1 Department of Medicine, Countess of Chester Hospital NHS Trust, Chester, U.K
2 Department of Medicine, University of Manchester, Manchester, U.K
3 Harvard Medical School, Boston, Massachusetts
4 General Internal Medicine Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
5 Boston University School of Public Health, Boston, Massachusetts
6 Medical University of South Carolina, Charleston, South Carolina
7 National Heart, Lung, and Blood Institutes Framingham Heart Study, Framingham, Massachusetts
Address correspondence and reprint requests to Martin K. Rutter, MD, Directorate of Medicine, Countess of Chester Hospital NHS Trust, Liverpool Road, Chester, CH2 1UL, U.K. E-mail: martin.rutter{at}coch.nhs.uk
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ABSTRACT |
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The National Cholesterol Education Program (NCEP) definition of the metabolic syndrome was proposed as a practical tool for identifying a high-risk cardiovascular disease (CVD) phenotype (1), and several studies have subsequently confirmed that the metabolic syndrome predicts incident CVD (25). The metabolic syndrome may also serve as a surrogate measure of the insulin resistance phenotype as it identifies a proportion of subjects with insulin resistance without directly measuring insulin action (69).
Insulin resistance may be causally related to increased CVD risk. Direct measurement of insulin resistance using the hyperinsulinemic-euglycemic clamp has practical limitations; consequently, data linking directly measured insulin resistance with CVD is derived mostly from cross-sectional studies (10) and rarely from prospective cohorts (11, 12).
Fasting insulin is a simple indirect measure of insulin resistance. Prospective studies using this measure have been equivocal or modest with regard to CVD risk (1316). Of the remaining indirect measures of insulin resistance, the homeostasis model assessment formula (HOMA-IR), requiring only fasting glucose and insulin measurements, is the most popular. HOMA-IR values correlate reasonably well with "gold standard" clamp-derived values (1719). Recently, Gutt et al. (20) proposed an index of insulin sensitivity (ISI0,120) that uses glucose and insulin levels before and after oral glucose loading. ISI0,120 values correlate well with directly measured insulin resistance (20) and have been shown to predict incident diabetes (19). Most (2123) but not all (24) studies have shown an association of the more sophisticated indirect measures of insulin resistance with CVD.
It is not known if the NCEP Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) definition of the metabolic syndrome identifies all the CVD risk associated with insulin resistance. We therefore studied baseline metabolic syndrome and two surrogate insulin resistance measures, HOMA-IR and ISI0,120, and their independent relation to 7-year CVD risk in the Framingham Offspring Study cohort.
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RESEARCH DESIGN AND METHODS |
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The metabolic syndrome was defined according to 2001 NCEP Adult Treatment Panel III guidelines (1). Plasma glucose was measured in fresh specimens with a hexokinase reagent kit (A-gent glucose test; Abbott, South Pasadena, CA). Glucose assays were run in duplicate; the intra-assay coefficient of variation was <3%. Fasting insulin levels were measured in plasma as total immunoreactive insulin and were standardized to serum levels for reporting purposes. The lower limit of sensitivity was 8.0 pmol/l (1.1 µU/ml) and the intra- and interassay coefficients of variation were 5.010.0%. Insulin resistance and sensitivity were defined using validated definitions: 1) HOMA-IR = [fasting glucose (mmol/l) x fasting insulin (µU/ml)]/22.5 (17, 18) and 2) ISI0,120 = (m/MPG)/log MSI, where m is [75,000 mg + (fasting glucose 2-h glucose) x 0.19 x body wt (kg)]/120 min, MPG is the mean of fasting and 2-h glucose concentrations (mg/dl), and MSI is the mean of fasting and 2-h insulin concentrations (mU/l) (20). Quartiles for the population distribution for the HOMA-IR were Q1, 2.215.12; Q2, 5.136.23; Q3, 6.247.95; and Q4, 7.9535.53 units; for the ISI0,120 they were Q1, 8.0921.60; Q2, 21.6125.90; Q3, 25.9130.33; and Q4, 30.3458.66 units. Total cholesterol and triglyceride levels were measured enzymatically, and the HDL cholesterol fraction was measured after precipitation of LDL and VLDL particles with dextran sulfate magnesium. The Framingham laboratory participates in the lipoprotein cholesterol laboratory standardization program administered by the Centers for Disease Control and Prevention (Atlanta, GA). Blood pressure was assessed as the average of two measurements taken after subjects had been seated for at least 5 min. Waist circumference was measured at the level of the umbilicus with the subject in the standing position. Subjects who reported smoking at least one cigarette per day during the year before the examination were classified as current smokers.
