SGLT-2 Inhibitors and Cardiovascular Protection: Lessons and Gaps in Understanding the Current Outcome Trials and Possible Benefits of Combining SGLT-2 Inhibitors With GLP-1 Agonists
Bibliographic record
Abstract
Landmark trials on diabetes control have shown variable results in terms of cardiovascular benefits, with the majority showing a favorable effect of glycemic control on microvascular and, more recently, macrovascular complications. However, some trials pointed out a CV hazard with tight diabetes mellitus (DM) control. Most of those trials were assessing the impact of glycemic control, more than evaluating the effect of a certain medication. In the last decade, food and drugs administration (FDA) has mandated that all new hypoglycemic agents run a CV outcome trial (CVOT) for safety in order to grant and sustain approval. The most stunning results came from relatively new agents in the field of diabetes management, sodium-glucose cotransporter-2 inhibitors (SGLT2i) and the glucagon-like peptide-1 agonists (GLP-1 agonists), details of these CVOTs will be addressed later in this document. SGLT2i effect on the cardiovascular system remains an area of extensive research. We aimed in this review to summarize what is the current evidence of cardiovascular protection upon using SGLT2i. Moreover, we wanted to raise a point that may be strongly adopted in the future, combining SGLT2i plus GLP-1 agonists, having a cardiovascular privilege in both molecules.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".