Abstract A10: Exposure to heterocyclic aromatic amines, genetic susceptibility and bulky DNA adduct levels in blood leukocytes
Notice bibliographique
Résumé
Abstract Background: There is compelling evidence to suggest that aspects of diet influence cancer risk; specifically, high meat consumption is associated with elevated risks. Exposure to heterocyclic aromatic amines (HAAs), carcinogens produced in meat when cooked at high temperatures, is a hypothesized explanation for the meat-cancer relationship. Reactive HAA metabolites form adducts with DNA; left unrepaired, adducts can induce mutations which may initiate/promote carcinogenesis. Genetic differences in the ability to biotransform HAAs (as conferred by polymorphisms in CYP1A1, CYP1A2, CYP1B1, NAT1 and NAT2) and repair DNA adducts (as conferred by polymorphisms in XPA, XPD and XRCC1) is postulated to modify the dietary HAAs-DNA adduct relationship. Methods: In a cross-sectional study of 99 healthy volunteers recruited from Kingston, Ontario, Canada, dietary exposure to HAAs and bulky DNA adduct levels in blood leukocytes was investigated. A detailed questionnaire was used in combination with a mutagen database to estimate average intake of dietary HAAs. Specifically, the detailed questionnaire obtained the average frequency of consumption, usual level of doneness and usual serving size of nine commonly consumed meats items with high HAA content. Bulky DNA adduct levels were measured in blood collected after an overnight fast using 32P-postlabelling. Least squares regression was used to examine the relationship between dietary HAA exposure and bulky DNA adduct levels in blood. Gene-diet interactions between dietary HAAs and genetic factors relevant to the biotransformation of HAAs and DNA repair were also examined. Results: No main effects of dietary exposure to HAAs on bulky DNA adduct levels were observed. However, polymorphisms in NAT1 were found to associate with bulky DNA adduct levels. Specifically, those with the putative NAT1*10 rapid acetylator phenotype had a lower adduct level 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 also observed between the NAT1*10 allele and dietary HAAs (p<0.05); specifically, among the slow acetylator phenotype, higher intakes of dietary HAAs were associated with an increase in DNA adduct levels compared to lower intakes. Conclusions: This study provides evidence of a biologic relationship between dietary HAAs, genetic susceptibility and bulky DNA adduct formation. The lack of a strong independent association between dietary HAAs and DNA adducts suggests that dietary HAAs are not a large contributor to bulky DNA adducts in this Canadian population; future studies should consider relevant gene-diet interactions to clarify the potential role of HAAs in carcinogenesis. Citation Format: Vikki Ho, Sarah Peacock, Thomas E. Massey, Roger W. L. Godschalk, Frederik-Jan van Schooten, Jian Chen, Will D. King. Exposure to heterocyclic aromatic amines, genetic susceptibility and bulky DNA adduct levels in blood leukocytes. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr A10.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».