Role of SGLT-2 Inhibitors in Ultrafiltration Failure in Peritoneal Dialysis: A Narrative Review
Bibliographic record
Abstract
Purpose of review: Sodium-glucose co-transporter-2 (SGLT-2) inhibitors are glucose lowering agents with protective effects on cardiovascular health and the ability to slow chronic kidney disease (CKD) progression. The benefits of SGLT-2 inhibitors have not been studied in patients with advanced CKD or on maintenance dialysis. Ultrafiltration failure is a common reason for failure of peritoneal dialysis (PD). Glucose transporters, such as SGLT-2, are involved in the progression to ultrafiltration failure, and hence, SGLT-2 inhibitors might be beneficial in patients on PD to prevent ultrafiltration failure. Source of information: Here, we review data from animal models and ongoing clinical trials of SGLT-2 inhibitors in advanced CKD, as well as considerations for a phase III trial in patients on PD. Methods: A literature search was conducted and information on clinical trials was obtained from clinicaltrials.gov. Key findings: Animal models of PD have shown upregulation of glucose transporters in the peritoneal membrane and a potential effect of SGLT-2 inhibitors on glucose absorption and ultrafiltration. Several clinical trials are currently ongoing with SGLT-2 inhibitors in patients on PD. We discuss their study designs and propose a mixed-methods, patient-centered approach to studying SGLT-2 inhibitors in PD patients. We also discuss the potential implications of SGLT-2 inhibitors on people living with kidney failure, especially in remote communities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".