Facts, noise and wishful thinking: muscle protein turnover in aging and human disuse atrophy
Bibliographic record
Abstract
Surprisingly little is known about the mechanisms of muscle atrophy with aging and disuse in human beings, in contrast to rodents, from which much has been extrapolated to explain the human condition. However, this extrapolation is likely unwarranted because the time course, extent of wasting, muscle fiber involvement and alterations of muscle protein turnover are all quite different in rodent and human muscle. Furthermore, there is little evidence that static indices of protein turnover represent dynamic changes and may be misleading. With disuse there are reductions in the rate of muscle protein synthesis (MPS) large enough to explain the atrophic loss of muscle protein without a concomitant increase in proteolysis. In aging, there is no evidence that there are marked alterations in basal muscle protein turnover in healthy individuals but instead the ability to maintain muscle after feeding is compromised. This anabolic resistance is evident with physical inactivity, which exacerbates the inability to maintain muscle mass with aging. The main conclusion of this review is that in uncomplicated, non-inflammatory disuse atrophy, the facilitative change causing loss of muscle mass is a depression of MPS, exacerbated by anabolic resistance during feeding, with possible adaptive depressions, rather than increases, of muscle proteolysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".