(Circulation. 1999;100:123-128.)
© 1999 American Heart Association, Inc.
Clinical Investigation and Reports |
Insulin Resistance Syndrome Predicts Coronary Heart Disease Events in Elderly Nondiabetic Men
From the Department of Medicine, University of Kuopio, Finland (P.L., L.M., K.P., M.L., J.K.), and the University of Texas Health Science Center at San Antonio (L.M.).
Correspondence to Markku Laakso, MD, Professor and Chair, Department of Medicine, University of Kuopio, PO Box 1777, FIN-70211 Kuopio, Finland. E-mail markku.laakso{at}uku.fi
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Abstract |
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Background—The role of a cluster of risk factors characteristic for the insulin resistance syndrome as a predictor for coronary heart disease (CHD) has not been studied previously.
Methods and Results—Clustering of cardiovascular risk factors was analyzed by factor analysis to investigate whether these clusters (factors) predict CHD events (CHD death or nonfatal myocardial infarction) in a nondiabetic population of 1069 subjects 65 to 74 years old from eastern Finland followed up for 7 years. There were 151 CHD events (92 for men, 59 for women) during the follow-up period. In men, factor 1 (the insulin resistance factor, which reflected primarily body mass index, waist-to-hip ratio, triglycerides, fasting plasma glucose, and insulin) (hazards ratio [HR] with 95% CI, 1.33, CI 1.08, 1.65, P=0.008), factor 2 (alcohol consumption, high HDL cholesterol, low triglycerides) (HR 0.78, CI 0.63, 0.96, P=0.020), factor 3 (age, systolic blood pressure, urinary albumin/creatinine ratio, left ventricular hypertrophy) (HR 1.52, CI 1.26, 1.83, P<0.001), and factor 4 (high total cholesterol and triglycerides) (HR 1.42, CI 1.15, 1.77, P=0.002) predicted CHD events in multivariate Cox regression analysis. In women, the insulin resistance factor did not predict CHD events (HR 1.06, CI 0.82, 1.36), but factor 2 (previous stroke, low HDL cholesterol and high triglycerides) (HR 1.34, CI 1.06, 1.69, P=0.014) and factor 3 (age, systolic blood pressure, urinary albumin/creatinine ratio, left ventricular hypertrophy) (HR 1.44, CI 1.15, 1.82, P=0.002) predicted CHD events.
Conclusions—Our study supports the notion that the insulin resistance syndrome is a risk factor for CHD in elderly men.
Key Words: insulin • coronary disease
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Introduction |
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Insulin resistance, characterized by decreased rates of insulin-mediated glucose uptake, is accompanied by hyperinsulinemia and adverse changes in cardiovascular risk factors, such as high triglycerides, low HDL cholesterol, and hypertension.1 2 Because insulin resistance is a multifaceted phenomenon, the term "insulin resistance syndrome" has been introduced.1
From the time of its introduction, the insulin resistance syndrome has been considered a risk factor for coronary heart disease (CHD),1 3 but definitive evidence for the causal link is lacking. Hyperinsulinemia, an integral feature of insulin resistance, has been shown to be associated with the risk of CHD in several prospective studies,2 4 5 but there is still controversy about its importance as a risk factor,6 particularly in women7 8 9 and in the elderly.8 10 11 12 Our knowledge of quantitatively measured insulin resistance as a risk factor for CHD is limited. Cross-sectional studies have indicated that insulin resistance is associated with ultrasonographically assessed atherosclerosis, even in the absence of hypertension and dyslipidemia,13 14 15 and with CHD verified by coronary angiography.16 17 No prospective studies have been published in which the degree of insulin resistance had been measured and correlated with the risk of CHD.2
There are no generally accepted criteria for the insulin resistance syndrome, which, in addition to the fact that interrelated variables typical of insulin resistance are not readily analyzed by conventional statistical techniques, has made it difficult to investigate the occurrence and consequences of the insulin resistance syndrome. Recently, factor analysis, a statistical technique for studies including interrelating variables, has been applied to investigate the clustering of cardiovascular risk factors in the insulin resistance syndrome,18 19 but to the best of our knowledge, there are no studies indicating that these clusters predict CHD events. Therefore, we applied factor analysis to investigate the clustering of cardiovascular risk factors, particularly those typical of insulin resistance, in a large, nondiabetic elderly Finnish population and investigated whether these clusters predict CHD events during the 7-year follow-up.