CVD assessment and follow-up.
Incident CVD was assessed using standard Framingham Heart Study criteria and was defined as any of the following: new-onset angina, fatal and nonfatal myocardial infarction or stroke, transient ischemic attack, heart failure, or intermittent claudication. Subjects free from CVD at the fifth (baseline) examination were followed for a median of 6.7 years to the seventh examination cycle (September 1998 to October 2001). Person-years of follow-up were accrued from baseline to the date of first event or censored at the date of the seventh examination if the subject was free of a CVD event.
Statistical analysis.
Descriptive statistics included means and medians for continuous variables and frequencies for categorical variables. The distributions of fasting insulin, HOMA-IR, and ISI0,120 were log transformed to improve normality before analysis. Tests for differences in age-adjusted mean HOMA-IR and ISI0,120 levels across levels of risk factors and individual components of the metabolic syndrome were conducted using multiple linear regression analysis. First-order sex interactions were then assessed between these risk factors and the measures of insulin resistance. Subjects were classified as having zero, one, two, three, four, or five components of the metabolic syndrome, and age-adjusted mean HOMA-IR and ISI0,120 levels were estimated for each group. Trends across groups were assessed using the 2 test. For the prediction of CVD, men and women were combined and first-order interaction terms for sex-by-insulin resistance measure interactions on the risk of CVD were examined. Because the interactions were not statistically significant, a sex-pooled Cox proportional hazards regression analysis was used to assess the association of risk factors with incident CVD. Similarly, first-order interaction terms for the impaired glucose tolerance x insulin resistance measure were not statistically significant; thus analysis was not stratified by glucose tolerance status. Models were adjusted for age, sex, LDL cholesterol, and smoking status. Risk factors were modeled as indicator variables, and hazard ratios (HRs) (95% CI) are presented. The overall predictive power of the models was assessed with the c-statistic representing the area under the receiver operating characteristic curve. All analyses were performed with SAS Version 8.2; a two-sided P < 0.05 was considered statistically significant.
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RESULTS |
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When subjects who developed diabetes during follow-up (n = 148) were excluded from the analysis, there was no substantive change in the results.
In age and sex-adjusted models that also adjusted for the components of the metabolic syndrome (systolic and diastolic blood pressure, triglycerides, and HDL cholesterol) treated either as continuous or categorical variables, neither the HOMA-IR nor the ISI0,120 was significantly related to CVD events (P 0.10 for all models).
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DISCUSSION |
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Our data on the prevalence of the metabolic syndrome in men and women and the comparison of insulin and HOMA-IR levels by sex suggest that the men in our sample were more insulin resistant than the women. Because the ISI0,120 is a measure of insulin sensitivity, lower levels in men than in women might have been expected. The unexpected finding of similar levels in men and women is probably explained by the calculation mode of the ISI0,120, which is directly related to body weight and inversely related to 2-h glucose and 2-h insulin levels. In our sample, the former was higher in men than in women and the latter two factors were lower in men.
Prospective study.
Many studies have been equivocal about the CVD risk associated with fasting insulin and glucose levels; nonetheless, we found an association between HOMA-IR and CVD, as have three previous studies (2123). Here we extended these findings by studying the HOMA-IR and ISI0,120 and assessing whether their relation to incident CVD was independent of the metabolic syndrome phenotype. This analysis is of particular importance because the metabolic syndrome phenotype has been proposed as a means of identifying subjects with insulin resistance who are at increased CVD risk (1). We have shown that individually all three indirect measures of insulin resistance predict CVD events. Our main finding was that after adjusting for the presence of the metabolic syndrome, the ISI0,120 was an independent CVD risk factor but the HOMA-IR was not. Our analysis demonstrated that the metabolic syndrome is an independent CVD risk factor after adjusting for the ISI0,120 or HOMA-IR. We have also shown that at the population level, the HOMA-IR, ISI0,120, and metabolic syndrome alone or in combination are equivalent in defining the CVD risk.
