Waist Circumference and Waist-to-Height Ratio as Predictors of Cardiovascular Disease Risk in Korean Adults

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1 ORIGINAL ARTICLE Epidemiology Circ J 9; 7: Waist Circumference and Waist-to-Height Ratio as Predictors of Cardiovascular Disease Risk in Korean Adults Sung-Hee Park, PhD; Soon-Ja Choi, MPH; Kwang-Soo Lee, PhD; Hyun-Young Park, PhD, MD Background: Obesity is associated with diabetes mellitus, hypertension, dyslipidemia and increased cardiovascular disease (CVD) risk. Anthropometric indices, such body mass index (), waist circumference (), and waist-to-height ratio (), were evaluated as predictors of the presence of CVD risk factors in Korean adults. Methods and Results: The data were obtained from the Third Korea National Health and Nutrition Examination Survey (KNHANES III, ). The study contained a nationally representative sample of Korean adults (,7 men,, women) aged years or older. The area under the receiver operating characteristic (ROC) curve for was higher than that for or with respect to diabetes mellitus and hypertension in both men and women, whereas was a better predictor for low high-density lipoprotein cholesterol in men. The cut-off value to predict diabetes mellitus, hypertension, and dyslipidemia was approximately. in men and. in women. The cut-offs varied from 8. to 8. cm in men and from 78. to 8.9 cm in women. The optimal cut-off point varied from. to.7 kg/m in both men and women. Conclusions: or may be a better predictor of CVD risk factors than in Korean adults. (Circ J 9; 7: ) Key Words: Cardiovascular disease; Body mass index; Obesity; Risk factors The prevalence of obesity has increased in Korea, as it has in other countries. Obesity is associated with diabetes mellitus, hypertension, dyslipidemia, and cardiovascular disease (CVD)., Obesity is defined as an excess of body fat, and anthropometric indices such as body mass index (), waist circumference (), waist-to-hip ratio (WHR), and waist-to-height ratio () have been developed to evaluate the obesity index. is often used to reflect total body fat, whereas, WHR, or may be used as surrogate markers of body fat centralization.,7 Central distribution of body fat, which suggests excessive deposition of intra-abdominal fat, is an important predictor of cardiovascular risk. 8 has been proposed as the best, simple measure of both intra-abdominal fat mass and total fat. 9, However, the measurement has been criticized for not taking into account differences in body height, and the value is a better predictor of cardiovascular risk factors. These anthropometric indices are associated with cardiovascular risk factors, such as type diabetes, hypertension, and dyslipidemia. However, controversy remains regarding the best anthropometric indices for CVD risk. Most studies examining the risk of adverse health associated with obesity have been based on data from the United States or Europe, with little data available on populations in the Asia-Pacific region. Several epidemiologic studies of Asian populations have shown that Asians have higher amounts of body fat corresponding to lower s and s than do Western populations,, perhaps leading to a greater prevalence of CVD risk factors at a lower in Asians than in Westerners.,7 Prevalence rates of obesity have been determined using kg/m in Western populations 8 and kg/m in Korean 9 and in other Asian populations. The prevalence of abdominal obesity was assessed according to criteria suggested by the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), the International Obesity Task Force Asian-Pacific region, and the cut-offs for Korean adults proposed by the Korean Society for the Study of Obesity ( 9 cm in men and 8 cm in women). 