Loss of GIPR in LEPR cells impairs glucose control by GIP and GIP:GLP-1 co-agonism without affecting body weight and food intake in mice
Bibliographic record
Abstract
OBJECTIVE: The glucose-dependent insulinotropic polypeptide (GIP) decreases body weight via central GIP receptor (GIPR) signaling, but the underlying mechanisms remain largely unknown. Here, we assessed whether GIP regulates body weight and glucose control via GIPR signaling in cells that express the leptin receptor (Lepr). METHODS: Hypothalamic, hindbrain, and pancreatic co-expression of Gipr and Lepr was assessed using single cell RNAseq analysis. Mice with deletion of Gipr in Lepr cells were generated and metabolically characterized for alterations in diet-induced obesity (DIO), glucose control and leptin sensitivity. Long-acting single- and dual-agonists at GIPR and GLP-1R were further used to assess drug effects on energy and glucose metabolism in DIO wildtype (WT) and Lepr-Gipr knock-out (KO) mice. RESULTS: Gipr and Lepr show strong co-expression in the pancreas, but not in the hypothalamus and hindbrain. DIO Lepr-Gipr KO mice are indistinguishable from WT controls related to body weight, food intake and diet-induced leptin resistance. Acyl-GIP and the GIPR:GLP-1R co-agonist MAR709 remain fully efficacious to decrease body weight and food intake in DIO Lepr-Gipr KO mice. Consistent with the demonstration that Gipr and Lepr highly co-localize in the endocrine pancreas, including the β-cells, we find the superior glycemic effect of GIPR:GLP-1R co-agonism over single GLP-1R agonism to vanish in Lepr-Gipr KO mice. CONCLUSIONS: GIPR signaling in cells/neurons that express the leptin receptor is not implicated in the control of body weight or food intake, but is of crucial importance for the superior glycemic effects of GIPR:GLP-1R co-agonism relative to single GLP-1R agonism.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".