Novel GLP-1 Analog Supaglutide Stimulates Insulin Secretion in Mouse and Human Islet Beta-Cells and Improves Glucose Homeostasis in Diabetic Mice
Bibliographic record
Abstract
Glucagon-like peptide-1(GLP-1), an incretin hormone plays an important role in regulating glucose homeostasis. The therapeutic use of native GLP-1 is inadequate due to its short in vivo half-life. We recently developed a novel GLP-1 mimetics supaglutide, and demonstrated that this formulation retained native GLP-1 biological activities and possessed long-lasting GLP-1 actions. In this study, we further examined its abilities in regulating blood glucose in diabetic mice. We found that supaglutide stimulated insulin secretion in both mouse and human islets in a dose-dependent fashion. Oral glucose tolerance test conducted in normal ICR mice showed that supaglutide significantly decreased postprandial glucose excursions in a dose-dependent fashion. In type 2 diabetic db/db mice, a single-dose injection of supaglutide significantly decreased blood glucose levels, and this efficacy was lasted for at least 72 h in a dose-dependent fashion. During a 4-week intervention course supaglutide (twice injections per week) dose-dependently and significantly decreased fasting and random blood glucose levels in hyperglycemic db/db mice. Supaglutide, at a dose of 1.2 mg/kg, significantly reduced serum fructosamine levels. This was associated with significant enlargement of beta-cell mass, increased pancreatic insulin content, and increased plasma insulin level. Notably, during the intervention course supaglutide significantly reduced body-weight gain in these obese diabetic mice, associated with reduced fat mass (but not the lean mass), improved lipid profile, i.e., declined serum triglyceride, and free fatty acid levels compared to the placebo control. These finding reveals that supaglutide exerts beneficial effects in regulating blood glucose and lipid homeostasis in diabetic db/db mice.
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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".