HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Permissions Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Ford, E. S. Search for Related Content PubMed PubMed Citation Articles by Ford, E. S. Social Bookmarking                What's this?

Risks for All-Cause Mortality, Cardiovascular Disease, and Diabetes Associated With the Metabolic Syndrome

A summary of the evidence

Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia

Address correspondence and reprint requests to Earl S. Ford, MD, MPH, Centers for Disease Control and Prevention, 4770 Buford Hwy., MS K66, Atlanta, GA 30341. E-mail: eford{at}cdc.gov

	ABSTRACT

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
OBJECTIVE—In recent years, several major organizations have endorsed the concept of the metabolic syndrome and developed working definitions for it. How well these definitions predict the risk for adverse events in people with the metabolic syndrome is only now being learned. The purpose of this study was to summarize the estimates of relative risk for all-cause mortality, cardiovascular disease, and diabetes reported from prospective studies in samples from the general population using definitions of the metabolic syndrome developed by the National Cholesterol Education Program (NCEP) and World Health Organization (WHO).

RESEARCH DESIGN AND METHODS—The author reviewed prospective studies from July 1998 through August 2004.

RESULTS—For studies that used the exact NCEP definition of the metabolic syndrome, random-effects estimates of combined relative risk were 1.27 (95% CI 0.90–1.78) for all-cause mortality, 1.65 (1.38–1.99) for cardiovascular disease, and 2.99 (1.96–4.57) for diabetes. For studies that used the most exact WHO definition of the metabolic syndrome, the fixed-effects estimates of relative risk were 1.37 (1.09–1.74) for all-cause mortality and 1.93 (1.39–2.67) for cardiovascular disease; the fixed-effects estimate was 2.60 (1.55–4.38) for coronary heart disease.

CONCLUSIONS—These estimates suggest that the population-attributable fraction for the metabolic syndrome, as it is currently conceived, is 6–7% for all-cause mortality, 12–17% for cardiovascular disease, and 30–52% for diabetes. Further research is needed to establish the use of the metabolic syndrome in predicting risk for death, cardiovascular disease, and diabetes in various population subgroups.

Abbreviations: NCEP, National Cholesterol Education Program • WHO, World Health Organization

	INTRODUCTION

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
Since the World Health Organization (WHO) and National Cholesterol Education Program (NCEP) produced their working definitions of the metabolic syndrome (1,2), a great deal of research has been undertaken to define its epidemiology. However, uncertainty exists about the clinical and public health importance of the metabolic syndrome (3,4). One way to address this uncertainty is to examine the nature of adverse events and the magnitude of the risks associated with the metabolic syndrome. Chief among these risks are all-cause mortality, cardiovascular disease, and diabetes. Although studies using nonstandard definitions of the metabolic syndrome have suggested that the risk of premature death and developing cardiovascular disease or diabetes is higher among people with the metabolic syndrome compared with those who did not have this syndrome, the risks for these outcomes associated with the new definitions of the metabolic syndrome are now emerging. Several studies have produced such risk estimates for all-cause mortality, cardiovascular disease, and diabetes. This report includes a review of these studies and a meta-analysis to determine summary estimates of risk.

	RESEARCH DESIGN AND METHODS

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
The term "metabolic syndrome" was used to perform a search of PubMed from July 1998, when the WHO definition was first published, through the end of February 2005. All abstracts were reviewed, and articles describing prospective studies were retrieved and evaluated. Only prospective studies that used either the NCEP definition, including those that had substituted BMI for waist circumference, or the WHO definition, including those with limited modifications, were included (Table 1). No attempt was made to locate unpublished studies or contact authors. In the case of duplicate analyses of the same dataset, only the first publication was included.

SEs for the estimates of relative risk were estimated from the CIs. For each study, a weight was calculated as the inverse of the variance (1/SE²). Fixed-effects estimates of relative risk were calculated according to the Mantel-Haenszel method (6). Random-effects estimates of relative risk were calculated using the approach by DerSimonian and Laird (7). Heterogeneity among studies was assessed using the Q test (7). Forest plots were reviewed. The influence of single studies on the summary estimates was also examined (8). Evidence for bias was assessed by examining funnel plots and assessing funnel plot asymmetry (9,10). Analyses were conducted in Stata 8.2 (11). The population-attributable fraction for adverse events associated with the metabolic syndrome was calculated from the following formula: [(P_o x (RR-1)]/{1 + [P_o x (RR-1)]}, where P_o is the proportion in the population with the metabolic syndrome (21.8%) (12) and RR represents the summary relative risk obtained from the meta-analysis.

