Glucagon receptor antagonist upregulates circulating GLP-1 level by promoting intestinal L-cell proliferation and GLP-1 production in type 2 diabetes
Bibliographic record
Abstract
OBJECTIVE: Glucagon receptor (GCGR) blockage improves glycemic control and increases circulating glucagon-like peptide-1 (GLP-1) level in diabetic animals and humans. The elevated GLP-1 has been reported to be involved in the hypoglycemic effect of GCGR blockage. However, the source of this elevation remains to be clarified. RESEARCH DESIGN AND METHODS: mice and high-fat diet+streptozotocin (HFD/STZ)-induced type 2 diabetic (T2D) mice. Blood glucose, glucose tolerance and plasma GLP-1 were evaluated during the treatment. The gut length, epithelial area, and L-cell number and proliferation were detected after the mice were sacrificed. Cell proliferation and GLP-1 production were measured in mouse L-cell line GLUTag cells, and primary mouse and human enterocytes. Moreover, GLP-1 receptor (GLP-1R) antagonist or protein kinase A (PKA) inhibitor was used in GLUTag cells to determine the involved signaling pathways. RESULTS: and HFD/STZ-induced T2D mice. Besides, the treatment promoted L-cell proliferation and LK-cell expansion, and increased the gut length, epithelial area and L-cell number in these two T2D mice. Similarly, our in vitro study showed that the GCGR mAb promoted L-cell proliferation and increased GLP-1 production in GLUTag cells, and primary mouse and human enterocytes. Furthermore, either GLP-1R antagonist or PKA inhibitor diminished the effects of GCGR mAb on L-cell proliferation and GLP-1 production. CONCLUSIONS: The elevated circulating GLP-1 level by GCGR mAb is mainly due to intestinal L-cell proliferation and GLP-1 production, which may be mediated via GLP-1R/PKA signaling pathways. Therefore, GCGR mAb represents a promising strategy to improve glycemic control and restore the impaired GLP-1 production in T2D.
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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) | 0.002 | 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".