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Methods |
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Baseline Study
The baseline examination including 1069 nondiabetic subjects was carried out in Kuopio, eastern Finland, in 1986 through 1988. The formation20 and representativeness21 of the study population have been described in detail previously. All subjects with a previous history of diabetes or newly diagnosed diabetes at the baseline study were excluded from the present analyses.
Weight, height, waist and hip circumference, and blood pressure were
measured as previously reported.20 A subject was defined
as having hypertension if systolic blood pressure was
160 mm Hg, or diastolic blood pressure was
95 mm Hg, or if the subject was taking drug treatment for
hypertension. With respect to alcohol consumption, subjects were
classified as alcohol users or nonusers. Smoking status was
defined as current smoking.
Chest pain symptoms suggestive of CHD were recorded with the Rose Cardiovascular Questionnaire.22 ECGs were classified according to the Minnesota code.23 Verified definite and possible myocardial infarction (MI) were defined according to the World Health Organization (WHO) MONICA project criteria24 as modified by the FINMONICA AMI Register Study Group.25 WHO criteria for definite and possible stroke were used in the ascertainment of the previous stroke.26 WHO diagnostic criteria for impaired glucose tolerance and diabetes mellitus were used in the classification of subjects without previously known diabetes.27
Blood samples were taken in the morning after a 12-hour overnight fast. All subjects underwent an oral glucose tolerance test (75 g glucose). Plasma glucose and insulin, serum lipids and lipoproteins, and urinary albumin were determined as previously described.11 20 The ratio of urinary albumin (mg/L) to urinary creatinine (mmol/L) (ACR) was used as a measure of albumin excretion.
The study was approved by the Ethics Committee of Kuopio University Hospital. All study subjects gave informed consent.
Follow-Up Study
The 7-year follow-up study was carried out during 1995. A postal
questionnaire was sent to every surviving participant of the original
study cohort. The questionnaire contained questions about hospital
admissions because of chest pain or symptoms suggestive of MI. Of the
1069 original nondiabetic baseline study participants, 867 subjects
were alive on June 30, 1995, of whom 839 responded to the questionnaire
(response rate, 97%).
Medical records of those participants who died during the 7-year follow-up (between the baseline study and June 30, 1995) and medical records of those who reported hospitalization due to symptoms suggestive of MI during the 7-year follow-up were reviewed by 2 of the authors (J.K. and P.L.). In addition, medical records of all nonresponders to the postal questionnaire were reviewed to verify definite or possible MIs and CHD deaths (J.K. and P.L.). Copies of death certificates of those who had died during the 7-year follow-up were obtained from medical records or from the files of the Central Statistical Office in Finland and reviewed (J.K. and P.L.). Therefore, all subjects from the original cohort were evaluated for CHD events. All deaths were coded according to the ninth revision of the International Classification of Diseases (ICD-9).28
CHD death during the follow-up was defined as a death resulting from CHD (ICD-9 codes 410 to 414). A new nonfatal MI during the 7-year follow-up was defined as follows: (1) a definite or possible MI verified at the hospital by the WHO criteria (WHO MONICA project criteria,24 as modified by the FINMONICA AMI Register Study Group,25 based on chest pain symptoms, ECG changes, and enzyme determinations) or (2) a new major Q-QS change on the ECG (progression from no Minnesota Q-QS code to 1.1 or 1.2, or from 1.3 to 1.1) for those who participated in the 3.5-year follow-up. CHD events included CHD death and definite or possible nonfatal MI. If a subject had >1 CHD event during the follow-up, only the first CHD event was included in statistical analyses.
Statistical Methods
Data analyses were conducted with the SPSS/PC+ programs.
Fasting and 2-hour insulin and triglycerides were
log-transformed for statistical analyses. The results for
continuous variables are given as mean±SEM or percentages.
