Anti-Oxidative Effect of Dapagliflozin, a Selective Sodium Glucose Transporter-2 Inhibitor, for Cardio-Renal Protection in Patients With Heart Failure With Reduced Ejection Fraction
Bibliographic record
Abstract
Background: Selective sodium glucose transporter-2 inhibitor (SGLT2i) has cardio-renal protective effects via osmotic diuresis and natriuresis, and other pleiotropic effects, such as anti-oxidative, anti-fibrotic, and anti-senescence effects, have been suggested. However, those pleiotropic effects have not yet been fully elucidated in a clinical study. Methods: We investigated the effects of SGLT2i in patients with heart failure with reduced ejection fraction (HFrEF). Twenty-five HFrEF patients who were initially treated with dapagliflozin from 2021 to 2023 at Fukuoka University Hospital were enrolled and we investigated their baseline characteristics, medications, clinical laboratory examination findings, echocardiography findings, and additional pleiotropic serum markers before administration of dapagliflozin and 6 months later. Results: . Only four patients (16.0%) had diabetes mellitus. With regard to medications, 64.0%, 76.0%, and 60.0% were already taking renin-angiotensin aldosterone system inhibitors, beta-blockers, and mineralocorticoid receptor antagonists, respectively, and these medications did not change significantly for 6 months. After treatment with dapagliflozin for 6 months, serum brain natriuretic peptide, left ventricular ejective function, hemoglobin, and urinary N-acetyl-β-D-glycosaminidase were significantly improved. In addition, high-sensitivity C-reactive protein and oxidative stress markers including myeloperoxidase, matrix metalloproteinase-1, and matrix metalloproteinase-9 significantly improved, while anti-fibrosis and anti-senescence markers did not. Conclusions: Dapagliflozin had anti-oxidative effects in patients with HFrEF, in addition to cardio-renal protective effects. These anti-oxidative effects could be related to the cardio-renal protective effects of SGLT2i, even in a clinical setting.
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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 | 0.000 |
| 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".