Prediction models for insulin resistance in the polycystic ovary syndrome

Women with the polycystic ovary syndrome (PCOS) have a high prevalence of insulin resistance, with consequent increased risk of metabolic diseases later in life. An early metabolic screening would therefore be of clinical relevance. By using stepwise regression analysis on several variables obtained in 72 women with PCOS, we constructed simple and reliable mathematical models predicting insulin sensitivity, as measured by the euglycaemic hyperinsulinaemic clamp. The normal ranges of insulin sensitivity were calculated from 81 non-hirsute, normally menstruating women with normal ovaries, and similar body mass index (BMI) and age as the women with PCOS. Measured variables included BMI, waist and hip circumferences, truncal-abdominal skin folds, circulating concentrations of gonadotrophins, androgens, sex hormone-binding globulin (SHBG), triglycerides, total cholesterol and cholesterol subfractions, fasting insulin, C-peptide and free fatty acids. The three best prediction models included waist circumference, together with insulin (model I: R(2) = 0.77), serum triglycerides (model II: R(2) = 0.65), and the subscapularis skin fold (model III: R(2) = 0. 64). Using reference limits for insulin sensitivity obtained in the 81 normal pre-menopausal women, the models identify insulin resistant women with PCOS. These simple and inexpensive models are potentially useful in clinical practice as an early screening in women with PCOS.