Notice bibliographique
Résumé
Abstract It is well known that macronutrient intake and exercise influence cancer risk (1). They also have important metabolic effects because they determine energy balance at the whole organism level. There is accumulating evidence that the association between obesity and cancer risk may be attributable to the endocrine-metabolic consequences of chronic positive energy balance. Examples of candidate mediators of the effect of excess energy intake on risk of cancer are increased insulin levels and decreased adiponectin levels (2,3). Mechanistically, by increasing PI3K pathway activation (due to hyperinsulinemia) or decreasing AMPK activation (due to decreased adiponectin (4)) in various renewing epithelial cell populations at risk for transformation, these hormonal changes favor cell survival and proliferation over apoptosis. This increases the probability of survival of a clone bearing an oncogenic mutation, which in turn increases risk of subsequent ‘hits’ leading to full transformation. Recent population studies demonstrating these associations will be reviewed. Importantly, the effect of energy balance on cancer risk varies substantially with cancer type: for example, obesity is strongly associated with endometrial but not lung cancer, consistent with the view that optimizing metabolic risk factors may be insufficient to reduce cancer incidence in settings where carcinogen exposure is high. Furthermore, unexpected interactions between metabolic risk factors and prevention strategies have been observed: the prostate cancer prevention trial, for example, demonstrated that insulin secretion rate as reflected by c-peptide level is an important risk factor for high grade disease, but that this c-peptide-associated risk is specifically attenuated by finasteride (5). While there is considerable evidence that the hormonal consequences of positive energy balance lead to increased risk for many cancers, the possibility that high circulating levels of specific nutrient molecules may predispose to carcinogenesis or neoplastic progression is now receiving attention, and there is early evidence that branched chain amino acids (BCAA) may be important in this regard (6,7). BCAAs favor activation of mTOR, which increases proliferation as well as rate of oxidative phosphorylation (8), which in turn may increase oxidative DNA damage. Future laboratory and clinical research may identify key serum hormonal and metabolic analytes that are altered by effective prevention measures (and may even be the mediators of their effects), and these may serve as useful surrogate endpoints in clinical trials of proposed pharmacologic cancer prevention strategies, including metformin (9), as well as lifestyle and nutritional interventions. (1) Pollak M. Do cancer cells care if their host is hungry? Cell Metab. 2009 9(5):401-3 (2) Bao Y et al. A prospective study of plasma adiponectin and pancreatic cancer risk in five US cohorts. J Natl Cancer Inst. 2013;105(2):95-103 (3) Pollak M. The insulin and insulin-like growth factor receptor family in neoplasia: an update. Nat Rev Cancer. 2012;12(3):159-69 (4) Zakikhani M et al. The effects of adiponectin and metformin on prostate and colon neoplasia involve activation of AMP-activated protein kinase. Cancer Prev Res. 2008 1(5):369-75 (5) Neuhouser ML et al. Finasteride modifies the relation between serum C-peptide and prostate cancer risk: results from the Prostate Cancer Prevention Trial. Cancer Prev. Res. 2010 3(3):279-89 (6) Mayer J et al. Elevated BCAA are an early event in pancreatic carcinoma development. Nature Med., in press (7) Liu KA et al. Leucine supplementation differentially enhances pancreatic cancer growth in lean and overweight mice. Cancer Metab. 2014 31;2(1):6. (8) Morita M et al. mTORC1 controls mitochondrial activity and biogenesis through 4E-BP-dependent translational regulation. Cell Metab. 2013;18(5):698-711. (9) Pollak M. Repurposing biguanides to target energy metabolism for cancer treatment. Nature Med. 2014 Jun;20(6):591-3. Citation Format: Michael N. Pollak. Metabolic approaches to cancer prevention. [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 PL04-01.
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 | 0,002 | 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) | 0,001 | 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 ».