Glucose-dependent insulinotropic polypeptide receptor signaling alleviates gut inflammation in mice
Bibliographic record
Abstract
Glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) are gut-derived peptide hormones that potentiate glucose-dependent insulin secretion. The clinical development of GIP receptor-GLP-1 receptor (GIPR-GLP-1R) multiagonists exemplified by tirzepatide and emerging GIPR antagonist-GLP-1R agonist therapeutics such as maritide is increasing interest in the extrapancreatic actions of incretin therapies. Both GLP-1 and GIP modulate inflammation, with GLP-1 also acting locally to alleviate gut inflammation in part through antiinflammatory actions on GLP-1R+ intestinal intraepithelial lymphocytes. In contrast, whether GIP modulates gut inflammation is not known. Here, using gain- and loss-of-function studies, we show that GIP alleviates 5-fluorouracil-induced (5FU-induced) gut inflammation, whereas genetic deletion of Gipr exacerbates the proinflammatory response to 5FU in the murine small bowel (SB). Bone marrow (BM) transplant studies demonstrated that BM-derived Gipr-expressing cells suppress 5FU-induced gut inflammation in the context of global Gipr deficiency. Within the gut, Gipr was localized to nonimmune cells, specifically stromal CD146+ cells. Hence, the extrapancreatic actions of GIPR signaling extend to the attenuation of gut inflammation, findings with potential translational relevance for clinical strategies modulating GIPR action in people with type 2 diabetes or obesity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".