American Journal of Cardiology
Volume 98, Issue 8 , Pages 1053-1056, 15 October 2006

Comparison of Body Mass Index Versus Waist Circumference With the Metabolic Changes That Increase the Risk of Cardiovascular Disease in Insulin-Resistant Individuals

Stanford University School of Medicine, Stanford, California.

Received 10 January 2006; received in revised form 8 May 2006; accepted 9 May 2006. published online 29 August 2006.

Article Outline

This study compared the abilities of body mass index (BMI) and waist circumference (WC) to identify resistance to insulin-mediated glucose uptake and related metabolic abnormalities in 261 apparently healthy patients. Insulin resistance and associated metabolic abnormalities occur more commonly in the overweight/obese, and these changes increase the risk of cardiovascular disease (CVD). Determining either their BMI or WC can identify patients more likely to experience the adverse effects of excess adiposity on CVD risk, and the relative clinical utility of these measurements is not clear. Therefore, insulin-mediated glucose uptake was quantified in 261 apparently healthy adults by determining the steady-state plasma glucose concentrations during the insulin suppression test; the higher the concentration, the greater the defect in insulin action. The fasting plasma glucose, triglyceride, and total, low-density lipoprotein, and high-density lipoprotein cholesterol concentrations were also measured, and the associations between these variables and the measurements of BMI and WC were determined. The greater the degree of adiposity, the higher the steady-state plasma glucose, fasting plasma glucose, and triglyceride concentrations, irrespective of the index of adiposity used. However, increases in the total and low-density lipoprotein cholesterol and decreases in the high-density lipoprotein cholesterol concentrations were only seen in those with higher BMI values. In conclusion, because BMI performed at least as well as WC in identifying differences in insulin sensitivity and multiple CVD risk factors, either estimate can be used to identify patients at increased CVD risk.

 

Emphasis on the importance of an assessment of abdominal obesity to help identify apparently healthy patients who are more likely to be insulin resistant and thereby at increased cardiovascular disease (CVD) risk1, 2, 3 is somewhat paradoxic, given the evidence from the National Health and Nutrition Examination Survey showing that measurements of body mass index (BMI) and waist circumference (WC) correlated highly (r ∼0.9), regardless of age, gender, or ethnicity.4 If the 2 measures of excess adiposity are so closely related, it is not immediately apparent why one should be more indicative of insulin resistance and its consequences than the other. Consequently, we hypothesized that either BMI or WC would be effective to a comparable degree in identifying insulin-resistant patients with the metabolic CVD risk factors associated with the defect in insulin action. The present study was initiated to test this formulation.

Back to Article Outline

Methods 

The study population consisted of 261 volunteers (140 women and 121 men) who had responded to advertisements describing our research interest in studying the role of insulin resistance in human disease. The studies were performed at the General Clinical Research Center at Stanford University Hospital (Stanford, California), all subjects gave informed consent, and the Stanford Human Subjects Committee approved the protocols.

Participants were in apparent good health, with normal physical examination findings and health histories, and were nondiabetic as defined by the American Diabetes Association,5 with normal blood counts and liver/kidney function test results. The WC (in centimeters) was determined, as described previously,6 and the BMI (in kilograms divided by meters squared) was calculated from the height and weight measurements of the subject in light clothing and no shoes. Patients were divided into 3 categories on the basis of the definitions proposed by the National Institutes of Health7: normal weight (BMI <25 kg/m2), overweight (BMI 25 to 29.9 kg/m2), and obese (BMI ≥30 kg/m2). Using the Adult Treatment Panel III criteria,2 the volunteers were also divided into 2 categories: abdominally obese (>88 cm for women and >102 cm for men) or nonabdominally obese.

