The <i>CYP1A2</i> Genotype Modifies the Association Between Coffee Consumption and Breast Cancer Risk Among <i>BRCA1</i> Mutation Carriers
Bibliographic record
Abstract
We have recently reported that, among BRCA1 mutation carriers, the consumption of caffeinated coffee was associated with a significant reduction in breast cancer risk. Because the metabolism of caffeine is primarily by CYP1A2, we examined whether or not the CYP1A2 genotype modifies the association between a history of coffee consumption and the risk of breast cancer. A common A to C polymorphism in the CYP1A2 gene is associated with decreased enzyme inducibility and impaired caffeine metabolism. Information regarding coffee consumption habits and the CYP1A2 genotype was available for 411 BRCA1 mutation carriers (170 cases and 241 controls). We estimated the odds ratios (ORs) and 95% confidence intervals (95% CIs) for breast cancer associated with the CYP1A2 genotype and a history of coffee consumption before age 35, adjusting for potential confounders. The CYP1A2 genotype did not affect breast cancer risk. Among women with at least one variant C allele (AC or CC), those who consumed coffee had a 64% reduction in breast cancer risk, compared with women who never consumed coffee (OR, 0.36; 95% CI, 0.18-0.73). A significant protective effect of coffee consumption was not observed among women with the CYP1A2 AA genotype (OR, 0.93; 95% CI, 0.49-1.77). Similar results were obtained when the analysis was restricted to caffeinated coffee. This study suggests that caffeine protects against breast cancer in women with a BRCA1 mutation and illustrates the importance of integrating individual genetic variability when assessing diet-disease associations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".