The Effects of Creatine Monohydrate and/or Whey Protein on the Muscle Protein Synthesis and Anabolic Signaling Responses in Non-Stressed C2C12 Murine Myotubes
Bibliographic record
Abstract
Background/Objectives: Creatine monohydrate (CRE) is a popular nutritional supplement that increases lean/muscle mass accretion. Although data regarding CRE and its effects on muscle protein synthesis are mixed, we hypothesized that CRE may potentiate/extend the anabolic response to essential amino acids given that CRE acts as a high-energy phosphate buffer to potentially amplify anabolic signaling. Therefore, we used an in vitro model to determine whether CRE synergistically enhances myotube protein synthesis and the anabolic signaling responses to EAA-rich whey protein (WP). Methods: C2C12 murine myotubes were treated with control media containing PBS (CTL), WP serum (5 mg/mL), CRE (10 mM), or WP + CRE. Myotubes were collected following 1, 4, and 24 h treatments (n = 6 replicates per treatment and time point) and assayed for relative creatine levels, myotube protein synthesis levels, and phosphorylation markers. Results: Cellular creatine levels were greater in CRE and WP + CRE versus CTL and WP at all treatment time points (p < 0.05). The protein synthesis levels with 4 hr treatments with WP and WP + CRE were greater compared to the CTL (p = 0.036 and p < 0.001, respectively), and 24 h levels were greater with WP versus other treatments (p < 0.05). p-p70S6K (Ser389) and p-rpS6 (Ser235/236) were greater with WP at 1 h compared to all other treatments (p < 0.05). No effects across time points were observed for p-mTOR (Ser2448), p-4E-BP1 (Thr37/46), or p-AMPKα (Thr172). Conclusions: WP increases protein synthesis and anabolic signaling with no additive effect from CRE. However, given that myotubes were not stressed nor stimulated to contract, such models are needed with the current treatment schematic to examine potential interactions.
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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 | 0.001 |
| 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".