Insulin-mediated glucose uptake was quantified by a modified version8 of the insulin suppression test, as described and validated by our research group.9, 10 After an overnight fast, an intravenous catheter was placed in each arm of the subject, 1 for the simultaneous 3-hour infusion of octreotide (0.27 μg/m2/min), insulin (32 mU/m2/min), and glucose (267 mg/m2/min) and 1 for the blood sampling. The plasma glucose and insulin concentrations were measured every 10 minutes during the 150- to 180-minute period, and then averaged to determine the steady-state plasma glucose and insulin concentrations. Because the steady-state plasma insulin concentrations were comparable in all patients, and the glucose infusion was identical, the resultant steady-state plasma glucose concentration provided a direct measure of the ability of insulin to mediate the disposal of a given glucose load (i.e., the higher the steady-state plasma glucose, the more insulin resistant the patient). Measurements of insulin-mediated glucose disposal with the insulin suppression test have been shown to be essentially identical to those obtained using the hyperinsulinemic, euglycemic clamp method.10

Plasma samples obtained after an overnight fast on the morning of the insulin suppression test were sent to Stanford Hospital’s core laboratory for measurement of plasma concentrations of fasting plasma triglycerides and total, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) cholesterol.

The results are expressed as the mean ± SEM. Statistical analyses were performed using Systat, version 7.0 (SPSS Inc., Chicago, Illinois). Statistical significance was evaluated by the Student’s t test, 1-way analysis of variance, and Bonferroni post hoc means comparisons and was set at p <0.05.

Back to Article Outline

Results 

The study population consisted primarily of white patients (71%) with approximately 16%, 9%, and 4% of Asian, Hispanic, and African-American ancestry, respectively. The mean and range for age, BMI, WC, and steady-state plasma glucose, fasting plasma glucose, triglyceride, and HDL cholesterol concentrations are presented in Table 1. These data indicated that the experimental population had a wide range of values for the 2 measures of adiposity, insulin action, and the metabolic variables associated with insulin resistance.

Table 1. Clinical characteristics of experimental population
CharacteristicMean ± SEMRange
Age (yrs)50±120–73
BMI (kg/m2)28.1±0.316.3–42.9
WC (cm)95±162–136
Steady-state plasma glucose (mg/dl)141±445–308
Glucose (mg/dl)95±163–125
Triglycerides (mg/dl)146±722–800
Total cholesterol (mg/dl)197±2104–358
LDL cholesterol (mg/dl)121±251–240
HDL cholesterol (mg/dl)51±124–114

Table 2 presents the steady-state plasma glucose, plasma glucose, triglyceride, and total, LDL, and HDL cholesterol concentrations of the nonobese (normal WC) and abdominally obese (obese WC) subgroups for comparison. These data showed that abdominally obese patients had significantly higher steady-state plasma glucose (p <0.001), glucose (p <0.001), and triglyceride (p = 0.01) concentrations than their normal WC counterparts. However, the differences in total, LDL, and HDL cholesterol concentrations between the 2 WC groups were not statistically significant.

Table 2. Metabolic variables in 261 study participants classified by waist circumference (WC)
VariableWC (cm)p Value⁎
Normal (n = 128)Obese (n = 133)
Steady-state plasma glucose (mg/dl)107±5174±6<0.001
Glucose (mg/dl)92±198±1<0.001
Triglycerides (mg/dl)128±10163±90.01
Total cholesterol (mg/dl)193±3201±30.09
LDL cholesterol (mg/dl)118±3124±30.14
HDL cholesterol (mg/dl)52±149±10.13

Data are expressed as mean ± SEM.

⁎Statistical significance determined by Student’s t test.

The changes in the insulin sensitivity and CVD factors as a function of differences in BMI are listed in Table 3. On 1-way analysis of variance, we found that every variable measured differed as a function of the BMI group. Furthermore, all the CVD risk factors were significantly different when normal-weight patients (BMI <25.0 kg/m2) were compared with obese subjects (BMI ≥30.0 kg/m2). Also, the steady-state plasma glucose concentrations of all 3 BMI groups were different from each other.

Table 3. Metabolic variables in 261 study participants classified by body mass index (BMI)
VariableBMI (kg/m2)p Value⁎
<25 (n = 68)25–29.9 (n = 106)≥30 (n = 87)
Steady-state plasma glucose (mg/dl)†91±5132±6192±8<0.001
Glucose (mg/dl)‡91±195±198±1<0.001
Triglycerides (mg/dl)‡98±7154±12173±12<0.001
Total cholesterol (mg/dl)§186±4201±4201±40.03
LDL cholesterol (mg/dl)‡111±3124±3126±40.01
HDL cholesterol (mg/dl)‡56±250±147±1<0.001

Data are expressed as mean ± SEM.

