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The Journal of Clinical Endocrinology & Metabolism Vol. 90, No. 10 5698-5703
Copyright © 2005 by The Endocrine Society

The Metabolic Syndrome, Insulin Resistance, and Cardiovascular Risk in Diabetic and Nondiabetic Patients

Christoph H. Saely, Stefan Aczel, Thomas Marte, Peter Langer, Guenter Hoefle and Heinz Drexel

Vorarlberg Institute for Vascular Investigation and Treatment (C.H.S., S.A., T.M., P.L., H.D.), Feldkirch 6800, Austria; and Department of Medicine, Academic Teaching Hospital Feldkirch (C.H.S., S.A., T.M., G.H., H.D.), Feldkirch 6800, Austria

Address all correspondence and requests for reprints to: Dr. Heinz Drexel, Carinagasse 47, A-6800 Feldkirch, Austria. E-mail: vivit{at}lkhf.at.


   Abstract
Top
 Abstract
Introduction
 Subjects and Methods
 Results
 Discussion
 References
 
Context: The contribution of insulin resistance per se to the vascular risk conferred by the metabolic syndrome (MetS) is not known; conversely, it is uncertain whether insulin resistance confers vascular risk beyond the entity of the MetS.

Objective: The objective of this study was to investigate the impact of the MetS (Adult Treatment Panel III criteria) and insulin resistance (as estimated by the homeostasis model assessment index) on the incidence of vascular events.

Design and Patients: This was a prospective cohort study enrolling 750 consecutive patients undergoing coronary angiography for the evaluation of coronary artery disease.

Setting: The study was performed at a tertiary care clinical research center.

Main Outcome Measure: The main outcome measure was the incidence of vascular events over 2.3 yr.

Results: Both the MetS and insulin resistance predicted vascular events after controlling for non-MetS risk factors [hazard ratio (HR), 2.74 (95% confidence interval, 1.71–4.39; P < 0.001) and 1.51 (1.24–1.84; P < 0.001), respectively]. After additional adjustment for insulin resistance, the MetS remained significantly predictive of vascular events [HR, 2.69 (1.57–4.64); P < 0.001], and conversely, insulin resistance remained significantly predictive of vascular events despite adjustment for the MetS [standardized HR, 1.41 (1.14–1.75); P = 0.002]. Additional adjustment for the presence of type 2 diabetes revealed that both the MetS [adjusted HR, 2.57 (1.47–4.51); P = 0.001] and homeostasis model assessment of insulin resistance [standardized adjusted HR, 1.37 (1.09–1.73); P = 0.007] significantly predicted vascular events independent from diabetes status.

Conclusions: Both the MetS and insulin resistance are strong and mutually independent predictors of vascular risk among angiographed coronary patients.


   Introduction
Top
 Abstract
 Introduction
Subjects and Methods
 Results
 Discussion
 References
 
THE METABOLIC SYNDROME (MetS) encompasses a cluster of metabolic risk factors associated with an increased risk for type 2 diabetes mellitus (1, 2, 3) and cardiovascular disease (3, 4, 5, 6, 7, 8, 9, 10). In the National Cholesterol Education Program’s Adult Treatment Panel III report (ATP III) the MetS has been defined as the presence of three of five quantitatively defined markers: abdominal obesity, high triglycerides, low levels of high-density lipoprotein (HDL) cholesterol, high blood pressure, and elevated fasting glucose (11).

Insulin resistance is a key player in the pathophysiology of the MetS and has even been postulated as its underlying cause (12). In general, the stigmata of the MetS are significantly associated with insulin resistance (13, 14). However, because the ATP III definition of the MetS does not include a measurement of insulin resistance, patients with the MetS are not necessarily insulin resistant (13, 15). Cardiovascular risk increases in parallel to insulin resistance both among patients with diabetes (16) and among nondiabetic patients (17, 18). Despite the established role of insulin resistance in the pathophysiology of the MetS and as a predictor of cardiovascular disease, it is still uncertain to what extent insulin resistance per se accounts for the increased vascular risk conferred by the MetS as defined by ATP III criteria.

