HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Citation Map Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Zhu, S. Articles by Heshka, S. Search for Related Content PubMed PubMed Citation Articles by Zhu, S. Articles by Heshka, S. Agricola Articles by Zhu, S. Articles by Heshka, S.

Race-ethnicity–specific waist circumference cutoffs for identifying cardiovascular disease risk factors^1,2,3

¹ From the Injury Research Center and the Department of Family and Community Medicine, Medical College of Wisconsin, Milwaukee (SZ); the New York Obesity Research Center, St Luke's–Roosevelt Hospital Center, Institute of Human Nutrition, College of Physicians and Surgeons, Columbia University, New York (SBH, ZW, AP, and SH); the Department of Public Health, Nagoya University Graduate School of Medicine, Nagoya, Japan (HT); and the Pediatric Unit, Verona University Medical School, Verona, Italy (AP)

² Supported by a grant from Pfizer Pharmaceutical Inc and National Institutes of Health grant PO1 DK42618.

³ Reprints not available. Address correspondence to S Zhu, Injury Research Center and Department of Family and Community Medicine, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226. E-mail: szhu{at}mcw.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Waist circumferences (WCs) in white men and women that represent a risk of cardiovascular disease (CVD) equivalent to that of body mass indexes (BMIs; in kg/m²) of 25 and 30 have been identified. However, WC cutoffs for other race-ethnicity groups remain unknown.

Objective: The objective was to determine WC cutoffs for CVD risk in non-Hispanic blacks (blacks), Mexican Americans (MA), and non-Hispanic whites (whites).

Design: Data from 10 969 participants in the third National Health and Nutrition Examination Survey (1988–1994) were analyzed. The presence of CVD risk factors was the main outcome. Sex- and race-ethnicity–specific WC cutoffs were determined with logistic regression models by linking WC cutoffs with equivalent CVD risk based on BMI cutoffs for overweight and obesity. WC cutoffs for metabolic syndrome risk factors were similarly calculated.

Results: Correlations between WC and lipid profiles, blood pressure, and glucose were significantly higher than those between BMI and these same variables in all groups. The WC cutoffs were 5–6 cm greater for white than for black men at BMIs between 25 and 40, and those for MA were intermediate. In women, few differences in WC cutoffs were observed between the groups. Simplified WC cutoffs corresponding to BMIs of 25 and 30, largely independent of age, for the 3 race-ethnicity groups were 89 and 101 cm for men and 83 and 94 cm for women. Minimal distances in receiver operating characteristic curves tended to be shorter when WC cutoffs rather than BMI cutoffs were used.

Conclusions: WC is a better indicator of CVD risk than is BMI in the 3 race-ethnicity groups studied. The proposed WC cutoffs are more sensitive than are BMI cutoffs in predicting CVD risk.

Key Words: Body mass index • BMI • metabolic risk factors • cardiovascular disease risk • ethnicity • obesity

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Waist circumference (WC) reflects the magnitude of abdominal adipose tissue deposits as well as total fat mass and thereby complements body mass index (BMI; in kg/m²) in the evaluation of obesity-associated cardiovascular disease (CVD) risk by providing a measure of body fat distribution (1–5). Several studies have shown that WC, a simple and clinically useful anthropometric measure, is complementary or superior to BMI in its association with CVD risk factors (2, 4–10).

In a previous study we reported WC cutoffs linking overweight and obesity to CVD risk factors. A WC of 90 cm for men and of 83 cm for women conferred a risk of CVD equivalent to a BMI of 25, whereas a WC of 100 cm for men and of 93 cm for women corresponded to CVD risk factors at a BMI of 30 in a white sample (approximately two-thirds non-Hispanic white and one-third Mexican American) from the third National Health and Nutrition Examination Survey (NHANES III, 1988–1994) (4). We now report race-ethnicity–specific WC cutoffs for non-Hispanic blacks (blacks), Mexican Americans, and non-Hispanic whites (whites), linking WC values to established BMI cutoffs with equivalent risk of CVD risk factors.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study population
This study analyzed data on black, Mexican American, and white subjects from NHANES III, conducted between 1988 and 1994, which used a stratified, multistage probability cluster sampling to assess the health and nutritional status of the noninstitutionalized US population. A total of 18 110 subjects aged 20 y were eligible for the study. We excluded 6946 subjects for whom demographic, socioeconomic, or dietary information was missing or who had fasted <6 h before venipuncture and 195 women who were pregnant or lactating at the time of evaluation. The remaining subjects were 5313 men (1337 blacks, 1564 Mexican Americans, and 2412 whites) and 5656 women (1577 blacks, 1427 Mexican Americans, and 2652 whites). Detailed information on NHANES III can be obtained elsewhere (11, 12).

