Joint Effects between UDP-Glucuronosyltransferase 1A7 Genotype and Dietary Carcinogen Exposure on Risk of Colon Cancer
Bibliographic record
Abstract
The UDP-glucuronosyltransferase 1A7 (UGT1A7) gene is polymorphic and encodes an enzyme involved in the detoxification of heterocyclic amines (HCA) and polycyclic aromatic hydrocarbons (PAH). Consumption of pan-fried and well-done meat are surrogates for HCA and PAH exposure and are possibly associated with colon cancer. We have evaluated whether UGT1A7 allelic variations are associated with colon cancer and whether UGT1A7 genotype modified associations among meat intake, exposure to HCAs and PAHs, and colon cancer in a population-based case-control study of African Americans (197 cases and 202 controls) and whites (203 cases and 210 controls). As part of a 150-item food frequency questionnaire, meat intake was assessed by cooking method and doneness and used to estimate individual HCA and PAH exposure. UGT1A7 alleles (UGT1A7*1, UGT1A7*2, UGT1A7*3, and UGT1A7*4) were measured and genotypes were categorized into predicted activity groups (high: *1/*1, *1/*2, *2/*2; intermediate: *1/*3, *1/*4, *2/*3; low: *3/*3, *3/*4, *4/*4). There was no association with UGT1A7 low versus high/intermediate genotype [odds ratio (OR), 1.1; 95% confidence interval (95% CI), 0.7-1.8], regardless of race. Greater than additive joint effects were observed for UGT1A7 low genotype and HCA-related factors. For example, equal to or greater than the median daily intake of the HCA, 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline (DiMeIQx) and having UGT1A7 low genotype was positively associated with colon cancer (OR, 2.4; 95% CI, 1.2-4.8), compared with less than the median daily intake and UGT1A7 high/intermediate genotypes. These data suggest that the associations among cooked meat-derived compound exposure, and colon cancer are modified by the UGT1A7 genotype.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".