Making a case for the combined use of SGLT2 inhibitors and GLP1 receptor agonists for cardiorenal protection
Bibliographic record
Abstract
Sodium glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RA) were initially approved to improve glycemic control in the treatment of type 2 diabetes. Clinical trials have also demonstrated beneficial effects with regards to cardiovascular and renal parameters. Beyond improving glycemic control, these therapies promote weight loss and lower blood pressure when used individually, and in an additive manner when used together. Accordingly, taking advantage of complementary mechanisms of action with the combined use of these two classes of agents to further improve cardiorenal outcomes is conceptually appealing, but has yet to be explored in detail in clinical trials. In this review, we discuss proposed mechanisms for renal protection, clinical benefits, and adverse events associated with the individual and combined use of SGLT2 inhibitors and GLP-1RA. The management of type 2 diabetes has significantly changed over the last few years, moving away from solely glycemic control towards the concurrent management of associated comorbidities in a patient population at significant risk of cardiovascular disease and progression of chronic kidney disease. It is from this perspective that we seek to outline the rationale for the sequential and/or combined use of SGLT2 inhibitors and GLP-1RA in patients with type 2 diabetes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".