Empagliflozin Blunts Worsening Cardiac Dysfunction Associated With Reduced NLRP3 (Nucleotide-Binding Domain-Like Receptor Protein 3) Inflammasome Activation in Heart Failure
Bibliographic record
Abstract
Background: Although empagliflozin was shown to profoundly reduce cardiovascular events in diabetic patients and blunt the decline in cardiac function in nondiabetic mice with established heart failure (HF), the mechanism of action remains unknown. Methods and Results: We treated 2 rodent models of HF with 10 mg/kg per day empagliflozin and measured activation of the NLRP3 (nucleotide-binding domain-like receptor protein 3) inflammasome in the heart. We show for the first time that beneficial effects of empagliflozin in HF with reduced ejection fraction (HF with reduced ejection fraction [HFrEF]; n=30–34) occur in the absence of changes in circulating ketone bodies, cardiac ketone oxidation, or increased cardiac ATP production. Of note, empagliflozin attenuated activation of the NLRP3 inflammasome and expression of associated markers of sterile inflammation in hearts from mice with HFrEF, implicating reduced cardiac inflammation as a mechanism of empagliflozin that contributes to sustained function in HFrEF in the absence of diabetes mellitus. In addition, we validate that the beneficial cardiac effects of empagliflozin in HF with preserved ejection fraction (HFpEF; n=9–10) are similarly associated with reduced activation of the NLRP3 inflammasome. Lastly, the ability of empagliflozin to reduce inflammation was completely blunted by a calcium (Ca 2+ ) ionophore, suggesting that empagliflozin exerts its benefit upon restoring optimal cytoplasmic Ca 2+ levels in the heart. Conclusions: These data provide evidence that the beneficial cardiac effects of empagliflozin are associated with reduced cardiac inflammation via blunting activation of the NLRP3 inflammasome in a Ca 2+ -dependent manner and hence may be beneficial in treating HF even in the absence of diabetes mellitus.
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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) | 0.001 | 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 | 0.001 |
| 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".