PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am Heart J. Author manuscript; available in PMC Mar 1, 2011.
Published in final edited form as:
PMCID: PMC2841510
NIHMSID: NIHMS176655
Variations in Prevalent Cardiovascular Disease and Future Risk by Metabolic Syndrome Classification in the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study
Todd M. Brown, MD, MSPH,1 Jenifer H. Voeks, PhD,2 Vera Bittner, MD, MSPH,1 and Monika M. Safford, MD3
1Division of Cardiovascular Diseases, University of Alabama at Birmingham, Birmingham, Alabama
2Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama
3Division of Preventive Medicine, University of Alabama at Birmingham, Birmingham, Alabama
Corresponding Author: Todd M. Brown, MD, MSPH Assistant Professor of Medicine UAB Division of Cardiovascular Diseases, LHRB 313 701 19th Street South, Birmingham, AL 35294 205-934-7336 (phone), 205-975-8568 (fax), tmbrown/at/uab.edu
Background
The International Diabetes Federation (IDF) and Adult Treatment Panel (ATP) III define metabolic syndrome (MetSyn) differently, with unclear implications for cardiovascular disease (CVD) risk.
Methods
We examined 22,719 participants in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. We classified participants as: no MetSyn, MetSyn by ATP-III and IDF criteria, MetSyn by ATP-only, or MetSyn by IDF-only. To assess current CVD, we determined the odds of self-reported CVD by MetSyn category using multivariable logistic regression, controlling for socio-demographic and behavioral factors. To estimate future coronary heart disease (CHD) risk, we calculated Framingham risk scores (FRS).
Results
Overall, 10,785 individuals (47%) had MetSyn. Of these, 79% had MetSyn by both definitions, 6% by ATP-only, and 14% by IDF-only. Compared to those without MetSyn, ATP-only individuals had the highest odds of current CVD and of having a FRS >20%. Also compared to those without MetSyn, IDF-only individuals had 43% higher odds of current CVD and two-fold increased odds of having a FRS >20%.
Conclusions
Consistent with previous reports, ATP-III MetSyn criteria identified individuals with increased odds of CVD and elevated future CHD risk. However, the IDF definition identified a clinically important number of additional individuals at excess CVD risk.
The metabolic syndrome (MetSyn) is highly prevalent in the United States (US) and associated with increased cardiovascular risk.13 The National Cholesterol Education Program Adult Treatment Panel-III (ATP-III) criteria are the most widely utilized MetSyn criteria in the US.45 The International Diabetes Federation (IDF) modified the ATP-III MetSyn classification by adopting ethnicity-specific cut-points for elevated waist circumference that are lower than those used by ATP-III and by requiring central obesity.6
While the IDF criteria identify more people as having MetSyn than ATP-III criteria,713 it is unclear whether people identified by only IDF criteria are at increased cardiovascular disease (CVD) risk.1423 Some studies suggest that the relatively modest number of individuals identified as having MetSyn by IDF criteria alone have little or no increased CVD risk compared with individuals without MetSyn.2429 However, these studies were relatively small and conducted primarily in European and Asian populations and do not reflect the importance of the IDF criteria in the US population. Furthermore, Wang et al30 concluded that among 6 different MetSyn definitions, the IDF definition best predicted incident stroke, suggesting that the IDF criteria do identify individuals at increased CVD risk.
To examine this issue in the US population, we compared the ATP-III and IDF MetSyn definitions in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study, a nationwide prospective cohort study of 30,228 individuals age 45 years and older, half African American, half female recruited between January 2003 and October 2007. We examine the association between ATP-III and IDF-defined MetSyn and prevalent CVD as well as estimated future coronary heart disease (CHD) risk using 10-year Framingham CHD risk scores (FRS).
REGARDS has been described previously.31 The cohort, by design, is 42% African American, and 55% female. Because the primary goals of REGARDS are to elucidate regional and racial differences in stroke, the Stroke Belt, located in the southeastern US, was over-sampled such that 20% of the overall cohort was selected from the “buckle” of the Stroke Belt (the coastal plain region of North Carolina, South Carolina, and Georgia); 30% from the rest of the Stroke Belt (the remaining parts of North Carolina, South Carolina, and Georgia plus Alabama, Mississippi, Louisiana, Arkansas, and Tennessee); and 50% from the remaining 40 contiguous states. Individuals identified by commercially available lists were contacted by mail and telephone. Upon enrollment, individuals underwent a computer assisted telephone interview followed by an in-home examination. During the telephone interview, demographic and self-reported medical information was obtained. During the in-home examination, the participant's blood pressure was measured, an electrocardiogram was performed, and blood and urine samples were obtained.
