Glucagon-Like Peptide-1 Receptor Agonists Across the Spectrum of Heart Failure
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have been used to reduce body weight in overweight or people with obesity and to improve glycemic control and cardiovascular outcomes among people with type 2 diabetes (T2D) and a high cardiovascular risk. However, the effects of GLP-1 RAs may be modified by the presence of heart failure (HF). In this review, we summarize the evidence for the use of GLP-1 RA across a patient's risk with a particular focus on HF. After a careful review of the literature, we challenge the current views about the use of GLP-1 RAs and suggest performing active HF screening (with directed clinical history, physical examination, an echocardiogram, and natriuretic peptides) before initiating a GLP-1 RA. After HF screening, we suggest GLP-1 RA treatment decisions as follows: (1) in people with T2D without HF, GLP-1 RAs should be used for reducing the risk of myocardial infarction and stroke, with a possible effect to reduce the risk of HF hospitalizations; (2) in patients with HF and preserved ejection fraction, GLP-1 RAs do not reduce HF hospitalizations but may reduce atherosclerotic events, and their use may be considered in an individualized manner; and (3) in patients with HF and reduced ejection fraction, the use of GLP-1 RAs warrants caution due to potential risk of worsening HF events and arrhythmias, pending risk-benefit data from further studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".