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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 |
| 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".