Treatment with Insulin Analog X10 and IGF-1 Increases Growth of Colon Cancer Allografts
Bibliographic record
Abstract
Obesity and type 2 diabetes are associated with an increased risk for development of certain forms of cancer, including colon cancer. The publication of highly controversial epidemiological studies in 2009 raised the possibility that use of the insulin analog glargine increases this risk further. However, it is not clear how mitogenic effects of insulin and insulin analogs measured in vitro correlate with tumor growth-promoting effects in vivo. The aim of this study was to examine possible growth-promoting effects of native human insulin, insulin X10 and IGF-1, which are considered positive controls in vitro, in a short-term animal model of an obesity- and diabetes-relevant cancer. We characterized insulin and IGF-1 receptor expression and the response to treatment with insulin, X10 and IGF-1 in the murine colon cancer cell line (MC38 cells) in vitro and in vivo. Furthermore, we examined pharmacokinetics and pharmacodynamics and monitored growth of MC38 cell allografts in mice with diet-induced obesity treated with human insulin, X10 and IGF-1. Treatment with X10 and IGF-1 significantly increased growth of MC38 cell allografts in mice with diet-induced obesity and we can therefore conclude that supra-pharmacological doses of the insulin analog X10, which is super-mitogenic in vitro and increased the incidence of mammary tumors in female rats in a 12-month toxicity study, also increase growth of tumor allografts in a short-term animal model.
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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 | 0.000 | 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 | 0.001 |
| 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 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".