	RESULTS

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
Characteristics of the studies included in the analyses are shown in Table 2.

View larger version (19K): [in this window] [in a new window]   Figure 1— A: Associations between metabolic syndrome, using the NCEP definition, and all-cause mortality. B: Associations between metabolic syndrome, using the NCEP definition, and cardiovascular disease. C: Associations between metabolic syndrome, using a modification of the NCEP definition, and cardiovascular disease. D: Associations between metabolic syndrome, using the original and modified NCEP definitions, and cardiovascular disease. , studies used the exact definition. , used a modified definition. CRP, C-reactive protein; M, men; W, women.

The authors of four other publications (16,22–24) modified the NCEP definition by using BMI instead of waist circumference to define the metabolic syndrome and produced five estimates of relative risk for cardiovascular disease. The random-effects estimate was 1.87 (95% CI 1.21–2.88) (P for heterogeneity <0.001) (Fig. 1C). Combining 13 estimates from 11 studies using the original or modified NCEP definition gave a random-effects estimate of 1.74 (1.43–2.12) (P for heterogeneity <0.001) (Fig. 1D). For seven studies (13,15,18,19,21,23,24) with eight estimates of relative risk that excluded participants with diabetes or participants who were using insulin, the random-effects estimate was 1.58 (1.33–1.87) (P for heterogeneity = 0.017). For five studies (14–17,22) that included participants with diabetes, the random-effects estimate was 2.02 (1.38–2.95) (P for heterogeneity <0.001). For three studies (13,17,21) that used the exact NCEP definition of the metabolic syndrome, the random-effects estimate of relative risk for coronary heart disease was 1.82 (1.38–2.38) (P for heterogeneity = 0.038). When three studies (13,16,17,21,23,24) that used the modified NCEP definition were added, the fixed-effects estimate was 1.54 (1.39–1.72) (P for heterogeneity = 0.051).

Diabetes.
Four studies (5,18,26,27) examined the risk of developing diabetes among people with the metabolic syndrome defined by NCEP criteria. The random-effects estimate of relative risk was 2.99 (95% CI 1.96–4.57) (P for heterogeneity = 0.001) (Fig. 2). Adding a study (23) that used a modification of the NCEP definition resulted in a random-effects estimate of 3.08 (2.16–4.40) (P for heterogeneity <0.001).

View larger version (11K): [in this window] [in a new window]   Figure 2— Associations between metabolic syndrome, using the NCEP definition, and diabetes.

Cardiovascular disease.
Among studies of cardiovascular disease, two (20,25) used the most exact WHO definition. For them, the fixed-effects estimate was 1.93 (95% CI 1.39–2.67) (P for heterogeneity = 0.527). Adding two other studies (13,15,20,25) that used a modification of the WHO definition gave a fixed-effects estimate of 1.89 (1.50–2.37) (P for heterogeneity = 0.497) for the four. Adding the results from a study that made extensive changes to the WHO definition changed the fixed-effects estimate to 2.06 (1.72–2.47) (P for heterogeneity = 0.509) (28). For coronary heart disease, the fixed-effects estimate for two studies (13,20) was 2.60 (1.55–4.38) (P for heterogeneity = 0.512).

Diabetes.
Three studies reported in two publications (5,27) examined the associations between the metabolic syndrome, as defined by modified WHO criteria, and the incidence of diabetes. However, for one (5), it was difficult to obtain risk estimates because the results were presented in a figure. For the other two studies, the unadjusted fixed-effects estimate was 6.08 (95% CI 4.76–7.76) (P for heterogeneity = 0.535).

Bias
No evidence of bias was found for studies of all-cause mortality or cardiovascular disease, except for the analysis of the four studies of cardiovascular disease and the metabolic syndrome using the WHO definition (intercept: 2.76, P = 0.044; slope: 0.01, P = 0.952). For the four studies of diabetes incidence, funnel plot asymmetry was present (intercept: 4.79, P = 0.035; slope: 0.22, P = 0.246).

