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Record W4249289759 · doi:10.1158/1538-7445.am2019-623

Abstract 623: Iron intake, oxidative stress-related genes, and breast cancer risk

2019· article· en· W4249289759 on OpenAlexaffabout
Vicky C. Chang, Michelle Cotterchio, Susan J. Bondy, Joanne Kotsopoulos

Bibliographic record

VenueCancer Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlutathione Transferases and Polymorphisms
Canadian institutionsCancer Care OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsBreast cancerMedicineOdds ratioPhysiologyInternal medicineCase-control studyOncologyCancerConfidence intervalPopulationEstrogen receptorGynecologyEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background: Iron has been shown to enhance breast carcinogenesis in animal models through generation of oxidative stress, although epidemiological evidence of the association between iron intake and breast cancer risk remains inconclusive. This study investigated associations between different types of iron intake and breast cancer risk, and whether these associations were modified by genetic polymorphisms in antioxidant enzymes, including manganese superoxide dismutase (MnSOD) and glutathione S-transferases M1 (GSTM1) and T1 (GSTT1). Methods: A population-based case-control study in Ontario, Canada recruited 3,030 breast cancer cases identified from the Ontario Cancer Registry and 3,402 controls from random digit dialing. Iron intake from foods and supplements was assessed using a 178-item food frequency questionnaire. Multivariable logistic regression analyses were used to evaluate associations between breast cancer risk and intakes of dietary, supplemental, and total (dietary plus supplemental) iron, among all women and stratified by menopausal status. Associations were also examined by hormone receptor [estrogen receptor (ER)/progesterone receptor (PR)] tumor subtype. Among women providing saliva (DNA) samples (1,696 cases and 1,761 controls), interactions between iron intake and genetic polymorphisms were assessed using the likelihood ratio test. Results: Among all women, intakes of dietary, supplemental, and total iron were not associated with breast cancer risk, overall or by tumor subtype. Among premenopausal women, there was an increase in breast cancer risk for the ER–PR– subtype associated with higher intakes (highest vs. lowest quintile) of dietary [odds ratio (OR) = 1.4; 95% confidence interval (CI): 0.9–2.4] and total (OR = 1.8; 95% CI: 1.1–2.9) iron (P for trend < 0.05). Among postmenopausal women, supplemental iron intake (>18 vs. 0 mg/day) was associated with reduced breast cancer risk (OR = 0.7; 95% CI: 0.5–0.9), with similar associations across tumor subtypes. Associations of dietary and total iron intake with overall breast cancer risk were modified by GSTT1 and/or GSTM1/T1 combined genotypes (P for interaction < 0.05). For example, among women with deletions in both GSTM1 and GSTT1 loci, higher dietary iron intake was associated with increased breast cancer risk (OR = 2.1; 95% CI: 1.1–4.2), whereas null or inverse associations were found among women with other GSTM1/T1 genotypes. Conclusions: Results from this study suggest that higher dietary and total iron intake may be associated with increased risk of ER–PR– breast cancer among premenopausal women, whereas higher supplemental iron intake may be associated with reduced postmenopausal breast cancer risk. In addition, associations between iron intake and breast cancer risk may be modified by polymorphisms in oxidative stress-related genes. Our ongoing work will investigate heme and non-heme iron intake in relation to breast cancer risk. Citation Format: Vicky C. Chang, Michelle Cotterchio, Susan J. Bondy, Joanne Kotsopoulos. Iron intake, oxidative stress-related genes, and breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 623.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.324
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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