Risk Factor Clustering in the Insulin Resistance Syndrome

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1 American Journal of Epidemiology Copyright 1998 by The Johns Hopkins University School of Hygiene and Public Health All rights reserved Vol. 148, No. 9 Printed in U.S.A. Risk Factor Clustering in the Insulin Resistance Syndrome The Strong Heart Study R. Stuart Gray, 1 Richard R. Fabsitz, 2 Linda D. Cowan, 3 Elisa T. Lee, 3 Barbara V. Howard, 1 and Peter J. Savage 2 The objective of this study was to examine how the major components of the insulin resistance syndrome relate to each other and to macrovascular disease in American Indians in the Strong Heart Study. The study cohort (4,228 resident tribal members years old) underwent a personal interview and a physical examination between July 1989 and January 1992 at three centers: Arizona, Oklahoma, and North and South Dakota; blood samples were drawn and a 75-g oral glucose tolerance test was performed. Factor analysis was used to assess the clustering and interdependence of groups of insulin resistance syndrome variables. Within both diabetic and nondiabetic groups, three factors emerged. In nondiabetic participants, a cluster of glucose, body mass index, and insulin accounted for 35% (male) and 32% (female) of the total variance in all variables considered, and a cluster of systolic blood pressure and diastolic blood pressure accounted for 25% and 22% in men and women, respectively. Both clusters were positively associated with coronary heart disease but not peripheral vascular disease. In diabetic participants, the combination of systolic and diastolic blood pressures was the most important factor, but the cluster was not associated with coronary heart disease or peripheral vascular disease. A component containing high density lipoprotein cholesterol, triglycerides, and glucose had a positive association with coronary heart disease in diabetic women and with peripheral vascular disease in both sexes. The association of clusters of risk factors and their relations with coronary heart disease provide important clues that may be used in understanding the metabolic disorders associated with insulin resistance and diabetes. Am J Epidemiol 1998; 148: coronary disease; factor analysis, statistical; Indians, North American; insulin resistance The insulin resistance syndrome has been proposed to include a set of metabolic and anthropometric characteristics of which glucose intolerance, hypertriglyceridemia, a reduced concentration of high density lipoprotein cholesterol, hyperinsulinemia, and central obesity are the predominant components (1). Other potential manifestations of the syndrome include hypertension (1, 2); a preponderance of small, dense, low density lipoprotein cholesterol (3); albuminuria (4); elevated concentrations of plasminogen activator inhibitor (5); and increased plasma uric acid (6). An elevated low density lipoprotein cholesterol concentration, which is a strong predictor of macrovascular disease (7), is most probably distinct from those other risk factors that constitute the insulin resistance syndrome. Although hyperinsulinemia is often found among subjects at risk of macrovascular disease (8- Received for publication June 18, 1997, and accepted for publication April 10, Medlantic Research Institute, Washington, DC. 2 National Heart, Lung, and Blood Institute, Bethesda, MD. 3 Department of Biostatistics and Epidemiology, University of Oklahoma, Oklahoma City, OK. Reprint requests to Dr. Barbara V. Howard, Medlantic Research Institute, 108 Irving Street, N.W., Washington, DC ), not all the features of the insulin resistance syndrome are necessarily expressed in any one individual, and there appear to be ethnic differences in the expression of this syndrome and its relation with macrovascular disease. For example, hypertension may not be associated with the insulin resistance syndrome in American Indians (11, 12), African Americans (11), or Pacific Islanders (13), as it is in white populations (14, 15). An appreciation of how the insulin resistance syndrome manifests within individuals or populations, with and without macrovascular disease, may further our understanding of whether and why the insulin resistance syndrome predicts atherosclerosis and to what extent this occurs in various ethnic groups. One statistical method of interpreting the interdependence of risk factors is factor analysis (16, 17). A factor analysis of the constituents of the insulin resistance syndrome has previously been undertaken by Edwards et al. (18) in a small (n = 281) population of predominantly white, nondiabetic women in California. No attempt was made to relate factors within the syndrome to macrovascular disease, and the study did not include men. Austin et al. (19) have used factor analysis to examine relations among lipoproteins in 869

