Glucagon-Like Peptide 1 Receptor Agonists and Heart Failure
Bibliographic record
Abstract
With worsening epidemiological trends for both the incidence and prevalence of type 2 diabetes mellitus (T2DM) and heart failure (HF) worldwide, it is critical to implement optimal prevention and treatment strategies for patients with these comorbidities, either alone or concomitantly. Several guidelines and consensus statements have recommended glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter type 2 inhibitors as add-ons to lifestyle interventions with or without metformin in those at high atherosclerotic cardiovascular disease risk. However, these recommendations are either silent about HF or fail to differentiate between the prevention of HF in those at risk versus the treatment of individuals with manifest HF. Furthermore, these documents do not differentiate among those with different HF phenotypes. This distinction, even though important, may not be critical for sodium-glucose cotransporter type 2 inhibitors in view of the consistent data for benefit for both atherosclerotic cardiovascular disease- and HF-related outcomes that have emerged from the regulatory-mandated cardiovascular outcome trials for all sodium-glucose cotransporter type 2 inhibitors and the recent DAPA-HF trial (Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction)demonstrating the benefit of dapagliflozin on HF-related outcomes in patients with HF with reduced ejection fraction with or without T2DM. However, the distinction may be crucial for glucagon-like peptide-1 receptor agonists and other antihyperglycemic agents. Indeed, in several of the new statements, glucagon-like peptide-1 receptor agonists are suggested treatment not only for patients with T2DM and atherosclerotic cardiovascular disease, but also in those with manifest HF, despite a lack of evidence for the latter recommendation. Although glucagon-like peptide-1 receptor agonists may be appropriate to use in patients at risk for HF, mechanistic insights and observations from randomized trials suggest no clear benefit on HF-related outcomes and even uncertainty regarding the safety in those with HF with reduced ejection fraction. Conversely, theoretical rationales suggest that these agents may benefit patients with HF with preserved ejection fraction. Considering that millions of patients with T2DM have HF, these concerns have public health implications that necessitate the thoughtful use of these therapies. Achieving this aim will require dedicated trials with these drugs in both patients who have HF with reduced ejection fraction and HF with preserved ejection fraction with T2DM to assess their efficacy, safety, and risk-benefit profile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".