0
We're unable to sign you in at this time. Please try again in a few minutes.
Retry
We were able to sign you in, but your subscription(s) could not be found. Please try again in a few minutes.
Retry
There may be a problem with your account. Please contact the AMA Service Center to resolve this issue.
Contact the AMA Service Center:
Telephone: 1 (800) 262-2350 or 1 (312) 670-7827  *   Email: subscriptions@jamanetwork.com
Error Message ......
Original Investigation |

Heterogeneity in the Prevalence of Risk Factors for Cardiovascular Disease and Type 2 Diabetes Mellitus in Obese Individuals:  Effect of Differences in Insulin Sensitivity FREE

Tracey McLaughlin, MD; Fahim Abbasi, MD; Cindy Lamendola, RN; Gerald Reaven, MD
[+] Author Affiliations

Author Affiliations: Divisions of Endocrinology (Dr McLaughlin) and Cardiovascular Medicine (Drs Abbasi and Reaven and Ms Lamendola), Stanford University School of Medicine, Stanford, Calif.


Arch Intern Med. 2007;167(7):642-648. doi:10.1001/archinte.167.7.642.
Text Size: A A A
Published online

Background  The possibility that substantial heterogeneity in metabolic abnormalities exists in moderately obese individuals has not been emphasized in studies of the effect of obesity on morbidity and mortality. We tested the hypothesis that risk factors for type 2 diabetes mellitus and cardiovascular disease vary dramatically in moderately obese individuals as a function of differences in a specific measure of insulin sensitivity.

Methods  Participants included 211 apparently healthy, obese (body mass index [calculated as weight in kilograms divided by height in meters squared], 30.0-34.9) volunteers for weight loss studies. Main outcome measures included insulin-mediated glucose uptake as quantified by the insulin suppression test and metabolic variables known to increase the risk for type 2 diabetes and cardiovascular disease.

Results  Insulin sensitivity varied 6-fold. When compared with the most insulin-sensitive third, the most insulin-resistant third of the population had significantly higher (P<.001) systolic and diastolic blood pressure (139 ± 20 vs 123 ± 18 mm Hg, and 83 ± 3 vs 75 ± 10 mm Hg, respectively), higher fasting and 2-hour oral glucose load concentrations (103 ± 11 vs 95 ± 11 mg/dL [5.7 ± 0.6 vs 5.3 ± 0.6 mmol/L], and 139 ± 30 vs 104 ± 19 mg/dL [7.7 ± 1.7 vs 5.8 ± 1.1 mmol/L], respectively), higher plasma triglyceride concentrations (198 ± 105 vs 114 ± 51 mg/dL [2.2 ± 1.2 vs 1.3 ± 0.6 mmol/L]), lower plasma high-density lipoprotein cholesterol concentrations (41 ± 9 vs 50 ± 13 mg/dL [1.1 ± 0.2 vs 1.3 ± 0.3 mmol/L]), and more prevalent impaired glucose tolerance (47% vs 2%).

Conclusions  The magnitude of risk factors for type 2 diabetes and cardiovascular disease varies markedly in moderately obese individuals as a function of differences in degree of insulin sensitivity. Because not all moderately obese individuals are at similar risk for developing type 2 diabetes and cardiovascular disease, intensive therapeutic interventions should be addressed to the insulin-resistant subset of this population.

The prevalence of obesity continues to increase in the United States,1,2 and both cross-sectional and prospective studies have demonstrated an association between obesity and mortality risk.37 The relationship between obesity and excess mortality is consistent with evidence810 that these individuals are at increased risk of essential hypertension, type 2 diabetes mellitus (DM2), and cardiovascular disease (CVD). In light of these findings, it is not surprising that a call to action has been issued to health care professionals to begin addressing the harmful effects of these dramatic changes in lifestyle.11

In contrast, recent evidence suggests that the increase in prevalence of overweight/obesity may not have as great a magnitude of effect on excess death as was feared.12 In this report, moderately obese participants (body mass index [BMI] [calculated as weight in kilograms divided by height in meters squared], 30.0-34.9) in the National Health and Nutrition Examination Surveys demonstrated only modestly increased mortality compared with individuals whose BMIs ranged from 18.5 to less than 25.0, with increased relative risk statistically significant in the first survey but not the second and third surveys.12 The authors suggested that the association between obesity and mortality may have decreased over time because of improvements in public health or medical care for obesity-related conditions. Indeed, analysis of trends in CVD risk factors shows decreases in the prevalence of hypercholesterolemia and high blood pressure and a stable prevalence of diabetes despite increasing prevalence of obesity.13