2 870 Gray et al. 204 Finnish men and women, and recently two abstracts using the technique were presented (20, 21). The Strong Heart Study has completed an initial cross-sectional analysis of the constituents of the insulin resistance syndrome among a large populationbased study of American Indians who were also assessed with respect to history or current electrocardiographic evidence of coronary heart disease and physical evidence of peripheral vascular disease. This population is of particular relevance to this form of inquiry given its remarkably high prevalence of insulin resistance as manifested by carbohydrate intolerance, central obesity, and hyperinsulinemia. We have therefore examined the data from the Strong Heart Study cohort by factor analysis to consider how the major components of the insulin resistance syndrome associate with one another and with macrovascular disease, and whether this clustering is similar to that described in other populations. We have also compared our findings in women and men and in nondiabetic and diabetic participants. MATERIALS AND METHODS Study design The study design, survey methods, and laboratory techniques of the Strong Heart Study have been previously reported (22, 23). Briefly, the study population included resident tribal members aged years who were examined between July 1989 and January 1992 at three study centers: Arizona, Oklahoma, and North and South Dakota. Participants were members of the following tribes: Pima/Maricopa/Papago of central Arizona in the Gila River, Salt River, and Ak Chin Indian communities; the seven tribes of southwestern Oklahoma (Apache, Caddo, Comanche, Delaware, Fort Sill Apache, Kiowa, and Wichita); the Oglala and Cheyenne River Sioux in South Dakota; and the Spirit Lake Community in the Fort Totten area of North Dakota. The wide range of tribes was included to represent the diversity of coronary heart disease that occurs among American Indian groups (22). Approximately 1,500 individuals from each of the three centers were included. Participation rates were 71 percent in the Arizona center, 61 percent in the Oklahoma center, and 53 percent in the Dakota center (24). Nonrespondents did not differ significantly from respondents in age, body mass index, or self-reported frequency of diabetes. Respondents were more often female and nonsmokers, and they had a slightly higher self-reported frequency of hypertension and obesity than did nonrespondents (24). For the prevalence rates of coronary heart disease and risk factors, the denominators are all tribal members aged years who attended the clinical examination. Clinical examination The clinical examination consisted of a personal interview and a physical examination. Participants reported in the morning after at least a 12-hour overnight fast. After informed consent was obtained, fasting blood samples were drawn for measurements of glucose, insulin, lipids, and lipoproteins. A 75-g oral glucose tolerance test was performed on all participants, except for diabetic persons treated with insulin or oral hypoglycemic agents or participants with a fasting glucose concentration of >12.5 mmol/liter (225 mg/dl) as determined by an Accu Check II (Baxter Healthcare Corporation, Grand Prairie, Texas) monitor. Triglycerides and glucose were determined by enzymatic methods using a Hitachi chemistry analyzer. High density lipoprotein cholesterol was determined in the supernatant following precipitation of apolipoprotein B containing lipoproteins by heparin manganese. Insulin was measured by a modification of the method of Morgan and Lazarow (25); it could not be measured in 65 patients with diabetes who showed evidence of anti-insulin antibodies. Anthropometric measurements included weight, height, and waist and hip circumferences measured with participants wearing light clothing and without shoes. Body mass index was defined as weight (kg)/ height (m) 2. Three consecutive measurements of blood pressure, using the first and fifth Korotkoff sounds, were performed with the participants seated, after 5 minutes of rest, on the right arm using the appropriate size cuff with a Baum mercury sphygmomanometer (W.A. Baum Company, Copiague, New York). The mean of the last two measurements was used to estimate the blood pressure. A 12-lead electrocardiogram was taken using a Marquette system (MAC-PC or MAC-12; Marquette Electronics, Milwaukee, Wisconsin). All electrocardiograms were read clinically by three staff cardiologists at the Fitzsimons Medical Center and were forwarded to the University of Minnesota electrocardiogram center for application of Minnesota codes (26). Right arm and bilateral ankle systolic blood pressure readings, measured using an Imex Doppler (Imex Medical Systems, Golden, Colorado), were taken separately while the patient was supine. Measurements of right arm, right ankle, and left ankle pressures were taken twice with both measurements recorded. The means of the two measurements for each leg and for the arm were used to calculate the ankle/brachial index, and the lower value of the two ankle/brachial indices was used to estimate peripheral vascular disease. Peripheral vascular disease was defined as an ankle/brachial index of <0.9.

