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Original Investigation |

Abdominal Adiposity and Mortality in Chinese Women FREE

Xianglan Zhang, MD, MPH; Xiao-Ou Shu, MD, PhD; Gong Yang, MD, MPH; Honglan Li, MD; Hui Cai, MD, PhD; Yu-Tang Gao, MD; Wei Zheng, MD, PhD
[+] Author Affiliations

Author Affiliations: Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tenn (Drs Zhang, Shu, Yang, Cai, and Zheng); and Department of Epidemiology, Shanghai Cancer Institute, Shanghai, China (Drs Li and Gao).


Arch Intern Med. 2007;167(9):886-892. doi:10.1001/archinte.167.9.886.
Text Size: A A A
Published online

Background  Increased abdominal adiposity has been linked to an increase in mortality in populations where many are overweight or obese; it is unclear whether the same is true in relatively lean populations.

Methods  We examined the association between waist-hip ratio and mortality in the Shanghai Women's Health Study, a population-based, prospective cohort study of Chinese women aged 40 to 70 years enrolled from December 28, 1996, through May 23, 2000, 95% of whom had a body mass index (calculated as weight in kilograms divided by height in meters squared) of less than 30.0. Included in this analysis were 72 773 nonsmoking women who had anthropometrics taken by trained interviewers at enrollment and who were followed up through December 31, 2004. Deaths were ascertained by biennial home visits and linkage with the vital statistics registry.

Results  During a mean follow-up of 5.7 years, 1456 deaths occurred. The waist-hip ratio was positively and significantly associated with deaths from all causes, cardiovascular disease, and diabetes (P<.01 for trend). A less significant positive association was found for death from cancer. After adjustment for body mass index and other potential confounders, the relative risks of total mortality were 1 (reference group), 1.28 (95% confidence interval [CI], 1.04-1.58), 1.40 (95% CI, 1.14-1.72), 1.54 (95% CI, 1.26-1.88), and 1.95 (95% CI, 1.60-2.38) across the lowest to the highest waist-hip ratio quintiles. The positive association appeared to be more evident in women with a lower body mass index. The relative risks of total mortality comparing the extreme waist-hip ratio quintiles were 2.36 (95% CI, 1.71-3.27), 1.60 (95% CI, 1.10-2.34), and 1.46 (95% CI, 0.97-2.20) for women with a body mass index of less than 22.3, 22.3 to 25.1, and 25.2 or greater, respectively.

Conclusion  Abdominal adiposity independently predicts mortality risk, particularly for nonobese women.

Figures in this Article

Obesity has been clearly shown to increase mortality. However, it remains controversial whether the relationship between body weight and mortality can be best described as linear, J-shaped, or U-shaped.16 Many methodologic limitations may have contributed to the conflicting results and led to an underestimate of the impact of obesity on mortality.5,7,8 Among them is the reduced validity of body mass index (BMI) (calculated as weight in kilograms divided by height in meters squared) as a measure of adiposity in the elderly, who tend to lose lean body mass and have a shift of body fat from peripheral to central sites with a concomitant increase in waist-hip ratio (WHR) (waist circumference divided by hip circumference) at the same level of BMI.79 Growing evidence suggests that the adverse effects of obesity are closely related to the distribution of body fat and that central (intra-abdominal) obesity is particularly detrimental.10 Measures of central adiposity, such as WHR, have been shown to be a better marker than BMI in predicting certain obesity-related health risks.6,11 A recent large-scale case-control study involving 27 098 participants from 52 countries reported that WHR, in comparison with BMI and other anthropometric measures, best predicted the risk of myocardial infarction across all age and ethnic groups.11 Waist-hip ratio was also found in a few cohort studies to be a significant predictor of mortality1,6,12,13 and potentially better than BMI at predicting mortality risk.6,12,13 Investigations of body fat distribution and mortality to date have been conducted mainly in Western populations, where overweight and obesity are prevalent, and most have used self-reported waist and hip circumferences. The accuracy of these self-reported measurements has been shown to decrease with increasing body size, and the measurement errors are further compounded in their ratio.14 Studies in relatively lean populations and, more importantly, with directly measured anthropometric variables are needed to further quantify the effect of body fat distribution on mortality.