Student's 2-tailed t test for independent samples or the
2 test was used in the assessment of
differences between the 2 groups, as appropriate.
Univariate and multivariate Cox regression
models29 were used to investigate the association of
cardiovascular risk factors with the incidence of CHD
events. Factor analysis consisting of (1) extraction of initial
components by use of principal-component analysis; (2) rotation
of components, resulting in elucidation of factors; and finally, (3)
interpretation of factors with loadings >0.40 (P<0.05) was
used to assess the relationship of several intercorrelated
variables.18 Principal-component analysis
identifies a minimum number of components that are transformed
(rotated) into interpretable factors. Interpretation is based on
correlations, called loadings, between the factors and the original
independent variables. Factors represent
physiological processes underlying the overall
relationship among the original independent
variables.19 The final number of factors was limited
to 4.
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Results |
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Of the 1069 nondiabetic subjects (396 men, 673 women) who had participated in the baseline study, 202 subjects (110 men [28%], 92 women [14%]) died during the 7-year follow-up. The number of CHD deaths was 49 (12%) in men and 27 (4%) in women. CHD events (CHD death or nonfatal MI) occurred in 92 men (23%) and in 59 women (9%).
The characteristics of the study population at baseline are
presented in Table 1
.
Women were more obese than men and more often had hypertension. Total
cholesterol and triglycerides as well as HDL
cholesterol and 2-hour insulin levels were higher in women
than in men. Smoking and alcohol consumption were more frequent among
men than among women.
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In men, previous stroke, left ventricular
hypertrophy, current smoking, waist-to-hip ratio,
hypertension, systolic blood pressure, low HDL
cholesterol, high triglycerides, high fasting
and 2-hour insulin levels, and high ACR were associated with the risk
of CHD events in univariate Cox regression analyses
(Table 2). In women, age, previous MI,
hypertension, systolic blood pressure, low HDL
cholesterol, high 2-hour insulin levels, and high ACR were
predictors of CHD events (Table 2).
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In men, previous stroke, left ventricular
hypertrophy, current smoking, and fasting insulin were
independent predictors of CHD events in the model including fasting
insulin in multivariate Cox regression analyses
(Table 3). The results were similar if
fasting insulin was replaced by 2-hour insulin in the model (left
ventricular hypertrophy, hazards ratio [HR]
2.31, 95% CI 1.41, 3.80, P<0.001; current smoking, HR
2.62, CI 1.61, 4.27, P<0.001; 2-hour insulin, HR 2.20, CI
1.10, 4.40, P=0.026; and high ACR, HR 1.58, CI 0.97, 2.56,
P=0.065). In women, in the model including fasting insulin,
independent risk factors for CHD events were age, previous MI,
increased systolic blood pressure, and low HDL
cholesterol, but not fasting insulin (Table 3). If
2-hour insulin was included in the model instead of fasting insulin,
neither HDL cholesterol (HR 1.91, CI 0.94, 3.88,
P=0.075) nor 2-hour insulin (HR 2.38, CI 0.90, 6.26,
P=0.080) was associated with CHD events.
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Varimax rotated factors and their loadings on original variables by
sex are shown in Table 4. In men, high
body mass index, high waist-to-hip ratio, high
triglycerides, and high fasting glucose and insulin levels,
all components of the insulin resistance syndrome, had significant
(>0.40) loadings on factor 1, which accounted for 17.7% of the total
variance. Alcohol consumption, high HDL cholesterol, and
low triglyceride levels loaded on factor 2. Age,
systolic blood pressure, ACR, and left ventricular
hypertrophy loaded on factor 3. Elevated total
cholesterol levels and triglyceride levels
loaded on factor 4. Altogether, these 4 factors accounted for 43.3% of
the total variance.
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Also in women, the variables characteristic of the insulin
resistance syndrome (high body mass index, high waist-to-hip ratio, low
HDL cholesterol, high triglycerides, and high
fasting glucose and insulin levels) had significant loadings on factor
1 (Table 4
). Previous stroke, together with high
triglycerides and low HDL cholesterol levels,
loaded on factor 2. Age, high systolic blood pressure, high
ACR, and left ventricular hypertrophy loaded on
factor 3. Current smoking and alcohol consumption (both inversely) and
high total cholesterol levels had significant loadings on
factor 4. These 4 factors accounted for 41.1% of the total
variance.
