Abstract PR06: Interactions between nonsteroidal anti-inflammatory drugs and other risk factors on colorectal cancer risk
Bibliographic record
Abstract
Abstract Background: Long-term use of aspirin and other nonsteroidal anti-inflammatory drugs (NSAIDs) has been shown to be protective against colorectal cancer (CRC). While aspirin is recommended to prevent cardiovascular disease and colorectal cancer in certain subgroups, a broad recommendation is not in place due to concerns about side effects. Evidence from clinical trials among patients with colorectal adenomas suggested stronger effect of aspirin among non-smokers, compared to current smokers; but the effect has not been evaluated for CRC risk. Methods: Using 11,894 cases and 15,999 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colon Cancer Family Registry (CCFR), we performed multivariate logistic regression models to test for the interaction between regular use of NSAIDs (aspirin and non-aspirin NSAIDs) and other CRC risk factors on CRC risk, including age, sex, body mass index (BMI), physical activity, smoking, alcohol consumption, screening, family history of CRC, hormone replacement therapy (HRT) use, as well as intake of fruit, vegetables, red meat, processed meat, dietary fiber, total calcium and total folate. Fixed-effects meta-analyses with inverse variance weighting were used to summarize the estimates from interactions and stratified analyses across studies for each risk factor. A p-value <0.05 was considered statistically significant. Results: Regular use of any NSAID, aspirin, or non-aspirin NSAIDs was statistically significantly associated with a lower risk of CRC across almost all subgroups stratified by sex, body mass index (BMI), smoking, alcohol intake, physical activity and dietary factors. The association between aspirin and CRC risk statistically significantly differed by smoking status after adjusting for other risk factors (p-interaction=0.048). Regular aspirin use was associated with a 25% lower risk of CRC among non-smokers (OR=0.75; 95% CI: 0.64, 0.87), while it was associated with 19% and 16% lower risk of CRC among smokers of lower pack-years (OR=0.81; 95% CI: 0.70, 0.94) and higher pack-years (OR=0.84; 95% CI: 0.73, 0.97), respectively. There was a suggestive interaction between regular use of aspirin and BMI (p-interaction=0.081), where the effect of regular use of aspirin on CRC risk was stronger among individuals with normal BMI (OR=0.77; 95% CI: 0.65, 0.92) and overweight (OR=0.77; 95% CI: 0.66, 0.90), and non-significant among the obese (OR=0.93; 95% CI: 0.80, 1.08). Similarly, the association between any NSAID use and CRC risk was suggested to differ by BMI (p-interaction=0.075). No interaction between non-aspirin NSAIDs and other risk factors of CRC was observed. Conclusions: Our results suggest that the association between regular use of aspirin, but not other NSAIDs, and CRC risk may be modified by smoking status and BMI. The beneficial effect of aspirin on CRC risk appears to be diminished among those with greater CRC risk due to obesity and heavy smoking. The observed effect modifications were borderline significant and did not account for multiple comparisons. This abstract is also being presented as Poster A33. Citation Format: Xiaoliang Wang, Andrew T. Chan, Martha L. Slattery, Jenny Chang Claude, John D. Potter, Steven Gallinger, Caan Bette, Johanna W. Lampe, Polly A. Newcomb, Niha Zubair, Li Hsu, Robert E. Schoen, Hermann Brenner, Loic Le Marchand, Ulrike Peters, Emily White. Interactions between nonsteroidal anti-inflammatory drugs and other risk factors on colorectal cancer risk. [abstract]. In: Proceedings of the AACR Special Conference on Colorectal Cancer: From Initiation to Outcomes; 2016 Sep 17-20; Tampa, FL. Philadelphia (PA): AACR; Cancer Res 2017;77(3 Suppl):Abstract nr PR06.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.028 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".