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Original Investigation |

Prevalence of the Metabolic Syndrome and Its Relation to All-Cause and Cardiovascular Mortality in Nondiabetic European Men and Women FREE

Gang Hu; Qing Qiao; Jaakko Tuomilehto; Beverley Balkau; Knut Borch-Johnsen; Kalevi Pyorala; for the DECODE Study Group
Arch Intern Med. 2004;164(10):1066-1076. doi:10.1001/archinte.164.10.1066.
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Background  Few studies have evaluated the associations between the metabolic syndrome (by any definition) and mortality. This study examined the age- and sex-specific prevalence of the metabolic syndrome and its association with all-cause and cardiovascular mortality in nondiabetic European men and women.

Methods  The study was based on 11 prospective European cohort studies comprising 6156 men and 5356 women without diabetes and aged from 30 to 89 years, and had a median follow-up of 8.8 years. A modification of the World Health Organization definition of the metabolic syndrome was used. The subjects were considered to have the metabolic syndrome if they had hyperinsulinemia and 2 or more of the following: obesity, hypertension, dyslipidemia, or impaired glucose regulation; however, other definitions were also studied. Hazard ratios for all-cause and cardiovascular mortality were estimated with Cox models in each cohort. Meta-analyses were performed to assess the overall association of the metabolic syndrome with mortality risk.

Results  The age-standardized prevalence of the metabolic syndrome was slightly higher in men (15.7%) than in women (14.2%). Of the 1119 deaths recorded during follow-up, 432 were caused by cardiovascular disease. The overall hazard ratios for all-cause and cardiovascular mortality in persons with the metabolic syndrome compared with persons without it were 1.44 (95% confidence interval [CI], 1.17-1.84) and 2.26 (95% CI, 1.61-3.17) in men and 1.38 (95% CI, 1.02-1.87) and 2.78 (95% CI, 1.57-4.94) in women after adjustment for age, blood cholesterol levels, and smoking.

Conclusions  The overall prevalence of the metabolic syndrome in nondiabetic adult Europeans is 15%. Nondiabetic persons with the metabolic syndrome have an increased risk of death from all causes as well as cardiovascular disease.

Figures in this Article

The concept of metabolic syndrome, also known as insulin resistance syndrome, was introduced by Reaven1 in 1988. The syndrome is characterized by hyperinsulinemia with underlying insulin resistance, and a cluster of other cardiovascular risk factors including impaired glucose regulation, elevated levels of triglycerides, decreased levels of high-density lipoprotein cholesterol (HDL-C), raised blood pressure (BP), and centrally distributed obesity. The pathogenesis of the syndrome is complex and so far incompletely understood; but the interaction of obesity, sedentary lifestyle, and dietary and genetic factors are known to contribute to its development.13 The most important dimension of the metabolic syndrome is its association with the risk of developing type 2 diabetes mellitus and atherosclerotic cardiovascular disease (CVD).

Clinical and epidemiological research on the metabolic syndrome have been hampered by a lack of agreement on the definition of the syndrome and on the cutoff points defining its components. To resolve this problem, the World Health Organization (WHO) Consultation for diabetes and its complications4 and the National Cholesterol Education Program (NCEP) Expert Panel5 have recently formulated definitions for the metabolic syndrome. The WHO definition was primarily given as a working definition, to be improved in due course, to facilitate research, in particular comparisons between studies. The metabolic syndrome was defined as insulin resistance (glucose uptake in the euglycemic clamp below the lowest quartile for the general population) or impaired glucose regulation (impaired fasting glycemia, impaired glucose tolerance, or type 2 diabetes), with 2 or more of the following: (1) a BP of 140/90 mm Hg or higher; (2) triglyceride levels of 150 mg/dL (1.7 mmol/L) or higher and/or HDL-C levels less than 35 mg/dL (0.9 mmol/L) in men and less than 39 mg/dL (1.0 mmol/L) in women; (3) waist-hip ratio greater than 0.90 in men and greater than 0.85 in women and/or body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters) greater than 30; or (4) microalbuminuria. The European Group for the Study of Insulin Resistance (EGIR) has proposed several modifications for the WHO working definition to identify the metabolic syndrome in nondiabetic individuals.6 Because the euglycemic hyperinsulinemic clamp technique is not applicable to epidemiological research or clinical practice, EGIR recommended that fasting plasma insulin rather than the euglycemic clamp be used as a proxy for the identification of insulin-resistant individuals, with plasma insulin concentrations above the highest quartile for the nondiabetic general population indicating the presence of insulin resistance. To provide simpler criteria for impaired glucose regulation, measuring the value of fasting plasma glucose (plasma glucose ≥110 mg/dL [6.1 mmol/L]) and omitting the oral glucose tolerance test was recommended. To comply with the recommendations of the Second Joint European Societies' task force on coronary heart disease prevention,7 the following criteria were recommended to define dyslipidemia: triglyceride level higher than 177 mg/dL (2.0 mmol/L) and/or HDL-C level less than 39 mg/dL (1.0 mmol/L) in both men and women. Furthermore, EGIR recommended that BMI be omitted from obesity criteria; that waist circumference (≥94 cm in men and ≥80 cm in women) be used instead of waist-hip ratio as an index of central obesity because it is simpler to measure and better correlated with intra-abdominal visceral adipose tissue mass8; and that microalbuminuria be omitted from the definition of the metabolic syndrome.

The NCEP definition of the metabolic syndrome was developed for clinical use.5 It does not include any estimation of insulin resistance and is based on the presence of 3 or more of the following components: (1) fasting plasma glucose concentration greater than 110 mg/dL (6.1 mmol/L); (2) a triglyceride concentration of 150 mg/dL (1.69 mmol/L) or greater; (3) a HDL-C concentration less than 40 mg/dL (1.04 mmol/L) in men and less than 50 mg/dL (1.29 mmol/L) in women; (4) a BP of 130/85 mm Hg or greater; and (5) a waist circumference greater than 102 cm in men and 88 cm in women. Recently, the American Association of Clinical Endocrinologists has also given its recommendations for the clinical detection of the metabolic syndrome (although they prefer the expression insulin resistance syndrome) in nondiabetic persons.9 Their recommendation follows that given by the NCEP but emphasizes that obesity, measured either as BMI or waist circumference, should be viewed as a physiological variable that increases insulin resistance rather than a parameter for the diagnosis of the syndrome. According to the American Association of Clinical Endocrinologists definition, nondiabetic persons have the insulin resistance syndrome if they meet 2 or more of the following criteria: (1) a triglyceride concentration of 150 mg/dL (1.69 mmol/L) or greater; (2) a HDL-C concentration less than 40 mg/dL (1.04 mmol/L) in men and less than 50 mg/dL (1.29 mmol/L) in women; (3) a BP of 130/85 mm Hg or greater or current use of antihypertensive medications; and (4) 2-hour glucose concentration between 140 and 199 mg/dL (7.8 and 11.09 mmol/L) (postload glucose rather than fasting glucose was chosen because of its greater sensitivity).

