Association of Genetic Liability to Allergic Diseases with Overall and Early-Onset Colorectal Cancer Risk: A Mendelian Randomization Study
Bibliographic record
Abstract
BACKGROUND: The tumor immunosurveillance theory supports that allergic conditions could decrease cancer risk. However, observational evidence yielded inconsistent results for the association between allergic diseases and colorectal cancer risk. We used Mendelian randomization (MR) to examine potential causal associations of allergies with the risk of overall and early-onset colorectal cancer. METHODS: Genome-wide association study summary statistical data were used to identify genetic variants associated with allergic diseases (Nvariants = 65) and individual allergic conditions (asthma, hay fever/allergic rhinitis, and eczema). Using two-sample MR, we examined these variants in relation to incident overall (Ncases = 52,775 cases) and early-onset colorectal cancer (Ncases = 6,176). The mediating role of white blood cells was examined using multivariable MR. RESULTS: In inverse-variance-weighted models, genetic liability to allergic diseases was inversely associated with overall {OR per log (odds) = 0.90 [95% confidence interval (CI), 0.85-0.96]; P < 0.01} and early-onset colorectal cancer [OR = 0.83 (95% CI, 0.73-0.95); P = 0.01]. Similar inverse associations were found for hay fever/allergic rhinitis or eczema, whereas no evidence of association was found between liability to asthma-related phenotypes and colorectal cancer risk. Multivariable MR adjustment for eosinophils weakened the inverse associations for liability to allergic diseases for overall [OR = 0.96 (95% CI, 0.89-1.03); P = 0.26] and early-onset colorectal cancer [OR = 0.86 (95% CI, 0.73-1.01); P = 0.06]. CONCLUSIONS: Our study supports a potential causal association between liability to allergic diseases, specifically hay fever/allergic rhinitis or eczema, and colorectal cancer, possibly at least in part mediated via eosinophil counts. IMPACT: Our results provide evidence that allergic responses may also have a role in immunosurveillance against 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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".