Use of isoflavone supplements is associated with reduced postmenopausal breast cancer risk
Bibliographic record
Abstract
Botanical supplements are widely used and contain diverse ingredients, including isoflavones. Food-based isoflavones have been associated with reduced breast cancer risk. However, no study has comprehensively evaluated supplements identified by isoflavone content and breast cancer risk. Associations between ever use of 28 isoflavone supplements and breast cancer risk in Ontario, Canada were evaluated using cases (n = 3,101) identified in 2002-2003 from the Ontario Cancer Registry and controls (n = 3,471) identified through random digit dialing methods. Multivariate logistic regression was used to estimate age-adjusted odds ratio (AOR) and 95% confidence intervals (CI). Several individual supplements were associated with reduced breast cancer risk (e.g., Natural HRT; AOR = 0.39; 95% CI: 0.22, 0.69; n(users) = 58). Use of any isoflavone supplements was associated with reduced risk when ≥ 3 were ever used (AOR = 0.68; 95% CI: 0.54, 0.86; n(users) = 332; p(trend) = 0.008) or any was taken >5 years (AOR = 0.75; 95% CI: 0.60, 0.94; n(users) = 325; p(trend) = 0.01); high content supplements were consistently associated with reduced risk. Risk reduction was confined to postmenopausal breast cancer for both individual and combined supplements, and was strongest in the latter among high content users who ever took ≥ 3 supplements (AOR = 0.55; 95% CI: 0.38, 0.81; n(users) = 118; p(trend) = 0.04) or any >5 years (AOR = 0.47; 95% CI: 0.27, 0.81; n(users) = 60; p(trend) = 0.03). Associations did not differ by estrogen-progesterone tumor receptor status. In conclusion, isoflavone supplements were associated with decreased postmenopausal breast cancer risk. Further research to examine these novel findings is warranted, given the low supplement use and potential limitations of our results.
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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 | 0.001 | 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".