We evaluated the association between WHR and the risk of death from all causes and from specific causes in a large cohort of middle-aged and older Chinese women with an average BMI of 24.0 at the time of enrollment in the Shanghai Women's Health Study (SWHS).

The SWHS is a population-based, prospective cohort study of adult Chinese women. The study was approved by the relevant institutional review boards for human research in China and the United States. The design and methods of the SWHS have been described in detail elsewhere.15 Briefly, from December 28, 1996, through May 23, 2000, the study recruited 74 942 women aged 40 to 70 years from selected urban communities of Shanghai, with a participation rate of 92.7%. Recruitment activities, including a detailed baseline survey and anthropometric measurements, were carried out at participants' homes by trained interviewers. Structured questionnaires were used during the survey to obtain information on demographics, diet and lifestyle habits, menstrual and reproductive history, hormone use, and medical history. The validity and reproducibility of the food frequency and physical activity questionnaires used in the SWHS have been demonstrated previously.16,17

ANTHROPOMETRY

Participants were asked to wear light indoor clothing when they were measured for weight, height, and circumferences of the waist and hips by trained interviewers. The measurement was conducted uniformly according to a standard protocol. Waist circumference was measured at 2.5 cm above the umbilicus and hip circumference at the level of maximum width of the buttocks with the subject in a standing position. Circumferences and heights were measured to the nearest 0.1 cm. Weight was measured to the nearest 0.1 kg using a digital weight scale that was calibrated every 6 months. All measurements were taken twice. A tolerance limit of 1 kg was set for weight measurement and 1 cm for height and circumference measurements. A third measurement was taken if the difference of the first 2 measurements was greater than the tolerance limit. Using the average of the 2 closest measurements, WHR and BMI were then calculated for the analysis.

END POINT ASCERTAINMENT

Participants were followed up by means of biennial in-person contact and record linkage to the Shanghai Cancer Registry and the Shanghai Vital Statistics Registry. For the present analysis, we used outcome data through December 31, 2004. As of this date, follow-up for the vital status of participants was more than 99% complete. The underlying cause of death was determined on the basis of death certificates and coded according to the codes of the International Classification of Diseases, Ninth Revision (ICD-9). The primary end point for the present analysis was death from all causes that occurred after the baseline survey, with follow-up through 2004. In addition, we examined deaths from cancer (ICD-9 codes 140-208), cardiovascular disease (CVD) (ICD-9 codes 390-459), stroke (ICD-9 codes 430-438), diabetes mellitus (ICD-9 code 250), and all other causes.

