Biomarkers of Micronutrients and Reproductive Hormones in Men with Infertility
Bibliographic record
Abstract
Background: Approximately 16% of couples in North America experience infertility, with male factor infertility implicated in roughly 30% of these cases. Reproductive hormones govern the reproductive system and, thus, impact fertility. Micronutrients play a role in modulating essential sites crucial for hormone synthesis and function, especially the testes and pituitary gland. Despite promising research from animal studies, the influence of micronutrients on male reproductive hormones in humans, especially in an infertile population, is unknown. The present thesis examined the association between biomarkers of pertinent micronutrients and reproductive hormones concentrations, including abnormal reproductive hormone levels, and determined whether age and BMI modified these associations among men experiencing infertility.Methods: Men experiencing infertility were recruited from Mount Sinai Hospital, Toronto. Using a cross-sectional design, serum was analyzed for ascorbic acid, vitamin B12, iron, ferritin, follicular stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (TT), prolactin and estradiol. Statistical analyses encompassed Spearman's rank correlations, linear regressions, effect modification linear regressions, logistic regressions, simple slope and Johnson-Neyman interval procedures. Results: Serum ascorbic acid was inversely associated with LH concentration (P = 0.01). An age-dependent (> 41.6 years) association was observed between serum ascorbic acid and TT concentration (P = 0.01). Serum vitamin B12 was associated with TT concentration (P = 0.03). Those in the mid-tertile (P = 0.03) and highest tertile (P = 0.02) of serum vitamin B12 had lower odds of TT deficiency compared to those in the lowest tertile. Serum iron was inversely associated with LH (P = 0.03) and prolactin (P = 0.003), while serum ferritin was inversely associated with both gonadotropins, FSH (P = 0.03) and LH (P = 0.02). Conclusions: Among males experiencing infertility, serum micronutrient concentrations are associated with serum reproductive hormone concentrations and improved hormonal profiles, and some of the effects appear to be age dependent. These findings carry practical implications for addressing male factor infertility. They suggest optimization of nutritional status may offer promising avenues for improving reproductive hormone profiles in men with infertility, paving the way for nutritional interventions to enhance fertility outcomes in males.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".