Glucagon-Like Peptide 1 Receptor Agonists Versus Sodium-Glucose Cotransporter 2 Inhibitors for Atherosclerotic Cardiovascular Disease in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Beyond improving hemoglobin A1c (HbA1c) in adults with type 2 diabetes, glucagon-like peptide 1 receptor agonists (GLP-1RA) have been approved for reducing risk of major adverse cardiovascular events (MACE) with established cardiovascular disease (CVD) or multiple CV risk factors. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) also reduced the risk for the primary composite CV outcome in patients with type 2 diabetes at high risk for CV events. In the American Diabetes Association (ADA) and European Association of Study in Diabetes (EASD) consensus report 2022, there is the description "In people with established atherosclerotic CVD (ASCVD) or with a high risk for ASCVD, GLP-1RA were prioritized over SGLT2i"; however, the evidence supporting such statement is limited. Therefore, we studied the superiority of GLP-1RA over SGLT2i for prevention of ASCVD from various viewpoints. We could not find a meaningful difference in the risk reduction in three-point MACE (3P-MACE), mortality due to any cause, mortality due to CV cause and nonfatal myocardial infarction between GLP-1RA and SGLT2i trials. The risk of nonfatal stroke decreased in all five GLP-1RA trials; however, two of three SGLT2i trials showed an increase in risk of nonfatal stroke. The risk of hospitalization for heart failure (HHF) decreased in all three SGLT2i trials, and one GLP-1RA trial showed an increase in risk of HHF. The risk reduction of HHF in SGLT2i trials was greater than that in GLP-1RA trials. These findings were consistent with current systematic reviews and meta-analyses. The risk reduction of 3P-MACE was significantly and negatively correlated with changes in HbA1c (R = -0.861, P = 0.006) and body weight (R = -0.895, P = 0.003) in GLP-1RA and SGLT2i trials. The studies using SGLT2i failed to reduce carotid intima media thickness (cIMT), the surrogate marker for atherosclerosis; however, several studies using GLP-1RA successfully reduced cIMT in patients with type 2 diabetes. Compared with SGLT2i, GLP-1RA had a higher probability of decreasing serum triglyceride. GLP-1RA have multiple vascular biological anti-atherogenic properties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".