As of June 2007, REGARDS had recruited 28,224 participants. Of these, we included the 22,719 participants (80%) with complete data for MetSyn components. Of the 5,505 individuals excluded from this analysis, 4,118 were excluded for not fasting at the time of phlebotomy or because their fasting status was unknown; 1,378 for missing data on a component of the MetSyn; and 9 for missing data on race or gender. Individuals excluded were of similar age (66 vs. 65 years) and gender (55% vs. 55% female) as those included. Individuals excluded were less likely to be from the stroke buckle (16%) than the stroke belt (21%) or non-belt (20%) and were more likely to be African American (23%) than white (17%).
Individuals meeting any 3 of the following criteria were classified as having ATP-III MetSyn: elevated waist circumference (>40 inches in men or >35 inches in women), hypertriglyceridemia (≥150 mg/dL), decreased high density lipoprotein-cholesterol (HDL-C) (<40 mg/dL in men or <50 mg/dL in women), elevated blood pressure (systolic blood pressure ≥130 mmHg, diastolic blood pressure ≥85 mmHg, history of hypertension, or use of medications for elevated blood pressure), or hyperglycemia (fasting glucose ≥100 mg/dL, history of diabetes, or use of medications for diabetes). To meet IDF criteria, individuals had to have an elevated waist circumference (≥37 inches in men or ≥31.5 inches in women) and 2 of the other 4 ATP-III criteria. Using these criteria, we classified participants into 1 of the following 4 groups: (1) no MetSyn, (2) MetSyn by both ATP-III and IDF criteria, (3) MetSyn by only ATP-III criteria, or (4) MetSyn by only IDF criteria.
We assessed the association between MetSyn classification and prevalent CVD among all 22,719 participants. CVD was defined as a self-reported history of any of the following: myocardial infarction, percutaneous coronary intervention, coronary artery bypass graft surgery, stroke, transient ischemic attack, carotid endarterectomy, carotid stenting, or surgery for peripheral arterial disease or abdominal aortic aneurysm. We performed multivariable logistic regression to determine the odds of self-reported CVD for each of the MetSyn classifications, incrementally controlling for demographic variables (age, race, gender, and geographic region), education and income, and behavioral risk factors (smoking status, alcohol use, and physical activity). Because the MetSyn includes individuals with and without diabetes, we performed these analyses in all participants (including those with and without diabetes) and separately in those without diabetes.
In order to compare the estimated future CHD risk of participants in each of the 4 MetSyn classifications, we calculated 10-year FRS for CHD death or myocardial infarction.32 This risk calculator considers diabetes a risk factor for coronary artery disease, but not a risk equivalent as defined by ATP-III.4 Therefore, we also calculated these FRS assuming that all diabetic participants had a FRS >20%. We then performed multivariable logistic regression to determine the odds of having a FRS >20% for each of the MetSyn classifications, controlling for demographic variables (age, race, gender, and geographic region), education and income, and behavioral risk factors (smoking status, alcohol use, and physical activity). We chose to perform separate models with and without adjustment for age and smoking as they are part of the FRS but not part of the MetSyn definition. This allowed us to assess the impact that MetSyn components have on the FRS. For this portion of the analysis, we excluded the 3,102 participants with a self-reported history of CHD (myocardial infarction, percutaneous coronary intervention, or coronary artery bypass graft surgery) and an additional 1,884 participants with evidence of myocardial infarction on their enrollment electrocardiogram who did not report a history of CHD. An additional 63 participants were missing data on other components of the FRS. Therefore, of the 22,719 participants, a total of 17,670 (78%) were included in these models.
