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Independent and opposite associations of waist and hip circumferences with diabetes, hypertension and dyslipidemia: the AusDiab Study

International Journal of Obesity volume 28, pages 402409 (2004) | Download Citation



OBJECTIVE: Fat distribution as measured by waist-to-hip ratio has been shown to be an important independent predictor of glucose intolerance. Few studies, however, have considered the contributions of the waist and hip circumferences independently. The aim of this study was to investigate the independent associations of waist and hip circumference with diabetes in a large population-based study, and to investigate whether they also apply to other major components of the metabolic syndrome (hypertension and dyslipidemia). In addition, as previous studies were performed in older persons, we investigated whether these associations were present across adult age groups.

METHODS: Weight, height, waist and hip circumferences were measured in 11 247 participants of the nationally representative Australian Diabetes, Obesity and Lifestyle (AusDiab) Study. HDL-cholesterol, triglycerides, fasting and 2-h postload glucose were determined, and diastolic and systolic blood pressure was measured. After exclusion of persons already known to have diabetes, hypertension or dyslipidemia, logistic and linear regression were used to study cross-sectional associations of anthropometric variables with newly diagnosed diabetes, hypertension and dyslipidemia, and with continuous metabolic measures, all separately for men (n=3818) and women (n=4582). Analyses were repeated in the same population stratified for age.

RESULTS: After adjustment for age, body mass index and waist, a larger hip circumference was associated with a lower prevalence of undiagnosed diabetes (odds ratio (OR) per one s.d. increase in hip circumference 0.55 (95% CI 0.41–0.73) in men and 0.42 (0.27–0.65) in women) and undiagnosed dyslipidemia (OR 0.58 (0.50–0.67) in men and 0.37 (0.30–0.45) in women). Associations with undiagnosed hypertension were weaker (OR 0.80 (0.69–0.93) in men and 0.88 (0.70–1.11) in women). As expected, larger waist circumference was associated with higher prevalence of these conditions. Similar associations were found using continuous metabolic variables as outcomes in linear regression analyses. Height partly explained the negative associations with hip circumference. When these analyses were performed stratified for age, associations became weaker or disappeared in the oldest age groups (age ≥75 y in particular), except for HDL-cholesterol.

CONCLUSION: We found independent and opposite associations of waist and hip circumference with diabetes, dyslipidemia and less strongly with hypertension in a large population-based survey. These results emphasize that waist and hip circumference are important predictors for the metabolic syndrome and should both be considered in epidemiological studies. The associations were consistent in all age groups, except in age ≥75 y. Further research should be aimed at verifying hypotheses explaining the ‘protective’ effect of larger hips.


Fat distribution, apart from overall obesity, is an important risk factor for type II diabetes and cardiovascular diseases.1,2,3,4 Most studies use the waist-to-hip ratio (WHR) for measuring fat distribution, or simply the waist circumference because the waist circumference alone is more strongly correlated to visceral fat than the WHR.5 Visceral fat has been shown to be strongly correlated to components of the metabolic syndrome, and is generally assumed to be a causal factor by releasing free fatty acids into the portal vein.6,7,8 However, results are not consistent, and some studies showed abdominal subcutaneous fat to be more strongly associated.9,10

A high WHR is generally taken to indicate an excess of visceral or abdominal fat, however, it can also be due to a smaller hip circumference.11,12 Therefore, recent studies have investigated the separate contributions of waist and hip circumferences to the glucose levels and type II diabetes.2,11,13,14,15 A larger waist circumference was associated with higher glucose levels and risk for developing type II diabetes. In contrast, a larger hip circumference was consistently associated with lower glucose levels and risk of type II diabetes, independently of waist circumference. These results also remained after adjustment for age and body mass index (BMI), possibly indicating a protective role of a larger hip circumference. The underlying mechanisms explaining these associations are unclear.