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 
One way to judge the utility of the WHO and NCEP definitions of the metabolic syndrome is to examine what outcomes are linked to it and the strength of these links. The sum of the evidence to date shows that the metabolic syndrome does an unremarkable job of predicting all-cause mortality (estimated summary relative risk of 1.2–1.4) and only a modest job of predicting cardiovascular disease (estimated summary relative risk of 1.7–1.9). However, it is more strongly associated with diabetes incidence.

The earliest publications (13,21) reported rather substantial associations between the metabolic syndrome and cardiovascular disease. Consequently, these reports have been cited as proof that the concept of the metabolic syndrome was meaningful and thus deserving diagnosis and treatment. However, in the case of the Finnish study, the estimates of relative risk were based on few events, and the weight of the study was small compared with later studies (13). The low-to-moderate summary estimates of relative risk are perhaps somewhat surprising, given that the metabolic syndrome includes several variables that are strong independent predictors of cardiovascular disease or diabetes.

The definitions of the metabolic syndrome developed by NCEP and WHO include people with diabetes. Diabetes is known to be a strong risk factor for cardiovascular disease. Studies that included participants with diabetes (2.02) produced higher summary risk estimates for cardiovascular disease than studies that excluded participants with diabetes (1.58).

At least three attempts (21,24,27) have been made to compare the predictive ability for cardiovascular disease of the metabolic syndrome with the Framingham Risk Score. In two studies, the metabolic syndrome was not found to improve the risk prediction beyond that achieved by the Framingham Risk Score. In a third study, however, the metabolic syndrome was a significant predictor of cardiovascular disease after adjustment for the Framingham Risk Score (24). In addition, the Diabetes Prediction Model was found to be superior to the metabolic syndrome in predicting risk for diabetes (27).

The majority of prospective studies have presented risks based on the NCEP definition. The estimates of relative risk from studies using the WHO definition are only slightly higher than those from studies using the NCEP definition. However, two studies (13,15) using both the NCEP and modified WHO definitions produced estimates for all-cause mortality and cardiovascular disease associated with the metabolic syndrome. On the basis of the NCEP definition, the fixed-effects estimates were 1.50 (95% CI 1.18–1.91) for all-cause mortality and 2.71 (1.91–3.83) for cardiovascular disease. When the modified WHO definition was used, the fixed-effects estimates were 1.37 (1.09–1.74) for all-cause mortality and 1.85 (1.34–2.55) for cardiovascular disease.

The mechanisms underlying the metabolic syndrome continue to be debated. Insulin resistance is thought by many to be the most important mechanism, and insulin resistance has been shown to predict cardiovascular disease (29). However, at least four studies (30,31,32,33) have shown that 50–70% of people with the metabolic syndrome have insulin resistance. Chronic activation of the immune system (34), disorders of the hypothalamic-pituitary-adrenal axis (35), altered glucocorticoid hormone action (36), chronic stress (37), and genetic factors may also be involved in the pathogenesis of the metabolic syndrome (38,39). The potential contributions of cytokines, hormones, and other molecules produced by adipocytes in the pathogenesis of the metabolic syndrome are being investigated.

Whether the adverse impact on health by the metabolic syndrome is greater than the sum of its parts remains unclear (40). One possible explanation for the low estimates of relative risk is that people who do not have the metabolic syndrome but who are obese or have hypertension, dyslipidemia, or hyperglycemia are included in the reference group, potentially raising the incidence rate in the reference group and thereby lowering estimates of relative risk. The definitions of the thresholds for defining abnormalities may have also factored in the estimates of relative risk. By using generally "liberal" thresholds, substantial numbers of people who are defined as having abnormalities may have had a relatively low risk of developing various adverse events.

As currently conceptualized, people with the metabolic syndrome experience an increased risk for adverse events that is not affected by the degree of severity of the individual components. However, it is quite likely that a risk gradient for adverse events occurs among people with the metabolic syndrome. Consideration should be given to developing a classification scheme for people with the metabolic syndrome that reflects the degree of abnormalities, analogous to classification schemes for blood pressure and BMI. The risks associated with such classes could then be prospectively evaluated.

Throughout its history, definitions of the metabolic syndrome have changed and are likely to evolve further. Already, a lower glucose threshold to define impaired fasting glucose (100 mg/dl) has been incorporated into the NCEP definition (40,41). How this change will affect risk estimates for adverse events remains to be determined. The WHO definition is difficult to implement in epidemiologic studies, as evidenced by the fact that most studies had to alter it.

