Resistance Training with Vascular Occlusion: Metabolic Adaptations in Human Muscle
Bibliographic record
Abstract
UNLABELLED: Two recent studies have reported increases in strength and whole muscle cross-sectional area after low-intensity resistance training (LIT) with vascular occlusion (OCC) that are greater than LIT alone (e.g., 22, 25). The OCC stress might be expected to induce metabolic alterations that are consistent with compromised oxygen delivery rather than an increase in strength per se, but this has not been studied. PURPOSE: We examined the effect of LIT and LIT+OCC on resting metabolites in m. biceps brachii and elbow flexor strength. METHODS: Eight men (19.5 +/- 0.4 yr) performed 8 wk of LIT at approximately 50% of one-repetition maximum (2 sessions per week; 3-6 sets, 8-10 repetitions, final set to failure); one arm trained with OCC and the other without (CON). :Biopsies obtained before and 72 h after the final training bout revealed that resting [glycogen] was higher (P <or= 0.05) in both arms after LIT (CON: 452 +/- 20 vs 325 +/- 28, OCC: 501 +/- 12 vs 332 +/- 28 mmol.kg-1 dry weight) and the increase was larger in the OCC arm (P <or= 0.05). Resting [ATP] was lower (P <or= 0.05) after LIT in both arms (CON: 20.5 +/- 0.5 vs 22.8 +/- 0.7, OCC: 18.2 +/- 0.6 vs 23.1 +/- 0.5 mmol.kg-1 dry weight), and the decrease was larger in the OCC arm (P <or= 0.05). Maximal isotonic and isokinetic elbow flexor strength increased (P <or= 0.05) after training to a similar extent in both arms. CONCLUSION: We conclude that [glycogen] was increased and [ATP] was decreased in resting human muscle, 72 h after an 8-wk LIT protocol. OCC potentiated the metabolic changes, perhaps by inducing an ischemic stimulus that enhanced muscle glucose transport and adenine nucleotide catabolism after LIT, but did not augment the increases in strength.
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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) | 0.003 | 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".