Review | Published:

Is the association of type II diabetes with waist circumference or waist-to-hip ratio stronger than that with body mass index?

European Journal of Clinical Nutrition volume 64, pages 3034 (2010) | Download Citation



In total, 17 prospective and 35 cross-sectional studies in adults aged 18–74 years, with the aim of comparing betweenbody mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) in their relation to the incidence and prevalence of type II diabetes, were reviewed. Among these studies, only a few have used C-statistic, paired homogeneity test or log-likelihood ratio test for formally comparing the differences. Five prospective studies, in which formal statistic tests have been made, came out with inconsistent findings: two results were in favour of WC in Mexicans African Americanss, respectively, one result was in favour of BMI in Pima Indians, and no difference was found in the other 2 studies. Among the 11 cross-sectional studies that have formally tested the differences, most found a higher odds ratio or slightly larger area under the ROC curve (AUC) for WC than for BMI. A meta-analysis based on the individual data of the Asian cohorts using a paired homogeneity test showed, however, that there was no difference in odds ratio between BMI and WC in Chinese, Japanese, Indian, Mongolian and Filipino men. In conclusion, all studies included in this review showed that either BMI or WC (WHR) predicted or was associated with type II diabetes independently, regardless of the controversial findings on which of these obesity indicators is better.


Diabetes increases dramatically worldwide (Zimmet et al., 2001) as a consequence of changes in lifestyle, including physical inactivity and unhealthy diet. Physical inactivity and obesity have been well recognized as major lifestyle-related risk factors for diabetes. In the light of evidence that the onset of diabetes can be prevented or delayed through lifestyle intervention, including weight reduction and increasing physical activity (Pan et al., 1997; Tuomilehto et al., 2001; Diabetes Prevention Program Research Group, 2002; Kosaka et al., 2005; Ramachandran et al., 2006), lifestyle intervention to prevent non-communicable diseases including diabetes has been included in the 2008–2013 Action Plan (World Health Organization, 2008). One of the objectives of this plan is to develop simple strategies to identify those at risk and provide them with early lifestyle interventions. As the glucose test is invasive, relatively expensive, time consuming and not easy to apply to mass-screening programmes, several other diagnostic tools, including obesity indicators such as waist circumference (WC) and body mass index (BMI), have been proposed and applied in diabetes prevention programmes in recent years (Rolka et al., 2001; Lindstrom and Tuomilehto, 2003; Schulze et al., 2007). However, controversial opinions exist on which of the obesity measures, WC (waist-to-hip ratio (WHR)) or BMI, is more strongly associated with the increased risk of type II diabetes. In this article, studies for comparing between BMI, WC and WHR in their relation to the incidence and prevalence of type II diabetes in adults were reviewed.

Materials and methods

Inclusion criteria

Publications of studies with the aim of comparing between BMI, WC and WHR in their relation to the incidence and prevalence of type II diabetes in adults were eligible for inclusion. A few studies that used unstandardized methods were excluded from this review because the results obtained could not be compared directly. For example, we have excluded studies that showed that an odds ratio for a BMI 28 kg/m2 was higher (or lower) than the odds ratio for a WC 94 cm, but did not mention why the cutoff values for BMI and WC were chosen and whether they are comparable, and there was no formal statistical test to support their conclusions also. Studies that have used quintiles or quartiles or s.d. changes in BMI and WC were included.

Data sources and limitations

The published articles related to these topics were searched from the PubMed from 1975 onwards or obtained through conferences and colleagues, and were reviewed by two independent researchers (RN and QQ). Most of the studies included individuals aged 18–74 years, except for one Chinese study (Woo et al., 2002). Participants in this Chinese study were older than 70 years and were selected from a list of recipients of Old Age and Disability Allowance. In different studies, waist has been measured in different anatomic locations, and diabetes was defined on the basis of either a previous history of diabetes or fasting plasma glucose or fasting plasma glucose plus 2-h post-challenge glucose test levels. Most of the studies are population based with random sampling approaches and a few are hospital based with participants coming for the health check-up as indicated in Supplementary Table 1.

