Comparative Effectiveness of Sodium-Glucose Cotransporter-2 Inhibitors Versus Glucagon-Like Peptide-1 Receptor Agonists in Reducing Cardiovascular Events in Patients With Type 2 Diabetes
Bibliographic record
Abstract
One of the primary causes of death and morbidity among individuals with type 2 diabetes mellitus (T2DM) is stroke. Despite their well-established cardiovascular advantages, there is insufficient data to determine how well sodium-glucose cotransporter-2 (SGLT2) inhibitors (SGLT2i) and glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1 RAs) prevent strokes. The research conducted a systematic review and performed a meta-analysis to assess the comparative effectiveness of SGLT2i versus GLP-1 RAs for preventing stroke incidents in T2DM patients. From 2020 to 2025, the study searched PubMed, Embase, and Web of Science following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Only observational, retrospective, and cohort studies comparing the outcomes of stroke by SGLT2i and GLP-1 RA were included in the study. For risk of bias assessment Newcastle Ottawa Scale (Version 2011) was used. For the meta-analysis, random effects methodology was used to analyze the hazard ratio (HR) with 95% CIs. The study used heterogeneity analysis in conjunction with sensitivity analyses. Eleven studies (with a total of over 500,000 participants) were included. The pooled HR for stroke was 0.92 (95% CI: 0.83-1.02), suggesting that SGLT2i and GLP-1 RA did not significantly differ from one another. Low to moderate heterogeneity was present (I2 = 27.4%). Sensitivity and subgroup analyses validated the findings' robustness. SGLT2i and GLP-1 RAs provided comparable protection against stroke in patients with T2DM. These findings will help clinicians in determining a suitable drug for T2DM patients against stroke.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.022 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".