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(Arteriosclerosis, Thrombosis, and Vascular Biology. 1997;17:2413-2417.)
© 1997 American Heart Association, Inc.

Articles

Does Insulin Resistance Unite the Separate Components of the Insulin Resistance Syndrome?

Evidence From the Miami Community Health Study

Richard P. Donahue; Judy A. Bean; Rosemary DeCarlo Donahue; Ronald B. Goldberg; ; Ronald J. Prineas

From the University of Miami School of Medicine, Department of Epidemiology and Public Health (R.P.D., J.A.B., R.J.P.); the University of Miami School of Nursing (R.D.D.); and the University of Miami School of Medicine, Department of Medicine R.B.G.), Miami, Fla.

Correspondence to Richard P Donahue PhD, MPH, SUNY Buffalo, Department of Social and Preventative Medicine, 270 Farber Hall, Buffalo, NY 14218 E-mail rdonahue{at}ubmed.buffalo.edu

	Abstract

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Abstract A number of coronary heart disease risk factors have been identified that often cluster together to increase the risk of macrovascular disease. This cluster is referred to as the insulin resistance syndrome, and the risk factors commonly include dyslipidemia, elevated blood pressure, an android pattern of body fat distribution, and glucose intolerance. Whether hyperinsulinemia or insulin resistance per se provides a common pathway for these metabolic abnormalities is unclear. The authors studied 50 nondiabetic persons who had completed a euglycemic hyperinsulinemic clamp protocol in addition to a 75-g oral glucose tolerance test and other measures of the coronary risk profile. Using principal-component analysis, we reduced nine coronary risk factors to two uncorrelated factors that explained 54.5% of the variance. Factor 1 consisted of positive loadings for uric acid, systolic and diastolic blood pressure, triglyceride concentration, and waist girth and negative loadings for HDL cholesterol and the rate of insulin-mediated glucose disposal (M, in milligrams per kilogram of body weight per minute). M also loaded on factor 2, along with fasting insulin and glucose concentrations, diastolic blood pressure, and waist girth. The observation that M loaded on both factors suggests that a resistance to insulin action may provide the mechanism uniting the features of the insulin resistance syndrome. Hyperinsulinemia with concomitant insulin resistance may be necessary to produce this metabolic derangement, as well as the increased risk of macrovascular complications.

Key Words: insulin • atherosclerosis • coronary risk factors

	Introduction

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A number of animal and human studies of serum insulin concentrations have led to the hypothesis that hyperinsulinemia directly contributes to CHD.^{1 2 3 4} These findings are supported by the observation that insulin is a strong mitogenic factor that increases endothelial–smooth muscle cell proliferation in cell culture.⁵ Fasting insulin concentration has also been positively associated with the degree of carotid atherosclerosis in population-based studies.⁶ Elevated insulin levels have alternatively been considered to be a marker for the IRS, and some investigators believe that it is this syndrome rather than circulating insulin levels that is the more important predictor of increased cardiovascular disease risk.^{7 8}

The IRS is generally defined as a cluster of metabolic abnormalities including dyslipidemia, elevated blood pressure, an android pattern of obesity, and increased glucose levels.⁹ Other factors may include elevated uric acid concentrations, an altered fibrinolytic/clotting system, and the presence of small, dense LDL particles.^{10 11 12 13} Whether insulin resistance, hyperinsulinemia, or both play a central role in the IRS is still in debate.^{14 15 16} Few epidemiological studies have included a direct measure of insulin resistance, eg, the euglycemic hyperinsulinemic clamp, along with measures of circulating insulin levels, to ascertain whether hyperinsulinemia or insulin resistance per se is the driving force producing the multiple abnormalities noted above.

Recently, principal-components analysis has been used to identify major factors of the IRS. This statistical tool is particularly useful when several variables are highly intercorrelated. Principal-components analysis is simply a reduction procedure that results in a limited number of components that account for most of the variance in a set of observed variables. Edwards and coworkers,¹⁷ using principal-component methods, identified an insulin/glucose factor, a body weight/fat distribution factor, and a lipid factor in a recent study of a group of 50-year-old women. A direct measure of insulin resistance was unavailable in that study. For the present report, we hypothesized that if insulin resistance unifies the various metabolic disturbances in this syndrome, then it should load on each factor of the IRS. If hyperinsulinemia is also important in the IRS, it should load on one but not all factors. We tested this hypothesis by using data from 50 nondiabetic persons who had undergone both a standard 75-g oral OGTT and a euglycemic hyperinsulinemic clamp as part of a comprehensive cardiovascular risk factor survey in Dade County, Florida, in 1991 to 1995.

