AJCN EB Program 2010
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wildman, R. P
Right arrow Articles by He, J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wildman, R. P
Right arrow Articles by He, J.
Agricola
Right arrow Articles by Wildman, R. P
Right arrow Articles by He, J.
American Journal of Clinical Nutrition, Vol. 82, No. 6, 1195-1202, December 2005
© 2005 American Society for Clinical Nutrition

ORIGINAL RESEARCH COMMUNICATION

Are waist circumference and body mass index independently associated with cardiovascular disease risk in Chinese adults?1,2,3

Rachel P Wildman, Dongfeng Gu, Kristi Reynolds, Xiufang Duan, Xiqui Wu and Jiang He

1 From the Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (RPW, KR, and JH); the Cardiovascular Institute and Fu Wai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China (DG, XD, and XW); the Department of Medicine, Tulane University School of Medicine, New Orleans, LA (JH); and Tulane Hypertension and Renal Center of Excellence, Tulane University Health Sciences Center, New Orleans, LA (JH)

2 Supported by a contractual agreement between Tulane University and Pfizer Inc. RPW and KR received partial support for the analysis and interpretation of these data from the National Institutes of Health (grant 1 K12 HD43451-01). JH received support from the National Heart, Lung, and Blood Institute, National Institutes of Health (grants HL68057 and HL72507).

3 Address reprint requests to RP Wildman, Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street, Suite 2000, SL-18, New Orleans, LA 70112. E-mail: rwildman{at}tulane.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: In Western populations, waist circumference (WC) is more predictive of cardiovascular disease (CVD) risk than is body mass index (BMI). It is unclear whether the same is true in Asian populations.

Objective: The objective was to examine the independent effects of WC and BMI on CVD risk factors in China.

Design: CVD risk factors, BMI, and WC were measured in a nationally representative cross-sectional study of 15 540 Chinese adults aged 35–74 y.

Results: Higher WC tertiles were associated with higher blood pressure and higher cholesterol, triacylglycerol, and glucose concentrations within each tertile of BMI and vice versa. In men, the odds of hypertension, dyslipidemia, and the metabolic syndrome (MS) increased with successive WC tertiles (1.0, 1.1, and 1.8, respectively, for hypertension; 1.0, 1.4, and 2.0, respectively, for dyslipidemia; and 1.0, 2.3, and 4.8, respectively, for MS; P for trend < 0.001 for all), even after adjustment for BMI. Similarly, the odds of hypertension, dyslipidemia, and MS increased with successive BMI tertiles (1.0, 1.5, and 2.6, respectively, for hypertension; 1.0, 1.3, and 1.8, respectively, for dyslipidemia; 1.0, 1.3, and 2.9, respectively for MS; P for trend < 0.001 for all), even after adjustment for WC. However, BMI tertiles were not associated with the odds of diabetes after adjustment for WC (P for trend = 0.67), whereas tertiles of WC were significantly associated with the odds of diabetes after adjustment for BMI (1.0, 1.6, and 2.1, respectively; P for trend = 0.002). The results were similar in women.

Conclusions: These data show that WC adds additional risk information to that of BMI in Chinese adults. Measurement of both WC and BMI in Chinese adults may enhance CVD risk stratification.

Key Words: Obesity • body mass index • waist circumference • abdominal obesity • CVD risk factors • China


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Obesity is associated with increased cardiovascular disease (CVD) risk factors and increased incident CVD events in both Western and Asian populations (1-7). Despite similar relations between standard risk factors and incident CVD in Chinese and US adults (8), recent data show differences between Western and Asian populations in the percentage of body fat associated with a given body mass index (BMI; in kg/m2). Asian populations have a greater percentage body fat at lower BMIs than do Western populations (9-11). Additionally, recent evidence also indicates that the current BMI and waist circumference cutoffs used in the World Health Organization's definitions of overweight and obesity, which were developed using Western populations, may need to be lowered for use in Asian populations (4, 12-14).

