Resistance-training-induced adaptations in skeletal muscle protein turnover in the fed state
Bibliographic record
Abstract
Resistance training changes the balance of muscle protein turnover, leading to gains in muscle mass. A longitudinal design was employed to assess the effect that resistance training had on muscle protein turnover in the fed state. A secondary goal was investigation of the potential interactive effects of creatine (Cr) monohydrate supplementation on resistance-training-induced adaptations. Young (N = 19, 23.7 +/- 3.2 year), untrained (UT), healthy male subjects completed an 8-week resistance-training program (6 d/week). Supplementation with Cr had no impact on any of the variables studied; hence, all subsequent data were pooled. In the UT and trained (T) state, subjects performed an acute bout of resistance exercise with a single leg (exercised, EX), while their contralateral leg acted as a nonexercised (NE) control. Following exercise, subjects were fed while receiving a primed constant infusion of [d5]- and [15N]-phenylalanine to determine the fractional synthetic and breakdown rates (FSR and FBR), respectively, of skeletal muscle proteins. Acute exercise increased FSR (UT-NE, 0.065 +/- 0.025 %/h; UT-EX, 0.088 +/- 0.032 %/h; P < 0.01) and FBR (UT-NE, 0.047 +/- 0.023 %/h; UT-EX, 0.058 +/- 0.026 %/h; P < 0.05). Net balance (BAL = FSR - FBR) was positive in both legs (P < 0.05) but was significantly greater (+65%) in the EX versus the NE leg (P < 0.05). Muscle protein FSR and FBR were greater at rest following T (FSR for T-NE vs. UT-NE, +46%, P < 0.01; FBR for T-NE vs. UT-NE, +81%, P < 0.05). Resistance training attenuated the acute exercise-induced rise in FSR (T-NE vs. T-EX, +20%, P = 0.65). The present results demonstrate that resistance training resulted in an elevated resting muscle protein turnover but an attenuation of the acute response of muscle protein turnover to a single bout of resistance exercise.
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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".