Sodium-glucose cotransporter 2 inhibitors in chronic kidney disease: A review of current evidence and clinical implications
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a progressive condition affecting millions worldwide, leading to substantial morbidity, mortality, and healthcare burden. While traditional treatments such as angiotensin-converting enzyme (ACE) inhibitors and angiotensin receptor blockers (ARBs) have been the cornerstone of CKD management, newer therapeutic approaches are needed to slow disease progression and improve outcomes. Sodium-glucose cotransporter 2 (SGLT2) inhibitors, initially developed as antihyperglycemic agents, have demonstrated significant renoprotective and cardioprotective effects beyond glucose control. This review aims to evaluate the current evidence on the efficacy, safety, and clinical implications of SGLT2 inhibitors in CKD, highlighting their mechanisms of action, benefits, limitations, and future research directions. A comprehensive literature search was conducted in PubMed, Google Scholar, and Medline using keywords related to SGLT2 inhibitors, CKD, and renal outcomes with no time limit. Studies included randomized controlled trials, cohort studies, and case-control studies examining the effects of SGLT2 inhibitors on renal and cardiovascular outcomes in CKD patients. The risk of bias was assessed using standard tools such as the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool. Clinical trials have demonstrated that SGLT2 inhibitors, including empagliflozin, canagliflozin, dapagliflozin, and ertugliflozin, significantly reduce CKD progression, lower albuminuria, and decrease the risk of cardiovascular events and all-cause mortality. These effects are observed in both diabetic and non-diabetic populations. Additionally, SGLT2 inhibitors exhibit renoprotective mechanisms such as reducing glomerular hyperfiltration, modulating tubuloglomerular feedback, and exerting anti-inflammatory and antifibrotic properties. However, potential adverse effects, including an initial decline in estimated glomerular filtration rate (eGFR), an increased risk of euglycemic diabetic ketoacidosis, and urinary tract infections, necessitate careful patient selection and monitoring. Emerging studies also explore the role of machine learning in optimizing SGLT2 inhibitor use for personalized treatment approaches. SGLT2 inhibitors have emerged as a transformative addition to CKD management, offering substantial renal and cardiovascular benefits. Despite safety concerns, their advantages outweigh the risks, warranting broader clinical implementation. Future research should focus on refining patient selection, optimizing treatment combinations, and leveraging data science to enhance therapeutic outcomes in CKD patients. • This paper analyzes SGLT2 inhibitors’ efficacy and safety in managing chronic kidney disease (CKD). • Highlights dual benefits of SGLT2 inhibitors in slowing CKD and improving cardiovascular outcomes. • Discusses SGLT2 inhibitors’ renoprotective effects, including reduced hyperfiltration and inflammation. • Examines risks like initial eGFR decline, diabetic ketoacidosis, and urinary tract infections. • Explores machine learning to optimize SGLT2 inhibitor use through personalized treatment and drug discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".