Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".