9 In addition, the prevalence of metabolic syndrome has been estimated according to the definitions proposed by the World Health Organization (WHO, 998), the International Diabetes Federation (), NCEP ATP III (), and the modified NCEP ATP III in Korean adults. Therefore, appropriate cut-off points for Koreans may differ from those currently recommended by the WHO. Those studies suggest the importance of applying ethnically appropriate cut-off values of anthropometric indices for assessing CVD risk factors. The purpose of this study was to evaluate,, and as anthropometric indices and to assess their respective associations with CVD risk factors in a Korean population. (Received March, 9; revised manuscript received April, 9; accepted May, 9; released online July 9, 9) Division of Cardiovascular and Rare Diseases, Center for Biomedical Sciences, National Institute of Health, Seoul, South Korea Mailing address: Hyun-Young Park, MD, PhD, Division of Cardiovascular and Rare Diseases, Center for Biomedical Sciences, National Institute of Health, Seoul -7, South Korea. hypark@nih.go.kr All rights are reserved to the Japanese Circulation Society. For permissions, please cj@j-circ.or.jp Circulation Journal Vol.7, September 9

2 PARK SH et al. Table. Characteristics of the Study Population in the Third Korea National Health and Nutrition Examination Survey (KNHANES III, ) Men (n=,7) Methods Women (n=,) P value Age (years) 7.±.7 7.±.7.97 (kg/m ).±..±. <. (cm) 8.± ±9. <..±..±.7. FBG (mg/dl) 98.±. 9.±9.9 <. SBP (mmhg).9±..7±8.7 <. DBP (mmhg) 8.7±. 7.8±. <. TC (mg/dl) 8.±. 8.±..9 TG (mg/dl).8±..±79.7 <. HDL-C (mg/dl).±..9±.8 <. LDL-C (mg/dl).±..±.. TC/HDL-C ratio.8±..9±.8 <. Diabetes mellitus, n (%) (.) 9 (7.) <. Hypertension, n (%) 7 (.) 7 (.) <. High TC, n (%) 9 (.) 89 (9.).7 High TG, n (%) (.) 9 (9.) <. Low HDL-C, n (%),8 (.) 79 (.) <. High LDL-C, n (%) (9.) 99 (.).9 Current drinker, n (%),898 (8.8), (.9) <. Current smoker, n (%),9 (7.) (.) <. Values are means ± SD or n (%). P values are from t-tests or chi-square tests for analysis of variance for continuous variables and categorical variables., body mass index;, waist circumference;, waist-to-height ratio; FBG, fasting blood glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. Study Population This study was based on data obtained from the Third Korea National Health and Nutrition Examination Survey (KNHANES III). KNHANES III was conducted as a crosssectional health survey of nationally representative samples of non-institutionalized Korean civilians by the Korean Ministry of Health and Welfare in. Based on the National Census Registry, sampling units (including, households) were randomly selected for the health interview survey and sampling units were randomly selected among the sampling frames for health examination, health behavior, and nutrition surveys. The health interview survey consisted of, individuals over the age of year. In all,,88 individuals completed the interviews and the response rate was 99.%. Of,8 participants, 7,97 subjects aged year and older completed the health examination survey (7.%). We excluded individuals with incomplete data for the standardized physical examination, laboratory tests, and anthropometric measures. In addition, pregnant women were excluded. This resulted in a final analytical sample of,9 subjects (,7 men and, women) aged years or older. Health Interview and Health Behavior Surveys Health interview and health behavior surveys were conducted in the households as face-to-face interviews by trained interviewers. Health interview and health behavior surveys included well-established questions to determine the demographic and socioeconomic characteristics of the subjects: age, education level, occupation, income, marital status, smoking habit, alcohol consumption, exercise, previous and current diseases, and family disease history. Using a standardized questionnaire, the participants were asked to report their smoking habits, including the number of cigarettes smoked per day and the duration of cigarette smoking in years, along with other health habits, including alcohol consumption. The participants were classified as current smokers if they smoked currently and had done so for at least year, nonsmokers if they had never smoked, and ex-smokers if they had smoked previously but had quit. Likewise, the subjects were classified as current drinkers, nondrinkers and ex-drinkers. Health Examination Survey Height and weight data were obtained using standardized techniques and equipment. was calculated by dividing weight by height squared (kg/m ). Using a fiberglass tape measure, was measured at the midpoint between the bottom of the rib cage and the top of the iliac crest. was calculated as divided by height. After min of rest while seated, blood pressure was measured times with -s intervals using a standard mercury sphygmomanometer. The average of the nd and rd measurements was used in the analysis. Blood samples were collected in the morning after fasting for at least 8 h. Fasting blood glucose, total cholesterol (TC), triglycerides (TG), and high-density lipoprotein cholesterol (HDL-C) levels were measured in a central, certified laboratory. Low-density lipoprotein cholesterol (LDL-C) levels were estimated indirectly using the Friedwald formula: LDL-C = TC {HDL-C + (TG/)}, for subjects with TG levels < mg/dl. Definition of CVD Risk Factors In the present study, we used the definition of CVD risk factors proposed by the NCEP ATP III. 7 Patients with fasting blood glucose mg/dl and/or physician-diagnosed diabetes mellitus and/or use of oral hypoglycemic agents were defined as having diabetes mellitus. Hypertension was defined as a systolic blood pressure mmhg and/or a diastolic blood pressure 9 mmhg and/or physician-diagnosed hypertension and/or the use of antihypertensive medication. The cut-off points were plasma TC mg/dl for high TC and/or use of medications to lower blood cholesterol, TG mg/dl for high TG, HDL-C < mg/dl in men and women for low HDL-C, and LDL-C mg/dl and/or use of medications to lower blood cholesterol for high LDL-C. Statistical Analysis The data were analyzed using SPSS version. for Windows (SPSS, Inc, Chicago, IL, USA). Receiver-operating characteristic (ROC) curves comparison was performed using MedCalc version (MedCalc software, Mariakerke, Belgium). Continuous variables are expressed as the mean ± SD, and discrete variables are expressed as numbers and proportions. Comparisons between groups were performed using Student s t-test for continuous and the chi-square test for categorical data. We used ROC analysis to compare predictive validity and to determine optimal cut-off values. ROC curves were plotted using measures of sensitivity (true-positive rate) and specificity (false-positive rate) based on the cut-off values for the various anthropometric indices. The ROC curves demonstrated the overall discriminatory power of a diagnostic test over the whole range of testing values. The area under the ROC curve (AUC) is a measure of the diagnostic power of a test. Circulation Journal Vol.7, September 9

3 Anthropometric Indices and CVD Risk Factors A perfect test will have an AUC of., and an AUC equal to. means the test performs no better than chance. Odds ratios (ORs) were calculated using multiple logistic regression analysis with adjustments for age and current smoking status. The adjusted ORs are presented with their 9% confidence intervals (CIs). P<. was considered to be statistically significant. Results Basic Characteristics of the Study Subjects The basic characteristics of the study population and the prevalence of CVD risk factors, stratified by gender, are shown in Table. The mean age of the study population was 7. years in both men and women, and the mean was. kg/m in men and. kg/m in women. The mean was 8. cm in men and 78. cm in women, whereas the mean was. in men and. in women. The percentage of male subjects with diabetes mellitus, hypertension, high TG and low HDL-C was higher than the corresponding percentage of female subjects. Approximately 8% of the men were drinkers and approximately 7% were current smokers. Roughly 7% of the women were drinkers and only % were current smokers. Table. Pearson s Correlation Coefficients Between Anthropometric Indices and CVD Risk Factors Men (n=,7) FBG SBP DBP.9.. TC TG.9.7. HDL-C LDL-C Women (n=,) FBG SBP DBP...8 TC..8. TG.7.7. HDL-C LDL-C Data are correlation coefficients. All correlation coefficients are significant at P<.. CVD, cardiovascular disease. Other abbreviations see Table. Table. AUC for Various Anthropometric Indices and CVD Risk Factors in Men and Women Risk factor Men (n=,7) Diabetes mellitus.7 (..7). (.98.8). (..7)*** Hypertension. (.7.).78 (..7).7 (.8.7)*** High TC.9 (..). (.79.). (.8.) High TG. (.8.8).7 (..9).7 (..9) Low HDL-C. (.9.7). (..78)**. (..) High LDL-C. (..).8 (.9.). (.98.7) Women (n=,) Diabetes mellitus.7 (..9).7 (.7.77).7 (.7.78)** Hypertension.8 (.9.7).7 (.7.7).78 (.7.799)*** High TC. (..8).78 (.8.77).98 (.7.77)** High TG.7 (..7).7 (.9.7).7 (.799.7) Low HDL-C.8 (.9.).9 (.7.7). (.9.7) High LDL-C. (..8).9 (..99).9 (.7.79)** Data are AUC (9% confidence interval). **P<., ***P<.. **, ***AUC is significantly larger than the next smaller AUC; significance was calculated only for the difference between parameters with the highest and second highest AUC. AUC, area under the ROC curve. Other abbreviations see Tables,. Table. Cut-Off Points for Anthropometric Indices Predictive of CVD Risk Factors Cut-off Sensitivity Specificity Cut-off Sensitivity Specificity Cut-off Sensitivity Specificity (kg/m ) (%) (%) (cm) (%) (%) (%) (%) Men (n=,7) Diabetes mellitus Hypertension High TC High TG Low HDL-C High LDL-C Women (n=,) Diabetes mellitus Hypertension High TC High TG Low HDL-C High LDL-C Abbreviations see Tables,. Circulation Journal Vol.7, September 9

4 PARK SH et al. 8 7 Diabetes mellitus 8 7 Diabetes mellitus Hypertension Hypertension High TC High TC Figure. Adjusted odds ratios of diabetes mellitus, hypertension, and high total cholesterol (TC) by body mass index (), waist circumference (), and waist-to-height ratio () quartiles. Adjusted for age and current smoking. quartiles were classified as follows: () <.9; ().9.9; ()..9; (). kg/m for men and () <.; ()..; ()..; (). kg/m for women. quartiles were classified as follows: () <78.; () ; () 8. 9.; () 9. cm for men and () <7.; () ; () ; () 8.8 cm for women. quartiles were classified as follows: () <.; ()..9; ()..; (). for men and () <.; ()..9; ()..; (). for women. M, men; W, women. Correlations Between Anthropometric Indices and CVD Risk Factors Pearson s correlation coefficients, as measured among the anthropometric indices and CVD risk factors, are shown in Table. The anthropometric indices all correlated significantly with the CVD risk factors for both men and women. The showed the highest correlation with systolic blood pressure and TC in both genders when compared with and. The correlation coefficient between and CVD risk factors was lower than that for either or. Cut-Off Points for the Various Anthropometric Indices to Predict CVD Risk Factors The AUCs of the anthropometric indices and CVD risk factors are shown in Table. Regarding diabetes mellitus and hypertension, the AUCs for were significantly higher than those for the and in both sexes. Additionally, the AUCs for were highest for high TC (AUC =.98) and high LDL-C (AUC =.9) in women, and the AUC for was significantly higher for low HDL-C (AUC =.) in men only. Table summarizes the cut-off points for the various anthropometric indices to Circulation Journal Vol.7, September 9

5 Anthropometric Indices and CVD Risk Factors 7 8 High TG 8 High TG Low HDL-C Low HDL-C High LDL-C High LDL-C Figure. Adjusted odds ratios of high triglycerides (TG) low high-density lipoprotein cholesterol (HDL-C), and high low-density lipoprotein cholesterol (LDL-C) by body mass index (), waist circumference (), and waist-to-height ratio () quartiles. Adjusted for age and current smoking. quartiles were classified as follows: () <.9; ().9.9; ()..9; (). kg/m for men and () <.; ()..; ()..; (). kg/m for women. quartiles were classified as follows: () <78.; () ; () 8. 9.; () 9. cm for men and () <7.; () ; () ; () 8.8 cm for women. quartiles were classified as follows: () <.; ()..9; ()..; (). for men and () <.; ()..9; ()..; (). for women. M, men; W, women. predict CVD risk factors using ROC analysis. For men, the optimal cut-off values for predicting diabetes mellitus, hypertension, and dyslipidemia (high TC, high TG, low HDL-C, and high LDL-C) varied from. to.7 kg/m. The optimal cut-off values varied from 8. to 8. cm, and the optimal cut-off value varied from.9 to.. In women, the cut-off points for association with diabetes mellitus, hypertension, and dyslipidemia varied between. and. kg/m, and the optimal cut-off value varied between 78. and 8.9 cm. The optimal cut-off