Data on the predictive power of the MetS in patients already affected by cardiovascular disease are scarce (19, 20). It has been demonstrated (20) that the MetS increases cardiovascular risk in women with coronary artery disease (CAD). However, the impact of the MetS on cardiovascular risk in the much larger population of men with established CAD remains unknown. Furthermore, there is a paucity of data about the prospective impact of the MetS in patients with diabetes. Therefore, we prospectively evaluated the impact of the MetS and insulin resistance on future vascular events among angiographed coronary patients enrolling in a large cohort that included men as well as women and both patients with and without type 2 diabetes mellitus.


   Subjects and Methods
Top
 Abstract
 Introduction
 Subjects and Methods
Results
 Discussion
 References
 
The design of this prospective cohort study has been described in detail previously (21). In brief, we enrolled 756 consecutive Caucasian patients referred to coronary angiography for routine evaluation of established or suspected CAD. Six patients with diabetes type 1 (C peptide negative) were excluded from the analyses. The ethics committee of the University of Innsbruck approved the present study, and all participants gave written informed consent.

At baseline, coronary angiography was performed with the Judkins technique. Stenoses of 50% or more were considered significant, and coronary arteries were defined as angiographically normal in the absence of any visible lumen narrowing at angiography, as described previously (22, 23).

From the 750 patients included in the analyses, 164 had diabetes mellitus according to World Health Organization (WHO) criteria (24), and 586 did not have diabetes. Among patients with diabetes, 42.7% were not receiving any antidiabetic medication, and 34.8%, 32.3%, 24.4%, and 1.2% were receiving, alone or in combination, sulfonylurea, biguanides, insulin, and {alpha}-glucosidase inhibitors, respectively. Overall, 64.7% of our patients were taking aspirin, 31.3% statins, 2.9% fibrates, 12.1% calcium antagonists, 47.1% ß-blocking agents, 36.0% angiotensin-converting enzyme inhibitors, and 3.9% angiotensin II-blocking agents. No patient was receiving thiazolidinediones.

According to ATP III criteria (11), the MetS was diagnosed in the presence of any three of the following: waist circumference greater than 102 cm in men and greater than 88 cm in women, triglycerides of 150 mg/dl (1.7 mmol/liter) or more, HDL cholesterol less than 40 mg/dl (1.0 mmol/liter) in men and less than 50 mg/dl (1.3 mmol/liter) in women, blood pressure of 130/85 mm Hg or higher, or fasting glucose of 110 mg/dl (6.1 mmol/liter) or higher. We defined a MetS score as the number of these traits in a given patient.

To measure insulin resistance, we used the homeostasis model assessment (HOMA) (25), which has been shown to be a reliable estimate of insulin resistance both among nondiabetic patients and patients with type 2 diabetes (26). Patients with diabetes who were receiving insulin treatment (n = 40) were excluded from the calculation of insulin resistance.

Prospective study

During a follow-up period of 2.3 ± 0.4 yr we recorded fatal and nonfatal cardiovascular end points, including coronary death (fatal myocardial infarction, sudden cardiac death, and mortality from congestive heart failure due to CAD), fatal ischemic stroke, nonfatal myocardial infarction, nonfatal ischemic stroke, and need for coronary artery bypass grafting, percutaneous coronary intervention, or noncoronary revascularization.

Statistical analysis

Differences in baseline characteristics were tested for statistical significance with the Mantel-Haenszel {chi}2 test for trend and the ordered Jonckheere-Terpstra test for categorical and continuous variables, respectively. The Wilcoxon-Gehan statistic was used to compare differences in the cumulative incidence rates of vascular events. Adjusted hazard ratios (HR) for the incidence of vascular events were derived from Cox proportional hazards models. For these calculations, continuous variables were z-transformed, and the trend over the categories of the MetS score was analyzed by introducing the MetS score as an ordinal variable in the Cox proportional hazards models. Results are given as the mean ± SD if not denoted otherwise. All statistical analyses were performed with the software package SPSS 12.0 for Windows (SPSS, Inc., Chicago, IL).


   Results
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
Discussion
 References
 
Prevalence of CAD and type 2 diabetes

The baseline demographic data of our 750 patients were characteristic for a cohort undergoing coronary angiography for the evaluation of CAD, with a mean age of 62.6 ± 10.4 yr, a preponderance of male gender (n = 509; 67.9%), and a high prevalence of type 2 diabetes mellitus (n = 164; 21.9%). Baseline angiography revealed CAD in 615 patients (82.0%), and 456 patients (60.8%) had significant coronary stenoses of 50% or more.