The results of 17 prospective studies and 35 cross-sectional studies, including large international collaborative studies (counted as one study), comparing between BMI and WC, are summarized and presented in Supplementary Tables 2 and 3. The relative risk (RR) or odds ratio for diabetes was estimated using either Cox regression analysis or logistic regression analysis corresponding to either a 1 s.d. increase (in BMI, WC, WHR, and so on) or dichotomous variables (top quintile or quartile vs the lowest quintile or quartile). Areas under the ROC curves (AUCs) with their 95% confidence intervals were reported in some of the studies. The differences between AUCs or between relative risks (or odds ratios) were formally tested in only a few studies (Supplementary Table 3).


Main findings from the prospective study

The follow-up length among the 17 prospective studies ranged from 3 to 15 years. One of these studies applied the paired homogeneity test, 3 compared the AUCs by applying the DeLong method for correlated data (C-statistic), 1 study reported results of both the log-likelihood ratio test and C-statistic, 12 studies did not perform any formal statistical test to compare between BMI and WC (or WHR). Therefore, the results from the 12 studies provide less convincing information to this review. Incidence of diabetes was defined on the basis of elevated fasting (7.8 or 7.0 mmol/l) and/or elevated post-load 2-h glucose level (11.1 mmol/l) in 11 studies, and on the basis of physician's judgement in 6 studies.

As shown in Supplementary Table 1, in some studies the relative risks or the AUCs for predicting development of diabetes were higher for WC than for BMI, but in other studies BMI was higher than WC. The variations in observations were independent of age, gender, ethnicity, diagnostic criteria for diabetes and methods in anthropometric measures. Two facts, however, need to be addressed when interpreting the findings: (1) the 95% confidence intervals for BMI and WC overlapped in all these studies and (2) no formal statistical tests were carried out in most of these studies. The five studies in which formal statistic tests have been applied produced different findings (Supplementary Table 3). The result was in favour of WC in Mexican Americans (Wei et al., 1997) and African Americans (Stevens et al., 2001), but in favour of BMI in Pima Indians (Tulloch-Reid et al., 2003) and the White American men (Stevens et al., 2001), and no difference was found in either the study (Diabetes Prevention Program Research Group, 2006) or the Mauritius Indian studies (Nyamdorj et al., 2009).

The San Antonio Heart Study among Mexican Americans in the United States (Wei et al., 1997), aged 25–64 years and followed up for 7 years, showed that WC was a better risk predictor for type II diabetes than BMI, but there was no difference between BMI and WHR. The AUC was statistically significantly larger for WC than for BMI in both women (P<0.001) and men (P=0.012) (Supplementary Table 1), but there was no difference between the WC and the WHR (P=0.07 in women and P=0.13 in men). The log-likelihood ratio test showed that the addition of BMI to the model with WC did not improve the model prediction based on the WC alone (P=0.53), but it improved prediction based on the WHR alone (P=0.0021); WC improved the model prediction based on either BMI alone (P=0.0006) or WHR alone (P=0.0004), and WHR improved the model prediction beyond that based on BMI alone (P=0.0017), but not based on WC alone (P=0.21) (Supplementary Table 1).

The Pima Indian study (Tulloch-Reid et al., 2003) is a population-based 5-year follow-up study among individuals >18 years. Applying the DeLong method for correlated data, the study showed that the AUC was slightly but statistically significantly larger for BMI than for either WC or WHR.

Again applying the DeLong method, data from the Diabetes Prevention Program (Diabetes Prevention Program Research Group, 2006) of the United States were analysed to compare the risk of each of the obesity indices. The mean age of individuals included in this intervention trial was 54 years, with a follow-up length of 3 years. Compared with BMI, there was no difference in the AUCs between WC and BMI and between WHR and BMI in the groups with placebo, with Metformin and with lifestyle interventions.

In the Atherosclerosis Risk in Community study among 12 814 Americans aged 45–64 years with a follow-up of 9 years, the AUC for prediction of type II diabetes for WC was significantly higher than for BMI in African men and women, but the AUC for WHR was lower than that for BMI in White men (Stevens et al., 2001).

In a recent study based on the data of the Mauritius Non-communicable Disease Survey of 3945 Indians and Creoles, aged 25–74 years with a maximum follow-up length of 11 years, Nyamdorj et al. showed that the risk size (relative risk) of BMI did not differ from that of WC or WHR when the paired homogeneity test was performed (Supplementary Table 3).

Main findings from the cross-sectional study

In total, 35 cross-sectional studies on the topic of interest were reviewed and the results are summarized in Supplementary Table 2. Among these studies, 2 performed the paired homogeneity test, 8 the C-statistic test 11 fitted all variables of interest in the same model but did not show the changes in the model prediction as compared with the nested model and 14 did not run any test.

