Novel Biological Action of the Dipeptidylpeptidase-IV Inhibitor, Sitagliptin, as a Glucagon-Like Peptide-1 Secretagogue
Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) is an incretin hormone secreted into the circulation by the intestinal L cell. The dipeptidylpeptidase-IV (DPP-IV) inhibitor, sitagliptin, prevents GLP-1 degradation and is used in the clinic to treat patients with type 2 diabetes mellitus, leading to improved glycated hemoglobin levels. When the effect of sitagliptin on GLP-1 levels was examined in neonatal streptozotocin rats, a model of type 2 diabetes mellitus, a 4.9 ± 0.9-fold increase in basal and 3.6 ± 0.4-fold increase in oral glucose-stimulated plasma levels of active GLP-1 was observed (P < 0.001), in association with a 1.5 ± 0.1-fold increase in the total number of intestinal L cells (P < 0.01). The direct effects of sitagliptin on GLP-1 secretion and L cell signaling were therefore examined in murine GLUTag (mGLUTag) and human hNCI-H716 intestinal L cells in vitro. Sitagliptin (0.1-2 μM) increased total GLP-1 secretion by mGLUTag and hNCI-H716 cells (P < 0.01-0.001). However, MK0626 (1-50 μM), a structurally unrelated inhibitor of DPP-IV, did not affect GLP-1 secretion in either model. Treatment of mGLUTag cells with the GLP-1 receptor agonist, exendin-4, did not modulate GLP-1 release, indicating the absence of feedback effects of GLP-1 on the L cell. Sitagliptin increased cAMP levels (P < 0.01) and ERK1/2 phosphorylation (P < 0.05) in both mGLUTag and hNCI-H716 cells but did not alter either intracellular calcium or phospho-Akt levels. Pretreatment of mGLUTag cells with protein kinase A (H89 and protein kinase inhibitor) or MAPK kinase-ERK1/2 (PD98059 and U0126) inhibitors prevented sitagliptin-induced GLP-1 secretion (P < 0.05-0.01). These studies demonstrate, for the first time, that sitagliptin exerts direct, DPP-IV-independent effects on intestinal L cells, activating cAMP and ERK1/2 signaling and stimulating total GLP-1 secretion.
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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) | 0.001 | 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".