Abstract
OBJECTIVE—The aim of this study was to examine how the major components of the metabolic syndrome relate to each other and to the development of diabetes using factor analysis.
RESEARCH DESIGN AND METHODS—The screening survey for type 2 diabetes was conducted in 1994, and a follow-up study of nondiabetic individuals at baseline was carried out in 1999 in the Beijing area. Among 934 nondiabetic and 305 diabetic subjects at baseline, factor analysis was performed using the principle components analysis with varimax orthogonal rotation of continuously distributed variables considered to represent the components of the metabolic syndrome. Fasting insulin was used as a marker for insulin resistance. Of the 559 subjects without diabetes at baseline, 129 developed diabetes during the 5-year follow-up. Factors identified at baseline were used as independent variables in univariate and multivariate logistic regression models to determine risk factor clusters predicting the development of diabetes.
RESULTS—Four factors were identified in nondiabetic and diabetic subjects. Fasting insulin levels, BMI, and waist-to-hip ratio were associated with one factor. Systolic and diastolic blood pressures were associated with the second factor. Two-hour postload plasma glucose (2-h PG) and serum insulin and fasting plasma glucose were associated with the third factor. Serum total cholesterol and triglycerides were associated with the fourth factor. The first and the third factors predicted the development of diabetes. In diabetic patients at baseline, the combination of systolic and diastolic blood pressure was the most important factor, and urinary albumin excretion rate clustered with fasting and 2-h PG levels.
CONCLUSIONS—Insulin resistance alone does not underlie all features of the metabolic syndrome. Different physiological processes associated with various components of the metabolic syndrome contain unique information about diabetes risk. Microalbunuria is more likely to be a complication of type 2 diabetes or hypertension than a marker for the metabolic syndrome.