Associations between Smoking, Alcohol Consumption, and Colorectal Cancer, Overall and by Tumor Microsatellite Instability Status
Bibliographic record
Abstract
INTRODUCTION: Both smoking and alcohol consumption have been associated with modestly increased risks of colorectal cancer (CRC). Reports have suggested that these associations may differ by tumor molecular subtype, with stronger associations for microsatellite unstable (MSI-H) tumors. METHODS: We used a population-based case-unaffected sibling design including 2,248 sibships (2,253 cases; 4,486 siblings) recruited to the Colon Cancer Family Registry to evaluate the association between smoking, alcohol consumption, and CRC. Associations were assessed using conditional logistic regression, treating sibship as the matching factor. RESULTS: Although there were no statistically significant associations between any smoking variable and CRC overall, smoking did confer an increased risk of certain types of CRC. We observed an association between pack-years of smoking and rectal cancer [odds ratio (OR), 1.85; 95% confidence interval (CI), 1.23-2.79 for >40 pack-years versus nonsmokers; P(trend) = 0.03], and there was an increased risk of MSI-H CRC with increasing duration of smoking (OR, 1.94; 95% CI, 1.09-3.46 for >30 years of smoking versus nonsmokers). Alcohol intake was associated with a modest increase in risk for CRC overall (OR, 1.21; 95% CI, 1.03-1.44 for 12+ drinks per week versus nondrinkers), with more marked increases in risk for MSI-L CRC (OR, 1.85; 95% CI, 1.06-3.24) and rectal cancer (OR, 1.48; 95% CI, 1.08-2.02). CONCLUSIONS: We found associations between cigarette smoking and increased risks of rectal cancer and MSI-H CRC. Alcohol intake was associated with increased risks of rectal cancer and MSI-L CRC. These results highlight the importance of considering tumor phenotype in studies of risk factors for CRC.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".