Factors assessed by factor analysis were included in the Cox
regression model to investigate whether they were risk factors for CHD
(Table 5). In men, factors 1 (the insulin
resistance factor), 2, 3, and 4 were all significantly associated with
the risk of CHD events in both univariate and
multivariate models (Table 5). In women, factors
2 and 3 were significantly associated with the risk of CHD events in
univariate and multivariate models, but
factors 1 and 4 were not (Table 5). HRs were considerably lower
than those given in Table 3, but this is because of the maximum
value of HR, which can only be 2.0 if factors are included in Cox
regression analysis. Basically similar results were obtained
when statistical analyses were performed only in normoglycemic
subjects (data not shown).
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Finally, we performed factor analysis in the whole study population, including both sexes (data not shown). Again, factor 1 was characterized by a clustering of high body mass index, high waist-to-hip ratio, high fasting insulin and glucose levels, high triglyceride levels, and low HDL cholesterol levels. Accordingly, the insulin resistance factor predicted CHD events during the follow-up both in univariate and in the multivariate Cox regression models (HR 1.31, CI 1.12, 1.53, P<0.001, and HR 1.35, CI 1.15, 1.59, P<0.001).
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Discussion |
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The significance of conventional cardiovascular risk factors, high total cholesterol, smoking, and hypertension has been demonstrated by a vast number of studies30 ; however, they do not explain all CHD morbidity. Insulin resistance syndrome, among other things, has been proposed as a new risk factor for CHD.1 2 Yet, the significance of the insulin resistance syndrome, characterized by clustering of interrelating variables, for increased risk of CHD is not easily demonstrated by conventional statistical methods. Recently, factor analysis, a technique that allows inclusion of intercorrelating variables in statistical analyses, has been applied to investigate the insulin resistance syndrome.18 19 31 In the present study, we applied factor analysis to demonstrate that the features of the insulin resistance syndrome clustered mainly on a single factor in both sexes in a large, nondiabetic elderly Finnish population. The novel finding of our study was that this insulin resistance factor predicted CHD events during the 7-year follow-up in men but not in women.
Previous studies have indicated that a clustering of cardiovascular risk factors typical of the insulin resistance syndrome can be demonstrated by factor analysis.18 19 31 In the population study by Meigs and coworkers,19 3 distinct factors were found. The first factor, suggested to represent central metabolic syndrome, included hyperinsulinemia, hyperglycemia, high levels of triglycerides, low HDL cholesterol, high body mass index, and high waist-to-hip ratio. The second factor included hyperinsulinemia and hyperglycemia and was thought to reflect impaired glucose tolerance. The third factor, called the hypertension factor, included systolic and diastolic blood pressure and body mass index.
In the present study, factor 1, or the insulin resistance factor, corresponded to the central metabolic syndrome factor in the report by Meigs and coworkers,19 except that in the present study, HDL cholesterol did not quite reach a significant loading in men. Consequently, hyperinsulinemia, hyperglycemia, obesity (especially central obesity), high levels of triglycerides, and probably also low HDL cholesterol appear to be essential features of the insulin resistance syndrome in different populations. In contrast, we did not find a separate impaired glucose tolerance factor, and although a separate hypertension factor was found (factor 3), it did not overlap with the insulin resistance factor but rather formed its own entity. Moreover, 2 factors not characterized by features of the insulin resistance syndrome were identified (factors 2 and 4). Although these 2 factors were not quite similar in men and in women, they basically consisted of various combinations of lifestyle factors, previous cardiovascular disease, and dyslipidemia.
