Is waist circumference a better predictor of blood pressure, insulin resistance and blood lipids than body mass index in young Chilean adults?
© Lara et al.; licensee BioMed Central Ltd. 2012
Received: 24 January 2012
Accepted: 26 July 2012
Published: 10 August 2012
It has been reported that waist circumference (WC) is a better predictor of cardiovascular risk factors than body mass index (BMI), although the findings have not been consistent. The aim of this study was to assess which measurement, BMI or WC, is more strongly associated with blood pressure, homeostatic model assessment (HOMA) and blood lipids in young Chilean adults.
999 subjects aged 22 to 28 years were randomly selected from a registry of individuals born between 1974 and 1978 at the Hospital of Limache, Chile. Weight, height, WC, blood pressure, HOMA and lipoproteins were assessed in a cross-sectional study.
In multivariable regressions BMI and WC were associated with blood pressure, HOMA and lipoproteins at similar level of explained variation (R2 between 1.6 % for Low Density Lipoproteins (LDL) and 15.6 %, the highest for HOMA and triglycerides) and similarly OR in standardised logistic regressions between 1.1 (95 % CI: 0.9 and 1.4) for LDL and 2.9 (95 % CI: 2.4 and 3.4) for elevated HOMA. When both WC and BMI were included in the model collinearity was high and only for HOMA was there a small independent contribution of each index (R2 = 1 %); for other outcomes the pattern was inconsistent.
The strength of the associations of WC and BMI for any cardiovascular risk factors was similar, but highest for HOMA and triglycerides. WC and BMI are equally useful for monitoring the consequences of obesity in young adults.
KeywordsWaist circumference body mass index cardiovascular risk factors young adults
As obesity is a risk factor for type 2 diabetes, hypertension, cardiovascular disease, dyslipidaemias and some types of cancer  it is important to choose the best measure of obesity to monitor its effects in populations.
Body mass index (BMI) has been the most used index to assess obesity . However, lately it has been suggested that waist circumference (WC), waist hip girth ratio, and WC height ratio are better measures of obesity than BMI in predicting cardiovascular risk factors [2–7]. It has been said that visceral fat, which is more correlated with WC than subcutaneous fat, has a direct link with the liver and greater lipolytic activity increasing the level of free fatty acids and decreasing insulin activity. In addition, visceral fat increases low density lipoproteins (LDL) and decreases high density lipoproteins (HDL) . In spite of the plausibility of this explanation, reports remain controversial because studies have not been consistent in showing the advantage of WC over BMI [9–12].
The great majority of the studies assessing which measure of obesity is best have included a wide age range and age has been treated as a confounder [3–5, 9–12]. There is little evidence that the published results would be applicable to young adults. This is a serious drawback as obesity increases markedly during the second and third decade of life [13, 14] and health practitioners need appropriate tools to advise young adults about the consequences of obesity. In relation to specific risk factors, few studies have assessed the measures of obesity in relation to insulin resistance . The great majority have used a diagnosis of type 2 diabetes [5, 9–11]. Such an approach limits the value of the evidence because type 2 diabetes is usually uncommon until late adulthood and would render any assessment less sensitive in youngsters; most individuals with high insulin resistance are not yet diabetic. Finally, few of the studies present their evidence in relation to the three main risk factors, namely, blood pressure, insulin resistance and blood lipids in the same population. It is important to study these points in order to learn about the consistency of the value of each obesity measure in relation to each risk factor and to compare their relative effect sizes in the same population.
In a study of young adults in Chile homeostatic model assessment (HOMA), blood pressure and blood lipids were assessed. The aim of this study is to assess whether BMI or WC is a better measure of obesity in relation to important cardiovascular risk factors in young adults.
Study population and design
Limache is a semi rural town of approximately 40 000 inhabitants (49 % men), located 108 km from the capital of Chile. Agriculture is the main economic activity and the poverty rate is similar to that of the country.