To gain further insights into the inverse association between hip circumference and glucose tolerance status, we were interested in seeing whether these associations could be extended to other populations and to other components of the metabolic syndrome apart from blood glucose levels. In addition, we were interested in examining if these associations were present in all age groups, as body composition changes with age. Therefore, we investigated the relationship of waist and hip circumferences with diabetes, hypertension and dyslipidemia in the Australian Diabetes, Obesity and Lifestyle (AusDiab) Study.


Study population

The Australian Diabetes, Obesity and Life style study (AusDiab) Study is a large, nationally representative cross-sectional study among 11 247 participants aged ≥25 y living in Australia. A detailed description of the methodology has been published elsewhere.16,17 Briefly, a nationally representative sample of the population was drawn from 42 randomly selected urban and nonurban areas (Census Collector Districts, CDs) across Australia. CDs containing <100 individuals aged ≥25 y, CDs classified as 100% rural, or CDs with a population comprising >10% Aboriginal or Torres Strait Islanders were excluded. All subjects aged ≥25 y were invited to attend the survey, which consisted of a short household interview followed by a biomedical examination at a study examination site within or close to the selected residential areas. From the 11 247 participants in the study, those already known to have diabetes (263 men and 212 women) and persons receiving medical treatment for hypertension (758 men and 1010 women) or dyslipidemia (471 men and 488 women) were excluded from the present analyses, because treatment could possibly influence the associations that we study. Also, women who reported to be pregnant (n=60) and subjects with missing data on BMI, waist or hip circumference were excluded (n=267). Therefore, analyses were performed in 8400 participants (3818 men and 4582 women). All responders gave written informed consent and ethical approval for the AusDiab Study was obtained from the ethics committee of the International Diabetes Institute.


Height was measured to the nearest 0.5 cm without shoes using a stadiometer. Weight was recorded to the nearest 0.1 kg with subjects wearing lights clothes only and no shoes, using a mechanical beam balance. Waist circumference was measured halfway between the lower rib and the iliac crest using a steel measuring tape. Hip circumference was measured at the widest point over the buttocks. Waist and hip circumference were measured in duplicate to the nearest 0.5 cm. If the difference between the two measurements was greater than 2 cm, a third measurement was taken and the mean of the two closest measurements was calculated. BMI was calculated as weight divided by height squared (kg/m2) and WHR was defined as waist circumference divided by hip circumference. Total body fat percentage was assessed by bioimpedance, using the Tanita body fat analyser (Model TBF-105, Tanita Corporation, Tokyo, Japan), which calculates body fat as a function of sex, height, weight and impedance.

A 75-g oral glucose tolerance test was performed to determine fasting and 2-h postload levels in all subjects, except those on insulin or oral diabetes medication. Plasma glucose levels, serum HDL-cholesterol and triglycerides were determined enzymatically using an Olympus AU600 automated analyser (Olympus Optical Co. Ltd, Tokyo, Japan), which uses the glucose oxidase method for glucose levels. Subjects who reported a history of physician-diagnosed diabetes and who (1) were taking oral glucose-lowering drugs or using insulin injections, or (2) had a fasting glucose value ≥7.0 mmol/l or postload glucose ≥11.1 mmol/l18 were classified as known diabetes. Dyslipidemia was defined as HDL-cholesterol <1.0 mmol/l or triglycerides ≥2.0 mmol/l.19

Blood pressure (BP) was measured in a seated position after at least 5 min rest with a Dinamap semiautomatic oscillometric recorder. A cuff of suitable size was applied to the participant's upper arm (the arm not used for blood collection), which was supported by the table at heart level. Three readings were taken at 1-min intervals. In one state only, BP was measured with a standard mercury sphygmomanometer using the first and fifth Korotkoff sounds, to the nearest 2 mmHg. Based on a comparison study, adjustment was made to all diastolic BP readings recorded using the sphygmomanometer. The mean of the first two readings was calculated unless the difference was greater than 10 mmHg. In that case, the two closest of three measurements was used. Participants were considered to be hypertensive if systolic BP was ≥140 mmHg or diastolic BP ≥90 mmHg.20

Statistical methods

All statistical analyses were performed separately for men and women. Differences in mean characteristics between men and women were tested for statistical significance by Student's t-test. Owing to their skewed distribution, median and interquartile range are reported for triglyceride and HDL-cholesterol levels, and differences between men and women were tested by Mann–Whitney's nonparametric test.

