Systematic review and meta‐analysis of protein intake to support muscle mass and function in healthy adults
Bibliographic record
Abstract
We performed a systematic review, meta-analysis, and meta-regression to determine if increasing daily protein ingestion contributes to gaining lean body mass (LBM), muscle strength, and physical/functional test performance in healthy subjects. A protocol for the present study was registered (PROSPERO, CRD42020159001), and a systematic search of Medline, Embase, CINAHL, and Web of Sciences databases was undertaken. Only randomized controlled trials (RCT) where participants increased their daily protein intake and were healthy and non-obese adults were included. Research questions focused on the main effects on the outcomes of interest and subgroup analysis, splitting the studies by participation in a resistance exercise (RE), age (<65 or ≥65 years old), and levels of daily protein ingestion. Three-level random-effects meta-analyses and meta-regressions were conducted on data from 74 RCT. Most of the selected studies tested the effects of additional protein ingestion during RE training. The evidence suggests that increasing daily protein ingestion may enhance gains in LBM in studies enrolling subjects in RE (SMD [standardized mean difference] = 0.22, 95% CI [95% confidence interval] 0.14:0.30, P < 0.01, 62 studies, moderate level of evidence). The effect on LBM was significant in subjects ≥65 years old ingesting 1.2-1.59 g of protein/kg/day and for younger subjects (<65 years old) ingesting ≥1.6 g of protein/kg/day submitted to RE. Lower-body strength gain was slightly higher by additional protein ingestion at ≥1.6 g of protein/kg/day during RE training (SMD = 0.40, 95% CI 0.09:0.35, P < 0.01, 19 studies, low level of evidence). Bench press strength is slightly increased by ingesting more protein in <65 years old subjects during RE training (SMD = 0.18, 95% CI 0.03:0.33, P = 0.01, 32 studies, low level of evidence). The effects of ingesting more protein are unclear when assessing handgrip strength and only marginal for performance in physical function tests. In conclusion, increasing daily protein ingestion results in small additional gains in LBM and lower body muscle strength gains in healthy adults enrolled in resistance exercise training. There is a slight effect on bench press strength and minimal effect performance in physical function tests. The effect on handgrip strength is unclear.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".