Dipeptidyl peptidase‐4 inhibition improves cardiac function in experimental myocardial infarction: Role of stromal cell‐derived factor‐1<i>α</i>
Bibliographic record
Abstract
BACKGROUND: In addition to degrading glucagon-like peptide-1 (GLP-1), dipeptidyl peptidase-4 (DPP-4) inactivates several chemokines, including stromal cell-derived factor-1α (SDF-1α), a pro-angiogenic and cardiomyocyte protective protein. We hypothesized that DPP-4 inhibition may confer benefit following myocardial infarction (MI) in the diabetic setting as a consequence of enhanced SDF-1α availability rather than potentiating GLP-1. To test this we compared the effects of saxagliptin with those of liraglutide and used the SDF-1α receptor (CXCR4) antagonist plerixafor. METHODS: Studies were conducted in streptozotocin-diabetic rats. Rats were randomized to receive saxagliptin (10 mg/kg per day), liraglutide (0.2 mg/kg, s.c., b.i.d.), plerixafor (1 mg/kg per day, s.c.), saxagliptin plus plerixafor or vehicle (1% phosphate-buffered saline). Two weeks later, rats underwent experimental MI, with cardiac function examined 4 weeks after MI. RESULTS: Glycemic control and MI size were similar in all groups. Four weeks after MI, mortality was reduced in saxagliptin-treated rats compared with vehicle treatment (P < 0.05). Furthermore, rats receiving saxagliptin had improved cardiac function compared with vehicle-treated rats (P < 0.05). Antagonism of CXCR4 prevented the improvement in cardiac function in saxagliptin-treated rats and was associated with increased mortality (P < 0.05). CONCLUSION: Saxagliptin-mediated DPP-4 inhibition, but not liraglutide-mediated GLP-1R agonism, improved cardiac function after MI independent of glucose lowering. These findings suggest that non-GLP-1 actions of DPP-4 inhibition, such as SDF-1α potentiation, mediate biological effects.
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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 | 0.000 |
| 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".