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Background— In addition to predicting cardiovascular disease (CVD) morbidity and mortality, the metabolic syndrome is strongly associated with the development of type 2 diabetes mellitus (DM), itself an important risk factor for CVD. Our objective was to compare the ability of various metabolic syndrome criteria (including those recently proposed by the International Diabetes Federation), markers of insulin resistance (IR) and inflammation, and impaired glucose tolerance (IGT) in the prediction of DM and to determine whether various proposed modifications to the National Cholesterol Education program (NCEP) metabolic syndrome definition improved predictive ability. Methods and Results— We examined 822 subjects in the Insulin Resistance Atherosclerosis Study aged 40 to 69 years who were nondiabetic at baseline. After 5.2 years, 148 individuals had developed DM. IGT, metabolic syndrome definitions, and IR markers all significantly predicted DM, with odds ratios ranging from 3.4 to 5.4 (all P P >0.05). Similarly, although IR and inflammation variables were significantly associated with incident DM when included in multivariate models with NCEP-defined metabolic syndrome (all P P >0.05). Conclusions— The International Diabetes Federation and NCEP metabolic syndrome definitions predicted DM at least as well as the World Health Organization definition, despite not requiring the use of oral glucose tolerance testing or measures of IR or microalbuminuria. Modifications or additions to the NCEP metabolic syndrome definition had limited impact on the prediction of DM.

Nonalcoholic fatty liver disease (NAFLD) is emerging as a component of the metabolic syndrome, although it is not known whether markers of NAFLD, including elevated concentrations of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALK), predict the development of metabolic syndrome. Our objective was to investigate the associations of elevated AST, ALT, and other liver markers, including C-reactive protein (CRP), with incident National Cholesterol Education Program–defined metabolic syndrome among 633 subjects in the Insulin Resistance Atherosclerosis Study who were free of metabolic syndrome at baseline. Insulin sensitivity (Si) and acute insulin response (AIR) were directly measured from the frequently sampled intravenous glucose tolerance test among African-American, Hispanic, and non-Hispanic white subjects aged 40–69 years. After 5.2 years, 127 individuals had developed metabolic syndrome. In separate logistic regression models adjusting for age, sex, ethnicity, clinic, and alcohol consumption, subjects in the upper quartiles of ALT, ALK, and CRP were at significantly increased risk of incident metabolic syndrome compared with those in the lowest quartile: ALT, odds ratio 2.50 (95% CI 1.38–4.51); ALK, 2.28 (1.24–4.20); and CRP, 1.33 (1.09–1.63). Subjects in the upper quartile of the AST-to-ALT ratio were at significantly reduced metabolic syndrome risk (0.40 [0.22–0.74]). After further adjustment for waist circumference, Si, AIR, and impaired glucose tolerance, the associations of ALT and the AST-to-ALT ratio with incident metabolic syndrome remained significant (ALT, 2.12 [1.10–4.09]; the AST-to-ALT ratio, 0.48 [0.25–0.95]). These associations were not modified by ethnicity or sex, and they remained significant after exclusion of former and heavy drinkers. In conclusion, NAFLD markers ALT and the AST-to-ALT ratio predict metabolic syndrome independently of potential confounding variables, including directly measured Si and AIR.

A limited number of studies have reported associations of markers of liver injury, including elevated concentrations of aspartate aminotransferase (AST) and alanine aminotransferase (ALT), with prospective risk of type 2 diabetes. However, only one study has adjusted for a detailed measure of insulin sensitivity (insulin sensitivity index [Si]), which is important given associations of obesity and Si with nonalcoholic fatty liver disease (NAFLD). Our objective was to investigate the associations of elevated AST and ALT with incident type 2 diabetes among 906 participants in the Insulin Resistance Atherosclerosis Study who were nondiabetic at baseline. Si and acute insulin response (AIR) were measured directly from the frequently sampled intravenous glucose tolerance test among black, Hispanic, and non-Hispanic white participants aged 40–69 years. After 5.2 years, 148 individuals had developed type 2 diabetes. Baseline AST and ALT were positively correlated with fasting insulin (r = 0.22 and r = 0.35, respectively), waist circumference (r = 0.18 and r = 0.34), and fasting glucose (r = 0.13 and r = 0.29) and inversely with Si (r = −0.18 and r = −0.30; all P < 0.0001). In separate logistic regression models adjusting for age, sex, ethnicity, clinical center, and alcohol consumption, participants in the highest quartiles (Q4) of AST and ALT were at significantly increased risk of incident type 2 diabetes compared with those in the lowest three quartiles (Q1–Q3): AST: odds ratio (OR) 1.73 (95% CI 1.17–2.57); ALT: OR 2.32 (1.36–3.75). After further adjustment for smoking, waist circumference, triglyceride, HDL, impaired glucose tolerance, Si, and AIR, both AST and ALT remained significantly associated with incident type 2 diabetes: AST, Q4 vs. Q1–Q3: OR 1.98 (1.23–3.17); ALT, Q4 vs. Q1–Q3: OR 2.00 (1.22–3.28). There were no interactions of sex, ethnicity, obesity, impaired glucose tolerance, or Si with AST or ALT in the prediction of type 2 diabetes. When entered into the same model with adjustment for demographic variables, both C-reactive protein and ALT independently predicted type 2 diabetes. In addition, AST and ALT were positively associated with incident type 2 diabetes after excluding former and moderate to heavy drinkers. In conclusion, AST and ALT independently predict type 2 diabetes. Baseline elevations of these markers may reflect NAFLD or related pathologies.

