Effects of a herbal supplement on estrogen metabolism in pre‐ and post‐menopausal women: a randomized controlled trial
Bibliographic record
Abstract
Objectives The major trial objective was to determine whether a breast health supplement containing indole-3-carbinol and hydroxymatairesinol lignan would alter estrogen metabolism to favour C-2 hydroxylation and reduce C-16 hydroxylation. Methods Forty-seven pre-menopausal and forty-nine post-menopausal women were recruited for this study, and were divided by random allocation into treatment and placebo group. Major treatment ingredients were HMR lignin and indole-3-carbinol, consumed for 28 days. At day 0 and day 28, blood samples were analysed for serum enterolactone concentrations, and first morning random urine samples were assessed for estrogen metabolites. Repeated measures ANOVA statistical testing was performed. Results In pre-menopausal women, treatment supplementation resulted in a significant increase (p<0.05) in urinary 2-hydroxyestrone (OHE) concentrations and in the 2:16 alpha-OHE ratio. In post-menopausal women, treatment supplementation resulted in a significant increase in urinary 2-OHE concentrations. In pre- and post-menopausal women combined, treatment supplementation produced a significant increase in urinary 2-OHE concentration and a trend (p=0.074) toward an increased 2:16 alpha-OHE ratio. There were no significant increases in serum enterolactone concentrations in the treatment or placebo groups. Conclusions Supplementation with a mixture of indole-3-carbinol and HMR lignan in women significantly increased estrogen C-2 hydroxylation. This may constitute a mechanism for the reduction of breast cancer risk as well as risk for other estrogen-related cancers. Research Support: Fem Med Formulas Limited Partnership
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".