Impact of a single bout of high-intensity interval exercise and short-term interval training on interleukin-6, FNDC5, and METRNL mRNA expression in human skeletal muscle
Bibliographic record
Abstract
Background Exercise promotes numerous phenotypic adaptations in skeletal muscle that contribute to improved function and metabolic capacity. An emerging body of evidence suggests that skeletal muscle also releases a myriad of factors during exercise, termed “myokines”. The purpose of this study was to examine the effects of high-intensity interval training (HIIT) on the acute regulation of the mRNA expression of several myokines, including the prototypical myokine interleukin-6 (IL-6), and recently identified myokines fibronectin type III domain-containing protein 5 (FNDC5) (irisin) and meteorin-like protein (METRNL). Methods Both before and after a 20-day period of twice-daily high-volume HIIT, 9 healthy males (20.5 ± 1.5 years performed a standardized bout of high-intensity interval exercise (HIIE; 5 × 4 min at ~80% pretraining peak power output) with skeletal muscle biopsy samples (vastus lateralis) obtained at rest, immediately following exercise, and at 3 h recovery. Results Before training, a single bout of HIIE increased IL-6 ( p < 0.05) and METRNL ( p < 0.05) mRNA expression measured at 3 h recovery when compared to rest. Following 20 days of HIIT, IL-6 and FNDC5 mRNA were increased at 3 h recovery from the standardized HIIE bout when compared to rest (both p < 0.05). Resting METRNL and FNDC5 mRNA expression were higher following training ( p < 0.05), and there was an overall increase in FNDC5 mRNA post-training (main effect of training, p < 0.05). Conclusion In human skeletal muscle (1) an acute bout of HIIE can induce upregulation of skeletal muscle IL-6 mRNA both before and after a period of intensified HIIT; (2) Resting and overall FNDC5 mRNA expression is increased by 20 days of HIIT; and (3) METRNL mRNA expression is responsive to both acute HIIE and short-term intense HIIT. Future studies are needed to confirm these findings at the protein and secretion level in humans.
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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".