STATISTICAL ANALYSIS

Of the 74 942 SWHS participants, only 2113 (2.8%) had ever smoked cigarettes, and they were excluded from the present analysis to avoid confounding by cigarette use. We also excluded those women who were pregnant (n = 10), were lost to follow-up (n = 10), or had missing data for anthropometric measurements (n = 39). After these exclusions (not mutually exclusive), 72 773 women remained for the analysis. Study participants were classified into 5 categories according to quintiles of WHR, with the lowest quintile serving as the reference category. Cox proportional hazards models were used, with age as the time scale to estimate relative risks (RRs) of death associated with WHR and their 95% confidence intervals (CIs) and to adjust for potential confounders.18 Entry time was defined as age at enrollment, and exit time was defined as age at death or censoring. Covariates included birth calendar year (7 categories); education level (4 categories); occupation (3 categories); family income (4 categories); menopausal status (premenopausal or postmenopausal); use of hormone therapy (yes or no); amount of regular exercise (hours per week, 4 categories); alcohol consumption (yes or no; using finer categories did not change the estimates because few women ever drank); intake of saturated fat, vegetables, and fruits (continuous); and BMI (linear and quadratic terms; both low and high BMI values were associated with increased mortality). Tests for linear trend in risk across WHR categories were performed by using the median value for each WHR category and modeling them as continuous variables. We also conducted analyses stratified by BMI level, age, menopausal status, and amount of exercise to further examine the independent effect of WHR and to evaluate possible effect modification. In addition, we used restricted cubic spline regression, a flexible statistical technique, to evaluate the association between WHR and mortality.19 Four knots were used for the analysis of total mortality and placed at the 5th, 35th, 65th, and 95th percentiles of the WHR distribution among the cohort. As relatively fewer deaths from specific causes occurred, 3 knots (placed at the 5th, 50th, and 95th percentiles) were used for the analysis of cause-specific mortality. To make the graph more stable and meaningful, those with a WHR below the 1st percentile or above the 99th percentile were deleted from the data set used to fit the spline model. Finally, we examined waist circumference and the ratio of waist circumference to height (waist-height ratio) and calculated c statistics to compare their predictive values. Statistical analyses were performed using SAS statistical software (version 9.1; SAS Institute Inc, Cary, NC). All statistical tests were based on 2-sided probability.

The mean age of the study population was 52 years at enrollment, and the mean WHR was 0.81. The prevalence of overweight or obesity (BMI ≥25.0) was 35.2%, and the prevalence of obesity (BMI ≥30.0) was 5.0%. Very few women ever drank alcoholic beverages or used hormonal therapy. Table 1 presents the age-adjusted baseline characteristics of study participants according to quintiles of WHR. Compared with those with a lower WHR, women with a higher WHR tended to be older, less educated, less likely to have a professional or administrative position, and more likely to have low family income. In addition, they exercised a little less and consumed less saturated fat. Vegetable or fruit intake did not appear to differ by WHR. Waist-hip ratio was moderately correlated with BMI (Pearson correlation, r = 0.46), whereas the correlation between waist circumference and BMI was high (r = 0.84).

Table Graphic Jump LocationTable 1. Age-Adjusted Baseline Characteristics of the Study Population According to Quintiles of WHR*

During a mean follow-up of 5.7 years, 1456 deaths were documented, including 732 from cancer (17.8% lung cancer, 12.4% colorectal cancer, 11.8% stomach cancer, 10.3% liver cancer, 8.5% breast cancer, and 39.3% other cancers), 357 from CVD (57.4% stroke, 20.2% coronary heart disease, and 22.4% other circulatory diseases), 99 from diabetes mellitus, and 268 from other causes. In age-adjusted analyses, BMI showed U-shaped associations with total and CVD mortality and no association with cancer mortality (data not shown). Compared with the third BMI quintile (22.9-24.5), the RRs of total mortality for the first (<21.1) and fifth (≥26.7) BMI quintiles were 1.46 (95% CI, 1.23-1.73) and 1.20 (95% CI, 1.02-1.41), respectively. The corresponding RRs for CVD mortality were 2.22 (95% CI, 1.51-3.25) and 2.06 (95% CI, 1.44-2.93), respectively. Further adjustment for sociodemographic and lifestyle factors did not markedly alter the results.

Table 2 summarizes the RRs and 95% CIs of total and cause-specific mortality according to WHR quintiles. Waist-hip ratio was positively and significantly associated with risk of death from all causes as well as from CVD, stroke, and diabetes in a dose-response fashion (P<.01 for trend). A less significant positive association was found for death from cancer. After adjustment for BMI and other potential confounders, women in the highest WHR quintile compared with those in the lowest quintile had an RR of 1.95 (95% CI, 1.60-2.38) for death from all causes, 2.74 (95% CI, 1.73-4.32) for death from CVD, 2.62 (95% CI, 1.40-4.89) for death from stroke, and 1.31 (95% CI, 1.00-1.71) for death from cancer. Additional adjustment for history of hypertension and diabetes (potential biological mediators) attenuated the RR for total mortality to 1.79 (95% CI, 1.47-2.19), CVD-related mortality to 2.41 (95% CI, 1.52-3.81), and stroke-related mortality to 2.23 (95% CI, 1.19-4.18), but did not change the RR estimate for cancer-related mortality.