REGARDS is supported by a cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, NIH, Department of Health and Human Service. During a portion of this work, Dr. Brown was supported in part by grant 5 T32 HS013852 from the Agency for Healthcare Research and Quality. Dr. Brown is currently supported by grant number KL2 RR025776 from the NIH National Center for Research Resources. Drs. Safford and Bittner were supported by NHLBI R01 HL80477-01A1. The authors are solely responsible for the design and conduct of this study, all study analyses, the drafting and editing of the paper, and its final contents.
Of the 22,719 participants, 10,785 (47%) met at least one definition for MetSyn. Of these, 8,571 (79%) had MetSyn by both criteria, 686 (6%) by only ATP-III criteria, and 1,528 (14%) by only IDF criteria. Participant characteristics are listed in Table 1. Compared with those meeting only ATP-III MetSyn criteria, individuals meeting only IDF criteria were more often African American, female, slightly better educated, and fewer smoked. Despite being more obese, they had lower overall mean values for fasting glucose and triglycerides, higher mean values for HDL-C, and similar mean values for blood pressure. As a result, fewer individuals meeting only IDF criteria had low HDL-C, high triglycerides, elevated blood pressure, or hyperglycemia than those meeting ATP-III criteria (Table 1).
Table 1
Table 1
Participant Characteristics by Metabolic Syndrome Classification in the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study.
Overall, 22% of participants reported a history of CVD, but this varied significantly by MetSyn classification. Only 17% of participants meeting no criteria for MetSyn reported a history of CVD; whereas, 32% of those meeting only ATP-III criteria, 27% of those meeting both ATP-III and IDF criteria, and 25% of those meeting only IDF criteria reported a history of CVD. Compared to those without MetSyn, individuals with MetSyn by any criteria had higher odds of self-reported CVD (adjusted odds ratio [AOR] 1.74 for both ATP-III and IDF, 1.68 for ATP-III-only, and 1.43 for IDF-only, Table 2), after adjusting for socio-demographic and behavioral risk factors. Among non-diabetic participants, individuals with MetSyn by any definition had significantly increased odds of prevalent CVD compared to those without MetSyn, although the magnitude was less than among all participants (including diabetics and non-diabetics), with a similar trend across the different MetSyn classifications (Table 2).
Table 2
Table 2
Odds of Prevalent Cardiovascular Disease* by Metabolic Syndrome Classification in the REasons for Geographic And Racial Differences in Stroke (REGARDS) Study.
We were also interested in whether individuals meeting ATP-III MetSyn criteria had greater odds of self-reported CVD than those identified by the IDF but not the ATP-III criteria. Compared to those meeting only IDF criteria, individuals meeting ATP-III criteria had significantly increased odds of self-reported CVD (AOR 1.22, 95% confidence interval [CI] 1.06–1.40), after adjusting for socio-demographic and behavioral risk factors. When restricting the analysis to only non-diabetic participants, those meeting ATP-III criteria had lower and not significantly increased odds of prevalent CVD than those meeting only IDF criteria (AOR 1.12, 95% CI 0.95–1.32).
Next, we estimated elevated future CHD risk by calculating FRS in those without prevalent CHD (Table 3). Individuals meeting only ATP-III MetSyn criteria had the largest proportion, with 34% having a FRS >20%. However, individuals meeting only IDF criteria, albeit having a lower proportion (13%) than those meeting ATP-III criteria, were twice as likely as those without MetSyn to have a FRS >20%. This pattern persisted when the analysis was restricted to only non-diabetic participants or when all diabetic participants were assumed to have a FRS >20% (Table 3). Compared to those without MetSyn, individuals with MetSyn by any criteria had higher odds of a FRS >20% (AOR 7.34 for both ATP-III and IDF, AOR 7.48 for ATP-III-only, and AOR 2.29 for IDF-only), after adjusting for socio-demographic and behavioral risk factors. Among non-diabetic participants, individuals with MetSyn by any criteria had significantly increased odds of having a FRS >20% compared to those without MetSyn, with a similar trend across the different MetSyn classifications. This trend was also similar when diabetic participants were assumed to have a FRS >20%, although the overall magnitude of the association was greater (Table 3). Additional multivariable models were performed without adjusting for age and smoking with similar results (data not shown).