HOMA-IR assesses insulin resistance in the fasting state rather than in the postprandial state, and it tends to represent hepatic rather than peripheral insulin resistance. It assesses the ß-cell response to energy stress by a process of theoretical modeling rather than by direct measurement of postprandial insulin and glucose levels. On the other hand, ISI0,120 is a more complex assessment of insulin resistance that accounts more for peripheral insulin resistance and glucose disposal and uses a direct measurement of the ß-cell response to energy stress.
Why is the ISI0,120 an independent CVD risk factor after accounting for the metabolic syndrome when the HOMA-IR is not? There are several potential explanations. First, fasting glucose is one of the five components of the NCEP metabolic syndrome and is also one of the two components of the HOMA-IR, but it is a relatively smaller component of the ISI0,120. The sharing of a common dominant variable could partly explain why the HOMA-IR does not remain a significant independent CVD risk factor after statistically adjusting for the metabolic syndrome. Second, if the CVD risk associated with the ISI0,120 reflects risk "due to" insulin resistance that is not fully captured by the HOMA-IR formula, then our data could be explained as the ability of the ISI0,120 to more accurately reflect insulin resistance than the HOMA-IR. Gutt et al. (20) have shown that the ISI0,120 is more strongly correlated with directly measured insulin sensitivity than the HOMA-IR formula values; on the other hand, Hanley et al. (19) found that the HOMA-IR and ISI0,120 were similarly related to insulin resistance. In a small study of 33 healthy volunteers, Soonthornpun et al. (27) found that the correlation with clamp-derived insulin resistance values was greater for indexes of insulin resistance derived from oral glucose tolerance test data (including the ISI0,120) when compared with those derived from fasting measures (including the HOMA-IR). In the present study, we did not directly measure insulin resistance and therefore we are unable to know whether the ISI0,120 or the HOMA-IR is superior in this regard. Third, differences in the statistical independence of the variables might explain the findings. Our cross-sectional data (Table 2) showed that the HOMA-IR was generally more strongly correlated with the individual components of the metabolic syndrome than the ISI0,120. Therefore, in a multivariable model including one of these measures and the metabolic syndrome, the greater statistical independence of the ISI0,120 might make it less likely to be displaced by the metabolic syndrome. Fourth, the ISI0,120 is a complex function of body weight and fasting and postchallenge glucose and insulin levels. It directly assesses ß-cell response to glucose loading and assesses peripheral insulin resistance, hepatic insulin resistance, and glucose disposal. Although the pathological conditions associated with hepatic insulin resistance are often associated with peripheral insulin resistance (18), this is not always the case (28), and it is possible that this distinction is important with regard to CVD risk. If the CVD risk associated with the ISI0,120 reflects risk "due to" insulin resistance, then it is possible that the ISI0,120 captures some aspects of insulin resistance, perhaps skeletal muscle insulin resistance or postprandial insulin resistance, or some part of ß-cell insufficiency that is not captured by the HOMA-IR or the NCEP metabolic syndrome definition. Fifth, the ISI0,120 could be linked to CVD events through factors related to insulin resistance that have not been captured by the HOMA-IR or the metabolic syndrome phenotype, such as inflammation, disorders of coagulation and fibrinolysis, postprandial lipemia, small dense LDL, albuminuria, and adiponectin. Previous studies have shown that 2-h insulin and 2-h glucose values are related to insulin resistance (29) and predict CVD (30, 31); however, in our analysis, the inclusion of these variables did not appear to explain the link between the ISI0,120 and CVD.
The potential mechanisms linking insulin resistance with CVD remain poorly understood but include inflammation (6, 32), impaired endothelial function (33), proliferation of vascular smooth muscle cells, increased sympathetic nervous system activity, and increased levels of free fatty acids. An attractive hypothesis is that insulin resistance leads to CVD through the development of diabetes. However, our exploratory analysis excluding subjects who developed diabetes during follow-up suggested that this mechanism explains only a small component of the link between insulin resistance and CVD.