Main outcome
The following CVD-related medical examination or laboratory data were selected: plasma glucose, systolic and diastolic blood pressure, total cholesterol, HDL cholesterol, triacylglycerol, and medication use for diabetes, hypertension, or dyslipidemia. LDL cholesterol was calculated as total cholesterol – HDL –triacylglycerol/5 (13). Three CVD risk factors were defined (14): 1) a high glucose concentration [a plasma glucose concentration > 125 mg/dL (>6.94 mmol/L) or current use of medication for diabetes], 2) high blood pressure (diastolic blood pressure 90 mm Hg, systolic blood pressure 140 mm Hg, or current use of medication for hypertension), and 3) dyslipidemia [LDL concentration 160 mg/dL (4.14 mmol/L), HDL concentration < 35 mg/dL (<0.91 mmol/L) for men, and <45 mg/dL (<1.17 mmol/L) for women or current use of medication for hypercholesterolemia]. The outcome variable was defined as subjects having one or more of these 3 CVD risk factors.

Main explanatory variables
BMI and WC were the main explanatory variables. BMI is defined as weight in kilogram divided by height squared in meters.

Covariates
The following information was included in the analysis: age (y), smoking and drinking habits, physical activity, economic and education levels, diet habits, and menopausal status for women. Smoking was coded as current, past, and never smoked. Past smokers were defined as those who reported that they had smoked 100 cigarettes during their lifetimes but did not currently smoke cigarettes. Drinking was categorized as heavy, moderate, and never drank. Heavy drinkers were subjects who ever drank 5 alcoholic beverages per day or drank beer, wine, or hard liquor one time per day during the past month. Physical activity levels were defined based on physical activity intensity rating scores obtained while the subjects were participating in various daily physical activities during the past month. The physical activity intensity rating scores were defined as the ratio of activity metabolic rate to resting metabolic rate (12). The physically inactive category included subjects with a total intensity rating score <3.5. The physically active category was defined as a total intensity rating score 12.5. The point at which the total intensity rating score equals 3.5 and 12.5 corresponds to 20th and 60th percentiles in the study samples, respectively. Education level was divided into 3 categories: <8 y, 8–12 y, and >12 y of education. Economic status was divided into 3 groups according to the previous year's household income: <$15 000, $15 001-$25 000, and >$25 000. Diet habits were categorized into 3 groups on the basis of dietary energy intake from carbohydrates (15): low (<40% of energy), moderate (40–60% of energy), and high (>60% of energy). Postmenopausal status was designated if there had been complete cessation of menses for 12 mo.

Statistical analysis
The statistical significance of differences in subject characteristics and the prevalence of CVD risk factors were evaluated with the use of the adjusted Wald test in blacks, Mexican Americans, and whites (16). A Bonferroni correction to the P values was used to compensate for the inflation of type I error due to multiple comparisons. Simple correlation analyses were applied to characterize the associations between WC and BMI with LDL, HDL, systolic and diastolic blood pressure, and glucose for 3 separate ethnic groups. Statistical comparisons of 2 dependent correlation coefficients between risk factor correlating with WC or BMI were made by using z tests (17).

Logistic regression analyses were applied to estimate ß coefficients for having CVD risk factors versus not having CVD risk factors for WC or BMI, with adjustment for age, cigarette smoking, alcohol consumption, physical activity, education and economic levels, diet habits, and menopausal status for women for the 3 separate race-ethnicity groups. Sex- and race-ethnicity–specific odds ratio (OR) equations were developed by comparing odds at one cutoff of WC or BMI for having CVD risk factors with the odds at a reference point. The reference point was set at the 25th percentile for BMI or WC in the sex- and race-ethnicity–specific study populations. These reference values were chosen because the BMI values corresponding to the 25th percentile in the study population are considered to have the lowest risk of death from any cause (18, 19). Thresholds for WC were identified where ORs for WC corresponded to those seen at BMIs of 18.5, 25, 30, 35, and 40 according to the sex- and race-ethnicity–specific OR equations. In addition, the interaction terms between age and WC or BMI were also tested in separate regression models in the 3 ethnic groups for men and women.