Table 3
Table 3
Proportion of Participants and Odds of Having a 10-Year Framingham Risk Score for CHD Death or Myocardial Infarction >20% by Metabolic Syndrome Classification in those Free of CHD* in the REasons for Geographic And Racial Differences in Stroke (more ...)
In this nationwide, community-based cohort of African American and White participants, individuals meeting ATP-III MetSyn criteria had the greatest prevalence of CVD and the largest proportion of individuals with elevated future CHD risk. Although less than those meeting ATPIII criteria, individuals meeting only IDF criteria had a 43% increased odds of prevalent CVD and a two-fold increased odds of having elevated future CHD risk compared with individuals without MetSyn. This suggests that in the US, the IDF criteria do identify additional individuals not identified by ATP-III criteria who are at increased risk for prevalent CVD and future cardiovascular events.
Many investigators have reported that the IDF criteria identify more individuals as having MetSyn, but that these individuals had similar cardiovascular risk as those with MetSyn by ATP-III criteria.723 These studies did not analyze individuals with only IDF MetSyn as a separate group as we have in this analysis. Given that the IDF and ATP-III MetSyn criteria similarly classify most people, it is not surprising that the IDF criteria, overall, did not add much predictive value to the ATP-III criteria in these studies.
In European and Asian populations, individuals meeting only IDF but not ATP-III MetSyn criteria have not demonstrated increased CVD risk compared with individuals without MetSyn. Athyros and others report that among 9,669 Greek adults, individuals meeting only IDF MetSyn criteria did not have an increased prevalence of CVD (10.5%) as compared to the study population as a whole (11.4%).24 In a cohort of 7,152 German men, Assman et al found that only 5.5% of individuals meeting only IDF MetSyn criteria developed CHD over 10 years of follow-up as compared to 3.4% of individuals without MetSyn (P=NS).25 Tong and associates report that among 4,350 Chinese individuals with diabetes mellitus, the incidence of CHD over a median follow-up of 7.1 years was 3.3 per 1,000 person-years (95% CI 1.2–5.3) in individuals meeting only IDF MetSyn criteria and 4.1 per 1,000 person-years (95% CI 2.8–5.3) in individuals without MetSyn.26 Monami et al indicate that 3-year survival among Italians with only IDF-defined MetSyn (91.8%) was not statistically different than individuals without MetSyn (93.7%).27 Saely and others report that among 750 Austrians referred for coronary angiography, those meeting only IDF MetSyn criteria had no difference in survival free of cardiovascular events than those without MetSyn after a mean follow-up of 3.9 years.28
Unlike these previous studies in predominantly European and Asian populations, our findings from a large, community-based, nationwide US cohort with large numbers of African Americans indicate that individuals identified as having MetSyn by IDF but not ATP-III criteria have increased odds for prevalent CVD and elevated future CHD risk compared to individuals without MetSyn. These individuals represent a clinically important number of Americans, since 14% of individuals in our study identified as having MetSyn only met IDF criteria. Previous smaller non-US cohorts were likely unable to assess the true cardiovascular risk of these individuals.
Our findings have important clinical implications. Cardiovascular risk increases prior to the clinical diagnosis of diabetes, and individuals with MetSyn who do not yet have diabetes are at increased CVD risk.2,3335 In our analysis, even non-diabetic individuals meeting only IDF MetSyn criteria had significantly increased odds for both prevalent CVD and high risk of future CHD compared to those without MetSyn. These findings suggest that there may be a continuum of risk with those without MetSyn being at lowest risk, those meeting only IDF criteria at higher risk, and those meeting ATP-III criteria at highest risk. Individuals meeting only IDF MetSyn criteria have a lower prevalence of traditional cardiovascular risk factors such as hyperglycemia, elevated blood pressure, and dyslipidemia than individuals meeting ATP-III MetSyn criteria. IDF-only individuals were less obese than those meeting both ATP-III and IDF MetSyn criteria, but more obese than those without MetSyn. Therapeutic lifestyle changes, which modify the MetSyn,3640 may be more important than pharmacologic interventions in these patients. However, individuals with MetSyn by only ATP-III criteria tended to be leaner and have more traditional cardiovascular risk factors and higher smoking rates; they might require more intensive pharmacologic intervention to modify cardiovascular risk.
