Effects of Citrulline alone or combined with exercise on muscle mass, muscle strength, and physical performance among older adults
Bibliographic record
Abstract
PURPOSE OF REVIEW: To Establish the potential of Citrulline supplementation (CIT) combined or not with exercise on muscle function and physical performance via a systematic review of randomized controlled trials (RCTs) in human aged 50 years and older. RECENT FINDINGS: Based on Preferred Reporting Items for Systematic Reviews and Meta-analysis guideline, 103 references have been identified. Among this number, only six RCTs (250 participants) matched the inclusion criteria and were included in the present systematic review. Among the included studies, five of six reported beneficial effects of CIT on muscle mass. Then, four of six studies reported CIT effects on muscle strength but also that CIT when combined to exercise results in further improvements in upper muscle strength. Finally, three of six studies reported beneficial CIT effect on physical performance and suggested that CIT with exercise displayed greater improvements in walking speed than exercise alone. SUMMARY: CIT supplementation seems to be able to improve muscular and physical factors in frail elderly people (malnourished, hypertensive, obese, dynapenic-obese) compared to placebo. More importantly, CIT combined to exercise is more efficient than exercise or CIT alone. However, because of the small number (six) and heterogeneity (dose, duration, population) of the studies realized in older adults, further studies are needed to confirm its promising potential.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".