Sperm Concentration Improvement May Be a Parameter Predicting Efficacy of FSH Therapy of Male Idiopathic Infertility.

Testis stimulation with follicle-stimulating hormone (FSH) is one of the empirical treatments proposed for male idiopathic infertility, although reliable markers to predict its efficacy are still lacking. This study aimed to identify parameters able to predict FSH efficacy in terms of pregnancy achievement. A real-world study was conducted, enrolling idiopathic infertile men treated with FSH 150IU three times weekly. Patients were treated until pregnancy achievement or for a maximum of two years and two visits were considered: V0 (baseline) and V1 (end of FSH treatment). Primary endpoints were the V1-V0 percentage change in sperm concentration, total sperm count, and total motile sperm number. In total, 48 pregnancies were recorded (27.7%) among 173 men (age 37.9 ± 6.2 years). All three endpoints increased after FSH administration, and only the V1-V0 percentage of sperm concentration significantly predicted pregnancy (p = 0.007). A V1-V0 sperm concentration of 30.8% predicted pregnancy, and the sperm concentration V1-V0 percentage (Y) required to obtain a pregnancy was predicted according to its baseline values (x): Y = 9.8433x2 - 203.67x + 958.29. A higher number of pregnancies was reached in men with baseline sperm concentration below 7.3 million/mL. Thus, the percentage of sperm concentration increasing after FSH administration could predict the treatment efficacy in terms of pregnancy. At the dosage used, the efficacy was significantly higher in patients with a starting sperm concentration < 7.3 mill/mL. Mathematical analyses identified a function able to predict the sperm concentration increase required to obtain a pregnancy in relation to the baseline sperm number.

Follicle-stimulating hormone effectiveness in male idiopathic infertility: What happens in daily practice?

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.

Genetic signature of differentiated thyroid carcinoma susceptibility: a machine learning approach.

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 three 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 datasets 1 and 2. Using these markers, PRS revealed that individuals in the fifth quintile had a seven-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.