This is a cross-sectional study of 999 adults (437 men and 562 women) aged between 22 and 28 years. The initial sample for the study of risks of cardiovascular disease was 1050 individuals, 250 fewer than for our asthma study . These subjects were selected by simple random sampling from a sampling frame of 3096 newborns registered between 1974 and 1978 in the hospital of Limache, Chile . 260 of the 1050 subjects (approximately 24.8 %) were randomly replaced because they were unavailable for examination, including 75 (seven per cent of the total sample) who were unwilling to participate. A further 51 (4.9 %) out of the 1050 selected subjects, after inclusion of the replacement fraction, did not consent to participate thus the final sample for analysis was 999. The addresses of participants were collated using information from the National Health Service, the National Registry and, if the participants had changed address, from relatives at the old address or neighbours. The participants were visited at home whenever possible. No data were missing for anthropometric measurements, blood pressure measurements and laboratory assessments.
This study was approved by the Ethics Committee of the Faculty of Medicine of the University of Chile. All individuals signed an informed consent form to participate in this study.
Collection of information
a) General and anthropometric characteristics
Socio-demographic information was collected using a questionnaire and was usually administered in the participant’s homes, whereas anthopometric measurements, blood pressure and a blood specimen were obtained by trained university nurses in the Limache hospital or a local health facility.
Weight was measured using electronic calibrated SECA scales to the nearest 100 grams. The subjects were weighed barefoot, wearing minimal clothing (T-shirt and trousers or blouse and skirt, and underwear), with the feet together in the centre of the weighing scales and the head looking forward. Height was measured using an anthropometer to the nearest 1 mm. Subjects stood barefoot on a flat surface, with their back against the instrument, and their head in the Frankfort position. BMI was expressed in kg/m2. WC was measured, using an inextensible tape, midway between the lower rib and the iliac crest after exhaling with the person standing.
b) Blood pressure, HOMA index and blood lipids
Blood pressure was taken with a digital automatic sphygmomanometer, Omron 740, with a self-inflating cuff. The mean of the last two of three blood pressure measurements was used for this analysis.
Blood samples, obtained following 12 hour fasting, were processed and frozen at the Limache hospital for analysis in the Laboratory of Nutrition of the Pontificia Universidad Catolica of Chile. Total cholesterol was measured using the enzymatic colorimetric method (Gesellschaft für biochemica und diagnosed Germany mbh), high density lipoprotein (HDL) by the precipitation technique of Seigler and Wu, and plasma triglycerides by the enzymatic method with HUMAN clarification factor (Gesellschaft für biochemica und diagnosed mbh Germany). Low-density lipoprotein (LDL) was calculated using the Friedewald formula .
The HOMA index was calculated by the formula: (insulin x glycaemia)/22.5 (insulin expressed in uU/ml and glucose in mmol/L) . Glycaemia was assessed by the enzymatic colorimetric method (GOD/PAD method, human diagnosed, Germany), and plasma insulin by radioimmunoassay (Insulin kit, DCP, Los Angeles, USA). As there is no international threshold definition of insulin resistance, a HOMA ≥2.53, equivalent to 1 standard deviation above the average HOMA value in 19 to 40 year olds in Santiago, Chile was used .
Statistical analyses were performed using the STATA software version 10.1.
First, using multiple linear regression analyses, we assessed the contribution of BMI and/or WC to each dependent variable (systolic blood pressure, diastolic blood pressure, HOMA index, total cholesterol, HDL, LDL and triglycerides) unadjusted and adjusted for age, sex, tobacco consumption and number of belongings (number of domestic appliances in working order in the household: refrigerator, gas-fuelled water heating, personal computer, washing machine and microwave oven). The effects of BMI and WC were assessed using the partial regression coefficients β (95 % Confidence Interval (CI)) and the percentage of explained variation attributable to each main factor (RBMI 2 and RWC 2). The residuals of each model were assessed for normality and homocedasticity.
Second, we performed multiple logistic regressions based on dependent binary variables: systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg , HOMA index ≥2.53 , total cholesterol ≥200 mg/dl, HDL ≤40 mg/dl, LDL ≥160 mg/dl and triglycerides ≥150 mg/dl . Odds ratios (OR) were calculated separately for the contribution to BMI and WC in relation to each binary dependent variable. As BMI and WC use different units they were standardised in Z-scores, based on the mean and one standard deviation of BMI and WC respectively.
