Exercise Intensity and Recovery on Circulating Brain-derived Neurotrophic Factor
Bibliographic record
Abstract
INTRODUCTION: Brain-derived neurotrophic factor (BDNF) is an exercise-induced neurotropin mediating neuroprotection and synaptic plasticity. Although exercise intensity is implicated as a potentially important mediator of BNDF release after exercise, the optimal exercise stimulus (interval vs continuous) and intensity (submaximal vs supramaximal) for augmenting circulating BDNF levels remains unknown. Irisin, an exercise-driven myokine, may also contribute to neuroprotection by upregulating BDNF. PURPOSE: To examine the response and recovery of plasma BDNF and irisin after acute exercise of differing intensities. METHODS: Eight males (23.1 ± 3.0 yr of age; V˙O2max 51.2 ± 4.4 mL·kg·min) completed four acute exercise sessions: 1) moderate-intensity continuous training (MICT, 65% V˙O2max); 2) vigorous-intensity continuous training (VICT, 85% V˙O2max); 3) sprint interval training (SIT, "all out"); and 4) no exercise (CTRL). Blood was collected preexercise as well as immediately, 30 min, and 90 min postexercise. Plasma BDNF and irisin were assessed with commercially available enzyme-linked immunosorbent assay kits. RESULTS: Plasma BDNF levels increased immediately after exercise in the SIT group (P < 0.0001) with plasma concentrations recovering 30 and 90 min postexercise. The BDNF levels after MICT were reduced 30 min postexercise compared with immediately postexercise (P = 0.0189), with no other changes across time points in MICT and VICT groups. Plasma BDNF area under the curve in SIT was significantly higher compared with CTRL, MICT, and VICT (P = 0.0020). No changes in plasma irisin across exercise groups and time points were found (P > 0.9999). CONCLUSIONS: Plasma BDNF levels increased in an intensity-dependent manner with SIT eliciting the highest BDNF concentration immediately postexercise. These results identify SIT as a time-efficient exercise modality to promote brain health through BDNF release.
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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) | 0.003 | 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".