Low-volume acute multi-joint resistance exercise elicits a circulating brain-derived neurotrophic factor response but not a cathepsin B response in well-trained men
Bibliographic record
Abstract
This study examined if acute multi-joint resistance exercises (RE; back squat, bench press, and deadlift) to volitional failure elicited a postexercise increase in the circulating response of biomarkers associated with neuroprotection. Thirteen males (age: 24.5 ± 3.8 years, body mass: 84.01 ± 15.44 kg, height: 173.43 ± 8.57 cm, training age: 7.1 ± 4.2 years) performed 4 sets to failure at 80% of a 1-repetition maximum on the squat, bench press, and deadlift in successive weeks. The measured biomarkers were brain-derived neurotrophic factor (BDNF), insulin-like growth factor 1 (IGF-1), cathepsin B (CatB), and interleukin 6 (IL-6). Biomarkers were assessed immediately before and 10-min after exercise. There was a main time effect (pre-exercise: 24.00 ± 0.61 to postexercise: 27.38 ± 0.48 ng/mL; p < 0.01) for BDNF with increases in the deadlift (p = 0.01) and bench press (p = 0.01) conditions, but not in the squat condition (p = 0.21). There was a main time effect (pre-exercise: 0.87 ± 0.16 to postexercise: 2.03 ± 0.32 pg/mL; p < 0.01) for IL-6 with a significant increase in the squat (p < 0.01), but not the bench press (p = 0.88) and deadlift conditions (p = 0.24). No main time effect was observed for either CatB (p = 0.62) or IGF-1 (p = 0.56). In summary, acute multi-joint RE increases circulating BDNF. Further, this investigation is the first to report the lack of a transient change of CatB to an acute RE protocol. Novelty Low-volume RE to failure can increase BDNF. Resistance training does not confer an acute Cat B response.
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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".