We also assessed two way interactions between sex and each independent variable on the response variable to decide if stratification by sex was necessary. We presented results separated by sex only when interaction was significant at p <0.01 or if the p-value was between 0.05 and 0.01, and the plotting of relevant values were considered relevant.
a) Characteristics of the study population
Prevalence and distribution of cardiovascular risk factors and socio-demographic factors by sex
Full time education (years)
Number of belongings
Systolic blood pressure (mm Hg)
Diastolic blood pressure (mm Hg)
Total cholesterol (mg/dl)
Systolic blood pressure ≥140 mm Hg
Diastolic blood pressure≥90 mm Hg
Total cholesterol ≥200 mg/dl
HDL ≤40 mg/dl
LDL ≥160 mg/dl
Triglycerides ≥150 mg/dl
b) Results from multiple linear regression analyses
Significant interactions were found in the multiple linear regressions between sex and both measures of obesity (BMI and WC) on triglycerides (p = 0.002 for both). Thus results are given separately by sex for triglycerides.
Association between BMI and/or WC and blood pressure, HOMA and blood lipids adjusted by age and sex
βcoefficient (95 % CI)a
Systolic blood pressure (mm Hg)
Diastolic blood pressure (mm Hg)
Total cholesterol (mg/dl)
BMI only in the model
(0.6 to 0.9)
(0.5 to 0.8)
(0.12 to 0.16)
(0.9 to 1.9)
(−0.8 to −0.5)
(0.5 to 1.5)
RBMI 2 b
WC only in the model
(0.2 to 0.3)
(0.2 to 0.3)
(0.05 to 0.06)
(0.3 to 0.8)
(−0.4 to −0.2)
(0.2 to 0.6)
RWC 2 c
BMI and WC included in the model
(0.3 to 1.0)
(0.3 to 0.8)
(0.02 to 0.12)
(−0.6 to 2.0)
(−0.4 to 0.4)
(−0.7 to 1.7)
RBMI 2 b
(−0.1 to 0.2)
(−0.05 to 0.2)
(0.01 to 0.05)
(−0.2 to 0.8)
(−0.4 to −0.1)
(−0.3 to 0.7)
RWC 2 c
When BMI and WC were both entered into the model for each dependent variable, adjusted for age and sex, the only outcome that both variables contributed independently to was HOMA. BMI, but not WC, was associated with systolic and diastolic blood pressure, while WC, but not BMI, was associated with HDL (Table 2). However, RBMI 2 and RWC 2 values were minimal in this analysis indicating a high level of collinearity between these BMI and WC.
Association between BMI and WC and plasma triglycerides by sex adjusted by age
β coefficient (95 % CI)
BMI only in the model
(3.4 to 5.1)
(5.3 to 9.3)
RBMI 2 a
WC only in the model
(1.4 to 2.1)
(2.2 to 3.7)
RWC 2 b
BMI and WC included in the model
(−0.8 to 3.5)
(−2.9 to 6.3)
RBMI 2 a
(0.4 to 2.1)
(0.6 to 4.1)
RWC 2 b
Residuals in all the multiple linear analysis were normally distributed.
c) Results from logistic regression analyses
The strength of the association between BMI or WC and blood pressure, lipids and HOMA was similar. The two measures were associated more strongly with HOMA and triglycerides than with any other cardiovascular risk factor both in the multiple linear and logistic regression analyses. The findings from multiple linear and logistic regression analyses were consistent for each dependent variable. The independent contribution of BMI and WC when significant was small highlighting the high collinearity between these two measures.
Interpretation of findings
The associations reported between measures of obesity and cardiovascular risk factors in this study underscore the potential for modifying the risk profile of individuals in a population. The 95 % CIs were narrow indicating that we can rely in the accuracy of our estimates. The effects were intermediate for HOMA, triglycerides and blood pressure, lower for HDL, and modest for total cholesterol and LDL. The ranking of effects for measures of obesity and dyslipidaemias were similar to those reported by Barzi and colleagues , though the approach used to assess the impact of measures of obesity was different to our study. We found that the association between WC or BMI and triglycerides was higher in men than women. A possible explanation would be that men have higher levels of blood triglycerides at this age which are susceptible to increase when BMI or WC increases, a finding also reported in a European study of 38 year olds .