Our analysis has a number of limitations. First, because it is cross-sectional, we are unable to definitively assess the ability of the different MetSyn classifications to predict future cardiovascular events and had to rely upon the FRS to estimate future cardiovascular risk. Although, this tool has been validated in African American populations.41 Second, we lack detailed knowledge about why patients were taking cholesterol-lowering medications. Therefore, we were not able to include the use of medications for the treatment of hypertriglyceridemia or low HDL-C as criteria for those respective MetSyn components. However, most patients taking cholesterol-lowering medications receive statins to treat elevated low density lipoprotein-cholesterol, thus this limitation likely had a minimal impact on the results. Third, REGARDS only enrolled African American and White US participants, limiting generalizability to other ethnic/racial groups within the US and in other regions of the world.
Our study indicates important differences between the ATP-III and IDF MetSyn criteria in the US. Those meeting only ATP-III criteria are an especially high risk, clinically distinct group appropriate for aggressive targeting. Contrary to previous reports, our findings suggest that in the US, the IDF MetSyn criteria identify a clinically important number of individuals at increased cardiovascular risk who may benefit from risk factor modification.
Acknowledgements
This research project is supported by a cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Service. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. Representatives of the funding agency have been involved in the review and approval of the manuscript but not directly involved in the collection, management, analysis or interpretation of the data. The authors acknowledge the participating investigators and institutions for their valuable contributions: The University of Alabama at Birmingham, Birmingham, Alabama (Study PI, Statistical and Data Coordinating Center, Survey Research Unit): George Howard DrPH, Leslie McClure PhD, Virginia Howard PhD, Libby Wagner MA, Virginia Wadley PhD, Rodney Go PhD, Monika Safford MD, Ella Temple, PhD, Margaret Stewart, MSPH, David Rhodes, RN; University of Vermont (Central Laboratory): Mary Cushman MD; Wake Forest University (ECG Reading Center): Ron Prineas MD, PhD; Alabama Neurological Institute (Stroke Validation Center, Medical Monitoring): Camilo Gomez MD, Susana Bowling MD; University of Arkansas for Medical Sciences (Survey Research): LeaVonne Pulley PhD; University of Cincinnati (Clinical Neuroepidemiology): Brett Kissela MD, Dawn Kleindorfer MD; Examination Management Services, Incorporated (In-Person Visits): Andra Graham; Medical University of South Carolina (Migration Analysis Center): Daniel Lackland, DrPH; Indiana University (Neuropsychology Center): Frederick Unverzagt, PhD; National Institute of Neurological Disorders and Stroke, National Institutes of Health (funding agency): Claudia Moy, Ph.D.
At the time of this work, Dr. Brown was supported in part by grant 5 T32 HS013852 from the Agency for Healthcare Research and Quality, Rockville, MD, USA. Dr. Brown is currently supported by grant number KL2 RR025776 from the NIH National Center for Research Resources. Drs. Safford and Bittner were supported by NHLBI R01 HL80477-01A1.
Footnotes
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
1. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US Adults: Findings from the Third National Health and Nutrition Examination Survey. JAMA. 2002;287(3):356–359. [PubMed]
2. Gami AS, Witt BJ, Howard DE, et al. Metabolic syndrome and risk of incident cardiovascular events and death. J Am Coll Cardiol. 2007;49(4):403–414. [PubMed]
3. Echahidi N, Pibarot P, Despres JP, et al. Metabolic syndrome increases operative mortality in patients undergoing coronary artery bypass grafting surgery. J Am Coll Cardiol. 2007;50(9):843–851. [PubMed]
4. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) JAMA. 2001;285(19):2486–2497. [PubMed]
5. Grundy SM, Brewer HB, Cleeman JI, et al. The Conference Participants Definition of the Metabolic Syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association Conference on Scientific Issues Related to Definition. Circulation. 2004;109(3):433–438. [PubMed]
6. The IDF consensus worldwide definition of the metabolic syndrome. Available from http://www.idf.org/webdata/docs/MetS_def_update2006.pdf. Accessed July 20, 2009.