More recently, a meta-analysis of 250 153 subjects with established CVD, enrolled in 40 studies, found that neither total nor CVD mortality was increased in moderately obese (BMI, ≥30.0-34.9) compared with normal-weight individuals.14 The results did not change significantly when adjusted for a number of other potentially confounding variables, leading the authors to conclude that “rather than prove that obesity is harmless, our data suggest that alternative methods might be needed to better characterize individuals who truly have an excess of body fat, compared with those in whom BMI is raised because of preserved muscle mass.”14(p676)

There is no reason to doubt that improved medical treatment has decreased the adverse clinical impact of obesity, and we do not suggest that obesity is harmless. However, we submit that the relationship between obesity and morbidity and mortality is more complicated than depicted in these reports. Specifically, we suggest that insulin resistance, and not obesity per se, is the major contributor to clinical outcomes associated with obesity. The degree of insulin resistance and the concomitant presence of associated metabolic abnormalities vary among obese individuals,15 and insulin resistance and/or hyperinsulinemia is an independent predictor of DM2, essential hypertension, and CVD.1621 Thus, it can be argued that the risk of developing these clinical syndromes will be accentuated in those obese individuals who are also more insulin resistant.

Based on these considerations, we thought it would be useful to confirm, in a much larger population, our previous findings2226 in small numbers of moderately obese individuals, stratified into insulin-resistant and insulin-sensitive subgroups, that metabolic risk factors for DM2 and CVD were confined to insulin-resistant individuals. Consequently, we quantified insulin-mediated glucose uptake and risk factors for the development of DM2 and CVD in 211 consecutive individuals who volunteered for weight loss studies at Stanford University, Stanford, Calif.

The study was conducted in a cross-sectional manner. We compiled data from all individuals with a BMI of 30.0 to 34.9 who volunteered for various weight loss studies conducted consecutively by our laboratory. All volunteers were respondents to advertisements requesting “healthy, moderately overweight volunteers for weight loss study,” which were posted in the major newspapers serving the San Francisco Bay area of California and Stanford University. Volunteers were required to have had a stable weight for 3 months, to not be taking corticosteroids or other medications known to alter glucose metabolism, to be free of major medical diseases with the exception of hypertension, to have a fasting plasma glucose concentration of less than 126 mg/dL (<7.0 mmol/L), and to have discontinued medication therapy to lower lipid levels for at least 4 weeks before testing insulin resistance and lipid and lipoprotein values. Subjects taking antihypertensive drugs were allowed to continue to receive their usual medications.

Included in this analysis were 211 individuals who met these eligibility requirements and who completed a history and physical examination, an oral glucose tolerance test, and a quantitative test for insulin-mediated glucose uptake. An additional 25 subjects who completed only the oral glucose tolerance test were not included in the analysis. Of the 211 subjects, 147 began 1 of 3 weight loss studies, and 111 completed their assigned study. These results are reported separately.22,2426 All studies were approved by the Stanford Human Subjects Committee, and all subjects gave written, informed consent.

On the initial visit, after a 12-hour overnight fast, height and weight in light clothing were obtained, and the BMI was calculated. Blood was drawn for measurement of plasma glucose level,15 after which a 75-g glucose beverage was administered. Blood was redrawn 2 hours later for a second measurement of glucose concentration.

On a separate visit, typically within 2 weeks, after a 12-hour overnight fast, blood was drawn for the measurement of lipid and lipoprotein concentrations,15 after which insulin-mediated glucose uptake was quantified by a modification27 of the insulin suppression test as originally described and validated.28,29 Briefly, subjects underwent infusion for 180 minutes with octreotide acetate (0.27 μg/m2 per minute), insulin human (25 mU/m2 per minute), and glucose (240 mg/m2 per minute). Blood was drawn at 10-minute intervals from 150 to 180 minutes of the infusion to measure plasma glucose and insulin concentrations, and the mean of these 4 values was used as the steady-state plasma insulin and steady-state plasma glucose (SSPG) concentrations for each individual. Because steady-state plasma insulin concentrations are similar in all subjects during these tests (approximately 60 μIU/mL [416.7 pmol/L]), the SSPG concentration provides a load: higher SSPG concentrations indicate that the individual is more insulin resistant. To explore the variability of risk markers as a function of insulin resistance, individuals were divided into tertiles on the basis of their SSPG concentration. It should be emphasized that the mean SSPG concentrations and the range of the values in the 3 tertiles thus formed were almost identical to the same values in a previous study from our research group of 490 apparently healthy individuals.30 Furthermore, in prospective studies of much smaller numbers of individuals, our group has shown that adverse clinical outcomes occurred significantly more frequently in the third of the population that was most insulin resistant.31,32

