Impact of whole-body and skeletal muscle composition on peak oxygen uptake in heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Aims Heart failure (HF) has a major impact on exercise tolerance that may (in part) be due to abnormalities in body and skeletal muscle composition. This systematic review and meta-analysis aims to assess how differences in whole-body and skeletal muscle composition between patients with HF and non-HF controls (CON) contribute to reduced peak oxygen uptake (VO2peak). Methods and results The PubMed database was searched from 1975 to May 2024 for eligible studies. Cross-sectional studies with measures of VO2peak, body composition, or muscle biopsies in HF and CON were considered. Out of 709 articles, 27 studies were included in this analysis. Compared with CON, VO2peak [weighted mean difference (WMD): −9.96 mL/kg/min, 95% confidence interval (CI): −11.71 to −8.21), total body lean mass (WMD: −1.63 kg, 95% CI: −3.05 to −0.21), leg lean mass (WMD: −1.38 kg, 95% CI: −2.18 to −0.59), thigh skeletal muscle area (WMD: −10.88 cm2 , 95% CI: −21.40 to −0.37), Type I fibres (WMD: −7.76%, 95% CI: −14.81 to −0.71), and capillary-to-fibre ratio (WMD: −0.27, 95% CI: −0.50 to −0.03) were significantly lower in HF. Total body fat mass (WMD: 3.34 kg, 95% CI: 0.35–6.34), leg fat mass (WMD: 1.37 kg, 95% CI: 0.37–2.37), and Type IIx fibres (WMD: 7.72%, 95% CI: 1.52–13.91) were significantly higher in HF compared with CON. Absolute VO2peak was significantly associated with total body and leg lean mass, thigh skeletal muscle area, and capillary-to-fibre ratio. Conclusion Individuals with HF display abnormalities in body and skeletal muscle composition including reduced lean mass, oxidative Type I fibres, and capillary-to-fibre ratio that negatively impact VO2peak.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".