Before the publication of the WHO and NCEP definitions of the metabolic syndrome, some prospective studies had already examined the association of the cluster of the metabolic syndrome components with the risk of CVD using their own cutoff values for the components. Based on a large population-based Italian study of 22 256 men and 18 495 women, Trevisan and colleagues10 reported that the risk of death from all causes as well as CVD increased with the number of metabolic abnormalities (high blood glucose level, high BP, low HDL-C level, and high triglyceride level) in both men and women. In men, the point estimates of hazard ratios were almost similar for participants with 3 abnormalities and those with the full cluster, about 2.0 for all-cause mortality and about 2.5 for CVD mortality. The Framingham Offspring Study, which included 2406 men and 2569 women aged from 19 to 74 years, examined the clustering of metabolic factors in relation to coronary heart disease (CHD) risk.11 The 6 metabolically linked risk factors considered were HDL-C level (the lowest sex-specific quintile); BMI; systolic BP; and triglycerides, glucose, and total cholesterol levels (the highest quintiles). With a cluster of 3 or more risk factors the risk of CHD increased 2.4-fold for men and 5.9-fold for women.

In the Botnia Study, a large family study of subjects with type 2 diabetes and their relatives in western Finland,12 the metabolic syndrome was defined using the WHO definition. The prevalence of the metabolic syndrome was 15% in nondiabetic men and 10% in nondiabetic women. In multivariate analyses of the data from the entire study population—it included diabetic subjects and combined data for men and women—the presence of the metabolic syndrome was associated with a 6-fold increase in CVD mortality during the 6.9-year follow-up. Another Finnish study, the Kuopio Ischemic Heart Disease Study, has recently reported the results of a 11.4-year follow-up study of middle-aged men that examined the predictive value of both the WHO and NCEP definitions of the metabolic syndrome and their modifications with regard to CVD and all-cause mortality.13 The prevalence of the metabolic syndrome ranged from 8.8% to 14.3% depending on the definition. The study found that all-cause mortality associated with the metabolic syndrome as defined by the WHO was increased 1.9-fold and CVD mortality 2.6-fold, whereas the NCEP definition less consistently predicted all-cause and CVD mortality. Two studies from the United States have recently reported estimates on the prevalence of the metabolic syndrome by the NCEP definition. A study based on the Third National Health and Nutrition Examination Survey (NHANES III) estimated that in the United States approximately 24% of adult men and 23% of adult women have the metabolic syndrome.14 In the Atherosclerosis Risk in Communities (ARIC) study population the prevalence of the metabolic syndrome by the NCEP definition was 28% in men and 34% in women.15 During the 11-year follow-up of the ARIC study population, the increase in CHD risk associated with the presence of the metabolic syndrome at baseline was 1.7-fold in men and 2.6-fold in women and the increase in the risk of stroke was 2.1-fold in men and 2.4-fold in women.

Undertaken in 1997 upon the initiative of the European Diabetes Epidemiology Group, the Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe (DECODE) study built a data set that included the baseline values needed to determine the presence of the metabolic syndrome using a modification of the WHO definition for 11 European study cohorts, and follow-up data on all-cause and CVD mortality. The aims of the present study were to examine the age- and sex-specific prevalence of the metabolic syndrome for the nondiabetic subjects of these study cohorts, and to assess by meta-analyses the predictive value of the metabolic syndrome with regard to all-cause and CVD mortality.

STUDY COHORTS AND METHODS

Centers in Europe that had performed population-based studies or large studies in occupational groups using the standard 2-hour 75-g oral glucose tolerance test were invited to participate in the DECODE study. The study populations and the methods used to recruit the participants have been described previously.1619 Individual data on fasting and 2-hour plasma glucose concentrations and a number of other variables were sent to the Diabetes and Genetic Epidemiology Unit of the National Public Health Institute in Helsinki, Finland, for data analyses. In this article, only the studies with follow-up data on cause-specific mortality and all required variables (BMI, BP, plasma triglyceride and/or HDL-C concentrations, fasting plasma insulin concentrations, and smoking habits) were included.

Eleven cohorts provided data on all-cause and CVD mortality, of which only 8 included data on women. Subjects with previously and newly diagnosed diabetes at baseline were excluded from the present analyses. The final study population comprised 6156 men and 5351 women aged from 30 through 89 years. The median duration of follow-up was 8.8 years (interquartile range, 6.8-9.6 years).

Plasma glucose concentration was determined in 9 of the 11 cohorts, whole blood glucose in 1, and serum glucose in 1. Before the data were analyzed, glucose concentrations were transformed to plasma glucose concentrations using the following equations:

(1) Plasma Glucose (millimoles per liter) = 0.558 + 1.119 × Whole Blood Glucose (millimoles per liter)

(2) Plasma Glucose (millimoles per liter) = − 0.137 + 1.047 × Serum Glucose (millimoles per liter)

The equations are based on 294 matched samples of whole blood (capillary and serum) glucose and plasma glucose concentration values obtained from a standard 75-g oral glucose tolerance test administered to 74 individuals at 0, 30, 60, and 120 minutes at the Diabetes and Genetic Epidemiology Unit, National Public Health Institute, Finland. The relationships between glucose concentrations as measured by the different methods used were estimated using a mixed model with random effects of the individual and sample developed in the Steno Diabetes Center in Denmark (this model was personally communicated to J.T. by B. Carstensen, PhD, in 2002).

Subjects were classified as never smokers, ex-smokers, and current smokers. Hyperinsulinemia was defined using the highest quartile of fasting plasma insulin as a threshold. Sex- and cohort-specific quartile cutoff points for fasting insulin were used in the analyses.

CLASSIFICATION OF GLUCOSE ABNORMALITIES

According to the 1999 WHO Consultation recommendations for the diagnosis of diabetes,4 subjects with previously diagnosed diabetes (treated with medication or diet, and a reported history of diabetes diagnosed by a physician) or with a fasting plasma glucose level of 126 mg/dL or greater (≥7.0 mmol/L) and/or 2-hour plasma glucose of 200 mg/dL or greater (≥11.1 mmol/L) were considered to have diabetes. Subjects with fasting plasma glucose level of 110 mg/dL or greater (≥6.1 mmol/L) but less than 126 mg/dL (<7.0 mmol/L) and/or 2-hour plasma glucose level of 140 mg/dL or greater (≥7.8 mmol/L) but less than 200 mg/dL (<11.1 mmol/L) were categorized as having impaired glucose regulation.