The presence of obesity-associated clinical conditions was defined according to national guidelines. Hypertension was defined as systolic blood pressure of 140 mm Hg or greater or diastolic blood pressure of 90 mm Hg or greater per the Seventh Report of the Joint National Committee.33 Hypertriglyceridemia was defined as a fasting plasma triglyceride (TG) level of greater than 200 mg/dL (2.3 mmol/L) and a low high-density lipoprotein cholesterol concentration (HDL-C) of less than 40 mg/dL (1.0 mmol/L) for men or less than 50 mg/dL (1.3 mmol/L) for women as per the Adult Treatment Panel III guidelines.34 Impaired fasting glucose level was defined as a fasting plasma glucose level of at least 100 mg/dL (5.6 mmol/L) and impaired glucose tolerance (IGT) as a plasma glucose concentration of at least 140 mg/dL (7.8 mmol/L) and less than 200 mg/dL (11.1 mmol/L) 2 hours after a 75-g oral glucose challenge, per American Diabetes Association guidelines.35

Statistical analyses were performed with SAS software (version 9.3.2; SAS Institute, Carey, NC). Unless otherwise indicated, values are presented as mean ± SD. Nonnormally distributed variables (TG concentrations) were log-transformed for statistical analyses. Comparison of continuous variables among these 3 tertiles (according to SSPG concentration) was performed with analysis of variance, with BMI, age, and sex as covariates to adjust for potential confounding. Categorical variables were compared across the 3 groups with χ2 testing. The Tukey adjustment for multiple comparisons was used for these analyses. Estimation of trends across tertiles of SSPG concentration used a general linear model for continuous variables and the Cochran-Armitage test for categorical variables. For obesity-associated clinical conditions, odds ratios and 95% confidence intervals for each end point were calculated for the top tertile of insulin resistance (tertile 3) compared with the lowest tertile of insulin resistance (tertile 1). Odds ratios and P values were adjusted for age, BMI, and sex. Relationships between all continuous variables are presented as Pearson correlation coefficients. Partial correlation coefficients are presented for SSPG value as a potential independent predictor for each other variable. Multivariate models were constructed for each variable with SSPG and all other risk factors included, with the exception of 2 colinear variables (systolic and diastolic blood pressure and fasting and 2-hour glucose concentrations). P<.05 was considered statistically significant for all analyses.

The SSPG concentrations and demographic variables of the experimental population divided into tertiles on the basis of their differences in insulin action are given in Table 1. By selection, SSPG concentrations in the 3 tertiles did not overlap and varied substantially, with the mean SSPG concentration in tertile 3, the most insulin-resistant group, 3-fold higher than the average of the most insulin-sensitive group (tertile 1). Despite inclusion of subjects within a relatively narrow range, there was a trend toward increased BMI values across the tertiles (P = .04). Otherwise, there were no significant differences among the tertiles.

Table Graphic Jump LocationTable 1. Demographic Characteristics of Obese Individuals According to the Tertile of SSPG Concentration

Table 2 compares the blood pressure of the 3 groups and the values of metabolic variables known to increase the risk of CVD and DM2. When adjusted for differences in age, sex, and BMI, the values of every risk factor measured varied as a function of degree of insulin resistance, with the exception of total and low-density lipoprotein cholesterol concentrations. A comparison of individual tertiles showed all variables to differ in tertiles 3 vs 1, and most of the values in tertile 3 were also significantly different from those in tertile 2. Perhaps of greatest clinical relevance was the dramatic difference in IGT between tertile 1 (2%) and tertile 3 (47%).

Table Graphic Jump LocationTable 2. Comparison of Cardiovascular and Diabetes Risk Factors in Obese Individuals According to the Tertile of SSPG Concentration*

Table 3 gives the odds ratio, unadjusted and adjusted for differences in age, sex, and BMI, of individuals in tertile 3 compared with tertile 1, of belonging to a diagnostic category at increased risk of developing CVD or DM2 as defined by the Seventh Report of the Joint National Committee,33 Adult Treatment Panel III,34 or American Diabetes Association.35 Similar to the results in Table 2, there was a trend toward greater risk as the SSPG concentration increased. Thus, subjects in tertile 2 had significantly greater risk of being hypertriglyceridemic and of having IGT than did those in tertile 1, whereas all 5 risk factors were significantly greater when those in tertile 3 were compared with individuals in tertile 1 (ranging from adjusted odds ratios of 3.0-54.8). The results of the univariate correlations between the experimental variables, shown in Table 4, indicate that every marker, with the exception of low-density lipoprotein cholesterol level, was significantly correlated with SSPG concentration. Body mass index, on the other hand, was significantly correlated only with fasting plasma glucose level. The relationship between SSPG concentration and each risk marker, with the exceptions of low-density lipoprotein cholesterol level, BMI, and fasting glucose level, remained statistically significant after adjustment for all other markers (partial correlation coefficients are given in Table 4).

