A Prospective Study of Meat and Meat Mutagens and Prostate Cancer Risk
Bibliographic record
Abstract
High-temperature cooked meat contains heterocyclic amines, including 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP), and polycyclic aromatic hydrocarbons, such as benzo(a)pyrene (BaP). In rodents, a high intake of PhIP induces prostate tumors. We prospectively investigated the association between meat and meat mutagens, specifically PhIP, and prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Diet was assessed using a 137-item food frequency questionnaire and a detailed meat-cooking questionnaire linked to a database for BaP and the heterocyclic amines 2-amino-3,8-dimethylimidazo[4,5-b]quinoxaline (MeIQx), 2-amino-3,4,8-trimethylimidazo[4,5-f]quinoxaline (DiMeIQx), and PhIP. During follow-up, we ascertained a total of 1,338 prostate cancer cases among 29,361 men; of these, 868 were incident cases (diagnosed after the first year of follow-up) and 520 were advanced cases (stage III or IV or a Gleason score of > or =7). Total, red, or white meat intake was not associated with prostate cancer risk. More than 10 g/d of very well done meat, compared with no consumption, was associated with a 1.4-fold increased risk of prostate cancer [95% confidence interval (95% CI), 1.05-1.92] and a 1.7-fold increased risk (95% CI, 1.19-2.40) of incident disease. Although there was no association with MeIQx and DiMeIQx, the highest quintile of PhIP was associated with a 1.2-fold increased risk of prostate cancer (95% CI, 1.01-1.48) and a 1.3-fold increased risk of incident disease (95% CI, 1.01-1.61). In conclusion, very well done meat was positively associated with prostate cancer risk. In addition, this study lends epidemiologic support to the animal studies, which have implicated PhIP as a prostate carcinogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".