Quantitative aspects of MetS and insulin resistance

From the total study cohort, 280 patients (37.3%) had three or more MetS traits and therefore fulfilled the criteria for the MetS. Table 1Go summarizes the baseline characteristics of our patients with zero through five markers of the MetS. The prevalence of type 2 diabetes mellitus increased with an increasing MetS score from 0% in patients with no MetS risk factor through 57.1% in patients with five MetS risk factors (Ptrend < 0.001). BMI, the prevalence of angiographic CAD, and, concordant with ATP III criteria for the diagnosis of the MetS, waist circumference, triglycerides, and systolic as well as diastolic blood pressures significantly increased, and HDL cholesterol significantly decreased through the categories of the MetS score (Ptrend < 0.001 for all). Low-density lipoprotein (LDL) cholesterol and the LDL peak particle diameter decreased with an increasing MetS score. HOMA insulin resistance increased significantly (Ptrend < 0.001) with an increasing number of MetS risk factors (Fig. 1Go).


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  		TABLE 1. Baseline characteristics by MetS score

 


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  		FIG. 1. Insulin resistance and MetS score. The MetS score is defined as the number of metabolic syndrome traits (Ptrend = 0.001).

 
Overall, HOMA insulin resistance was higher among patients with type 2 diabetes than among nondiabetic patients (6.09 ± 4.96 vs. 2.78 ± 2.75; P < 0.001). The increase in HOMA insulin resistance from patients with zero through patients with five MetS traits was observed in patients with type 2 diabetes (HOMA insulin resistance, 3.57 ± 2.97, 2.50 ± 0.95, 4.24 ± 3.86, 6.55 ± 5.21, 6.92 ± 5.31, and 8.87 ± 4.96, respectively; Ptrend < 0.001) as well as in nondiabetic patients (HOMA insulin resistance, 1.62 ± 1.07, 1.94 ± 1.23, 2.90 ± 2.92, 3.18 ± 1.90, 4.66 ± 4.50, and 7.71 ± 5.46, respectively; Ptrend < 0.001). Serum fasting insulin levels in nondiabetic patients were strongly correlated to HOMA insulin resistance (r = 0.985; P < 0.001) and, like HOMA insulin resistance, significantly increased from values in nondiabetic patients with zero MetS traits through nondiabetic patients with five MetS traits (serum fasting insulin, 6.77 ± 4.19, 7.83 ± 4.66, 10.93 ± 9.24, 11.61 ± 6.30, 18.80 ± 23.73, and 25.00 ± 15.16 µU/ml, respectively; Ptrend < 0.001).

Incidence of vascular events

During a mean ± SD follow-up time of 2.3 ± 0.4 yr, we recorded 95 vascular end points (encompassing 26 coronary deaths, three fatal ischemic strokes, 10 nonfatal myocardial infarctions, 10 nonfatal ischemic strokes, 15 coronary artery bypass graftings, 14 percutaneous coronary interventions, and 17 revascularizations at the carotid or peripheral arteries). The incidence of vascular events was higher in men (n = 509) than in women (n = 241; 16.9% vs. 8.9%; P = 0.006), it was higher in patients with type 2 diabetes (n = 164) than in nondiabetic patients (n = 586; 21.9% vs. 12.2%; P = 0.003), and it was higher among patients with significant coronary artery stenoses of 50% or more at baseline (n = 456) than in patients without such lesions (n = 294; 20.4% vs. 4.7%; P < 0.001).

Event-free survival was significantly lower (P < 0.001) in patients with the MetS than in patients without the MetS (Fig. 2Go). In Cox regression analysis, adjusting for age, gender, smoking, BMI, and LDL cholesterol, the MetS proved independently predictive for the incidence of vascular events [adjusted HR, 2.74 (95% confidence interval, 1.71–4.39); P < 0.001].



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  		FIG. 2. Event-free survival in patients with and without the MetS. The survival curves show the incidence of vascular events in patients with the MetS and in those without the MetS (P < 0.001). Solid line, No MetS; broken line, MetS.