Table Graphic Jump LocationTable 3. Rates of Adverse Clinical Outcomes Associated With Insulin Resistance in Tertiles 2 and 3 of SSPG Concentration Compared With Tertile 1
Table Graphic Jump LocationTable 4. Correlation Matrix Showing Relationships Among BMI, SSPG Concentration, and Other Cardiovascular Risk Markers: Univariate and Multivariate Analyses

Our cross-sectional analysis of 211 apparently healthy individuals with BMIs ranging from 30.0 to 34.9 demonstrates that a large degree of variability in both insulin resistance and related metabolic risk factors existed in the study population, despite the fact that every participant was obese. For example, SSPG concentrations varied 6-fold (from 55 to 230 mg/dL [3.1 to 12.8 mmol/L]), as did plasma TG concentrations (from 47 to 319 mg/dL [0.5 to 3.6 mmol/L]). Furthermore, as emphasized in Table 4, with the exception of the low-density cholesterol level, there was a significant correlation between SSPG concentrations and all of the CVD risk factors, even after adjustment for associated metabolic risk factors. Although insulin resistance (SSPG concentration) and obesity (BMI) were modestly related (r = 0.17), the only metabolic risk factor correlated with BMI was the fasting glucose concentration. Thus, at the simplest level, it is quite clear that there are considerable differences in insulin action and associated metabolic abnormalities within a group of apparently healthy, obese individuals, and that CVD and DM2 risk markers differ significantly as a function of insulin resistance.

The disparity in risk for DM2 and CVD of the 3 groups of equally obese individuals is also manifest when compared on the basis of categorical variables. According to conventionally accepted criteria, subjects in tertile 3 (vs those in tertile 1) were more likely to be hypertensive and dyslipidemic (high TG and low high-density lipoprotein cholesterol levels), and to have impaired fasting glucose levels and IGT. Thus, it appears that profound differences in the relative risk for DM2 and CVD exist within a population of apparently healthy, moderately obese individuals, and that they are related to the degree of insulin resistance.

Turning to the relationship between obesity and morbidity and mortality, the results in Tables 2 and 3 not only document the greatly increased risk of CVD and DM2 in the most insulin-resistant third of the moderately obese population, they also clearly indicate that values of the metabolic risk factors in the third of the obese group that was most insulin sensitive were not particularly abnormal in view of currently accepted guidelines.3335 The average blood pressure values in the insulin-sensitive third were within a normal, healthy range; they were not dyslipidemic on average; their mean plasma glucose concentration 120 minutes after the oral glucose challenge was 104 mg/dL (5.8 mmol/L); and only 2% had IGT.

These findings provide evidence suggesting that (1) the inability of Flegal et al12 and Romero-Corral and colleagues14 to document a powerful adverse effect of moderate obesity cannot be entirely explained by better health care and/or the inability of BMI to identify individuals at risk for CVD and DM2 and (2) consideration must also be given to the fact that profound differences in CVD and DM2 risk factors exist in all individuals who are classified as being moderately obese.

The fact that only a proportion of moderately obese individuals appears to be at increased risk for DM2 and CVD must be viewed in the context of realizing that it is only this subset that benefits significantly from weight loss.2226 Given the difficulty of achieving weight loss in overweight/obese individuals, it seems mandatory to identify those obese individuals who are at greatest risk for DM2 and CVD.36,37 In that context, we have presented evidence37 that the plasma concentration ratio of TG/high-density lipoprotein cholesterol is a good surrogate marker of insulin resistance, and a ratio of 3.0 or higher can help to identify moderately obese individuals who demonstrate both insulin resistance and the dyslipidemia characteristic of this defect in insulin action. Although this approach may not be useful in all races38—and it and all surrogate markers of insulin resistance should be validated in minority groups—it is an example of a means by which obese individuals at high risk may be targeted for intensive efforts for risk reduction to prevent DM2 and CVD. Furthermore, because the insulin-resistant subgroup of moderately obese individuals benefits significantly more from weight loss than does the insulin-sensitive subgroup or the subgroup of non–insulin-resistant but equally obese individuals,2226 identification of this high-risk subgroup takes on even more clinical relevance. It has been debated whether obesity might represent a useful addition to the Framingham Risk score.39 The results of our study suggest that perhaps a marker of insulin resistance, rather than obesity, might be a more appropriate addition to this tool for identifying high-risk individuals.

