What have we learned about renal protection from the cardiovascular outcome trials and observational analyses with SGLT2 inhibitors?
Bibliographic record
Abstract
Over the past 5 years, sodium-glucose cotransport 2 (SGLT2) inhibitors have been increasingly regarded as glycaemic agents with cardiovascular (CV) and renal protective effects. The CV benefits of SGLT2 inhibitors have been well established in patients with type 2 diabetes (T2D) and a range of CV comorbidities at baseline. Subsequently, the renal benefits of SGLT2 inhibitors were established in the CREDENCE trial, a dedicated renal outcome trial where canagliflozin reduced the primary composite renal outcome by 30%. In light of these trials, clinical practice guidelines have rapidly evolved, recommending the use of SGLT2 inhibitors as renal and cardioprotective agents in appropriate patient populations. Accordingly, it is important to have an in-depth understanding of the evidence underlying the use of SGLT2 inhibitors in patients with T2D based on published clinical trials and real-world evidence (RWE) studies, as well as information related to potential safety concerns. To accomplish this, we reviewed the evidence for renal protection and safety with SGLT2 inhibitors in the EMPA-REG OUTCOME, CANVAS Program and DECLARE-TIMI 58 CV safety trials, and in the growing body of evidence emerging from real-world studies. This body of work has shown that SGLT2 inhibitors reduce the risk of surrogate renal endpoints such as albuminuria and mitigate the risk of hard renal endpoints including doubling of serum creatinine and end-stage kidney disease in patients with T2D.
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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 | 0.329 | 0.576 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".