3 Risk Factor Clustering: The Strong Heart Study 871 Definitions of terms The participants' medical histories included the Rose questionnaire for angina pectoris (27). Criteria used to define prevalent coronary heart disease have been previously described (26). Definite myocardial infarction was determined by Minnesota coded Q wave changes on electrocardiogram or by a history of myocardial infarction verified as definite by chart review and confirmed by a Strong Heart Study cardiologist (22). Possible myocardial infarction included electrocardiograms with a broader range of Minnesota codes or a history of myocardial infarction verified as possible by chart review and confirmed by a Strong Heart Study cardiologist (22). Criteria for definite coronary heart disease included definite myocardial infarction, evidence in the medical record of coronary angioplasty or bypass surgery, thrombolytic therapy, a positive angiogram, or angina pectoris by Rose questionnaire when accompanied by Minnesota code 4.1 or 5.1 or a verified history of possible myocardial infarction. Possible coronary heart disease included an electrocardiogram with a broad range of Minnesota codes, angina pectoris by Rose questionnaire, or a history of myocardial infarction by interview. For the analyses, coronary heart disease was defined as the sum of definite plus possible coronary heart disease. Participants were classified as diabetic according to World Health Organization criteria (28) if they were taking insulin or oral antidiabetic medication or if they had fasting glucose concentrations that were ^7.8 mmol/liter (140 mg/dl) or 2-hour glucose levels of >11.0 mmol/liter (>200 mg/dl) after a 75-g oral glucose tolerance test. In this analysis, the nondiabetic group included those with normal (fasting and 2-hour glucose levels of 7.8 mmol/liter (140 mg/dl)) and impaired (a fasting glucose level of <7.8 mmol/liter (140 mg/dl) and a 2-hour glucose level of mmol/liter ( mg/dl)) glucose tolerance. Statistical methods Due to the variability in data collection methods, these analyses are purposely limited to factors that satisfy two criteria: 1) they must be among the most frequently hypothesized as variables to the insulin resistance syndrome; and 2) they must be available in all of the studies participating in this analytical exercise. Thus, this analysis is limited to the following seven variables: 1) systolic blood pressure, 2) diastolic blood pressure, 3) high density lipoprotein cholesterol, 4) triglycerides, 5) glucose, 6) body mass index, and 7) insulin. These variables were available in 4,228 (93 percent) of the 4,549 participants. Analyses were performed separately by sex for diabetic and nondiabetic participants; triglycerides and insulin were log transformed. All variables were adjusted for center and age effects using linear regression. Initial estimates of association between variables were based on Pearson's product-moment correlations. It was likely that the known effects of diabetes and the degree of metabolic control over several of the risk factors (e.g., lipoprotein level, blood pressure) would distort the relations among the various risk factors in a manner that would be more dependent upon the short-term degree of glucose control than upon long-term linkages among the risk factors. Factor analyses were used to account for the overlapping variability (communality) of intercorrelated variables by defining a set of new composite independent hypothetical variables. Factor analyses for this article used the method of principal components from the Proc Factor program of SAS version 6.0 software (SAS Institute, Inc., Cary, North Carolina). This procedure provides orthogonal (mathematically independent) factors that are linear combinations of the original variables. Factors are selected on the basis of maximizing the amount of remaining variation contained in each additional factor as it is selected. Factor loadings associated with each variable within a factor represent the correlation of that variable with the corresponding factor. Factors were initially selected based on the commonly used criterion of the eigenvalue (the variance attributable to the factor) >1, but that criterion was relaxed to >0.9 in one subgroup (nondiabetic males) to determine if the allowance of a third factor would result in a set of factors similar to those of other subgroups. Factor solutions are not unique; many sets of loadings may result in the same correlation structure. Thus, an additional step in factor analysis is to simplify the factor structure to provide more meaningful solutions. Selected factors from the initial solution were rotated (redefined in n-space) using a "varimax procedure" that maintains the independence of the factors from one another but seeks to simplify or more clearly define the factors by maximizing (force factor loadings close to one) and minimizing (force factor loadings close to zero) factor loadings. A factor structure is simplest when each variable has a nonzero loading on only one common factor (29). Factor loadings of the rotated factors are presented along with the percentage of variance explained. Factor loadings of >0.4 (absolute value) are considered definitive of the factor for interpretation, because those variables share at least 15 percent of variance with the factor (17). Factor scores are calculated as part of the program and used in subsequent analyses to test for an association with prevalent coronary heart disease and peripheral vascular disease based on t tests of those with versus those without the characteristic.

