The effect of caffeine on skeletal muscle anabolic signaling and hypertrophy
Bibliographic record
Abstract
Caffeine is a widely consumed stimulant with the potential to enhance physical performance through multiple mechanisms. However, recent in vitro findings have suggested that caffeine may block skeletal muscle anabolic signaling through AMP-activated protein kinase (AMPK)-mediated inhibition of mechanistic target of rapamycin (mTOR) signaling pathway. This could negatively affect protein synthesis and the capacity for muscle growth. The primary purpose of this study was to assess the effect of caffeine on in vivo AMPK and mTOR pathway signaling, protein synthesis, and muscle growth. In cultured C2C12 muscle cells, physiological levels of caffeine failed to impact mTOR activation or myoblast proliferation or differentiation. We found that caffeine administration to mice did not significantly enhance the phosphorylation of AMPK or inhibit signaling proteins downstream of mTOR (p70S6k, S6, or 4EBP1) or protein synthesis after a bout of electrically stimulated contractions. Skeletal muscle-specific knockout of LKB1, the primary AMPK activator in skeletal muscle, on the other hand, eliminated AMPK activation by contractions and enhanced S6k, S6, and 4EBP1 activation before and after contractions. In rats, the addition of caffeine did not affect plantaris hypertrophy induced by the tenotomy of the gastrocnemius and soleus muscles. In conclusion, caffeine administration does not impair skeletal muscle load-induced mTOR signaling, protein synthesis, or muscle hypertrophy.
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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 | 0.000 | 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) | 0.001 | 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 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".