Tirzepatide inhibits tumor growth in mice with diet-induced obesity
Bibliographic record
Abstract
Abstract Tirzepatide, a drug used in management of type II diabetes, is an activator of both glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) receptors. Tirzepatide treatment leads to weight loss in murine models of obesity, and clinical trials have shown the drug can lead to weight loss up to ∼ 20% in overweight patients. Obesity has been shown to increase risk and/or to worsen prognosis of certain common cancers, including colon cancer, but the effect of tirzepatide on neoplasia has not been examined in detail. We studied the effects of this drug on the murine MC38 colon cancer model, which has previously shown to exhibit accelerated growth in hosts with diet-induced obesity. Tirzepatide did not cause tumor regression, but reduced tumor growth rates by ∼ 50%. This was associated with substantial reductions in food intake, and in circulating levels of insulin and leptin. Tirzepatide had no effect on MC38 cancer cell proliferation in vitro , and the effect of tirzepatide on tumor growth in vivo could be phenocopied in placebo treated mice simply by restricting food intake to the amount consumed mice receiving the drug. This provides evidence that the drug acts indirectly to inhibit tumor growth. Our findings raise the possibility that use of tirzepatide or similar agents may benefit patients with obesity-related cancers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".