	Methods

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Subjects
This study has been described previously.¹⁸ In brief, the Miami Community Health Study is an epidemiological study designed to comprehensively assess the physiological and behavioral correlates of blood pressure and other cardiovascular disease risk factors in African-Americans, Cuban-Americans, and non-Hispanic whites living in Dade County, Florida. The study cohort was selected from census tracts (1990 US Census) located within 10 miles of the University of Miami School of Medicine, with populations composed of at least 80% of the targeted ethnic group. Participants were recruited and examined between 1991 and 1995. The overall participation rate was 53%.

Beginning in January 1993, 58 of 107 (54.2%) consecutive subjects examined that year participated in a substudy designed to directly measure insulin resistance using the euglycemic hyperinsulinemic clamp technique as previously discussed.¹⁹ Too few Cuban-Americans volunteered for the clamp study to provide meaningful results, so their data were omitted (n=5), as were the data of 3 people with missing information on one or more variables, leaving a total of 50 participants for this report.

Prior to selection for the current investigation, all subjects underwent extensive examinations for cardiovascular risk factor testing, which included a standard 75-g OGTT. Persons testing positive for diabetes mellitus (fasting glucose 140 mg/dL or a 2-hour glucose >200 mg/dL) were ineligible to participate in the clamp protocol. IGT was defined according to World Health Organization criteria (fasting glucose <140 mg/dL and 2-hour glucose between 140 and 200 mg/dL).²⁰ Glucose was assayed by the glucose-oxidase method (Yellow Springs Instruments). Insulin was assayed with a double-antibody radioimmunoassay technique (Diagnostic Products Corp). The interassay coefficient of variation for fasting glucose was 2.7% and that for fasting insulin was 15%. Intra-assay coefficients were <2% and 11%, respectively. Other components of the clinical examination included measurements of several cardiovascular risk factors, including a lipoprotein profile, uric acid level, resting blood pressure, and anthropometry. Total cholesterol was measured enzymatically²¹ (Roche reagent for cholesterol). HDL cholesterol was measured after precipitation of the apoB-containing lipoproteins with MgCl₂. Triglycerides were also measured enzymatically (Roche reagent for triglycerides). The inter-assay coefficients of variation for HDL cholesterol and triglycerides were 2.5% and 5.3%, respectively. Blood pressure was measured three times using a standard mercury manometer by trained and certified technicians.²² The onset of the first phase (systolic) and fifth phase (diastolic) Korotkoff sounds were recorded. The mean of the second and third measures was used in analyses.

All anthropometric measurements were made with the participant wearing light clothes without shoes. Because the waist circumference is more closely associated with the metabolic parameters under investigation,²³ we chose to use this measure over other anthropometric indices in these analyses. Waist girth was measured in the standing position by applying a linen tape measure horizontally midway between the iliac crest and the lowest lateral portion of the rib cage and anteriorly midway between the umbilicus and the xiphoid process. This measure is the smallest circumference at waist level. The mean of two measurements (recorded to the nearest 0.5 cm) was used. The study was approved by the University of Miami School of Medicine Internal Review Board, and all participants gave written informed consent.

Hyperinsulinemic Euglycemic Clamp
Participants were required to fast for 10 to 12 hours the night before their clinic visit. No formal instructions were given with respect to prior dietary intake. Sensitivity to insulin-mediated glucose disposal was determined with the hyperinsulinemic euglycemic clamp procedure.¹⁹ At 8:30 AM, compliance with fasting instructions was determined (noncompliance resulted in rescheduling). An angiocatheter (20 gauge) was placed in the right forearm for infusion and a second angiocatheter (22 gauge) placed retrogradely in a vein in the left hand or wrist. Blood samples were obtained for fasting glucose and insulin concentrations. An insulin primer was infused over a 10-minute period followed by a constant infusion of 40.0 µU per meter squared per minute for 120 minutes. Euglycemia was maintained within 5% of the fasting value by a variable infusion of 20% dextrose solution. The serum glucose level was determined every 5 minutes and the rate of glucose infusion adjusted accordingly. Euglycemia was maintained for 2 hours. During the second hour in steady-state hyperinsulinemia, the quantity of glucose metabolized was calculated as the mean of the glucose infusion rate. This infusion rate (M) reflects the total insulin-stimulated glucose metabolism and assumes complete suppression of hepatic glucose output. Radiolabeled glucose was not used to quantify hepatic glucose output because the insulin dose used in this study (1 µU per kilogram of body weight per minute) has been shown to effectively suppress endogenous glucose production in healthy persons.²⁴ Upon termination of the clamp procedure, the participant remained in the research center until their glucose concentrations returned to baseline.