Table Graphic Jump LocationTable 2. Relative Risks (RRs) of Deaths From All Causes and From Specific Causes According to Quintiles of WHR

The Figure shows the shape of the dose-response relation between all-cause and cause-specific mortality and WHR on a continuous basis. Overall, as WHR increased, so did the adjusted RRs of mortality. When WHR was analyzed as a continuous variable, for each 0.05 (1-SD) increase in WHR, the risk for total mortality increased by 18% (RR, 1.18; 95% CI, 1.12-1.25).

Place holder to copy figure label and caption
Figure.

Multivariate relative risks of total mortality (A) and mortality from cardiovascular disease (CVD) (B) and cancer (C) according to waist-hip ratio (WHR). Multivariate relative risks were estimated from a restricted cubic spline Cox regression model. Point estimates are indicated by a solid line and 95% confidence intervals by dashed lines. The median value was treated as the reference point.

Graphic Jump Location

In further stratified analyses (Table 3), the positive association between WHR and total mortality persisted across all strata defined by BMI level, menopausal status, age, or amount of regular exercise. The magnitude of the association appeared to be greater in women with lower BMIs. The multivariate RRs of total mortality comparing the extreme WHR quintiles were 2.36 (95% CI, 1.71-3.27), 1.60 (95% CI, 1.10-2.34), and 1.46 (95% CI, 0.97-2.20) for women with BMIs of less than 22.3, 22.3 to 25.1, and 25.2 or greater, respectively. Results of tests for multiplicative interaction, however, were not significant. There was no indication of effect modification by menopausal status, age, or amount of regular exercise.

Table Graphic Jump LocationTable 3. Multivariate Relative Risks (RRs) of Total Mortality According to WHR, Stratified by BMI, Menopausal Status, Age, or Exercise*

We also examined waist and hip circumferences individually in relation to mortality (data not shown). When an adjustment was not made for BMI in multivariate analyses, only mortality from stroke was related to greater waist circumference, with an RR of 2.00 (95% CI, 1.08-3.71) for the highest vs the lowest quintile. After adjustment for BMI, however, positive associations emerged for total mortality (RR, 1.95; 95% CI, 1.46-2.60) and death from CVD (RR, 1.68; 95% CI, 0.94-3.01), diabetes (RR, 6.37; 95% CI, 2.00-20.33), cancer (RR, 1.61; 95% CI, 1.07-2.42), and other causes (RR, 2.22; 95% CI, 1.15-4.27) (P<.05 for trend). Waist circumference appeared to be more predictive for women 50 years or younger than for women older than 50 years, with an RR for total mortality of 4.16 (95% CI, 2.09-8.31) for younger women vs 1.62 (95% CI, 1.17-2.23) for older women (P = .02 for interaction). Overall, no independent association was found for hip circumference.

In addition, we assessed RRs of death associated with waist-height ratio, another measure of central obesity. As with waist circumference, positive associations emerged only when the multivariate analyses adjusted for BMI (RR of total mortality comparing the extreme quintiles of waist-height ratio, 2.18; 95% CI, 1.62-2.93). The c statistics were 0.773, 0.771, and 0.772 for fully adjusted models of total mortality that included WHR, waist circumference, and waist-height ratio, respectively.

