268-OR: The Combined Use of SGLT2 Inhibitors and GLP-1 Receptor Agonists on the Risk of Cardiovascular Events among Patients with Type 2 Diabetes
Bibliographic record
Abstract
Background: While sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are known to have cardiovascular benefits when used individually, it remains unknown whether their combined use is associated with further reductions in cardiovascular events in the real-world setting. Objective: To determine whether the SGLT2 inhibitor-GLP-1 RA combination is associated with a decreased risk of major adverse cardiovascular events (MACE) compared with SGLT-2 inhibitors alone. Methods: Using the United Kingdom Clinical Practice Research Datalink linked with the Hospital Episode Statistics and Office for National Statistics databases, we identified all patients, >18 years of age, who initiated an SGLT2 inhibitor from 2013 to 2020. Using a prevalent new-user design, we matched 8980 patients initiating an SGLT2 inhibitor-GLP-1 RA combination, in a 1:1 ratio, with patients using SGLT2 inhibitors alone on time-conditional propensity scores. The primary outcome was MACE (myocardial infarction, ischemic stroke, and cardiovascular mortality). Secondary outcomes were the individual components of MACE and heart failure. Patients were followed using an on-treatment approach and Cox proportional hazards regression models were fit to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for each outcome. Results: Compared with SGLT2 inhibitors alone, the SGLT2 inhibitor-GLP-1 RA combination was associated with a 34% decreased risk of MACE (HR: 0.66, 95% CI: 0.48-0.91). In secondary analyses, the combination was associated with a decreased risk of myocardial infarction (HR: 0.64, 95 CI: 0.42-0.98), while the other outcomes generated more modest effects with CIs including the null. Conclusions: The results of this real-world study suggest that the SGLT2 inhibitor-GLP-1 RA combination is associated with further reductions in the risk of MACE, compared with the SGLT2 inhibitors alone. Disclosure L.Azoulay: Advisory Panel; Pfizer Inc., Consultant; Pfizer Inc., Speaker's Bureau; Roche Diagnostics. N.Simms-williams: None. H.Yin: None. S.Lu: None. N.Treves: None. O.Yu: Consultant; Novo Nordisk. Funding Canadian Institutes of Health Research (FDN143328)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".