Relationship between bone mineral density and insulin resistance in polycystic ovary syndrome
ABSTRACT The aim of the present study was to evaluate whether there is a relationship between bone mineral density (BMD) and insulin resistance and hyperinsulinemia in women with polycystic ovary syndrome (PCOS). The study consisted of 28 amenorrheic women with PCOS and 11 amenorrheic women without PCOS. Fifteen healthy women with normal ovulatory function, matched for age and body mass index (BMI), served as controls. BMD was measured at the lumbar spine and left femoral neck with dual-energy X-ray absorptiometry. Blood samples were obtained to measure serum levels of insulin, follicle-stimulating hormone, luteinizing hormone, sex hormone-binding globulin (SHBG), total and free testosterone, androstenedione and estradiol by radioimmunassay. Insulin resistance was estimated by the in sulin tolerance test (ITT), and K(ITT) was taken as the insulin sensitivity index. In the PCOS group, K(ITT) was significantly lower and insulin levels were higher than in either of the control groups (P < 0.001). BMD in the PCOS group was lower than in the healthy group and higher than in the amenorrheic control group (P < 0.05). In the PCOS group, there were positive correlations of BMD of the lumbar spine with insulin (r = 0.42: P < 0.05) and negative correlations of BMD with K(ITT) (r = -0.58; P < 0.001) and SHBG (r = -0.38; P < 0.05). The inverse association of BMD and K(ITT) was independent of BMI, insulin, SHBG, androstenedione, and free testosterone. In conclusion, insulin resistance and hyperinsulinemia in women with PCOS may be a relative protective factor against bone mineral loss.
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ABSTRACT: The standardized uptake value (SUV) has gained recognition in recent years as a semiquantitative evaluation parameter in positron emission tomography (PET) studies. However, there is as yet no consensus on the way in which this index should be determined. One of the confusing factors is the normalisation procedure. Among the proposed anthropometric parameters for normalisation is lean body mass (LBM); LBM has been determined by using a predictive equation in most if not all of the studies. In the present study, we assessed the degree of agreement of various LBM predictive equations with a reference method. Secondly, we evaluated the impact of predicted LBM values on a hypothetical value of 2.5 SUV, normalised to LBM (SUV(LBM)), by using various equations. The study population consisted of 153 women, aged 32.3+/-11.8 years (mean+/-SD), with a height of 1.61+/-0.06 m, a weight of 71.1+/-17.5 kg, a body surface area of 1.77+/-0.22 m(2) and a body mass index of 27.6+/-6.9 kg/m(2). LBM (44.2+/-6.6 kg) was measured by a dual-energy X-ray absorptiometry (DEXA) method. A total of nine equations from the literature were evaluated, four of them from recent PET studies. Although there was significant correlation between predicted and measured LBM values, 95% limits of agreement determined by the Bland and Altman method showed a wide range of variation in predicted LBM values as compared with DEXA, no matter which predictive equation was used. Moreover, only one predictive equation was not statistically different in the comparison of means (DEXA and predicted LBM values). It was also shown that the predictive equations used in this study yield a wide range of SUV(LBM) values from 1.78 to 5.16 (29% less or 107% more) for an SUV of 2.5. In conclusion, this study suggests that estimation of LBM by use of a predictive equation may cause substantial error for an individual, and that if LBM is chosen for the SUV normalisation procedure, it should be measured, not predicted.European journal of nuclear medicine and molecular imaging 06/2003; 29(12):1630-8. DOI:10.1007/s00259-002-0974-3 · 5.22 Impact Factor