Factor analysis, a multivariate correlation technique, has been used to provide insight into the underlying structure of the metabolic syndrome. The majority of previous factor analyses, however, have used only surrogate measures of insulin sensitivity; very few have included nontraditional cardiovascular disease (CVD) risk factors such as plasminogen activator inhibitor (PAI)-1, fibrinogen, and C-reactive protein (CRP); and only a limited number have assessed the ability of factors to predict type 2 diabetes. The objective of this study was to investigate, using factor analysis, the clustering of metabolic and inflammation variables using data from 1,087 nondiabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS) and to determine the association of these clusters with risk of type 2 diabetes at follow-up. This study includes information on directly measured insulin sensitivity (Si) from the frequently sampled intravenous glucose tolerance test among African-American, Hispanic, and non-Hispanic white subjects aged 40–69 years. Principal factor analysis of data from nondiabetic subjects at baseline (1992–1994) identified three factors, which explained 28.4, 7.4, and 6% of the total variance in the dataset, respectively. Based on factor loadings of ≥ 0.40, these factors were interpreted as 1) a “metabolic” factor, with positive loadings of BMI, waist circumference, 2-h glucose, log triglyceride, and log PAI-1 and inverse loadings of log Si + 1 and HDL; 2) an “inflammation” factor, with positive loadings of BMI, waist circumference, fibrinogen, and log CRP and an inverse loading of log Si + 1; and 3) a “blood pressure” factor, with positive loadings of systolic and diastolic blood pressure. The results were similar within strata of ethnicity, and there were only subtle differences in sex-specific analyses. In a prospective analysis, each of the factors was a significant predictor of diabetes after a median follow-up period of 5.2 years, and each factor remained significant in a multivariate model that included all three factors, although this three-factor model was not significantly more predictive than models using either impaired glucose tolerance or conventional CVD risk factors. Factor analysis identified three underlying factors among a group of inflammation and metabolic syndrome variables, with insulin sensitivity loading on both the metabolic and inflammation variable clusters. Each factor significantly predicted diabetes in multivariate analysis. The findings support the emerging hypothesis that chronic subclinical inflammation is associated with insulin resistance and comprises a component of the metabolic syndrome.

OBJECTIVE—To prospectively investigate predictors of the incident metabolic syndrome in nondiabetic adults. RESEARCH DESIGN AND METHODS—This analysis included 714 white, black, and Hispanic participants in the Insulin Resistance Atherosclerosis Study (IRAS) who were free of the metabolic syndrome at baseline; 139 of these developed the metabolic syndrome in the subsequent 5 years. We examined measures of glucose (fasting and 2 h), insulin (fasting and 2 h, acute insulin response, insulin sensitivity [Si], and proinsulin), lipids (HDL and triglycerides), blood pressure (systolic and diastolic), waist circumference, and baseline physical activity (total energy expenditure) as predictors of the metabolic syndrome. Logistic regression models were adjusted for age, sex, study site, ethnicity, and impaired glucose tolerance. Signal detection analysis was used to identify the characteristics of the highest risk group. RESULTS—The best predictors of incident metabolic syndrome were waist circumference (odds ratio [OR] 1.7 [1.3–2.0] per 11 cm), HDL cholesterol (0.6 [0.4–0.7] per 15 mg/dl), and proinsulin (1.7 [1.4–2.0] per 3.3 pmol/l). Signal detection analysis identified waist circumference (>89 cm in women, >102 cm in men) as the optimal predictor. CONCLUSIONS—These findings suggest that obesity may precede the development of other metabolic syndrome components. Interventions that address obesity and reduce waist circumference may reduce the incidence of the metabolic syndrome in nondiabetic adults.

