Notice bibliographique
Résumé
Epidemiological evidence points towards the role of diets high in red and processed meats in the cancers of many tissue types. The mechanisms by which meat consumption may lead to cancer risk are not well understood. Meat is a complex exposure, i.e. a mixture of various potential carcinogens that may individually or collectively contribute to cancer risk. In these studies we investigated whether carcinogens that accumulate during the process of high temperature cooking of meats, or to due to cooking of meats to high levels of doneness, might increase the risk of prostate and colorectal cancer risk. These carcinogens include heterocyclic amines (HCAs) and polycyclic aromatic hydrocarbons (PAHs). Moreover, diets high in processed meats may contribute to exposure to N-nitroso compounds (NOCs), which can also be formed endogenously when diets are high in red meat intake. To better understand which of these carcinogens might be more likely to explain the observed association between meat and cancer we considered variation in genes that metabolize HCAs, PAHs or N-nitroso compounds (NOCs), and genes that repair DNA damage caused by these exposures, to test for gene-exposure interactions. Given that some of these genes play specific roles in the metabolism of each type of carcinogen, finding of an interaction would help us identify which substrate is more likely to play a role in meat-induced carcinogenesis. Diets high in meat, as well as dairy, are also a rich source of phytanic acid. This branched-fatty acid has also been postulated to play a role in cancer. Therefore, we also investigated whether variants in AMACR, an enzyme that plays a key role in the metabolism of phytanic acid ,may associate with prostate cancer risk. ❧ We tested our hypotheses for prostate cancer using the resources of the California Collaborative Case Control Study of Prostate Cancer that included food frequency questionnaire (FFQ) data for 1,096 controls, 717 localized and 1,140 advanced cases. We conducted analyses for colorectal cancer risk in participants of the Colorectal Family Registry (CFR), an international consortium of six centers from the United States, Canada and Australia.We had food frequency questionnaire data for 3,364 probands, 1,942 familial controls (siblings/ spouse) and 1,620 population based controls. ❧ In the prostate cancer study, pan-fried red meat and white fish were associated with a statistically significant increased risk of advanced prostate cancer. Since pan-frying is associated with the formation of HCAs, but not PAHs or NOCs, we speculate that dietary HCAs may be relevant risk factors of advanced prostate cancer. However estimated levels of total dietary heterocyclic amines using the CHARRED database were not associated with risk of prostate cancer. We observed evidence of modification of the effect of 'estimated mutagenic activity' on advanced prostate cancer risk by a promoter polymorphism in PTGS2, an enzyme that plays a role in activation of both PAHs and HCAs in the prostate as well as in inflammation. We did not find any evidence in support of the hypothesis that phytanic acid may mediate associations between meat products and prostate cancer risk. ❧ In the colorectal study, we did not observe any associations for well done meat types, oven broiled or grilled meat types with colorectal cancer risk. However, of the 6 meat types for which we had pan-frying information, three (beef, spam and sausage) showed a statistically significant increased risk of colorectal cancer. As with the prostate cancer study, estimated levels of heterocyclic amines were not associated with the risk of colorectal cancer. The findings of our studies point towards at least a partial role for meat cooking related exposures and risk of cancers, and have potential implications both in understanding the mechanisms of meat related carcinogenesis and in the public health goal of cancer prevention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».