Differential Antidiabetic Efficacy of Incretin Agonists Versus DPP-4 Inhibition in High Fat–Fed Mice
Bibliographic record
Abstract
OBJECTIVE: We examined whether chronic administration of a glucagon-like peptide 1 (GLP-1) receptor agonist exendin-4 (Ex-4), a glucose-dependent insulinotropic polypeptide (GIP) receptor agonist D-Ala(2)-GIP (DA-GIP), or a dipeptidyl peptidase-4 (DPP-4) inhibitor (DPP-4i) des-fluoro-sitagliptin produced comparable antidiabetic actions in high fat-fed mice. RESEARCH DESIGN AND METHODS: High fat-fed mice were administered twice-daily injections of Ex-4, DA-GIP, vehicle (saline), or vehicle with the addition of des-fluoro-sitagliptin (DPP-4i) in food to produce sustained inhibition of DPP-4 activity. RESULTS AND CONCLUSIONS: Mice treated with vehicle alone or DA-GIP exhibited progressive weight gain, whereas treatment with Ex-4 or DPP-4i prevented weight gain. Although Ex-4 improved oral glucose tolerance and insulin-to-glucose ratios after an intraperitoneal glucose tolerance test (IPGTT), DPP-4i had no significant effect after IPGTT but improved glucose excursion and insulin levels after an oral glucose tolerance test. The extent of improvement in glycemic control was more sustained with continuous DPP-4 inhibition, as evidenced by loss of glucose control evident 9 h after peptide administration and a significant reduction in A1C observed with DPP-4i but not with DA-GIP or Ex-4 therapy. DA-GIP, but not Ex-4 or DPP-4i, was associated with impairment in insulin sensitivity and increased levels of plasma leptin and resistin. Although none of the therapies increased beta-cell mass, only Ex-4-treated mice exhibited increased pancreatic mRNA transcripts for Irs2, Egfr, and Gck. These findings highlight significant differences between pharmacological administration of incretin receptor agonists and potentiation of endogenous GLP-1 and GIP via DPP-4 inhibition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".