In clinic and research settings, overweight and obesity have most often been defined by BMI. However, BMI does not take into account the distribution of fat on the body. Because abdominal fat deposition has emerged as a strong risk factor for CVD, studies have begun to explore whether abdominal obesity adds independent CVD risk information to that gained through measurement of BMI (15-21). Most studies have shown an independent effect of abdominal obesity on CVD risk factors, most often measured via waist circumference (15-19). However, most of these studies have been done in Western populations. Those done in Asian populations either did not assess the independent effects of BMI and waist circumference (22) or found conflicting results (20, 21).

Given the differences identified above between Western and Asian populations in the predictive qualities of obesity cutoffs and in the percentage of body fat related to a given BMI, data from Western populations concerning the independent effects of BMI and waist circumference may not be directly applicable to Asian populations. The aim of the current study was to assess whether waist circumference is associated with CVD risk factors independent of BMI in a nationally representative sample of Chinese adults. Associations were examined by men and women, separately.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Sample design
A detailed description of the study population and methods of the International Collaborative Study of CVD in Asia (InterASIA) was published elsewhere (23). Briefly, InterASIA used a 4-stage stratified sampling method to select a nationally representative sample of the general population in China aged 35–74 y. The sampling process was stratified by rural versus urban areas and North versus South. The final stage of sampling was stratified by sex (50% men and 50% women) and by age on the basis of 1990 China census data. Only 1 participant was selected from each household, without replacement.

A total of 19 012 individuals were randomly selected from 20 primary sampling units (street districts in urban areas or townships in rural areas) and invited to participate. A total of 15 838 individuals completed the survey and examination—a response rate of 83.3% (82.1% in men, 84.5% in women, 82.2% in urban areas, and 84.4% in rural areas).

The Institutional Review Board at Tulane University Health Sciences Center approved the InterASIA study. In addition, ethics committees and other relevant regulatory bodies in China approved the study. Informed consent was obtained from each participant before data collection.

Anthropometric measurement
Data collection was conducted in examination centers at local health stations or in community clinics in the participants' residential area. During examinations, a standard questionnaire assessing demographic information and medical history was administered by trained research staff.

Body weight, height, and waist circumference were measured by trained observers according to a standard protocol. A certified double balance placed on a firm surface was used to measure body weight, whereas a Frankfort place positioned at a 90 ° angle against a wall-mounted metal tape was used to measure height. Waist circumference was measured 1 cm above the naval with a standard tape measure. BMI was calculated as weight (in kg)/height squared (in m).

Blood pressure measurement
Three blood pressure measurements were obtained by trained nurses and physicians according to a standard protocol, adapted from American Heart Association recommendations, while the participants were in a sitting position after ≥5 min of rest (24). A standard mercury sphygmomanometer was used with 1 of 4 cuff sizes (pediatric, regular adult, large adult, or thigh) based on the participant's arm circumference. Participants were advised to refrain from coffee, tea, or alcohol intake; cigarette smoking; and vigorous exercise for ≥30 min before their examination. All study investigators and staff successfully completed a training program orienting them to both the aims of the study and the specific tools and methods used.

Hypertension was defined as self-reported usage of antihypertensive medication within the past 2 wk or an average systolic blood pressure ≥ 140 mm Hg or an average diastolic blood pressure ≥ 90 mm Hg.

