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(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

Päivi Lempiäinen, MD; Leena Mykkänen, MD; Kalevi Pyörälä, MD; Markku Laakso, MD; Johanna Kuusisto, MD

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

	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

	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 women^{7 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.

	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.

	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.

View this table: [in this window] [in a new window]   Table 1. Baseline Characteristics of the Study Subjects by Sex

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).

View this table: [in this window] [in a new window]   Table 2. Risk Factors of CHD Events in Elderly Nondiabetic Men and Women During 7 Years of Follow-Up, Univariate Cox Regression Analysis

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.

View this table: [in this window] [in a new window]   Table 3. Risk Factors of CHD Events in Elderly Nondiabetic Men and Women During 7-year Follow-Up in Multivariate Cox Regression Analysis

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.

View this table: [in this window] [in a new window]   Table 4. Factors and Loadings on Different Variables in Elderly Nondiabetic Men and Women, Factor Analysis

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).

View this table: [in this window] [in a new window]   Table 5. Association of 4 Factors Derived From Factor Analysis With CHD Events in Elderly Nondiabetic Men and Women, Cox Regression Analysis

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).

	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.

	Acknowledgments

 
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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				S. B. Seidelmann, C. De Luca, R. L. Leibel, J. L. Breslow, A. R. Tall, and C. L. Welch Quantitative Trait Locus Mapping of Genetic Modifiers of Metabolic Syndrome and Atherosclerosis in Low-Density Lipoprotein Receptor-Deficient Mice: Identification of a Locus for Metabolic Syndrome and Increased Atherosclerosis on Chromosome 4 Arterioscler. Thromb. Vasc. Biol., January 1, 2005; 25(1): 204 - 210. [Abstract] [Full Text] [PDF]

				U. Salmenniemi, E. Ruotsalainen, J. Pihlajamaki, I. Vauhkonen, S. Kainulainen, K. Punnonen, E. Vanninen, and M. Laakso Multiple Abnormalities in Glucose and Energy Metabolism and Coordinated Changes in Levels of Adiponectin, Cytokines, and Adhesion Molecules in Subjects With Metabolic Syndrome Circulation, December 21, 2004; 110(25): 3842 - 3848. [Abstract] [Full Text] [PDF]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]

				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]