Comparison of regional fat mass measurement by whole body DXA scans and anthropometric measures to predict insulin resistance in women with polycystic ovary syndrome and controls

Introduction: Polycystic ovary syndrome (PCOS) is characterized by obesity and insulin resistance. Measures of regional obesity may be used to predict insulin resistance. In the present study we compared fat distribution in patients with PCOS vs. controls and established the best measure of fat mass to predict insulin resistance in patients with PCOS. Material and methods: The study was cross-sectional in an academic tertiary-care medical center with 167 premenopausal women with PCOS and 110 controls matched for ethnicity, BMI and age. Total and regional fat and lean body mass were assessed by whole body dual-energy X-ray absorptiometry (DXA) scans. Anthropometric measures (BMI, waist) and fasting metabolic analyses [insulin, glucose, lipids, Homeostasis model assessment (HOMA-IR), lipid accumulation product, and visceral adiposity index] were determined. Trial registration numbers: NCT00451568, NCT00145340. Results: Women with PCOS had higher central fat mass (waist, waist–hip ratio, and upper/lower fat ratio) compared with controls. In bivariate associations, the strongest associations were found between HOMA-IR and the fat mass measures trunk fat (r = 0.59), waist (r = 0.57) and BMI (r = 0.56), all p < 0.001. During multiple regression analyses, trunk fat, waist and BMI were the best predictors of HOMA-IR (R ²= 0.48, 0.49, and 0.47, respectively). Conclusions: Women with PCOS were characterized by central obesity. Trunk fat, waist and BMI were the best predictors of HOMA-IR in PCOS, but only limited information regarding insulin resistance was gained by whole body DXA scan.