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 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 | 0.001 |
| 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".