Characteristics of Different Obesity Metabolic Indexes and their Correlation with Insulin Resistance in Patients with Polycystic Ovary Syndrome.

This study is aimed to investigate the characteristics of different obesity metabolic indexes [body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), cardiometabolic index (CMI), body roundness index (BRI), visceral adiposity index (VAI), and lipid aggregation products (LAP)] and their correlation with insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). The study was conducted on 140 subjects with PCOS and 133 control subjects aged 18-44 years. According to insulin resistance index (HOMA-IR) ≥ 2.69 and HOMA-IR < 2.69, PCOS group members were divided into insulin resistance group and non-insulin resistance group. Anthropometric and serological characteristics of the population with PCOS focused on calculating different obesity metabolic indexes and HOMA-IR. BMI, WC, WHR, WHtR, CMI, BRI, VAI, and LAP were significantly higher in PCOS patients than in the control group, and the differences were all statistically significant (P < 0.05); In the insulin resistance group of PCOS patients, BMI, WC, WHR, WHtR, CMI, BRI, VAI, and LAP were significantly higher than in the non-insulin resistance group (P < 0.05). In PCOS patients, BMI (r = 0.658, P < 0.001), WC (r = 0.0.662, P < 0.001), WHR (r = 0.377, P < 0.001), WHtR (r = 0.660, P < 0.001), CMI (r = 0.698, P < 0.001), BRI (r = 0.757, P < 0.001), VAI (r = 0.640, P < 0.001), and LAP (r = 0.767, P < 0.001) were positively correlated with IR. Obesity metabolic indexes associated with PCOS were elevated in the PCOS group compared to the control group, and in the PCOS insulin-resistant group compared to the non-insulin resistant group. Novel obesity metabolic indexes, especially CMI, BRI and LAP, might be more appropriate for evaluating the risk of concurrent IR in PCOS.

The Relationship Between Anti-Müllerian Hormone and Body Composition Components in Patients With Polycystic Ovary Syndrome of Reproductive Age.

To investigate and analyze the relationship between body composition components, including Body Mass Index (BMI), body fat percentage, waist-to-hip ratio, and visceral fat index, and Anti-Müllerian Hormone (AMH) levels in patients diagnosed with Polycystic Ovary Syndrome (PCOS), The study aims to provide a comprehensive understanding of how various aspects of body composition impact AMH levels. This study enrolled 167 women with PCOS of reproductive age. Serum AMH level and body composition were measured, and the correlation between body composition elements and AMH levels was analyzed. AMH level was negatively correlated with body weight, BMI, fat-free mass, body fat percentage, waist-hip ratio, and visceral fat level (P < 0.01). And negatively correlated with skeletal muscle ratio ( P = 0.003). AMH level remained significantly associated with BMI ( P = 0.028), body fat percentage ( P = 0.040), waist-hip ratio (P = 0.003), and visceral fat level ( P = 0.040) after age was included and a multiple linear regression model was established. After adjusting for age, BMI was still significantly associated with AMH (P = 0.029). At the same time, there was no obvious linear correlation between BSA and AMH. The results showed that AMH levels were significantly different among the three groups (9.53 ± 5.12 vs 6.98 ± 3.35 vs 6.38 ± 3.38, P < 0.001; ng/mL). The level of AMH in the non-central obesity group was higher than that in the central obesity group (9.68 ± 5.22vs7.09 ± 3.83, P < 0.001; ng/mL). In PCOS patients, those who are more obese have lower AMH levels, indicating poorer Ovarian Reserve. BMI may independently affect AMH levels, apart from age, BSA, and other factors. Ovarian function in centrally obese patients is poorer than in those with non-central obesity.

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.

A Novel Consistent Random Forest Framework: Bernoulli Random Forests.

Random forests (RFs) are recognized as one type of ensemble learning method and are effective for the most classification and regression tasks. Despite their impressive empirical performance, the theory of RFs has yet been fully proved. Several theoretically guaranteed RF variants have been presented, but their poor practical performance has been criticized. In this paper, a novel RF framework is proposed, named Bernoulli RFs (BRFs), with the aim of solving the RF dilemma between theoretical consistency and empirical performance. BRF uses two independent Bernoulli distributions to simplify the tree construction, in contrast to the RFs proposed by Breiman. The two Bernoulli distributions are separately used to control the splitting feature and splitting point selection processes of tree construction. Consequently, theoretical consistency is ensured in BRF, i.e., the convergence of learning performance to optimum will be guaranteed when infinite data are given. Importantly, our proposed BRF is consistent for both classification and regression. The best empirical performance is achieved by BRF when it is compared with state-of-the-art theoretical/consistent RFs. This advance in RF research toward closing the gap between theory and practice is verified by the theoretical and experimental studies in this paper.