Statistical Methods
Factor Analysis
Factor analysis was performed in three steps: (1) use of principal-component analysis to identify the initial components; (2) varimax rotation, which yielded the factors; and (3) interpretation of the factors. Steps 1 and 2 provide independent factors that are linear combinations of the original variables. Factors are selected on the basis of maximizing the amount of remaining variation explained as each additional factor is selected. A total of nine CHD risk factor variables were included in the factor analysis. These included fasting insulin, insulin-mediated glucose disposal rate (M), HDL cholesterol, triglycerides, systolic blood pressure, diastolic blood pressure, uric acid, waist girth, and fasting glucose. Principal-component analysis extracted two components with eigenvalues >1, and these components were retained for varimax rotation. These components are referred to as factors. Factor loadings may be interpreted as correlation coefficients and were used to interpret the factors.^{25 26} Only variables with a loading 0.4 were used for interpretive purposes. To address our main hypotheses, these analyses were performed twice; once with fasting insulin and the risk factors (without M) to examine the effects of fasting insulin as a marker of insulin resistance and again with fasting insulin, M, and the risk factors. All analyses were performed using the Proc Factor program in the Statistical Analysis System.²⁷ Fasting insulin and triglyceride concentrations were logarithmically transformed (natural log) to improve normality.

	Results

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As shown in Table 1, one half of the study participants were men and were nearly equally divided between African-Americans and non-Hispanic whites. The average age of the participants was 35.3±5.9 years. Sixteen percent (8/50) had IGT on the basis of the results of the OGTT. The average M value was 7.9±3.3 mg · kg^-1 · min^-1 and the mean fasting insulin 12.2±15.0 µU/mL. By design, the participants were generally healthy. The mean systolic and diastolic blood pressure was 109.1±12.2 and 74.1±10.5 mm Hg, respectively and the mean HDL cholesterol concentration 45.9±11.3 mg/dL. Triglyceride levels averaged 115.1±51.6 mg/dL, and the mean waist circumference was 87.0±13.0 cm. Table 2 presents partial Spearman correlation coefficients (adjusted for age, sex, and ethnicity) among the variables included in the factor analysis. Fasting insulin level was positively correlated with waist girth, blood pressure, and fasting glucose. Somewhat surprisingly, no meaningful correlation was observed between fasting insulin and HDL cholesterol or triglycerides, which were more strongly correlated with the M value. Further analyses showed that insulin was related to HDL cholesterol (r_s=-.24) and triglycerides (r_s=.25) in African-Americans only. As expected, the M value was inversely correlated with fasting insulin (r_s=-.29), triglycerides, blood pressure, fasting glucose, and uric acid and positively associated with HDL cholesterol (r_s=.30). The waist girth was inversely related to both M and HDL cholesterol (r_s=-.74 and -.25), and positively with triglycerides (r_s=.29) and blood pressure levels (r_s=.45 to .47). These correlations provide support for the associations among CHD risk factors observed in the IRS.

View this table: [in this window] [in a new window]   Table 1. Selected Characteristics of Study Sample (n=50)

View this table: [in this window] [in a new window]   Table 2. Partial1 Spearman Correlation Coefficients Among Selected Variables

Two models were analyzed by using principal-component analyses. The first model included all of the variables of interest except M. The second model added M to the list of risk factors. In the first model (Table 3A), two factors were identified. Factor 1 included positive loadings for uric acid, blood pressure level, triglycerides, and waist girth. A negative loading was observed for HDL cholesterol, whereas fasting insulin failed to load. This factor explained 43.3% of the total variance. Fasting insulin level loaded on the second factor, which also included positive loadings for fasting glucose and diastolic blood pressure. This factor explained an additional 13.5% of the variance.

View this table: [in this window] [in a new window]   Table 3A. Results of Factor Analysis: Factors and Factor Loadings Without M

In model 2 (Table 3B), factor 1 resembled that of model 1 with the addition of a negative loading of M to the factor (ie, decreased insulin sensitivity or increased insulin resistance). Of particular importance is the observation that M and fasting insulin loaded together on factor 2. Fasting glucose, diastolic blood pressure, and waist girth also loaded on factor 2. This model explained a similar proportion of the total variance (54.5%) as did model 1 (56.8%).

