Impact of Insulin Resistance on Risk of Type 2 Diabetes and Cardiovascular Disease in People With Metabolic Syndrome

  1. James B. Meigs, MD, MPH1,
  2. Martin K. Rutter, MD23,
  3. Lisa M. Sullivan, PHD4,
  4. Caroline S. Fox, MD, MPH5,
  5. Ralph B. D'Agostino, Sr., PHD6 and
  6. Peter W.F. Wilson, MD7
  1. 1Harvard Medical School and the General Medicine Division, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
  2. 2The Manchester Diabetes Centre, Manchester Royal Infirmary, Manchester, U.K.
  3. 3Division of Cardiovascular and Endocrine Sciences, School of Medicine, University of Manchester, Manchester, U.K.
  4. 4Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
  5. 5Division of Endocrinology, Metabolism, and Diabetes, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, and the National Heart, Lung, and Blood Institute's Framingham (Mass) Heart Study
  6. 6Department of Mathematics, Statistics, and Consulting Unit, Boston University, Boston, Massachusetts
  7. 7Emory University School of Medicine, Atlanta, Georgia
  1. Address correspondence and reprint requests to James B. Meigs, MD, MPH, General Medicine Division, Massachusetts General Hospital, 50 Staniford St., 9th Floor, Boston, MA 02114. E-mail: jmeigs{at}partners.org

Abstract

OBJECTIVE—Metabolic syndrome increases the risk for type 2 diabetes and cardiovascular disease (CVD) and may be associated with insulin resistance.

RESEARCH DESIGN AND METHODS—We tested the hypothesis that the metabolic syndrome confers risk with or without concomitant insulin resistance among 2,803 Framingham Offspring Study subjects followed up to 11 years for new diabetes (135 cases) or CVD (240 cases). We classified subjects by presence of metabolic syndrome (using the National Cholesterol Education Program's [NCEPs] Third Adult Treatment Panel [ATP III], International Diabetes Federation [IDF], or European Group for the Study of Insulin Resistance [EGIR] criteria) and insulin resistance (homeostasis model assessment of insulin resistance ≥75th percentile) and used separate risk factor–adjusted proportional hazards models to estimate relative risks (RRs) for diabetes or CVD using as referents those without insulin resistance, metabolic syndrome, or without both.

RESULTS—Fifty-six percent of individuals with ATP III, 52% with IDF, and 100% with EGIR definitions of metabolic syndrome had insulin resistance. Insulin resistance increased risk for diabetes (RR 2.6 [95% CI 1.7–4.0]) and CVD (1.8 [1.4–2.3]) as did metabolic syndrome for diabetes (ATP III, 3.5 [2.2–5.6]; IDF, 4.6 [2.7–7.7]; and EGIR, 3.3 [2.1–5.1]) and CVD (ATP III, 1.8 [1.4–2.3]; IDF, 1.7 [1.3–2.3]; and EGIR, 2.1 [1.6–2.7]). Relative to those without either metabolic syndrome or insulin resistance, metabolic syndrome and insulin resistance increased risk for diabetes (ATP III, 6.0 [3.3–10.8] and IDF, 6.9 [3.7–13.0]) and CVD (ATP III, 2.3 [1.7–3.1] and IDF, 2.2 [1.6–3.0]). Any instance of metabolic syndrome without insulin resistance increased risk for diabetes approximately threefold (P < 0.001); IDF metabolic syndrome without insulin resistance (RR 1.6, P = 0.01), but not ATP III metabolic syndrome without insulin resistance (RR 1.3, P = 0.2), increased risk for CVD.

CONCLUSIONS—Metabolic syndrome increased risk for diabetes regardless of insulin resistance. Metabolic syndrome by ATP III criteria may require insulin resistance to increase risk for CVD. The simultaneous presence of metabolic syndrome and insulin resistance identifies an especially high-risk individual.

People with the cluster of risk factors including obesity, impaired fasting glucose (IFG), hypertension, low HDL cholesterol, and elevated triglycerides are thought to have the “metabolic syndrome,” reflecting underlying insulin resistance. Both the metabolic syndrome and insulin resistance are factors in the development of type 2 diabetes and cardiovascular disease (CVD) (1). Several competing definitions of metabolic syndrome are in use, and each is differently linked to the presence of insulin resistance. These definitions include that of the National Cholesterol Education Program (NCEP) Third Adult Treatment Panel (ATP III) (2), the International Diabetes Federation (IDF) (3), and the European Group for the Study of Insulin Resistance (EGIR) (4). The EGIR definition requires the presence of insulin resistance plus any two other metabolic traits; ATP III and IDF definitions require at least three metabolic traits but do not require the presence of insulin resistance. In studies of ATP III metabolic syndrome, as many as half of subjects do not have insulin resistance (57).

