The Role of Milk- and Soy-Based Protein in Support of Muscle Protein Synthesis and Muscle Protein Accretion in Young and Elderly Persons
Why this work is in the frame
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Bibliographic record
Abstract
The balance between muscle protein synthesis (MPS) and muscle protein breakdown (MPB) is dependent on protein consumption and the accompanying hyperaminoacidemia, which stimulates a marked rise in MPS and mild suppression of MPB. In the fasting state, however, MPS declines sharply and MPB is increased slightly. Ultimately, the balance between MPS and MPB determines the net rate of muscle growth. Accretion of new muscle mass beyond that of normal growth can occur following periods of intense resistance exercise. Such muscle accretion is an often sought-after goal of athletes. There needs to be, however, an increased appreciation of the role that preservation of muscle can play in offsetting morbidities associated with the sarcopenia of aging, such as type 2 diabetes and declines in metabolic rate that can lead to fat mass accumulation followed by the onset or progression of obesity. Emerging evidence shows that consumption of different types of proteins can have different stimulatory effects on the amplitude and possibly duration that MPS is elevated after feeding; this may be particularly significant after resistance exercise. This effect may be due to differences in the fundamental amino acid composition of the protein (i.e., its amino acid score) and its rate of digestion. Milk proteins, specifically casein and whey, are the highest quality proteins and are quite different in terms of their rates of digestion and absorption. New data suggest that whey protein is better able to support MPS than is soy protein, a finding that may explain the greater ability of whey protein to support greater net muscle mass gains with resistance exercise. This review focuses on evidence showing the differences in responses of MPS, and ultimately muscle protein accretion, to consumption of milk- and soy-based supplemental protein sources in humans.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it