Dapagliflozin's Association With Cardiorenal Outcomes and Apolipoprotein M Levels in HFrEF Patients
Bibliographic record
Abstract
BACKGROUND: Apolipoprotein M (ApoM) is associated with lower mortality in heart failure (HF) patients and protects against cardiac and kidney injury in mice. OBJECTIVES: The authors investigated dapagliflozin's cardiorenal effects by studying its association with ApoM in patients with HF with reduced ejection fraction. METHODS: We performed a secondary analysis of DEFINE-HF (Dapagliflozin Effects on Biomarkers, Symptoms, and Functional Status in Patients with HF with Reduced Ejection Fraction) to assess dapagliflozin's effects on ApoM, N-terminal pro B-type natriuretic peptide (NT-proBNP), and urine albumin-creatinine ratio (UACR) changes from baseline to 12 weeks. RESULTS: Of 263 randomized patients, 236 had ApoM values at baseline (mean 0.641 ± 0.181 μM) and 12 weeks. Dapagliflozin did not significantly affect ApoM vs placebo. However, each 0.1 μM increase in ApoM was associated with a significant decrease in log-transformed NT-proBNP overall (β = -0.11, P = 0.006), particularly in dapagliflozin-treated patients (β = -0.19, P < 0.001; P interaction = 0.025). The inverse relationship between ApoM and NT-proBNP varied by changes in UACR. Dapagliflozin-treated patients with reduced UACR at 12 weeks (n = 53, 22%) experienced a mean NT-proBNP reduction of -0.28 per 0.1 μM increase in ApoM (P < 0.001), compared to a smaller reduction in those without UACR change (-0.07, P = 0.47). Placebo-treated patients with reduced UACR over 12 weeks did not show significant NT-proBNP changes (β = -0.17, P = 0.11). CONCLUSIONS: Dapagliflozin did not significantly alter ApoM overall; however, an inverse association between ApoM and NT-proBNP was observed in dapagliflozin-treated patients with albuminuria. While some NT-proBNP reductions were seen in the placebo group, the significant interaction with treatment allocation suggests a potential dapagliflozin-mediated effect.
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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) | 0.000 | 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) | 0.000 | 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".