Most of the studies that were reviewed adjusted their analyses for various potential confounders, but the degree of adjustment varied. Adjusting for variables such as age, smoking status, lipids, or lipid patterns that are not part of the definition of the metabolic syndrome seems reasonable. An important consideration in choosing potential confounders is deciding whether the factors under consideration are part of the etiologic chain. This can be a challenging decision, however, because the interrelationships among the many anthropometric and physiologic abnormalities are complex. For example, inadequate physical activity and poor dietary habits (especially excess energy intake) lead to excess weight and insulin resistance. In turn, these factors can produce a variety of abnormalities that are collectively termed the metabolic syndrome. Thus, adjusting for physical activity and energy intake could be construed as adjusting for variables that are part of the causal chain and are risk factors for the metabolic syndrome. Adjusting for variables such as inflammatory markers that may be caused by obesity and insulin resistance may be adjusting for variables that lie in the causal chain but are sequellae of obesity/insulin resistance/metabolic syndrome.

The summary of relative risks suggest that the population-attributable fraction due to the metabolic syndrome is limited. Using the NCEP definition of the metabolic syndrome, the population-attributable fraction is 6% for all-cause mortality, 12% for cardiovascular disease, and 30% for diabetes. Using the WHO definition, the population-attributable fraction is 7% for all-cause mortality, 17% for cardiovascular disease, and 52% for diabetes. Although these population-attributable fractions could be important, they need to be compared with the population-attributable fraction calculated from the sum of the population-attributable fractions of each component or analogous measures. In the case of diabetes, the population-attributable fraction for BMI alone has been estimated to be as high as 70% (42).

The population-attributable fraction may be larger in certain population subgroups. For example, the prevalence of the metabolic syndrome increases with age, reaching a prevalence of 40% in people aged 60 years (12). If estimates of relative risk in this age-group are similar to those calculated in this report, something that still needs to be established, the population-attributable fraction for cardiovascular disease might be 17%. Similarly, some evidence suggests that the risk for cardiovascular disease may be higher among women than men (15,21), although in other studies, no such sex-specific difference has been observed (17,24). If women are at higher risk, then the population-attributable fraction among women would exceed that among men, given that the prevalence of the syndrome is similar between the two sexes.

In conclusion, the evidence from published studies suggests that the ability of current definitions of the metabolic syndrome to predict the future risk of all-cause mortality and cardiovascular disease in the general population may be limited. The metabolic syndrome does a better job of predicting the future risk of diabetes. Given the attention that the metabolic syndrome has received in recent years, establishing how well the metabolic syndrome predicts future adverse health outcomes is a matter of some urgency. To improve our current understanding of the prognostic value of the metabolic syndrome, more research is needed that specifically addresses the issue of whether the metabolic syndrome improves risk prediction for adverse events above that of its individual components. In addition, studies in various population subgroups may be helpful in assessing how well the metabolic syndrome predicts risk for future adverse health events.

	Footnotes

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

Received for publication January 7, 2005. Accepted for publication March 28, 2005.

	References

TOP ABSTRACT INTRODUCTION RESEARCH DESIGN AND METHODS RESULTS CONCLUSIONS References

 

1. Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 15:539–553, 1998[Medline]
   
2. National Institutes of Health: Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III): Executive Summary. Washington, DC, U.S. Govt. Printing Office, 2001 (NIH Publication no. 01-3670)
   
3. Vinicor F, Bowman B: The metabolic syndrome: the emperor needs some consistent clothes (Letter). Diabetes Care 27:1243, 2004[Free Full Text]
   
4. Ford ES: Insulin resistance syndrome: the public health challenge. Endocr Pract 2(Suppl. 9):23–25, 2003
   
5. Laaksonen DE, Lakka HM, Niskanen LK, Kaplan GA, Salonen JT, Lakka TA: Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am J Epidemiol 156:1070–1077, 2002[Abstract/Free Full Text]
   
