SGLT-2 inhibitors improve cardiovascular and renal outcomes in patients with CKD: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract The effect of sodium-glucose co-transporter-2 (SGLT-2) inhibitors on cardiovascular and renal outcomes has not been systematically reviewed across baseline kidney function groups. We conducted a systematic review and meta-analysis of randomized control trials (RCTs) with SGLT-2 inhibitors in patients with and without CKD. We performed a PubMed/Medline search of randomized, placebo-controlled, event-driven outcome trials of SGLT-2 inhibitors versus active or placebo control in patients with and without diabetes from inception to November 2022. CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m 2 (PROSPERO registration CRD4202016054). The primary outcome was cardiovascular death. Secondary outcomes included hospitalization for heart failure, major adverse cardiovascular events, CKD progression, all-cause mortality, treatment discontinuation, and acute kidney injury (AKI). The relative risk (RR) was estimated using a random-effects model. Twelve RCTs were included in this meta-analysis (89,191 patients, including 38,949 with eGFR < 60 ml/min/1.73m 2 ). Use of an SGLT-2 inhibitor in patients with CKD was associated with a lower incidence of cardiovascular death (RR 0.87; 95% CI 0.79–0.95) and of heart failure (RR 0.67; 95% CI 0.61–0.75), compared with placebo. Heart failure risk reduction with SGLT-2 inhibitors was larger among patients with CKD compared with patients without CKD (RR for the interaction 0.87, 95% CI 0.75–1.02, and p-value for interaction 0.08). SGLT-2 inhibitors were associated with a lower incidence of CKD progression among patients with pre-existing CKD: RR 0.77 (95% CI 0.68–0.88), compared with placebo. Among patients with CKD, a lower risk of AKI (RR 0.82; 95% CI 0.72–0.93) and treatment discontinuation was seen with SGLT-2 inhibitors compared with placebo. SGLT-2 inhibitors offer substantial protection against cardiovascular and renal outcomes in patients with CKD. These results strongly advocate in favor of using them in patients with CKD and keeping them as kidney function declines.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".