Iron intake, body iron status, and risk of breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Iron has been shown to promote breast carcinogenesis in animal models through generation of oxidative stress and interaction with estrogen. Heme iron, which is found exclusively in animal-sourced foods, is suggested to have a more detrimental effect. Epidemiological evidence of the association between iron and breast cancer risk remains inconclusive and has not been comprehensively summarized. This systematic review and meta-analysis evaluated associations between both iron intake and body iron status and breast cancer risk. Four electronic databases (MEDLINE, EMBASE, CINAHL, and Scopus) were searched up to December 2018 for studies assessing iron intake and/or biomarkers of iron status in relation to breast cancer risk. Using random-effects meta-analyses, pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated comparing the highest vs. lowest category of each iron measure. Dose-response meta-analyses were also performed to investigate linear and nonlinear associations. A total of 27 studies were included in the review, of which 23 were eligible for meta-analysis of one or more iron intake/status measures. Comparing the highest vs. lowest category, heme iron intake was significantly associated with increased breast cancer risk, with a pooled RR of 1.12 (95% CI: 1.04–1.22), whereas no associations were found for dietary (1.01, 95% CI: 0.89–1.15), supplemental (1.02, 95% CI: 0.91–1.13), or total (0.97, 95% CI: 0.82–1.14) iron intake. Associations of iron status indicators with breast cancer risk were generally in the positive direction; however, a significant pooled RR was found only for serum/plasma levels (highest vs. lowest) of iron (1.22, 95% CI: 1.01–1.47), but not for ferritin (1.13, 95% CI: 0.78–1.62), transferrin saturation (1.16, 95% CI: 0.91–1.47), or total iron-binding capacity (1.10, 95% CI: 0.97–1.25). In addition, a nonlinear dose-response was observed for heme iron intake and serum iron (both P nonlinearity < 0.05). Heme iron intake and serum iron levels may be positively associated with breast cancer risk. Although associations were modest, these findings may have public health implications given the widespread consumption of (heme) iron-rich foods. In light of methodological and research gaps identified, further research is warranted to better elucidate the relationship between iron and breast cancer risk.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".