6. Mantel N, Haenszel W: Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22:719–748, 1959
   
7. DerSimonian R, Laird N: Meta-analysis in clinical trials. Control Clin Trials7:177–188, 1986
   
8. Tobias A: sbe26: assessing the influence of a single study in meta-analysis. Stata Tech Bull 47:15–17, 1999
   
9. Begg CB, Mazumdar M: Operating characteristics of a rank correlation test for publication bias. Biometrics 50:1088–1101, 1994[Medline]
   
10. Egger M, Davey Smith G, Schneider M, Minder CE: Bias in meta-analysis detected by a simple, graphical test. BMJ 315:629–634, 1997[Abstract/Free Full Text]
    
11. Sterne JAC, Bradburn MJ, Egger M: Meta-analysis in stata. In Systematic Reviews in Health Care. Egger M, Smith GD, Altman DG, Eds. London, BMJ Books, 1995. Update is available from http://www.bmjpg.com/books/sysrev/chapter18.pdf. Accessed 26 August 2004
    
12. 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 287:356–359, 2002[Abstract/Free Full Text]
    
13. Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, Salonen JT: The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 288:2709–2716, 2002[Abstract/Free Full Text]
    
14. Katzmarzyk PT, Church TS, Blair SN: Cardiorespiratory fitness attenuates the effects of the metabolic syndrome on all-cause and cardiovascular disease mortality in men. Arch Intern Med 164:1092–1097, 2004[Abstract/Free Full Text]
    
15. Hunt KJ, Resendez RG, Williams K, Haffner SM, Stern MP: National Cholesterol Education Program versus World Health Organization metabolic syndrome in relation to all-cause and cardiovascular mortality in the San Antonio Heart Study. Circulation 110:1251–1257, 2004[Abstract/Free Full Text]
    
16. Ford ES: The metabolic syndrome and mortality from cardiovascular disease and all-causes: findings from the National Health and Nutrition Examination Survey II Mortality Study. Atherosclerosis 173:309–314, 2004[Medline]
    
17. Onat A, Ceyhan K, Basar O, Erer B, Toprak S, Sansoy V: Metabolic syndrome: major impact on coronary risk in a population with low cholesterol levels: a prospective and cross-sectional evaluation. Atherosclerosis 165:285–292, 2002[Medline]
    
18. Resnick HE, Jones K, Ruotolo G, Jain AK, Henderson J, Lu W, Howard BV: Insulin resistance, the metabolic syndrome, and risk of incident cardiovascular disease in nondiabetic american indians: the Strong Heart Study. Diabetes Care 26:861–867, 2003[Abstract/Free Full Text]
    
19. Rutter MK, Meigs JB, Sullivan LM, D’Agostino RB Sr, Wilson PW: C-reactive protein, the metabolic syndrome, and prediction of cardiovascular events in the Framingham Offspring Study. Circulation 110:380–385, 2004[Abstract/Free Full Text]
    
20. Bonora E, Kiechl S, Willeit J, Oberhollenzer F, Egger G, Bonadonna RC, Muggeo M: Carotid atherosclerosis and coronary heart disease in the metabolic syndrome: prospective data from the Bruneck Study. Diabetes Care 26:1251–1257, 2003[Abstract/Free Full Text]
    
21. McNeill AM, Rosamond WD, Girman CJ, Golden SH, Schmidt MI, East HE, Ballantyne CM, Heiss G: The metabolic syndrome and 11-year risk of incident cardiovascular disease in the atherosclerosis risk in communities study. Diabetes Care 28:385–390, 2005[Abstract/Free Full Text]
    
22. Ridker PM, Buring JE, Cook NR, Rifai N: 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, 2003[Abstract/Free Full Text]
    
23. Sattar N, Gaw A, Scherbakova O, Ford I, O’Reilly DS, Haffner SM, Isles C, Macfarlane PW, Packard CJ, Cobbe SM, Shepherd J: 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, 2003[Abstract/Free Full Text]
    
24. Girman CJ, Rhodes T, Mercuri M, Pyorala K, Kjekshus J, Pedersen TR, Beere PA, Gotto AM, Clearfield M, the 4S Group, the AFCAPS/TexCAPS Research Group: 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, 2004[Medline]
    
25. Isomaa B, Almgren P, Tuomi T, Forsen B, Lahti K, Nissen M, Taskinen MR, Groop L: Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 24:683–689, 2001[Abstract/Free Full Text]
    
