Neurotrophic growth factor response to lower body resistance training in older adults
Bibliographic record
Abstract
Multimodal cognitive interventions (resistance training + cognitive training; RT + CT) may synergistically improve cognitive function in older adults. RT stimulates the release of neurotrophic growth factors (NGF) which orchestrate structural and functional brain plasticity in the aging brain, while CT increases regional cerebral blood flow. Timing the administration of CT following a bout of RT, when NGF levels are at their highest, may maximize the blood‐borne dose of NGF delivered to active brain tissue. We refer to this as the ‘anabolic window’. Currently, when and how much NGF levels in the blood increase following a bout of RT is poorly understood. The purpose of this work was to characterize the timing and magnitude of insulin like growth factor‐1 (IGF‐1) and brain derived neurotrophic factor (BDNF) levels in venous blood for 2 hours following an acute bout of RT. Methods 10 older adults (ages 60 – 77) performed 1 hour of lower‐body RT and rested for 2 hours post‐exercise. Blood was taken before and for 2 hours after RT at set time points. This procedure was repeated after 8‐weeks of regular RT. Results BDNF increased immediately post‐exercise then returned to resting levels before and after 8 weeks of RT. IGF‐1 levels did not change. Conclusions these data are the first to show an increase in BDNF immediately following RT in older adults. This may represent the anabolic window for performing CT. NSERC
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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".