DEFINITION OF THE METABOLIC SYNDROME

A modification of the WHO definition of the metabolic syndrome was used. The subjects were considered to have the metabolic syndrome if they had hyperinsulinemia as defined by EGIR (fasting plasma insulin values greater than those for the highest sex- and cohort-specific quartile of the nondiabetic background population), and 2 or more of the following components: (1) obesity, defined by a BMI greater than 30; (2) hypertension, defined as a systolic BP of 140 mm Hg or greater and/or a diastolic BP of 90 mm Hg or greater, or use of antihypertensive drugs; (3) dyslipidemia, raised plasma triglyceride levels (≥150 mg/dL [1.7 mmol/L]) and/or low HDL-C levels (<35 mg/dL [0.9 mmol/L] in men and <39 mg/dL [1.0 mmol/L] in women); and (4) impaired glucose regulation. We could not include the waist-hip ratio in the definition of obesity because waist and hip circumference measurements were only available in 6 studies. Because HDL-C measurements had not been made in the Helsinki Policemen Study, the presence of dyslipidemia was based on triglyceride level in this study. Information on the use of hypolipidemic drugs was not available, but at the time of the baseline examinations of the study cohorts the use of such drugs was very rare. Microalbuminuria was omitted from the definition because data were not available.

We also analyzed our data using a stricter definition of the metabolic syndrome: the presence of hyperinsulinemia plus 3 or more of the other components. To obtain information on the incremental value of hyperinsulinemia in the definition of the syndrome, the data were also analyzed without inclusion of hyperinsulinemia in the definitions.

FOLLOW-UP

In all studies vital status and the date and cause of death for those deceased were recorded for each subject attending the baseline examination. Subjects who had left the country and for whom the vital status could not be confirmed were treated as censored at the time of emigration. The follow-up was almost complete, from 98% to 100%.18 The International Classification of Diseases (eighth, ninth, and tenth revisions) were used for coding the causes of death; the codes used for CVD were 401-448 (eighth and ninth revisions) and I10-I79 (tenth revision).

STATISTICAL ANALYSIS

Data analyses were performed with the statistical software SPSS for Windows, version 11.0 (SPSS Inc, Chicago, Ill). Sex-specific prevalence of individual metabolic abnormalities of the metabolic syndrome according to cohort- and sex-specific quartiles of fasting insulin in the pooled data were tested using logistic regression after adjustment for age and center. Age- and sex-specific prevalence of the metabolic syndrome and its components in the pooled data set comprising 8 DECODE studies that included men and women were calculated by 10-year age intervals and were age-standardized by the direct method using a standard European population aged from 40 through 89 years.20

The Cox proportional hazards model was used to estimate the association of the metabolic syndrome with the risks of all-cause and CVD mortality by sex in each cohort, adjusting for age, cholesterol, and smoking. Meta-analyses were performed to assess the overall association of the metabolic syndrome with the risks of all-cause and CVD mortality using a fixed effect approach according to known methods.21 A fixed rather than a random effects approach was chosen because the Q statistic for measuring study-to-study variation in effect size was not statistically significant. The results are reported as hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality. In the meta-analyses of data for CVD mortality in women, only the 7 studies in which the number of CVD deaths was at least 4 for both sexes were included. Calculations of sensitivity, specificity, and positive predictive value of the different combinations of the components of the metabolic syndrome with regard to all-cause and CVD mortality were made using the pooled data set of these 7 studies.

PREVALENCE

The number of subjects in the 11 study cohorts and their age and sex distribution are shown in Table 1. There was a wide range of medians for continuous risk factors between cohorts (Table 2). The prevalence of obesity, hypertension, dyslipidemia, and impaired glucose regulation, as well as the prevalence of a combination of 2 or more and 3 or more of the above components increased gradually over the quartiles of fasting insulin for both men and women (Table 3). Furthermore, the prevalence of individual metabolic abnormalities and of patients with 2 or more and 3 or more of the above components increased markedly in the highest quartile of fasting insulin values.

Table Graphic Jump LocationTable 1. Demographic Characteristics of Subjects and Number of All-Cause and Cardiovascular Deaths During Follow-up in 11 DECODE Study Cohorts*
Table Graphic Jump LocationTable 2. Medians or Prevalence of Metabolic Risk Factors at Baseline in Pooled Study Cohorts by Sex*
Table Graphic Jump LocationTable 3. Prevalence of Individual Components of the Metabolic Syndrome According to Cohort- and Sex-Specific Quartiles of Plasma Insulin Levels in the Pooled DECODE Study Data by Sex*

Table 4 illustrates within a narrow age band, 50 through 59 years, the prevalence of individual components of the metabolic syndrome and their combinations in the 8 DECODE study cohorts that included men and women. There were differences in the prevalence of almost all risk factor components of the metabolic syndrome, particularly the prevalence of obesity, between the 8 DECODE study cohorts. Dyslipidemia showed less variation between the cohorts analyzed by sex, although there was a marked difference between men and women. There were also marked differences between populations in the prevalence of the metabolic syndrome by different definitions.

Table Graphic Jump LocationTable 4. Prevalence of the Components of the Metabolic Syndrome and Their Combinations Among Subjects Aged 50 Through 59 Years in Each Study Cohort by Sex

Table 5 shows the prevalence of the components of the metabolic syndrome and their combinations in the pooled data set comprising 8 DECODE studies. Men had a significantly higher age-standardized prevalence of hypertension, dyslipidemia, impaired glucose regulation, and any combinations of the metabolic syndrome, but a lower age-standardized prevalence of obesity than women. The prevalence of the individual components of the metabolic syndrome and their different combinations increased with age for both men and women. However, the age-related increase in the prevalence of obesity and dyslipidemia in both men and women, impaired glucose regulation in men, 2 or more and 3 or more of the components in men, hyperinsulinemia plus 2 or more of the components in men, and hyperinsulinemia plus 3 or more of the components in women, seemed to attenuate in the oldest age group (70-89 years). Figure 1 shows the prevalence of the metabolic syndrome per the modified WHO definition (hyperinsulinemia plus 2 or more of the other components) by age among men and women in the 4 studies (Cremona Study, Newcastle Study, Goodinge Study, and Northern Sweden Monitoring of Trends and Determinants in Cardiovascular Disease [MONICA] study) that included at least 4 age groups. The prevalence of the metabolic syndrome increased steeply with age, reaching its peak in the 60- through 69-year age group in men, and, in women, either in the 60- through 69-year age group or in the 70- through 89-year age group.