4 872 Gray et al. RESULTS Table 1 shows the numbers of male and female participants from each center according to diabetic status and their respective mean ages. Also shown in table 1 are the percentages of participants from each center with coronary heart disease and peripheral vascular disease. Table 2 shows the means and standard deviations for systolic blood pressure, diastolic blood pressure, high density lipoprotein cholesterol, triglycerides, fasting glucose, body mass index, and insulin of nondiabetic and diabetic men and women. Table 3 shows the age- and center-adjusted Pearson's correlation coefficients among the variables for nondiabetic and diabetic men and women. The results of the factor analysis are presented in table 4 and figure 1. Within both the nondiabetic and the diabetic groups, three factors emerged with eigenvalues of >0.9 in men and women, and each of these was subjected to rotation. Nondiabetic participants Nondiabetic male participants. The glucose/ obesity factor was characterized by positive correlations with glucose, body mass index, and log insulin. This factor accounted for 34.9 percent of the total variance attributable to all the variables we considered, and it was positively associated with evidence of TABLE 1. Prevalences of coronary heart disease and peripheral vascular disease by center, sex, and diabetic status, Strong Heart Study, Arizona Oklahoma Dakotas Arizona Oklahoma Dakotas No Diabetic (%) Nondiabetic Diabetic M@s.n age (years) 54.5 (8.3)* 54.6 (7.6) 55.2 (8.2) 56.1 (8.3) 55.8 (7.9) 55.2 (7.8) 54.8 (7.3) 56.6 (7.8) 58.0 (8.3) 58.9 (7.8) 56.7 (7.6) 58.1 (8.0) * Numbers in parentheses, standard deviation. Coronary heart disease (%) Peripheral vascular disease (%) coronary heart disease (p < 0.001) but not with peripheral vascular disease (p = 0.88). The blood pressure factor was characterized by positive correlations with systolic and diastolic blood pressures. This factor accounted for 23.7 percent of the total variance and was positively associated with coronary heart disease (p = ) but not with peripheral vascular disease (p ). The dyslipidemia factor was characterized by positive correlations with log triglycerides and log insulin and by a negative correlation with high density lipoprotein cholesterol. This factor accounted for 13 percent of the total variance but was associated with neither coronary heart disease (p = 0.16) nor peripheral vascular disease (p = 0.68). Nondiabetic female participants. The glucose/ obesity factor was similar in nondiabetic men and women, being characterized by positive correlations with glucose, body mass index, and log insulin. The glucose/obesity factor accounted for 31.6 percent of the total variance and was positively associated with evidence of coronary heart disease (p < 0.05) but not with peripheral vascular disease (p = 0.11). The blood pressure factor was similar in nondiabetic men and women as well, having positive correlations with systolic and diastolic blood pressures. The blood pressure factor accounted for 22.2 percent of the total variance and was positively associated with coronary heart disease (p < 0.05) but not with peripheral vascular disease (p = 0.43). The dyslipidemia factor was characterized by a positive correlation with log triglycerides and an inverse correlation with high density lipoprotein cholesterol, but it did not include log insulin as was found in nondiabetic men. The dyslipidemia factor accounted for 15.8 percent of the total variance and was associated with neither coronary heart disease (p = 0.15) nor peripheral vascular disease (p 0.51). Diabetic participants Diabetic male participants. The blood pressure factor was characterized by positive correlations with systolic and diastolic blood pressures; this factor accounted for 25.2 percent of the total variance and was therefore the most prominent factor, in contrast to our findings in nondiabetic participants, but it was associated with neither coronary heart disease (p = 0.31) nor peripheral vascular disease (p = 0.82). The glucose/dyslipidemia factor was characterized by positive correlations with log triglycerides and glucose and by an inverse correlation with high density lipoprotein cholesterol. The glucose/dyslipidemia factor accounted for 23.8 percent of the variance, was not associated with coronary heart disease (p = 0.74), but was positively associated with peripheral vascular disease (p < 0.05). The obesity/insulin factor was char-