Laboratory methods and definition of the metabolic syndrome
Overnight fasting blood samples were drawn by venipuncture to measure serum total cholesterol, HDL cholesterol, triacylglycerols, and glucose. All blood samples were analyzed at a central laboratory in the Department of Population Genetics at Fuwai Hospital of the Chinese Academy of Medical Sciences in Beijing. Specimens were stored at –70 °C at exam centers and shipped monthly by air to the central laboratory, where samples were frozen at –70 ° until they could be analyzed. The central laboratory participates in the Lipid Standardization Program of the US Centers for Disease Control and Prevention and the National Heart, Lung, and Blood Institute. Total cholesterol, HDL-cholesterol, and triacylglycerol concentrations were analyzed enzymatically on a Hitachi 7060 Clinical Analyzer (Hitachi High-Technologies Corporation, Tokyo, Japan) with the use of commercial reagents (25). LDL-cholesterol concentrations were calculated by using the Friedewald equation for the participants who had triacylglycerol concentrations <400 mg/dL (26). Dyslipidemia was defined as a total cholesterol concentration ≥200 mg/dL, an LDL-cholesterol concentration ≥130 mg/dL, or an HDL-cholesterol concentration <35 mg/dL. The same HDL cutoff was used for both men and women because of similar concentrations between men and women in China (27). Plasma glucose was measured with the use of a modified hexokinase enzymatic method. Diabetes was defined as a fasting plasma glucose concentration ≥126 mg/dL, the use of insulin or oral hypoglycemic agents, or a self-reported history of diabetes. The metabolic syndrome was defined according to the National Cholesterol and Education Program Adult Treatment Panel III definition as the presence of ≥3 of the following conditions: a waist circumference >88 cm for women or >102 cm for men, a triacylglycerol concentration ≥150 mg/dL, an HDL-cholesterol concentration <50 for women or < 40 for men, a blood pressure ≥130/85 mm Hg or the use of antihypertensive medications within the past 2 wk, or a fasting glucose concentration ≥110 mg/dL or the use of oral hypoglycemic agents within the past 2 wk (28).

Statistical methods
Of the 15 838 participants who completed the InterASIA examination and survey, the following were excluded from the current analyses: 298 for being outside of the 35–74 y age range, 281 for missing laboratory data, and an additional 20 for missing BMI or waist circumference data. Therefore, data from 15 239 participants were used in the current analyses.

Analyses were weighted to represent the total Chinese population aged 35–74 y. Weights were calculated on the basis of the 2000 China population census data and took into account several features of the survey, including oversampling for specific age or geographic subgroups, nonresponse, and other demographic or geographic differences between the sample and total China population. SEs were calculated by using a technique appropriate to the complex survey design. Age standardization was performed by using the direct method with the 2000 Chinese population aged 35–74 y as the standard population. Sex-specific tertiles of waist circumference and BMI were used for analysis. Age-standardized means for each of 7 continuous risk factor measurements (systolic blood pressure, diastolic blood pressure, total cholesterol, HDL cholesterol, LDL cholesterol, triacylglycerols, and glucose) were calculated jointly for waist circumference and BMI tertiles. To assess linear trends across tertiles of waist circumference and BMI in associations with continuous CVD risk factors, tertiles of waist circumference and BMI (eg, values of 1, 2, and 3) were treated as continuous covariates in the same linear regression models for the prediction of continuous risk factors. To determine whether waist circumference or BMI evidenced stronger associations with continuous CVD risk factors, t tests were performed to test the difference between the 2 ß values. Weighted partial correlation analysis with the use of Fisher's z transformation was used to determine age-adjusted correlation coefficients and 95% CIs between continuous measures of waist circumference or BMI and continuous CVD risk factor measures. Logistic regression analysis was used to determine the odds ratios of hypertension, dyslipidemia, diabetes, and the metabolic syndrome associated with each tertile of waist circumference and BMI after adjustment for one another (both in tertiles and as continuous variables) and age. To assess the significance of a linear trend across waist circumference and BMI tertiles in logistic regression analyses, waist circumference and BMI tertiles were entered into logistic regression modeling as continuous covariates. Last, the age-standardized prevalence of ≥2 risk factors was determined jointly for waist circumference and BMI tertiles. All analyses, except age-adjusted partial correlation coefficients, were conducted with the use of SUDAAN software (version 8.0; Research Triangle Institute, Research Triangle Park, NC). Age-adjusted correlation coefficients were calculated with the use of SAS software (version 8.2; SAS Institute Inc, Cary, NC).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Sex-specific, age-standardized, mean CVD risk factors by waist circumference and BMI tertiles are shown in Tables 1Go and 2Go. For both men and women, CVD risk factors tended to increase across waist circumference tertiles within each BMI tertile. Similarly, CVD risk factors tended to increase across BMI tertiles within each waist circumference tertile. There were differences between waist circumference and BMI in the strength of many of these relations. In men, graded relations with systolic and diastolic blood pressures were stronger across BMI tertiles than across waist circumference tertiles (P for difference in ß values = 0.032 for systolic and 0.003 for diastolic blood pressures), and graded relations with triacylglycerols and glucose were stronger across waist circumference tertiles than across BMI tertiles (P for difference in ß values <0.001 for triacylglycerols and <0.025 for glucose). In women, graded relations with systolic blood pressure, diastolic blood pressure, total cholesterol, and LDL cholesterol were stronger across BMI tertiles than across waist circumference tertiles (P for difference in ß values <0.001 for all), and graded relations with HDL cholesterol and triacylglycerols were stronger across waist circumference tertiles than across BMI tertiles (P for difference in ß values <0.001 for HDL cholesterol and 0.046 for triacylglycerols).