The present study is the first to indicate that the insulin resistance syndrome predicts CHD events. The mechanisms by which the insulin resistance syndrome appears to induce, or at least enhance, atherogenesis are largely unknown, but several mechanisms can be suggested. Adverse changes in cardiovascular risk factors may induce atherogenesis, or hyperinsulinemia may directly accelerate atherogenesis in the arterial wall.2 32 Insulin resistance may also cause cardiovascular disease by some as yet unidentified mechanisms.33 Although our study cannot discover the mechanisms by which the insulin resistance syndrome causes CHD, it suggests that hypertension is not responsible for the association, because systolic blood pressure did not load on the insulin resistance factor. Moreover, the present study indicates that impaired glucose tolerance is not the mechanism explaining excess CHD, because the insulin resistance factor predicted CHD in normoglycemic men as well. Finally, low HDL cholesterol is not likely to explain the association, because HDL cholesterol levels did not load significantly on the insulin resistance factor in men.
In our study, the insulin resistance syndrome predicted CHD events in men but not in women. The number of CHD events was substantially smaller in women than in men, and nonsignificant results in women may be due to the type 2 error. However, our findings are in accordance with previous studies suggesting that hyperinsulinemia is not as important a risk factor in women as it is in men.7 8 So far, only 1 prospective study9 has demonstrated an association between hyperinsulinemia and CHD risk in women.
Not surprisingly, the hypertension factor and the factor characterized by dyslipidemia and previous vascular disease also predicted CHD events both in men and in women. Our findings contribute to the evidence that conventional cardiovascular risk factors—hypertension, dyslipidemia, and previous CHD—are important risk factors for CHD in elderly subjects as well.
In conclusion, our population-based prospective study on elderly subjects demonstrates that cardiovascular risk factors typical of the insulin resistance syndrome cluster and that this clustering predicts CHD events, at least in men. Therefore, in addition to classic risk factors, the insulin resistance syndrome should be considered a significant contributor to cardiovascular disease.
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Acknowledgments |
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This study was supported by grants from the Academy of Finland, the Finnish Heart Foundation, the Aarne and Aili Turunen Foundation, and the Finnish Cultural Foundation of Northern Savo.
Received October 20, 1998; revision received April 19, 1999; accepted April 22, 1999.
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 Y. W. Aude, A. S. Agatston, F. Lopez-Jimenez, E. H. Lieberman, Marie Almon, M. Hansen, G. Rojas, G. A. Lamas, and C. H. Hennekens The National Cholesterol Education Program Diet vs a Diet Lower in Carbohydrates and Higher in Protein and Monounsaturated Fat: A Randomized Trial Arch Intern Med, October 25, 2004; 164(19): 2141 - 2146. [Abstract] [Full Text] [PDF]
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J.-j. Wang, Q. Qiao, M. E. Miettinen, J. Lappalainen, G. Hu, and J. Tuomilehto The Metabolic Syndrome Defined by Factor Analysis and Incident Type 2 Diabetes in a Chinese Population With High Postprandial Glucose Diabetes Care, October 1, 2004; 27(10): 2429 - 2437. [Abstract] [Full Text] [PDF]
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J.-Y. Oh, Y. S. Hong, Y.-A. Sung, and E. Barrett-Connor Prevalence and Factor Analysis of Metabolic Syndrome in an Urban Korean Population Diabetes Care, August 1, 2004; 27(8): 2027 - 2032. [Abstract] [Full Text] [PDF]
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 Z. H. Huang, D. Gu, and T. Mazzone Oleic Acid Modulates the Post-translational Glycosylation of Macrophage ApoE to Increase Its Secretion J. Biol. Chem., July 9, 2004; 279(28): 29195 - 29201. [Abstract] [Full Text] [PDF]