Sex-specific tertiles of waist circumference and of hip circumference were created, and unadjusted and BMI- and age-adjusted prevalences of undiagnosed diabetes, hypertension and dyslipidemia were calculated in nine subgroups created according to these tertiles (3 × 3 table).

Logistic regression was used to study the independent associations of continuous waist and hip circumferences with undiagnosed diabetes, hypertension and dyslipidemia. Odds ratios (ORs) with 95% confidence intervals (CIs) are reported, expressed per one s.d. increase in waist or hip circumference.

Since using cut points for defining the metabolic disorders implies a loss of information, we also studied relationships between anthropometric variables and continuous metabolic variables by use of linear regression. Waist and hip circumference were both included as independent variables with each of the metabolic variables (fasting glucose, postload glucose, systolic BP, diastolic BP, triglycerides or HDL-cholesterol) as the dependent variable. Triglycerides and HDL-cholesterol were ln-transformed because of their skewed distribution. Regression analyses were also performed additionally adjusted for age, BMI or height. Effect modification by age was tested by adding interaction terms (waist × age and hip × age) to the regression models that already included age. In addition, regression analyses were also performed stratified for age, using 10-y intervals. Multicolinearity was studied by the tolerance statistic. If the tolerance was <0.1, the stability of the regression model was considered to be disturbed by multicolinearity. To make the results of linear regression models comparable among different independent variables, we report standardized beta values. All analyses were performed using SPSS Version 10.0.5 for Windows (SPSS, Chicago, IL, USA).


The characteristics of the study population are shown in Table 1. Although men had a higher BMI and WHR than women, women had a higher total body fat percentage. Except for body fat percentage and postload glucose, men had a worse metabolic profile than women.

Table 1: Characteristics of the study populationa

Figure 1 presents the age- and BMI-adjusted prevalence of undiagnosed diabetes, hypertension and dyslipidemia separately for men and women, in tertiles of waist and hip circumferences. In persons with a large waist and a small hip circumference, the prevalence of undiagnosed diabetes was the highest. A similar pattern was shown for the prevalence of undiagnosed hypertension and undiagnosed dyslipidemia.

Figure 1
Figure 1

BMI- and age-adjusted prevalence of undiagnosed diabetes (a), hypertension (b) and dyslipidemia (c) within tertiles of low, median and high (1–3) waist and hip circumferences, separately for men and women.

Adding both waist and hip circumferences as continuous variables to one logistic regression model, a larger waist circumference was associated with a greater risk for having undiagnosed diabetes, hypertension and dyslipidemia. In contrast, a larger hip circumference was associated with a lower risk after adjustment for waist circumference. Adjustment for BMI weakened the associations with waist circumference, but not the associations with hip circumference (Table 2). Similar results were shown using fat percentage measured by impedance instead of BMI (data not shown). If prevalent cases (ie already using medication) were not excluded for these analyses, the results were also very similar (data not shown). In univariate models, waist and hip were both significantly positively associated with a greater risk of having undiagnosed diabetes, hypertension and dyslipidemia (data not shown).

Table 2: Relative risk (ORs with 95% CI) for having undiagnosed diabetes, hypertension or dyslipidemia associated with 1 s.d. increase in waist or hip circumference

It is shown in Table 3 that waist circumference was associated in the expected manner with all continuous metabolic variables, while hip showed opposite associations, although not statistically significant with diastolic BP in women and with HDL-cholesterol in men. Adjustment for age alone attenuated all associations with the glucose and blood pressure variables, but strengthened the associations with lipids. Adjustment for BMI alone weakened all associations with waist circumference, but strengthened all associations with hip circumference. Similar results were found if fat percentage was used instead of BMI. Adjustment for height alone affected all associations leading to weaker but still significant associations (data not shown). The associations also remained similar if only subjects with normal glucose tolerance were studied (ie subjects with newly detected diabetes, impaired fasting glucose and impaired glucose tolerance were excluded from the analyses), or if only subjects with normal lipids were studied (ie subjects with newly detected dyslipidemia were excluded). If subjects with undiagnosed hypertension were excluded, the associations with BP became weaker. In univariate models, waist and hip were both significantly positively associated with all the continuous metabolic variables (data not shown).

