Matcha Green Tea Promotes Training-induced Anabolic Response of Skeletal Muscle in Mice
Bibliographic record
Abstract
Matcha green tea contains various bioactive compounds, such as catechins and dietary fibers. Daily consumption of matcha green tea enhances exercise-induced adaptations to skeletal muscle hypertrophy and strength in humans. However, interventional studies on the efficacy and mechanism of micro-compounds in muscle adaptation to resistance training are limited. In this study, we examined the effects of matcha green tea on anabolic regulators in the skeletal muscles in association with the gut environment during high-intensity intermittent exercise in mice. Male ICR mice were divided into sedentary, exercise, and matcha groups. The matcha group was orally administered matcha (200 µL, 10 mg of matcha powder/mL) daily. The exercise and matcha groups were subjected to three sets of high-intensity intermittent exercises for 3 min, with intervals of 5 min, five times per week. After 1 or 4 weeks, hindlimb muscle, plasma, and fecal samples were collected. After 1 week of training, the phosphorylation levels of p70S6K (Thr421/Ser424) and pERK (Thr202/Tyr204) in the gastrocnemius muscle were higher in the matcha group than that in the sedentary group. After 4 weeks, the gastrocnemius muscle weight was higher in the matcha group than that in the sedentary group. The abundance of Clostridium coccoides in the feces tended to be higher in the matcha group than that in the exercise group. Furthermore, muscle weight was positively correlated with the abundance of Clostridium coccoides. Matcha green tea consumption with high-intensity intermittent exercise promotes skeletal muscle protein synthesis, which may be associated with the microbiota composition.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".