To eliminate the potential influence of CVD or diabetes history on the relation of BMI and WC with CVD risk factors, we repeated the analysis excluding subjects who had CVD or diabetes history (ie, a history of type 2 diabetes, hypertension, heart attack, congestive heart failure, or stroke) but did not have any of the 3 CVD risk factors at the time of survey.

To investigate whether the choice of dependent variables would affect WC cutoffs, we also used components of metabolic syndrome risk factors as outcomes. Four metabolic syndrome risk factors were selected from 5 metabolic syndrome criteria, excluding the WC criteria (20): 1) high triacylglycerol concentrations (150 mg/dL, or 1.69 mmol/L), 2) low HDL-cholesterol concentrations (<40 mg/dL, or <1.03 mmol/L, for men; <50 mg/dL, or <1.29 mmol/L, for women), 3) high blood pressure (systolic 130 mm Hg or diastolic 85 mm Hg), and 4) high fasting plasma glucose concentrations (110 mg/dL, or 6.11 mmol/L). Subjects with 1, 2, or 3 of these metabolic syndrome risk factors were considered to have risk factors, and the logistic regression analyses were carried out separately in the 3 ethnic groups for men and women.

Sensitivity and specificity analyses, including positive predictive value (PV+) and negative predictive value (PV–), and the distance between a point on the receiver operating characteristic (ROC) curve and maximum sensitivity and specificity (21), were then conducted to test BMIs of 25 and 30 and the corresponding cutoffs for WC derived from regression analysis for identifying the presence of one or more CVD risk factors.

Statistical significance was set at P < 0.05 unless otherwise indicated. All analyses were carried out by using STATA statistical software (version 7.0 for WINDOWS; Stata Corporation, College Station, TX) to calculate weighted means, percentages, parameter coefficients derived from regression models, and SEs with adjustments for the complex NHANES III sample design. SAS statistical software (version 8 for WINDOWS; SAS Institute Inc, Cary, NC) was used to calculate the correlation coefficients between BMI and lipid profiles, blood pressure, and glucose or between WC and lipid profiles, blood pressure, and glucose when unweighted data were used.

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Subject characteristics
Subject characteristics are summarized in Table 1. The sex-by-race interactions were significant for most of the variables. White men and women were oldest, whereas Mexican Americans were the youngest among the 3 ethnic groups. Height and weight were lowest among Mexican American men and were significantly different from white and black men. BMI was not significantly different among men in the 3 ethnic groups, whereas WC was largest for white, intermediate for Mexican American, and smallest for black men. Mexican American women had the lowest height and weight, whereas black women had the highest height and weight. Black women had the highest BMI and WC, whereas white women had the lowest BMI and WC.

Table 1

Correlation coefficients for WC and BMI with lipid profiles, blood pressure, and glucose are shown in Table 2. Except for the correlation coefficients of WC and BMI with HDL in all groups in men and in black and white women, and with diastolic blood pressure in white men and women, all correlation coefficients between WC and lipid profiles, blood pressure, and glucose were significantly higher than those of BMI. The correlation coefficients between WC and BMI ranged from 0.88 to 0.92 in the 6 groups.

Table 3

Figure 1

Figure 2

View larger version (31K): [in this window] [in a new window]   FIGURE 1. Odds ratio for having one or more cardiovascular disease (CVD) risk factors derived from logistic regression models for BMI. Reference BMI (kg/m2): 23.0 for black, 23.9 for Mexican American, and 23.6 for white men; 23.5 for black, 23.5 for Mexican American, and 21.7 for white women.

View larger version (29K): [in this window] [in a new window]   FIGURE 2. Odds ratio for having one or more cardiovascular disease (CVD) risk factors derived from logistic regression models for waist circumference (WC). Reference WC (cm): 81.5 for black, 85.9 for Mexican American, and 87.7 for white men; 80.4 for black, 80.0 for Mexican American, and 76.2 for white women.