Table Graphic Jump LocationTable 5. Prevalence of the Components of the Metabolic Syndrome and Their Combinations in the Pooled Data of 8 DECODE Studies by Sex and Age in Subjects 40 Years or Older*
Place holder to copy figure label and caption

Prevalence of the metabolic syndrome by the modified World Health Organization definition (hyperinsulinemia plus 2 or more of the other components) by age among men (A) and women (B) in 4 studies that included at least 4 age groups (Cremona Study, Newcastle Study, Goodinge Study, and Northern Sweden Monitoring of Trends and Determinants in Cardiovascular Disease [MONICA]).

Graphic Jump Location
ASSOCIATION WITH ALL-CAUSE AND CARDIOVASCULAR MORTALITY

During the follow-up (median duration, 8.8 years), 1119 deaths were recorded, of which (39%) were ascribed to CVD (Table 1). The results of the meta-analyses of the risk of all-cause and CVD mortality associated with the metabolic syndrome defined by different sets of its components are summarized in Table 6. These results are from 7 studies including both men and women and with at least 4 CVD deaths among women in each study. In men, with different definitions of the metabolic syndrome, multivariate-adjusted overall HRs associated with the presence of the metabolic syndrome ranged from 1.39 to 1.47 for all-cause mortality and from 1.74 to 2.26 for CVD mortality, relative to men without the metabolic syndrome. In women, the corresponding HRs ranged from 1.23 to 1.49 and from 1.56 to 2.78. In meta-analyses of data for men in all 11 studies, the HRs associated with the metabolic syndrome ranged from 1.26 to 1.40 for all-cause mortality and from 1.41 to 1.81 for CVD mortality, all these HRs being statistically significant (detailed data not shown). As a definition of the metabolic syndrome without inclusion of hyperinsulinemia, the presence of 2 or more of its components was, for both men and women, a poorer predictor of all-cause mortality than the presence of 3 or more of the components. Inclusion of hyperinsulinemia with 2 or more of the other components led to an improvement in the predictive value, particularly with regard to CVD mortality, whereas its inclusion with 3 or more of the other components had very little effect on the predictive value.

Table Graphic Jump LocationTable 6. Meta-analyses of the Association of the Metabolic Syndrome With the Risk of All-Cause and Cardiovascular Mortality in 7 DECODE Study Cohorts by Sex*

Table 7 shows estimates of sensitivity, specificity, and positive predictive value for the different definitions of the metabolic syndrome. Sensitivity estimates for any definition of the metabolic syndrome were rather similar and low for both men and women, and became lower with stricter definitions. Specificity estimates were rather high and similar for both sexes, and improved with stricter definitions. Because the absolute risks of death from all causes and CVD during the follow-up period were lower for women than for men, the estimates for positive predictive value of the metabolic syndrome with any definition were also lower for women.

Table Graphic Jump LocationTable 7. Sensitivity, Specificity, and Positive Predictive Value of Different Definitions of the Metabolic Syndrome in Men and Women in the Pooled Data of 7 DECODE Study Cohorts*

The present study based on 11 European study cohorts showed that 15.7% of nondiabetic men and 14.2% of nondiabetic women had the metabolic syndrome as defined by a modified WHO definition (hyperinsulinemia plus 2 or more other components including obesity, hypertension, dyslipidemia, and impaired glucose regulation). When the syndrome was defined by the presence of 2 or more components without hyperinsulinemia, the prevalence was 35.3% for men and 29.9% for women; by the presence of 3 or more components without hyperinsulinemia, it was 12.4% for men and 10.7% for women; and by the presence of 3 or more components plus hyperinsulinemia, it was 7.7% for men and 6.3% for women. The presence of the metabolic syndrome by the modified WHO definition was associated with a 1.4-fold increase in the risk of all-cause mortality for both men and women, and with a 2.3-fold increase in the risk of CVD mortality for men and 2.8-fold for women.

PREVALENCE OF THE METABOLIC SYNDROME

The prevalence rates of the metabolic syndrome reported in the different studies have varied widely, mainly because of differences in the definitions of the syndrome and also, in part, because of differences in the characteristics of the populations studied.1015,2227 In the Finnish Botnia study, the prevalence of the metabolic syndrome using the original WHO definition was 15% in nondiabetic men and 10% in nondiabetic women.12 The Botnia study, however, was not a truly population-based study because it recruited people with type 2 diabetes and their relatives, and thus diabetes was present in more than one third of the cohort. An investigation conducted by EGIR regarding the prevalence of the metabolic syndrome in 8 European study cohorts using the WHO definition and its EGIR modification included a total of 8200 men and 9363 women.24 In nondiabetic subjects the prevalence of the syndrome by the WHO definition in persons aged 40 through 55 years varied between study cohorts from 7% to 36% in men and from 5% to 22% in women. When defined by the EGIR criteria, the syndrome was less frequent. In our study, the between-cohort differences in the prevalence of the metabolic syndrome by the modified WHO definition were also wide in persons aged from 50 through 59 years (7%-23% in men and 9%-18% in women). These marked differences in the prevalence of the metabolic syndrome between our study cohorts were found to be due to large between-cohort differences in the prevalences of its components, in particular prevalences for obesity, hypertension, and impaired glucose regulation. A study based on the NHANES III survey that used the NCEP definition and did not exclude individuals with diabetes was recently conducted in the United States. It estimated that approximately 23% of US adults have the metabolic syndrome but found that there were marked differences in prevalence among ethnic groups and noted that the prevalence was highest for Mexican Americans and lowest for blacks of both sexes.25 The Strong Heart Study used the NCEP definition and reported a high prevalence of the metabolic syndrome for American Indians aged from 45 through 74 years (55.2% for the entire study population and 35% for nondiabetic individuals).27 Ford and colleagues26 compared the prevalence of the metabolic syndrome in the NHANES III survey according to the WHO and NCEP definitions and found that the 2 definitions gave a similar prevalence for the entire sample but that, for some subgroups, they produced a substantial discordance. In our study cohorts the prevalence of the metabolic syndrome with any definition increased with age and reached its peak in the seventh decade for men and in the seventh or eighth decade for women, which is in close agreement with the findings reported from the NHANES III survey.25

