Comparative Cardiovascular Outcomes of Dapagliflozin Versus Empagliflozin in Patients With Type 2 Diabetes: A Meta-Analysis
Bibliographic record
Abstract
Sodium-glucose co-transporter-2 (SGLT2) inhibitors have demonstrated significant cardiovascular benefits in patients with type 2 diabetes. However, head-to-head comparisons between dapagliflozin and empagliflozin, two widely prescribed SGLT2 inhibitors, remain limited. This meta-analysis aimed to directly compare the cardiovascular outcomes of these agents in patients with type 2 diabetes. We conducted a comprehensive literature search across multiple databases and included eight retrospective studies enrolling 280,617 patients (158,352 receiving empagliflozin and 122,265 receiving dapagliflozin). The primary outcome was major adverse cardiovascular events (MACE), with secondary outcomes including all-cause mortality, myocardial infarction, and stroke. Our pooled analysis revealed no significant difference in MACE risk between empagliflozin and dapagliflozin (RR: 1.04; 95% CI: 0.96 to 1.13). Similarly, no significant differences were observed for all-cause mortality (RR: 1.05; 95% CI: 0.96 to 1.15), myocardial infarction (RR: 1.04; 95% CI: 0.94 to 1.16), or stroke (RR: 1.00; 95% CI: 0.91 to 1.09). Subgroup analyses by gender, atherosclerotic cardiovascular disease, and chronic kidney disease status showed consistent results. However, in patients with heart failure, a trend toward reduced MACE risk was observed with empagliflozin (RR: 0.90; 95% CI: 0.82 to 1.00). Despite pharmacokinetic differences between these agents, our findings suggest comparable cardiovascular outcomes in patients with type 2 diabetes, with potentially enhanced benefits of empagliflozin in those with heart failure. However, due to lack of studies, this finding should be interpreted with caution. These results provide valuable insights for clinical decision-making when selecting SGLT2 inhibitors for cardiovascular risk reduction in diabetic patients. Further prospective studies are warranted to confirm these findings and explore potential mechanistic differences between these agents.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".