View this table:
[in this window]
[in a new window]
  		TABLE 1. Age-standardized mean (and 95% CIs) cardiovascular disease risk factors by BMI and waist circumference tertiles for men in China1

 

View this table:
[in this window]
[in a new window]
  		TABLE 2. Age-standardized mean (and 95% CIs) cardiovascular disease risk factors by BMI and waist circumference tertiles for women in China1

 
Age-adjusted partial correlation coefficients for waist circumference and BMI with continuous CVD risk factors are shown in Table 3Go. Both waist circumference and BMI were strongly associated with each of the CVD risk factors and had coefficients of similar magnitude for each of the CVD risk factors.


View this table:
[in this window]
[in a new window]
  		TABLE 3. Age-adjusted partial correlation coefficients (and 95% CIs) between waist circumference or BMI and cardiovascular disease risk factors for Chinese adults1

 
Odds ratios and 95% CIs for hypertension, dyslipidemia, diabetes, and the metabolic syndrome associated with tertiles 2 and 3 of BMI and waist circumference compared with the first tertile are shown in Table 4Go. Odds ratios for waist circumference tertiles were adjusted for BMI tertile and vice versa. Waist circumference and BMI had independent effects on the odds of hypertension, dyslipidemia, and the metabolic syndrome in both men and women, with graded increases in the odds of hypertension, dyslipidemia, and the metabolic syndrome with increasing waist circumference and BMI tertiles. However, for diabetes, waist circumference was significantly related to diabetes independent of BMI, but BMI was not related to diabetes independent of waist circumference. This was consistent in both men and women. Additionally, when continuous BMI or waist circumference was adjusted for, results were remarkably similar. Three percent of the subjects reported a history of clinical CVD (either myocardial infarction, stroke, peripheral vascular disease, or congestive heart failure). The analyses in Table 3Go were rerun excluding these subjects, and the results were nearly identical to those reported.


View this table:
[in this window]
[in a new window]
  		TABLE 4. Adjusted odds ratios (ORs) (and 95% CIs) for cardiovascular disease risk factors by tertiles of BMI and waist circumference for men and women in China1

 
The prevalence of ≥2 risk factors (eg, hypertension, dyslipidemia, diabetes, or the metabolic syndrome) by waist circumference and BMI tertiles is presented for men and women separately in Figure 1Go. For both men and women, the greatest prevalence of ≥2 risk factors was seen in those who were in the top tertile of both waist circumference and BMI. Additionally, within each BMI tertile in women and within BMI tertiles 2 and 3 in men, the prevalence of ≥2 risk factors was higher across higher waist circumference tertiles (P for trend across waist circumference tertiles <0.001 for both men and women). Within each waist circumference tertile, the prevalence of ≥2 risk factors was higher across higher BMI tertiles in both men and women (P for trend across BMI tertiles <0.001 for both men and women).



View larger version (60K):
[in this window]
[in a new window]
  		FIGURE 1.. Age-standardized prevalence of ≥2 risk factors for cardiovascular disease (hypertension, dyslipidemia, diabetes, and metabolic syndrome) by waist circumference and BMI tertiles for men and women in China. P for trend < 0.001 across BMI and waist circumference tertiles, and P < 0.001 for interaction in both men and women; these data were generated by using direct standardization with weights.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
Our results indicate that BMI and waist circumference are independently associated with CVD risk factors in Chinese adults, with both contributing important CVD risk information. These findings are similar to those in Western populations, which indicate that waist circumference is a predictor of CVD risk independent of BMI (15, 16, 18, 19). These findings are also similar to those in a population sample of Hong Kong Chinese (21). However, these data contrast those from a worksite sample of Hong Kong Chinese for whom waist circumference was predictive of CVD risk independent of BMI, but BMI was not predictive of CVD risk once adjusted for waist circumference (20). Use of a worksite sample, rather than a representative population sample, as used in the current study, may underlie the discrepant results.

