Incretin Action in the Pancreas: Potential Promise, Possible Perils, and Pathological Pitfalls
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are incretin hormones that control the secretion of insulin, glucagon, and somatostatin to facilitate glucose disposal. The actions of incretin hormones are terminated via enzymatic cleavage by dipeptidyl peptidase-4 (DPP-4) and through renal clearance. GLP-1 and GIP promote β-cell proliferation and survival in rodents. DPP-4 inhibitors expand β-cell mass, reduce α-cell mass, and inhibit glucagon secretion in preclinical studies; however, whether incretin-based therapies sustain functional β-cell mass in human diabetic subjects remains unclear. GLP-1 and GIP exert their actions predominantly through unique G protein-coupled receptors expressed on β-cells and other pancreatic cell types. Accurate localization of incretin receptor expression in pancreatic ductal or acinar cells in normal or diabetic human pancreas is challenging because antisera used for detection of the GLP-1 receptor often are neither sufficiently sensitive nor specific to yield reliable data. This article reviews recent advances and controversies in incretin hormone action in the pancreas and contrasts established mechanisms with areas of uncertainty. Furthermore, methodological challenges and pitfalls are highlighted and key areas requiring additional scientific investigation are outlined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".