Effect of an acute exercise bout on immediate post‐exercise irisin concentration in adults: A meta‐analysis
Bibliographic record
Abstract
Irisin is a recently discovered myokine that increases adipocyte metabolism, induces further "browning" of white adipose tissue, and enhances glucose metabolism. No study has ever determined how an acute bout of exercise impacts immediate post-exercise irisin concentration using a meta-analytic approach. The purpose of this study is to determine the impact of an acute bout of exercise on the magnitude of post-exercise irisin concentration in adults using meta-analytic procedures. Searches were performed on PubMed, EMBASE, CINAHL, PEDro, SCOPUS, and SPORTDiscus databases. Effect summaries were obtained using random-effects models. Random-effects single and multiple meta-regressions were performed to determine relationships between, and potential confounding effects of, variables of interest. Ten articles were retained for the final meta-analysis, producing 21 study estimates. An acute bout of exercise was accompanied by a post-exercise average increase in irisin concentration of 15.0 (95% CI: 10.8%-19.3%). There was no significant relationship between post-exercise irisin concentration and age, intensity of aerobic exercise, or type of exercise training session (resistance vs aerobic training). Fitness level and body mass index were identified as significant predictive variables for post-exercise irisin concentration. However, a multiple meta-regression model identified fitness level as the single best predictor, with being fit (21.1%±2.2%) associated with a nearly twofold increase in post-exercise irisin concentration, compared with being unfit (11.8%±2.1%). Immediately following an acute bout of exercise, irisin concentration increases substantially in adults, with fitness level as an important modifier for the effect.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.052 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".