Gene‐diet interactions in exposure to heterocyclic aromatic amines and bulky DNA adduct levels in blood leukocytes
Bibliographic record
Abstract
Heterocyclic aromatic amines (HAAs), carcinogens produced in meat when cooked at high temperatures, are an emerging biologic explanation for the meat-colorectal cancer relationship. HAAs form DNA adducts; left unrepaired, adducts can induce mutations, which may initiate/promote carcinogenesis. The purpose of this research was to investigate the relationship between dietary HAAs, genetic susceptibility and bulky DNA adduct levels. Least squares regression was used to examine the relationship between dietary HAA exposure and bulky DNA adduct levels in blood measured using (32)P-postlabeling among 99 healthy volunteers. Gene-diet interactions between dietary HAAs and genetic factors relevant to the biotransformation of HAAs and DNA repair were also examined. No main effects of dietary HAAs on bulky DNA adduct levels was found. However, those with the putative NAT1 rapid acetylator phenotype had lower adduct levels than those with the slow acetylator phenotype (P = 0.02). Furthermore, having five or more 'at-risk' genotypes was associated with higher bulky DNA adduct levels (P = 0.03). Gene-diet interactions were observed between NAT1 polymorphisms and dietary HAAs (P < 0.05); among the slow acetylator phenotype, higher intakes of HAAs were associated with an increase in DNA adduct levels compared to lower intakes. This study provides evidence of a biologic relationship between dietary HAAs, genetic susceptibility and bulky DNA adduct formation. However, the lack of a strong main effect of HAAs suggests that dietary HAAs are not a large contributor to bulky DNA adducts in this population; future studies should consider relevant gene-diet interactions to clarify the role of HAAs in carcinogenesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".