Our conclusions must be tempered by the following caveats. First, because our subjects were largely white and because we studied only moderately obese individuals, our results may not apply to individuals who are nonwhite or who are overweight (BMI, ≥25.0 and <29.9) or severely obese (BMI, ≥35.0). Furthermore, our population consisted of volunteers who responded to advertisements for weight loss studies, and may thus not reflect the general population of obese individuals. In addition, we excluded individuals with known disease; therefore, our findings may not apply to less healthy obese individuals. Perhaps of even greater importance is that this was a cross-sectional study, with risk factors for CVD and DM2, rather than disease, as the outcomes. Thus, despite the fact that IGT was present in only 2% of the most insulin-sensitive third of the population compared with 42% in the most insulin-resistant third, it does not necessarily mean that the rate of developing DM2 would differ in these 2 groups. Obviously, the answers to all of these issues depend on the results of future prospective studies performed in different ethnic groups that include appropriate phenotyping at baseline.

We have demonstrated that there is considerable disparity in insulin resistance and associated metabolic risk factors for CVD and DM2 in moderately obese, otherwise healthy individuals (BMI, 30.0-34.9). The fact that not all obese individuals are at equal risk of developing DM2 and CVD may contribute to recent findings of an adverse effect on morbidity and mortality that was less than expected in moderately obese individuals.12,14 To a large extent, the effects of obesity on the increasing risk of DM2 and CVD are primarily because being overweight/obese makes it more likely that a given individual will be insulin resistant. In light of the obesity epidemic, it is essential that we optimally stratify (according to risk) individuals in whom aggressive early intervention may prevent DM2 and CVD. Rather than limit our risk evaluation to the identification of obesity alone, we should focus our efforts on identifying high-risk, insulin-resistant, obese individuals.

Correspondence: Tracey McLaughlin, MD, Division of Endocrinology, Stanford University School of Medicine, Room S025, 300 Pasteur Dr, Stanford, CA 94305-5103 (tmclaugh@stanford.edu).

Accepted for Publication: September 8, 2006.

Author Contributions:Study concept and design: McLaughlin, Abbasi, and Reaven. Acquisition of data: McLaughlin, Abbasi, and Lamendola. Analysis and interpretation of data: McLaughlin and Reaven. Drafting of the manuscript: McLaughlin and Reaven. Critical revision of the manuscript for important intellectual content: McLaughlin, Lamendola, and Reaven. Statistical analysis: McLaughlin. Obtained funding: McLaughlin. Administrative, technical, and material support: McLaughlin, Abbasi, and Lamendola. Study supervision: McLaughlin, Abbasi, and Reaven.

Financial Disclosure: None reported.

Funding/Support: This study was supported in part by grants RR2HLL406 and RR00070 from the National Institutes of Health.

