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Metabolic and Inflammation Variable Clusters and Prediction of Type 2 Diabetes

Factor Analysis Using Directly Measured Insulin Sensitivity

¹ Division of Clinical Epidemiology, University of Texas Health Sciences Center at San Antonio, San Antonio, Texas
² Leadership Sinai Centre for Diabetes, Departments of Public Health Sciences and Medicine, Mt. Sinai Hospital and University of Toronto, Toronto, Ontario
³ Department of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, North Carolina
⁴ National Heart, Lung, and Blood Institute, Division of Epidemiology and Clinical Applications, Bethesda, Maryland
⁵ Departments of Pathology and Biochemistry, College of Medicine, University of Vermont, Burlington, Vermont
⁶ Division of Clinical Epidemiology and Preventive Medicine, UCLA School of Medicine, Los Angeles, California

Factor analysis, a multivariate correlation technique, has been used to provide insight into the underlying structure of the metabolic syndrome. The majority of previous factor analyses, however, have used only surrogate measures of insulin sensitivity; very few have included nontraditional cardiovascular disease (CVD) risk factors such as plasminogen activator inhibitor (PAI)-1, fibrinogen, and C-reactive protein (CRP); and only a limited number have assessed the ability of factors to predict type 2 diabetes. The objective of this study was to investigate, using factor analysis, the clustering of metabolic and inflammation variables using data from 1,087 nondiabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS) and to determine the association of these clusters with risk of type 2 diabetes at follow-up. This study includes information on directly measured insulin sensitivity (S_i) from the frequently sampled intravenous glucose tolerance test among African-American, Hispanic, and non-Hispanic white subjects aged 40–69 years. Principal factor analysis of data from nondiabetic subjects at baseline (1992–1994) identified three factors, which explained 28.4, 7.4, and 6% of the total variance in the dataset, respectively. Based on factor loadings of 0.40, these factors were interpreted as 1) a "metabolic" factor, with positive loadings of BMI, waist circumference, 2-h glucose, log triglyceride, and log PAI-1 and inverse loadings of log S_i + 1 and HDL; 2) an "inflammation" factor, with positive loadings of BMI, waist circumference, fibrinogen, and log CRP and an inverse loading of log S_i + 1; and 3) a "blood pressure" factor, with positive loadings of systolic and diastolic blood pressure. The results were similar within strata of ethnicity, and there were only subtle differences in sex-specific analyses. In a prospective analysis, each of the factors was a significant predictor of diabetes after a median follow-up period of 5.2 years, and each factor remained significant in a multivariate model that included all three factors, although this three-factor model was not significantly more predictive than models using either impaired glucose tolerance or conventional CVD risk factors. Factor analysis identified three underlying factors among a group of inflammation and metabolic syndrome variables, with insulin sensitivity loading on both the metabolic and inflammation variable clusters. Each factor significantly predicted diabetes in multivariate analysis. The findings support the emerging hypothesis that chronic subclinical inflammation is associated with insulin resistance and comprises a component of the metabolic syndrome.

Abbreviations: AA, African American; AROC, area under the receiver operating characteristic; CHS, Cardiovascular Health Study; CRP, C-reactive protein; CVD, cardiovascular disease; HA, Hispanic American; HS, Hispanic; IGT, impaired glucose tolerance; IRAS, Insulin Resistance Atherosclerosis Study; NHW, non-Hispanic white; PAI, plasminogen activator inhibitor

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