Revisiting the Complexity of GLP-1 Action from Sites of Synthesis to Receptor Activation
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) is produced in gut endocrine cells and in the brain, and acts through hormonal and neural pathways to regulate islet function, satiety, and gut motility, supporting development of GLP-1 receptor (GLP-1R) agonists for the treatment of diabetes and obesity. Classic notions of GLP-1 acting as a meal-stimulated hormone from the distal gut are challenged by data supporting production of GLP-1 in the endocrine pancreas, and by the importance of brain-derived GLP-1 in the control of neural activity. Moreover, attribution of direct vs indirect actions of GLP-1 is difficult, as many tissue and cellular targets of GLP-1 action do not exhibit robust or detectable GLP-1R expression. Furthermore, reliable detection of the GLP-1R is technically challenging, highly method dependent, and subject to misinterpretation. Here we revisit the actions of GLP-1, scrutinizing key concepts supporting gut vs extra-intestinal GLP-1 synthesis and secretion. We discuss new insights refining cellular localization of GLP-1R expression and integrate recent data to refine our understanding of how and where GLP-1 acts to control inflammation, cardiovascular function, islet hormone secretion, gastric emptying, appetite, and body weight. These findings update our knowledge of cell types and mechanisms linking endogenous vs pharmacological GLP-1 action to activation of the canonical GLP-1R, and the control of metabolic activity in multiple organs.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".