The point estimate of the odds ratio or the AUC for prevalent diabetes was higher for WC or WHR than for BMI in most of the comparisons, but the confidence intervals were overlapped for most of the reports (Supplementary Table 2). The variations were not explained by differences in ethnicity, age and gender. Studies that tested the differences in the AUCs and the strength of the odds ratio of the risk factors are summarized and presented in Supplementary Table 3. One study ON Australian aboriginal people and Torres Strait islanders (Wang et al., 2007), applying the DeLong method, revealed that the AUC was larger for WHR than for others (BMI, WC and waist-to-stature ratio), but the data were not shown, and hence the study is not included in Supplementary Table 3. As shown in Supplementary Table 3, most of these cross-sectional studies revealed that the AUC was slightly larger for WC or for WHR than for BMI. The odds ratio was also stronger for WC or WHR than for BMI in a study made by Huxley et al. (2008) using the paired homogeneity test. However, a meta-analysis based on the individual data of the Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria for Diabetes in Asia study applying the paired homogeneity test showed that there was no difference in odds ratio in Chinese, Japanese, Indian, Mongolian and Filipino men (Nyamdorj et al., 2008, 2009). The BMI was found to be inferior to WC, WHR or the waist-to-stature ratio in only the Filipino women.

Meta-analysis based on AUC data

Recently, a meta-analytic study by Lee et al. (2008) has analysed the data of AUCs on the basis of one prospective and eight cross-sectional studies. The combined AUC for predicting type II diabetes in men was 0.672 (95% confidence interval 0.646, 0.697) for BMI, 0.701 (0.670, 0.732) for WC, 0.721 (0.664, 0.778) for WHR and 0.726 (0.698, 0.754) for waist-to-height ratio. The corresponding figures for women were 0.693 (0.629, 0.757), 0.744 (0.695, 0.794), 0.748 (0.687, 0.810) and 0.756 (0.700, 0.811), respectively. The test for heterogeneity between each of the abdominal obesity measures with BMI showed significant differences between BMI and waist-to-height ratio in only men (P<0.01). The criteria for diagnosing diabetes and the method of measuring WC were not described.


The evidence based on the prospective studies equally favoured all anthropometric measures of BMI, WC, WHR and the waist-to-stature ratio. But most of the cross-sectional studies showed that WC or WHR discriminate better the cases with diabetes from those without, as compared with BMI. As the number of the prospective studies is limited and covered only limited ethnic groups, the evidence obtained is less convincing and difficult to generalize. The cross-sectional study itself provides only possible association or evidence. Nevertheless, all these studies have shown that either BMI or WC predicted or was associated with increased diabetes risk, independent of other factors.

Conflict of interest

The authors declare no conflict of interest.


  1. , , (2006). Obesity indices and cardiovascular risk factors in Thai adults. Int J Obes (Lond) 30, 1782–1790.

  2. , , (1999). Body fat distribution and the risk of non-insulin-dependent diabetes mellitus in the Omani population. East Mediterr Health J 5, 14–20.

  3. , , , , , (2004). Visceral and central abdominal fat and anthropometry in relation to diabetes in Asian Indians. Diabetes Care 27, 2948–2953.

  4. , , , , , et al. (2007). International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in 168 000 primary care patients in 63 countries. Circulation 116, 1942–1951.

  5. , , , (2001). Anthropometric indexes in the prediction of type 2 diabetes mellitus, hypertension and dyslipidaemia in a Mexican population. Int J Obes Relat Metab Disord 25, 1794–1799.

  6. , , , , , et al. (2008). Body fat distribution and the risk of hypertension and diabetes among Japanese men and women. Hypertens Res 31, 851–857.

  7. , , , , , et al. (2001). Predictive risk factors for deterioration from normoglycemic state to type 2 diabetes mellitus or impaired glucose tolerance in a Tunisian urban population. Diabetes Metab 27, 487–495.

  8. , , , , , (2006). Waist-to-thigh ratio can also be a better indicator associated with type 2 diabetes than traditional anthropometrical measurements in Taiwan population. Ann Epidemiol 16, 321–331.

  9. , , , (2007). Identifying cut-points in anthropometric indexes for predicting previously undiagnosed diabetes and cardiovascular risk factors in the Tongan population. Obes Res Clin Prac 1, 17–25.

