Aspirin, Ibuprofen, and the Risk of Colorectal Cancer in Lynch Syndrome
Bibliographic record
Abstract
BACKGROUND: Inheritance of a germline mutation in one of the DNA mismatch repair (MMR) genes MLH1, MSH2, MSH6, and PMS2 causes a high risk of colorectal and other cancers (Lynch Syndrome). Use of aspirin has been shown to be associated with a reduced risk of colorectal cancer for the general population as well as for MMR gene mutation carriers. The aim of this study was to determine whether use of aspirin and ibuprofen in a nontrial setting is associated with the risk of colorectal cancer risk for MMR gene mutation carriers. METHODS: We included 1858 participants in the Colon Cancer Family Registry who had been found to have a pathogenic germline mutation in a MMR gene (carriers). We used weighted Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). All statistical tests were two-sided. RESULTS: A total of 714 carriers (38%) were diagnosed with colorectal cancer at a mean age of 42.4 (standard deviation 10.6) years. A reduced risk of colorectal cancer was associated with aspirin use (for 1 month to 4.9 years: HR = 0.49, 95% CI = 0.27 to 0.90, P = .02; for ≥5 years: HR = 0.25, 95% CI = 0.10 to 0.62, P = .003) and ibuprofen use (for 1 month to 4.9 years: HR = 0.38, 95% CI = 0.18 to 0.79, P = .009; for ≥5 years: HR = 0.26, 95% CI = 0.10 to 0.69, P = .007), compared with less than one month of use. CONCLUSION: Our results provide additional evidence that, for MMR gene mutation carriers, use of aspirin and ibuprofen might be effective in reducing their high risk of colorectal cancer.
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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 | 0.003 |
| 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 | 0.001 | 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".