Different cutoff points and other differences in the criteria for the individual components of the metabolic syndrome will lead to differences in their prevalences and thereby will have an important impact on the prevalence of the metabolic syndrome. In the pooled data from 8 cohorts used in our study, including data for subjects 40 years or older, the overall prevalence of hypertension by our modified WHO criteria (≥140/90 mm Hg or use of antihypertensive drugs) was 48.5% for men and 45.2% for women; by the NCEP criteria (≥130/85 mm Hg or use of antihypertensive drugs), however, it was 66.5% for men and 60.6% for women. As to the overall prevalence of impaired glucose regulation in the same pooled data, by the criteria used in our study (fasting plasma glucose ≥110 mg/dL [6.1 mmol/L] but <126 mg/dL [7.0 mmol/L] and/or 2-hour plasma glucose ≥140 mg/dL [7.8 mmol/L]) but <200 mg/dL [11.1 mmol/L]) it was 22.2% for men and 17.0% for women; by the NCEP criterion based only on fasting plasma glucose (≥110 mg/dL), however, it was 15.7% in men and 8.3% in women. Direct comparison of the prevalence of dyslipidemia by the WHO criteria (triglycerides ≥150 mg/dL [1.7 mmol/L] and/or HDL-C <35 mg/dL [0.9 mmol/L] in men and <39 mg/dL [1.0 mmol/L] in women) and the prevalence of lipid abnormalities by the NCEP criteria for lipid abnormalities was not possible. This is because the NCEP criteria for elevated triglycerides and low HDL-C are considered to be separate components of the metabolic syndrome. We were able to compare prevalences obtained for obesity by different criteria in the pooled data of 6 DECODE study cohorts from which all the anthropometric measurements needed were available. In this pooled data set the following prevalences for obesity were obtained: using the original WHO criteria (waist-hip ratio >0.90 in men and >0.85 in women and/or BMI >30) obesity prevalence was 68.8% for men and 36.1% for women; using our criterion (BMI ≥30) it was 14.1% for men and 18.3% for women, using the NECP criterion (waist circumference >102 cm for men and >88 cm for women) it was 22.1% for men and 33.7% for women, and applying the much stricter waist circumference criteria proposed by EGIR (≥94 cm for men and ≥80 cm for women) it was 51.7% for men and 60.9% for women.

In our modified WHO definition, the presence of hyperinsulinemia, defined by the cutoff for the cohort- and sex-specific highest quartile for fasting plasma insulin, is the prerequisite for the syndrome; additionally, the presence of any combination of 2 or more of the other components is required. Compared with the prevalence of the syndrome, if defined by the presence of 2 or more of the other components, inclusion of hyperinsulinemia reduced the overall prevalence by more than one half. If the syndrome was defined by the presence of 3 or more of the other components, inclusion of hyperinsulinemia reduced the prevalence by two fifths.

METABOLIC SYNDROME AND RISK OF CVD

Prospective studies on the relationship between the metabolic syndrome and CVD risk are still scanty. Results from a large population-based Italian study,10 the Framingham Offspring Study,11 the Botnia Study,12 the Kuopio Ischemic Heart Disease Study,13 and the ARIC study15 have shown that the presence of the metabolic syndrome is associated with a significantly increased risk of all-cause mortality and CVD morbidity and mortality regardless of its definitions. However, in the Strong Heart Study of nondiabetic American Indians the metabolic syndrome by the NCEP definition did not predict an increase in incident CVD events.27 In our study, the presence of the metabolic syndrome by the modified WHO definition was a statistically significant predictor of all-cause and CVD mortality in both men and women after adjustment for age, cholesterol levels, and smoking. Although comparison with findings from other prospective studies have to be made with caution because of differences in the characteristics of the study populations and in the definitions of the metabolic syndrome, our findings of a 1.4-fold increase in all-cause mortality and of a more than 2-fold increase in CVD mortality for both men and women are similar to the findings of the Kuopio Ischemic Heart Disease Study obtained by applying the WHO and NCEP definitions of the syndrome in the male population,13 and they also concur with the findings of the ARIC study15 on the association of the metabolic syndrome by the NCEP definition with the risk of incident CHD and stoke events in men and women. Our results, in accordance with the findings from the ARIC study,15 indicate that the increase in the relative risk associated with the metabolic syndrome is similar in men and women.

The pooled data of 11 DECODE study cohorts showed that 43.8% of all deaths were from CVD in men and 24.0% in women. During the follow-up (median, 8.8 years) the absolute risk of all-cause and CVD mortality was rather low in our study cohorts that excluded persons with diabetes and comprised mainly middle-aged men and women, and therefore it is understandable that rather low sensitivity estimates were obtained for different definitions of the metabolic syndrome in the prediction of all-cause and CVD death. The same applied to estimates of their positive predictive value (Table 7). However, estimates obtained for their specificity were reasonably high, particularly for the stricter definitions.

Following an EGIR recommendation primarily given for clinical research purposes, we used hyperinsulinemia, defined by the cutoff for the highest fasting plasma insulin quartile, as a proxy for insulin resistance. The precision and standardization of plasma insulin measurements have improved with the introduction of methods that are specific for intact (true) insulin; however, intraindividual variation in plasma insulin levels is wide and a single plasma insulin measurement does not always give a correct estimate of the "usual" plasma insulin level of an individual.28 This explains, in part, why the correlation of fasting plasma insulin concentration (obtained using either the euglycemic clamp29 or the insulin suppression test30) is only about 0.60. It means that fasting plasma insulin concentration explains only about 40% of the variance in insulin resistance. Although it has been proposed that fasting plasma insulin could be used as amarker of insulin resistance in the clinical screening of individuals with the metabolic syndrome and concomitant high risk of CVD,31 for the reasons given above a single plasma insulin measurement is far from a perfect marker in the classification of individuals with regard to their insulin resistance.

If insulin resistance is considered to be the key underlying feature of the metabolic syndrome, as proposed by Reaven,1 it is important to recognize 2 facts in the assessment of the performance of different definitions for the syndrome, with or without a surrogate marker for insulin resistance. First, insulin resistance is a continuous rather than a dichotomous characteristic; and second, the prevalence of other components of the syndrome and their combinations increases with increasing levels of the surrogate marker, fasting plasma insulin (Table 3). In another study based on the same 11 DECODE study cohorts we have demonstrated by meta-analyses that there was a statistically significant positive association between fasting plasma insulin and the risk of CVD death, and that this association was largely independent of other risk factors, including those risk factors considered to belong to the core components of the metabolic syndrome (DECODE Study Group, unpublished data, 2003). This association was found to be similar for men and women and could be demonstrated not only by defining hyperinsulinemia as the highest quartile of fasting plasma insulin levels, but also using fasting insulin as a continuous variable. This finding is compatible with the view that the association of plasma insulin level and the underlying insulin resistance with the risk of CVD death is not found only in the most insulin-resistant individuals but may be continuous in character.

