Liraglutide and Weight Loss Among Patients with Advanced Heart Failure and a Reduced Ejection Fraction: Insights from the FIGHT Trial
Bibliographic record
Abstract
AIMS: Obesity is present in up to 45% of patients with heart failure (HF). Liraglutide, a glucagon-like peptide-1 (GLP-1) receptor antagonist, facilitates weight loss in obese patients. The efficacy of liraglutide as a weight loss agent among patients with HF and reduced ejection fraction (HFrEF) and a recent acute HF hospitalization remains unknown. METHODS AND RESULTS: The Functional Impact of GLP-1 for Heart Failure Treatment study randomized 300 patients with HFrEF (ejection fraction ≤ 40%), both with and without diabetes and a recent HF hospitalization to liraglutide or placebo. The primary outcome for this post hoc analysis was the change in weight from baseline to last study visit. We conducted an 'on-treatment' analysis of patients with at least one follow-up visit on study drug (123 on liraglutide and 124 on placebo). The median age was 61 years, 21% were female, and 69% of patients had New York Heart Association functional Class III or IV symptoms. The median ejection fraction was 25% (25th, 75th percentile 19-32%). Liraglutide use was associated with a significant weight reduction [liraglutide -1.00 lbs vs. placebo 2.00 lbs; treatment difference -4.10 lbs; 95% confidence interval (CI) -7.94, -0.25; P = 0.0367; percentage treatment difference -2.07%, 95% CI -3.86, -0.28; P = 0.0237]. Similar results were seen after multivariable adjustments. Liraglutide also significantly reduced triglyceride levels (liraglutide 7.5 mg/dL vs. placebo 12.0 mg/dL; treatment difference -33.1 mg/dL; 95% CI -60.7, -5.6; P = 0.019). CONCLUSIONS: Liraglutide is an efficacious weight loss agent in patients with HFrEF. These findings will require further exploration in a well-powered cardiovascular outcomes trial.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".