Dipeptidyl peptidase-4 inhibition improves left ventricular function in chronic kidney disease
Bibliographic record
Abstract
PURPOSE: Heart failure with preserved ejection fraction (HFpEF) is a common comorbidity in people with chronic kidney disease (CKD) for which no evidence-based treatment currently exists. Recently, a group of anti-hyperglycemic agents used in the treatment of Type 2 diabetes, termed incretin-based therapies, have come under scrutiny for their putative glucose-independent effects on cardiac function. In the present study, the actions of the dipeptidyl peptidase-4 (DPP-4) inhibitor class of incretin-based therapy in preventing HFpEF induced by chronic renal impairment were investigated. METHODS: Sham-operated and subtotally-nephrectomized rats were randomized to receive the DPP-4 inhibitors, linagliptin or sitagliptin for seven weeks before assessment of cardiac and renal structure and function. RESULTS: Analysis of pressure-volume loops revealed that both linagliptin and sitagliptin prevented the development of cardiac diastolic dysfunction, with cardiac collagen I synthesis also being reduced by DPP-4 inhibition. These attenuating cardiac effects occurred without change in renal function or structure where, in the doses administered, neither linagliptin nor sitagliptin affected GFR decline, proteinuria, renal fibrosis or the increased urinary excretion of biomarkers of renal toxicity. CONCLUSION: The beneficial cardiac effects of DPP-4 inhibition, in the absence of a concurrent improvement in renal dysfunction, raise the possibility that these agents may confer cardiovascular advantages in the CKD population.
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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) | 0.000 | 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".