In this large cohort study of Chinese women, a positive monotonic dose-response relationship was observed between WHR and the risk of death. This positive association was independent of BMI and sociodemographic and lifestyle factors. Several features distinguish this study from previous studies. To our knowledge, this is the first cohort study that has evaluated mortality risk associated with body fat distribution in Asian women. Nearly two thirds of the study population had a BMI of less than 25.0, providing a unique opportunity to evaluate the influence of body fat distribution on mortality among lean or normal-weight individuals. None of the participants included in the analyses had ever smoked cigarettes and, thus, confounding by smoking was not a concern for this study. All anthropometric variables were measured directly rather than self-reported, which eliminated the possibility of any bias from self-reports. Our finding of a positive relationship between WHR and mortality regardless of BMI underscores the importance of incorporating fat distribution measurement in assessing obesity-related health risk and supports the use of WHR as a measure of choice to improve risk assessment. Given the possibility that a healthy body weight, currently defined as a BMI of 18.5 to 24.9, might provide some false assurance to those who are apparently lean but have a high degree of abdominal adiposity, it is important to raise awareness among those subjects and their health care providers of the deleterious effects of abdominal obesity.

Our results are consistent with those from studies of white women that support an independent contribution of body fat distribution to mortality and the usefulness of WHR as a predictor of mortality.1,6,12 In the Nurses' Health Study1,20 and the Iowa Women's Health Study,6,21 a positive association of WHR with mortality, independent of BMI, was consistently found during short- and longer-term follow-ups. Waist-hip ratio showed a stronger association with mortality from CVD than from cancer in the Iowa Women's Health Study, as it did in our study. Coronary heart disease was the cause that contributed most to the association with CVD-related mortality in the Iowa study, whereas stroke was the predominant cause in our study. Likewise, in a Swedish cohort of middle-aged and older women, WHR was directly related to the risk of all-cause mortality after adjustment for overall body fat.12 No association, however, was observed between WHR and mortality in a small Dutch cohort of elderly women.22 The WHR also appears to predict mortality risk in men, but the evidence has been limited and inconclusive.12,13,22

Waist circumference has been proposed in recent years to replace WHR as an indicator of abdominal adiposity, because it is simpler to measure and interpret.23 However, waist circumference is highly correlated not only with visceral fat but also with BMI, and it reflects both general and central adiposity, whereas WHR correlates less strongly with BMI and thus may provide more specific information about regional fat distribution.6 In our study and in the Iowa Women's Health Study,6,21 WHR appeared to be a more robust predictor of mortality than waist circumference was. The WHR was also found to be a better predictor than waist circumference of coronary heart disease in our previous study of the same cohort24 and in the Nurses' Health Study.25 The analysis with c statistics in the present study, however, failed to demonstrate that one measure of abdominal adiposity is superior to another.

It is believed that abdominal obesity directly underlies insulin resistance and systemic inflammation, which, in turn, leads to the metabolic syndrome, CVD, and certain cancers.9,10 Expanded abdominal fat deposits provide a major source of increased production of C-reactive protein, tumor necrosis factor α, interleukin 6, plasminogen activator inhibitor 1, angiotensinogen, and vascular endothelial growth factor, all of which contribute importantly to the development and progression of many obesity-related diseases.10

Limitations of our study need to be considered when interpreting the results. The maximum follow-up time of 7.5 years in our study is relatively short. Although the short interval between anthropometric measurement and event reduces the influence of fluctuations in body weight and fat distribution over time on the results, it raises concern about the effect of baseline illness. We therefore conducted sensitivity analyses excluding deaths that occurred within the first 2 or 3 years of follow-up and excluding subjects with a history of CVD or cancer at baseline and found no material change in the risk estimates for total mortality. Despite having carefully adjusted for a range of potential confounding variables, including sociodemographic factors, physical activity, and other lifestyle and dietary factors, we could not completely rule out the possibility of residual confounding due to unmeasured or inaccurately measured covariates. For example, the questionnaire-based physical activity assessment used in our study, although validated, is crude and prone to misclassification; consequently, residual confounding from physical activity or fitness status cannot be dismissed. On the other hand, our study focused on WHR, a more specific measure of abdominal fat distribution, which may be less subject than BMI to the influence of these confounders.

