Dipeptidyl Peptidase IV-Resistant [<scp>d</scp>-Ala2]Glucose-Dependent Insulinotropic Polypeptide (GIP) Improves Glucose Tolerance in Normal and Obese Diabetic Rats
Bibliographic record
Abstract
The therapeutic potential of glucose-dependent insulinotropic polypeptide (GIP) for improving glycemic control has largely gone unstudied. A series of synthetic GIP peptides modified at the NH(2)-terminus were screened in vitro for resistance to dipeptidyl peptidase IV (DP IV) degradation and potency to stimulate cyclic AMP and affinity for the transfected rat GIP receptor. In vitro experiments indicated that [D-Ala(2)]GIP possessed the greatest resistance to enzymatic degradation, combined with minimal effects on efficacy at the receptor. Thus, [D-Ala(2)]GIP(1--42) was selected for further testing in the perfused rat pancreas and bioassay in conscious Wistar and Zucker rats. When injected subcutaneously in normal Wistar, Fa/?, or fa/fa Vancouver Diabetic Fatty (VDF) Zucker rats, both GIP and [D-Ala(2)]GIP significantly reduced glycemic excursions during a concurrent oral glucose tolerance test via stimulation of insulin release. The latter peptide displayed greater in vivo effectiveness, likely because of resistance to enzymatic degradation. Hence, despite reduced bioactivity in diabetic models at physiological concentrations, GIP and analogs with improved plasma stability still improve glucose tolerance when given in supraphysiological doses, and thus may prove useful in the treatment of diabetic states.
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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) | 0.001 | 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".