  10. , , , , , et al. (2003). Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med 254, 555–563.

  11. Diabetes Prevention Program Research Group (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med 346, 393–403.

  12. Diabetes Prevention Program Research Group (2006). Relationship of body size and shape to the development of diabetes in the diabetes prevention program. Obesity (Silver Spring) 14, 2107–2117.

  13. , , , , (2007). How does ethnicity affect the association between obesity and diabetes? Diabet Med 24, 1199–1204.

  14. , , (2004). Waist-to-hip ratio is a better screening measure for cardiovascular risk factors than other anthropometric indicators in Tehranian adult men. Int J Obes Relat Metab Disord 28, 1325–1332.

  15. , , , , , et al. (2000). Associations of general and abdominal obesity with multiple health outcomes in older women: the Iowa Women's Health Study. Arch Intern Med 160, 2117–2128.

  16. , , , (2002). Body mass index (BMI) and waist circumference (WC) as screening tools for cardiovascular risk factors in Guadeloupean women. J Clin Epidemiol 55, 990–996.

  17. , , , (2007). The prospective association of general and central obesity variables with incident type 2 diabetes in adults, Tehran lipid and glucose study. Diabetes Res Clin Pract 76, 449–454.

  18. , , , (1998). Associations of body composition with type 2 diabetes mellitus. Diabet Med 15, 129–135.

  19. , , (2003). Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann Epidemiol 13, 683–691.

  20. , , , , , et al. (2007). Central rather than overall obesity is related to diabetes in the Chinese population: the InterASIA study. Obesity (Silver Spring) 15, 2809–2816.

  21. , , , , , et al. (2008). Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes Rev 9 (Suppl 1), 53–61.

  22. , , , , , et al. (2003). Detection of cardiovascular risk factors by indices of obesity obtained from anthropometry and dual-energy X-ray absorptiometry in Japanese individuals. Int J Obes Relat Metab Disord 27, 232–237.

  23. , , (2007). Does waist circumference predict diabetes and cardiovascular disease beyond commonly evaluated cardiometabolic risk factors? Diabetes Care 30, 3105–3109.

  24. , , (2007). Adiposity, physical fitness and incident diabetes: the physical activity longitudinal study. Diabetologia 50, 538–544.

  25. , , , , , et al. (2008). A comparison of anthropometric indices for predicting hypertension and type 2 diabetes in a male industrial population of Chennai, South India. Ethn Dis 18, 31–36.

  26. , , , (1999). Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese. Int J Obes Relat Metab Disord 23, 1136–1142.

  27. , , (2005). Prevention of type 2 diabetes by lifestyle intervention: a Japanese trial in IGT males. Diabetes Res Clin Pract 67, 152–162.

  28. , , , , (2007). Overall and central obesity and risk of type 2 diabetes in U.S. black women. Obesity (Silver Spring) 15, 1860–1866.

  29. , , , (1997). Correlation between cardiovascular disease risk factors and simple anthropometric measures. Canadian Heart Health Surveys Research Group. CMAJ 157 (Suppl 1), S46–S53.

  30. , , , (2008). Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol 61, 646–653.

  31. , , , , , et al. (2002). Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord 26, 1232–1238.

  32. , (2003). The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26, 725–731.

  33. , , , , , et al. (2007). Which obesity index best explains prevalence differences in type 2 diabetes mellitus? Obesity (Silver Spring) 15, 1294–1301.

  34. , (2007). Cut-off values for anthropometric variables that confer increased risk of type 2 diabetes mellitus and hypertension in Iraq. Arch Med Res 38, 253–258.

  35. , , , , (2006). Body fat distribution and risk of type 2 diabetes in the general population: are there differences between men and women? The MONICA/KORA Augsburg cohort study. Am J Clin Nutr 84, 483–489.

  36. , , , , (2007). Measures of adiposity and cardiovascular disease risk factors. Obesity (Silver Spring) 15, 785–795.

  37. , , (2004). Detection of cardiovascular risk factors by anthropometric measures in Tehranian adults: receiver operating characteristic (ROC) curve analysis. Eur J Clin Nutr 58, 1110–1118.

  38. , , , , , et al. (2008). BMI compared with central obesity indicators in relation to diabetes and hypertension in Asians. Obesity (Silver Spring) 16, 1622–1635.

  39. , , , , , et al. (2009). BMI compared with central obesity indicators as a predictor of diabetes incidence in Mauritius. Obesity (Silver Spring) 17, 342–348.

