Prescribing SGLT2 Inhibitors in Patients With CKD: Expanding Indications and Practical Considerations
Bibliographic record
Abstract
SGLT2 inhibitors have emerged as a key disease-modifying therapy to prevent the progression of chronic kidney disease (CKD). These agents prevent decline in kidney function through reduction in glomerular hypertension mediated through tubuloglomerular feedback independent of their effect on glycemic control. The proliferation of clinical trials on SGLT2 inhibitors has rapidly expanded the approved clinical indications for these agents beyond patients with diabetes mellitus (DM). We review the current indications for SGLT2 inhibitors in patients with and without diabetic kidney disease, including new evidence for use in patients with heart failure with or without reduced ejection fraction, stage 4 CKD, and chronic glomerulonephritis. The EMPA-KIDNEY trial was recently stopped early for efficacy suggesting that SGLT2 inhibitors may soon be indicated for patients with CKD without albuminuria. We review practical considerations for prescription of SGLT2 inhibitors, including the anticipated acute decline in estimated glomerular filtration rate (eGFR) on initiation, initiating the lowest dosage used in clinical trials, volume status considerations, and adverse event mitigation. Combination therapy in patients with DM may be considered with agents, including glucagon-like peptide-1 receptor agonists (GLP-1-RAs), novel mineralocorticoid receptor antagonists, and selective endothelin receptor antagonists to reduce residual albuminuria and cardiovascular risk.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".