The finding that both waist circumference and BMI contribute independent CVD risk information is not surprising given that each has certain limitations. BMI has been found to be less predictive in elderly adults, in whom decreases in both body height and muscle mass are common (29, 30), whereas waist circumference may have more error in very obese individuals. Because each measure has limitations, the joint measurement of BMI and waist circumference provides more information concerning CVD risk than does the measurement of either alone. In its 1997 report on the global obesity epidemic, the World Health Organization (WHO) recommended the assessment of both BMI and waist circumference for the determination of CVD risk and subsequent treatment strategies (31). An expert panel convened by the National Institutes of Health (NIH) in 1998 to establish evidence-based clinical guidelines for the identification and treatment of overweight and obesity in the United States adopted these same recommendations (32) and further expanded them in 2000 (33). As part of these recommendations, the measurement of waist circumference in addition to BMI is especially indicated for overweight and class I obese individuals (BMI: 25–34.9) to allow for more precise CVD risk determination and, therefore, appropriate treatment actions. For example, these recommendations encourage physicians to set specific treatment goals and institute maintenance counseling for individuals with a BMI of 25–29.9 and a high waist circumference (>102 cm for men and >88 cm for women) but not for individuals with that same BMI but a low waist circumference (32). However, the recommendations of the WHO and NIH guidelines appear to be largely derived from data in Western, primarily white, populations. Subsequent analyses from the third National Health and Nutrition Examination Survey supported the enhanced risk stratification afforded by measurement of waist circumference in addition to BMI in a population that included both Hispanics and non-Hispanic blacks (34). The additional risk information gained by measurement of waist circumference was especially impressive for women. Overweight women (BMI: 25–29.9) with a high waist circumference were between 2 and 4 times as likely to have hypertension, type 2 diabetes, hypercholesterolemia, and the metabolic syndrome than were overweight women with a low waist circumference. Class I obese women (BMI: 30.0–34.9) with high waist circumference values were between 15 and 29 times as likely to have the above risk factors than were women with a low waist circumference (34). Our results extend the findings from white, non-Hispanic black, and Hispanic adults to Chinese adults, which suggests that the measurement of both BMI and waist circumference in Chinese adults would lead to better CVD risk stratification. Measurement of waist circumference in addition to BMI in China may enhance clinical practice for overweight and mildly obese Chinese adults.

These results showed that blood pressure and LDL cholesterol were more strongly associated with BMI than with waist circumference. This finding is surprising and contrasts with the findings of previous studies in both Asian and Western populations, which found waist circumference to be a stronger risk factor for all CVD risk factors, including blood pressure and lipid values (20, 21, 35). However, the current data also indicated that waist circumference was more strongly associated with glucose than was BMI in men, with HDL cholesterol in women, and with triacylglycerols and diabetes in both sexes. These findings agree with those of previous studies in Asian populations (20, 21) and also agree with evidence linking intraabdominal fat, specifically, to the development of the metabolic syndrome (36). Lipid and glucose disturbances are defining features of both the metabolic syndrome and diabetes (28, 37).