Recently, the metabolic syndrome (MetS) has attracted much attention as a risk cluster for cardiovascular disease. Although it is believed that individuals with the MetS have insulin resistance (IR), there are few data using direct measures of IR such as glucose clamps or frequently sampled intravenous glucose tolerance tests (FSIGTTs). We examined associations of MetS with FSIGTT-derived measures of insulin sensitivity and secretion among nondiabetic subjects in the Insulin Resistance Atherosclerosis Study. Two sets of MetS criteria were evaluated: those from the 1999 World Health Organization (WHO) and the 2001 National Cholesterol Education Program (NCEP). Both WHO and NCEP MetS definitions were significantly associated with risk of being in the lowest quartile of directly measured insulin sensitivity (P < 0.0001 for all subjects as well as within ethnic subgroups). However, the associations with WHO-MetS were stronger for all subjects combined (WHO: odds ratio [OR] = 10.2; 95% CI 7.5–13.9; NCEP: OR = 4.6; 3.4–6.2) and in separate analyses of non-Hispanic whites, blacks, and Hispanics. WHO and NCEP MetS definitions were also significantly associated with risk of being in the lowest quartile of insulin sensitivity–adjusted acute insulin response (AIR) and disposition index (DI; all P < 0.01), although the associations were generally weaker than those for insulin sensitivity and there was no difference between the two definitions in all subjects combined (low AIR, WHO: OR = 1.7, 1.2–2.4; NCEP: OR = 1.7, 1.2–2.5). There were, however, a number of ethnic differences, including a stronger association of NCEP-MetS with low AIR among blacks. WHO-MetS was significantly more sensitive than NCEP-MetS in detecting low insulin sensitivity (65.4 vs. 45.6%, respectively; P < 0.0001), with no significant differences in specificity between the definitions (84.4 vs. 84.6%; P = 0.91), although WHO-MetS had a larger area under the receiver operating characteristic curve (75% vs. 65%; P < 0.0001). In conclusion, although both the WHO and NCEP MetS criteria identify nondiabetic individuals with low insulin sensitivity, the associations were notably stronger using the WHO definition. The definitions are generally less useful for identifying those with low AIR or DI, although NCEP-MetS seems to differentiate black subjects with insulin secretion defects.

To determine and formally compare the ability of simple indexes of insulin resistance (IR) to predict type 2 diabetes, we used combined prospective data from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study, which include well-characterized cohorts of non-Hispanic white, African-American, Hispanic American, and Mexican subjects with 5–8 years of follow-up. Poisson regression was used to assess the ability of each candidate index to predict incident diabetes at the follow-up examination (343 of 3,574 subjects developed diabetes). The areas under the receiver operator characteristic (AROC) curves for each index were calculated and statistically compared. In pooled analysis, Gutt et al.’s insulin sensitivity index at 0 and 120 min (ISI0,120) displayed the largest AROC (78.5%). This index was significantly more predictive (P < 0.0001) than a large group of indexes (including those by Belfiore, Avignon, Katz, and Stumvoll) that had AROC curves between 66 and 74%. These findings were essentially similar both after adjustment for covariates and when analyses were conducted separately by glucose tolerance status and ethnicity/study subgroups. In conclusion, we found substantial differences between published IR indexes in the prediction of diabetes, with ISI0,120 consistently showing the strongest prediction. This index may reflect other aspects of diabetes pathogenesis in addition to IR, which might explain its strong predictive abilities despite its moderate correlation with direct measures of IR.

Factor analysis, a multivariate correlation technique, has been used to provide insight into the underlying structure of metabolic syndrome, which is characterized by physiological complexity and strong statistical intercorrelation among its key variables. The majority of previous factor analyses, however, have used only surrogate measures of insulin sensitivity. In addition, few have included members of multiple ethnic groups, and only one has presented results separately for subjects with impaired glucose tolerance. The objective of this study was to investigate, using factor analysis, the clustering of physiologic variables using data from 1,087 nondiabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS). This study includes information on the directly measured insulin sensitivity index (SI) from intravenous glucose tolerance testing among African-American, Hispanic, and non-Hispanic white subjects aged 40–69 years at various stages of glucose tolerance. Principal factor analysis identified two factors that explained 28 and 9% of the variance in the dataset, respectively. These factors were interpreted as 1) a “ metabolic” factor, with positive loadings of BMI, waist, fasting and 2-h glucose, and triglyceride and inverse loadings of log(SI+1) and HDL; and 2) a “blood pressure” factor, with positive loadings of systolic and diastolic blood pressure. The results were unchanged when surrogate measures of insulin resistance were used in place of log(SI+1). In addition, the results were similar within strata of sex, glucose tolerance status, and ethnicity. In conclusion, factor analysis identified two underlying factors among a group of metabolic syndrome variables in this dataset. Analyses using surrogate measures of insulin resistance suggested that these variables provide adequate information to explore the underlying intercorrelational structure of metabolic syndrome. Additional clarification of the physiologic characteristics of metabolic syndrome is required as individuals with this condition are increasingly being considered candidates for behavioral and pharmacologic intervention.

