Efficacy of interventions to alter measures of fat-free mass in people with COPD: a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction Low fat-free mass (FFM) is linked to poor health outcomes in COPD, including impaired exercise tolerance and premature death. The aim of this systematic review was to synthesise evidence on the effectiveness of interventions for increasing FFM in COPD. Methods Searches of electronic databases (MEDLINE, Cochrane Library, Embase, Web of Science, Scopus) and trial registers ( ClinicalTrials.gov ) were undertaken from inception to August 2022 for randomised studies of interventions assessing measures of FFM in COPD. The primary outcome was change in FFM (including derivatives). Secondary outcomes were adverse events, compliance and attrition. Results 99 studies (n=5138 people with COPD) of 11 intervention components, used alone or in combination, were included. Exercise training increased mid-thigh cross-sectional area (k=3, standardised mean difference (SMD) 1.04, 95% CI 0.02–2.06; p=0.04), but not FFM (k=4, SMD 0.03, 95% CI −0.18–0.24; p=0.75). Nutritional supplementation significantly increased FFM index (k=11, SMD 0.31, 95% CI 0.13–0.50; p<0.001), but not FFM (k=19, SMD 0.16, 95% CI −0.06–0.39; p=0.16). Combined exercise training and nutritional supplementation increased measures related to FFM in 67% of studies. Anabolic steroids increased FFM (k=4, SMD 0.98, 95% CI 0.24–1.72; p=0.009). Neuromuscular electrical stimulation increased measures related to FFM in 50% of studies. No interventions were more at risk of serious adverse events, low compliance or attrition. Discussion Exercise training and nutritional supplementation were not effective in isolation to increase FFM, but were for localised muscle and index measures, respectively. Combined, exercise and nutritional supplementation shows promise as a strategy to increase FFM in COPD. Anabolic steroids are efficacious for increasing FFM in COPD.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.037 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".