A strength of the current study was that these data are from a representative sample of Chinese adults. Therefore, these results can be generalized to the adult population of mainland China aged 35–74 y. In addition, this study was conducted in a population at low risk of CVD, in whom lifestyle intervention or pharmacologic treatment for CVD risk factors is rare. Therefore, these factors did not confound associations with waist circumference or BMI. Data from a Western population of older women show that waist circumference and BMI are independently predictive of incident diabetes, hypertension, and death from a CVD cause (38). However, prospective data in other populations have shown that BMI is no longer associated with CVD incidence and mortality after adjustment for either waist circumference or waist-to-hip ratio, whereas waist circumference and waist-to-hip ratio remained strong predictors of CVD incidence and mortality after adjustment for BMI (39-41). A meta-analysis of cohort studies conducted in China, including both men and women, found that BMI values were strongly associated with incident coronary heart disease and stroke; every 2-kg/m2 increase in baseline BMI was associated with a 15.4% increase in the relative risk of coronary heart disease and an 18.8% increase in the relative risk of ischemic stroke (7). However, few studies have assessed the effects of waist circumference on incident disease or the independent effects of waist circumference and BMI on incident disease in Chinese adults. In a cohort of Chinese women, the relative risk of coronary heart disease increased in a stepwise fashion across tertiles of both BMI and waist circumference when each was analyzed separately (42). However, when waist circumference was adjusted for BMI, it was no longer significantly associated with incident coronary heart disease (42). Similarly, when BMI was adjusted for waist-to-hip ratio, it was no longer significantly associated with incident coronary heart disease (42). BMI adjusted for waist circumference were not presented. Whether waist circumference and BMI are independently associated with incident CVD in the Chinese population requires additional prospective studies in populations of Chinese adults that include subjects at low, intermediate, and high risk of CVD.

These data show that waist circumference adds additional CVD risk information to that of BMI, and vice versa, in both Chinese men and women. Given that CVD is now the leading cause of death in China, with mortality expected to increase over the next decade (43, 44), as well as the strong associations between obesity and CVD and its risk factors in Chinese adults (3, 7, 45), it is imperative that the identification and treatment of overweight and obesity in China becomes a key focus of the Chinese health care community. Our results suggest that the issuance of clinical treatment guidelines for overweight and obesity in Chinese adults that include the measurement of both waist circumference and BMI will greatly enhance the ability of Chinese health care providers to accurately assess CVD risk and implement efficient treatment strategies.


   ACKNOWLEDGMENTS
 
RPW performed all data analyses and wrote the manuscript. DG and JH aided in the design of the experiment and the collection of data and provided significant advice and consultation on the manuscript. KR aided in data management of study data and provided significant advice and consultation. XD and XW aided in the collection of data and provided significant advice and consultation on the manuscript. Several researchers employed by Pfizer Inc were members of the steering committee that designed the study; however, the study was conducted, analyzed, and interpreted by the investigators independently of the sponsor.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