It should be noted that our data are entirely consistent with the hypothesis that all three surrogate markers for insulin resistance studied could reflect aspects of the pathogenesis of CVD that are not related to insulin resistance. In other words, the individual components of the ISI0,120 (glucose, insulin, and body weight), HOMA-IR (glucose and insulin), and metabolic syndrome (obesity, hypertension, hyperglycemia, and dyslipidemia) could be acting directly as CVD risk factors, independent of their relation to insulin resistance. Because we did not directly measure insulin resistance, we cannot estimate what proportion of the measured CVD risk was explained by insulin resistance itself independent of these other possible explanations.
Our analysis highlights the loss of potentially valuable CVD risk information through categorizing subjects as having or not having the NCEP-defined metabolic syndrome. In the models that were adjusted for the individual nonglucose components of the metabolic syndrome (blood pressure, waist measurement, and lipids), the ISI0,120 was not significantly related to incident CVD, whereas it was significantly related to incident CVD when it was included in a model with the metabolic syndrome (yes/no), as shown in Table 3. The models that adjusted for the individual components of the metabolic syndrome do not clarify the mechanisms leading to CVD because all components of the metabolic syndrome are related to insulin resistance (Table 2). Thus, insulin resistance could cause CVD through increased waist circumference, dyslipidemia, and/or hypertension; however, because of the intercorrelations, the data are also consistent with the hypothesis that the features of metabolic syndrome lead to CVD through insulin resistance.
HRs versus population risk.
We have shown that the ISI0,120 and metabolic syndrome are independent CVD risk factors but that the inclusion of both variables in the multivariable model produced only a small increase in the c-statistic when compared with that obtained using either variable on its own. The explanation for this apparent paradox was provided in a recent study that showed that for a risk factor to be effective for population risk stratification, the associated HR has to be "of a magnitude rarely seen in epidemiological studies" (34). We have provided evidence of a small but statistically significant HR for the association between insulin resistance and CVD. However, the true strength and nature of the association between insulin resistance and CVD is difficult to determine from this analysis (see STUDY LIMITATIONS below). For example, the true effect of insulin resistance in the pathogenesis of CVD might be best reflected by the results of model A (Table 3) in which the influence of the ISI0,120 was unadjusted for the presence of the metabolic syndrome.
Study limitations.
We had only fasting and 2-h oral glucose tolerance test information and did not directly measure insulin resistance. We also excluded subjects with CVD and diabetes at baseline to reduce the effect of medical therapies on CVD outcomes; therefore, we have probably provided conservative estimates of the strength of the association among metabolic syndrome, insulin resistance, and CVD events. The size of our male, female, and impaired glucose tolerance subgroups has limited the statistical power to perform stratified survival analysis. Our study sample was largely white, which may limit generalizability to other ethnic groups.
Clinical implications.
Recent clamp studies have raised concerns that the current NCEP definition of the metabolic syndrome has low sensitivity for identifying insulin resistance in subjects (8,9); the question that must then be asked is whether this has clinical implications. Our data suggest that the metabolic syndrome, HOMA-IR, and ISI0,120 each significantly contribute to CVD risk but that none is superior alone or in combination for population-level prediction of CVD risk.
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Conclusion. |
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ACKNOWLEDGMENTS |
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The authors acknowledge the contributions of Jessica A. Perhanidis, who performed a large proportion of the statistical analysis; Dr. David Nathan (Massachusetts General Hospital), who facilitated the insulin assays; and Dr. Murray W. Stewart and Rita Patwardhan from GlaxoSmithKline, who provided scientific expertise and helped with coordinating the study.
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FOOTNOTES |
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CVD, cardiovascular disease; HOMA-IR, homeostasis model assessment of insulin resistance; ISI, insulin sensitivity index; NCEP, National Cholesterol Education Program.
Received for publication December 5, 2004 and accepted in revised form August 1, 2005
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REFERENCES |
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