Waist circumference cutoffs
Sex- and race-ethnicity–specific WC cutoffs corresponding to the selected BMIs are presented in Table 4. The WC cutoffs in men varied among the race-ethnicity groups but were similar in women. In men, the WC cutoffs were 5–6 cm greater for white men than for black men at every BMI level from 25 to 40, with Mexican American men intermediate. In women, there were almost no differences in WC cutoffs among the 3 race-ethnic groups.

Table 4

The WC cutoffs corresponding to BMIs of 25 and 30, when various metabolic syndrome risk factors were used as outcomes, are shown in Table 5. The WC cutoffs were very similar to the cutoffs obtained from the main models presented in Table 4.

Table 6

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

Our previous study reported a WC of 90 and 100 cm for men and of 83 and 93 cm for women that are equivalent in risk to a BMI of 25 and 30 in the white US population (4). The definition of race for whites in NHANES III includes approximately one-third of Mexican Americans and two-thirds of non-Hispanic whites when a race-ethnicity definition is adopted. Studies have reported that non-Hispanic whites, compared with Mexican Americans, have a higher prevalence of metabolic syndrome (23, 24) and are more prone to develop hyperinsulinemia, insulin resistance, and an unfavorable distribution of body fat (25, 26). Using a race-ethnicity definition in the present study enabled us to analyze the relations between WC, BMI, and CVD risk factors separately for non-Hispanic blacks, Mexican Americans, and non-Hispanic whites.

Gillum (27) reported that blacks have a higher mortality from coronary heart disease (CHD) than do other ethnic groups. Compared with whites, blacks also have a 60% higher incidence of type 2 diabetes (28) and are more insulin resistant at a similar degree of adiposity (25, 26, 29, 30). However, in this sample, we observed a significantly lower prevalence of CVD risk only for Mexican American men; no significant difference was found among different ethnic groups for women or between blacks and whites for men. In addition, at the same BMI or WC, white women have the highest OR of having CVD risk, followed by Mexican American and black women (Figure 2). Using the same data, Park et al (23) observed a lower OR for metabolic syndrome in blacks than in whites after controlling for various confounding factors. The reasons for these race-ethnicity differences are unknown. However, at the same WC, blacks have relatively smaller depots of insulin resistance–related visceral adipose tissue than do whites (31).

Unfortunately, NHANES III did not have information for other minority groups, such as Asians. A recently released WHO guideline for obesity screening (32) suggested additional public health action points for Asian populations, ie, a BMI of 23 represents increased risk and a BMI of 27.5 represents high risk. However, there are still no clear WC cutoffs for Asians. A guideline published by the Japan Society for the Study of Obesity defined a WC of > 85 cm for men and of 90 for women in association with a BMI of 25 as a diagnostic criterion for visceral fat obesity (33). This criterion reports a WC cutoff for women that is greater than that for men. Thus, further study of WC cutoffs for Asian populations seems in order.

The WC cutoffs identified in the present study correspond to the established BMI ranges for normal weight, overweight, and obesity and are largely independent of age and ethnicity. This is because the ORs derived from regression models were based on a comparison with the ORs for CVD risks in subjects at the 25th percentile of an ethnic-specific population and because the relations of WC and BMI with CVD risk were similar across the different race-ethnicity groups.

Our study clarified the importance of WC as a CVD predictor because WC was a better indicator of CVD risk than was BMI in all 3 ethnic groups. In addition, this study also extends our previous study and provides additional information (4) on race-ethnicity–specific WC cutoffs. A set of simplified WC cutoffs suitable for all 3 race-ethnicity groups with a stronger test performance than BMI alone can be used as guidance in clinical and prevention settings.

	ACKNOWLEDGMENTS

 
SZ, SBH, and SH designed the study. SZ analyzed the data, wrote the manuscript, and collected and assembled the data. SZ, SBH, HT, ZW, AP, and SH interpreted the data, provided advice or consultation, and gave final approval of the manuscript. SZ and SBH obtained funding. None of the authors had a conflict of financial or personal interest in any company or organization sponsoring this study.

