Effects of Supplementation with Milk Proteins on Body Composition and Anthropometric Parameters: A Systematic Review and Dose–Response Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: There is no consensus regarding the impacts of supplementation with milk proteins (MPs) on body composition (BC). This systematic review and dose–response meta-analysis of randomized controlled trials (RCTs) assessed the effects of MP, casein protein (CP), and whey protein (WP) supplementation on BC and anthropometric parameters. Methods: A comprehensive search was performed in several databases to identify eligible RCTs published until October 2025. Random-effects models were applied to estimate the pooled effects of MP supplementation on anthropometric parameters. Results: A total of 150 RCTs were included. MP supplementation substantially increased lean body mass (LBM) (weighted mean difference (WMD): 0.41 kg; 95% CI: 0.19, 0.62; p < 0.001) and fat-free mass (FFM) (WMD: 0.67 kg; 95% CI: 0.40, 0.94; p < 0.001). It also significantly reduced body fat percentage (BFP) (WMD: −0.66%; 95% CI: −1.03, −0.28; p = 0.001), fat mass (FM) (WMD: −0.66 kg; 95% CI: −0.91, −0.41; p < 0.001), and waist circumference (WC) (WMD: −0.69 cm; 95% CI: −1.16, −0.22; p = 0.004). No considerable effects were observed for muscle mass (MM), body mass index (BMI), and body weight (BW). Dose–response analysis revealed that MP dosage was associated with significant changes in BFP, LBM, and MM. Conclusions: MP supplementation was associated with favorable modifications in body composition, including increases in LBM and FFM, as well as reductions in FM, BFP, and WC. These findings provide coherent and consistent evidence supporting the potential role of MP supplementation in targeted body composition management.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| 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 | 0.003 | 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".