 
Subgroup analyses

Figure 3AGo depicts the results of important subgroup analyses. The MetS proved significantly predictive for vascular events among both men and women [adjusted HR, 2.45 (1.47–4.11; P = 0.001) and 5.03 (1.46–17.03; P = 0.010), respectively]. Furthermore, the MetS was significantly predictive for vascular events among patients with type 2 diabetes as well as among nondiabetic patients [adjusted HR, 4.51 (1.29–15.75; P = 0.018) and 2.37 (1.32–4.25; P = 0.004, respectively]. Among patients with significant coronary stenoses of 50% or more, the MetS was a significant predictor of vascular events [adjusted HR, 2.42 (1.48–3.97); P < 0.001]. The adjusted HR for patients without significant CAD [2.28 (0.50–10.34); P = 0.285] was not significantly different from the HR for those with significant CAD (for interaction of MetS x CAD, P = 0.982). However, because of the low absolute number of end points in patients without significant CAD, the confidence interval was wide. In subgroup analyses with respect to both gender and the presence of significant coronary stenoses of 50% or more at baseline, the MetS proved significantly predictive for vascular events among men with significant stenoses [n = 350; adjusted HR, 2.18 (1.28–3.72); P = 0.004]; among women with significant stenoses (n = 106), the adjusted HR was 3.95 (0.96–15.87; P = 0.053).



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  		FIG. 3. A, Adjusted HRs for the incidence of vascular events in patients with the MetS for the total cohort and for study subgroups. B, Adjusted HRs according to categories of the MetS score; the MetS score is defined as the number of MetS traits.

 
Increase in vascular risk with increasing number of MetS traits

The incidence of vascular events increased with an increasing MetS score. For patients with zero through five MetS risk factors, the respective incidence rates were 8.8, 12.0, 10.7, 14.1, 25.0, and 32.3% (Ptrend < 0.001). Compared with patients without any MetS risk factor, the HRs adjusted for non-MetS risk factors were 2.03 (0.68–6.06), 2.10 (0.69–6.38), 3.04 (0.99–9.33), 7.34 (2.47–21.85), and 14.37 (4.21–49.12)] for patients with one through five MetS risk factors (Fig. 3BGo; through the categories of the MetS score Ptrend < 0.001).

Independent effects of MetS and insulin resistance on the incidence of events

Like the MetS, HOMA insulin resistance proved significantly predictive of vascular events in our cohort of coronary patients [standardized HR controlled for age, gender, smoking, BMI, and LDL cholesterol, 1.51 (1.24–1.84); P < 0.001]. Interestingly, however, after additional adjustment for HOMA insulin resistance, the MetS remained significantly predictive for the incidence of vascular events, with an adjusted HR of 2.69 (1.57–4.64; P < 0.001). Conversely, insulin resistance added incremental prognostic value to the clinical diagnosis of the metabolic syndrome; in the regression model described above, it proved significantly (P = 0.002) predictive for the incidence of vascular events even after additional adjustment for the presence of the metabolic syndrome, with a standardized adjusted HR of 1.41 (1.14–1.75).

Additional adjustment for the presence of type 2 diabetes revealed that both the MetS [adjusted HR, 2.57 (1.47–4.51); P = 0.001] and HOMA insulin resistance [standardized adjusted HR, 1.37 (1.09–1.73); P = 0.007] significantly predicted vascular events independent from diabetes status. Consistently, the trend toward an increased incidence of vascular events with an increasing number of MetS traits remained significant after additional adjustment for HOMA insulin resistance and diabetes status (Ptrend < 0.001), with adjusted HRs of 1.53 (0.50–4.67), 1.02 (0.31–3.33), 2.03 (0.63–6.55), 4.45 (1.38–14.32), and 7.49 (1.97–28.49) for patients with one through five MetS traits.