	REFERENCES

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 

1. Han TS, Leer EM, Seidell JC, Lean MEJ. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ 1995;311:1401-5.[Abstract/Free Full Text]
2. Pouliot MC, Despres JP, Lemieux S, et al. Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Card 1994;73:460-8.
3. Ross R, Leger L, Morris D, de Guise J, Guardo R. Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol 1992;72:787-95.[Abstract/Free Full Text]
4. Zhu S, Wang ZM, Heshka S, Heo M, Faith MS, Heymsfield SB. Waist circumference and obesity-associated risk factors among non-Hispanic whites in the third National Health and Nutrition Examination Survey: clinical action thresholds. Am J Clin Nutr 2002;76:743-9.[Abstract/Free Full Text]
5. Zhu S, Heshka S, Wang Z, et al. Combination of body-mass index and waist circumference for predicting metabolic risk factors in whites. Obes Res 2004;12:633-45.[Medline]
6. Stevens J, Keil JE, Rust PF, Tyroler HA, Davis CE, Gazes PC. Body mass index and body girths as predictors of mortality in black and white women. Arch Intern Med 1992;152:1257-62.[Abstract]
7. Pi-Sunyer FX. Obesity: criteria and classification. Proc Nutr Soc 2000;59:505-9.[Medline]
8. Onat A. Waist circumference and waist-to-hip in Turkish adults: interrelation with other risk factors and association with cardiovascular disease. Int J Cardiol 1999;70:43-50.[Medline]
9. Lean MEJ, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ 1995;311:158-61.[Abstract/Free Full Text]
10. Janssen I, Katzmarzyk PT, Ross R. Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr 2004;79:379-84.[Abstract/Free Full Text]
11. National Center for Health Statistics. The third National Health and Nutrition Examination Survey, 1988-1994. Plan and operations procedures manuals. Hyattsville, MD: Centers for Disease Control and Prevention, 1996.
12. Centers for Disease Control and Prevention. The third National Health and Nutrition Examination Survey (NHANES III 1988-94) reference manuals and reports. Bethesda, MD: National Center for Health Statistics, 1996.
13. 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 1972;18:499-502.[Abstract]
14. US Department of Health and Human Service. The practical guide—identification, evaluation, and treatment of overweight and obesity in adults. Washington, DC: US DHHS, 2000. (NIH Publication no. 00-4084.)
15. US Department of Health and Human Service. Healthy people 2010. 2nd ed. With understanding and improving health and objectives for improving health. 2 vols. Washington, DC: US Government Printing Office, 2000.
16. Korn EL, Graubard BI. Analysis of health surveys. New York: Wiley & Sons, Inc, 1999.
17. Steiger JH. Tests for comparing elements of a correlation matrix. Psychol Bull 1980;87:245-61.
18. Stevens J, Cai J, Pamuk ER, Williamson DF, Thun MJ, Wood JL. The effect of age on the association between body-mass index and mortality. N Engl J Med 1998;338:1-7.[Abstract/Free Full Text]
19. Calle EE, Thun MJ, Peterlli JM, Rodrigues C, Heath CW. Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 1999;341:1097-105.[Abstract/Free Full Text]
20. Expert Panel on 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 (Adult Treatment Panel III). JAMA 2001;285:2486-96.[Free Full Text]
21. Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993;39:561-77.[Abstract/Free Full Text]
22. World Health Organization. Obesity, preventing and managing the global epidemic—report of a WHO consultation on obesity. Geneva: WHO, 1997.
23. Park Y, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB. The metabolic syndrome: prevalence and associated risk factors findings in the US population from the third National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med 2003;163:427-36.[Abstract/Free Full Text]
24. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: findings from the third National Health and Nutrition Examination Survey. JAMA 2002;287:356-9.[Abstract/Free Full Text]
25. Okosun IS, Liao Y, Rotimi CN, Prewitt TE, Cooper RS. Abdominal adiposity and clustering of multiple metabolic syndrome in white, black and Hispanic Americans. Ann Epidemiol 2000;10:263-70.[Medline]
26. Haffner SM, Stern MP, Hazuda HP, Pugh JA, Patterson JK, Malina RM. Upper body and centralized adiposity in Mexican Americans and non-Hispanic whites: relationship to body mass index and other behavioral and demographic variables. Int J Obes 1986;10:493-502.[Medline]
27. Gillum RF. Cardiovascular disease in the United States: an epidemiologic overview. In: Saunders E, ed. Cardiovascular disease in blacks. Philadelphia: FA Davis, 1991:3-16.
28. Harris MI. Non-insulin dependent diabetes mellitus in black and white Americans. Diabetes Metab Rev 1990;6:71-90.[Medline]
29. Haffner SM, D'Agostino R Jr, Saad MF, et al. Increased insulin resistance and insulin secretion in non-diabetic African-Americans, and Hispanics compared to non-Hispanic whites: the Insulin Resistance and Atherosclerosis Study. Diabetes 1996;45:742-8.[Abstract]
30. Karter AJ, Mayer-Davis EJ, Selby JV, et al. Insulin sensitivity and abdominal obesity in African-American, Hispanic, and non-Hispanic white men and women: the Insulin Resistance and Atherosclerosis Study. Diabetes 1996;45:1547-55.[Abstract]
31. Brancati FL, Linda Kao WH, Folsom AR, Watson RL, Szklo M. Incident type 2 diabetes mellitus in African American and white adults. JAMA 2000;283:2253-9.[Abstract/Free Full Text]
32. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363:157-63.[Medline]
33. The 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;66:987-92.[Medline]

