Milk protein ingestion does not enhance recovery from muscle-damaging resistance exercise in untrained males and females: a randomized controlled trial
Bibliographic record
Abstract
Milk-based proteins are a common choice of post-exercise nutrition to enhance exercise recovery and adaptation. Peri-exercise milk protein ingestion may attenuate exercise-induced muscle damage (EIMD), which is a particular risk to untrained individuals. However, most research has been conducted with males, and due to potential sex differences in EIMD, research with both sexes is required. This parallel-group randomized controlled trial examined the impact of milk protein ingestion on recovery from EIMD. Untrained males and females performed a single bout of leg-based resistance exercise and consumed a milk protein (MILK-PRO: n = 4 males, n = 8 females) or isoenergetic control (CON: n = 4 males, n = 8 females) supplement over 4 days post-exercise (17 doses total). Maximum strength was assessed ≥3 wk pre- and 72 and 168 h post-exercise, and measures of leg circumference, range of motion, muscle soreness, pressure-pain threshold (PPT), and serum creatine kinase concentration ([CK]) were conducted pre-, immediately post-, and 24, 48, 72, and 168 h post-exercise. Resistance exercise induced mild muscle damage that was not attenuated with MILK-PRO relative to CON. Peak increases in [CK] and reductions in PPT were greater in males compared with females. Changes in other markers were comparable between sexes. We conclude that moderate resistance exercise in naïve individuals induces muscle damage without compromising muscle strength. We support sex differences in EIMD and emphasize the need for further research with both sexes. Milk protein ingestion was not beneficial for recovery from EIMD, thus alternative management strategies should be investigated. This trial was prospectively registered at ClinicalTrials.gov PRS (protocol ID: 290580A).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".