Optimal Cutoff Points for Anthropometric Variables to Predict Insulin Resistance in Polycystic Ovary Syndrome

Quantitative Proteomics Reveals That a Prognostic Signature of the Endometrium of the Polycystic Ovary Syndrome Women Based on Ferroptosis Proteins

OBJECTIVE

We aimed to study the relationship between ferroptosis proteins and reproductive outcomes of infertile patients with PCOS and construct the related prognostic model.

METHODS

These endometrium samples of the study were collected from 33 women with PCOS and 7 control women with successful pregnancies at the Reproductive Center of Lanzhou University Second Hospital, September 2019 to September 2020. The 40 patients' endometrium was identified the differentially expressed proteins (DEPs) using liquid chromatography tandem mass spectrometry. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) showed that the DEPs related pathways and functions between PCOS and controls. Subsequently, univariate Cox regression analysis and Lasso regression were used to identifying independent prognostic ferroptosis proteins, which were utilized to establish a prognostic model. Then the performance of the prognostic model was evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Then clinical data and prognostic model were used to predict the reproductive outcomes of PCOS patients by constructing the nomograms. Finally, we performed the single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status.

RESULTS

A total of 5331 proteins were identified, 391 proteins were differentially expressed in the PCOS and controls. The KEGG analysis revealed that the ferroptosis pathway was significantly different between PCOS and controls. 5 ferroptosis proteins (GPX4, DPP4, G6PD, PCBP1, and PCBP2) prognostic model (FerSig) was constructed via Cox regression and Lasso regression. Patients were separated into high and low-risk groups according to the FerSig. Kaplan-Meier curve showed that patients in the low-risk group had much better reproductive outcomes than those in the high-risk group. The DCA showed that the risk score was an independent predictive factor for reproductive outcomes. Compared with clinical data, ROC curve analysis indicated the FerSig proteins as a potential diagnostic and prognostic factor in PCOS patients. Functional analysis revealed that the FerSig proteins and immune microenvironment were correlated to the prognosis of PCOS.

CONCLUSION

The prognostic model focused on the FerSig proteins could predict the reproductive outcomes of PCOS patients with decreased endometrial receptivity, and provided theoretical basis for individualized treatment.

Body Mass, Total Body Fat Percentage, and Visceral Fat Level Predict Insulin Resistance Better Than Waist Circumference and Body Mass Index in Healthy Young Male Adults in Indonesia

The incidence of obesity which leads to insulin resistance (IR) and metabolic disorder is increasing in developing countries, including Indonesia. Male adults have a higher risk of abdominal obesity than females. This is associated with cardiometabolic disorders. Several anthropometric measurements have been proposed to predict IR. The aim of this study was to investigate whether body mass, body mass index (BMI), waist circumference (WC), body fat percentage (BF) or visceral fat level (VF) could become a better predictor of IR in healthy young male adults. A total of 140 healthy young male adults ranging from 18⁻25 years were recruited in the study. Insulin resistance was measured by calculating their Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Subjects with a HOMA-IR value ≥75th percentile, with cut off 3.75, were defined as IR. Anthropometric measurements including body weight, BMI, and WC were performed, whereas BF and VC were measured using bioelectrical impedance analysis (BIA). IR had a strong correlation with body weight, BMI, WC, BF, and VF. In the area under the curve of body mass, BF and VF were slightly greater than WC and BMI. Anthropometric measurements correlated strongly with IR but body weight, BF, VF had a stronger correlation than WC and BMI in healthy young male adults.