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Factor Analysis of Metabolic Syndrome Using Directly Measured Insulin Sensitivity

The Insulin Resistance Atherosclerosis Study

¹ Division of Clinical Epidemiology, University of Texas Health Sciences Center at San Antonio, San Antonio, Texas
² Division of Epidemiology and Biostatistics, Samuel Lunenfeld Research Institute, Mt. Sinai Hospital, Toronto, Canada
³ Division of Research, Kaiser Permanente, Oakland, California
⁴ Department of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, North Carolina
⁵ Division of Epidemiology and Clinical Applications, National Heart, Lung and Blood Institute, Bethesda, Maryland
⁶ Departments of Pathology and Biochemistry, College of Medicine, University of Vermont, Burlington, Vermont
⁷ Department of Medicine, University of Southern California School of Medicine, Los Angeles, California

Factor analysis, a multivariate correlation technique, has been used to provide insight into the underlying structure of metabolic syndrome, which is characterized by physiological complexity and strong statistical intercorrelation among its key variables. The majority of previous factor analyses, however, have used only surrogate measures of insulin sensitivity. In addition, few have included members of multiple ethnic groups, and only one has presented results separately for subjects with impaired glucose tolerance. The objective of this study was to investigate, using factor analysis, the clustering of physiologic variables using data from 1,087 nondiabetic participants in the Insulin Resistance Atherosclerosis Study (IRAS). This study includes information on the directly measured insulin sensitivity index (S_I) from intravenous glucose tolerance testing among African-American, Hispanic, and non-Hispanic white subjects aged 40–69 years at various stages of glucose tolerance. Principal factor analysis identified two factors that explained 28 and 9% of the variance in the dataset, respectively. These factors were interpreted as 1) a " metabolic" factor, with positive loadings of BMI, waist, fasting and 2-h glucose, and triglyceride and inverse loadings of log(S_I+1) and HDL; and 2) a "blood pressure" factor, with positive loadings of systolic and diastolic blood pressure. The results were unchanged when surrogate measures of insulin resistance were used in place of log(S_I+1). In addition, the results were similar within strata of sex, glucose tolerance status, and ethnicity. In conclusion, factor analysis identified two underlying factors among a group of metabolic syndrome variables in this dataset. Analyses using surrogate measures of insulin resistance suggested that these variables provide adequate information to explore the underlying intercorrelational structure of metabolic syndrome. Additional clarification of the physiologic characteristics of metabolic syndrome is required as individuals with this condition are increasingly being considered candidates for behavioral and pharmacologic intervention.

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