A robust test to infer that BMI and WC are equally useful in the assessment of obesity was that BMI and WC made only a small independent contribution (approximately one percent) or no contribution at all to the studied outcomes when both were included in the model; BMI and WC made an independent contribution to HOMA, BMI made a contribution to blood pressure levels and WC to HDL. Others studies have also shown that the use of BMI and WC together offer only a marginal advantage [23, 24], with the exception of Zhu and colleagues .
Our second test to show that the two measures, BMI and WC, were of similar value was that they were associated with the same strength in terms of OR and R2 with each of the seven outcomes. Our results are consistent with Huxley and colleagues in relation to hypertension  and appear consistent with a receiving operating characteristic (ROC) analysis and assessment of area under the curve [4, 11], but not with NHANES findings which showed that WC was a more suitable measure than BMI . Our findings regarding measures of obesity and HOMA were consistent with a metaanalysis of follow-up studies which used type 2 diabetes as outcome  and another two meta-analyses [10, 11], despite slightly stronger associations with WC in some subgroup analyses which was also reported in a ROC analysis . As in our study, the strength of association between BMI or WC and dyslipidaemia variables was found to be similar in another study .
Our results and many other reports show no difference between BMI and WC in the assessment of coronary risk factors [4, 9, 11, 12]. However, there are studies that have shown an advantage of WC over BMI both slight [10, 11] and meaningful [2, 5, 26, 27]. In addition WC was shown to be more related to cardiovascular mortality than BMI in a recently published meta-analysis . The variation of results between studies is large. This may be due to the study designs, the age at which BMI and WC of the participants were measured and the outcome used in these studies (risk of cardiovascular disease, a meaningful cardiovascular event or cardiovascular mortality). The main contribution of our study is that it focuses on young adults, an area that it has been rarely addressed in the literature.
Our findings suggest that in young adults BMI is as appropriate a measure of obesity as WC. We were unable to find other studies in 20–29 year olds. In older ages obesity increases, mainly in women, and the cardiovascular risk factors are more common than in young age therefore the level of association between BMI or WC and each response variable could be different [9, 11, 29, 30]. However, our results showed levels of association similar to those reported in other studies carried out in adults with a broader age range [4, 10–12]. It is also possible that some differences between studies may be due to ethnicity [10–12]. Our study is based in a group with an admixture of Spanish and native Indians and we could not make comparisons between groups.
Another important fact is that the biggest OR for each indicator was with resistance to insulin, reinforcing the idea that both excess BMI and/or WC are associated with the development of type 2 diabetes [31, 32] and would support the view that insulin resistance provides an enabling environment for the subsequent development of other cardiovascular risk factors .
Strengths and weaknesses of this study
The strengths of this population study are: the high response rate; the reliability of the measurements taken by trained professionals; the appropriate sample size for the analysis undertaken; the assessment of the most important cardiovascular risk factors in one study; the use of insulin resistance which allowed us to include participants at high risk of type 2 diabetes in a population of young adults; and the inclusion of both multiple linear regression to assess the association throughout the range of values and logistics regression using standardization to compare the effects of BMI and WC.
The cross-sectional design of this study can be considered a weakness. However, reverse causality i.e. blood pressure level, dyslipidaemia or insulin resistance causing obesity, is implausible. Our study based on a young population could not assess major cardiovascular events and mortality.
Another limitation of our study is that we did not assess waist hip girth ratio because we did not measure hip girth. We did not include waist girth height ratio because reference values are not readily available and the most readily available are for BMI and WC.
Adolphe Quetelet proposed the BMI index approximately 175 years ago (Sur l'homme et le développement de ses facultés, ou Essai de physique sociale. 2 volumes 1835). Its value as a tool to assess obesity continues despite the existence of other measures such as WC. The success of BMI is based on its simplicity and the reliability of its component measurements, the ease of use of reference values of obesity and, as demonstrated in our study, the level of association with major cardiovascular risk factors at a similar level to their association with WC.
