Is ovarian volume a good predictor to determine metabolic syndrome development in polycystic ovary patients. (3rd April 2019) - Sipahi, Mehmet; Tokgöz, Vehbi Yavuz; Keskin, Özlem; Atasever, Melahat; Menteşe, Ahmet; Demir, Selim
Abstract: The aim of this study was to investigate the findings of ultrasound that could predict the metabolic syndrome (MetS) which may develop in polycystic ovary syndrome (PCOS) patients. A total of 96 consecutive PCOS patients, who were scheduled for any gynaecologic examination from January 2015 to January 2016 and who were eligible for the study, were prospectively enrolled in it. About 15.6% of PCOS patients were diagnosed with MetS. The mean age of the MetS patients and the non-MetS patients were 25.8 and 23.3, respectively ( p  = .056). The mean ovary volume was calculated as being 11.7 mL in the MetS patients and as 9.6 mL in the non-MetS patients ( p  = .027). The Doppler and the other ultrasound findings were compared between the groups and no significant difference was observed. When a receiver operator characteristic curve analysis was conducted for the ovarian volume to predict MetS, the area under curve was 0.67 (95% CI, 0.52–0.81). The optimum cut-off point for OV was determined at 9.2 mL, with the sensitivity and specificity of 80.0% and 50.6%, respectively. The risk of developing MetS appears to be higher in PCOS patients with higher OV values. Impact statement: What is already known on this subject? Metabolic syndrome is not rare in PCOS patients. There are several studies to specify a predictor for MetS development in PCOS. Most are biochemical predictors, such as hyperandrogenemia, a visceral adiposity index, lipid accumulation product, adiponectin index and a leptin-to-adiponectin ratio. What do the results of this study add? The ultrasound markers to predict the insulin resistance at PCOS is already used, but are new for predicting MetS. What are the implications of these findings for clinical practice and/or further research? Ultrasound is an available tool in most clinics and predicting MetS is important for the future health problems of PCOS patients.