Flegal  KMCarroll  MDOgden  CLJohnson  CL Prevalence and trends in obesity among US adults, 1999-2000. JAMA 2002;2881723- 1727
PubMed Link to Article
Hedley  AAOgden  CLJohnson  CLCarroll  MDCurtin  LRFlegal  KM Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. JAMA 2004;2912847- 2850
PubMed Link to Article
Allison  DBFontaine  KRManson  JEStevens  JVanItallie  TB Annual deaths attributable to obesity in the United States. JAMA 1999;2821530- 1538
PubMed Link to Article
Fontaine  KRRedden  DTWang  CWestfall  AOAllison  DB Years of life lost due to obesity. JAMA 2003;289187- 193
PubMed Link to Article
Hu  FBWillett  WCLi  TStampfer  MJColditz  GAManson  JE Adiposity as compared with physical activity in predicting mortality among women. N Engl J Med 2004;3512694- 2703
PubMed Link to Article
Calle  EEThun  MJPetrelli  JMRodriguez  CHeath  CW Body-mass index and mortality in a prospective cohort of US adults. N Engl J Med 1999;3411097- 1105
PubMed Link to Article
Mokdad  AHMarks  JSStroup  DFGerberding  JL Actual causes of death in the United States, 2000. JAMA 2004;2911238- 1245[published correction appears in JAMA. 2005;293:298].
PubMed Link to Article
West  KMKalbfleisch  JM Influence of nutritional factors on prevalence of diabetes. Diabetes 1971;2099- 108
PubMed
Havlik  RJHubert  HBFabsitz  RRFeinleib  M Weight and hypertension. Ann Intern Med 1983;98855- 859
PubMed Link to Article
Rimm  EBStampfer  MJGiovannucci  E  et al.  Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men. Am J Epidemiol 1995;1411117- 1127
PubMed
Manson  JESkerrett  PJGreenland  PVanItallie  TB The escalating pandemics of obesity and sedentary lifestyle. Arch Intern Med 2004;164249- 258
PubMed Link to Article
Flegal  KMGraubard  BIWilliamson  DFGail  MH Excess deaths associated with underweight, overweight, and obesity. JAMA 2005;2931861- 1867
PubMed Link to Article
Gregg  EWCheng  YJCadwell  BL  et al.  Secular trends in cardiovascular disease risk factors according to body mass index in US adults. JAMA 2005;2931868- 1874
PubMed Link to Article
Romero-Corral  AMontori  VMSomers  VK  et al.  Association of body weight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet 2006;368666- 678
PubMed Link to Article
Abbasi  FBrown  BW  JrLamendola  CMcLaughlin  TReaven  GM Relationship between obesity, insulin resistance, and coronary heart disease risk. J Am Coll Cardiol 2002;40937- 943
PubMed Link to Article
Ferrannini  EBuzzigoli  GBonadona  R Insulin resistance in essential hypertension. N Engl J Med 1987;317350- 357
PubMed Link to Article
Reaven  GM Insulin resistance/compensatory hyperinsulinemia, essential hypertension, and cardiovascular disease. J Clin Endocrinol Metab 2003;882399- 2403
PubMed Link to Article
Lillioja  SMott  DMSpraul  M  et al.  Insulin resistance and insulin secretory dysfunction as precursors of non–insulin dependent diabetes mellitus. N Engl J Med 1993;3291988- 1992
PubMed Link to Article
Warram  JHMartin  BCKrowlewski  AS  et al.  Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the off-spring of the diabetic parents. Ann Intern Med 1990;113909- 915
PubMed Link to Article
Després  J-PLamarche  BMauriége  P  et al.  Hyperinsulinemia as an independent risk factor for ischemic heart disease. N Engl J Med 1996;334952- 957
PubMed Link to Article
Reaven  GM The insulin resistance syndrome. Curr Atheroscler Rep 2003;5364- 371
PubMed Link to Article
McLaughlin  TAbbasi  FKim  H-SLamendola  CSchaaf  PReaven  GM Relationship between insulin resistance, weight loss, and coronary heart disease risk in healthy, obese women. Metabolism 2001;50795- 800
PubMed Link to Article
McLaughlin  TAbbasi  FLamendola  C  et al.  Differentiation between obesity and insulin resistance in the association with C-reactive protein. Circulation 2002;1062908- 2912
PubMed Link to Article
McLaughlin  TStuhlinger  MLamendola  C  et al.  Plasma asymmetric dimethylarginine concentrations are elevated in obese insulin-resistant women and fall with weight loss. J Clin Endocrinol Metab 2006;911896- 1900
PubMed Link to Article
McLaughlin  TAbbasi  FCarantoni  MSchaaf  PReaven  G Differences in insulin resistance do not predict weight loss in response to hypocaloric diets in healthy obese women. J Clin Endocrinol Metab 1999;84578- 581
PubMed
McLaughlin  TCarter  SLamendola  C  et al.  Effects of moderate variations in macronutrient composition on weight loss and reduction in cardiovascular disease risk in obese, insulin-resistant adults. Am J Clin Nutr 2006;84813- 821
PubMed
Pei  DJones  CNOBhargava  RChen  Y-DIReaven  GM Evaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test. Diabetologia 1994;37843- 845
PubMed Link to Article
Shen  S-WReaven  GMFarquhar  JW Comparison of impedance to insulin mediated glucose uptake in normal and diabetic subjects. J Clin Invest 1970;492151- 2160
PubMed Link to Article
Greenfield  MSDoberne  LKraemer  FBTobey  TAReaven  GM Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp. Diabetes 1981;30387- 392
PubMed Link to Article
Yeni-Komshian  HCarantoni  MAbbasi  FReaven  GM Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy, nondiabetic volunteers. Diabetes Care 2000;23171- 175
PubMed Link to Article
Yip  JFacchini  FSReaven  GM Resistance to insulin-mediated glucose disposal as a predictor of cardiovascular disease. J Clin Endocrinol Metab 1998;832773- 2776
PubMed Link to Article
Facchini  FSHua  NAbbasi  FReaven  GM Insulin resistance as a predictor of age-related diseases. J Clin Endocrinol Metab 2001;863574- 3578
PubMed Link to Article
 The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.  Bethesda, Md US Dept of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute2003;NIH publication 03-5233
 Third Report of the National Cholesterol Education Program Expert Panel on Detection of High Blood Cholesterol in Adults (Adult Treatment Panel III) Executive Summary.  Bethesda, Md National Cholesterol Education Program, National Heart, Lung, and Blood Institute, National Institutes of Health2001;NIH publication 01-3670
Genuth  SAlberti  KGBennett  P  et al. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus, Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;263160- 3167
PubMed Link to Article
Reaven  GM Identifying the overweight patient who will benefit the most by losing weight. Ann Intern Med 2003;138420- 423
PubMed Link to Article
McLaughlin  TAbbasi  FCheal  KChu  JLamendola  CReaven  G Use of metabolic markers to identify overweight individuals who are insulin resistant. Ann Intern Med 2003;139802- 809
PubMed Link to Article
Reaven  GMcLaughlin  T Why the plasma triglyceride/high-density lipoprotein cholesterol concentration ratio does not predict insulin resistance in African Americans [letter]. Arch Intern Med 2006;166249
PubMed Link to Article
Wilson  PWD’Augustino  RBLevy  DBelanger  AMSilbershatz  HKannel  WB Prediction of coronary heart disease using risk factor categories. Circulation 1998;971837- 1847
PubMed Link to Article