varied between. and. in women. ORs of CVD Risk Factors by Anthropometric Indices Figures and show the ORs for predicting the presence of CVD risk factors according to the anthropometric indices, categorized by quartiles for men and women. The increase in the ORs, according to the,, and quartiles, was significant after adjusting for age and current smoking status in men and women. The cut-off values for high,, and were included in the second quartiles for men and in the second or third quartiles for women. The and were superior to in their association with diabetes mellitus, hypertension, high TG and low Circulation Journal Vol.7, September 9

6 8 PARK SH et al. Table. Odds Ratios (9% Confidence Interval) for CVD Risk Factors According to Category and Category in Men and Women (cm) <8 8 <8 8 <.. <.. <.. <.. Men (n=7) (n=) (n=) (n=,) (n=,8) (n=87) (n=9) (n=,8) Diabetes mellitus.. (..9)*..7 (.7.7).. (.8.97) Hypertension..79 (..9).. (.7.7)..9 (.7.)..97 (..)* High TC.. (.7.).. (..98)..7 (.9.)..9 (.97.9) High TG..9 (. 7.).. (..9).. (.7.7)..7 (..) Low HDL-C..9 (..).. (..9)..7 (..9)..9 (.8.) High LDL-C..7 (..)..88 (..)*.. (.9.7) Women (n=,7) (n=9) (n=) (n=,) (n=,89) (n=7) (n=) (n=7) Diabetes mellitus..7 (..)..9 (..8).. (.7.) Hypertension.. (.7.)*.. (..)*.. (.79.) High TC..9 (.7.9).. (..).. (..)* High TG.. (.9.7).. (..)..9 (..8) Low HDL-C.. (..78)..89 (.8.)..9 (.7.) High LDL-C..8 (.7.9)..79 (..8)..9 (.98.9) *P<., P<., and P<.. Abbreviations see Table. HDL-C in both sexes. Both male and female subjects in the highest group for had the higher risk prediction for diabetes mellitus, and subjects in the highest group for had the highest risk prediction for high TG levels and low HDL-C. The combined effects of and for predicting the presence of CVD risk factors are presented Table. The logistic regression analysis showed that both male and female subjects with <8 cm and. had a higher risk of CVD compared with subjects with <8 cm and <.. Also, in men with 8 cm and., the trend toward CVD risk factors was higher than in those with 8 cm and <.. In women with 8 cm and., the risk of CVD were no greater than for those with 8 cm and <., with the exception of those with hypertension (OR:., 9%CI:.., P<.). Discussion Obesity is associated with various cardiovascular risk factors and obesity-related diseases, such as coronary heart disease and stroke, are the major causes of death in Korea. 8 The definitions of overweight ( 9.9 kg/m ) and obesity ( kg/m ) recommended by the WHO are a result of the relationship between and morbidity and mortality outcomes. 9 Recently, WHR and values have been proposed as more sensitive measurements of visceral obesity and more indicative of cardiovascular risk. Recent studies in Asian populations have shown that Asians have a higher percentage of body fat at lower s and s than Western populations.,,, Therefore, increased health risks associated with obesity appear to occur at a lower in Asians. The prevalence of obesity is.% for men and 8.% for women in Korea ( kg/m ). The prevalence of central obesity is.% in men and.% in women according to the criteria for Koreans (9 cm for men and 8 cm for women).,9 Variations in measurement levels and differences in cut-off values between men and women and among various ethnic groups may prevent accurate assessment of the global prevalence of central obesity. Our study was a population-based examination of gender-specific cut-off points for,, and in a Korean population based on current levels of CVD risk factors. In the ROC analysis, proved to be significantly more predictive of diabetes mellitus and hypertension in men, and high TC and high LDL-C values were more reliable than or in the women in our study. Schneider et al showed that regarding dyslipidemia and type diabetes, the AUCs for were significantly higher than for,, hip circumference, or WHR in both sexes. Higher and were significantly associated with dyslipidemia in the Korean adult population. The is a surrogate measure of abdominal obesity, as manifested by an enlarged abdomen with a disproportionately short stature. Several studies in adults have shown that is a good predictor of diabetes, hypertension, and dyslipidemia., When we calculated the ORs that allow for adjustment for age and smoking status, was still a slightly better predictor of diabetes mellitus in men and women, whereas was slightly superior for high TG and low HDL-C in men, and hypertension and dyslipidemia in women, in our study. This could be related to the fact that the predictive power of each anthropometric variable is population-dependent and varies among different ethnic groups. 