In subgroup analyses with respect to diabetes status, HOMA insulin resistance proved significantly predictive of vascular events among patients with type 2 diabetes [standardized adjusted HR, 1.56 (1.18–2.06); P = 0.002]; among nondiabetic patients, the increase in vascular risk associated with HOMA insulin resistance was not significant [standardized adjusted HR, 1.30 (0.86–1.98); P = 0.210]. However, the impact of HOMA insulin resistance on the incidence of vascular events was not significantly different between patients with type 2 diabetes and nondiabetic patients (for interaction of HOMA insulin resistance x diabetes status, P = 0.383). When fasting insulin was used as a measure of insulin resistance among nondiabetic patients, the results were similar to those obtained with HOMA insulin resistance; the association between fasting insulin and the incidence of vascular events in nondiabetic patients did not reach statistical significance [standardized adjusted HR, 1.39 (0.93–1.93); P = 0.116], whereas the MetS remained significantly predictive of vascular events in nondiabetic patients after adjustment for fasting insulin [HR, 2.22 (1.12–4.41); P = 0.023].

Consistent with the results from the total study cohort, the MetS remained significantly predictive of vascular events after adjustment for HOMA insulin resistance in both the subgroup of patients with type 2 diabetes [adjusted HR, 4.05 (1.14–14.42); P = 0.031] and the subgroup of nondiabetic patients [adjusted HR, 2.31 (1.15–4.65); P = 0.019]. As for the total study cohort, HOMA insulin resistance conversely added incremental prognostic value to the clinical diagnosis of the MetS among patients with type 2 diabetes, with a standardized adjusted HR of 1.55 (1.16–2.08; P = 0.003). Among nondiabetic patients, HOMA insulin resistance was not significantly predictive of the incidence of vascular events after adjustment for the MetS [HR, 1.21 (0.75–1.94); P = 0.442]. Again, the impact of HOMA insulin resistance on the incidence of vascular events was not significantly different between patients with type 2 diabetes and nondiabetic patients (for interaction of HOMA insulin resistance x diabetes status, P = 0.299).


   Discussion
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
References
 
From our data we conclude that the MetS and insulin resistance are mutually independent predictors of vascular risk among angiographed coronary patients: insulin resistance does not account for the full amount of risk conferred by the MetS stigmata, and conversely, insulin resistance provides prognostic information beyond the clinical entity of the MetS. Moreover, this study is the first to demonstrate that the MetS (as defined by ATP III criteria) significantly increases cardiovascular risk in men with established CAD and patients with type 2 diabetes mellitus.

The MetS, as defined by ATP III criteria, significantly increased cardiovascular risk among our patients with type 2 diabetes. Because diabetes is associated with a 2- to 3-fold increase in cardiovascular risk (27), cardiovascular risk factors in patients with diabetes deserve particular attention. However, there is a paucity of prospective data on the impact of the MetS on the incidence of vascular events in patients with diabetes. Recently, Bonora et al. (28) reported an increased risk of cardiovascular events in diabetic patients with the MetS defined according to WHO criteria, which include insulin resistance per se, whereas Bruno et al. (29) found similar HRs for cardiovascular mortality in diabetic patients with and without the WHO-defined MetS. However, on the basis of ATP III criteria, before our investigation no data had been published for diabetic patients on the prospective impact of the MetS on the incidence of vascular events. Also, we provide the first report that the MetS is significantly predictive for future vascular events among men with established CAD, thereby extending a previous observation on the impact of the MetS on women with CAD (20) to the larger population of male coronary patients.

Insulin resistance is regarded as a key player in the pathophysiology of the MetS (12, 30). In our investigation, insulin resistance increased gradually from patients with none through those with five markers of the MetS. This increasing severity of insulin resistance with an increasing number of markers of the MetS reflects and confirms the pathophysiological rationale behind the ATP III criteria for the diagnosis of the MetS.

In Cox regression models controlling for non-MetS risk factors, both the MetS and insulin resistance were significant predictors of vascular events in our cohort of coronary patients. Importantly, the MetS remained significantly predictive for the incidence of vascular events even after adjustment for HOMA insulin resistance. This result suggests that insulin resistance alone does not explain the full amount of future vascular risk that is conferred by the MetS stigmata; the MetS, as defined by ATP III criteria, conferred vascular risk beyond insulin resistance. Conversely, insulin resistance was significantly predictive for vascular events even after adjustment for the MetS. This suggests that insulin resistance exerts additional vascular risk beyond current ATP III MetS criteria. These prospective results from our investigation are well in line with a recent cross-sectional study reporting independent associations with coronary artery calcification for both the MetS and insulin resistance after mutual adjustment (31).

