Coffee consumption promotes skeletal muscle hypertrophy and myoblast differentiation
Bibliographic record
Abstract
Coffee is a widely consumed beverage worldwide and is believed to help prevent the occurrence of various chronic diseases. However, the effect of coffee on skeletal muscle hypertrophy, differentiation and the mechanisms of action responsible have remained unclear. To investigate the effect of coffee on skeletal muscle hypertrophy, mice were fed a normal diet or a normal diet supplemented with 0.3% coffee or 1% coffee. Coffee supplementation was observed to increase skeletal muscle hypertrophy, while simultaneously upregulating protein expression of total MHC, MHC2A, and MHC2B in quadricep muscle. Myostatin expression was also attenuated, and IGF1 was upregulated with subsequent phosphorylation of Akt and mTOR, while AMPK phosphorylation was attenuated. Coffee also increased the grip strength and PGC-1α protein expression, and decreased the expressions of TGF-β and myostatin in tricep muscle. Coffee activated the MKK3/6-p38 pathway and upregulated PGC-1α, which may play a role in promoting myogenic differentiation and myogenin expression in C2C12 cells. These results suggest that coffee increases skeletal muscle function and hypertrophy by regulating the TGF-β/myostatin - Akt - mTORC1.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".