Mammographic Density in Relation to Daidzein-Metabolizing Phenotypes in Overweight, Postmenopausal Women
Bibliographic record
Abstract
Circulating hormones are associated with mammographic density, an intermediate marker of breast cancer risk. Differences in circulating hormones, including estrone and testosterone, have been observed in premenopausal women based on their capacity to metabolize daidzein, an isoflavone found predominantly in soybeans. Equol and O-desmethylangolensin (O-DMA) are products of intestinal bacterial metabolism of daidzein. There is interindividual variability in the capacity to produce daidzein metabolites; individuals can be equol producers or non-producers and O-DMA producers or non-producers. We tested the hypothesis that daidzein-metabolizing phenotypes are associated with mammographic density. Participants were recruited from among 92 sedentary, postmenopausal women, ages 50 to 75 years, who participated in a 1-year physical activity intervention. Pre-intervention mammographic density was determined using a computer-assisted, gray-scale thresholding technique. Fifty-five of these women consumed supplemental soy protein (>10 mg daidzein/d) for 3 days and collected a first-void urine sample on the fourth day to determine daidzein-metabolizing phenotypes. Equol and O-DMA concentrations were measured using gas chromatography-mass spectrometry. Associations between daidzein-metabolizing phenotypes and percent mammographic density were adjusted for age, maximum adult weight, gravidity, family history of breast cancer, and serum follicle-stimulating hormone and free testosterone concentrations. Mammographic density was 39% lower in equol producers compared with non-producers (P = 0.04). O-DMA producers had mammographic density 69% greater than non-producers (P = 0.05). These results suggest that particular intestinal bacterial profiles are associated with postmenopausal mammographic density, and these associations are not entirely explained by differences in reproductive or anthropometric characteristics or circulating hormones.
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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) | 0.001 | 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".