There are few population-based data comparing how well the ATP III or IDF metabolic syndrome definitions identify subjects with insulin resistance (8) or comparing how well ATP III, IDF, or EGIR metabolic syndrome definitions predict subsequent risk for incident diabetes (9,10) or CVD (1113). In addition, while it has been implied that the presence of the metabolic syndrome is a surrogate for the presence of insulin resistance, there are few data on diabetes or CVD risk associated with metabolic syndrome in the absence of insulin resistance or in the presence of both metabolic syndrome and insulin resistance. With this background in mind, we performed an analysis in the Framingham Offspring Study using three metabolic syndrome definitions in a test of the hypothesis that metabolic syndrome confers risk for subsequent development of diabetes or CVD with or without concomitant insulin resistance.

RESEARCH DESIGN AND METHODS—

The Framingham Offspring Study is a community-based prospective observational study of CVD and its risk factors (14). During the fifth exam cycle (the baseline exam, 1991–1995), 3,799 participants fasted overnight and had a standardized medical examination including a 2-h oral glucose tolerance test (OGTT). Of 3,799 participants, we excluded those with prevalent diabetes (n = 429), prevalent CVD (n = 269), or missing information on covariates (n = 298), which left 2,803 subjects for analysis. Subjects were followed from baseline over a mean of 6.8 years for new cases of diabetes and a mean of 11.6 years for first CVD events. The institutional review board of Boston University approved the study protocol, and all subjects gave written informed consent at each examination.

Clinical definitions and laboratory methods

We defined metabolic syndrome according to updated 2005 NCEP ATP III (2), IDF (3), and EGIR (4) criteria. Key features and differences among these three metabolic syndrome definitions are as follows: ATP III metabolic syndrome requires the presence of any three of five traits (a large waist circumference, IFG, low HDL cholesterol, high triglycerides, or hypertension), IDF requires a large waist circumference plus any two of the preceding metabolic traits, and EGIR requires insulin resistance plus any two of the preceding traits with the exception that lipid traits are not counted separately (low HDL cholesterol or high triglycerides count as one trait). The other major differences are that a larger waist defines “large waist circumference” for ATP III (≥102 cm in men or ≥88 cm in women) than for IDF or EGIR (≥94 cm in white men or ≥80 cm in white women), and by current criteria, IFG is defined by a fasting plasma glucose (FPG) 5.6–6.9 mmol/l in the ATP III and IDF definitions and an FPG 6.1–6.9 mmol/l in the EGIR definition. We measured insulin resistance with the homeostasis model using the following validated formula: homeostasis model assessment of insulin resistance (HOMA-IR) = (fasting glucose [mmol/l] × fasting insulin [μU/ml])/22.5 (15,16). Values of HOMA-IR in each quarter were 2.21–5.07 units (quarter 1), 5.08–6.18 units (quarter 2), 6.19–7.84 units (quarter 3), and 7.85–30.80 units (quarter 4). We defined insulin resistance as a HOMA-IR value >75th percentile (7.84 units) among nondiabetic subjects (4). We measured height, weight, and waist circumference with the subject standing in light clothes. Waist circumference was measured at the level of the umbilicus. Pairwise intertechnician (three technicians) intraclass correlations that were performed periodically for waist circumference quality control ranged from 0.96 to 0.99. Blood pressure values were taken as the mean of two measurements after the subject had been seated for at least 5 min. Those who reported smoking cigarettes regularly during the year before the exam were considered current smokers. We based a positive parental history of diabetes on self-report of diabetes in one or both parents (17). We defined IGT as a 2-h OGTT glucose level 7.8–11.0 mmol/l. Laboratory methods for glucose, insulin, and lipid assays have been previously published (18).

Diabetes and CVD assessment

We defined diabetes at the baseline exam as FPG ≥7.0 mmol/l, 2-h OGTT glucose ≥11.1 mmol/l, or use of hypoglycemic drug therapy. We defined diabetes at follow-up as development of FPG ≥7.0 mmol/l or new use of hypoglycemic drug therapy during the study interval. Over 99% of diabetes among Framingham offspring is type 2 diabetes (18). We defined baseline and follow-up CVD by standard Framingham Heart Study criteria as any of the following: new onset angina, fatal and nonfatal myocardial infarction or stroke, transient ischemic attack, heart failure, or intermittent claudication (19).

