Novel GLP-1 analog supaglutide improves glucose homeostasis in diabetic monkeys
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) is an insulinotropic hormone and plays an important role in regulating glucose homeostasis. GLP-1 has a short half-life (t1/2 < 2 min) due to degrading enzyme dipeptidyl peptidase-IV and rapid kidney clearance, which limits its clinical application as a therapeutic reagent. We demonstrated recently that supaglutide, a novel GLP-1 mimetic generated by recombinant fusion protein techniques, exerted hypoglycemic and β-cell trophic effects in type 2 diabetes db/db mice. In the present study, we examined supaglutide's therapeutic efficacy and pharmacokinetics in diabetic rhesus monkeys. We found that a single subcutaneous injection of supaglutide of tested doses transiently and significantly reduced blood glucose levels in a dose-dependent fashion in the diabetic monkeys. During a 4-week intervention period, treatment of supaglutide of weekly dosing dose-dependently decreased fasting and random blood glucose levels. This was associated with significantly declined plasma fructosamine levels. The repeated administration of supaglutide remarkably also decreased body weight in a dose-dependent fashion accompanied by decreased food intake. Intravenous glucose tolerance test results showed that supaglutide improved glucose tolerance. The intervention also showed enhanced glucose-stimulated insulin secretion and improved lipid profile in diabetic rhesus monkeys. These results reveal that supaglutide exerts beneficial effects in regulating blood glucose and lipid homeostasis in diabetic rhesus monkeys.
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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) | 0.001 | 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".