Bibliographic record
Abstract
CN08-04 * Hyperinsulinemia is usually associated with reduced insulin signalling in classic target tissues for insulin action, such as liver, muscle, and fat. Intake of energy in excess of requirements often leads to insulin resistance, obesity, and hyperinsulinemia, although this is influenced by genetic factors and varies between individuals in human populations and between mouse strains in laboratory models. * Hyperinsulinemia and obesity are becoming more common in affluent societies, largely due to decreasing physical activity coupled with ample availability of calorie-dense foods. It is important to recognize that hyperinsulinemia is not always associated with obesity: in affluent societies, many individuals meet criteria for characterization as obese, normal weight (MONW). * Recent studies (for example Ma J, Li H, Pollak M, Kurth T, Giovannucci E, Stampfer M, abstract A204, AACR Frontiers in Cancer Prevention, 2006) provide early evidence hyperinsulinemia is associated with poor prognosis for common cancers. These studies are consistent with earlier observations suggesting that obesity (Calle E et al NEJM 1999: 341:1097-1105) and hyperglycemia ( Jee S et al JAMA 2005: 293: 194-202) are associated with increased cancer mortality. While they are many metabolic abnormalities in subjects who are hyperglycemic, hyperinsulinemic, and obese that might be causally associated with the increased cancer mortality observed, one obvious candidate is insulin itself. This involves the hypothesis that in hyperinsulinemic, insulin resistant subjects, neoplastic tissue may not share the insulin resistance present in the normal host tissues, but rather remain insulin sensitive, in a hyperinsulinemic milieu. * As an early step to explore this hypothesis, we have confirmed and extended recent reports from several groups in documenting the presence of insulin receptors on primary human cancers, including those of breast, colon, and prostate. We also recognize that drugs known to lower insulin levels, such as metformin, would be predicted to have antineoplastic activity if this hypothesis is valid. Early population studies ( Bowker S et al Diabetes Care 2006: 29:254-258; Evans J et al BMJ 2005: 330: 1304-1305) are consistent with this possibility. I will describe recent studies with laboratory models that are also consistent with the hypothesis. These models suggest that the in vivo antineoplastic activity of metformin is restricted to hosts rendered insulin resistant by overfeeding; little activity was seen under control dietary conditions. The in vivo activity of the drug is correlated with reduction of insulin receptor activation in neoplastic tissue, suggesting reduction of insulin levels is a contributing mechanism, although the growth inhibition via AMPK activation described in vitro (Zakikhani M, Dowling R, Fantus I, Sonenberg N, Pollak M Cancer Res 2006:10269-73) may also play a role. * Taken together, the ongoing work is consistent with the possibility that excess insulin is a risk factor for poor cancer outcome. As hyperinsulinemia is common and is modifiable by lifestyle and drug therapy, further research is justified. The early results suggest that benefits of interventions in this area may be restricted to metabolically defined subsets of patients, a point which should be taken into account in the design of future intervention trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".