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 K P Morgan, A Kapur, and K J Beatt Anatomy of coronary disease in diabetic patients: an explanation for poorer outcomes after percutaneous coronary intervention and potential target for intervention Heart, July 1, 2004; 90(7): 732 - 738. [Abstract] [Full Text] [PDF]
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 A. J.G. Hanley, A. Festa, R. B. D'Agostino, L. E. Wagenknecht, P. J. Savage, R. P. Tracy, M. F. Saad, and S. M. Haffner Metabolic and Inflammation Variable Clusters and Prediction of Type 2 Diabetes: Factor Analysis Using Directly Measured Insulin Sensitivity Diabetes, July 1, 2004; 53(7): 1773 - 1781. [Abstract] [Full Text] [PDF]
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D. A. Lawlor, S. Ebrahim, M. May, and G. Davey Smith (Mis)use of Factor Analysis in the Study of Insulin Resistance Syndrome Am. J. Epidemiol., June 1, 2004; 159(11): 1013 - 1018. [Abstract] [Full Text] [PDF]
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M. A. Austin, K. L. Edwards, M. J. McNeely, W. L. Chandler, D. L. Leonetti, P. J. Talmud, S. E. Humphries, and W. Y. Fujimoto Heritability of Multivariate Factors of the Metabolic Syndrome in Nondiabetic Japanese Americans Diabetes, April 1, 2004; 53(4): 1166 - 1169. [Abstract] [Full Text] [PDF]
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Y.-C. Lin, F.-Y. Chu, C.-C. Fu, and J.-D. Chen Prevalence and Risk Factors for Angina in Elderly Taiwanese J. Gerontol. A Biol. Sci. Med. Sci., February 1, 2004; 59(2): M161 - 165. [Abstract] [Full Text] [PDF]
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 J. Skrha, T. Haas, G. Sindelka, M. Prazny, J. Widimsky, D. Cibula, and S. Svacina Comparison of the Insulin Action Parameters from Hyperinsulinemic Clamps with Homeostasis Model Assessment and QUICKI Indexes in Subjects with Different Endocrine Disorders J. Clin. Endocrinol. Metab., January 1, 2004; 89(1): 135 - 141. [Abstract] [Full Text] [PDF]
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W. Tang, M. B. Miller, S. S. Rich, K. E. North, J. S. Pankow, I. B. Borecki, R. H. Myers, P. N. Hopkins, M. Leppert, and D. K. Arnett Linkage Analysis of a Composite Factor for the Multiple Metabolic Syndrome: The National Heart, Lung, and Blood Institute Family Heart Study Diabetes, November 1, 2003; 52(11): 2840 - 2847. [Abstract] [Full Text] [PDF]
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J. B. Meigs, P. W.F. Wilson, D. M. Nathan, R. B. D'Agostino Sr., K. Williams, and S. M. Haffner Prevalence and Characteristics of the Metabolic Syndrome in the San Antonio Heart and Framingham Offspring Studies Diabetes, August 1, 2003; 52(8): 2160 - 2167. [Abstract] [Full Text] [PDF]
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R. H. Knopp, B. Retzlaff, B. Fish, C. Walden, S. Wallick, M. Anderson, K. Aikawa, and S. E. Kahn Effects of Insulin Resistance and Obesity on Lipoproteins and Sensitivity to Egg Feeding Arterioscler. Thromb. Vasc. Biol., August 1, 2003; 23(8): 1437 - 1443. [Abstract] [Full Text] [PDF]
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H.P. Kopp, C.W. Kopp, A. Festa, K. Krzyzanowska, S. Kriwanek, E. Minar, R. Roka, and G. Schernthaner Impact of Weight Loss on Inflammatory Proteins and Their Association With the Insulin Resistance Syndrome in Morbidly Obese Patients Arterioscler. Thromb. Vasc. Biol., June 1, 2003; 23(6): 1042 - 1047. [Abstract] [Full Text] [PDF]
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S. J. Robins, H. B. Rubins, F. H. Faas, E. J. Schaefer, M. B. Elam, J. W. Anderson, and D. Collins Insulin Resistance and Cardiovascular Events With Low HDL Cholesterol: The Veterans Affairs HDL Intervention Trial (VA-HIT) Diabetes Care, May 1, 2003; 26(5): 1513 - 1517. [Abstract] [Full Text] [PDF]
