Biomarkers of Iron Are Associated with Anterior-Pituitary-Produced Reproductive Hormones in Men with Infertility
Bibliographic record
Abstract
Approximately 16% of North American couples are affected by infertility, with 30% of cases being attributable to male factor infertility. The regulation of reproductive hormones via the hypothalamic–pituitary–gonadal axis is important for spermatogenesis and subsequently male fertility. Maintaining iron homeostasis is critical to normal reproductive physiological function. This cross-sectional study’s objective was to determine the association between serum biomarkers of iron and reproductive hormones. Men experiencing infertility (n = 303) were recruited from Mount Sinai Hospital, Toronto. Serum was analyzed for iron and ferritin as biomarkers of iron status and reproductive hormones (follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, and prolactin), which were the primary outcome. Associations were determined using non-parametric Spearman’s rank correlation coefficient, linear regressions, and logistic regressions. A significant independent monotonic inverse relationship between serum iron and prolactin (p = 0.0002) was found. In linear regression analyses, iron was inversely associated with luteinizing hormone (unadjusted p = 0.03, adjusted p = 0.03) and prolactin (unadjusted p = 0.001 and adjusted p = 0.003). Serum ferritin was inversely associated with both gonadotropins, follicle-stimulating hormone (adjusted p = 0.03), and luteinizing hormone (adjusted p = 0.02). These findings suggest that biomarkers of iron are associated with pituitary-produced reproductive hormones, which play a role in the hypothalamic–pituitary–gonadal signaling pathway involved in spermatogenesis, testicular testosterone production, and male fertility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".