P2-114 Heterocyclic aromatic amines and cancer risk - a study of dietary exposure and biomarkers of early biologic effect
Bibliographic record
Abstract
Background Heterocyclic aromatic amines (HAAs) are formed during the cooking of meats at high temperatures and are a suspected risk factor for cancer. However, inconsistent results have been reported on the HAA-cancer relationship in epidemiologic studies. This is potentially due to the difficulty in measuring HAA exposures and variation in individual susceptibilities to HAAs. Metabolites of HAAs form DNA adducts in cells, an initiating step in chemical carcinogenesis, which may represent an early carcinogenic effect of HAA exposure. Methods This cross-sectional study aims to provide further understanding of the relationship between dietary exposure to HAAs and levels of HAA-DNA adducts measured in easily accessible white blood cells among a sample of 125 healthy volunteers. A detailed questionnaire was used in combination with a database that estimates average intake of HAAs in cooked meats. A blood sample was used to quantify HAA-DNA adduct concentration and determine polymorphisms in genes involved in HAA metabolism and DNA repair. Results In the preliminary data, HAA-DNA adducts were detectable in 17 of 23 individuals. Results show that dietary HAAs were predictive of adduct levels (Spearman Correlation Coefficient=0.39, p=0.06). Further analyses on the remaining cohort will be conducted to model adduct levels as a function of dietary HAAs and other relevant dietary, lifestyle and genetic factors; gene-diet interactions will also be explored. Conclusion This research aims to contribute to understanding the initial steps in this potentially carcinogenic pathway between meat consumption and cancer - important for assessing causality and the prevention of modifiable exposures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".