The promoter sequence variant -278A in the CYP7 gene, which encodes cholesterol 7-alpha hydroxylase, was previously reported to be associated with reduced plasma low density lipoprotein (LDL) cholesterol concentration. We tested for association of CYP7-278A with plasma lipoprotein traits in samples taken from three distinct Canadian populations: 594 Alberta Hutterites, 325 Ontario Oji-Cree and 190 Keewatin Inuit. The CYP7-278A allele frequencies in these three groups were 0.708, 0.466 and 0.490, respectively. The frequencies of CYP7-278A/A homozygotes were 0.481, 0.215 and 0.247, respectively. In the Hutterites, CYP7-278A was associated with reduced plasma HDL-cholesterol and apolipoprotein AI concentration. In the Oji-Cree, CYP7-278A was not significantly associated with any plasma lipoprotein trait. In the Inuit CYP7-278A was associated with elevated plasma total and LDL-cholesterol. There was no consistent relationship between the population mean plasma LDL-cholesterol concentration and the population CYP7-278A frequency. Our findings suggest that the common -278A promoter variant of CYP7 was inconsistently associated with variation in plasma LDL- and HDL-cholesterol in samples from three independent populations. The inconsistencies could be due to differences in genetic background or to unspecified environmental or genetic factors.

Previous studies have indicated that β-cell dysfunction predicts the development of diabetes, although it is unknown whether the use of combinations of insulin secretory measures further improves prediction. The Insulin Resistance Atherosclerosis Study is a prospective, multicenter, epidemiological study of the relationship between insulin sensitivity and the risk of diabetes and cardiovascular disease. At baseline, fasting concentrations of insulin, intact proinsulin (PI), and split PI were measured, and acute insulin response (AIR) was determined during a frequently sampled intravenous glucose tolerance test (FSIGTT). Subjects who were nondiabetic at baseline (n = 903) were reexamined after 5 years of follow-up; 148 had developed diabetes. In separate logistic regression models adjusted for age, sex, clinic, and ethnicity, 1 SD differences in measures of β-cell dysfunction were associated with diabetes incidence (AIR: odds ratio [OR] 0.37, 95% CI 0.27–0.52; intact PI: OR 1.90, 95% CI 1.57–2.30; split PI: OR 1.94, 95% CI 1.63–2.31). After additional adjustment for BMI, impaired glucose tolerance, and insulin sensitivity, these measures continued to be significantly associated with risk of diabetes (all P < 0.0001). Furthermore, in models that included both PI and AIR, each was an independent predictor, and individuals who had combined low AIR and high PI experienced the highest diabetes risk. In conclusion, both low AIR and high PI independently predicted diabetes in a well-characterized multiethnic population. Although fasting PI is simpler to assess, determining AIR from an FSIGTT may further improve prediction. If pharmacological agents to prevent diabetes are proved to be efficacious in ongoing clinical trials, then it may be beneficial to perform FSIGTTs to identify better (for intensive intervention) prediabetic subjects who would ultimately require lifelong pharmacological therapy.

Type 2 diabetes is associated with a marked increase in the risk of coronary heart disease (CHD).1 In addition, small increases in glucose concentrations in the nondiabetic range also may be associated with an increased risk of CHD (see the Diabetes Epidemiology: Collaborative Analysis of Diagnostic Criteria in Europe [DECODE] study2). However, the increase in CHD in diabetic subjects may not be entirely due to increases in glucose levels per se, because in many studies, including the United Kingdom Prospective Diabetes Study (UKPDS),3 although glucose levels were significantly related to CHD, the magnitude of the association is only modest. One explanation for the increased risk of CHD in diabetics may be the insulin resistance syndrome.4 A related possibility may be the atherogenicity of the prediabetic state.4 Indeed, insulin resistance, rather than decreased insulin secretion, seems to be most responsible for the increased cardiovascular risk factors in the prediabetic state (ie, increased triglyceride and blood pressure and decreased high-density lipoprotein cholesterol [HDL-C] even before the onset of diabetes).5 In some studies, insulin concentrations (as a surrogate for insulin resistance) have been strongly related to CHD6; however, in other studies that include meta-analysis, insulin concentrations have been only modestly associated with CHD.7 See Circulation . 2002;105:1311–1316 Until recently, most insulin immunoassays cross-reacted …