Liraglutide and Weight Loss Among Patients with Advanced Heart Failure and a Reduced Ejection Fraction: Insights from the FIGHT Trial
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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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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 it