⁎Statistical significance by analysis of variance.

†p <0.001 for pairwise comparisons of BMI groups <25 versus 25 to 29.9, <25 versus ≥30, and 25 to 29.9 versus ≥30 kg/m2

‡p <0.05 for pairwise comparisons of BMI groups <25 versus 25 to 29.9 and <25 versus ≥30 kg/m2

§p <0.05 for pairwise comparison of BMI groups <25 versus ≥30 kg/m2.

The results in Table 2, Table 3 indicate that the insulin sensitivity and related metabolic CVD risk factors worsened as a function of increased obesity, regardless of whether BMI or WC was used as the index of excess adiposity. To further assess the clinical relevance of using either BMI or WC to identify patients at increased CVD risk, we first used the BMI criteria to identify overweight/obese subjects. (The overweight/obese groups were combined into 1 group for this analysis because of the similarity of their values [Table 3]). We then applied the WC criteria to identify abdominally obese subjects. The metabolic characteristics of the subjects identified by the 2 different obesity criteria were then calculated. These data are listed in Table 4, and more patients met the criteria for being overweight/obese (n = 193) than for being abdominally obese (n = 133). It can be seen that the values for all the CVD risk factors measured were almost identical, irrespective of whether BMI or WC was used to classify the groups.

Table 4. Metabolic variables in 261 study participants classified by overweight/obese body mass index (BMI) or obese waist circumference (WC)
VariableBMI (≥25 kg/m2) (n = 193)WC (>88 cm for women; >102 cm for men) (n = 133)
Steady-state plasma glucose (mg/dl)159±5174±6
Glucose (mg/dl)96±198±1
Triglycerides (mg/dl)163±8163±9
Total cholesterol (mg/dl)200±3201±3
LDL cholesterol (mg/dl)123±3124±3
HDL cholesterol (mg/dl)49±149±1

Data are expressed as mean ± SEM.

Back to Article Outline

Discussion 

The results of this study are discussed under 2 general topics. The first issue is the impact that the use of either of the 2 indexes of adiposity had on the ability to identify insulin-resistant patients displaying the metabolic abnormalities associated with this defect in insulin action that increase CVD risk. In this case, the results in Table 2, Table 3 demonstrate that increases in either BMI or WC are associated with a decrease in insulin sensitivity (higher steady-state plasma glucose) and higher glucose and triglyceride concentrations. However, significant changes in total, LDL, and HDL cholesterol concentrations only varied as a function of obesity when the BMI criteria were used. Furthermore, the results in Table 4 indicate that the values for all the CVD risk factors were almost the same for patients defined as overweight/obese (BMI criteria) or abdominally obese (WC criteria). Because all the metabolic changes determined in this study predicted for CVD,11, 12, 13, 14 it appears that either index of obesity, BMI or WC, will identify, to a comparable degree, those insulin-resistant patients with the associated metabolic abnormalities that increase CVD risk.

The second issue is the clinical utility of using determinations of WC or BMI to identify patients who, because they are obese, are more likely to be insulin resistant and display the CVD risk factors associated with this defect in insulin action. The results in Table 4 suggest that it might not make a great deal of difference which approach is used. Because considerably more patients were overweight/obese than abdominally obese and the metabolic CVD risk factors were not different in the 2 groups, it would seem that measuring BMI may actually be of greater clinical utility. It is certainly difficult to conclude from these data that BMI measurements provide less clinically relevant information than determinations of WC.

