Causal Effects of Lifetime Smoking on Breast and Colorectal Cancer Risk: Mendelian Randomization Study
Bibliographic record
Abstract
Abstract Background: Observational evidence has shown that smoking is a risk factor for breast and colorectal cancer. We used Mendelian randomization (MR) to examine causal associations between smoking and risks of breast and colorectal cancer. Methods: Genome-Wide Association Study summary data were used to identify genetic variants associated with lifetime amount of smoking (n = 126 variants) and ever having smoked regularly (n = 112 variants). Using two-sample MR, we examined these variants in relation to incident breast (122,977 cases/105,974 controls) and colorectal cancer (52,775 cases/45,940 controls). Results: In inverse-variance weighted models, a genetic predisposition to higher lifetime amount of smoking was positively associated with breast cancer risk [OR per 1-SD increment: 1.13; 95% confidence interval (CI): 1.00–1.26; P = 0.04]; although heterogeneity was observed. Similar associations were found for estrogen receptor–positive and estrogen receptor–negative tumors. Higher lifetime amount of smoking was positively associated with colorectal cancer (OR per 1-SD increment, 1.21; 95% CI, 1.04–1.40; P = 0.01), colon cancer (OR, 1.31; 95% CI, 1.11–1.55; P < 0.01), and rectal cancer (OR, 1.36; 95% CI, 1.07–1.73; P = 0.01). Ever having smoked regularly was not associated with risks of breast (OR, 1.01; 95% CI, 0.90–1.14; P = 0.85) or colorectal cancer (OR, 0.97; 95% CI, 0.86–1.10; P = 0.68). Conclusions: These findings are consistent with prior observational evidence and support a causal role of higher lifetime smoking amount in the development of breast and colorectal cancer. Impact: The results from this comprehensive MR analysis indicate that lifetime smoking is a causal risk factor for these common malignancies.
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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 | 0.043 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".