7. Ford ES. Prevalence of the Metabolic Syndrome Defined by the International Diabetes Federation Among Adults in the US. Diabetes Care. 2005;28(11):2745–2749. [PubMed]
8. Rathmann W, Haastert B, Icks A, et al. Prevalence of the Metabolic Syndrome in the Elderly Population According to IDF, WHO, and NCEP Definitions and Associations with C-Reactive Protein: The KORA Survey 2000. Diabetes Care. 2006;29(2):461. [PubMed]
9. Lorenzo C, Serrano-Rios M, Martinez-Larrad MT, et al. Geographic Variations of the International Diabetes Federation and the National Cholesterol Education Program–Adult Treatment Panel III Definitions of the Metabolic Syndrome in Nondiabetic Subjects. Diabetes Care. 2006;29(3):685–691. [PubMed]
10. Adams RJ, Appleton S, Wilson DH, et al. Population Comparison of Two Clinical Approaches to the Metabolic Syndrome: Implications of the new International Diabetes Federation Consensus Definition. Diabetes Care. 2005;28(11):2777–2779. [PubMed]
11. Guerrero-Romero F, Rodriguez-Moran M. Concordance Between the 2005 International Diabetes Federation Definition for Diagnosing Metabolic Syndrome with the National Cholesterol Education Program Adult Treatment Panel III and the World Health Organization Definitions. Diabetes Care. 2005;28(10):2588–2589. [PubMed]
12. Bo S, Ciccone G, Pearce N, et al. Prevalence of undiagnosed metabolic syndrome in a population of adult asymptomatic subjects. Diabetes Research and Clinical Practice. 2007;75(3):362–365. [PubMed]
13. Gonzalez-Ortiz M, Martinez-Abundis E, Jacques-Camarena O, et al. Prevalence of metabolic syndrome in adults with excess of adiposity: comparison of the Adult Treatment Panel III criteria with the International Diabetes Federation definition. Acta Diabetol. 2006;43(3):84–86. [PubMed]
14. Katzmarzyk PT, Janssen I, Ross R, et al. The importance of waist circumference in the definition of metabolic syndrome. Diabetes Care. 2006;29(2):404–409. [PubMed]
15. Lorenzo C, Williams K, Hunt KJ, et al. The National Cholesterol Education Program-Adult Treatment Panel III, International Diabetes Federation, and World Health Organization definitions of the metabolic syndrome as predictors of incident cardiovascular disease and diabetes. Diabetes Care. 2007;30(1):8–13. [PubMed]
16. De Simone G, Devereux RB, Chinali M, et al. Prognostic impact of metabolic syndrome by different definitions in a population with high prevalence of obesity and diabetes. Diabetes Care. 2007;30(7):1851–1856. [PubMed]
17. Sandhofer A, Iglseder B, Paulweber B, et al. Comparison of different definitions of the metabolic syndrome. Eur J Clin Invest. 2007;37(2):109–116. [PubMed]
18. Skilton MR, Moulin P, Serusclat A, et al. A comparison of the NCEP-ATPIII, IDF, and AHA/NHLBI metabolic syndrome definitions with relation to early carotid atherosclerosis in subjects with hypercholesterolemia or at risk of CVD: Evidence for sex-specific differences. Atherosclerosis. 2007;190(2):416–422. [PubMed]
19. Nilsson PM, Engstrom G, Hedblad B. The metabolic syndrome and incidence of cardiovascular disease in non-diabetic subjects – a population-based study comparing three different definitions. Diabetic Medicine. 2007;24(5):464–472. [PubMed]
20. Boulon C, Lafitte M, Richeboeuf V, et al. Prevalence of metabolic syndrome after acute coronary syndrome and its prognostic significance. Am J Cardiol. 2006;98(11):1429–1134. [PubMed]
21. Lawlor DA, Smith GD, Ebrahim S. Does the new International Diabetes Federation definition of the metabolic syndrome predict CHD any more strongly than older definitions? Findings from the British Women's Heart and Health Study. Diabetologia. 2006;49(1):41–48. [PubMed]
22. The DECODE study group. Qiao Q. Comparison of different definitions of the metabolic syndrome in relation to cardiovascular mortality in European men and women. Diabetologia. 2006;49(12):2837–2846. [PubMed]