  1. Wilson PW, D'Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and obesity as determinants of cardiovascular risk: the Framingham experience. Arch Intern Med 2002; 162: 1867–72.[Abstract/Free Full Text]
  2. Stevens J, Cai J, Evenson KR, Thomas R. Fitness and fatness as predictors of mortality from all causes and from cardiovascular disease in men and women in the lipid research clinics study. Am J Epidemiol 2002; 156: 832–41.[Abstract/Free Full Text]
  3. Wildman RP, Gu D, Reynolds K, Duan X, He J. Appropriate body mass index and waist circumference cut-points for categorization of overweight and central adiposity among Chinese adults. Am J Clin Nutr 2004; 80: 1129–36.[Abstract/Free Full Text]
  4. Pan WH, Flegal KM, Chang HY, Yeh WT, Yeh CJ, Lee WC. Body mass index and obesity-related metabolic disorders in Taiwanese and US whites and blacks: implications for definitions of overweight and obesity for Asians. Am J Clin Nutr 2004; 79: 31–9.[Abstract/Free Full Text]
  5. Zhou B, Wu Y, Yang J, Li Y, Zhang H, Zhao L. Overweight is an independent risk factor for cardiovascular disease in Chinese populations. Obes Rev 2002; 3: 147–56.[Medline]
  6. Zhou BF, Cooperative Meta-Analysis Group of the Working Group on Obesity in China. 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 2002; 15: 83–96.[Medline]
  7. Zhou BF. Effect of body mass index on all-cause mortality and incidence of cardiovascular diseases–report for meta-analysis of prospective studies open optimal cut-off points of body mass index in Chinese adults. Biomed Environ Sci 2002; 15: 245–52.[Medline]
  8. Liu J, Hong Y, D'Agostino RB Sr, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA 2004; 291: 2591–9.[Abstract/Free Full Text]
  9. Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord 1998; 22: 1164–71.[Medline]
  10. Chang CJ, Wu CH, Chang CS, et al. Low body mass index but high percent body fat in Taiwanese subjects: implications of obesity cutoffs. Int J Obes Relat Metab Disord 2003; 27: 253–9.[Medline]
  11. He M, Tan KC, Li ET, Kung AW. Body fat determination by dual energy X-ray absorptiometry and its relation to body mass index and waist circumference in Hong Kong Chinese. Int J Obes Relat Metab Disord 2001; 25: 748–52.[Medline]
  12. Moon OR, Kim NS, Jang SM, Yoon TH, Kim SO. The relationship between body mass index and the prevalence of obesity-related diseases based on the 1995 National Health Interview Survey in Korea. Obes Rev 2002; 3: 191–6.[Medline]
  13. Vikram NK, Pandey RM, Misra A, Sharma R, Devi JR, Khanna N. Non-obese (body mass index < 25 kg/m2) Asian Indians with normal waist circumference have high cardiovascular risk. Nutrition 2003; 19: 503–9.[Medline]
  14. Deurenberg-Yap M, Chew SK, Deurenberg P. Elevated body fat percentage and cardiovascular risks at low body mass index levels among Singaporean Chinese, Malays and Indians. Obes Rev 2002; 3: 209–15.[Medline]
  15. Seidell JC, Cigolini M, Charzewska J, Ellsinger BM, Dibiase G. Fat distribution in European women—a comparison of anthropometric measurements in relation to cardiovascular risk-factors. Int J Epidemiol 1990; 19: 303–8.[Abstract/Free Full Text]
  16. Seidell JC, Cigolini M, Charzewska J, Ellsinger BM, Deslypere JP, Cruz A. Fat distribution in European men—a comparison of anthropometric measurements in relation to cardiovascular risk-factors. Int J Obes Relat Metab Disord 1992; 16: 17–22.[Medline]
  17. Iwao S, Iwao N, Muller DC, Elahi D, Shimokata H, Andres R. Does waist circumference add to the predictive power of the body mass index for coronary risk? Obes Res 2001; 9: 685–95.[Medline]
  18. Ardern CI, Katzmarzyk PT, Janssen I, Ross R. Discrimination of health risk by combined body mass index and waist circumference. Obes Res 2003; 11: 135–42.[Medline]
  19. Zhu S, Heshka S, Wang Z, et al. Combination of BMI and waist circumference for identifying cardiovascular risk factors in whites. Obes Res 2004; 12: 633–45.[Medline]
  20. Ko GTC, Chan JCN, Woo J, et al. Simple anthropometric indexes and cardiovascular risk factors in Chinese. Int J Obes Relat Metab Disord 1997; 21: 995–1001.[Medline]
  21. Ho SC, Chen YM, Woo JLF, Leung SSF, Lam TH, Janus ED. Association between simple anthropometric indices and cardiovascular risk factors. Int J Obes Relat Metab Disord 2001; 25: 1689–97.[Medline]
  22. Huang KC, Lin WY, Lee LT, et al. Four anthropometric indices and cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord 2002; 26: 1060–8.[Medline]
  23. He J, Neal B, Gu D, et al. International Collaborative Study of Cardiovascular Disease in Asia: design, rationale, and preliminary results. Ethnic Dis 2004; 14: 260–8.[Medline]