26. Lorenzo C, Okoloise M, Williams K, Stern MP, Haffner SM, the San Antonio Heart Study: The metabolic syndrome as predictor of type 2 diabetes: the San Antonio Heart Study. Diabetes Care 26:3153–3159, 2003[Abstract/Free Full Text]
    
27. Stern MP, Williams K, Gonzalez-Villalpando C, Hunt KJ, Haffner SM: Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease? Diabetes Care 27:2676–2681, 2004[Abstract/Free Full Text]
    
28. Hu G, Qiao Q, Tuomilehto J, Balkau B, Borch-Johnsen K, Pyorala K, the DECODE Study Group: Prevalence of the metabolic syndrome and its relation to all-cause and cardiovascular mortality in nondiabetic European men and women. Arch Intern Med 164:1066–1076, 2004[Abstract/Free Full Text]
    
29. Sowers JR, Frohlich ED: Insulin and insulin resistance: impact on blood pressure and cardiovascular disease. Med Clin North Am 88:63–82, 2004[Medline]
    
30. Hanley AJ, Wagenknecht LE, D’Agostino RB Jr, Zinman B, Haffner SM: Identification of subjects with insulin resistance and ß-cell dysfunction using alternative definitions of the metabolic syndrome. Diabetes 52:2740–2747, 2003[Abstract/Free Full Text]
    
31. Ascaso JF, Pardo S, Real JT, Lorente RI, Priego A, Carmena R: Diagnosing insulin resistance by simple quantitative methods in subjects with normal glucose metabolism. Diabetes Care 26:3320–3325, 2003[Abstract/Free Full Text]
    
32. Liao Y, Kwon S, Shaughnessy S, Wallace P, Hutto A, Jenkins AJ, Klein RL, Garvey WT: Critical evaluation of adult treatment panel III criteria in identifying insulin resistance with dyslipidemia. Diabetes Care 27:978–983, 2004[Abstract/Free Full Text]
    
33. Cheal KL, Abbasi F, Lamendola C, McLaughlin T, Reaven GM, Ford ES: Relationship to insulin resistance of the adult treatment panel III diagnostic criteria for identification of the metabolic syndrome. Diabetes 53:1195–1200, 2004[Abstract/Free Full Text]
    
34. Duncan BB, Schmidt MI: Chronic activation of the innate immune system may underlie the metabolic syndrome. Sao Paulo Med J 119:122–127, 2001[Medline]
    
35. Bjorntorp P, Rosmond R: The metabolic syndrome: a neuroendocrine disorder? Br J Nutr 83(Suppl. 1):S49–S57, 2000
    
36. Whorwood CB, Donovan SJ, Flanagan D, Phillips DI, Byrne CD: Increased glucocorticoid receptor expression in human skeletal muscle cells may contribute to the pathogenesis of the metabolic syndrome. Diabetes 51:1066–1075, 2002[Abstract/Free Full Text]
    
37. Vitaliano PP, Scanlan JM, Zhang J, Savage MV, Hirsch IB, Siegler IC: A path model of chronic stress, the metabolic syndrome, and coronary heart disease. Psychosom Med 64:418–435, 2002[Abstract/Free Full Text]
    
38. Groop L: Genetics of the metabolic syndrome. Br J Nutr 83(Suppl. 1):S39–S48, 2000
    
39. Arya R, Blangero J, Williams K, Almasy L, Dyer TD, Leach RJ, O’Connell P, Stern MP, Duggirala R: Factors of insulin resistance syndrome–related phenotypes are linked to genetic locations on chromosomes 6 and 7 in nondiabetic Mexican Americans. Diabetes 51:841–847, 2002[Abstract/Free Full Text]
    
40. Grundy SM, Brewer HB Jr, Cleeman JI, Smith SC Jr, Lenfant C, the American Heart Association, the National Heart, Lung, and Blood Institute: Definition of metabolic syndrome: report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation 109:433–438, 2004[Free Full Text]
    
41. Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, Kitzmiller J, Knowler WC, Lebovitz H, Lernmark A, Nathan D, Palmer J, Rizza R, Saudek C, Shaw J, Steffes M, Stern M, Tuomilehto J, Zimmet P, the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26:3160–3167, 2003[Free Full Text]
    
42. Ford ES, Williamson DF, Liu S: Weight change and diabetes incidence: findings from a national cohort of US adults. Am J Epidemiol 146:214–222, 1997[Abstract/Free Full Text]
    