Statistical analysis

We used χ2 tests or ANOVA to test differences in baseline characteristics by metabolic syndrome and insulin resistance categories. We used the κ statistic to assess the level of agreement between metabolic syndrome definitions, where poor agreement is considered κ < 0.20; fair, κ = 0.21–0.40; moderate, κ = 0.41–0.60; substantial, κ = 0.61–0.80; and very good agreement, κ > 0.80 (20). Subjects were followed from baseline through the seventh (1998–2001) exam for diabetes and through December 2004 for CVD events. Risk for diabetes or CVD was examined in separate analyses. We calculated incidence rates for diabetes or CVD as the number of diabetes or CVD events divided by person-years of follow-up in each category. For diabetes incidence, we used the exam visit date on which a new case of diabetes was identified as the date of diagnosis. For CVD events, we used the actual date of the event as the date of diagnosis and, for subjects without events, the date of their last follow-up exam as the censoring date. We used hazard ratios from proportional hazards regression models (accounting for interval censoring for diabetes events) to estimate relative risks (RRs) and 95% CIs for incident diabetes or CVD conditioned on baseline metabolic syndrome or insulin resistance categories. Models were adjusted for age and sex and used those with no metabolic syndrome or insulin resistance as the referent groups. Multivariable models were adjusted for major disease risk factors beyond those comprising the metabolic syndrome. Models predicting incident diabetes included covariates for age, sex, parental history of diabetes, BMI (calculated as weight in kilograms divided by the square of the height in meters), and IGT. Multivariable models predicting incident CVD included covariates for age, sex, LDL cholesterol level, and smoking. First-order sex–by–metabolic syndrome interaction terms were not significant (in part because there were too few events in some subgroups to calculate stable sex-specific risk estimates); thus, we did not conduct sex-specific analyses. For instance, even in the sex-combined analysis, in the smallest groups (no metabolic syndrome but with insulin resistance), we had only 30–40% power to detect a difference in the observed proportions at α = 0.05. We used areas under the receiver operating characteristic curve (AROC) to compare the ability of metabolic syndrome and/or insulin resistance to discriminate future diabetes or CVD risk. The AROC is interpreted as the probability that the modeled phenotype(s) correctly discriminates subjects developing end points from those without end points, where 0.5 is chance discrimination and 1.0 is perfect discrimination. We estimated the population attributable risk (PAR) percent for diabetes or CVD associated with exposure categories (for instance, the ATP III metabolic syndrome and insulin resistance) as PAR percent = {proportion of cases in the exposure category × [(relative riskexposure category − 1)/relative riskexposure category]} × 100 (20). We performed all analyses using SAS (SAS Institute, Cary, NC) and considered a two-sided value of P < 0.05 to be statistically significant.

RESULTS—

The mean age of the study population overall was 54 years (range 26–82), and 55% were women. Baseline characteristics of study subjects are shown in Table 1. Diabetes and CVD risk factor levels were generally more adverse among people with insulin resistance than those without insulin resistance and most adverse among those with metabolic syndrome and insulin resistance. Among 2,803 people, the prevalence of ATP III metabolic syndrome was 27.8%, of IDF metabolic syndrome 34.2%, and of EGIR metabolic syndrome 19.1%. By definition, the prevalence of insulin resistance was 25%. Among those with ATP III metabolic syndrome, the prevalence of insulin resistance was 56.4%, among those with IDF metabolic syndrome, 52%, and, by definition, 100% among those with EGIR metabolic syndrome. The prevalence of insulin resistance in those without ATP III metabolic syndrome was 12.8% and among those without IDF metabolic syndrome 11.0%. There was substantial agreement in metabolic syndrome classification by ATP III versus IDF criteria (κ statistic 0.77 overall; women, 0.81; men, 0.71) and moderate agreement between IDF and EGIR or ATP III criteria (IDF vs. EGIR, κ statistic 0.50; EGIR vs. ATP III, κ statistic 0.53).

Incidence rates for diabetes stratified by the presence or absence of metabolic syndrome and insulin resistance were generally similar for all three metabolic syndrome definitions (Fig. 1). The diabetes incidence rates were dramatically higher for those with both metabolic syndrome and insulin resistance compared with the other categories. Similar relationships were apparent for the incidence of CVD.

