Flaxseed and its lignan and oil components: can they play a role in reducing the risk of and improving the treatment of breast cancer?
Bibliographic record
Abstract
Flaxseed (FS), rich in the phytoestrogen lignans and α-linolenic acid-rich oil, has been suggested to have an anticancer effect. Questions remain whether FS and its lignan and oil components are effective in reducing breast cancer risk and tumour growth, and can interact beneficially with breast cancer drugs. To find answers, in vitro, animal, observational, and clinical studies on FS and its lignan and oil components were reviewed. The majority of studies in various rodent models show that 2.5%-10% FS diet or the equivalent amount of lignan or oil reduces tumour growth. Ten percent FS and equivalent lignans do not interfere with but rather increase the effectiveness of tamoxifen (80 mg/day) while the 4% FS oil increases trastuzumab/Herceptin (2.5 mg/kg) effectiveness. Observational studies show that FS and lignan intake, urinary excretion, or serum levels are associated with reduced risk, particularly in postmenopausal women. Lignans reduce breast cancer and all-cause mortality by 33%-70% and 40%-53%, respectively, without reducing tamoxifen effectiveness. Clinical trials show that FS (25 g/day with 50 mg lignans; 32 days) reduces tumour growth in breast cancer patients and lignans (50 mg/day; 1 year) reduces risk in premenopausal women. Mechanisms include decreased cell proliferation and angiogenesis and increased apoptosis through modulation of estrogen metabolism and estrogen receptor and growth factor receptor signalling pathways. More clinical trials are needed but current overall evidence indicates that FS and its components are effective in the risk reduction and treatment of breast cancer and safe for consumption by breast cancer patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".