HOMA insulin resistance was significantly higher in the subgroup of patients with diabetes than in the subgroup of nondiabetic individuals. In subgroup analyses, the MetS strongly affected cardiovascular prognosis in both patients with type 2 diabetes and nondiabetic individuals; the increased risk associated with insulin resistance, in contrast, was carried primarily by the patients with diabetes. However, there was no statistically significant difference in the impact of insulin resistance on the incidence of vascular events between patients with diabetes and nondiabetic subjects, and from the results of our subgroup analyses, we cannot exclude that a significant association between insulin resistance and vascular events could be demonstrable in nondiabetic coronary patients with the greater statistical power of a larger study or a longer period of follow-up. Recently, Stern et al. (32) reported data from a population with a high proportion of Mexican-Americans, in whom, among subjects with a history of cardiovascular disease, the ability of the MetS to predict cardiovascular mortality was entirely due to the inclusion of diabetic patients among those with the MetS. In that population, no additional effect of the MetS on cardiovascular mortality was found after adjustment for diabetes mellitus. However, in our investigation, adjustment for diabetes status in regression analyses confirmed that both the MetS and insulin resistance were strong and significant predictors of vascular events in the total study population, independent of diabetes status.

Our study shows that the MetS identifies coronary patients at a high cardiovascular risk. However, our data also indicate that cardiovascular risk increases gradually with the number of MetS stigmata. Patients with four and, even more so, those with five of these markers are at a particularly high cardiovascular risk. Therefore, our results suggest that considering the number of risk factors that cluster in an individual enhances the prognostic strength of MetS, at least in an unselected angiography population.

Our investigation is characterized by the typical limitations and strengths of studies enrolling patients who undergo coronary angiography for the evaluation of CAD. This is a selected group of patients; our results therefore are not necessarily applicable to the general population. However, these patients undergoing coronary angiography are at a high absolute risk for future cardiovascular events, and prospective data on cardiovascular risk factors in this patient cohort are of major clinical importance.

We conclude that both the clinical diagnosis of the MetS and biomarkers of insulin resistance should be considered in cardiovascular risk estimation. Whether pharmacological improvement of the MetS and insulin sensitivity, as with thiazolidinedione treatment, translates into improved cardiovascular outcome is a question of major clinical importance. Indeed, this hypothesis has been investigated in a large-scale clinical trial with pioglitazone (33), the results of which are awaited soon.


   Acknowledgments
 
We thank Drs. Werner Benzer, Hannes Holzmueller, Wolfgang Fuchs, and Wolfgang Metzler for performing expert coronary angiographies; Drs. Willi Moll and Michael Hubmann for performing laboratory analyses; and Dr. Elmar Bechter, Amt der Vorarlberger Landesregierung, for providing us with mortality data from Statistic Austria. We are grateful to Dr. Egmond Frommelt and the Liechtenstein Global Trust Bank, to Franz Rauch and the Vorarlberger Industriellenvereinigung, to Dr. Peter Woess and the Vorarlberger Aerztekammer, and to Luis Patsch, Director, Vorarlberger Landeskrankenhaus-Betriebsgesellschaft, for continuously supporting our research institute.


   Footnotes
 
This work was supported by Institute for Clinical Chemistry, Feldkirch, Austria, Liechtenstein Global Trust Bank (Bendern, Principality of Liechtenstein), Vorarlberger Industriellenvereinigung (Bregenz, Austria), Vorarlberger Aerztekammer (Dornbirn, Austria), and Vorarlberger Landeskrankenhaus-Betriebsgesellschaft (Feldkirch, Austria).

First Published Online August 9, 2005

Abbreviations: CAD, Coronary artery disease; HDL, high-density lipoprotein; HOMA, homeostasis model assessment; HR, hazard ratio; LDL, low-density lipoprotein; MetS, metabolic syndrome.

Received April 12, 2005.

Accepted July 29, 2005.