CiteULike

Del.icio.us

Digg

Reddit

Technorati

This article has been cited by other articles:

				G. Bianchi, V. Rossi, A. Muscari, D. Magalotti, M. Zoli, and the Pianoro Study Group Physical activity is negatively associated with the metabolic syndrome in the elderly QJM, September 1, 2008; 101(9): 713 - 721. [Abstract] [Full Text] [PDF]

				E. S. Ford, C. Li, and N. Sattar Metabolic Syndrome and Incident Diabetes: Current state of the evidence Diabetes Care, September 1, 2008; 31(9): 1898 - 1904. [Abstract] [Full Text] [PDF]

				H. K. Choi, M. A. De Vera, and E. Krishnan Gout and the risk of type 2 diabetes among men with a high cardiovascular risk profile Rheumatology, August 18, 2008; (2008) ken305v1. [Abstract] [Full Text] [PDF]

				A. Peter, C. Weigert, H. Staiger, K. Rittig, A. Cegan, P. Lutz, F. Machicao, H.-U. Haring, and E. Schleicher Induction of stearoyl-CoA desaturase protects human arterial endothelial cells against lipotoxicity Am J Physiol Endocrinol Metab, August 1, 2008; 295(2): E339 - E349. [Abstract] [Full Text] [PDF]

				M. L. A. de Kroon, C. M. Renders, E. C. C. Kuipers, J. P. van Wouwe, S. van Buuren, G. A. de Jonge, and R. A. Hirasing Identifying metabolic syndrome without blood tests in young adults--The Terneuzen Birth Cohort Eur J Public Health, July 4, 2008; (2008) ckn056v1. [Abstract] [Full Text] [PDF]

				Y. Zhang, X. Li, D. Zou, W. Liu, J. Yang, N. Zhu, L. Huo, M. Wang, J. Hong, P. Wu, et al. Treatment of Type 2 Diabetes and Dyslipidemia with the Natural Plant Alkaloid Berberine J. Clin. Endocrinol. Metab., July 1, 2008; 93(7): 2559 - 2565. [Abstract] [Full Text] [PDF]

				K. C. Hoang, H. Ghandehari, V. A. Lopez, M. G. Barboza, and N. D. Wong Global Coronary Heart Disease Risk Assessment of Individuals With the Metabolic Syndrome in the U.S. Diabetes Care, July 1, 2008; 31(7): 1405 - 1409. [Abstract] [Full Text] [PDF]

				A. E. Mark and I. Janssen Relationship between screen time and metabolic syndrome in adolescents J. Public Health Med., June 1, 2008; 30(2): 153 - 160. [Abstract] [Full Text] [PDF]

				S.-J. Hwang, M.-Y. Lin, H.-C. Chen, S.-C. Hwang, W.-C. Yang, C.-C. Hsu, H.-C. Chiu, and L.-W. Mau Increased risk of mortality in the elderly population with late-stage chronic kidney disease: a cohort study in Taiwan Nephrol. Dial. Transplant., May 1, 2008; (2008) gfn222v1. [Abstract] [Full Text] [PDF]

				R. J Kaaja Metabolic syndrome and the menopause Menopause Int, March 1, 2008; 14(1): 21 - 25. [Abstract] [Full Text] [PDF]

				G. Hu, J. Lindstrom, P. Jousilahti, M. Peltonen, L. Sjoberg, R. Kaaja, J. Sundvall, and J. Tuomilehto The Increasing Prevalence of Metabolic Syndrome among Finnish Men and Women over a Decade J. Clin. Endocrinol. Metab., March 1, 2008; 93(3): 832 - 836. [Abstract] [Full Text] [PDF]

				J.-P. Despres, P. Poirier, J. Bergeron, A. Tremblay, I. Lemieux, and N. Almeras From individual risk factors and the metabolic syndrome to global cardiometabolic risk Eur. Heart J. Suppl., March 1, 2008; 10(suppl_B): B24 - B33. [Abstract] [Full Text] [PDF]

				C. M. Schooling, C. Q. Jiang, T. H. Lam, W. S. Zhang, K. K. Cheng, and G. M. Leung Life-Course Origins of Social Inequalities in Metabolic Risk in the Population of a Developing Country Am. J. Epidemiol., February 15, 2008; 167(4): 419 - 428. [Abstract] [Full Text] [PDF]