Regression models confirmed that all three metabolic syndrome definitions conferred generally similar risk for incident diabetes (Table 2). Of the three, IDF metabolic syndrome was associated with a perhaps slightly higher age–to–sex–to–risk factor–adjusted RR for diabetes (4.6) than was ATP III metabolic syndrome (3.5) or EGIR metabolic syndrome (3.3); all had a somewhat higher RRs for diabetes than did insulin resistance (2.6). In fully adjusted models, insulin resistance without metabolic syndrome (by any definition) was not associated with a significantly increased risk for diabetes, but this was the most uncommon subgroup contributing the fewest events (Fig. 1). Metabolic syndrome without insulin resistance was associated with a significant approximate threefold increased risk. ATP III or IDF metabolic syndrome and insulin resistance were associated with a six- to sevenfold increased risk for diabetes, consistent with an additive (on the log scale) effect of metabolic syndrome and insulin resistance on diabetes risk. In all models, first-order interaction terms for metabolic syndrome by insulin resistance were not significant (all P > 0.6), confirming that metabolic syndrome did not confer greater diabetes risk in the presence of insulin resistance than in the absence of insulin resistance. As we have previously shown that the number of metabolic syndrome–related traits is positively associated with risk for diabetes (1), it was perhaps not surprising that EGIR metabolic syndrome (which, although requires insulin resistance, is a sum of as few as three to as many as five traits) conferred a lower RR (3.3) than ATP III or IDF metabolic syndrome and insulin resistance (which, at minimum, represent a sum of as few as four and as many as six traits). To demonstrate this point, we conducted a subsidiary analysis of ATP III or IDF metabolic syndrome and insulin resistance but with metabolic syndrome defined by as any two or more component traits. In this analysis, RRs were very similar as for EGIR metabolic syndrome. For instance, the risk for diabetes relative to all subjects without ATP III metabolic syndrome (two or more traits) and without insulin resistance was 3.03 (95% CI 1.93–4.74). In fully adjusted models, all three metabolic syndrome definitions, insulin resistance, and their joint combinations were associated with similar discriminatory capacity for diabetes (adjusted AROCs 0.83–0.85), and metabolic syndrome with insulin resistance accounted for 42–66% of diabetes risk in the population (Table 2).

Also shown in Table 2 are RRs for CVD. As previously reported (1), adjusted RRs for CVD associated with metabolic syndrome were substantially lower than for diabetes (Table 2), ranging from 1.7 to 2.1. Insulin resistance conferred similar (1.8) risk for CVD as did the metabolic syndrome by any definition. In fully adjusted models, insulin resistance without metabolic syndrome did not confer significantly increased risk for CVD, and only IDF metabolic syndrome without insulin resistance significantly increased risk for CVD. As for diabetes, RR sizes were consistent with an additive effect of metabolic syndrome and insulin resistance on risk of CVD, and first-order interaction terms for metabolic syndrome by insulin resistance were not significant (all P > 0.3). In fully adjusted models, all metabolic syndrome definitions were associated with similar discriminatory capacity for CVD (adjusted AROCs 0.73–0.74), and metabolic syndrome and insulin resistance accounted for 14–23% of CVD risk in the population.

Additional subsidiary analyses

FPG is a component of the metabolic syndrome definitions, and it is also a component of HOMA-IR. This could potentially lead to overfitting FPG in statistical models that include both metabolic syndrome and HOMA-IR. We therefore used hyperinsulinemia (greater than the upper quartile of fasting serum insulin in subjects without diabetes) as an alternative measure of insulin resistance. The results of this analysis were virtually identical to those using HOMA-IR (not shown).

The increased CVD risk associated with metabolic syndrome might be explained, at least in part, by incident diabetes; therefore, we performed an analysis that excluded those who developed diabetes from the analysis of CVD events. This analysis yielded a modest (∼10–15% lower) attenuation of the RRs associated with metabolic syndrome and insulin resistance for incident diabetes and CVD, but the results of the analysis, including significance levels, were essentially unchanged (not shown).

