Inhibition of Dipeptidyl Peptidase IV With Sitagliptin (MK0431) Prolongs Islet Graft Survival in Streptozotocin-Induced Diabetic Mice
Bibliographic record
Abstract
OBJECTIVE: Dipeptidyl peptidase-IV (DPP-IV) inhibitors have been introduced as therapeutics for type 2 diabetes. They partially act by blocking degradation of the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), thus increasing circulating levels of active hormones. In addition to their insulinotropic actions, GLP-1 and GIP also promote beta-cell proliferation and survival, and DPP-IV inhibitors exert similar effects in rodent type 2 diabetes models. The study objective was to establish whether DPP-IV inhibitor treatment prolonged survival of transplanted islets and to determine whether positron emission tomography (PET) was appropriate for quantifying the effect of inhibition on islet mass. RESEARCH DESIGN & METHODS: Effects of the DPP-IV inhibitor MK0431 (sitagliptin) on glycemic control and functional islet mass in a streptozotocin (STZ)-induced type 1 diabetes mouse model were determined with metabolic studies and microPET imaging. RESULTS: The type 1 diabetes mouse model exhibited elevated plasma DPP-IV levels that were substantially inhibited in mice on an MK0431 diet. Residual beta-cell mass was extremely low in STZ-induced diabetic mice, and although active GLP-1 levels were increased by the MK0431 diet, there were no significant effects on glycemic control. After islet transplantation, mice fed normal diet rapidly lost their ability to regulate blood glucose, reflecting the suboptimal islet transplant. By contrast, the MK0431 group fully regulated blood glucose throughout the study, and PET imaging demonstrated a profound protective effect of MK0431 on islet graft size. CONCLUSIONS: Treatment with a DPP-IV inhibitor can prolong islet graft retention in an animal model of type 1 diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".