Sodium/glucose cotransporter 2 inhibitors in chronic kidney disease and heart failure: ready for prime time in patients without diabetes
Bibliographic record
Abstract
PURPOSE OF REVIEW: The benefits of sodium/glucose cotransporter 2 (SGLT2) inhibitors seem to extend beyond glycemic control. We review recent randomized trial evidence evaluating SGLT2 inhibition in nondiabetic settings, including in patients with chronic kidney disease (CKD) and heart failure (HF). RECENT FINDINGS: DAPA-CKD, DAPA-HF and EMPEROR-Reduced compared SGLT2 inhibitors to placebo, enrolling 5868 patients without diabetes. In DAPA-CKD, patients with an estimated glomerular filtration rate (eGFR) of 25-75 ml/min/1.73 m2 and macroalbuminuria irrespective of kidney disease aetiology had improved cardiovascular and kidney outcomes if randomized to receive SGLT2 inhibitors (primary composite endpoint: hazard ratio [HR] 0.61, 95% CI 0.51-0.72; absolute risk reduction [ARR] 5.3%). In DAPA-HF and EMPEROR-Reduced, participants with reduced ejection fraction (HFrEF) had improved cardiovascular outcomes when an SGLT2 inhibitor was added to guideline-directed medical therapy, mainly through a reduction in HF hospitalizations (HR 0.70, 95% CI 0.59-0.83; ARR 3.7% and HR 0.69, 95% CI 0.59-0.81; ARR 5.1% with dapagliflozin and empagliflozin, respectively). In all 3 trials, the benefits were not modified by diabetes, baseline eGFR or proteinuria. SUMMARY: SGLT2 inhibitors improve kidney and HF outcomes in patients with high-risk CKD and HFrEF, irrespective of diabetes. Clinicians should become more comfortable prescribing these medications as we await studies that may further broaden their indications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".