Stimulatory effect of imeglimin on incretin secretion
Bibliographic record
Abstract
AIMS/INTRODUCTION: Imeglimin is a new antidiabetic drug structurally related to metformin. Despite this structural similarity, only imeglimin augments glucose-stimulated insulin secretion (GSIS), with the mechanism underlying this effect remaining unclear. Given that glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) also enhance GSIS, we examined whether these incretin hormones might contribute to the pharmacological actions of imeglimin. MATERIALS AND METHODS: Blood glucose and plasma insulin, GIP, and GLP-1 concentrations were measured during an oral glucose tolerance test (OGTT) performed in C57BL/6JJcl (C57BL/6) or KK-Ay/TaJcl (KK-Ay) mice after administration of a single dose of imeglimin with or without the dipeptidyl peptidase-4 inhibitor sitagliptin or the GLP-1 receptor antagonist exendin-9. The effects of imeglimin, with or without GIP or GLP-1, on GSIS were examined in C57BL/6 mouse islets. RESULTS: Imeglimin lowered blood glucose and increased plasma insulin levels during an OGTT in both C57BL/6 and KK-Ay mice, whereas it also increased the plasma levels of GIP and GLP-1 in KK-Ay mice and the GLP-1 levels in C57BL/6 mice. The combination of imeglimin and sitagliptin increased plasma insulin and GLP-1 levels during the OGTT in KK-Ay mice to a markedly greater extent than did either drug alone. Imeglimin enhanced GSIS in an additive manner with GLP-1, but not with GIP, in mouse islets. Exendin-9 had only a minor inhibitory effect on the glucose-lowering action of imeglimin during the OGTT in KK-Ay mice. CONCLUSIONS: Our data suggest that the imeglimin-induced increase in plasma GLP-1 levels likely contributes at least in part to its stimulatory effect on insulin 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) | 0.001 | 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) | 0.002 | 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".