This study demonstrates that WC does not offer any advantage over BMI regarding a range of major cardiovascular risk factors; both are associated with cardiovascular risk factors with similar strength. There is no great advantage in using both measures of obesity for population monitoring as their independent contribution is only marginal. This study shows that BMI, which can be reliably measured, is as appropriate for monitoring obesity as WC. BMI could be more useful in populations where the measurement of WC might be problematic either because the available facilities do not allow privacy or because the measurement of WC is less acceptable than measurement of weight and height. It is also advantageous that the threshold values of BMI for overweight and obesity are similar for both sexes and are widely known by practitioners.
This study was funded by the Chilean National Fund for Scientific and Technological Development (Fondecyt) N 101057.
- World Health Organization: Obesity: preventing and managing the global epidemic. http://www.who.int/nutrition/publications/obesity/WHO_TRS_894/en/index.html,
- Huxley R, Mendis S, Zheleznyakov E, Reddy S, Chan J: Body mass index, waist circumference and waist:hip ratio as predictors of cardiovascular risk–a review of the literature. Eur J Clin Nutr. 2010, 64 (1): 16-22.View ArticlePubMedGoogle Scholar
- Ho SY, Lam TH, Janus ED: Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann Epidemiol. 2003, 13 (10): 683-691.View ArticlePubMedGoogle Scholar
- Lee CM, Huxley RR, Wildman RP, Woodward M: Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008, 61 (7): 646-653.View ArticlePubMedGoogle Scholar
- Menke A, Muntner P, Wildman RP, Reynolds K, He J: Measures of adiposity and cardiovascular disease risk factors. Obesity (Silver Spring). 2007, 15 (3): 785-795.View ArticleGoogle Scholar
- Janssen I, Katzmarzyk PT, Ross R: Waist circumference and not body mass index explains obesity-related health risk. Am J Clin Nutr. 2004, 79 (3): 379-384.PubMedGoogle Scholar
- 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 (4): 743-749.PubMedGoogle Scholar
- Chan DC, Barrett HP, Watts GF: Dyslipidemia in visceral obesity: mechanisms, implications, and therapy. Am J Cardiovasc Drugs. 2004, 4 (4): 227-246.View ArticlePubMedGoogle Scholar
- Vazquez G, Duval S, Jacobs DR: Silventoinen K: Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: a meta-analysis. Epidemiol Rev. 2007, 29: 115-128.View ArticlePubMedGoogle Scholar
- Huxley R, James WP, Barzi F, Patel JV, Lear SA, Suriyawongpaisal P, Janus E, Caterson I, Zimmet P, Prabhakaran D, et al: Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes Rev. 2008, 9 (Suppl 1): 53-61.View ArticlePubMedGoogle Scholar
- Nyamdorj R, Qiao Q, Lam TH, Tuomilehto J, Ho SY, Pitkaniemi J, Nakagami T, Mohan V, Janus ED, Ferreira SR: BMI compared with central obesity indicators in relation to diabetes and hypertension in Asians. Obesity (Silver Spring). 2008, 16 (7): 1622-1635.View ArticleGoogle Scholar
- Barzi F, Woodward M, Czernichow S, Lee CM, Kang JH, Janus E, Lear S, Patel A, Caterson I, Patel J, et al: The discrimination of dyslipidaemia using anthropometric measures in ethnically diverse populations of the Asia-Pacific Region: the Obesity in Asia Collaboration. Obes Rev. 2010, 11 (2): 127-136.View ArticlePubMedGoogle Scholar
- Kelly T, Yang W, Chen CS, Reynolds K, He J: Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond). 2008, 32 (9): 1431-1437.View ArticleGoogle Scholar
- Low S, Chin MC, Deurenberg-Yap M: Review on epidemic of obesity. Ann Acad Med Singapore. 2009, 38 (1): 57-59.PubMedGoogle Scholar
- Liu Y, Tong G, Tong W, Lu L, Qin X: Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic risk factors in Chinese subjects?. BMC Public Health. 2011, 11: 35-View ArticlePubMedPubMed CentralGoogle Scholar