Table 3: Associations (standardised betas) of waist and hip circumference with variables of the metabolic syndrome after adjustment for each other, and additionally adjusted for age and BMI or for age and height

Results of linear regression analyses stratified for age are shown in Table 4. The number of subjects in each age group was smaller in older age groups, but was at least 166 per group. The associations of fasting and postload glucose levels with waist and hip circumferences in each age group were similar to those in the total population and reasonably consistent over all age groups, except for men aged ≥65 y and women ≥75 y. In these older age groups, the associations of both waist and hip circumference became weaker, disappeared or even went in the opposite direction. Also, the associations of anthropometric variables with BP disappeared in these older age groups, but the associations with BP became weak in all age groups. Associations with lipids mainly remained similar in each age group, except for the associations with triglycerides in the oldest age groups (age ≥75 y). None of the linear regression models was disturbed by multicolinearity.

Table 4: Associations (standardised betas) of waist and hip circumference (adjusted for each other and for BMI) with variables of the metabolic syndrome, stratified for age


In the present study, we have confirmed in a large population-based sample with a wide age range that, after taking waist circumference into account, a larger hip circumference is associated with a lower prevalence of undiagnosed diabetes. We have extended these findings to other components of the metabolic syndrome, that is hypertension and dyslipidemia. In particular, the associations with diabetes and dyslipidemia were strong and consistent across all ages, except age ≥75 y.

Earlier studies investigating the separate contributions of waist and hip circumferences to diabetes also have demonstrated an apparent ‘protective’ effect of a large hip circumference.11,13,15 Similar associations were found not only for risk of future diabetes2,14 but also for incident self-reported myocardial infarction, combined cardiovascular diseases, and mortality of these diseases.14 Negative associations using thigh circumference instead of hip circumference were shown in women, but less strongly in men.2,15 However, previous studies have been limited by not basing the diagnosis of diabetes on an OGGT,11,13,14 or by restriction to a particular age group,2,11,14,15 or gender.14

Several mechanisms could explain the inverse association between hip circumference and metabolic disturbances. Firstly, larger hips could indicate larger skeletal frame. Adjustment for height as another measure for frame size explained part of the associations. Height can be interpreted as an indicator of muscle mass, as it is strongly correlated with lean mass.21 Secondly, larger hips could also directly reflect greater muscle mass. Difference in glucose metabolism between Swedish and Indian men has been demonstrated to be more strongly linked to differences in leg muscle mass and not visceral fat.12 Although insulin resistance affects all tissues, skeletal muscle is the main target of insulin and one major site of insulin resistance. Finally, hip circumference could reflect femoral fat mass accumulation, which could have a protective effect additional to muscle mass. Fat tissue in the femoral or gluteal region has been suggested to play a protective role, because adipose tissue in these regions is less sensitive to lipolytic stimuli.22 Lipoprotein lipase (LPL) activity has been shown to be greater in femoral subcutaneous adipose fat compared to visceral fat.23 These regions are therefore more likely to buffer the circulation of FFA after a meal, by taking them up and suppressing lipolytic release into the circulation.24 A diminished entrapment capacity of adipose tissue leads to increased lipid fluxes in the circulation leading to ectopic fat storage in liver, skeletal muscle and pancreas, leading to insulin resistance and beta cell dysfunction.25,26,27,28,29 In humans, larger leg fat mass by dual-energy X-ray absorptiometry or larger thigh fat area by computed tomography has been found to be associated with favourable levels of cardiovascular risk factors including lipids and glucose levels in a few studies now.30,31,32,33,34,35

