Clustering of Cardiovascular Risk Factors Associated With the Insulin Resistance Syndrome

Autor: Roberto Castello, Romolo M. Dorizzi, Michele Muggeo, Giovanna Spiazzi, Giacomo Zoppini, M. Elisabetta Zanolin, Paolo Moghetti, Flavia Tosi
Rok vydání: 2006
Předmět:
Zdroj: Diabetes Care. 29:372-378
ISSN: 1935-5548
0149-5992
DOI: 10.2337/diacare.29.02.06.dc05-1478
Popis: OBJECTIVE—Hyperinsulinemia is often associated with several metabolic abnormalities and increased blood pressure, which are risk factors for cardiovascular disease. It has been hypothesized that insulin resistance may underlie all these features. However, recent data suggest that some links between insulin resistance and these alterations may be indirect. The aim of our study was to further investigate this issue in a sample of young hyperandrogenic women, who often show insulin resistance and other metabolic abnormalities typical of the insulin resistance syndrome. RESEARCH DESIGN AND METHODS—We tested the hypothesis of a single factor underlying these features by principal component analysis, which should recognize one component if a single mechanism explains this association. The analysis was carried out in a sample of 255 young nondiabetic hyperandrogenic women. Variables selected for this analysis included the basic features of the insulin resistance syndrome and some endocrine parameters related to hyperandrogenism. RESULTS—Principal component analysis identified four separate factors, explaining 64.5% of the total variance in the data: the first included fasting and postchallenge insulin levels, BMI, triglycerides, HDL cholesterol, and uric acid; the second, BMI, blood pressure, and serum free testosterone; the third, fasting plasma glucose, postchallenge glucose and insulin levels, serum triglycerides, and free testosterone; and the fourth, postchallenge plasma insulin, serum free testosterone, and gonadotropin-releasing hormone agonist–stimulated 17-hydroxyprogesterone. CONCLUSIONS—These results support the hypothesis of multiple determinants in the clustering of abnormalities in the so-called insulin resistance syndrome.
Databáze: OpenAIRE