Figures

Tables

Table Graphic Jump LocationTable 1. Demographic Characteristics of Obese Individuals According to the Tertile of SSPG Concentration
Table Graphic Jump LocationTable 2. Comparison of Cardiovascular and Diabetes Risk Factors in Obese Individuals According to the Tertile of SSPG Concentration*
Table Graphic Jump LocationTable 3. Rates of Adverse Clinical Outcomes Associated With Insulin Resistance in Tertiles 2 and 3 of SSPG Concentration Compared With Tertile 1
Table Graphic Jump LocationTable 4. Correlation Matrix Showing Relationships Among BMI, SSPG Concentration, and Other Cardiovascular Risk Markers: Univariate and Multivariate Analyses

References

Flegal  KMCarroll  MDOgden  CLJohnson  CL Prevalence and trends in obesity among US adults, 1999-2000. JAMA 2002;2881723- 1727
PubMed Link to Article
Hedley  AAOgden  CLJohnson  CLCarroll  MDCurtin  LRFlegal  KM Prevalence of overweight and obesity among US children, adolescents, and adults, 1999-2002. JAMA 2004;2912847- 2850
PubMed Link to Article
Allison  DBFontaine  KRManson  JEStevens  JVanItallie  TB Annual deaths attributable to obesity in the United States. JAMA 1999;2821530- 1538
PubMed Link to Article
Fontaine  KRRedden  DTWang  CWestfall  AOAllison  DB Years of life lost due to obesity. JAMA 2003;289187- 193
PubMed Link to Article
Hu  FBWillett  WCLi  TStampfer  MJColditz  GAManson  JE Adiposity as compared with physical activity in predicting mortality among women. N Engl J Med 2004;3512694- 2703
PubMed Link to Article
Calle  EEThun  MJPetrelli  JMRodriguez  CHeath  CW Body-mass index and mortality in a prospective cohort of US adults. N Engl J Med 1999;3411097- 1105
PubMed Link to Article
Mokdad  AHMarks  JSStroup  DFGerberding  JL Actual causes of death in the United States, 2000. JAMA 2004;2911238- 1245[published correction appears in JAMA. 2005;293:298].
PubMed Link to Article
West  KMKalbfleisch  JM Influence of nutritional factors on prevalence of diabetes. Diabetes 1971;2099- 108
PubMed
Havlik  RJHubert  HBFabsitz  RRFeinleib  M Weight and hypertension. Ann Intern Med 1983;98855- 859
PubMed Link to Article
Rimm  EBStampfer  MJGiovannucci  E  et al.  Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men. Am J Epidemiol 1995;1411117- 1127
PubMed
Manson  JESkerrett  PJGreenland  PVanItallie  TB The escalating pandemics of obesity and sedentary lifestyle. Arch Intern Med 2004;164249- 258
PubMed Link to Article
Flegal  KMGraubard  BIWilliamson  DFGail  MH Excess deaths associated with underweight, overweight, and obesity. JAMA 2005;2931861- 1867
PubMed Link to Article
Gregg  EWCheng  YJCadwell  BL  et al.  Secular trends in cardiovascular disease risk factors according to body mass index in US adults. JAMA 2005;2931868- 1874
PubMed Link to Article
Romero-Corral  AMontori  VMSomers  VK  et al.  Association of body weight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet 2006;368666- 678
PubMed Link to Article
Abbasi  FBrown  BW  JrLamendola  CMcLaughlin  TReaven  GM Relationship between obesity, insulin resistance, and coronary heart disease risk. J Am Coll Cardiol 2002;40937- 943
PubMed Link to Article
Ferrannini  EBuzzigoli  GBonadona  R Insulin resistance in essential hypertension. N Engl J Med 1987;317350- 357
PubMed Link to Article
Reaven  GM Insulin resistance/compensatory hyperinsulinemia, essential hypertension, and cardiovascular disease. J Clin Endocrinol Metab 2003;882399- 2403
PubMed Link to Article
Lillioja  SMott  DMSpraul  M  et al.  Insulin resistance and insulin secretory dysfunction as precursors of non–insulin dependent diabetes mellitus. N Engl J Med 1993;3291988- 1992
PubMed Link to Article
Warram  JHMartin  BCKrowlewski  AS  et al.  Slow glucose removal rate and hyperinsulinemia precede the development of type II diabetes in the off-spring of the diabetic parents. Ann Intern Med 1990;113909- 915
PubMed Link to Article
Després  J-PLamarche  BMauriége  P  et al.  Hyperinsulinemia as an independent risk factor for ischemic heart disease. N Engl J Med 1996;334952- 957
PubMed Link to Article
Reaven  GM The insulin resistance syndrome. Curr Atheroscler Rep 2003;5364- 371