The problems inherent in the use of plasma insulin measurements in clinical practice being recognized, 3 or more of its other components appeared to be the most useful definition of the metabolic syndrome for clinical practice because it gives reasonable prevalence estimates, 12.9% for men and 11.0% for women. Of the individuals fulfilling this definition, 59.5% of the men and 60.0% of the women were in the highest quartile of plasma insulin levels. Estimates for the sensitivity, specificity, and positive predictive value of this definition were close to those for our modified WHO definition that included plasma insulin.

STUDY LIMITATIONS AND STRENGTHS

The principal limitation of our study was that the protocols and methods used for the assessment of risk factors at baseline had not been uniform in the 11 DECODE study cohorts, because studies included in the DECODE study had originally been conducted independently of each other. To overcome the influence of diversity in the methods used for the determination of fasting plasma insulin, reflected in large differences in the medians and interquartile ranges between populations, we used sex- and cohort-specific cutoffs for the highest insulin quartile for the definition of hyperinsulinemia. Other continuous risk factor variables were, however, used as provided and methodological differences leading even to relatively minor shifts in the distributions of these variables from their "true" distributions will have had their influence on the prevalences of individual risk factor components and their combinations. As mentioned earlier, the 11 study populations did not all have data on waist-hip ratio, which is included, in addition to BMI, in the original WHO criteria for obesity. According to comparisons made in a subset of 6 studies, the use of BMI only led to a markedly lower prevalence of obesity than the rates obtained with the original WHO criteria. Another limitation of our study was that, owing to the lack of uniformity of data collection at baseline, we could not exclude individuals with prevalent CVD. In general, the relationships of the risk factors with the risk of CVD events tend to become somewhat weaker after the advent of clinically manifest CVD because the history of a previous acute CVD event is a very strong predictor of subsequent events. Thus, the inclusion of individuals with prevalent CVD may have led to a reduction in the risk ascribed to the metabolic syndrome. Excluding from our analyses persons with previously known and newly detected diabetes strengthened our study in 2 ways. First, it is known that fasting insulin concentration falls after the onset of diabetes as a result of β-cell failure and by this exclusion we avoided the resulting uncertainty in the use of fasting insulin as a marker of insulin resistance. Second, by excluding persons with diabetes at baseline, although we did not have information on the development of diabetes during the follow-up, it became unlikely that diabetes would have explained the increased mortality risk of individuals with the metabolic syndrome.

The main strength of our study is that it is based on a large number of middle-aged and elderly European men and women followed up for a median of 8.8 years, which led to a relatively large number of deaths from all causes and CVD. This data set allowed us to examine the age- and sex-specific prevalence of the metabolic syndrome and its components in study cohorts from different European countries. Finally, meta-analyses of the DECODE study data allowed a comparison of the predictive value of the metabolic syndrome with regard to all-cause and CVD mortality for men and women.

IMPLICATIONS FOR FURTHER RESEARCH

The results of our study, in concurrence with findings from other recent studies,1013,15 leave no doubt about the increased risk of CVD in individuals with the metabolic syndrome. In addition to the modified WHO definition of the metabolic syndrome, we also examined the prevalence of the syndrome and its predictive value by other definitions based on the components of the WHO definitions. Our findings regarding the prevalence of the syndrome by different definitions and the sensitivity, specificity, and positive predictive value of these definitions with regard to risk of death from all causes and CVD point out that much further research is needed to identify the best possible definition for the metabolic syndrome for the purposes of preventive clinical practice. Further work is in progress based on data of those DECODE study cohorts from which all variables needed for the comparison of the WHO, EGIR, and NCEP definitions are available. It is likely that substantial differences in the criteria and in the cutoffs for some components of the syndrome will lead to differences in the phenotype of persons deemed to have the metabolic syndrome by these definitions.

The results from clinical trials conducted in China, Finland, and the United States have demonstrated that lifestyle interventions (reduction of obesity, increased physical activity, and dietary changes) reduce the risk of progression from impaired glucose tolerance to type 2 diabetes and also improve several CVD risk factors.3234 Lifestyle intervention would obviously be a relevant strategy for the management of the metabolic syndrome, and may substantially reduce the risk of CVD associated with the syndrome. The effectiveness of lifestyle interventions needs, however, to be tested in large-scale trials. Before such trials can be adequately planned, further research is needed to validate and improve methods for the identification of individuals with the metabolic syndrome who are at high risk for CVD and premature mortality.

Corresponding author and reprints: Gang Hu, MD, Diabetes and Genetic Epidemiology Unit, Department of Epidemiology and Health Promotion, National Public Health Institute, Mannerheimintie 166, FIN-00300 Helsinki, Finland (e-mail: hu.gang@ktl.fi).

Accepted for publication June 30, 2003.

This analysis has been carried out with the help of grants from Novartis Pharma AG, Basel, Switzerland; AstraZeneca R&D Mölndal, Sweden; and Finnish Academy grants 46558, 76502, 77618, 204274, and 205657.

The DECODE study was funded by Novo Nordisk, Bagsvaerd, Denmark.