The increased diabetes risk associated with metabolic syndrome could be largely explained by the IFG component of the definition. To explore this further, we excluded subjects with IFG from the analysis, and we used hyperinsulinemia as a measure of insulin resistance (rather than HOMA-IR) to remove fasting glucose as an exposure variable. In this analysis, ATP III and IDF metabolic syndrome remained significant predictors of incident diabetes in age- and sex-adjusted analyses (ATP III metabolic syndrome [no IFG] RR = 2.2, P = 0.02; IDF metabolic syndrome [no IFG] RR = 3.2, P = 0.0003), but neither remained significant predictors in multivariable-adjusted analyses (ATP III metabolic syndrome [no IFG] RR = 1.1, P = 0.9; IDF metabolic syndrome [no IFG] RR = 1.9, P = 0.06). Compared with the referent group, hyperinsulinemic subjects with ATP III (no IFG) or IDF (no IFG) metabolic syndrome were at increased risk for incident diabetes in age- and sex-adjusted analyses (ATP III metabolic syndrome [no IFG] and hyperinsulinemia RR = 4.2, P = 0.0003; IDF metabolic syndrome [no IFG] and hyperinsulinemia RR = 6.0, P < 0.0001) but only IDF metabolic syndrome (no IFG)/hyperinsulinemic subjects remained at increased risk in multivariable-adjusted models (ATP III metabolic syndrome [no IFG] and hyperinsulinemia RR = 1.7, P = 0.3; IDF metabolic syndrome [no IFG] and hyperinsulinemia RR = 3.1, P < 0.01). This analysis suggests that the covariates in the multivariate models, parental history of diabetes, and IGT largely accounted for the association of nonglucose metabolic syndrome traits with risk of diabetes.

CONCLUSIONS—

This study provides several insights. First, the level of agreement among ATP III, IDF, and EGIR metabolic syndrome definitions is moderate or better, but only half of individuals with ATP III or IDF metabolic syndrome are insulin resistant defined by the conventional top quartile of the HOMA-IR distribution. Second, individuals with ATP III or IDF metabolic syndrome but without insulin resistance are at increased risk for diabetes, and those with IDF metabolic syndrome but without insulin resistance are at increased risk for CVD; however, the joint presence of insulin resistance and metabolic syndrome indicates substantially increased risk for diabetes or CVD. Lastly, all three metabolic syndrome definitions, insulin resistance, and their joint combinations are associated with similar discriminatory capacity and PAR percent for incident diabetes or CVD. The data support the general concept of risk factor clustering as a diabetes and CVD risk factor and suggest that adding measurement of insulin resistance helps to identify increased risk in individuals with metabolic syndrome. On a population level, diagnosis of risk factor clustering with or without insulin resistance leads to equivalent ability to sort groups into higher- and lower-risk categories.

Our data confirm those of other population studies showing a higher prevalence of IDF metabolic syndrome compared with ATP III metabolic syndrome (2226) largely due to lower thresholds for elevated waist circumference in IDF versus ATP III metabolic syndrome. Few studies have examined the prevalence of EGIR metabolic syndrome (27). Its relatively low prevalence in Framingham offspring is accounted for by the requirement for insulin resistance and higher thresholds defining elevated glucose, triglycerides, and blood pressure. A relatively high level of agreement between ATP III and IDF metabolic syndrome definitions is not surprising since both definitions share common risk factors defined by generally similar thresholds. Our data also confirm other studies showing that only about half of people with metabolic syndrome have evidence of insulin resistance (5,6,8). In a prior analysis from Framingham, we showed that even among obese subjects with ATP III metabolic syndrome, the prevalence of insulin resistance was only 68% (7). This present study extends these data, and we show that the widely promoted ATP III and IDF metabolic syndrome definitions are not, as commonly stated (2,3), synonymous with insulin resistance. Only the EGIR definition, which requires the presence of insulin resistance, represents a true “insulin resistance syndrome.”

Several groups have promoted different definitions of metabolic syndrome, all of which are assumed to represent an insulin resistance syndrome. In this context, the present study adds data to the literature by comparing the performance of the most widely promoted definitions with and without concomitant insulin resistance. Prior data from Mexican-American, white, black, and Chinese samples suggest that ATP III and IDF metabolic syndrome definitions confer roughly equivalent risk for diabetes (9,10), an observation that we confirm in a large, unselected community-based white sample. We extend this observation to show that metabolic syndrome alone, but perhaps not the uncommon insulin resistance alone, increases risk for diabetes and that metabolic syndrome and insulin resistance have additive effects increasing diabetes risk. Data from Pima Indians also show that independent physiologic domains derived from factor analysis have additive effects on diabetes risk (28). The present data suggest that the presence of insulin resistance in addition to metabolic syndrome adds to diabetes risk in individuals. However, in the absence of metabolic syndrome, we did not find insulin resistance to be a significant diabetes risk factor. However, the insulin-resistance effect was in the direction of increased risk, and the additive pattern of risk in the group with metabolic syndrome and insulin resistance suggests that low power in the group with metabolic syndrome but without insulin resistance accounted in part for the statistically nonsignificant association with diabetes. In addition, a nonsignificant effect in this group might be explained by imprecision in the true estimation of insulin resistance inherent in use of proxy measures or the possibility that metabolic correlates of insulin resistance confer much of its diabetogenic effect (9). However, metabolic syndrome is a risk factor for diabetes, even in the absence of insulin resistance, and the clearly additive effects of metabolic syndrome and insulin resistance on diabetes risk suggest that both are independent determinants of diabetes and may operate by at least partially distinct pathways.

