The Influence Training Load and Volume on Anabolic Signalling and Muscle Hypertrophy with Resistance Training
Bibliographic record
Abstract
Recent evidence suggests that maximal fiber activation and contraction volume, regardless of training intensity, are fundamental variables driving muscle protein synthesis. PURPOSE:To determine the influence of high and low relative loads (i.e, % of 1RM) and training volume (1 vs 3 sets) on muscle hypertrophy and intramuscular signalling after 10 weeks of resistance training. To examine if acute p70S6K phosphorylation is related to hypertrophy after resistance training. METHODS:: 18 subjects (Age=19.6±0.8; BMI=23.6±2.1) completed 10 weeks of unilateral knee extension resistance training. Subjects were randomized into two of three training conditions: 30% of 1RM for three sets (30RM); 80% of 1RM (80RM) for 1 set; or 80RM for a 3 sets. All training sessions were performed until volitional fatigue (i.e., failure). Training was performed three times per week and followed by ingestion of 30g of protein. Isometric strength testing and MRI scans were performed before and after training to determine muscle strength and volume. Biopsies were taken from the vastus lateralis of each leg at rest and 1 h after the first training session to measure anabolic signalling. RESULTS: Muscle volume increased in each group, but there was no difference between conditions (30RM: 6.0±6.1%, 80RM for 3 set: 6.5±4.4% and 1 set: 3.0±2.6%; P<0.001). Isometric strength increased significantly in all groups (P< 0.001), 30RM (26±8%), 80RM 3 set (46±11%) and 1 set (29±8%) conditions. Phosphorylation of p70S6K at 1 h after the first training session was elevated significantly in both 80RM groups 3 set (1.7-fold) and 1 set (1.5-fold). However there was no change (P=0.01) in the 30RM condition. There was no correlation between initial p70S6K phosphorylation and the extent of muscle hypertrophy (r = 0.14, P=0.42). Akt phosphorylation was increased in all conditions (main effect for time, P=0.004). CONCLUSIONS: Light training loads were equally effective as heavy loads for inducing muscular hypertrophy. Training volume did not significantly affect hypertrophy; however, the 80RM 3 set condition gained more than twice the muscle volume of the 80RM 1 set condition (P=0.27) suggesting that further investigation is required. In contrast to previous reports there was no relationship between acute p70S6K phosphorylation and hypertrophy in the current study
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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 | 0.001 |
| 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) | 0.002 | 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".