Alcohol Consumption, Cigarette Smoking, and Risk of Breast Cancer for <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers: Results from The BRCA1 and BRCA2 Cohort Consortium
Bibliographic record
Abstract
Abstract Background: Tobacco smoking and alcohol consumption have been intensively studied in the general population to assess their effects on the risk of breast cancer, but very few studies have examined these effects in BRCA1 and BRCA2 mutation carriers. Given the high breast cancer risk for mutation carriers and the importance of BRCA1 and BRCA2 in DNA repair, better evidence on the associations of these lifestyle factors with breast cancer risk is essential. Methods: Using a large international pooled cohort of BRCA1 and BRCA2 mutation carriers, we conducted retrospective (5,707 BRCA1 mutation carriers and 3,525 BRCA2 mutation carriers) and prospective (2,276 BRCA1 mutation carriers and 1,610 BRCA2 mutation carriers) analyses of alcohol and tobacco consumption using Cox proportional hazards models. Results: For both BRCA1 and BRCA2 mutation carriers, none of the smoking-related variables was associated with breast cancer risk, except smoking for more than 5 years before a first full-term pregnancy (FFTP) when compared with parous women who never smoked. For BRCA1 mutation carriers, the HR from retrospective analysis (HRR) was 1.19 [95% confidence interval (CI), 1.02–1.39] and the HR from prospective analysis (HRP) was 1.36 (95% CI, 0.99–1.87). For BRCA2 mutation carriers, smoking for more than 5 years before an FFTP showed an association of a similar magnitude, but the confidence limits were wider (HRR = 1.25; 95% CI, 1.01–1.55 and HRP = 1.30; 95% CI, 0.83–2.01). For both carrier groups, alcohol consumption was not associated with breast cancer risk. Conclusions: The finding that smoking during the prereproductive years increases breast cancer risk for mutation carriers warrants further investigation. Impact: This is the largest prospective study of BRCA mutation carriers to assess these important risk factors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".