective studies. Lancet 2009;373: Bigaard J, Frederiksen K, Tjǿnneland A, et al. Body fat and fat-free mass and all-cause mortality. Obes Res 2004;12: Allison DB, Zhu SK, Plankey M, Faith MS, Heo M. Differential associations of body mass index and adiposity with all-cause mortality among men in the first and second National Health and Nutrition Examination Surveys (NHANES I and NHANES II) follow-up studies. Int J Obes Relat Metab Disord 2002;26: Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B. Mortality associated with body fat, fat-free mass and body mass index among 60-year-old mass and body mass index among 60-year-old Swedish men-a 22-year follow-up. The study of men born in Int J Obes Relat Metab Disord 2000;24: Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW Jr. Body-mass index and mortality in a prospective cohort of U.S. Adults. N Engl J Med 1999;341: Zheng W, McLerran DF, Rolland B, et al. Association between body-mass index and risk of death in more than 1 million Asians. N Engl J Med 2011;364: Jee SH, Sull JW, Park J, et al. Body-mass index and mortality in Korean men and women. N Engl J Med 2006;355: Lohman TG, Houtkooper L, Going SB. Body fat measurement goes high-tech. Not all are created equal. Health & Fitness J 1997;1: Goh VH, Tain CFT, Tong TYY, Mok HPP, Wong MT. Are BMI and other anthropometric measure appropriate as indices for obesity? A study in an Asian population. J Lipid Res 2004;45: Kyle UG, Bosaeus I, De Lorenzo DD, et al. Bioelectrical impedance analysis part I: review of principles and methods. Clin Nutr 2004;23: Houtkooper LB, Lohman TG, Going LS, Howell WH. Why bioelectrical impedance analysis should be used for estimated adiposity. Am J Clin Nutr 1996;64(suppl):436S 48S. 38. Sievenpiper JL, Jenkins DJA, Josse RG, Leiter LA, Vuksan V. Simple skinfold-thickness measurements complement conventional anthropometric assessments in predicting glucose tolerance. Am J Clin Nutr 2001; 73: Korea Center for Disease Control & Prevention, Ministry of Health and Welfare. The Annual Statistical Report of Health and Welfare. Version current 1 January Available from: jb/sjb0501vw.jsp?par_menu_id=03&menu_id=030501&board_ ID=320&BOARD_FLAG=00&CONT_SEQ=178471&page=1 (cited 23 October 2009).