Sodium-glucose cotransporter 2 inhibition in non-diabetic kidney disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have proven cardiorenal protection in patients with diabetes and chronic kidney disease (CKD) as seen in cardiovascular outcome trials (CVOTs) and CREDENCE. In this review, we aim to discuss the mechanisms of kidney protection with SGLT2 inhibition as well as review the results of multiple translational studies and clinical trials of SGLT2 inhibition in the nondiabetic kidney disease (non-DKD) population. RECENT FINDINGS: The application of SGLT2 inhibitors as dedicated kidney-protective agents continues to evolve with the publication of the dapagliflozin in patients with chronic kidney disease (DAPA CKD) trial, which extends their cardiorenal protection to patients with nondiabetic CKD. This trial was preceded by CREDENCE, a dedicated kidney outcome study in participants with DKD that demonstrated a 30% reduction in the risk of the composite kidney outcome. From a physiological perspective, mechanistic benefits of SGLT2 inhibitors are independent of their glucose-lowering effects as demonstrated in preclinical studies and post hoc analyses of dedicated CVOTs in participants with type 2 diabetes. From a clinical perspective, there is a growing body of evidence for kidney protection in nondiabetes mellitus patients. SUMMARY: There exists strong rationale for SGLT2 inhibition to be incorporated into standard of care for appropriate groups of patients with nondiabetic kidney disease.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".