Effects of Citrulline or Watermelon Supplementation on Body Composition: A Systematic Review and Dose–Response Meta-Analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: L-Citrulline (CIT) is a non-essential amino acid abundant in watermelon and commonly used as a dietary supplement to enhance exercise performance. Although its benefits for endurance and resistance training are well documented, its effects on body composition remain uncertain. This systematic review and dose-response meta-analysis aimed to assess the impact of CIT supplementation on anthropometric parameters. METHODS: A comprehensive search of major databases identified relevant randomized controlled trials (RCTs) published until March 2025. A random-effects model was used to synthesize the data. RESULTS: Twenty-one RCTs were included. Overall, CIT supplementation had no substantial effects on body mass index (BMI), body weight, fat mass (FM), waist circumference (WC), body fat percentage (BFP), and fat-free mass (FFM). Subgroup analyses revealed reductions in FM among participants over 40 years of age and in those administered more than 6 g/day of CIT. Interventions lasting 3 to 8 weeks were associated with a significant increase in FFM. Dose-response analyses suggested a non-linear association between CIT supplementation duration and changes in FM and FFM. CONCLUSIONS: CIT supplementation appears to have no overall effect on body composition. However, exploratory findings indicated potential benefits at higher doses or shorter durations. Rigorous trials controlling for dietary intake and training variables are needed to clarify its long-term effects.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".