Growth Differentiation Factor-15 and the Effect of Dapagliflozin in Heart Failure: Insights From the DAPA-HF Trial
Bibliographic record
Abstract
BACKGROUND: Growth differentiation factor (GDF)-15, a stress-induced cytokine implicated in systemic energy homeostasis, is associated with adverse outcomes in heart failure (HF). This study evaluated the associations between baseline GDF-15 and the clinical characteristics and outcomes in patients with HF with reduced ejection fraction in the DAPA-HF (Dapagliflozin and Prevention of Adverse-outcomes in Heart Failure) trial. The effect of the sodium-glucose cotransporter-2 inhibitor dapagliflozin on circulating GDF-15 levels and the effect of dapagliflozin on clinical outcomes in relation to baseline GDF-15 concentrations were also examined. METHODS AND RESULTS: DAPA-HF was a randomized trial of dapagliflozin in patients with HF and LVEF ≤ 40%. GDF-15 was measured at baseline and 12 months. The primary outcome was the composite of worsening HF or cardiovascular death. The median baseline GDF-15 level was 1888 (IQR: 1323-2755) pg/mL. Higher GDF-15 levels were associated with older age, lower body mass index, and greater HF symptom burden. There was a stepwise increase in adjusted risk for the primary outcome across quartiles of baseline GDF-15 (adjusted hazard ratio [Q4 vs Q1] 2.30, 95% CI 1.66-18; P trend < 0.001). Dapagliflozin did not significantly change GDF-15 concentrations over 12 months, compared to placebo (placebo-corrected relative change +4%; 95% CI, -2% to +10%). The relative effect of dapagliflozin on the primary outcome was consistent across GDF-15 quartiles (P interaction = 0.96), with a greater absolute benefit in those with higher GDF-15 (P trend < 0.01). CONCLUSIONS: In DAPA-HF, GDF-15 was independently prognostic of worsening HF or cardiovascular death. Absolute risk reduction with dapagliflozin was greater in patients with higher baseline GDF-15, but the benefit was not associated with an effect on GDF-15 itself.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".