- Rona RJ, Smeeton NC, Bustos P, Amigo H, Diaz PV: The early origins hypothesis with an emphasis on growth rate in the first year of life and asthma: a prospective study in Chile. Thorax. 2005, 60 (7): 549-554.View ArticlePubMedPubMed CentralGoogle Scholar
- Wu AH, Contois JH, Cole TG: Reflex testing I: algorithm for lipid and lipoprotein measurement in coronary heart disease risk assessment. Clin Chim Acta. 1999, 280 (1–2): 181-193.View ArticlePubMedGoogle Scholar
- Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985, 28 (7): 412-419.View ArticlePubMedGoogle Scholar
- Acosta AM, Escalona M, Maiz A, Pollak F, Leighton F: Determination of the insulin resistance index by the Homeostasis Model Assessment in a population of Metropolitan Region in Chile. Rev Med Chil. 2002, 130 (11): 1227-1231.View ArticlePubMedGoogle Scholar
- Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL, Jones DW, Materson BJ, Oparil S, Wright JT, et al: The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003, 289 (19): 2560-2572.View ArticlePubMedGoogle Scholar
- Expert Panel on Detection E: 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 (19): 2486-2497.View ArticleGoogle Scholar
- Seidell JC, Cigolini M, Deslypere JP, Charzewska J, Ellsinger BM, Cruz A: Body fat distribution in relation to serum lipids and blood pressure in 38-year-old European men: the European fat distribution study. Atherosclerosis. 1991, 86 (2–3): 251-260.View ArticlePubMedGoogle Scholar
- Farin HM, Abbasi F, Reaven GM: Body mass index and waist circumference both contribute to differences in insulin-mediated glucose disposal in nondiabetic adults. Am J Clin Nutr. 2006, 83 (1): 47-51.PubMedGoogle Scholar
- Kiernan M, Winkleby MA: Identifying patients for weight-loss treatment: an empirical evaluation of the NHLBI obesity education initiative expert panel treatment recommendations. Arch Intern Med. 2000, 160 (14): 2169-2176.View ArticlePubMedGoogle Scholar
- Zhu S, Heshka S, Wang Z, Shen W, Allison DB, Ross R, Heymsfield SB: Combination of BMI and Waist Circumference for Identifying Cardiovascular Risk Factors in Whites. Obes Res. 2004, 12 (4): 633-645.View ArticlePubMedGoogle Scholar
- Rexrode KM, Carey VJ, Hennekens CH, Walters EE, Colditz GA, Stampfer MJ, Willett WC, Manson JE: Abdominal adiposity and coronary heart disease in women. JAMA. 1998, 280 (21): 1843-1848.View ArticlePubMedGoogle Scholar
- Folsom AR, Stevens J, Schreiner PJ, McGovern PG: Body mass index, waist/hip ratio, and coronary heart disease incidence in African Americans and whites. Atherosclerosis Risk in Communities Study Investigators. Am J Epidemiol. 1998, 148 (12): 1187-1194.PubMedGoogle Scholar
- Czernichow S, Kengne AP, Stamatakis E, Hamer M, Batty GD: Body mass index, waist circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk?: evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies. Obes Rev. 2011, 12 (9): 680-687.PubMedPubMed CentralGoogle Scholar
- Esmaillzadeh A, Mirmiran P, Azizi F: 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. 2004, 28 (10): 1325-1332.View ArticlePubMedGoogle Scholar
- Esmaillzadeh A, Mirmiran P, Azizi F: Comparative evaluation of anthropometric measures to predict cardiovascular risk factors in Tehranian adult women. Public Health Nutr. 2006, 9 (1): 61-69.View ArticlePubMedGoogle Scholar
- Garg A: Regional adiposity and insulin resistance. J Clin Endocrinol Metab. 2004, 89 (9): 4206-4210.View ArticlePubMedGoogle Scholar
- Lebovitz HE, Banerji MA: Point: visceral adiposity is causally related to insulin resistance. Diabetes Care. 2005, 28 (9): 2322-2325.View ArticlePubMedGoogle Scholar
- DeFronzo RA: Insulin resistance: a multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidaemia and atherosclerosis. Neth J Med. 1997, 50 (5): 191-197.View ArticlePubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/638/prepub