P1266Empagliflozin improves left ventricular diastolic function in db/db mice without altering cardiac expression of enzymes relevant for ketone body or branched chain amino acid catabolism
Bibliographic record
Abstract
Background: Empagliflozin dependent SGLT2-Inhibition was recently found to reduce cardiovascular mortality and heart failure hospitalization in patients with type 2 diabetes in the EMPAREG-OUTCOME trial. The relevant mechanisms for this favorable effect remain poorly understood but might relate to a switch of cardiac substrate utilization toward oxidation of ketone and branched chain amino acid catabolism. Methods and results: To investigate the effects of Empagliflozin on cardiac function and substrate utilization in diabetes we treated db/db mice (n=13/group; 6 weeks of age) on a high fat diet with or without Empagliflozin (150 mg/kg diet) and compared these to heterozygote db/wt controls (n=12/group). Empagliflozin significantly increased urinary glucose excretion (534.6 mmol/L vs 409.5 mmol/L vs.; p<0.01) and reduced fasting blood glucose levels (99.2 mg/dl vs 168.3 mg/dl; p<0.001), while not affecting body weight. After a period of 4.5 weeks 6 control db/db mice died, but none of the Empagliflozin treated db/db or heterozygote db/wt controls. This was accompanied by improved left ventricular diastolic function of Empagliflozin treated db/db vs. db/db control mice during a dobutamine stress test (dp/dt min: -7560.01 mmHg/s vs -3947 mmHg/s; p<0.05). At a molecular level Empagliflozin significantly increased cardiac Akt-phosphorylation as an indicator of insulin sensitivity and augmented cardiac expression of the fatty acid transporter CD36. The rate limiting enzymes of ketone body (BDH-1, SCOT) or branched chain amino acid catabolism (BCKDHA-P) were not altered by Empagliflozin treatment, despite BDH-1 being significantly reduced in db/db relative to heterozygote db/wt control mice.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".