Phytoestrogen intake and other dietary risk factors for low motile sperm count and poor sperm morphology
Bibliographic record
Abstract
BACKGROUND: Few potentially modifiable risk factors of male infertility have been identified, and while different diets and food groups have been associated with male infertility, evidence linking dietary factors including phytoestrogens and semen quality is limited and contradictory. OBJECTIVES: To study the associations between phytoestrogen intake and other dietary factors and semen quality. MATERIALS AND METHODS: /mL) and poor sperm morphology (PM: <4% normal morphology) used unconditional logistic regression, accounting for clustering of subjects within the clinics, first without, and then with, adjustment for confounders associated with that outcome. RESULTS: High consumption of daidzein (≥13.74 μg/d), a phytoestrogen found in soy products, was a protective factor for MSC with an odds ratio (95%CI) of 0.58 (0.42-0.82) after adjustment for clustering and potential confounding. Dietary risk factors for PM after similar adjustment showed that drinking whole milk (OR 0.67, 95%CI 0.47-0.96) and eating red meat were protective with an OR 0.67 (0.46-0.99) for eating red meat >3 times/wk. DISCUSSION: In this case-referent study of men attending an infertility clinic for fertility diagnosis, we have identified that low MSC is inversely associated with daidzein intake. In contrast, daidzein intake was not associated with PM but eating red milk and drinking whole milk were protective. CONCLUSIONS: Dietary factors associated with semen quality were identified, suggesting that male fertility might be improved by dietary changes.
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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 | 0.001 |
| 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) | 0.005 | 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".