The foregoing discussion concerning risk for diabetes also applies to risk for CVD. In addition, the present study underscores that, by any definition, and regardless of the presence or absence of insulin resistance, metabolic syndrome is a far more powerful risk factor for diabetes than for CVD (1). Several studies have examined the risk for CVD associated with metabolic syndrome by various definitions, and they have found that all definitions of metabolic syndrome are generally associated with a twofold increased RR for new CVD (1013,26). One can conclude that none of the competing metabolic syndrome definitions provides a distinct advantage over the others as a CVD risk prediction tool. We previously published an analysis of Framingham data demonstrating that ATP III metabolic syndrome and insulin resistance were independently associated with incident CVD over 7 years of follow-up (29). The present study advances that analysis by extending surveillance up to 11 years, dichotomizing metabolic syndrome and insulin resistance into clinically identifiable groups and demonstrating a clear additive effect of metabolic syndrome and insulin resistance on both diabetes and CVD risk. Elsewhere (30), we have advocated the measurement of insulin resistance as part of metabolic syndrome to render it a true insulin resistance syndrome as in the EGIR definition. The present study suggests that including insulin resistance as a component of risk factor clustering might have value on the individual clinical level in people with metabolic syndrome but is probably unnecessary to further discriminate high-risk groups on the population level.

Strengths of this study include a large, prospectively evaluated, community-based sample assessed for standardized exposures and outcomes. There are limitations to this study including that we did not have adequate sample size to subdivide the sample by sex. We did not directly measure insulin resistance; use of proxy measures will misclassify some people and diminish the true magnitude of associations of insulin resistance with outcomes. Finally, the Framingham population is white, so findings may have limited generalizability.

In summary, prospective analysis demonstrates that ATP III and IDF metabolic syndrome are not synonymous with an insulin-resistant phenotype. The presence of insulin resistance substantially increases individual diabetes or CVD risk in people with metabolic syndrome, but population risk prediction using metabolic syndrome is similar with or without concurrent insulin resistance. A clinical trial may be needed to test whether clinical use of insulin resistance adds value to care aimed at reducing individual metabolic risk. No one metabolic syndrome definition offers a clear advantage for diabetes or CVD risk detection. As such, consensus on a definition for risk factor clustering would be helpful and, regardless of how defined, risk factor clustering identifies individuals and groups at marked increased risk for future diabetes and modest risk for future CVD.

Figure 1—

Annualized incidence rates of type 2 diabetes (DM; left panel) or CVD (right panel) by ATP III (□), IDF (▪), or EGIR (Graphic) metabolic syndrome (MetS) with or without insulin resistance (IR). Number of type 2 diabetes or CVD events are given below each category. *By definition, all subjects with EGIR metabolic syndrome had insulin resistance.

Table 1—

Baseline characteristics according to ATP III metabolic syndrome and insulin resistance category

Table 2—

RRs for incident type 2 diabetes or CVD by ATP III, IDF, or EGIR metabolic syndrome and/or insulin resistance category

Acknowledgments

This study was supported by the National Heart, Lung, and Blood Institute's Framingham Heart Study (contract no. N01-HC-25195), a grant from GlaxoSmithKline, and by an American Diabetes Association Career Development Award to J.B.M. The funding agencies had no influence over the decision to publish the findings.

The authors thank Peter Shrader, MS, for assistance with the statistical analyses.

Footnotes

  • Published ahead of print at http://care.diabetesjournals.org on 26 January 2007. DOI: 10.2337/dc06-2484.

    J.B.M. currently has research grants from GlaxoSmithKline, Wyeth, and sanofi-aventis and serves on safety or advisory boards for GlaxoSmithKline, Merck, and Eli Lilly.

    P.W.F.W. is supported by grants from GlaxoSmithKline and Wyeth.

    A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

    The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C Section 1734 solely to indicate this fact.

    • Accepted January 18, 2007.
    • Received December 7, 2006.

References

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  1. Diabetes Care vol. 30 no. 5 1219-1225
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