Plasma irisin levels progressively increase in response to increasing exercise workloads in young, healthy, active subjects
Bibliographic record
Abstract
BACKGROUND: Irisin, a recently discovered myokine, has been shown to induce browning of white adipose tissue, enhancing energy expenditure and mediating some of the beneficial effects of exercise. We aimed to estimate the time frame of changes in irisin levels after acute exercise and the effect of different exercise workloads and intensities on circulating irisin levels immediately post-exercise. METHODS: In a pilot study, four healthy subjects (22.5±1.7 years) underwent maximal workload exercise (maximal oxygen consumption, VO2 max) and blood was drawn at prespecified intervals to define the time frame of pre- and post-exercise irisin changes over a 24-h period. In the main study, 35 healthy, non-smoking (23.0±3.3 years) men and women (n=20/15) underwent three exercise protocols ≥48-h apart, in random order: i) maximal workload (VO2 max); ii) relative workload (70% of VO2 max/10 min); and iii) absolute workload (75 W/10 min). Blood was drawn immediately pre-exercise and 3 min post-exercise. RESULTS: In the pilot study, irisin levels increased by 35% 3 min post-exercise, then dropped and remained relatively constant. In the main study, irisin levels post-exercise were significantly higher than those of pre-exercise after all workloads (all, P<0.001). Post-to-pre-exercise differences in irisin levels were significantly different between workloads (P=0.001), with the greatest increase by 34% following maximal workload (P=0.004 vs relative and absolute). CONCLUSIONS: Circulating irisin levels were acutely elevated in response to exercise, with a greater increase after maximal workload. These findings suggest that irisin release could be a function of muscle energy demand. Future studies need to determine the underlying mechanisms of irisin release and explore irisin's therapeutic potential.
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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) | 0.001 | 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".