Doneness preferences, meat and meat-derived heterocyclic amines intake, and N-acetyltransferase 2 polymorphisms: association with colorectal adenoma in Japanese Brazilians
Bibliographic record
Abstract
Intake of heterocyclic amines (HCAs) and other mutagenic compounds formed during cooking has been hypothesized to be responsible for the positive association observed between red meat and colorectal cancer. We evaluated whether well-done/very well-done preferences for various meat and fish items, higher intakes of meat and fish, and meat-derived and fish-derived HCA are associated with the risk of colorectal adenoma (CRA) in a Japanese-Brazilian population. We selected 302 patients with adenoma and 403 control individuals who underwent total colonoscopy between 2007 and 2013, and collected information on aspects of meat intake using a detailed questionnaire. We also estimated HCA intake of the study participants using an HCA database that matched the cooking methods of this population. Latent class analysis on the basis of response to doneness preferences for different cooking methods of commonly consumed meat and fish items identified four distinct subgroups. Compared with the subgroup characterized by a preference for rare/medium well-done cooking for most meat and fish items, the odds ratio of CRA for the well-done/very well-done preference subgroup was 1.19 (95% confidence interval: 0.51-2.75). High intake of mixed-meat dishes was suggestively associated inversely with CRA, whereas a high intake of poultry was associated positively with CRA. No clear association with intake of total or specific HCAs and no effect modification by N-acetyltransferase 2 acetylation genotype were observed. We found no statistically significant associations between meat and HCA intake and CRA. These findings do not support a positive association between meat and meat-derived HCA intake and the risk of CRA.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".