5 Risk Factor Clustering: The Strong Heart Study 873 TABLE 2. Arizona Oklahoma Dakotas Arizona Oklahoma Dakotas Mean anthropometric and metabolic characteristics by center, sex, and diabetic status, Strong Heart Study, Blood pressure (mmhg) Systolic (17.5)* 126.5(19.1) 127.6(16.4) (18.7) 122.0(16.8) 118.9(17.4) 131.9(18.6) (22.2) (17.6) 133.2(19.5) (18.8) (19.8) Diastolic 79.9(10.1) 75.9 (9.4) 80.0 (9.6) 74.7 (9.3) 76.1 (10.1) 73.2 (9.9) 81.5(9.1) 75.8 (9.1) 81.3(10.5) 75.9(10.2) 79.2(10.8) 74.1 (10.2) * Numbers in parentheses, standard deviation. High density iipoproicin cholesterol (mg/dl) 44.3 (14.8) 47.5 (12.0) 43.1 (12.2) 52.5 (14.7) 46.2 (15.2) 51.2(14.8) 43.1 (13.9) 44.2 (10.5) 38.8 (10.5) 44.5 (10.2) 38.2 (9.6) 44.3(11.8) Nondiabetic IDiabetic Tri n h/f*p ri rl p ^ 11 >yiy vci IUCO (mg/dl) 127.5(170.9) 121.2(64.4) (80.0) (87.1) 121.9(93.2) 119.0(62.2) (196.8) 169.0(118.6) (103.8) 173.0(112.2) 214.8(398.7) (181.4) GIUCOSG (mg/dl) 104.6(11.6) 106.9(12.1) (12.6) (12.5) 102.5(13.0) (11.0) (77.5) (84.3) (66.8) (79.2) (75.0) 203.3(81.1) Body mass index (kg/m*) 31.6(7.5) 34.2 (7.6) 29.2 (4.6) 29.7 (5.9) 27.7 (4.8) 29.2 (5.6) 31.2(7.0) 32.9 (6.6) 32.1 (6.0) 33.5 (6.2) 30.5 (4.3) 31.4(5.4) Inci imn 11 louiii 1 (microunits/ml) 18.1 (14.9) 22.3(15.3) 14.8 (10.6) 15.5 (15.0) 12.8 (18.4) 13.9 (9.7) 23.8 (16.3) 30.4 (31.0) 26.3 (22.5) 30.1 (28.7) 23.9 (29.2) 26.6 (23.1) acterized by positive correlations with body mass index and log insulin and by an inverse correlation with high density lipoprotein cholesterol. The obesity/ insulin factor accounted for 19.6 percent of the variance but was associated with neither coronary heart disease (p ) nor peripheral vascular disease (p = 0.98). Diabetic female participants. The blood pressure factor was similar in diabetic men and women, having positive correlations with systolic and diastolic blood pressures. This factor accounted for 23.9 percent of the variance and was therefore the most important factor, but it was associated with neither coronary heart disease (p = 0.22) nor peripheral vascular disease (p = ). The glucose/dyslipidemia factor was similar in diabetic men and women, having positive correlations with log triglycerides and glucose and an inverse correlation with high density lipoprotein cholesterol. This factor accounted for 22.3 percent of the variance and was positively associated with coronary heart disease (p = ) and peripheral vascular disease (p < ). The obesity/insulin factor was characterized by correlations with body mass index and log insulin. This factor accounted for 19.6 percent of the variance and was associated with neither coronary heart disease (p 0.12) nor peripheral vascular disease (p = 0.22). DISCUSSION When many factors help to determine the risk of development of coronary heart disease, it has been the epidemiologist's convention to consider the weight of each risk factor in turn by multivariate analysis, thus adjusting for bias introduced by confounding variables. The importance of any individual risk factor can thus be assessed independently of the influence of other, allied risk factors. This approach, however, fails to take account of the manner in which related factors may confer risk in concert. Moreover, this approach obscures attempts to gain an understanding of the manner in which risk factors are interrelated. In this article, we have used the technique of factor analysis to evaluate the strength of associations among various components of the insulin resistance syndrome and to distinguish between their relations with cardiovascular disease. This allows an unbiased assessment of the interrelations among the many variables that are associated with the insulin resistance syndrome. This analysis is part of a series of such studies in different ethnic populations within North America, each conducted in the same manner for the National Heart, Lung, and Blood Institute Workshop on Metabolic Cardiovascular Risk Factor Clustering Syndrome, September 11-12, Fasting insulin concentrations, acknowledged to be highly correlated with direct measures of insulin action (11, 30, 31), are elevated in the Strong Heart Study population (32), indicating a high degree of insulin resistance. In the Strong Heart Study population, these high fasting insulin concentrations correlate very strongly with body mass index, triglycerides, and

6 874 Gray et al. TABLE 3. Age- and center-adjusted Pearson's correlation coefficients of risk variables by sex and diabetic status: Strong Heart Study, Systolic blood pressure Diastolic blood pressure High density lipoprotein cholesterol Log triglycerides Glucose Body mass index Log insulin Systolic blood pressure Diastolic blood pressure High density lipoprotein cholesterol Log triglycerides Glucose Body mass index Log insulin *p<0.05;** p<. Sex Blood pressure Systolic 0.68* 0.65* 0.11* ** * 0.16* 0.09* 0.16* 0.66* 0.62* 0.09** 0.09* 0.11* * Diastolic inondiabetic * 0.09* 0.10* 0.14* 0.16* 0.14* 0.16* Diabetic * -* 0.08* 0.02 High density Iqpoprotein cholesterol -0.43* -0.35* -0.14* -0.15* -0.33* -0.20* -0.39* -0.31* -0.39* -0.39* -0.17* -0.14* -0.20* -** -0.23* -0.16* Log triglycerides 0.23** 0.10* 0.22* 0.36* 0.23* 0.32* 0.25* ** 0.16* 0.08* Glucose 0.29* 0.27* 