Strategic Ingestion of High-Protein Dairy Milk during a Resistance Training Program Increases Lean Mass, Strength, and Power in Trained Young Males
Bibliographic record
Abstract
Background: We evaluated the effects of high-protein dairy milk ingestion on changes in body composition, strength, power, and skeletal muscle regulatory markers following 6 weeks of resistance training in trained young males. Methods: Thirty resistance-trained young males (age: 27 ± 3 years; training experience: 15 ± 2 months) were randomly assigned to one of two groups: high-protein dairy milk (both whey and casein) + resistance training (MR; n = 15) or isoenergetic carbohydrate (maltodextrin 9%) + resistance training (PR; n = 15). Milk and placebo were ingested immediately post-exercise (250 mL; 30 g protein) and 30 min before sleep (250 mL; 30 g protein). Before and after 6 weeks of linear periodized resistance training (4 times/week), body composition (bioelectrical impedance), strength, power, and serum levels of skeletal muscle regulatory markers (insulin-like growth factor 1 (IGF-1), growth hormone, testosterone, cortisol, follistatin, myostatin, and follistatin–myostatin ratio) were assessed. Results: The MR group experienced a significantly higher (p < 0.05) increase in lean mass, strength, and power (upper- and lower-body) than the PR group. Further, IGF-1, growth hormone, testosterone, follistatin, and follistatin–myostatin ratio were significantly increased, while cortisol and myostatin significantly decreased in the MR group than the PR group (p < 0.05). Conclusions: The strategic ingestion of high-protein dairy milk (post-exercise and pre-sleep) during 6 weeks of resistance training augmented lean mass, strength, power, and altered serum concentrations of skeletal muscle regulatory markers in trained young males compared to placebo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".