Reaven  GM Banting Lecture 1988: role of insulin resistance in human disease. Diabetes. 1988;371595- 1607
PubMed
Liese  ADMayer-Davis  EJHaffner  SM Development of the multiple metabolic syndrome: an epidemiological perspective. Epidemiol Rev. 1998;20157- 172
PubMed
Laws  AReaven  GM Insulin resistance and risk factors for coronary heart disease. Bailliers Clin Endocrinol Metab. 1993;71063- 1078
Alberti  KZimmet  Pfor a WHO Consultation, Diagnosis and Classification of Diabetes Mellitus.  Geneva, Swizerland World Health Organization1999;Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications. Part 1.
Not Available, 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;2852486- 2497
PubMed
Balkau  BCharles  MA Comment on the provisional report from the WHO consultation: European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 1999;16442- 443
PubMed
Wood  DADe Backer  GFaergeman  OGraham  IMancia  GPyörälä  K Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and Other Societies on Coronary Prevention. Eur Heart J. 1998;191434- 1503
PubMed
Pouliot  MDesprés  JPMelieux  S  et al.  Waist circumference and abdominal sagittal diameter: the best simple anthropometric measures of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. 1994;73460- 468
PubMed
Bloomgarden  ZT American Association of Clinical Endocrinologists (AACE) Consensus Conference on the Insulin Resistance Syndrome: 25–26 August 2002, Washington, DC. Diabetes Care. 2003;26933- 939
PubMed
Trevisan  MLiu  JBahsas  FBMenotti  A Syndrome X and mortality: a population-based study: Risk Factor and Life Expectancy Research Group. Am J Epidemiol. 1998;148958- 966
PubMed
Wilson  PWKannel  WBSilbershatz  HD'Agostino  RB Clustering of metabolic factors and coronary heart disease. Arch Intern Med. 1999;1591104- 1109
PubMed
Isomaa  BAlmgren  PTuomi  T  et al.  Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care. 2001;24683- 689
PubMed
Lakka  HMLaaksonen  DELakka  TA  et al.  The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;2882709- 2716
PubMed
Ford  ESGiles  WHDietz  WH Prevalence of the metabolic syndrome among US adults: findings from the Third National Health and Nutrition Examination Survey. JAMA. 2002;287356- 359
PubMed
McNeill  AMGirman  CJRosamond  WBallantyne  CM A new definition of the metabolic syndrome predicts incident coronary heart disease and stroke.  Presented at: 75th Scientific Session of the American Heart Association November 17-20, 2002 Chicago, IllAbstract
DECODE Study Group, Will new diagnostic criteria for diabetes mellitus change phenotype of patients with diabetics? reanalysis of European epidemiological data. BMJ. 1998;317371- 375
PubMed
DECODE Study Group, Is fasting glucose sufficient to define diabetes? epidemiological data from 20 European studies. Diabetologia. 1999;42647- 654
PubMed
DECODE Study Group, Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria. Lancet. 1999;354617- 621
PubMed
DECODE Study Group, Glucose tolerance and cardiovascular mortality: comparison of the fasting and the 2-hour diagnostic criteria. Arch Intern Med. 2001;161397- 404
PubMed
Waterhouse  JMuir  CCorrea  PPowell  J Cancer Incidence in Five Continents. Vol 3. Lyon, France IARC Scientific Publications1976;456
Fleiss  JH The statistical basis of meta-analysis. Stat Methods Med Res. 1993;2121- 145
PubMed
Bonora  EKiechl  SWilleit  J  et al.  Prevalence of insulin resistance in metabolic disorders: the Bruneck Study. Diabetes. 1998;471643- 1649
PubMed
Rantala  AOKauma  HLilja  MSavolainen  MJReunanen  AKesäniemi  YA Prevalence of the metabolic syndrome in drug-treated hypertensive patients and control subjects. J Intern Med. 1999;245163- 174
PubMed
Balkau  BCharles  MADrivsholm  T  et al.  Frequency of the WHO metabolic syndrome in European cohorts, and an alternative definition of an insulin resistance syndrome. Diabetes Metab. 2002;28364- 376
PubMed
Park  YWZhu  SPalaniappan  LHeshka  SCarnethon  MRHeymsfield  SB The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Intern Med. 2003;163427- 436
PubMed
Ford  ESGiles  WH A comparison of the prevalence of the metabolic syndrome using two proposed definitions. Diabetes Care. 2003;26575- 581
PubMed
Resnick  HEJones  KRuotolo  G  et al.  Insulin resistance, the metabolic syndrome, and risk of incident cardiovascular disease in nondiabetic American Indians: the Strong Heart Study. Diabetes Care. 2003;26861- 867
PubMed
Mooy  JMGrootenhuis  PAde Vries  H  et al.  Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia. 1996;39298- 305
PubMed
Laakso  M How good a marker is insulin level for insulin resistance? Am J Epidemiol. 1993;137959- 965
PubMed
Yeni-Komshian  HCarantoni  MAbbasi  FReaven  GM Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers. Diabetes Care. 2000;23171- 175
PubMed
Després  JPLamarche  BMauriège  PCantin  BLupien  PJDagenais  GR Risk factors for ischaemic heart disease: is it time to measure insulin? Eur Heart J. 1996;171453- 1454
PubMed
Pan  XRLi  GWHu  YH  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20537- 544
PubMed
Tuomilehto  JLindström  JEriksson  JG  et al.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;3441343- 1350
PubMed
Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346393- 403
PubMed

Box Section Ref ID

The DECODE Study Group

The following study centers and investigators were included in this study: Finland: Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki (Aulikki Nissinen, Juha Pekkanen, and Jaakko Tuomilehto) (East-West Finland Study); Department of Epidemiology and Health Promotion, National Public Health Institute, Helsinki (Jaakko Tuomilehto, Pekka Jousilahti, and Jaana Lindström) (FIN-MONICA); Department of Medicine, University of Kuopio, Kuopio (Marja Pyörälä and Kalevi Pyörälä) (Helsinki Policemen Study); Italy: Institute of Medical Statistics–University of Milan and Epidemiology Unit, S. Raffaele Institute, Milan (Giuseppe Gallus and Maria Paola Garancini (Cremona Study); the Netherlands: Institute for Research in Extramural Medicine, Vrije Universiteit Medical Center, Amsterdam (Lex M. Bouter, Jacqueline M. Dekker, Robert J. Heine, Giel Nijpels, and Coen D. A. Stehouwer (The Hoorn Study); the National Institute of Public Health and the Environment, Bitlhoven (Edith J. M. Feskens and Daan Kromhout) (Zutphen Elderly Study); Poland: Department of Clinical Epidemiology and Population Studies, Institute of Public Health, Collegium Medicum, Jagiellonian University, Krakow (Andrzej Pajak) (POL-MONICA Study, Krakow); Sweden: Department of Medicine, University of Umeå, (Mats Eliasson, Birgitta Stegmayr, and Vivan Lundberg) (Northern Sweden MONICA Study, 2 cohorts); England: Department of Public Health and Primary Care, University of Cambridge, Cambridge (N. J. Wareham) (Isle of Ely Diabetes Project); the University of Newcastle (Nigel Unwin, Naseer Ahmad, K. George, M. M. Alberti, Louise Hayes) (Newcastle Heart Project); the Centre for Diabetes and Cardiovascular Risk and Department of Primary Health Care, University College London Medical School, London (J. S. Yudkin, M. Gould, A. Haines, and R. W. Morris) (The Goodinge Study).

Figures

Place holder to copy figure label and caption

Prevalence of the metabolic syndrome by the modified World Health Organization definition (hyperinsulinemia plus 2 or more of the other components) by age among men (A) and women (B) in 4 studies that included at least 4 age groups (Cremona Study, Newcastle Study, Goodinge Study, and Northern Sweden Monitoring of Trends and Determinants in Cardiovascular Disease [MONICA]).