In line with the latter mechanism, the strongest negative associations of the hip circumference would be expected to be observed with triglycerides, which we found in the present study, followed by slightly weaker but relevant associations with glucose levels. After adjustment for BMI, BP showed no strong association with the fat distribution measures. This could indicate that overall obesity plays a more important role in hypertension than does fat distribution, which is in line with recently reported data in Swedish women, where BMI was more strongly correlated with diastolic and systolic BP than was WHR. Only BMI and change in BMI predicted future hypertension.36 There is also much debate regarding the role of insulin resistance in the regulation of BP and only a modest association between higher insulin sensitivity and lower risk of development of hypertension has been shown recently.37 Factors as salt and alcohol intake and physical activity might be other important factors determining BP and hypertension.38

Previous studies that investigated separate contributions of waist and hip could or did not distinguish between different age groups. In the present study, associations between anthropometrics and metabolic variables seemed to apply for all ages, but disappeared after the age of 75 y. The association with glucose levels disappeared at a younger age in men than in women (65 vs 75 y, respectively). The fact that associations between anthropometric measures and disease are weaker in older age groups may be explained by the healthy survivor effect. Also, other diseases may interfere with the associations of interest, and finally, anthropometric variables may reflect something different at older ages.

A limitation of the present study is that it is cross-sectional and causality cannot be assumed. Underlying factors may influence both metabolic disturbances and abdominal fat distribution. An increased activity of the sympathetic nervous system and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis have both been associated with an increased WHR and with metabolic disturbances.39,40 An increased sensitivity of the HPA axis leads to high glucocorticoid and low growth hormone levels, and disturbances in sex hormones, which all have been associated with insulin resistance as well as abdominal fat distribution.41 Also, intrauterine growth retardation (low birth weight) has been linked with both unfavourable WHR and disease at adult age (Barker hypothesis).42,43 An altered development of the pancreas or altered insulin sensitivity of target tissues may be some of the foetal changes causing type II diabetes later.44 A lower birth weight has recently been shown to be associated with lower lean mass in later life.45

In summary, we found independent and opposite relations of waist and hip circumferences with diabetes, hypertension and dyslipidemia in a large population-based Australian study. The associations with diabetes and dyslipidemia were strong and consistent across all ages, except age ≥75 y. Although the adverse effects of a higher waist circumference were stronger, the opposite associations of a larger hip circumference were also strong and emphasize that waist and hip circumference are important predictors for the metabolic syndrome and should both be considered in epidemiological studies. Further research should explore the possible mechanisms explaining these associations.


  1. 1.

    , , , , , , , , . Relation of impaired fasting and postload glucose with incident type 2 diabetes in a Dutch population: the Hoorn Study. JAMA 2001; 285: 2109–2113.

  2. 2.

    , , , , , , , , , . Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. Am J Clin Nutr 2003; 77: 1192–1197.

  3. 3.

    , , , . Abdominal obesity is associated with increased risk of acute coronary events in men. Eur Heart J 2002; 23: 706–713.

  4. 4.

    , , , , , . Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis 1990; 10: 497–511.

  5. 5.

    , , , . Predicting intra-abdominal fatness from anthropometric measures: the influence of stature. Int J Obes Relat Metab Disord 1997; 21: 587–593.

  6. 6.

    . Metabolic implications of body fat distribution. Diabetes Care 1991; 14: 1132–1143.

  7. 7.

    , , , , , , , . The insulin resistance-dyslipidemic syndrome: contribution of visceral obesity and therapeutic implications. Int J Obes Relat Metab Disord 1995; 19 (Suppl 1): S76–S86.

  8. 8.

    . Insulin resistance in type 2 diabetes: role of fatty acids. Diabetes Metab Res Rev 2002; 18 (Suppl 2): S5–S9.

  9. 9.

