Anti-Müllerian hormone measurement for the diagnosis of polycystic ovary syndrome

Objective: Anti-Müllerian hormone (AMH) is derived from the small antral follicles, and an elevated level has been suggested to add value to the Rotterdam criteria for the diagnosis of PCOS in cases of diagnostic uncertainty. Therefore, the role of AMH in the classical phenotype of PCOS was defined within a Caucasian population. Design: This was a cross-sectional study. Patients: Sixty Five women without PCOS and 110 women with PCOS fulfilling all 3 diagnostic Rotterdam criteria. Measurements: The main outcomes were the utility of serum AMH for the diagnosis of PCOS and its relationship to the metabolic parameters. Results: Anti-Müllerian hormone was increased in PCOS compared to controls (P  < .001). Areas under the receiver operator curve showed AMH to be predictive of PCOS (0.76) using a cut-off AMH of 46 pmol/L, which is derived from the 95 th percentile of the controls that gave a 41% sensitivity and 86% specificity; an AMH cut-off of 35 pmol/L gave a 55% sensitivity and 79% specificity. Age- and BMI-adjusted multiple logistic regression showed that AMH was more predictive of PCOS independently of either serum testosterone (T) (OR = 4.04; 95% CI 1.42-11.11; P =.007) or free androgen index (FAI) (OR = 3.90; 95% CI 1.40-10.83; P =.009). Conclusion: Whilst an elevated AMH has poor sensitivity, it is fourfold more likely to be associated with a diagnosis of PCOS, and supplementary to biochemical parameters will make a positive diagnosis of PCOS in 22% of patients when neither serum testosterone nor FAI is elevated

Adiponectin in Women with Polycystic Ovary Syndrome

BACKGROUND: Though adiponectin has been associated with insulin resistance and cardiovascular risk factors, the relationship between adiponectin and polycystic ovary syndrome (PCOS) remains controversial. The aim of this study was to compare adiponectin level in women with PCOS and without PCOS, and to investigate the relationship between adiponectin level and metabolic variables including insulin resistance. METHODS: 60 women with PCOS were enrolled along with a control group of 80 healthy women, matched for age and body mass index (BMI). We measured hormonal and metabolic parameters, as well as the plasma adiponectin concentration of each participant. We estimated the insulin sensitivity according to the quantitative insulin sensitivity check index (QUICKI). RESULTS: The PCOS group displayed significantly lower level of adiponectin (P < 0.001) after adjustment for age, BMI, mean blood pressure, fasting glucose, fasting insulin, and several metabolic parameters. Adiponectin levels were positively correlated with QUICKI in the PCOS group (P < 0.001) and the control group (P = 0.03). Following step-wise multiple regression analysis, however, adiponectin level was positively correlated with QUICKI in the control group only (P = 0.03). In addition, adiponectin level was found to be independently associated with HDL-cholesterol level (P < 0.001) and BMI (P = 0.02) in the PCOS group and independently associated with HDL-cholesterol (P = 0.02) in the control group. CONCLUSION: We report decreased adiponectin level in PCOS patients in relation to controls independently of insulin resistance or other metabolic factors. And adiponectin is associated with both lipid metabolism and obesity, which, in turn, is related to insulin resistance in PCOS. Further studies are needed to clarify the mechanism of adiponectin in PCOS.ope

Lipidomic analysis of plasma samples from women with polycystic ovary syndrome

Abstract Polycystic ovary syndrome (PCOS) is a common disorder affecting between 5 and 18 % of females of reproductive age and can be diagnosed based on a combination of clinical, ultrasound and biochemical features, none of which on its own is diagnostic. A lipidomic approach using liquid chromatography coupled with accurate mass high-resolution mass-spectrometry (LCHRMS) was used to investigate if there were any differences in plasma lipidomic profiles in women with PCOS compared with control women at different stages of menstrual cycle. Plasma samples from 40 women with PCOS and 40 controls aged between 18 and 40 years were analysed in combination with multivariate statistical analyses. Multivariate data analysis (LASSO regression and OPLSDA) of the sample lipidomics datasets showed a weak prediction model for PCOS versus control samples from the follicular and mid-cycle phases of the menstrual cycle, but a stronger model (specificity 85 % and sensitivity 95 %) for PCOS versus the luteal phase menstrual cycle controls. The PCOS vs luteal phase model showed increased levels of plasma triglycerides and sphingomyelins and decreased levels of lysophosphatidylcholines and phosphatidylethanolamines in PCOS women compared with controls. Lipid biomarkers of PCOS were tentatively identified which may be useful in distinguishing PCOS from controls especially when performed during the menstrual cycle luteal phase