Dehydroepiandrosterone with a high-fat diet treatment at inducing polycystic ovary syndrome in rat model.

OBJECTIVE: This study aimed to evaluate the effects of dehydroepiandrosterone (DHEA) and DHEA combined with a high-fat diet (HFD) treatment of reproductive and endocrine metabolism in rats and then identify an ideal model of polycystic ovary syndrome (PCOS). METHODS: Three-week-old female Sprague-Dawley rats were injected subcutaneously with DHEA or oil, fed with or without a HFD, for 21 days, during which body weight, feed intake, and estrous cycle monitoring were carried out. Fasting blood glucose was measured, and serum fasting insulin, testosterone, dihydrotestosterone (DHT), estradiol, progesterone, luteinizing hormone (LH), anti-Müllerian hormone (AMH), and follicle-stimulating hormone (FSH) were estimated by ELISA. Serum total cholesterol (TC), total triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured by colorimetric assay. Whereas, histologic changes in rat ovaries were evaluated by H&E staining. Ovarian steroid hormone synthases and their protein levels (StAR, 3ß-HSD2, 17ß-HSD1, CYP11A1, CYP17A1, and CYP19A1) were examined by Western blotting. RESULTS: Both DHEA and DHEA + HFD-treated rats lost a regular estrous cycle; had polycystic ovarian changes, significantly higher serum fasting insulin and testosterone levels; and increased ovarian StAR, 3ß-HSD2, and CYP11A1 protein levels. Additionally, rats in the DHEA + HFD-treated group were obese; had elevated fasting blood glucose, TG, DHT, AMH levels and LH:FSH ratios; increased ovarian 17ß-HSD1 protein levels. CONCLUSION: DHEA combined with HFD treatment is more effective at inducing PCOS than DHEA alone. The reproductive and endocrine metabolic aspects of this method are more consistent with the clinical characteristics of PCOS patients.

Relationship between serum apelin, visfatin levels, and body composition in Polycystic Ovary Syndrome patients.

OBJECTIVE: To investigate the relationship between body composition and serum visfatin and apelin levels in patients with polycystic ovary syndrome (PCOS). METHODS: In this prospective observational study, the differences in body composition, levels of gonadal hormone concentrations, glucose metabolism, apelin, and visfatin were compared between PCOS patients and the control group. PCOS patients were further divided into different subgroups according to different obesity criteria and the differences between serum visfatin and apelin levels in different subgroups were compared. Finally, the correlation of serum visfatin levels and apelin levels with body composition, and metabolism-related indicators in PCOS patients was explored. RESULTS: A total collected 178 cases of PCOS patients and 172 cases of healthy women (control group) between 2020 July and 2021 November. In PCOS patients, their weight, Body Mass Index (BMI), Waist Hip Rate (WHR), Fat-Free Mass Index (FFMI), Percent Body Fat (PBF), Fat mass index (FMI), PBF of Arm, PBF of Leg, PBF of the Trunk, Visceral Fat Level (VFL), fasting insulin (FINS), Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) and Luteinizing hormone (LH) were significantly higher than in the control group (all P < 0.001), Percent Skeletal Muscle (PSM), PSM of Leg, and PSM of the Trunk were significantly decreased than in the control group (all P < 0.001). The PCOS patients had significantly higher serum visfatin levels and apelin levels compared with the control group (all P < 0.001). In PBF > 35 % PCOS patients, the apelin and visfatin levels were significantly higher than the PBF ≤ 35 % PCOS patients. In WHR ≥ 0.85 and BMI ≥ 24 kg/m2 PCOS patients, the visfatin levels were significantly higher than the WHR < 0.85 and BMI < 24 kg/m2 PCOS patients. Serum apelin and visfatin positively correlated with BMI level, WHR, FFMI, PBF, FMI, PBF of arms, PBF of legs, PBF of the trunk, VFL, FBG, HOMA-IR index and negatively correlated with PSM, PSM of legs, and PSM of the trunk (all P < 0.001). CONCLUSIONS: Compared with healthy women, Patients with PCOS have an increased fat content in various parts of the body, reduced skeletal muscle content, and are often complicated by metabolic abnormalities. Serum visfatin and apelin correlated not only with obesity, fat mass, and fat distribution but also with muscle mass and distribution. It may be possible to reduce the long-term risk of metabolic disease in PCOS through the monitoring and management of the body composition in PCOS patients or to reflect the therapeutic effect of PCOS.

Rapid identification of endometrial hyperplasia and endometrial endometrioid cancer in young women.

PURPOSE: We investigated endometrial hyperplasia (EH) and endometrial endometrioid cancer (EEC) and developed a nomogram model to predict the EH/EEC risk and improve patients' clinical prognosis. METHODS: Data were collected from young females (age: ≤ 40 years) who complained of abnormal uterine bleeding (AUB) or abnormal ultrasound endometrial echoes. The patients were randomly divided into training and validation cohorts at a 7:3 ratio. The risk factors for EH/EEC were determined through the optimal subset regression analysis and a prediction model was developed. We used the concordance-index (C-index), and calibration plots in the training and validation sets to assess the prediction model. We drew the ROC curve in the validation set and calculated the area under the curve (AUC), as well as its accuracy, sensitivity, specificity, negative predictive value, and positive predictive value, and finally, converted the nomogram into a web page dynamic nomogram. RESULTS: Predictors included in the nomogram model were body mass index (BMI), polycystic ovary syndrome (PCOS), anemia, infertility, menostaxis, AUB type, and endometrial thickness. The C-index of the model in the training and validation sets were 0.863 and 0.858. The nomogram model had good discriminatory power and was well-calibrated. According to the prediction model, the AUC of EH/EC, EH without atypia, and AH/EC were 0.889, 0.867, and 0.956, respectively. CONCLUSIONS: The nomogram of EH/EC is significantly associated with risk factors, namely BMI, PCOS, anemia, infertility, menostaxis, AUB type, and endometrial thickness. The nomogram model can be used to predict the EH/EC risk and rapidly screen risk factors in a women population with high risk.