4.7 Article

Factor analysis of metabolic syndrome using directly measured insulin sensitivity - The Insulin Resistance Atherosclerosis Study

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DIABETES
卷 51, 期 8, 页码 2642-2647

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AMER DIABETES ASSOC
DOI: 10.2337/diabetes.51.8.2642

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  1. NHLBI NIH HHS [U01-HL47892, U01-HL47889, U01-HL47887, U01-HL47902] Funding Source: Medline
  2. NIDDK NIH HHS [DK-29867] Funding Source: Medline
  3. PHS HHS [R01 58329] Funding Source: Medline

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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 (SI) 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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