Physiological and Pharmacological Mechanisms through which the DPP-4 Inhibitor Sitagliptin Regulates Glycemia in Mice
Bibliographic record
Abstract
Inhibition of dipeptidyl peptidase-4 (DPP-4) activity improves glucose homeostasis through a mode of action related to the stabilization of the active forms of DPP-4-sensitive hormones such as the incretins that enhance glucose-induced insulin secretion. However, the DPP-4 enzyme is highly expressed on the surface of intestinal epithelial cells; hence, the role of intestinal vs. systemic DPP-4 remains unclear. To analyze mechanisms through which the DPP-4 inhibitor sitagliptin regulates glycemia in mice, we administered low oral doses of the DPP-4 inhibitor sitagliptin that selectively reduced DPP-4 activity in the intestine. Glp1r(-/-) and Gipr(-/-) mice were studied and glucagon-like peptide (GLP)-1 receptor (GLP-1R) signaling was blocked by an i.v. infusion of the corresponding receptor antagonist exendin (9-39). The role of the dipeptides His-Ala and Tyr-Ala as DPP-4-generated GLP-1 and glucose-dependent insulinotropic peptide (GIP) degradation products was studied in vivo and in vitro on isolated islets. We demonstrate that very low doses of oral sitagliptin improve glucose tolerance and plasma insulin levels with selective reduction of intestinal but not systemic DPP-4 activity. The glucoregulatory action of sitagliptin was associated with increased vagus nerve activity and was diminished in wild-type mice treated with the GLP-1R antagonist exendin (9-39) and in Glp1r(-/-) and Gipr(-/-) mice. Furthermore, the dipeptides liberated from GLP-1 (His-Ala) and GIP (Tyr-Ala) deteriorated glucose tolerance, reduced insulin, and increased portal glucagon levels. The predominant mechanism through which DPP-4 inhibitors regulate glycemia involves local inhibition of intestinal DPP-4 activity, activation of incretin receptors, reduced liberation of bioactive dipeptides, and activation of the gut-to-pancreas neural axis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".