Nevertheless, our study of relatively lean Chinese women, taken with studies of white women, suggests that increased abdominal adiposity contributes independently to an increase in mortality; WHR predicts mortality risk and can be used as a measure of choice to enhance risk evaluation.

Correspondence: Xianglan Zhang, MD, MPH, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, 1215 21st Ave S, 6000 Medical Center E, North Tower, Nashville, TN 37232-8300 (Xianglan.zhang@vanderbilt.edu).

Accepted for Publication: November 30, 2006.

Author Contributions:Study concept and design: Zhang, Shu, Yang, Cai, Gao, and Zheng. Acquisition of data: Shu, Yang, Li, Cai, Gao, and Zheng. Analysis and interpretation of data: Zhang, Shu, Yang, Cai, Gao, and Zheng. Drafting of the manuscript: Zhang, Li, and Cai. Critical revision of the manuscript for important intellectual content: Zhang, Shu, Yang, Cai, Gao, and Zheng. Statistical analysis: Zhang, Yang, and Cai. Obtained funding: Shu and Zheng. Administrative, technical, and material support: Yang, Li, Gao, and Zheng. Study supervision: Gao and Zheng.

Financial Disclosure: None reported.

Funding/Support: This study was supported by research grant R01CA70867 from the National Institutes of Health.

Hu  FBWillett  WCLi  TStampfer  MJColditz  GAManson  JE Adiposity as compared with physical activity in predicting mortality among women. N Engl J Med 2004;3512694- 2703
PubMed Link to Article
Flegal  KMGraubard  BIWilliamson  DFGail  MH Excess deaths associated with underweight, overweight, and obesity. JAMA 2005;2931861- 1867
PubMed Link to Article
Gu  DHe  JDuan  X  et al.  Body weight and mortality among men and women in China. JAMA 2006;295776- 783
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Jee  SHSull  JWPark  J  et al.  Body-mass index and mortality in Korean men and women. N Engl J Med 2006;355779- 787
PubMed Link to Article
Adams  KFSchatzkin  AHarris  TB  et al.  Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med 2006;355763- 778
PubMed Link to Article
Folsom  ARKushi  LHAnderson  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;1602117- 2128
PubMed Link to Article
Willett  WCHu  FBColditz  GAManson  JE Underweight, overweight, obesity, and excess deaths. JAMA 2005;294551
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Willett  WCDietz  WHColditz  GA Guidelines for healthy weight. N Engl J Med 1999;341427- 434
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Calle  EEKaaks  R Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer 2004;4579- 591
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Berg  AHScherer  PE Adipose tissue, inflammation, and cardiovascular disease. Circ Res 2005;96939- 949
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Yusuf  SHawken  SOunpuu  S  et al. INTERHEART Study Investigators, Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005;3661640- 1649
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Lahmann  PHLissner  LGullberg  BBerglund  G A prospective study of adiposity and all-cause mortality: the Malmo Diet and Cancer Study. Obes Res 2002;10361- 369
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Kalmijn  SCurb  JDRodriguez  BLYano  KAbbott  RD The association of body weight and anthropometry with mortality in elderly men: the Honolulu Heart Program. Int J Obes Relat Metab Disord 1999;23395- 402
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Rimm  EBStampfer  MJColditz  GAChute  CGLitin  LBWillett  WC Validity of self-reported waist and hip circumferences in men and women. Epidemiology 1990;1466- 473
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Zheng  WChow  WHYang  G  et al.  The Shanghai Women's Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol 2005;1621123- 1131
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Shu  XOYang  GJin  F  et al.  Validity and reproducibility of the food frequency questionnaire used in the Shanghai Women's Health Study. Eur J Clin Nutr 2004;5817- 23
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Matthews  CEShu  XOYang  G  et al.  Reproducibility and validity of the Shanghai Women's Health Study physical activity questionnaire. Am J Epidemiol 2003;1581114- 1122
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Zhang  XShu  XOGao  YT  et al.  Anthropometric predictors of coronary heart disease in Chinese women. Int J Obes Relat Metab Disord 2004;28734- 740
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Figures

Place holder to copy figure label and caption
Figure.

