Lower Risk of CV Events and Death Associated with Initiation of SGLT2 vs. DPP-4 Inhibitors—Analysis from the CVD-REAL 2 Study
Bibliographic record
Abstract
DPP-4 inhibitors and SGLT2 inhibitors are widely used in T2D. Clinical trials demonstrated lower risk of CV events with SGLT2i, and a neutral effect on CV events with DPP-4i. However, large comparative studies are lacking. We compared the risk of death, hospitalization for heart failure (HHF), MI and stroke in patients starting the SGLT2i dapagliflozin (DAPA) vs. any DPP-4i, using real world data from S. Korea, Japan, Israel, and Canada. Patients initiating DAPA or DPP-4i were identified via national registries, claims, and medical records. Propensity scores for SGLT2i initiation were developed in each country, with 1:1 matching. Hazard ratios were assessed by country and pooled using weighted meta-analysis, with an intent-to-treat approach. In total, 128,066 patients were included (mean age 55 years, 46% women, 25% with history of CVD). Post-match, baseline characteristics were balanced across matched groups. Initiation of DAPA vs. DPP-4i was associated with significantly lower risk of death, HHF, MI and stroke (Figure 1). In a large cohort of T2D patients seen in clinical practice across 4 countries, 75% without established CVD, initiation of DAPA was associated with lower risk of CV events (including stroke) and death compared with DPP-4i. Disclosure S. Kohsaka: Research Support; Self; Bayer Yakuhin, Ltd.. Speaker's Bureau; Self; Bayer Yakuhin, Ltd.. Research Support; Self; Daiichi Sankyo Company, Limited. Speaker's Bureau; Self; Bristol-Myers Squibb Company. C.S. Lam: Research Support; Self; Boston Scientific Corporation, Bayer AG, Thermofisher, Medtronic, Vifor Pharma. Consultant; Self; Bayer AG, Novartis AG, AstraZeneca, Janssen Research & Development, Menarini Group, Abbott, Roche Diagnostics Corporation, Boehringer Ingelheim GmbH, Merck & Co., Inc.. D. Kim: None. A. Karasik: Stock/Shareholder; Self; Novo Nordisk A/S. Research Support; Self; Novo Nordisk A/S. Advisory Panel; Self; Novo Nordisk A/S, AstraZeneca. Research Support; Self; AstraZeneca. Consultant; Self; Boehringer Ingelheim GmbH. Advisory Panel; Self; Merck & Co., Inc., GlucoMe. N. Tangri: Research Support; Self; AstraZeneca. Advisory Panel; Self; AstraZeneca, Otsuka Holdings Co., Ltd. S. Goh: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim GmbH, Amgen Inc., Novo Nordisk A/S, Sanofi-Aventis, Servier. M. Thuresson: Consultant; Self; AstraZeneca. H. Chen: Employee; Self; AstraZeneca. F. Surmont: Employee; Self; AstraZeneca. N. Hammar: Employee; Self; AstraZeneca. Stock/Shareholder; Self; AstraZeneca. P. Fenici: Employee; Self; AstraZeneca. M. Kosiborod: Advisory Panel; Self; AstraZeneca. Consultant; Self; AstraZeneca, Amgen Inc., Sanofi, Boehringer Ingelheim GmbH, GlaxoSmithKline plc., Glytec Systems, Merck & Co., Inc., Novo Nordisk A/S, Janssen Pharmaceuticals, Inc., ZS Pharma, Inc., Intarcia Therapeutics, Inc., Novartis AG. Research Support; Self; AstraZeneca, Boehringer Ingelheim GmbH.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.026 |
| 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 | 0.003 |
| 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".