Our conclusion that the association between measurements of BMI and insulin resistance, and related abnormalities, is comparable to that between WC and these metabolic CVD risk factors is obviously in conflict with the view of the Adult Treatment Panel III and the International Diabetes Federation.2, 3 Furthermore, it is contrary to reports emphasizing the relation between abdominal obesity in general, or visceral fat specifically, and the prediction of the development of the clinical syndromes related to insulin resistance.15, 16, 17, 18, 19, 20 However, their point of view is far from unanimous. For example, increases in visceral obesity did not correlate with decreases in insulin-mediated glucose disposal in Pima Indians,21 and BMI was the estimate of adiposity with the highest hazard ratio in the prediction of type 2 diabetes.22 Furthermore, adding WC to the multivariate model did not enhance its predictive ability. Similarly, a prospective study of Mexican-Americans23 reported that those patients with the highest baseline plasma glucose and insulin values were most likely to develop type 2 diabetes, independent of differences in age, BMI, or central obesity. In addition, a prospective study in a predominantly white population concluded that “overall and abdominal adiposity strongly and independently predict risk of type 2 diabetes.”24 The results of studies in several ethnic groups have shown that BMI is more strongly associated with blood pressure than abdominal obesity,25, 26, 27 and a similar conclusion was reached concerning the presence of carotid atherosclerosis in Japanese men.28 The clustering of dyslipidemia, hyperuricemia, diabetes, and hypertension described in whites and African-Americans was most strongly related to the insulin concentration, although the magnitude decreased when adjusted for differences in BMI and abdominal obesity.29 Thus, although WC is a significant predictor of clinical outcomes linked to insulin resistance, considerable evidence has shown that overall obesity, as estimated by BMI, effectively identifies patients more likely to develop the clinical syndromes associated with the defect in insulin action.

Perhaps of greater clinical relevance is the evidence summarized in a recent report30 that studies demonstrating the relation between increased abdominal obesity and adverse clinical consequences used measurements of WC made at ≥14 different anatomic sites and that measurements made at the 4 most commonly used sites yielded quite different absolute values for WC. On the basis of these observations, and the results of our study, it does not seem that knowledge of the WC provides any unique clinical insight and that either the BMI or WC can be used by clinicians to increase their suspicion that a specific patient might be insulin resistant, with the metabolic abnormalities that increase their risk of CVD.