0.35* 0.37* ** * Body mass index 0.59* 0.53* 0.41* 0.33* Log insulin glucose and inversely with high density lipoprotein cholesterol. The insulin resistance syndrome occurs in American Indians as in other ethnic populations, and its features such as obesity, low high density lipoprotein cholesterol, and hyperglycemia all present with high frequency (32). Although the fasting insulin concentration is a reliable surrogate index of insulin resistance in the nondiabetic population, it is accepted that in diabetic patients, fasting insulin concentrations can no longer be regarded as a satisfactory index of insulin resistance because of "Starling's law of the pancreas," that is, increasing hyperglycemia and insulin resistance accompanied by a decline in fasting insulin level (33). Moreover, hyperglycemia per se might be expected to influence the pattern of dyslipidemia and, particularly, triglyceride concentration. Further, coronary heart disease and peripheral vascular disease are much more common in diabetic than nondiabetic populations. For these reasons, and because the prevalence of diabetes is very high (40-70 percent), we performed the analysis separately in diabetic and nondiabetic individuals. Inclusion of participants with impaired glucose tolerance in the nondiabetic group ensures an adequate range of glucose ( mg/dl) to assess relations between the variables and glycemia. Among both male and female nondiabetic participants, the factor analysis yielded a cluster of variables the glucose/obesity factor that alone accounted for almost 35 percent and 32 percent in men and women, respectively, of the total variance attributable to all the risk factors under consideration. The glucose/obesity factor included obesity-associated components of the insulin resistance syndrome, comprising body mass index, glucose, and insulin. Edwards et al. (18) have presented almost identical findings in nondiabetic, predominantly white women, in whom waist circumference accompanied weight, fasting insulin, and glucose as a grouping. The waist/hip ratio in the American Indian population is highly correlated with body mass index because virtually all Strong Heart Study participants have central obesity. The present study shows this cluster of insulin resistance syndrome-

7 Risk Factor Clustering: The Strong Heart Study 875 TABLE 4. Results of factor analysis, with factors and loadings, the percentage of variance explained, and f test results of factor scores by status for coronary heart disease (CHD) and peripheral vascular disease (PVD) by diabetic status and sex. Strong Heart Study, Factors Glucose/obesity Blood pressure Dyslipidemia Blood pressure Glucose/dyslipidemia Obesity /insulin Blood pressure Systolic t 0.90t t 0.90t Diastollc j- 0.90t 0.89J High density lipoprotein cholesterol f -0.76t t 0.72t -0.40t Correlation coefficients LOQ triglycerides t 0.86t f 0.81 f Glucose Nondiabetic 0.75f 0.68t Diabetic t 0.58t Body mass index 0.76f 0.80f t 0.77t Log insulin 0.73t 0.78t t t 0.80t % of variance explained Relation with CHD <0.001 <0.05 < p values* * p values for test of differences in factor scores of those with versus those without characteristic. Factor loadings represent the correlations between individual variables and each factor. Significance of correlations is expressed as p values (not as correlation coefficients). t Considered meaningful correlation. Relation with PVD <0.05 < related risk factors to be significantly related to coronary heart disease in both nondiabetic men and women. Body mass index, having the highest loading within this factor, previously has been reported to be unrelated to peripheral vascular disease in a Caucasian population (34, 35) as we now describe in an American Indian population. Failure to demonstrate any relation between the glucose/obesity factor and peripheral vascular disease suggests that the etiology of peripheral vascular disease may be unrelated to insulin resistance in nondiabetic individuals. The insulin resistance syndrome in the American Indian population differs from that found in whites in that there is no significant association between the fasting insulin concentration and systolic or diastolic blood pressure when account is taken of confounding variables (12). Conversely, an association between insulin resistance and hypertension has been described in white populations (14, 15). A similar disparity has been shown in African-American and Pacific Island populations within whom hypertension also appears to be independent of (11, 13), or only weakly related to (36), hyperinsulinemia. Blood pressure was included among the variables because of previous research proposing its link to insulin resistance in other populations, but factor analysis has further highlighted the independence of hypertension and insulin resistance within the male and female