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Demographic Characteristics of Subjects and Number of All-Cause and Cardiovascular Deaths During Follow-up in 11 DECODE Study Cohorts*
Table Graphic Jump LocationTable 2. Medians or Prevalence of Metabolic Risk Factors at Baseline in Pooled Study Cohorts by Sex*
Table Graphic Jump LocationTable 3. Prevalence of Individual Components of the Metabolic Syndrome According to Cohort- and Sex-Specific Quartiles of Plasma Insulin Levels in the Pooled DECODE Study Data by Sex*
Table Graphic Jump LocationTable 4. Prevalence of the Components of the Metabolic Syndrome and Their Combinations Among Subjects Aged 50 Through 59 Years in Each Study Cohort by Sex
Table Graphic Jump LocationTable 5. Prevalence of the Components of the Metabolic Syndrome and Their Combinations in the Pooled Data of 8 DECODE Studies by Sex and Age in Subjects 40 Years or Older*
Table Graphic Jump LocationTable 6. Meta-analyses of the Association of the Metabolic Syndrome With the Risk of All-Cause and Cardiovascular Mortality in 7 DECODE Study Cohorts by Sex*
Table Graphic Jump LocationTable 7. Sensitivity, Specificity, and Positive Predictive Value of Different Definitions of the Metabolic Syndrome in Men and Women in the Pooled Data of 7 DECODE Study Cohorts*

References

Reaven  GM Banting Lecture 1988: role of insulin resistance in human disease. Diabetes. 1988;371595- 1607
PubMed
Liese  ADMayer-Davis  EJHaffner  SM Development of the multiple metabolic syndrome: an epidemiological perspective. Epidemiol Rev. 1998;20157- 172
PubMed
Laws  AReaven  GM Insulin resistance and risk factors for coronary heart disease. Bailliers Clin Endocrinol Metab. 1993;71063- 1078
Alberti  KZimmet  Pfor a WHO Consultation, Diagnosis and Classification of Diabetes Mellitus.  Geneva, Swizerland World Health Organization1999;Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications. Part 1.
Not Available, 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;2852486- 2497
PubMed
Balkau  BCharles  MA Comment on the provisional report from the WHO consultation: European Group for the Study of Insulin Resistance (EGIR). Diabet Med. 1999;16442- 443
PubMed
Wood  DADe Backer  GFaergeman  OGraham  IMancia  GPyörälä  K Prevention of coronary heart disease in clinical practice: recommendations of the Second Joint Task Force of European and Other Societies on Coronary Prevention. Eur Heart J. 1998;191434- 1503
PubMed
Pouliot  MDesprés  JPMelieux  S  et al.  Waist circumference and abdominal sagittal diameter: the best simple anthropometric measures of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol. 1994;73460- 468
PubMed
Bloomgarden  ZT American Association of Clinical Endocrinologists (AACE) Consensus Conference on the Insulin Resistance Syndrome: 25–26 August 2002, Washington, DC. Diabetes Care. 2003;26933- 939
PubMed
Trevisan  MLiu  JBahsas  FBMenotti  A Syndrome X and mortality: a population-based study: Risk Factor and Life Expectancy Research Group. Am J Epidemiol. 1998;148958- 966
PubMed
Wilson  PWKannel  WBSilbershatz  HD'Agostino  RB Clustering of metabolic factors and coronary heart disease. Arch Intern Med. 1999;1591104- 1109
PubMed
Isomaa  BAlmgren  PTuomi  T  et al.  Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care. 2001;24683- 689
PubMed
Lakka  HMLaaksonen  DELakka  TA  et al.  The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002;2882709- 2716
PubMed
Ford  ESGiles  WHDietz  WH Prevalence of the metabolic syndrome among US adults: findings from the Third National Health and Nutrition Examination Survey. JAMA. 2002;287356- 359
PubMed
McNeill  AMGirman  CJRosamond  WBallantyne  CM A new definition of the metabolic syndrome predicts incident coronary heart disease and stroke.  Presented at: 75th Scientific Session of the American Heart Association November 17-20, 2002 Chicago, IllAbstract
DECODE Study Group, Will new diagnostic criteria for diabetes mellitus change phenotype of patients with diabetics? reanalysis of European epidemiological data. BMJ. 1998;317371- 375
PubMed
DECODE Study Group, Is fasting glucose sufficient to define diabetes? epidemiological data from 20 European studies. Diabetologia. 1999;42647- 654
PubMed
DECODE Study Group, Glucose tolerance and mortality: comparison of WHO and American Diabetes Association diagnostic criteria. Lancet. 1999;354617- 621
PubMed
DECODE Study Group, Glucose tolerance and cardiovascular mortality: comparison of the fasting and the 2-hour diagnostic criteria. Arch Intern Med. 2001;161397- 404
PubMed
Waterhouse  JMuir  CCorrea  PPowell  J Cancer Incidence in Five Continents. Vol 3. Lyon, France IARC Scientific Publications1976;456
Fleiss  JH The statistical basis of meta-analysis. Stat Methods Med Res. 1993;2121- 145
PubMed
Bonora  EKiechl  SWilleit  J  et al.  Prevalence of insulin resistance in metabolic disorders: the Bruneck Study. Diabetes. 1998;471643- 1649
PubMed
Rantala  AOKauma  HLilja  MSavolainen  MJReunanen  AKesäniemi  YA Prevalence of the metabolic syndrome in drug-treated hypertensive patients and control subjects. J Intern Med. 1999;245163- 174
PubMed
Balkau  BCharles  MADrivsholm  T  et al.  Frequency of the WHO metabolic syndrome in European cohorts, and an alternative definition of an insulin resistance syndrome. Diabetes Metab. 2002;28364- 376
PubMed
Park  YWZhu  SPalaniappan  LHeshka  SCarnethon  MRHeymsfield  SB The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988-1994. Arch Intern Med. 2003;163427- 436
PubMed
Ford  ESGiles  WH A comparison of the prevalence of the metabolic syndrome using two proposed definitions. Diabetes Care. 2003;26575- 581
PubMed
Resnick  HEJones  KRuotolo  G  et al.  Insulin resistance, the metabolic syndrome, and risk of incident cardiovascular disease in nondiabetic American Indians: the Strong Heart Study. Diabetes Care. 2003;26861- 867
PubMed
Mooy  JMGrootenhuis  PAde Vries  H  et al.  Intra-individual variation of glucose, specific insulin and proinsulin concentrations measured by two oral glucose tolerance tests in a general Caucasian population: the Hoorn Study. Diabetologia. 1996;39298- 305
PubMed
Laakso  M How good a marker is insulin level for insulin resistance? Am J Epidemiol. 1993;137959- 965
PubMed
Yeni-Komshian  HCarantoni  MAbbasi  FReaven  GM Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers. Diabetes Care. 2000;23171- 175
PubMed
Després  JPLamarche  BMauriège  PCantin  BLupien  PJDagenais  GR Risk factors for ischaemic heart disease: is it time to measure insulin? Eur Heart J. 1996;171453- 1454
PubMed
Pan  XRLi  GWHu  YH  et al.  Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance: the Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20537- 544
PubMed
Tuomilehto  JLindström  JEriksson  JG  et al.  Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;3441343- 1350
PubMed
Diabetes Prevention Program Research Group, Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346393- 403
PubMed

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