    , , , . Subcutaneous abdominal fat and thigh muscle composition predict insulin sensitivity independently of visceral fat. Diabetes 1997; 46: 1579–1585.

  10. 10.

    , , , , . Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest 1995; 96: 88–98.

  11. 11.

    , , , . Narrow hips and broad waist circumferences independently contribute to increased risk of non-insulin-dependent diabetes mellitus. J Intern Med 1997; 242: 401–406.

  12. 12.

    , , . Computed tomography-determined body composition in relation to cardiovascular risk factors in Indian and matched Swedish males. Metabolism 1996; 45: 634–644.

  13. 13.

    , , , . Waist and hip circumferences have independent and opposite effects on cardiovascular disease risk factors: the Quebec Family Study. Am J Clin Nutr 2001; 74: 315–321.

  14. 14.

    , , , , . Larger hip circumference independently predicts health and longevity in a Swedish female cohort. Obes Res 2001; 9: 644–646.

  15. 15.

    , , , , , , , , . Larger thigh and hip circumferences are associated with better glucose tolerance: the Hoorn Study. Obes Res 2003; 11: 104–111.

  16. 16.

    , , , , , , , . The Australian Diabetes, Obesity and Lifestyle Study (AusDiab)—methods and response rates. Diabetes Res Clin Pract 2002; 57: 119–129.

  17. 17.

    , , , , , , , , , , , . The rising prevalence of diabetes and impaired glucose tolerance: the Australian Diabetes, Obesity and Lifestyle Study. Diabetes Care 2002; 25: 829–834.

  18. 18.

    WHO. Definition, diagnosis and classification of diabetes mellitus and its complications. Department of Noncommunicable Disease Surveillance : Geneva; 1999.

  19. 19.

    , , , . Diabetic dyslipidaemia. Australian Diabetes Society position statement. Med J Aust 1995; 162: 91–93.

  20. 20.

    1999 World Health Organization-International Society of Hypertension Guidelines for the Management of Hypertension. Guidelines subcommittee. J Hypertens 1999; 17: 151–183.

  21. 21.

    , , , , , , , . Appendicular skeletal muscle mass: effects of age, gender, and ethnicity. J Appl Physiol 1997; 83: 229–239.

  22. 22.

    . Differences in lipolysis between human subcutaneous and omental adipose tissues. Ann Med 1995; 27: 435–438.

  23. 23.

    , , , , , , . Fat cell metabolism in different regions in women. Effect of menstrual cycle, pregnancy, and lactation. J Clin Invest 1985; 75: 1973–1976.

  24. 24.

    . Adipose tissue as a buffer for daily lipid flux. Diabetologia 2002; 45: 1201–1210.

  25. 25.

    , . Increased fat intake, impaired fat oxidation, and failure of fat cell proliferation result in ectopic fat storage, insulin resistance, and type 2 diabetes mellitus. Ann NY Acad Sci 2002; 967: 363–378.

  26. 26.

    . Banting lecture 2001: dysregulation of fatty acid metabolism in the etiology of type 2 diabetes. Diabetes 2002; 51: 7–18.

  27. 27.

    , , . Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am J Clin Nutr 2000; 71: 885–892.

  28. 28.

    , , , , , , , . Fat accumulation in the liver is associated with defects in insulin suppression of glucose production and serum free fatty acids independent of obesity in normal men. J Clin Endocrinol Metab 2002; 87: 3023–3028.

  29. 29.

    , , , , , , , , . Liver-fat accumulation and insulin resistance in obese women with previous gestational diabetes. Obes Res 2002; 10: 859–867.

  30. 30.

    , , , , . Regional fat distribution in women and risk of cardiovascular disease. Am J Clin Nutr 1997; 65: 855–860.

  31. 31.

    , , , , . Contributions of total and regional fat mass to risk for cardiovascular disease in older women. Am J Physiol Endocrinol Metab 2002; 282: E1023–E1028.

  32. 32.