PubMed Link to Article
McLaughlin  TAbbasi  FKim  H-SLamendola  CSchaaf  PReaven  GM Relationship between insulin resistance, weight loss, and coronary heart disease risk in healthy, obese women. Metabolism 2001;50795- 800
PubMed Link to Article
McLaughlin  TAbbasi  FLamendola  C  et al.  Differentiation between obesity and insulin resistance in the association with C-reactive protein. Circulation 2002;1062908- 2912
PubMed Link to Article
McLaughlin  TStuhlinger  MLamendola  C  et al.  Plasma asymmetric dimethylarginine concentrations are elevated in obese insulin-resistant women and fall with weight loss. J Clin Endocrinol Metab 2006;911896- 1900
PubMed Link to Article
McLaughlin  TAbbasi  FCarantoni  MSchaaf  PReaven  G Differences in insulin resistance do not predict weight loss in response to hypocaloric diets in healthy obese women. J Clin Endocrinol Metab 1999;84578- 581
PubMed
McLaughlin  TCarter  SLamendola  C  et al.  Effects of moderate variations in macronutrient composition on weight loss and reduction in cardiovascular disease risk in obese, insulin-resistant adults. Am J Clin Nutr 2006;84813- 821
PubMed
Pei  DJones  CNOBhargava  RChen  Y-DIReaven  GM Evaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test. Diabetologia 1994;37843- 845
PubMed Link to Article
Shen  S-WReaven  GMFarquhar  JW Comparison of impedance to insulin mediated glucose uptake in normal and diabetic subjects. J Clin Invest 1970;492151- 2160
PubMed Link to Article
Greenfield  MSDoberne  LKraemer  FBTobey  TAReaven  GM Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp. Diabetes 1981;30387- 392
PubMed Link to Article
Yeni-Komshian  HCarantoni  MAbbasi  FReaven  GM Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy, nondiabetic volunteers. Diabetes Care 2000;23171- 175
PubMed Link to Article
Yip  JFacchini  FSReaven  GM Resistance to insulin-mediated glucose disposal as a predictor of cardiovascular disease. J Clin Endocrinol Metab 1998;832773- 2776
PubMed Link to Article
Facchini  FSHua  NAbbasi  FReaven  GM Insulin resistance as a predictor of age-related diseases. J Clin Endocrinol Metab 2001;863574- 3578
PubMed Link to Article
 The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure.  Bethesda, Md US Dept of Health and Human Services, National Institutes of Health, National Heart, Lung, and Blood Institute2003;NIH publication 03-5233
 Third Report of the National Cholesterol Education Program Expert Panel on Detection of High Blood Cholesterol in Adults (Adult Treatment Panel III) Executive Summary.  Bethesda, Md National Cholesterol Education Program, National Heart, Lung, and Blood Institute, National Institutes of Health2001;NIH publication 01-3670
Genuth  SAlberti  KGBennett  P  et al. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus, Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 2003;263160- 3167
PubMed Link to Article
Reaven  GM Identifying the overweight patient who will benefit the most by losing weight. Ann Intern Med 2003;138420- 423
PubMed Link to Article
McLaughlin  TAbbasi  FCheal  KChu  JLamendola  CReaven  G Use of metabolic markers to identify overweight individuals who are insulin resistant. Ann Intern Med 2003;139802- 809
PubMed Link to Article
Reaven  GMcLaughlin  T Why the plasma triglyceride/high-density lipoprotein cholesterol concentration ratio does not predict insulin resistance in African Americans [letter]. Arch Intern Med 2006;166249
PubMed Link to Article
Wilson  PWD’Augustino  RBLevy  DBelanger  AMSilbershatz  HKannel  WB Prediction of coronary heart disease using risk factor categories. Circulation 1998;971837- 1847
PubMed Link to Article

Correspondence

CME
Meets CME requirements for:
Browse CME for all U.S. States
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
Submit a Comment

Multimedia

Some tools below are only available to our subscribers or users with an online account.

Web of Science® Times Cited: 65

Related Content

Customize your page view by dragging & repositioning the boxes below.

Articles Related By Topic
Related Collections
PubMed Articles