Effects of glucagon‐like peptide‐1 receptor agonists on cardiovascular and renal outcomes: A <scp>meta‐analysis</scp> and <scp>meta‐regression</scp> analysis
Bibliographic record
Abstract
Abstract Aim To evaluate the cardiovascular and renal outcomes of glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) and the associations between these outcomes and HbA1c or weight reduction. Materials and Methods We searched PubMed/MEDLINE, EMBASE, and CENTRAL databases for randomized, placebo‐controlled trials of GLP‐1 RAs reporting major adverse cardiovascular events (MACE; a composite of cardiovascular mortality, stroke, and myocardial infarction) as the primary outcome. We conducted a meta‐regression analysis of primary and secondary outcomes with HbA1c or weight reduction following a meta‐analysis with a random‐effects model for these outcomes. Results We extracted data of 60 800 individuals from eight eligible studies (ELIXA, LEADER, SUSTAIN‐6, EXSCEL, HARMONY, PIONEER 6, REWIND, and AMPLITUDE‐O). GLP‐1 RAs reduced MACE (hazard ratio [HR] 0.86; 95% CI: 0.80‐0.93; P < .001) and secondary outcomes including the composite renal outcome (0.80; 0.73‐0.87; P < .001). In meta‐regression analysis, every 1% reduction in HbA1c was associated with 26% and 35% decreases in the logarithm of HR of MACE ( P = .044; R 2 = 0.65) and the composite renal outcome ( P = .040; R 2 = 0.85), respectively. On the contrary, weight reduction was not associated with any outcome, including MACE ( P = .390). Conclusions The reduction in HbA1c, but not body weight, is associated with cardiovascular and renal outcomes. The magnitude of HbA1c reduction can be a surrogate for the cardiovascular and renal benefits of treatment with GLP‐1 RAs.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.060 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".