This article has been cited by other articles:

				J. C K Wells Ethnic variability in adiposity and cardiovascular risk: the variable disease selection hypothesis Int. J. Epidemiol., September 27, 2008; (2008) dyn183v1. [Abstract] [Full Text] [PDF]

				R. Ness-Abramof and C. M. Apovian Waist Circumference Measurement in Clinical Practice Nutr Clin Pract, August 1, 2008; 23(4): 397 - 404. [Abstract] [Full Text] [PDF]

				S. K. Kumanyika, E. Obarzanek, N. Stettler, R. Bell, A. E. Field, S. P. Fortmann, B. A. Franklin, M. W. Gillman, C. E. Lewis, W. C. Poston II, et al. Population-Based Prevention of Obesity: The Need for Comprehensive Promotion of Healthful Eating, Physical Activity, and Energy Balance: A Scientific Statement From American Heart Association Council on Epidemiology and Prevention, Interdisciplinary Committee for Prevention (Formerly the Expert Panel on Population and Prevention Science) Circulation, July 22, 2008; 118(4): 428 - 464. [Abstract] [Full Text] [PDF]

				E Koch, M Bogado, F Araya, T Romero, C Diaz, L Manriquez, M Paredes, C Roman, A Taylor, and A Kirschbaum Impact of parity on anthropometric measures of obesity controlling by multiple confounders: a cross-sectional study in Chilean women J. Epidemiol. Community Health, May 1, 2008; 62(5): 461 - 470. [Abstract] [Full Text] [PDF]

				C. K. Good, N. Holschuh, A. M. Albertson, and A. L. Eldridge Whole Grain Consumption and Body Mass Index in Adult Women: An Analysis of NHANES 1999-2000 and the USDA Pyramid Servings Database J. Am. Coll. Nutr., February 1, 2008; 27(1): 80 - 87. [Abstract] [Full Text] [PDF]

				E. van den Berg, G. J. Biessels, A. J.M de Craen, J. Gussekloo, and R.G.J. Westendorp The metabolic syndrome is associated with decelerated cognitive decline in the oldest old Neurology, September 4, 2007; 69(10): 979 - 985. [Abstract] [Full Text] [PDF]

				Y. Wang and M. A. Beydoun The Obesity Epidemic in the United States--Gender, Age, Socioeconomic, Racial/Ethnic, and Geographic Characteristics: A Systematic Review and Meta-Regression Analysis Epidemiol. Rev., May 17, 2007; (2007) mxm007v1. [Abstract] [Full Text] [PDF]

				X. Gao, M. E. Nelson, and K. L. Tucker Television Viewing Is Associated With Prevalence of Metabolic Syndrome in Hispanic Elders Diabetes Care, March 1, 2007; 30(3): 694 - 700. [Abstract] [Full Text] [PDF]

				G. de Simone Left Ventricular Hypertrophy in Blacks and Whites: Different Genes or Different Exposure? Hypertension, July 1, 2005; 46(1): 23 - 24. [Full Text] [PDF]

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Citation Map Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Zhu, S. Articles by Heshka, S. Search for Related Content PubMed PubMed Citation Articles by Zhu, S. Articles by Heshka, S. Agricola Articles by Zhu, S. Articles by Heshka, S.