Human islets contain a subpopulation of glucagon-like peptide-1 secreting α cells that is increased in type 2 diabetes
Bibliographic record
Abstract
OBJECTIVES: Our study shows that glucagon-like peptide-1 (GLP-1) is secreted within human islets and may play an unexpectedly important paracrine role in islet physiology and pathophysiology. It is known that α cells within rodent and human pancreatic islets are capable of secreting GLP-1, but little is known about the functional role that islet-derived GLP-1 plays in human islets. METHODS: We used flow cytometry, immunohistochemistry, perifusions, and calcium imaging techniques to analyse GLP-1 expression and function in islets isolated from cadaveric human donors with or without type 2 diabetes. We also used immunohistochemistry to analyse GLP-1 expression within islets from pancreatic biopsies obtained from living donors. RESULTS: We have demonstrated that human islets secrete ∼50-fold more GLP-1 than murine islets and that ∼40% of the total human α cells contain GLP-1. Our results also confirm that dipeptidyl peptidase-4 (DPP4) is expressed in α cells. Sitagliptin increased GLP-1 secretion from cultured human islets but did not enhance glucose-stimulated insulin secretion (GSIS) in islets from non-diabetic (ND) or type 2 diabetic (T2D) donors, suggesting that β cell GLP-1 receptors (GLP-1R) may already be maximally activated. Therefore, we tested the effects of exendin-9, a GLP-1R antagonist. Exendin-9 was shown to reduce GSIS by 39% and 61% in ND islets and T2D islets, respectively. We also observed significantly more GLP-1+ α cells in T2D islets compared with ND islets obtained from cadaveric donors. Furthermore, GLP-1+ α cells were also identified in pancreatic islet sections obtained from living donors undergoing surgery. CONCLUSIONS: In summary, we demonstrated that human islets secrete robust amounts of GLP-1 from an α cell subpopulation and that GLP-1R signalling may support GSIS to a greater extent in T2D islets.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".