Dissociation of Increases in PGC-1α and Its Regulators from Exercise Intensity and Muscle Activation Following Acute Exercise
Bibliographic record
Abstract
Muscle activation as well as changes in peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1α) following high-intensity interval exercise (HIIE) were examined in young healthy men (n = 8; age, 21.9±2.2 yrs; VO2peak, 53.1±6.4 ml/min/kg; peak work rate, 317±23.5 watts). On each of 3 visits HIIE was performed on a cycle ergometer at a target intensity of 73, 100, or 133% of peak work rate. Muscle biopsies were taken at rest and three hours after each exercise condition. Total work was not different between conditions (∼730 kJ) while average power output (73%, 237±21; 100%, 323±26; 133%, 384±35 watts) and EMG derived muscle activation (73%, 1262±605; 100%, 2089±737; 133%, 3029±1206 total integrated EMG per interval) increased in an intensity dependent fashion. PGC-1α mRNA was elevated after all three conditions (p<0.05), with a greater increase observed following the 100% condition (∼9 fold, p<0.05) compared to both the 73 and 133% conditions (∼4 fold). When expressed relative to muscle activation, the increase in PGC-1α mRNA for the 133% condition was less than that for the 73 and 100% conditions (p<0.05). SIRT1 mRNA was also elevated after all three conditions (∼1.4 fold, p<0.05), with no difference between conditions. These findings suggest that intensity-dependent increases in PGC-1α mRNA following submaximal exercise are largely due to increases in muscle recruitment. As well, the blunted response of PGC-1α mRNA expression following supramaximal exercise may indicate that signalling mediated activation of PGC-1α may also be blunted. We also indentify that increases in PDK4, SIRT1, and RIP140 mRNA following acute exercise are dissociated from exercise intensity and muscle activation, while increases in EGR1 are augmented with supramaximal HIIE (p<0.05).
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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".