   References
Top
 Abstract
 Introduction
 Subjects and Methods
 Results
 Discussion
 References
 

  1. Haffner SM, Valdez RA, Hazuda HP, Mitchell BD, Morales PA, Stern MP 1992 Prospective analysis of the insulin-resistance syndrome (syndrome X). Diabetes 41:715–722[Abstract]
  2. Resnick HE, Jones K, Ruotolo G, Jain AK, Henderson J, Lu W, Howard BV 2003 Insulin resistance, the metabolic syndrome, and risk of incident cardiovascular disease in nondiabetic American Indians: the Strong Heart Study. Diabetes Care 26:861–867[Abstract/Free Full Text]
  3. Sattar N, Gaw A, Scherbakova O, Ford I, O’Reilly DS, Haffner SM, Isles C, Macfarlane PW, Packard CJ, Cobbe SM, Shepherd J 2003 Metabolic syndrome with and without C-reactive protein as a predictor of coronary heart disease and diabetes in the West of Scotland Coronary Prevention Study. Circulation 108:414–419[Abstract/Free Full Text]
  4. Rutter MK, Meigs JB, Sullivan LM, D’Agostino RB, Wilson PW 2004 C-Reactive protein, the metabolic syndrome, and prediction of cardiovascular events in the Framingham Offspring Study. Circulation 110:380–385[Abstract/Free Full Text]
  5. Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, Salonen JT 2002 The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 288:2709–2716[Abstract/Free Full Text]
  6. Schillaci G, Pirro M, Vaudo G, Gemelli F, Marchesi S, Porcellati C, Mannarino E 2004 Prognostic value of the metabolic syndrome in essential hypertension. J Am Coll Cardiol 43:1817–1822[Abstract/Free Full Text]
  7. Isomaa B, Almgren P, Tuomi T, Forsen B, Lahti K, Nissen M, Taskinen MR, Groop L 2001 Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 24:683–689[Abstract/Free Full Text]
  8. Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Bonadonna RC, Muggeo M 2003 Carotid atherosclerosis and coronary heart disease in the metabolic syndrome: prospective data from the Bruneck study. Diabetes Care 26:1251–1257[Abstract/Free Full Text]
  9. Ridker PM, Buring JE, Cook NR, Rifai N 2003 C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14 719 initially healthy American women. Circulation 107:391–397[Abstract/Free Full Text]
  10. Katzmarzyk PT, Church TS, Janssen I, Ross R, Blair SN 2005 Metabolic syndrome, obesity, and mortality: impact of cardiorespiratory fitness. Diabetes Care 28:391–397[Abstract/Free Full Text]
  11. Adult Treatment Panel III 2001 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 285:2486–2497[Free Full Text]
  12. Hanley AJ, Karter AJ, Festa A, D’Agostino Jr R, Wagenknecht LE, Savage P, Tracy RP, Saad MF, Haffner S 2002 Factor analysis of metabolic syndrome using directly measured insulin sensitivity: The Insulin Resistance Atherosclerosis Study. Diabetes 51:2642–2647[Abstract/Free Full Text]
  13. Cheal KL, Abbasi F, Lamendola C, McLaughlin T, Reaven GM, Ford ES 2004 Relationship to insulin resistance of the adult treatment panel III diagnostic criteria for identification of the metabolic syndrome. Diabetes 53:1195–1200[Abstract/Free Full Text]
  14. Carr DB, Utzschneider KM, Hull RL, Kodama K, Retzlaff BM, Brunzell JD, Shofer JB, Fish BE, Knopp RH, Kahn SE 2004 Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes 53:2087–2094[Abstract/Free Full Text]
  15. Hanley AJ, Wagenknecht LE, D’Agostino Jr RB, Zinman B, Haffner SM 2003 Identification of subjects with insulin resistance and ß-cell dysfunction using alternative definitions of the metabolic syndrome. Diabetes 52:2740–2747[Abstract/Free Full Text]
  16. Bonora E, Formentini G, Calcaterra F, Lombardi S, Marini F, Zenari L, Saggiani F, Poli M, Perbellini S, Raffaelli A, Cacciatori V, Santi L, Targher G, Bonadonna R, Muggeo M 2002 HOMA-estimated insulin resistance is an independent predictor of cardiovascular disease in type 2 diabetic subjects: prospective data from the Verona Diabetes Complications Study. Diabetes Care 25:1135–1141[Abstract/Free Full Text]
  17. Bressler P, Bailey SR, Matsuda M, DeFronzo RA 1996 Insulin resistance and coronary artery disease. Diabetologia 39:1345–1350[CrossRef][Medline]
