Alcohol Consumption and the Risk of Colorectal Cancer for Mismatch Repair Gene Mutation Carriers
Bibliographic record
Abstract
Abstract Background: People with germline mutation in one of the DNA mismatch repair (MMR) genes have increased colorectal cancer risk. For these high-risk people, study findings of the relationship between alcohol consumption and colorectal cancer risk have been inconclusive. Methods: 1,925 MMR gene mutations carriers recruited into the Colon Cancer Family Registry who had completed a questionnaire on lifestyle factors were included. Weighted Cox proportional hazard regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for the association between alcohol consumption and colorectal cancer. Results: Colorectal cancer was diagnosed in 769 carriers (40%) at a mean (SD) age of 42.6 (10.3) years. Compared with abstention, ethanol consumption from any alcoholic beverage up to 14 g/day and >28 g/day was associated with increased colorectal cancer risk (HR, 1.50; 95% CI, 1.09–2.07 and 1.69; 95% CI, 1.07–2.65, respectively; Ptrend = 0.05), and colon cancer risk (HR, 1.78; 95% CI, 1.27–2.49 and 1.94; 95% CI, 1.19–3.18, respectively; Ptrend = 0.02). However, there was no clear evidence for an association with rectal cancer risk. Also, there was no evidence for associations between consumption of individual alcoholic beverage types (beer, wine, spirits) and colorectal, colon, or rectal cancer risk. Conclusions: Our data suggest that alcohol consumption, particularly more than 28 g/day of ethanol (∼2 standard drinks of alcohol in the United States), is associated with increased colorectal cancer risk for MMR gene mutation carriers. Impact: Although these data suggested that alcohol consumption in MMR carriers was associated with increased colorectal cancer risk, there was no evidence of a dose-response, and not all types of alcohol consumption were associated with increased risk. Cancer Epidemiol Biomarkers Prev; 26(3); 366–75. ©2016 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".