    , , , , , , , . Visceral obesity in men. Associations with glucose tolerance, plasma insulin, and lipoprotein levels. Diabetes 1992; 41: 826–834.

  33. 33.

    , , , , , . Fat distribution, physical activity, and cardiovascular risk factors. Med Sci Sports Exerc 1997; 29: 362–369.

  34. 34.

    , , , . Contributions of regional adipose tissue depots to plasma lipoprotein concentrations in overweight men and women: possible protective effects of thigh fat. Metabolism 1991; 40: 733–740.

  35. 35.

    , , , , . Peripheral adiposity exhibits an independent dominant antiatherogenic effect in elderly women. Circulation 2003; 107: 1626–1631.

  36. 36.

    , , , , . Blood pressure and pulse pressure development in a population sample of women with special reference to basal body mass and distribution of body fat and their changes during 24 years. Int J Obes Relat Metab Disord 2003; 27: 128–133.

  37. 37.

    , , , . Insulin sensitivity and the risk of incident hypertension: insights from the Insulin Resistance Atherosclerosis Study. Diabetes Care 2003; 26: 805–809.

  38. 38.

    , , . Nutritional factors in the control of blood pressure and hypertension. Nutr Clin Care 2002; 5: 9–19.

  39. 39.

    , , , , , , , , . The activity of the hypothalamic–pituitary–adrenal axis and the sympathetic nervous system in relation to waist/hip circumference ratio in men. Obes Res 2000; 8: 487–495.

  40. 40.

    . Body fat distribution, insulin resistance, and metabolic diseases. Nutrition 1997; 13: 795–803.

  41. 41.

    , . Visceral fat in relation to health: is it a major culprit or simply an innocent bystander? Int J Obes 1997; 21: 626–631.

  42. 42.

    , . Fetal origins of adult disease: epidemiology and mechanisms. J Clin Pathol 2000; 53: 822–828.

  43. 43.

    . Fetal origins of coronary heart disease. BMJ 1995; 311: 171–174.

  44. 44.

    , . Pancreatic development and adult diabetes. Pediatr Res 2000; 48: 269–274.

  45. 45.

    , , , , . Programming of lean body mass: a link between birth weight, obesity, and cardiovascular disease? Am J Clin Nutr 2003; 77: 726–730.

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MBS received grants from the European Foundation for the Study of Diabetes and Van Walree Fund, Royal Netherlands Academy of Arts and Sciences. We are most grateful to the following for their support of the AusDiab study: The Commonwealth Department of Health and Aged Care, Eli Lilly (Aust) Pty Ltd, Janssen—Cilag (Aust) Pty Ltd, Knoll Australia Pty Ltd, Merck Lipha s.a., Alphapharm Pty Ltd, Merck Sharp & Dohme (Aust), Roche Diagnostics, Servier Laboratories (Aust) Pty Ltd, SmithKline Beecham International, Pharmacia and Upjohn Pty Ltd, BioRad Laboratories Pty Ltd, HITECH Pathology Pty Ltd, the Australian Kidney Foundation, Diabetes Australia (Northern Territory), Queensland Health, South Australian Department of Human Services, Tasmanian Department of Health and Human Services, Territory Health Services, Victorian Department of Human Services and Health Department of Western Australia. For their invaluable contribution to the field activities of AusDiab, we are enormously grateful to Annie Allman, Marita Dalton, Adam Meehan, Claire Reid, Alison Stewart, Robyn Tapp and Fay Wilson.

Author information


  1. International Diabetes Institute, Caulfield, Victoria, Australia

    • M B Snijder
    • , P Z Zimmet
    •  & J E Shaw
  2. Institute for Extramural Medicine, VU University Medical Center, Amsterdam, The Netherlands

    • M B Snijder
    • , M Visser
    • , J M Dekker
    •  & J C Seidell
  3. Department of Nutrition and Health, Faculty of Earth and Life Sciences, Vrije Universiteit, Amsterdam, The Netherlands

    • J C Seidell


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Correspondence to M B Snijder.

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