A Muscle-Centric Perspective on Intermittent Fasting: A Suboptimal Dietary Strategy for Supporting Muscle Protein Remodeling and Muscle Mass?
Bibliographic record
Abstract
Muscle protein is constantly "turning over" through the breakdown of old/damaged proteins and the resynthesis of new functional proteins, the algebraic difference determining net muscle gain, maintenance, or loss. This turnover, which is sensitive to the nutritional environment, ultimately determines the mass, quality, and health of skeletal muscle over time. Intermittent fasting has become a topic of interest in the health community as an avenue to improve health and body composition primarily via caloric deficiency as well as enhanced lipolysis and fat oxidation secondary to attenuated daily insulin response. However, this approach belies the established anti-catabolic effect of insulin on skeletal muscle. More importantly, muscle protein synthesis, which is the primary regulated turnover variable in healthy humans, is stimulated by the consumption of dietary amino acids, a process that is saturated at a moderate protein intake. While limited research has explored the effect of intermittent fasting on muscle-related outcomes, we propose that infrequent meal feeding and periods of prolonged fasting characteristic of models of intermittent fasting may be counter-productive to optimizing muscle protein turnover and net muscle protein balance. The present commentary will discuss the regulation of muscle protein turnover across fasted and fed cycles and contrast it with studies exploring how dietary manipulation alters the partitioning of fat and lean body mass. It is our position that intermittent fasting likely represents a suboptimal dietary approach to remodel skeletal muscle, which could impact the ability to maintain or enhance muscle mass and quality, especially during periods of reduced energy availability.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".