23. Targher G, Bertolini L, Tessari R, et al. The International Diabetes Federation definition of the metabolic syndrome independently predicts future cardiovascular events in type 2 diabetic patients. The Valpolicella Heart Disease Study. Diabetic Medicine. 2006;23(11):1270–1271. [PubMed]
24. Athyros VG, Ganotakis ES, Elisaf MS, et al. Prevalence of vascular disease in metabolic syndrome using three proposed definitions. Int J Cardiol. 2007;117(2):204–210. [PubMed]
25. Assmann G, Guerra R, Fox G, et al. Harmonizing the definition of the metabolic syndrome: Comparison of the criteria of the Adult Treatment Panel III and the International Diabetes Federation in United States American and European populations. Am J Cardiol. 2007;99(4):541–548. [PubMed]
26. Tong PC, Kong AP, So WY, et al. The usefulness of the International Diabetes Federation and the National Cholesterol Education Program's Adult Treatment Panel III definitions of the metabolic syndrome in predicting coronary heart disease in subjects with type 2 diabetes. Diabetes Care. 2007;30(5):1206–1211. [PubMed]
27. Monami M, Marchionni N, Masotti G, et al. IDF and ATP-III definitions of metabolic syndrome in the prediction of all-cause mortality in type-2 diabetes patients. Diabetes, Obesity, and Metabolism. 2007;9(3):350–353. [PubMed]
28. Saely CH, Koch L, Schmid F, et al. Adult Treatment Panel III 2001 but not International Diabetes Federation 2005 criteria of the metabolic syndrome predict clinical cardiovascular events in subjects who underwent coronary angiography. Diabetes Care. 2006;29(4):901–907. [PubMed]
29. Brown TM, Vaidya D, Rogers WJ, et al. Does prevalence of the metabolic syndrome in women with coronary artery disease differ by the ATP III and IDF criteria? J Womens Health. 2008;17(5):841–847. [PMC free article] [PubMed]
30. Wang J, Ruotsalainen S, Moilanen L, et al. The metabolic syndrome predicts incident stroke: a 14-year follow-up study in elderly people in Finland. Stroke. 2008;39(4):1078–1083. [PubMed]
31. Howard VJ, Cushman M, Pulley LV, et al. The REasons for Geographic And Racial Differences in Stroke (REGARDS) study: objectives and design. Neuroepidemiology. 2005;25(3):135–143. [PubMed]
32. Wilson PW, D'Agostino RB, Levy D, et al. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–1847. [PubMed]
33. Hu FB, Stampfer MJ, Haffner SM, et al. Elevated risk of cardiovascular disease prior to clinical diagnosis of type 2 diabetes. Diabetes Care. 2002;25(7):1129–1134. [PubMed]
34. Coutinho M, Gerstein HC, Wang Y, et al. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years. Diabetes Care. 1999;22(2):233–240. [PubMed]
35. Levitan EB, Song Y, Ford ES, et al. Is nondiabetic hyperglycemia a risk factor for cardiovascular disease? A meta-analysis of prospective studies. Arch Int Med. 2004;164(19):2147–2155. [PubMed]
36. Shubair MM, Kodis J, McKelvie RS, et al. Metabolic profile and exercise capacity outcomes. J Cardiopulm Rehabil. 2004;24(6):405–413. [PubMed]
37. Stewart KJ, Bacher AC, Turner K, et al. Exercise and risk factors associated with metabolic syndrome in older adults. Am J Prev Med. 2005;28(1):9–18. [PubMed]
38. Katzmarzyk PT, Leon AS, Wilmore JH, et al. Targeting the metabolic syndrome with exercise: evidence from the HERITAGE Family Study. Med Sci Sports Exerc. 2003;35(10):1703–1709. [PubMed]
39. Grundy SM, Hansen B, Smith SC, et al. Conference Participants Clinical management of metabolic syndrome. Report of the American Heart Association/National Heart, Lung, and Blood Institute/American Diabetes Association Conference on Scientific Issues Related to Management. Circulation. 2004;109(4):551–556. [PubMed]
40. Bassand J-P. Managing cardiovascular risk in patients with metabolic syndrome. Clinical Cornerstone. 2006;8(Suppl 1):S7–S14. [PubMed]
41. D'Agostino RB, Grundy S, Sullivan LM, et al. The CHD Risk Prediction Group Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA. 2001;286(2):180–187. [PubMed]