nondiabetic American Indian population. Thus, for each sex, a measure of both systolic and diastolic blood pressures constituted a grouping that accounted for 24 percent and 22 percent of the variance in nondiabetic men and women, respectively. This blood pressure factor was not significantly correlated with the fasting insulin concentration in nondiabetics. Within a factor analysis of nondiabetic, predominantly white women (18), systolic and diastolic blood pressures cosegregated with the fasting insulin concentration, confirming the association between hypertension and insulin resistance in that population, in direct contrast with our findings in American Indians. The relation between coronary heart disease and the grouping of systolic and diastolic blood pressures in men and women in the Strong Heart Study population accords with previous findings (37). Although peripheral vascular disease has been associated with systolic blood pressure in Caucasian populations (34, 35) and in the Strong Heart Study population of American Indians (38), diastolic blood pressure shows

8 876 Gray et al. NONDIABETIC 34.9% 28.4% 31.6% >^H X 30.4% 13.0% 23.7% 22.2% 15.8% Glucose/obesity factor: BMI, glucose, log insulin Blood pressure factor SBP, DBP Dyslipidemia factor: log TG, log insulin, 1/HDL cholesterol Unattributable Glucose/obesity factor: BMI, glucose, log insulin Blood pressure factor: SBP, DBP Dyslipidemia factor: log TG, 1/HDL cholesterol Unattributable 25.2% 31.4% DIABETIC 23.9% 34.2% 23.8% 19.6% Blood pressure factor: SBP, DBP Glucose/dyslipidemia factor: log TG, glucose, 1/HDL cholesterol E3 Obesity/insulin factor: BMI, log insulin, 1/HDL cholesterol Unattributable 22.3% 19.6% Blood pressure factor: SBP, DBP E9 Glucose/dyslipidemia factor: log TG, glucose, 1/HDL cholesterol E3 Obesity/insulin factor: BMI, log insulin Unattributable FIGURE 1. Percentage of variance explained by factor analysis of risk variables comprising the insulin resistance syndrome, by diabetic status and sex, Strong Heart Study, BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL, high density lipoprotein. no such association. Because the blood pressure factor comprises systolic and diastolic blood pressures equally, it is perhaps not surprising that we failed to demonstrate any relation between the blood pressure factor and peripheral vascular disease. It has been traditional to regard insulin resistance as being strongly related to dyslipidemia (1). Central obesity is perceived as pivotal in this relation, whereby increased free fatty acid flux from insulin-resistant visceral fat stores to the liver encourages hepatic secretion of very low density lipoproteins (39). Impaired lipolysis, consequent upon insulin resistance, restricts the transfer of apoproteins and cholesterol ester from triglyceride-rich lipoproteins, leading to reduced high density lipoprotein cholesterol levels. Our observations in nondiabetic men show that fasting insulin is a constituent of the dyslipidemia factor in this subgroup, suggesting that insulin resistance is intimately allied to dyslipidemia. In the present study, in nondiabetic women, the correlation with insulin did not reach significance in the cluster including triglycerides and high density lipoprotein cholesterol in any other subgroup. Edwards et al. (18) also did not find insulin to be a significant component of the cluster including triglycerides and high density lipoprotein cholesterol. Thus, in two ethnic groups, there appear to be sex differences in the associations among insulin, triglycerides, and high density lipoprotein cholesterol. Considering the strength of the association between coronary heart disease and dyslipidemia in Caucasian nondiabetic subjects (40), it is perhaps surprising that no such association was evident in this American Indian population. Our failure to demonstrate any relation between dyslipidemia and peripheral vascular disease is, nevertheless, in accordance with some (34) but not all (35) reports in nondiabetic Caucasian populations. Diabetes in American Indians is characterized by profound insulin resistance (41). Although the mean fasting insulin concentration is higher than that found in nondiabetic subjects, suggesting a greater degree of