  24. Perloff D, Grim C, Flack J, et al. Human blood pressure determination by sphygmomanometry. Circulation 1993; 88: 2460–70.[Free Full Text]
  25. Allain CC, Poon LS, Chan CSG, Richmond W, Fu PC. Enzymatic determination of total serum cholesterol. Clin Chem 1974; 20: 470–5.[Abstract]
  26. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without the use of preparative ultracentrifuge. Clin Chem 1972; 18: 499–502.[Abstract]
  27. He J, Gu D, Reynolds K, et al. Serum total and lipoprotein cholesterol levels and awareness, treatment, and control of hypercholesterolemia in China. Circulation 2004; 110: 405–11.[Abstract/Free Full Text]
  28. 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: 2486–97.[Free Full Text]
  29. Visscher TL, Seidell JC, Molarius A, van der Kuip D, Hofman A, Witteman JC. A comparison of body mass index, waist-hip ratio and waist circumference as predictors of all-cause mortality among the elderly: the Rotterdam study. Int J Obes Relat Metab Disord 2001; 25: 1730–5.[Medline]
  30. Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996; 143: 228–39.[Abstract/Free Full Text]
  31. WHO. Obesity: preventing and managing the global epidemic. Report of a WHO consultation on obesity, Geneva, 1997. Geneva, Switzerland: World Health Organization, 1998. (WHO/NUT/NCD/98.1.)
  32. Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med 1998; 158: 1855–67.[Free Full Text]
  33. National Institutes of Health, National Heart, Lung and Blood Institute Joint National Committee on Prevention Detection Evaluation and Treatment of High Blood Pressure, North American Association for the Study of Obesity. The practical guide: identification, evaluation, and treatment of overweight and obesity in adults. Bethesda, MD: NIH, 2000. (NIH publication no. 00-4084, 1-84.)
  34. Janssen I, Katzmarzyk PT, Ross R. Body mass index, waist circumference, and health risk: evidence in support of current National Institutes of Health guidelines. Arch Intern Med 2002; 162: 2074–9.[Abstract/Free Full Text]
  35. Zhu S, Wang Z, Heshka S, Heo M, Faith MS, Heymsfield SB. Waist circumference and obesity-associated risk factors among 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]
  36. Carr DB, Utzschneider KM, Hull RL, et al. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes 2004; 53: 2087–94.[Abstract/Free Full Text]
  37. Goldberg IJ. Clinical review 124: diabetic dyslipidemia: causes and consequences. J Clin Endocrinol Metab 2001; 86: 965–71.[Free Full Text]
  38. Folsom AR, Kushi LH, Anderson KE, et al. Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women's Health Study. Arch Intern Med 2000; 160: 2117–28.[Abstract/Free Full Text]
  39. Prineas RJ, Folsom AR, Kaye SA. Central adiposity and increased risk of coronary artery disease mortality in older women. Ann Epidemiol 1993; 3: 35–41.[Medline]
  40. Kanaya AM, Vittinghoff E, Shlipak MG, et al. Association of total and central obesity with mortality in postmenopausal women with coronary heart disease. Am J Epidemiol 2003; 158: 1161–70.[Abstract/Free Full Text]
  41. Wang Z, Hoy WE. Waist circumference, body mass index, hip circumference and waist-to-hip ratio as predictors of cardiovascular disease in Aboriginal people. Eur J Clin Nutr 2004; 58: 888–93.[Medline]
  42. Zhang X, Shu XO, Gao YT, et al. Anthropometric predictors of coronary heart disease in Chinese women. Int J Obes Relat Metab Disord 2004; 28: 734–40.[Medline]
  43. Murray CJ, Lopez AD. Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet 1997; 349: 1269–76.[Medline]
  44. Murray CJL, Lopez AD. Global pattern of cause of death and burden of disease in 1990, with projections to 2020 investing in health research and development. Report of the ad hoc Committee on Health Research Relation to Future Intervention Options. Geneva, Switzerland: WHO, 1996.
  45. Ko GT, Chan JC, Cockram CS, Woo J. Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese. Int J Obes Relat Metab Disord 1999; 23: 1136–42.[Medline]
Received for publication May 4, 2005. Accepted for publication August 12, 2005.




This article has been cited by other articles:


Home page
 Epidemiol RevHome page
W. Yang, T. Kelly, and J. He
Genetic Epidemiology of Obesity
Epidemiol. Rev., June 12, 2007; (2007) mxm004v1.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Purchase Article
Right arrow View Shopping Cart
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Wildman, R. P
Right arrow Articles by He, J.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Wildman, R. P
Right arrow Articles by He, J.
Agricola
Right arrow Articles by Wildman, R. P
Right arrow Articles by He, J.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS