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Record W2221711924 · doi:10.1159/000439110

Relationship between Caffeine and Levels of DNA Repair and Oxidative Stress in Women with and without a <b><i>BRCA1</i></b> Mutation

2015· article· en· W2221711924 on OpenAlexaff
Dina Nikitina, Chen Zhou, Katherine A. Vallis, Aletta Poll, Peter Ainsworth, Steven A. Narod, Joanne Kotsopoulos

Bibliographic record

VenueLifestyle Genomics · 2015
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoWomen's College Hospital
Fundersnot available
KeywordsOxidative stressCaffeineMutationGeneticsChemistryMolecular biologyBiologyGeneBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Coffee consumption has been associated with a reduction in breast cancer risk among women with a BRCA1 mutation. The objective of this study was to evaluate whether major contributors of caffeine intake are associated with a reduction in DNA damage and/or oxidative stress in women with and without a BRCA1 mutation. METHODS: Coffee, tea, soda and total caffeine consumption was collected by a dietary history questionnaire, and DNA repair capacity in lymphocytes was assessed by the comet assay (tail moments), micronucleus test (per 1,000 binucleated cells) and analysis of γ-H2AX staining (nuclear foci). The thiobarbituric acid-malondialdehyde and DTNB assays were used to estimate serum lipid peroxidation (µmol/l) and protein oxidation (µmol/l), respectively. RESULTS: Among all women, high levels of caffeine and caffeinated coffee intake were associated with significantly lower levels of micronuclei (138.50 vs. 97.67, p = 0.04, and 138.12 vs. 97.70, p = 0.04). There was no significant relationship between caffeine, coffee, tea and soda intake and the other markers of DNA repair capacity and oxidative stress among all women and in analyses stratified by BRCA1 mutation status. CONCLUSION: The chemopreventive effects of coffee and/or caffeine may be associated with improved capacity to efficiently repair DNA damage.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.044
GPT teacher head0.299
Teacher spread0.255 · 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 designObservational
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".

Quick stats

Citations24
Published2015
Admission routes1
Has abstractyes

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