Distinct Identity of GLP-1R, GLP-2R, and GIPR Expressing Cells and Signaling Circuits Within the Gastrointestinal Tract
Bibliographic record
Abstract
Enteroendocrine cells directly integrate signals of nutrient content within the gut lumen with distant hormonal responses and nutrient disposal via the production and secretion of peptides, including glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide 1 (GLP-1) and glucagon-like peptide 2 (GLP-2). Given their direct and indirect control of post-prandial nutrient uptake and demonstrated translational relevance for the treatment of type 2 diabetes, malabsorption and cardiometabolic disease, there is significant interest in the locally engaged circuits mediating these metabolic effects. Although several specific populations of cells in the intestine have been identified to express endocrine receptors, including intraepithelial lymphocytes (IELs) and αβ and γδ T-cells (Glp1r+) and smooth muscle cells (Glp2r+), the definitive cellular localization and co-expression, particularly in regards to the Gipr remain elusive. Here we review the current state of the literature and evaluate the identity of Glp1r, Glp2r, and Gipr expressing cells within preclinical and clinical models. Further elaboration of our understanding of the initiating G-protein coupled receptor (GPCR) circuits engaged locally within the intestine and how they become altered with high-fat diet feeding can offer insight into the dysregulation observed in obesity and diabetes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".