Glucagon‐like peptide 1 receptor agonists in patients with type 2 diabetes with and without chronic heart failure: A meta‐analysis of randomized placebo‐controlled outcome trials
Bibliographic record
Abstract
AIM: Glucagon-like peptide 1 receptor agonists (GLP1-RA) reduce atherosclerotic events in patients with type 2 diabetes (T2D) and a high cardiovascular risk. The effect of GLP1-RA to reduce heart failure (HF) has been inconsistent across T2D trials, and individual trials were underpowered to assess the effect of GLP1-RA according to HF history. In this meta-analysis we aim to assess the effect of GLP1-RA in patients with and without HF history in stable ambulatory patients with T2D. METHODS: Random-effects meta-analysis of placebo-controlled trials. The hazard ratio (HR) and 95% confidence intervals (95% CI) were extracted from the treatment effect estimates of HF subgroup analyses reported in each individual study. The primary outcome was a composite of HF hospitalization or cardiovascular death. RESULTS: In total, 54 092 patients with T2D from seven randomized controlled trials were included, of whom 8460 (16%) had HF history. Compared with placebo, GLP1-RA did not reduce the composite of HF hospitalization or cardiovascular death in patients with HF history: HR 0.96, 95% CI: 0.84-1.08, but reduced this outcome in patients without HF history: HR 0.84, 95% CI: 0.76-0.92. GLP1-RA did not reduce all-cause death in patients with HF history: HR 0.98, 95% CI: 0.86-1.11, but reduced mortality in patients without HF history: HR 0.85, 95% CI: 0.79-0.92. GLP1-RA reduced atherosclerotic events regardless of HF history: HR 0.85, 95% CI: 0.75-0.97 with HF, and HR 0.88, 95% CI: 0.83-0.93 without HF. CONCLUSIONS: Treatment with GLP1-RA did not reduce HF hospitalizations and mortality in patients with concomitant T2D and HF, but may prevent new-onset HF and mortality in patients with T2D without HF. The reduction of atherosclerotic events with GLP1-RA was not influenced by HF history status.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.049 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".