Objectives: To assess the effectiveness of follicle-stimulating hormone (FSH) administration in male idiopathic infertility in a clinical setting. Methods:A retrospective real-world study was carried out, including all consecutive FSH-treated infertile men attending the Andrology Unit of Modena (Italy) from June 2015 to May 2022. Medical history, physical and andrological examinations, hormonal and seminal parameters, therapeutic management and pregnancy data were collected. The primary endpoint was the number of pregnancies obtained after FSH administration, whereas semen parameters change was the secondary outcome.Results: A total of 194 of 362 (53.6%) infertile men, eligible according to the Italian Health System regulations, were treated with FSH (mean age 37.9 ± 6.1 years). Following FSH administration (mean therapy duration 9.1 ± 7.1 months), 43 pregnancies were recorded (27.6%), of which 22 occurred naturally and 21 after assisted reproduction.A significant increase in sperm concentration (9.9 ± 12.2 vs. 18.9 ± 38.9 million/mL, p = 0.045) was detected after treatment, together with a significant increase in normozoospermia (from 1.0% to 5.1%, p = .044) and a reduction in azoospermia rate (from 9.8% to 7%, p = 0.044). Dividing the cohort in FSH-responders and non-responders, in terms of pregnancy achieved, higher sperm concentrations (15.7 ± 26.6 vs. 22.2 ± 25.7 million/mL, p = 0.033) and progressive sperm motility (18.0 ± 18.2 vs. 27.3 ± 11.3, p = 0.044) were found in pregnancy group. Conclusion:Our experience suggests that FSH, empirically administered to men with idiopathic infertility, leads to pregnancy in one out of four patients and increases sperm concentration. Although the expected limits because of a real-world data study, the number of FSH-treated patients required to achieve one pregnancy seems to be lower in clinical setting if compared to previously published data.
To identify a peculiar genetic combination predisposing to differentiated thyroid carcinoma (DTC), we selected a set of single-nucleotide polymorphisms (SNPs) associated with DTC risk, considering polygenic risk score (PRS), Bayesian statistics, and a machine learning (ML) classifier to describe cases and controls in 3 different datasets. Dataset 1 (649 DTC, 431 controls) has been previously genotyped in a genome-wide association study (GWAS) on Italian DTC. Dataset 2 (234 DTC, 101 controls) and dataset 3 (404 DTC, 392 controls) were genotyped. Associations of 171 SNPs reported to predispose to DTC in candidate studies were extracted from the GWAS of dataset 1, followed by replication of SNPs associated with DTC risk (P<0.05) in dataset 2. The reliability of the identified SNPs was confirmed by PRS and Bayesian statistics after merging the three datasets. SNPs were used to describe the case/control state of individuals by ML classifier. Starting from 171 SNPs associated with DTC, 15 were positive in both the datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a 7-fold increased risk of DTC than those in the first. Bayesian inference confirmed that the selected 15 SNPs differentiate cases from controls. Results were corroborated by ML, finding a maximum AUC of about 0.7. A restricted selection of only 15 DTC-associated SNPs is able to describe the inner genetic structure of Italian individuals and ML allows a fair prediction of case or control status based solely on the individual genetic background.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.