  18. Howard G, O’Leary DH, Zaccaro D, Haffner S, Rewers M, Hamman R, Selby JV, Saad MF, Savage P, Bergman R 1996 Insulin sensitivity and atherosclerosis. The Insulin Resistance Atherosclerosis Study (IRAS) investigators. Circulation 93:1809–1817[Abstract/Free Full Text]
  19. Girman CJ, Rhodes T, Mercuri M, Pyorala K, Kjekshus J, Pedersen TR, Beere PA, Gotto AM, Clearfield M 2004 The metabolic syndrome and risk of major coronary events in the Scandinavian Simvastatin Survival Study (4S) and the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS). Am J Cardiol 93:136–141[CrossRef][Medline]
  20. Marroquin OC, Kip KE, Kelley DE, Johnson BD, Shaw LJ, Bairey Merz CN, Sharaf BL, Pepine CJ, Sopko G, Reis SE 2004 Metabolic syndrome modifies the cardiovascular risk associated with angiographic coronary artery disease in women: a report from the Women’s Ischemia Syndrome Evaluation. Circulation 109:714–721[Abstract/Free Full Text]
  21. Drexel H, Aczel S, Marte T, Benzer W, Langer P, Moll W, Saely CH 2005 Is atherosclerosis in diabetes and impaired fasting glucose driven by elevated LDL cholesterol or by decreased HDL cholesterol? Diabetes Care 28:101–107[Abstract/Free Full Text]
  22. Drexel H, Amann FW, Rentsch K, Neuenschwander C, Luethy A, Khan SI, Follath F 1992 Relation of the level of high-density lipoprotein subfractions to the presence and extent of coronary artery disease. Am J Cardiol 70:436–440[CrossRef][Medline]
  23. Drexel H, Amann FW, Beran J, Rentsch K, Candinas R, Muntwyler J, Luethy A, Gasser T, Follath F 1994 Plasma triglycerides and three lipoprotein cholesterol fractions are independent predictors of the extent of coronary atherosclerosis. Circulation 90:2230–2235[Abstract/Free Full Text]
  24. Alberti KG, Zimmet PZ 1998 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 15:539–553[CrossRef][Medline]
  25. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC 1985 Homeostasis model assessment: insulin resistance and ß-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–419[CrossRef][Medline]
  26. Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, Monauni T, Muggeo M 2000 Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity. Diabetes Care 23:57–63[Abstract]
  27. Stamler J, Vaccaro O, Neaton JD, Wentworth D 1993 Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care 16:434–444[Abstract]
  28. Bonora E, Targher G, Formentini G, Calcaterra F, Lombardi S, Marini F, Zenari L, Saggiani F, Poli M, Perbellini S, Raffaelli A, Gemma L, Santi L, Bonadonna RC, Muggeo M 2004 The metabolic syndrome is an independent predictor of cardiovascular disease in type 2 diabetic subjects. Prospective data from the Verona Diabetes Complications Study. Diabet Med 21:52–58[CrossRef][Medline]
  29. Bruno G, Merletti F, Biggeri A, Bargero G, Ferrero S, Runzo C, Prina CS, Pagano G, Cavallo-Perin P 2004 Metabolic syndrome as a predictor of all-cause and cardiovascular mortality in type 2 diabetes: the Casale Monferrato Study. Diabetes Care 27:2689–2694[Abstract/Free Full Text]
  30. Reaven GM 1988 Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 37:1595–1607[Abstract]
  31. Reilly MP, Wolfe ML, Rhodes T, Girman C, Mehta N, Rader DJ 2004 Measures of insulin resistance add incremental value to the clinical diagnosis of metabolic syndrome in association with coronary atherosclerosis. Circulation 110:803–809[Abstract/Free Full Text]
  32. Stern MP, Williams K, Hunt KJ 2005 Impact of diabetes/metabolic syndrome in patients with established cardiovascular disease. Atherosclerosis 6(Suppl):3–6
  33. Charbonnel B, Dormandy J, Erdmann E, Massi-Benedetti M, Skene A 2004 The prospective pioglitazone clinical trial in macrovascular events (PROactive): can pioglitazone reduce cardiovascular events in diabetes? Study design and baseline characteristics of 5238 patients. Diabetes Care 27:1647–1653[Abstract/Free Full Text]

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