Prevalence, Pattern and Predictors for Dyslipidemia of Chinese Women With Polycystic Ovary Syndrome

Objective: To investigate the prevalence, pattern and risk predictors for dyslipidemia among Chinese women with polycystic ovary syndrome (PCOS).Study Design and Methods: A total of 1,000 women diagnosed as PCOS by modified Rotterdam criteria were enrolled in 27 hospitals across China in a randomized controlled trial. Anthropometric, metabolic parameters, sex hormone, and lipid levels were measured at the baseline visit. Dyslipidemia was defined according to total cholesterol (TC), low density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) level. Independent t-test and logistic regression were used to identify predictors for dyslipidemia. Area under the receiver operating characteristic curve (AUC) was calculated.Results: A total of 41.3% of the women had dyslipidemia, and the prevalence of abnormal TC, LDL-C, HDL-C, and TG were 8.6, 9.1, 26.9, and 17.5%, respectively. Logistic regression found that age, waist circumference, insulin, follicle-stimulating hormone, and sex hormone-binding globulin were independent predictors for dyslipidemia. When combining these predictors, the AUC was 0.744. The cut-off points were age >28.5 years, waist circumference >86.5 cm, insulin >96.0 pmol/L, follicle-stimulating hormone <5.6 mIU/mL, and sex hormone-binding hormone <31.0 nmol/L, respectively.Conclusion: Dyslipidemia was common in Chinese women with PCOS, and low HDL-C level was the predominant lipid abnormality. Age, waist circumference, follicle-stimulating hormone, insulin and sex hormone-binding globulin were predictive for dyslipidemia among Chinese women with PCOS.

Decreased Risk of Ovarian Cancer Associated With rs9898876 Sex Hormone-binding Globulin Gene Variant

Background. Ovarian cancer (OC) is one of the most common gynecologic cancers,with significant morbidity and mortality. The risk of OCis influenced by hormone status, of which sex hormone-binding globulin (SHBG), whichinfluences the serum availability of steroid sex hormones, is implicated in the pathogenesis and evolution of OC. The aim of this study is to evaluate the involvement of common SHBG gene variants in OC susceptibility and evolution. Materials. A case control study including 71 OC patients and 74 cancer-free controls, who were genotyped for rs9898876, rs13894, rs1799941 and rs6257 SHBG SNP. Genotyping was done by the allelic discrimination method, using VIC- and FAM-labeled primers.Results. The minor allele frequencies of rs9898876, rs13894, rs1799941 and rs6257 SHBG SNP was comparable between OC cases and control women, implying no significant associations of the tested variants and overall OC risk. Taking homozygous wild-type genotype as reference (OR=1.00), heterozygous rs9898876 (G/T), and minor allele-carrying genotypes [G/T+T/T] were associated with reduced risk of OC. Whilers9898876 heterozygosity (G/T) was predictive of OC occurrence, no significant association of the remaining three tested SNPs was noted with altered risk of OC. Irrespective of FIGO staging, the four tested SHBG SNPs were not associated with the clinical progression of OC.Conclusion. In conclusion, SHBG rs9898876 is associated with a decreased risk of OC, and thus constitutes a potential diagnostic biomarker of OC.

Genetically predicted sex hormone binding globulin and ischemic heart disease in men and women: a univariable and multivariable Mendelian randomization study

Men are more vulnerable to ischemic heart disease (IHD) than women, possibly due to testosterone. Correspondingly, sex hormone binding globulin (SHBG) which lowers circulating testosterone might protect men against IHD. SHBG may also affect IHD independent of testosterone, which has not previously been examined. To assess the sex-specific role of SHBG in IHD, in univariable Mendelian randomization (MR), we used sex-specific, genome-wide significant genetic variants to predict SHBG, and examined their association with IHD in the UK Biobank. We also replicated using genetic instruments from Japanese men and applied to Biobank Japan. To assess the role of SHGB independent of testosterone in men, we used multivariable MR controlling for testosterone. Genetically predicted SHBG was associated with lower IHD risk in men [odds ratio (OR) 0.78 per standard deviation, 95% confidence interval (CI) 0.70 to 0.87], and the association was less clear in women. The estimates were similar in Japanese. The inverse association remained after controlling for testosterone in men (OR 0.79, 95% CI 0.71 to 0.88). SHBG might lower the risk of IHD in men, with a role independent of testosterone. Exploring intervention strategies that increase SHBG is important for targeting IHD treatments.

Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body’s life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.