View this table: [in this window] [in a new window]   Table 3B. Results of Factor Analysis: Factors and Factor Loadings With M

	Discussion

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The purpose of these analyses was to examine whether insulin resistance (or conversely, insulin sensitivity) measured directly with the euglycemic hyperinsulinemic clamp could unify the various metabolic parameters commonly referred to as the IRS. Most previous epidemiological reports examining the metabolic components of the IRS have included only fasting insulin level as the primary measurement of insulin resistance. Although fasting insulin is considered a relatively good indicator of insulin resistance among nondiabetic persons, it explains <40% of the variance in insulin resistance.²⁸ It is possible that insulin resistance per se has effects on atherosclerosis in addition to those of hyperinsulinemia. Thus, direct measurement of insulin resistance is a critical and unique addition to this area of inquiry. Our results provide new and provocative evidence that insulin level and insulin resistance may have joint effects on the IRS but that the latter may unify the many components.

In both models (Tables 3A and 3B), factor 1 appears to reflect similar pathophysiologies, including a lipid factor (HDL cholesterol and triglycerides), blood pressure, a fat distribution factor (waist girth), and uric acid. Factor 2 could be considered an insulin/glucose/blood pressure factor. Insulin resistance loaded on both factors (Table 3B), suggesting that it may unify these variables in the IRS. The components of each factor as well as the number of factors may differ from study to study, however, depending on the variables available for inclusion and the sample size. Standardized analyses across different studies using the same core set of variables would be of great interest in this regard.

Insulin resistance still has not been definitively shown to precede the IRS. Baron and coworkers²⁹ have advanced the notion that in the presence of hypertension, an increase in peripheral insulin resistance results in reduced skeletal blood flow. Because transport of insulin across the capillary-endothelium influences the rate of insulin action, the resultant peripheral hyperinsulinemia and insulin resistance may be secondary phenomena. The study of Baron et al, however, was cross-sectional, and it is just as likely that insulin resistance may reflect early vascular and/or endothelial dysfunction. This may lead to a self-sustaining IRS in some people. Clearly, intervention studies are required to clarify these issues.

Direct comparisons of our results with others are difficult due to the different racial and sex compositions of the study samples, differences in the risk factors included, and differences in sample sizes. Edwards et al¹⁷ studied a sample of women aged 50 years and found three factors that explained >60% of the variance of the IRS. These factors were interpreted as (1) body weight/fat distribution; (2) insulin/glucose, including systolic blood pressure; and (3) lipids. Our results identified many of the same variables (though only two factors) but further suggested that when direct measurement of insulin resistance was available, it loaded on both factors, suggesting that resistance to insulin action may unify the various components of the IRS. Other features of the IRS, including small, dense LDL particles and fibrinolytic/clotting factors, were not assessed in this study, and thus, it is not possible to know how they would have loaded in these analyses.

Another distinguishing feature of the current study is that the analyses included both men and women and both white and African-American participants. Analyses stratified separately by sex and ethnicity revealed generally similar results as those presented above. The small poststratification sample sizes limit the statistical power, however, to definitively address these issues.

As noted by others,³⁰ the syndrome of insulin resistance remains poorly defined as a clinically observable entity. The multitude of components comprising the IRS are correlated to such an extent that statistical methods to discern independence, such as multiple linear regression, may limit rather than deepen our understanding of this cluster.³¹ The use of principal-component analysis is one multivariate method that may help clarify whether insulin resistance is the pivotal force underlying this constellation of CHD risk factors.

One potential limitation of this study, however, is that our insulin assay measured proinsulin to some degree. Nagi et al³² have shown that among patients with non–insulin-dependent diabetes, proinsulin levels are associated with an atherogenic lipid profile. This finding was not observed among Pima Indians with IGT or normoglycemia.³³ Thus, it is unlikely that our results were biased to any major extent, as we excluded persons with non–insulin-dependent diabetes mellitus and only 8 of our study subjects had IGT.

In summary, our results suggest that hyperinsulinemia may not be the main culprit in the IRS, and by extension, may not be sufficient to produce macrovascular disease, eg, insulinoma. Hyperinsulinemia may require the presence of insulin resistance to produce the elevated risk in cardiovascular disease that has been observed in many but not all epidemiological studies.^{34 35} Although the results of the current report do not directly address this latter point, they are consistent with such a hypothesis.

	Selected Abbreviations and Acronyms

 

CHD = coronary heart disease IGT = impaired glucose tolerance IRS = insulin resistance syndrome OGTT = oral glucose tolerance test

	Acknowledgments

 
This work was supported by NIH grant HL 44600 (to R.P.D.). The authors wish to acknowledge the contributions of Killiam Lopez, Delia A Stephens, and Linda Jones in preparation of this manuscript.

Received April 7, 1997; accepted June 9, 1997.

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