Cardiovascular, Renal, and Metabolic Outcomes of Dapagliflozin Versus Placebo in a Primary Cardiovascular Prevention Cohort: Analyses From DECLARE-TIMI 58
Bibliographic record
Abstract
OBJECTIVE International guidelines propose prescribing sodium–glucose cotransporter 2 (SGLT2) inhibitors to patients with type 2 diabetes (T2D) as secondary prevention in patients with established atherosclerotic cardiovascular disease (ASCVD) or for primary prevention of cardiovascular events in high-risk patients with multiple risk factors (MRF) for ASCVD. The current analyses expand on the cardiovascular renal and metabolic effects of SGLT2 inhibitors in MRF patients. RESEARCH DESIGN AND METHODS In DECLARE-TIMI 58, 17,160 patients with T2D and MRF (59.4%) or established ASCVD (40.6%) were randomized to dapagliflozin versus placebo; patients were followed for a median of 4.2 years. The cardiovascular and renal outcomes in the MRF cohort were studied across clinically relevant subgroups for treatment effect and subgroup-based treatment interaction. RESULTS Among patients with MRF, the reduction with dapagliflozin in risk of cardiovascular death or hospitalization for heart failure (CVD/HHF) (hazard ratio [HR] 0.84, 95% CI 0.67–1.04) and the renal-specific outcome (HR 0.51, 95% CI 0.37–0.69) did not differ from that for patients with ASCVD (Pinteraction 0.99 and 0.72, respectively). The effect on CVD/HHF was entirely driven by a reduction in HHF (HR 0.64, 95% CI 0.46–0.88). The benefits of dapagliflozin on HHF and on the renal-specific outcome, among the subset with MRF, were directionally consistent across clinically relevant subgroups. At 48 months, HbA1c, weight, systolic blood pressure, and urinary albumin–to–creatinine ratio were lower with dapagliflozin versus placebo and estimated glomerular filtration rate was higher (P < 0.001). CONCLUSIONS In patients with T2D and MRF, dapagliflozin reduced the risk of HHF and adverse renal outcomes regardless of baseline characteristics. These analyses support the benefit of dapagliflozin for important outcomes in a broad primary prevention population.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".