Back to Article Outline

References 

  1. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications (Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation). Diabet Med. 1998;15:539–553
  2. Expert Panel on the Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA. 2001;285:2846–2897
  3. Alberti KGMM, Zimmet P, Shaw J IDF Epidemiology Task Force Consensus Group. The metabolic syndrome–a new worldwide definition. Lancet. 2005;366:1059–1062
  4. Ford ES, Mokdad AH, Giles WH. Trends in waist circumference among U.S. adults. Obes Res. 2003;11:1223–1231
  5. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2004;27(suppl 1):S5–S10
  6. Centers for Disease Control and Prevention. The Third National Health and Nutrition Examination Survey (NHANES III 1988–94) Reference Manuals and Reports. Bethesda: National Center for Health Statistics; 1996;(CD-ROM)
  7. National Institutes of Health. Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: the Evidence Report. Bethesda: National Institutes of Health, United States Department of Health and Human Services; 1998;
  8. Pei D, Jones CN, Bhargava R, Chen YD, Reaven GM. Evaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test. Diabetologia. 1994;37:843–845
  9. Shen SW, Reaven GM, Farquhar JW. Comparison of impedance to insulin-mediated glucose uptake in normal and diabetic subjects. J Clin Invest. 1970;49:2151–2160
  10. Greenfield MS, Doberne L, Kraemer F, Tobey TA, Reaven GM. Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp. Diabetes. 1981;30:387–392
  11. Pyorala K. Relationship of glucose tolerance and plasma insulin to the incidence of coronary heart disease: results from two population studies in Finland. Diabetes Care. 1979;2:131–141
  12. Castelli WP, Garrison RJ, Wilson PWF, Abbott RO, Kalonsdian S, Kannel WB. Incidence of coronary heart disease and lipoprotein cholesterol levels: the Framingham Study. JAMA. 1986;256:2385–2387
  13. Austin MA, Hokanson JE, Edwards KI. Hypertriglyceridemia as a cardiovascular risk factor. Am J Cardiol. 1998;81(suppl):7B–12B
  14. Facchini FS, Hua N, Abbasi F, Reaven GM. Insulin resistance as a predictor of age-related diseases. J Clin Endocrinol Metab. 2001;86:3574–3578
  15. Kissebah AH, Vydelingum N, Murray R, Evans DJ, Hartz AJ, Kalkhoff RK, et al. Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab. 1982;54:254–260
  16. Sparrow D, Borkan GA, Gerzof SG, Wisniewski C, Silbert CK. Relationship of fat distribution to glucose tolerance: results of computed tomography in male participants of the Normative Aging Study. Diabetes. 1986;35:411–415
  17. Bjorntorp P. Adipose distribution, plasma insulin, and cardiovascular disease. Diabete Metab. 1987;13:381–385
  18. Despres JP, Nadeau A, Tremblay A, Ferland M, Lupien PJ. Role of deep abdominal fat in the association between regional adipose tissue distribution and glucose tolerance in obese women. Diabetes. 1989;38:304–309
  19. Pouliot MC, Despres JP, Nadeau A, Moorjani S, Prud’homme D, Lupien PJ, et al. Visceral obesity in men: associations with glucose tolerance, plasma insulin, and lipoprotein levels. Diabetes. 1992;41:826–834
  20. Kissebah AH, Krakower GR. Regional adiposity and morbidity. Physiol Rev. 1994;74:761–811
  21. Gautier JF, Milner MR, Elam E, Chen K, Ravussin E, Pratley RE. Visceral adipose tissue is not increased in Pima Indians compared with equally obese Caucasians and is not related to insulin action or secretion. Diabetologia. 1999;42:28–34
  22. Tulloch-Reid MK, Williams DE, Looker HC, Hanson RL, Knowler WC. Do measures of body fat distribution provide information on the risk of type 2 diabetes in addition to measures of general obesity?. Diabetes Care. 2003;26:2556–2561
  23. Haffner SM, Stern MP, Mitchell BD, Hazuda HP, Patterson JK. Incidence of type II diabetes in Mexican Americans predicted by fasting insulin and glucose levels, obesity, and body fat distribution. Diabetes. 1990;39:283–299
  24. Wang Y, Rimm EB, Stampfer MJ, Willett WC, Hu FB. Comparison of abdominal adiposity and overall obesity in predicting risk of type 2 diabetes among men. Am J Clin Nutr. 2005;81:555–563
  25. 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;86:251–260
  26. Folsom AR, Li Y, Pao X, Cen R, Zhang K, Lin X, et al. Body mass, fat distribution and cardiovascular risk factors in a lean population of south China. J Clin Epidemiol. 1994;47:173–181
  27. Sakurai Y, Kono S, Shinchi K, Honjo S, Todoroki I, Wakabayashi K, et al. Relation of waist-hip ratio to glucose tolerance, blood pressure, and serum lipids in middle-aged Japanese males. Int J Obes. 1995;19:632–637
  28. Takami R, Takeda N, Hayashi M, Sasaki A, Kawachi S, Yoshino K, et al. Body fatness and fat distribution as predictors of metabolic abnormalities and early carotid atherosclerosis. Diabetes Care. 2001;24:1248–1252
  29. Schmidt MI, Duncan BB, Watson RL, Sharrett AR, Brancati FL, Heiss G. A metabolic syndrome in whites and African-Americans. Diabetes Care. 1996;19:414–418
  30. Wang J, Thornton JC, Bari S, Williamson B, Gallagher D, Heymsfield SB, et al. Comparisons of waist circumferences measured at 4 sites. Am J Clin Nutr. 2003;77:379–384

 This study was supported by Grant 5 T32 HL07708 from the National Institutes of Health, Bethesda, Maryland, and Stanford Vascular Biology and Medicine Training, Stanford, California; and by Grant RR-00070 from the National Institutes of Health, Bethesda, Maryland.

PII: S0002-9149(06)01281-1

doi:10.1016/j.amjcard.2006.05.025

American Journal of Cardiology
Volume 98, Issue 8 , Pages 1053-1056, 15 October 2006

Article Tools