Multivariate relative risks of total mortality (A) and mortality from cardiovascular disease (CVD) (B) and cancer (C) according to waist-hip ratio (WHR). Multivariate relative risks were estimated from a restricted cubic spline Cox regression model. Point estimates are indicated by a solid line and 95% confidence intervals by dashed lines. The median value was treated as the reference point.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Age-Adjusted Baseline Characteristics of the Study Population According to Quintiles of WHR*
Table Graphic Jump LocationTable 2. Relative Risks (RRs) of Deaths From All Causes and From Specific Causes According to Quintiles of WHR
Table Graphic Jump LocationTable 3. Multivariate Relative Risks (RRs) of Total Mortality According to WHR, Stratified by BMI, Menopausal Status, Age, or Exercise*

References

Hu  FBWillett  WCLi  TStampfer  MJColditz  GAManson  JE Adiposity as compared with physical activity in predicting mortality among women. N Engl J Med 2004;3512694- 2703
PubMed Link to Article
Flegal  KMGraubard  BIWilliamson  DFGail  MH Excess deaths associated with underweight, overweight, and obesity. JAMA 2005;2931861- 1867
PubMed Link to Article
Gu  DHe  JDuan  X  et al.  Body weight and mortality among men and women in China. JAMA 2006;295776- 783
PubMed Link to Article
Jee  SHSull  JWPark  J  et al.  Body-mass index and mortality in Korean men and women. N Engl J Med 2006;355779- 787
PubMed Link to Article
Adams  KFSchatzkin  AHarris  TB  et al.  Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med 2006;355763- 778
PubMed Link to Article
Folsom  ARKushi  LHAnderson  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;1602117- 2128
PubMed Link to Article
Willett  WCHu  FBColditz  GAManson  JE Underweight, overweight, obesity, and excess deaths. JAMA 2005;294551
PubMed Link to Article
Willett  WCDietz  WHColditz  GA Guidelines for healthy weight. N Engl J Med 1999;341427- 434
PubMed Link to Article
Calle  EEKaaks  R Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms. Nat Rev Cancer 2004;4579- 591
PubMed Link to Article
Berg  AHScherer  PE Adipose tissue, inflammation, and cardiovascular disease. Circ Res 2005;96939- 949
PubMed Link to Article
Yusuf  SHawken  SOunpuu  S  et al. INTERHEART Study Investigators, Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 2005;3661640- 1649
PubMed Link to Article
Lahmann  PHLissner  LGullberg  BBerglund  G A prospective study of adiposity and all-cause mortality: the Malmo Diet and Cancer Study. Obes Res 2002;10361- 369
PubMed Link to Article
Kalmijn  SCurb  JDRodriguez  BLYano  KAbbott  RD The association of body weight and anthropometry with mortality in elderly men: the Honolulu Heart Program. Int J Obes Relat Metab Disord 1999;23395- 402
PubMed Link to Article
Rimm  EBStampfer  MJColditz  GAChute  CGLitin  LBWillett  WC Validity of self-reported waist and hip circumferences in men and women. Epidemiology 1990;1466- 473
PubMed Link to Article
Zheng  WChow  WHYang  G  et al.  The Shanghai Women's Health Study: rationale, study design, and baseline characteristics. Am J Epidemiol 2005;1621123- 1131
PubMed Link to Article
Shu  XOYang  GJin  F  et al.  Validity and reproducibility of the food frequency questionnaire used in the Shanghai Women's Health Study. Eur J Clin Nutr 2004;5817- 23
PubMed Link to Article
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