Semaglutide in HFpEF across obesity class and by body weight reduction: a prespecified analysis of the STEP-HFpEF trial
Bibliographic record
Abstract
Abstract In the STEP-HFpEF trial, semaglutide improved symptoms, physical limitations and exercise function and reduced body weight in patients with obesity phenotype of heart failure and preserved ejection fraction (HFpEF). This prespecified analysis examined the effects of semaglutide on dual primary endpoints (change in Kansas City Cardiomyopathy Questionnaire-Clinical Summary Score (KCCQ-CSS) and body weight) and confirmatory secondary endpoints (change in 6-minute walk distance (6MWD), hierarchical composite (death, HF events, change in KCCQ-CSS and 6MWD) and change in C-reactive protein (CRP)) across obesity classes I–III (body mass index (BMI) 30.0–34.9 kg m − 2 , 35.0–39.9 kg m − 2 and ≥40 kg m − 2 ) and according to body weight reduction with semaglutide after 52 weeks. Semaglutide consistently improved all outcomes across obesity categories ( P value for treatment effects × BMI interactions = not significant for all). In semaglutide-treated patients, improvements in KCCQ-CSS, 6MWD and CRP were greater with larger body weight reduction (for example, 6.4-point (95% confidence interval (CI): 4.1, 8.8) and 14.4-m (95% CI: 5.5, 23.3) improvements in KCCQ-CSS and 6MWD for each 10% body weight reduction). In participants with obesity phenotype of HFpEF, semaglutide improved symptoms, physical limitations and exercise function and reduced inflammation and body weight across obesity categories. In semaglutide-treated patients, the magnitude of benefit was directly related to the extent of weight loss. Collectively, these data support semaglutide-mediated weight loss as a key treatment strategy in patients with obesity phenotype of HFpEF. ClinicalTrials.gov identifier: NCT04788511 .
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".