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 J. M. McCaffery, R. Niaura, J. F. Todaro, G. E. Swan, and D. Carmelli Depressive Symptoms and Metabolic Risk in Adult Male Twins Enrolled in the National Heart, Lung, and Blood Institute Twin Study Psychosom Med, May 1, 2003; 65(3): 490 - 497. [Abstract] [Full Text] [PDF]
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B.-J. Shen, J. F. Todaro, R. Niaura, J. M. McCaffery, J. Zhang, A. Spiro III, and K. D. Ward Are Metabolic Risk Factors One Unified Syndrome? Modeling the Structure of the Metabolic Syndrome X Am. J. Epidemiol., April 15, 2003; 157(8): 701 - 711. [Abstract] [Full Text] [PDF]
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E. Bonora, S. Kiechl, J. Willeit, F. Oberhollenzer, G. Egger, R. C. Bonadonna, and M. Muggeo Carotid Atherosclerosis and Coronary Heart Disease in the Metabolic Syndrome: Prospective data from the Bruneck Study Diabetes Care, April 1, 2003; 26(4): 1251 - 1257. [Abstract] [Full Text] [PDF]
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E. S. Ford and W. H. Giles A Comparison of the Prevalence of the Metabolic Syndrome Using Two Proposed Definitions Diabetes Care, March 1, 2003; 26(3): 575 - 581. [Abstract] [Full Text] [PDF]
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G. Paradisi, H. O. Steinberg, M. K. Shepard, G. Hook, and A. D. Baron Troglitazone Therapy Improves Endothelial Function to Near Normal Levels in Women with Polycystic Ovary Syndrome J. Clin. Endocrinol. Metab., February 1, 2003; 88(2): 576 - 580. [Abstract] [Full Text] [PDF]
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 M. Laakso and J. Kuusisto Diabetology for cardiologists Eur. Heart J. Suppl., January 1, 2003; 5(suppl_B): B5 - B13. [Abstract] [PDF]
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 H.-M. Lakka, D. E. Laaksonen, T. A. Lakka, L. K. Niskanen, E. Kumpusalo, J. Tuomilehto, and J. T. Salonen The Metabolic Syndrome and Total and Cardiovascular Disease Mortality in Middle-aged Men JAMA, December 4, 2002; 288(21): 2709 - 2716. [Abstract] [Full Text] [PDF]
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S. H. Golden, A. R. Folsom, J. Coresh, A. R. Sharrett, M. Szklo, and F. Brancati Risk Factor Groupings Related to Insulin Resistance and Their Synergistic Effects on Subclinical Atherosclerosis: The Atherosclerosis Risk in Communities Study Diabetes, October 1, 2002; 51(10): 3069 - 3076. [Abstract] [Full Text] [PDF]
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R. L. Hanson, G. Imperatore, P. H. Bennett, and W. C. Knowler Components of the "Metabolic Syndrome" and Incidence of Type 2 Diabetes Diabetes, October 1, 2002; 51(10): 3120 - 3127. [Abstract] [Full Text] [PDF]
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L. P. Palaniappan, M. R. Carnethon, and S. P. Fortmann Heterogeneity in the Relationship Between Ethnicity, BMI, and Fasting Insulin Diabetes Care, August 1, 2002; 25(8): 1351 - 1357. [Abstract] [Full Text] [PDF]
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P. Marques-Vidal, E. Mazoyer, V. Bongard, P. Gourdy, J.-B. Ruidavets, L. Drouet, and J. Ferrieres Prevalence of Insulin Resistance Syndrome in Southwestern France and Its Relationship With Inflammatory and Hemostatic Markers Diabetes Care, August 1, 2002; 25(8): 1371 - 1377. [Abstract] [Full Text] [PDF]
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 V.-P. Valkonen, M. Kolehmainen, H.-M. Lakka, and J. T Salonen Insulin resistance syndrome revisited: application of self-organizing maps Int. J. Epidemiol., August 1, 2002; 31(4): 864 - 871. [Abstract] [Full Text] [PDF]
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A. J.G. Hanley, A. J. Karter, A. Festa, R. D'Agostino Jr., L. E. Wagenknecht, P. Savage, R. P. Tracy, M. F. Saad, and S. Haffner Factor Analysis of Metabolic Syndrome Using Directly Measured Insulin Sensitivity: The Insulin Resistance Atherosclerosis Study Diabetes, August 1, 2002; 51(8): 2642 - 2647. [Abstract] [Full Text] [PDF]