7 In the current study, in both sexes, with increasing quartile of,, and, there were significantly higher risks of diabetes mellitus, hypertension, and dyslipidemia. According to the multiple logistic regression analysis reported herein, the third quartile of,, and showed a greater than -fold increased risk for developing diabetes mellitus, hypertension, and dyslipidemia in both men and women. The risk of diabetes mellitus and hypertension increased at the third quartile of (..9 kg/m in men and.. kg/m in women), this quartile being much lower than the current WHO cut-off point of. kg/m of obesity. In the ROC analysis, the optimal cut-off point of for predicting CVD risk factors was.8. kg/m for men and.. kg/m for women. A study based on the KNHANES III revealed that the ORs of diabetes mellitus and hypertension were.7-fold and.- fold for men and.-fold and.7-fold for women, respectively, at a cut-off point of. kg/m. These findings strongly suggest that the value of kg/m can serve as the cut-off point for obesity determination in Korea. The WHO recommends the use of pre-specified cut-off points for,, and WHR to standardize comparisons Circulation Journal Vol.7, September 9

7 Anthropometric Indices and CVD Risk Factors within and between populations. 8 Currently such cut-off points are derived from studies among European populations and thus may not be applicable to other ethnic groups. The location of the measurement of (at the umbilical level) by Japan Society for the Study of Obesity was different from that of the WHO and Asia-Pacific perspective (measured at the midpoint between the lower border of the rib cage and the iliac crest). According to Hsieh et al, the average of the s measured at the umbilical level may be slightly larger than those measured at the midpoint between the lower border of the rib cage and the iliac crest. In the current study, we measured according to the WHO recommendation at the midpoint between the lower border of the rib cage and the iliac crest. Most studies have suggested a cut-off of kg/m for men and women and a cut-off of 8 8 cm (for men) and 7 8 cm (for women) from Korean or Asian- Pacific background.,,9 The cut-offs recommended herein were identified as the and values that best balanced sensitivity and specificity of the measurement applied. Based on the sensitivity, specificity, and ROC calculations, these data suggest a of kg/m and a of 8 cm (for men) or 8 cm (for women) as more appropriate cut-off values for the designation of overweight and central adiposity in the Korean population, cut-offs similar to those reported in other studies of Asian populations. Hsieh et al recently proposed a cut-off value of. for, as anything over this value is associated with an increased cardiovascular risk in normal and overweight Japanese adults. has been suggested as a common measure of central obesity for Asian populations and many studies have proposed a boundary value, cut-off level of. for both sexes.,, However, in Korean adult populations, a of. was included for the third quartile in men and women and our study suggests the use of a similar cut-off range.9.. Subjects with a 8 or <8 cm (for men) or <8 cm (for women) with. had a significantly greater CVD risk than those with a 8 or <8 cm for men or <8 cm for women with <.. It is interesting that both the and cut-off values (<8 cm and., respectively) were able to identify individuals within the Korean population with cardiovascular risk among those with central obesity. A subject with a 8 cm and a high (.) had no higher risk than one with a 8 cm and a low (<.) of having CVD risk factors, except among women with hypertension. Study Limitations First, it relates the risk of CVD