English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

and Objectives  About 30-75% of infertile men are diagnosed with idiopathic infertility, thereby lacking major causative factors to explain their impaired fertility status. In this study, we used a large cohort of idiopathic infertile men to determine whether subgroups could be identified by an unbiased clustering approach and whether underlying etiologic factors could be delineated.    Patients and Methods  From our in-house database Androbase ® , we retrospectively selected patients (from 2008 to 2018) with idiopathic male infertility (azoo- to normozoospermia) who fit the following selection criteria: FSH ≥ 1 IU/l, testosterone ≥ 8 nmol/l, ejaculate volume ≥ 1.5 ml. Patients with genetic abnormalities or partners with female factors were excluded.  For the identified study population (n=2742), we used common andrologic features (somatic, semen and hormonal parameters, including the  FSHB  c.-211G&amp;gt;T (rs10835638) single nucleotide polymorphism) for subsequent analyses. Cluster analyses were performed for the entire study population and for two sub-cohorts, which were separated by total sperm count (TSC) thresholds: Cohort A (TSC ≥ 1 mill/ejac; n=2422) and Cohort B (TSC &amp;lt; 1 mill/ejac; n=320). For clustering, the  partitioning around medoids  method was employed, and the quality was evaluated by average silhouette width.    Results  The applied cluster approach for the whole study population yielded two separate clusters, which showed significantly different distributions in bi-testicular volume, FSH and  FSHB  genotype. Cluster 1 contained all men homozygous for G (wildtype) in  FSHB  c.-211G&amp;gt;T (100%), while Cluster 2 contained most patients carrying a T allele (&amp;gt;96.6%). In the analyses of sub-cohorts A/B, two clusters each were formed too. Again, the strongest segregation markers between the respective clusters were bi-testicular volume, FSH and  FSHB  c.-211G&amp;gt;T.    Conclusion  With this first unbiased approach for revealing putative subgroups within a heterogenous group of idiopathic infertile men, we did indeed identify distinct patient clusters. Surprisingly, across all diverse phenotypes of infertility, the strongest segregation markers were  FSHB  c.-211G&amp;gt;T, FSH, and bi-testicular volume. Further, Cohorts A and B were significantly separated by  FSHB  genotype (wildtype vs. T-allele carriers), which supports the notion of a contributing genetic factor. Consequently,  FSHB  genotyping should be implemented as diagnostic routine in patients with idiopathic infertility.

FSHB Genotype Identified as a Relevant Diagnostic Parameter Revealed by Cluster Analysis of Men With Idiopathic Infertility