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A. J.G. Hanley, K. Williams, M. P. Stern, and S. M. Haffner Homeostasis Model Assessment of Insulin Resistance in Relation to the Incidence of Cardiovascular Disease: The San Antonio Heart Study Diabetes Care, July 1, 2002; 25(7): 1177 - 1184. [Abstract] [Full Text] [PDF]
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J. D. Mills, M. W. Mansfield, and P. J. Grant Tissue Plasminogen Activator, Fibrin D-Dimer, and Insulin Resistance in the Relatives of Patients With Premature Coronary Artery Disease Arterioscler. Thromb. Vasc. Biol., April 1, 2002; 22(4): 704 - 709. [Abstract] [Full Text] [PDF]
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R. Arya, J. Blangero, K. Williams, L. Almasy, T. D. Dyer, R. J. Leach, P. O'Connell, M. P. Stern, and R. Duggirala Factors of Insulin Resistance Syndrome-Related Phenotypes Are Linked to Genetic Locations on Chromosomes 6 and 7 in Nondiabetic Mexican-Americans Diabetes, March 1, 2002; 51(3): 841 - 847. [Abstract] [Full Text] [PDF]
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 J. D Mills and P. J Grant Insulin resistance, haemostatic factors and cardiovascular risk The British Journal of Diabetes & Vascular Disease, January 1, 2002; 2(1): 19 - 26. [Abstract] [PDF]
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M. Laakso Insulin resistance and cardiovascular disease The British Journal of Diabetes & Vascular Disease, January 1, 2002; 2(1_suppl): S9 - S11. [Abstract] [PDF]
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J. Kuusisto, P. Lempiainen, L. Mykkanen, and M. Laakso Insulin Resistance Syndrome Predicts Coronary Heart Disease Events in Elderly Type 2 Diabetic Men Diabetes Care, September 1, 2001; 24(9): 1629 - 1633. [Abstract] [Full Text] [PDF]
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A. Kareinen, L. Viitanen, P. Halonen, S. Lehto, and M. Laakso Cardiovascular Risk Factors Associated With Insulin Resistance Cluster in Families With Early-Onset Coronary Heart Disease Arterioscler. Thromb. Vasc. Biol., August 1, 2001; 21(8): 1346 - 1352. [Abstract] [Full Text] [PDF]
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C. Indolfi, D. Torella, L. Cavuto, A. M. Davalli, C. Coppola, G. Esposito, M. V. Carriero, A. Rapacciuolo, E. Di Lorenzo, E. Stabile, et al. Effects of Balloon Injury on Neointimal Hyperplasia in Streptozotocin-Induced Diabetes and in Hyperinsulinemic Nondiabetic Pancreatic Islet-Transplanted Rats Circulation, June 19, 2001; 103(24): 2980 - 2986. [Abstract] [Full Text] [PDF]
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P. A. Sakkinen, P. Wahl, M. Cushman, M. R. Lewis, and R. P. Tracy Clustering of Procoagulation, Inflammation, and Fibrinolysis Variables with Metabolic Factors in Insulin Resistance Syndrome Am. J. Epidemiol., November 15, 2000; 152(10): 897 - 907. [Abstract] [Full Text] [PDF]
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J. B. Meigs Invited Commentary: Insulin Resistance Syndrome? Syndrome X? Multiple Metabolic Syndrome? A Syndrome At All? Factor Analysis Reveals Patterns in the Fabric of Correlated Metabolic Risk Factors Am. J. Epidemiol., November 15, 2000; 152(10): 908 - 911. [Abstract] [Full Text] [PDF]
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M. Pyorala, H. Miettinen, P. Halonen, M. Laakso, and K. Pyorala Insulin Resistance Syndrome Predicts the Risk of Coronary Heart Disease and Stroke in Healthy Middle-Aged Men : The 22-Year Follow-Up Results of the Helsinki Policemen Study Arterioscler. Thromb. Vasc. Biol., February 1, 2000; 20(2): 538 - 544. [Abstract] [Full Text] [PDF]
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J. D. Mills, M. W. Mansfield, and P. J. Grant Tissue Plasminogen Activator, Fibrin D-Dimer, and Insulin Resistance in the Relatives of Patients With Premature Coronary Artery Disease Arterioscler. Thromb. Vasc. Biol., April 1, 2002; 22(4): 704 - 709. [Abstract] [Full Text] [PDF]
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