to, and in a cross-sectional setting using the occurrence of established risk factors as a proxy risk estimate. This indicates the need for prospective studies that relate anthropometric indices to clinical CVD mortality and all-cause mortality. Second, we had no data on body fat mass. Further studies may need to consider body fat mass and its relationship with surrogate anthropometric indices. In conclusion, or may be better indicators of obesity-related CVD risk factors than. Our study suggests that the cut-off values for,, or to define obesity should be much lower in Korea than in Western countries. Further investigations are necessary before and can be used for assessing risk factors in the clinical setting for the management of diabetes mellitus, hypertension, and dyslipidemia in Korean populations. 9 Acknowledgment This study was supported by an intramural grant of the National Institute of Health, Korea (8-8--). References. In-Depth Analysis of the Third Korea National Health and Nutrition Examination Survey (KNHANES III). Seoul: Korea Centers for Disease Control and Prevention, 7.. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM. Prevalence of overweight and obesity among US children, adolescents, and adults, 999. JAMA ; 9: Bermudez OI, Tucker KL. Total and central obesity among elderly Hispanics and the association with Type diabetes. Obes Res ; 9:.. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: A -year followup of participants in the Framingham Heart Study. Circulation 98; 7: Lukaski HC. 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8 PARK SH et al. wide definition: A Consensus Statement from the International Diabetes Federation. Diabet Med ; : 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 (Adult Treatment Panel III). JAMA ; 8: Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation ; : The Third Korea National Health and Nutrition Examination Survey (KNHANES III). Seoul: Korean Ministry of Health and Welfare, Korea Centers for Disease Control and Prevention, 7.. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 97; 8: Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation ; :. 8. Annual report on the cause of death statistics. Seoul: Korea National Statistical Office,. 9. Brundtland GH. From the World Health Organization: Reducing risks to health, promoting healthy life. JAMA ; 88: 97.. Hyun YJ, Kim OY, Jang Y, Ha JW, Chae JS, Kim JY, et al. Evaluation of metabolic syndrome risk in Korean premenopausal women: Not waist circumference but visceral fat. Circ J 8; 7: 8.. Li R, Lu W, Jia J, Zhang S, Shi L, Li Y, et al. Relationships between indices of obesity and its cardiovascular comorbidities in a Chinese population. Circ J 8; 7: Schneider HJ, Glaesmer H, Klotsche J, Bohler S, Lehnert H, Zeiher AM, et al. Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab 7; 9: Jeong SK, Seo MW, Kim YH, Kweon SS, Nam HS. Does waist indicate dyslipidemia better than in Korean adult population? J Korean Med Sci ; : 7.. Hsieh SD, Yoshinaga H, Muto T. Waist-to-height ratio, a simple and practical index for assessing central fat distribution and metabolic risk in Japanese men and women. Int J Obes Relat Metab Disord ; 7:.. Sayeed MA, Mahtab H, Latif ZA, Khanam PA, Ahsan KA, Banu A, et al. Waist-to-height ratio is a better obesity index than body mass index and waist-to-hip ratio for predicting diabetes, hypertension and lipidemia. Bangladesh Med Res Counc Bull ; 9:.. Molarius A, Seidell JC. Selection of anthropometric indicators for classification of abdominal fatness: A critical review. Int J Obes Relat Metab Disord 998; : Lear SA, Chen MM, Frohlich JJ, Birmingham CL. The relationship between waist circumference and metabolic risk factors: Cohorts of European and Chinese descent. Metabolism ; : Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser ; 89: i-. 9. Zhou BF. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: Study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci ; : Ho SY, Lam TH, Janus ED. Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann Epidemiol ; : 8 9. Circulation Journal Vol.7, September 9

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