  40. , , , , , et al. (1997). Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care 20, 537–544.

  41. , (2005). Anthropometric indices as screening tools for cardiovascular risk factors in Singaporean women. Asia Pac J Clin Nutr 14, 74–79.

  42. , , , , , et al. (2004). Temporal changes in prevalence of diabetes and impaired glucose tolerance associated with lifestyle transition occurring in the rural population in India. Diabetologia 47, 860–865.

  43. , , , , , (2006). The Indian diabetes prevention programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia 49, 289–297.

  44. , , , , , et al. (2001). Performance of recommended screening tests for undiagnosed diabetes and dysglycemia. Diabetes Care 24, 1899–1903.

  45. , , , , , (2003). Anthropometric cutoff points for predicting chronic diseases in the Mexican National Health Survey 2000. Obes Res 11, 442–451.

  46. , , , , (2002). Predicting incident diabetes in Jamaica: the role of anthropometry. Obes Res 10, 792–798.

  47. , , , , , et al. (2007). Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab 92, 589–594.

  48. , , , , , (2006). Comparison of anthropometric characteristics in predicting the incidence of type 2 diabetes in the EPIC-Potsdam study. Diabetes Care 29, 1921–1923.

  49. , , , , , et al. (2007). An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 30, 510–515.

  50. , , , , , et al. (2003). Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn study. Am J Clin Nutr 77, 1192–1197.

  51. , , , , , et al. (2001). Sensitivity and specificity of anthropometrics for the prediction of diabetes in a biracial cohort. Obes Res 9, 696–705.

  52. , , , , , et al. (2001). Sensitivity and specificity of anthropometrics for the prediction of diabetes in a biracial cohort. Obesity 9, 696–705.

  53. , , , , (2005). Fat distribution is strongly associated with plasma glucose levels and diabetes in Thai adults-the InterASIA study. Diabetologia 48, 657–660.

  54. , , , , , (2004). Impact of obesity and body fat distribution on cardiovascular risk factors in Hong Kong Chinese. Obes Res 12, 1805–1813.

  55. , , , , (2003). Do measures of body fat distribution provide information on the risk of type 2 diabetes in addition to measures of general obesity? Comparison of anthropometric predictors of type 2 diabetes in Pima Indians. Diabetes Care 26, 2556–2561.

  56. , , , , , et al. (2001). Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med 344, 1343–1350.

  57. , , , , (2005). Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr 81, 555–563.

  58. , (2004). Body size measurements as predictors of type 2 diabetes in Aboriginal people. Int J Obes Relat Metab Disord 28, 1580–1584.

  59. , , , , (2007). Anthropometric indices and their relationship with diabetes, hypertension and dyslipidemia in Australian Aboriginal people and Torres Strait Islanders. Eur J Cardiovasc Prev Rehabil 14, 172–178.

  60. , , , (1997). Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans--a 7-year prospective study. Obes Res 5, 16–23.

  61. , , , , , (2005). Are waist circumference and body mass index independently associated with cardiovascular disease risk in Chinese adults? Am J Clin Nutr 82, 1195–1202.

  62. , , , (2002). Is waist circumference a useful measure in predicting health outcomes in the elderly? Int J Obes Relat Metab Disord 26, 1349–1355.

  63. World Health Organization (2008). 2008–2013 Action Plan for the Global Strategy for the Prevention and Control of Non-Communicable Disease. WHO: Geneva.

  64. , , (2001). Global and societal implications of the diabetes epidemic. Nature 414, 782–787.

Download references


The earlier version of this paper was prepared as a background paper for the WHO Expert Consultation on waist circumference and waist–hip ratio (Geneva, 8–11 December 2008). We owe our sincere thanks to all experts who gave comments to improve the paper. This work has been financially supported by the Academy Finland (118492).

Author information


  1. Department of Public Health, University of Helsinki, Helsinki, Finland

    • Q Qiao
    •  & R Nyamdorj
  2. National Institute for Health and Welfare, Helsinki, Finland

    • Q Qiao
    •  & R Nyamdorj


  1. Search for Q Qiao in:

  2. Search for R Nyamdorj in:

Corresponding author

Correspondence to Q Qiao.

Supplementary information

Word documents

  1. 1.

    Supplementary Table 1

About this article

Publication history





Supplementary Information accompanies the paper on European Journal of Clinical Nutrition website (

Further reading