Albiglutide in patients with type 2 diabetes and heart failure: a post‐hoc analysis from Harmony Outcomes
Bibliographic record
Abstract
AIM: Glucagon-like peptide-1 receptor agonists (GLP1-RA) improve cardiovascular outcomes in patients with type 2 diabetes (T2D). However, some studies suggest that their effects in patients with heart failure (HF) may be attenuated. We aimed to explore the effects of the GLP1-RA albiglutide on HF outcomes in patients with and without HF history enrolled in the Harmony Outcomes trial. METHODS AND RESULTS: Harmony Outcomes enrolled patients with T2D and cardiovascular disease randomized to either albiglutide or placebo over a median follow-up of 1.6 years. A total of 9462 patients were included, of whom 1922 (20%) had HF history. Patients with HF had more cardiovascular comorbidities, poorer renal function, and had a three to four-fold higher risk of HF events compared to patients without HF. Compared to placebo, the effect of albiglutide on the composite of cardiovascular death or HF hospitalization was more pronounced among patients without HF (hazard ratio [HR] 0.73, 95% confidence interval [CI] 0.56-0.95) than in patients with HF (HR 1.06, 95% CI 0.79-1.43) (interaction p = 0.062). A similar pattern was observed for HF hospitalizations (interaction p = 0.025). The effect of albiglutide on cardiovascular death, sudden death or 'pump failure' death, and all-cause mortality was also attenuated among patients with HF history, but without significant interaction (p > 0.1). The benefit of albiglutide to reduce atherosclerotic events was consistent regardless of HF history. CONCLUSIONS: In patients with T2D and cardiovascular disease, albiglutide appeared to have no effect in reducing HF-related events among patients with HF history. These findings, placed in the context of other trials, suggest that GLP1-RA may not improve HF outcomes in patients with HF.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".