Impact of acute circuit training on irisin in younger and older overweight adults
Bibliographic record
Abstract
Studies show aerobic exercise increases irisin and leads to health benefits. The impact of circuit training (CT) on irisin in overweight younger and older adults is unknown. The objectives were to determine whether, during an acute bout of CT, changes in irisin differed between overweight younger and older adults, and if irisin is associated with body composition, fitness level, or muscle strength. Inactive, overweight adults aged between 19–35 (25.9 ± 5.0; n = 15) and 60–75 years (67.7 ± 4.1; n = 14) participated in this study. The primary exposure variable was an acute bout of CT (12–15 repetitions; 65–70% of 1-repetition maximum; 3 loops). The primary outcome measure was the concentration of irisin determined by ELISA before, during, and after exercise. Repeated-measures analyses showed no effect of time on irisin levels during acute CT, and no interaction effect between age and time (p > 0.05). No associations were observed between changes in irisin and body composition, fitness, or strength (p > 0.05). In conclusion, acute CT does not increase irisin in overweight individuals, and irisin is not associated with the measured outcomes. Further studies are needed to elucidate the release of irisin by different types of exercise across the lifespan. This trial was registered at clinicaltrials.gov (NCT03715088). Novelty: Younger and older adults show a similar irisin response to an acute bout of circuit training. Irisin response is not associated with measures of body composition, cardiorespiratory fitness, or muscle 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".