				C. Bianchi, R. Miccoli, G. Penno, and S. Del Prato Primary Prevention of Cardiovascular Disease in People With Dysglycemia Diabetes Care, February 1, 2008; 31(Supplement_2): S208 - S214. [Abstract] [Full Text] [PDF]

				H. I Katcher, R. S Legro, A. R Kunselman, P. J Gillies, L. M Demers, D. M Bagshaw, and P. M Kris-Etherton The effects of a whole grain enriched hypocaloric diet on cardiovascular disease risk factors in men and women with metabolic syndrome Am. J. Clinical Nutrition, January 1, 2008; 87(1): 79 - 90. [Abstract] [Full Text] [PDF]

				R. P F Dullaart, A. K Groen, G. M Dallinga-Thie, R. de Vries, W. J Sluiter, and A. van Tol Fibroblast cholesterol efflux to plasma from metabolic syndrome subjects is not defective despite low high-density lipoprotein cholesterol Eur. J. Endocrinol., January 1, 2008; 158(1): 53 - 60. [Abstract] [Full Text] [PDF]

				C. E. O'Neil and T. A. Nicklas State of the Art Reviews: Relationship Between Diet/ Physical Activity and Health American Journal of Lifestyle Medicine, December 1, 2007; 1(6): 457 - 481. [Abstract] [PDF]

				L. Berglund, M. Lefevre, H. N Ginsberg, P. M Kris-Etherton, P. J Elmer, P. W Stewart, A. Ershow, T. A Pearson, B. H Dennis, P. S Roheim, et al. Comparison of monounsaturated fat with carbohydrates as a replacement for saturated fat in subjects with a high metabolic risk profile: studies in the fasting and postprandial states Am. J. Clinical Nutrition, December 1, 2007; 86(6): 1611 - 1620. [Abstract] [Full Text] [PDF]

				J. Sierra-Johnson, A. Romero-Corral, V. K. Somers, F. Lopez-Jimenez, G. Walldius, A. Hamsten, M.-L. Hellenius, and R. M. Fisher ApoB/apoA-I ratio: an independent predictor of insulin resistance in US non-diabetic subjects Eur. Heart J., November 1, 2007; 28(21): 2637 - 2643. [Abstract] [Full Text] [PDF]

				L. Guize, F. Thomas, B. Pannier, K. Bean, B. Jego, and A. Benetos All-Cause Mortality Associated With Specific Combinations of the Metabolic Syndrome According to Recent Definitions Diabetes Care, September 1, 2007; 30(9): 2381 - 2387. [Abstract] [Full Text] [PDF]

				R. W. Grant Invited Commentary: Untangling the Web of Diabetes Causality in African Americans Am. J. Epidemiol., August 15, 2007; 166(4): 388 - 390. [Abstract] [Full Text] [PDF]

				A. Emili, H. Abushomar, and K. Nair Treating metabolic syndrome: Lifestyle change or medication? Can Fam Physician, July 1, 2007; 53(7): 1203 - 1205. [Full Text] [PDF]

				B. M.Y. Cheung, N. M.S. Wat, Y. B. Man, S. Tam, G. N. Thomas, G. M. Leung, C. H. Cheng, J. Woo, E. D. Janus, C. P. Lau, et al. Development of Diabetes in Chinese With the Metabolic Syndrome: A 6-year prospective study Diabetes Care, June 1, 2007; 30(6): 1430 - 1436. [Abstract] [Full Text] [PDF]

				A. Kadota, A. Hozawa, T. Okamura, T. Kadowak, K. Nakmaura, Y. Murakami, T. Hayakawa, Y. Kita, A. Okayama, Y. Nakamura, et al. Relationship Between Metabolic Risk Factor Clustering and Cardiovascular Mortality Stratified by High Blood Glucose and Obesity: NIPPON DATA90, 1990-2000 Diabetes Care, June 1, 2007; 30(6): 1533 - 1538. [Abstract] [Full Text] [PDF]

				N. R. Poa and P. F. Edgar Insulin Resistance Is Associated With Hypercortisolemia in Polynesian Patients Treated With Antipsychotic Medication Diabetes Care, June 1, 2007; 30(6): 1425 - 1429. [Abstract] [Full Text] [PDF]