9 Risk Factor Clustering: The Strong Heart Study 877 insulin resistance, the relation between log insulin and glucose is distorted by a failure of pancreatic insulin secretion at high fasting glucose concentrations (33). Thus, an inverse correlation was observed between log insulin and glucose in diabetic men and women of the Strong Heart Study population. The potential, therefore, for factor analysis to provide insight into the relation of insulin resistance syndrome with macrovascular disease within the diabetic population may be limited in the absence of a better marker, whether direct or indirect, of insulin resistance. Notwithstanding this caveat, factor analysis among diabetic participants showed that the greatest amount of variance was attributable to the cluster that included measures of systolic and diastolic blood pressures, accounting for 25 percent and 24 percent in men and women, respectively. That the blood pressure factor should be independent of dyslipidemia and all other variables often associated with insulin resistance is in accordance with our observations in the nondiabetic population. Although no relation was observed between the blood pressure factor and macrovascular disease in either sex in this cross-sectional study, it remains possible that such an association will emerge on prospective analysis. In both diabetic men and women, hypertriglyceridemia and a reduced concentration of high density lipoprotein cholesterol cosegregated with one another and with glucose (the glucose/dyslipidemia factor). Insulin resistance originally may have been responsible for the emergence of this factor, although glycemic control, itself, may directly influence lipid metabolism in individuals with frank hyperglycemia. It is of interest that the glucose/dyslipidemia factor was found to be associated with peripheral vascular disease in diabetic men and with both coronary heart disease and peripheral vascular disease in diabetic women, suggesting that dyslipidemia may play a more important role in atherogenesis in the diabetic than in the nondiabetic population. However, these results may be influenced by the fact that there are few cases of peripheral vascular disease among nondiabetics. Likewise, the grouping of glucose with dyslipidemia and its association with macrovascular disease in the diabetic population may suggest that the degree of hyperglycemia is relevant to the pathogenesis of both dyslipidemia and macrovascular disease. The therapeutic implication of this interpretation is to suggest that improvement in glycemic control may help to prevent atherosclerosis. The obesity/insulin factor includes body mass index and insulin in both diabetic men and women. A strong correlation (r = 0.40, p < 0.001, and r = 0.33, p < 0.001) was observed between insulin and body mass index in diabetic men and women. As pancreatic insulin secretion fails at higher glucose concentrations, so the patient's weight inevitably falls due to glycosuria, thus probably preserving the relation between insulin and body mass index. Thus, the relation governing these two variables should be preserved in spite of the otherwise unreliable nature of log insulin as an index of insulin resistance. Our explanation may gain support from the observation that these two variables cosegregate consistently in each sex. The sexes differ to the extent that a reduced high density lipoprotein cholesterol concentration was found only within the dyslipidemia factor in diabetic men. The dyslipidemia factor was related to coronary heart disease and peripheral vascular disease in diabetic men and women. In summary, the technique of factor analysis has revealed consistent clusters of variables that differ in nondiabetic and diabetic individuals. Blood pressure was not related to insulin in this population. In nondiabetics, dyslipidemia is related to insulin, whereas dyslipidemia associates with glucose in diabetics. The factors containing insulin or blood pressure were related to coronary heart disease in nondiabetics, whereas in diabetics, coronary heart disease was related to the cluster that contained glucose. Thus, cluster analysis has assisted in the distinction between the metabolic links among factors relative to the insulin resistance syndrome. It will be important to confirm these observations with longitudinal analysis. ACKNOWLEDGMENTS This study was conducted by cooperative agreement grants (U01-HL41642, U01HL41652, and UL01HL41654) from the National Heart, Lung, and Blood Institute. A Wellcome Research travel grant and financial support through the Myre Sim Travel and Education Fund were provided to Dr. R. S. Gray. The authors acknowledge the assistance and cooperation of the AkChin Tohono O'odham (Papago)/Pima, Apache, Caddo, Cheyenne River Sioux, Comanche, Delaware, Spirit Lake Community, Fort Sill Apache, Gila River, Pima/Maricopa, Kiowa, Oglala Sioux, Salt River Pima/Maricopa, and Wichita Indian communities, without whose support this study would not have been possible. The authors also thank the Indian Health Service hospitals and clinics at each center and Betty Jarvis, Takir AH, and Allan Crawford, Directors of the Strong Heart Study clinics, and their staffs. Further, the authors acknowledge the assistance of Dr. Oscar Go in data analysis and the editorial assistance of Ellen Shair. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Indian Health Service.

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