Red Meat Intake, NAT2, and Risk of Colorectal Cancer: A Pooled Analysis of 11 Studies
Bibliographic record
Abstract
BACKGROUND: Red meat intake has been associated with risk of colorectal cancer, potentially mediated through heterocyclic amines. The metabolic efficiency of N-acetyltransferase 2 (NAT2) required for the metabolic activation of such amines is influenced by genetic variation. The interaction between red meat intake, NAT2 genotype, and colorectal cancer has been inconsistently reported. METHODS: We used pooled individual-level data from the Colon Cancer Family Registry and the Genetics and Epidemiology of Colorectal Cancer Consortium. Red meat intake was collected by each study. We inferred NAT2 phenotype based on polymorphism at rs1495741, highly predictive of enzyme activity. Interaction was assessed using multiplicative interaction terms in multivariate-adjusted models. RESULTS: From 11 studies, 8,290 colorectal cancer cases and 9,115 controls were included. The highest quartile of red meat intake was associated with increased risk of colorectal cancer compared with the lowest quartile [OR, 1.41; 95% confidence interval (CI), 1.29-1.55]. However, a significant association was observed only for studies with retrospective diet data, not for studies with diet prospectively assessed before cancer diagnosis. Combining all studies, high red meat intake was similarly associated with colorectal cancer in those with a rapid/intermediate NAT2 genotype (OR, 1.38; 95% CI, 1.20-1.59) as with a slow genotype (OR, 1.43; 95% CI, 1.28-1.61; P interaction = 0.9). CONCLUSION: We found that high red meat intake was associated with increased risk of colorectal cancer only from retrospective case-control studies and not modified by NAT2 enzyme activity. IMPACT: Our results suggest no interaction between NAT2 genotype and red meat intake in mediating risk of colorectal cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".