The Role of GLP-1R and GIPR Agonism in Heart Failure
Bibliographic record
Abstract
Heart failure (HF) is a clinical syndrome characterized by signs and symptoms of structural and functional cardiac abnormalities. It is corroborated by elevated N-terminal pro‑B‑type natriuretic peptide (NT-proBNP) levels and objective evidence of pulmonary or systemic congestion. More than 100,000 Canadians are diagnosed with HF annually. For years, HF has been classified based on left ventricular ejection fraction (LVEF). HF with reduced ejection fraction (HFrEF) refers to symptomatic HF with an LVEF <40%. However, if the LVEF is >50%, this is known as HF with preserved ejection fraction (HFpEF). In HFpEF, obesity is commonly implicated in the disease pathophysiology, and is present in up to 80% of people with this condition. Obesity contributes to concentric heart remodelling through mechanisms such as insulin resistance, diabetes, hyperlipidemia, visceral adipose tissue expansion, and myocardial steatosis. Additionally, obesity leads to a pro-inflammatory state which affects the vasculature and visceral organs.2 Glucagon‑like peptide-1 receptor agonists (GLP‑1RAs), such as semaglutide, have shown promise in weight reduction across multiple Phase 3 clinical trials. Agents combining GLP-1RA and glucose-dependent insulinotropic peptide receptor (GIPR) agonism, such as tirzepatide, have also contributed to clinically significant weight loss. As such, their impact in addressing obesity‑related HFpEF is under investigation. This paper reviews the data on GLP-1RAs and tirzepatide in patients with HF across the LVEF spectrum, with a particular focus on those with HFpEF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".