Iron intake, oxidative stress‐related genes and breast cancer risk
Bibliographic record
Abstract
Iron has been suggested to contribute to breast cancer development through oxidative stress generation. Our study investigated associations between iron intake and breast cancer risk, overall and by menopausal and estrogen receptor/progesterone receptor (ER/PR) status, and modification by oxidative stress‐related genetic polymorphisms (MnSOD, GSTM1 and GSTT1). A population‐based case–control study (3,030 cases and 3,402 controls) was conducted in Ontario, Canada. Iron intake (total, dietary, supplemental, heme, nonheme) was assessed using a validated food frequency questionnaire. Odds ratios (OR) and 95% confidence intervals (CI) were estimated from multivariable logistic regression models. Interactions between iron intake and genotypes were assessed among 1,696 cases and 1,761 controls providing DNA. Overall, no associations were observed between iron intake and breast cancer risk. Among premenopausal women, total, dietary and dietary nonheme iron were positively associated with ER–/PR– breast cancer risk (all ptrend < 0.05). Among postmenopausal women, supplemental iron was associated with reduced breast cancer risk (OR>18 vs. 0 mg/day = 0.68, 95% CI: 0.51–0.91), and dietary heme iron was associated with an increased risk, particularly the ER–/PR– subtype (ORhighest vs. lowest quintile = 1.69, 95% CI: 1.16–2.47; ptrend = 0.02). Furthermore, GSTT1 and combined GSTM1/GSTT1 polymorphisms modified some of the associations. For example, higher dietary iron was most strongly associated with increased breast cancer risk among women with GSTT1 deletion or GSTM1/GSTT1 double deletions (pinteraction < 0.05). Findings suggest that iron intake may have different effects on breast cancer risk according to menopausal and hormone receptor status, as well as genotypes affecting antioxidant capacity.
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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".