Impact of GLP-1 receptor agonist-based therapies on cardiovascular and renal outcomes in diabetic and non-diabetic patients with CKD
Bibliographic record
Abstract
BACKGROUND: The effect of glucagon-like peptide-1 (GLP-1) receptor agonists-based therapies 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 controlled trials (RCTs) with GLP-1 Receptor Agonists (RAs) in patients with and without chronic kidney disease (CKD). METHODS: We performed a PubMed/Medline search of randomized, placebo-controlled, event-driven outcome trials of GLP-1 RAs versus placebo in patients with and without diabetes from inception to January 2025. CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2. The primary outcome was major adverse cardiovascular events (MACE). Secondary outcomes included hospitalization for heart failure, CKD progression, cardiovascular and all-cause mortality. The relative risk (RR) was estimated using a random-effects model. RESULTS: Nine RCTs were included with a total of 75,088 patients, including 17,568 with eGFR < 60 ml/min/1.73m2. Use of an GLP-1 RA in patients with CKD was associated with a lower incidence of MACE (RR 0.84; 95% CI 0.74-0.95; P 0.006) and of CKD progression (RR 0.85, 95% CI 0.77-0.94; P 0.002), compared with placebo. There was no differential treatment effect of GLP-1 RA on these endpoints by CKD status at baseline. CONCLUSIONS: GLP-1 RAs offer substantial cardiovascular and renal protection in patients with CKD. These findings support their use in CKD patients and confirms that these therapies may be continued 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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".