Oligozoospermia or low sperm count is a leading cause of male infertility worldwide. Despite decades of work on non-coding RNAs (ncRNAs) as regulators of spermatogenesis, fertilization, and male fertility, the literature on the function of long non-coding RNAs (lncRNAs) in human oligozoospermia is scarce. We integrated lncRNA and mRNA sequencing data from 12 human normozoospermic and oligozoospermic samples and comprehensively analyzed the function of differentially expressed lncRNAs (DE lncRNAs) and mRNAs (DE mRNAs) in male infertility. The target genes of DE lncRNAs were identified using a Gaussian graphical model. Gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways were primarily enriched in protein transport and localization to the endoplasmic reticulum (ER). The lncRNA–mRNA co-expression network revealed cis- and trans-regulated target genes of lncRNAs. The transcriptome data implicated DE lncRNAs and DE mRNAs and their target genes in the accumulation of unfolded proteins in sperm ER, PERK-EIF2 pathway-induced ER stress, oxidative stress, and sperm cell apoptosis in individuals with oligozoospermia. These findings suggest that the identified lncRNAs and pathways could serve as effective therapeutic targets for male infertility.

Aim of the present study was to explore the evaluative effectiveness of age, ovarian volume (OV), antral follicle count (AFC), serum follicle-stimulating hormone (FSH), anti-Müllerian hormone (AMH), AFC/Age ratio, AMH/Age ratio, FSH/luteinizing hormone (LH) ratio, and ovarian response prediction index (ORPI) to determine which could more advantageously assess ovarian reserve and response. This research enrolled 319 consecutive infertile women who had undergone in vitro fertilization-ET/intracytoplasmic sperm injection (IVF-ET/ICSI) treatments. Abovementioned variables were measured and calculated. Receiver operating characteristic (ROC) curve analysis was used to analyze the predictive accuracy of variables and to calculate cut-off values and corresponding sensitivity and specificity. Our study revealed that the significant variables for evaluating a decline in ovarian reserve include age, OV, FSH, AFC/Age ratio, AMH/Age ratio, and ORPI. Moreover, the area under the curve (AUC) of AFC/Age ratio was higher than other 5 variables (AUC = 1.000), and the cut-off value of AFC/Age ratio was 0.111 (sensitivity 100.00%, specificity 100.00%). The significant variables forecasting excessive ovarian response were age, AFC, AMH, FSH, AFC/Age ratio, AMH/Age ratio, FSH/LH ratio, and ORPI, and the significant variables forecasting poor ovarian response were AMH, LH, OV, AFC/Age ratio, AMH/Age ratio, and FSH/LH ratio. When ORPI was used to predict excessive response, the cut-off value of ORPI was 0.880 (sensitivity 84.72%, specificity 67.32%) and ORPI presented better effectiveness. When used to predict poor response, the evaluative effectiveness of 6 variables was almost similar, although the AUC of AFC/Age ratio presented the largest value. Regarding the infertile women, AFC/Age ratio performed better than did the other variables in evaluating ovarian reserve, and it offered excellent effectiveness in predicting poor ovarian response, however, ORPI presented better effectiveness in predicting excessive ovarian response.

This multi-center, cross-sectional study investigated the association between serum testosterone (T) levels, serum sex hormone-binding globulin (SHBG) levels, and the risk of metabolic syndrome (MS) in 3332 adult Chinese men. The prevalence of MS was 34.7%, and men with MS had lower serum levels of total T (TT) and SHBG than those without MS (P < 0.001). There was no significant difference in serum free T (FT) levels between subjects with and without MS (P = 0.627). In logistic regression analysis, the association between MS and serum SHBG levels persisted after adjusting for age, body mass index (BMI), smoking and drinking status, and serum TT (odds ratio [OR] 0.962, 95% confidence interval [95% CI] 0.954−0.969, P< 0.01). However, the association between serum TT level and the risk of MS was weak after adjusting for age, BMI, SHBG level, and smoking and drinking status (OR 0.981, 95% CI 0.960−1.007). Our study reveals that both serum TT and SHBG levels, but not serum FT, are inversely associated with the prevalence of MS and that serum SHBG is an independent and dominant risk factor for MS.