Physical Exercise Improves Peripheral BDNF Levels and Cognitive Functions in Mild Cognitive Impairment Elderly with Different BDNF Val66Met Genotypes
Bibliographic record
Abstract
The benefits of physical exercise on improvements in brain-derived neurotrophic factor (BDNF) levels and cognitive functioning have been reported in the literature. However, the variability of individual responses may be linked to genetic differences. BDNF is considered one of the most plausible factors involved in the cognitive benefits associated with physical activity practice. A single nucleotide polymorphism localized in the gene that codes BDNF results in a missense mutation that promotes an amino acid substitution (Val66Met) in the protein. This process has been associated with decreased levels of BDNF secretion, with corresponding impairments in specific cognitive functions. Therefore, the objective of this study was to analyze the effects of a multimodal physical exercise program on peripheral BDNF levels and cognitive functions in elderly individuals with mild cognitive impairment (MCI). The participants were genotyped for the BDNF Val66Met polymorphism. Cognitive functions were assessed by the Montreal Cognitive Assessment (MoCA) prior to and after the intervention. Forty-five participants were assigned to the control and trained groups. The trained group participated in a multimodal physical training for a 16-week period. The results showed a significant between-subjects interaction (p < 0.05), which indicates the beneficial contribution of training on cognitive functions independent of the BDNF genotype. However, only participants with BDNF-Met genotypes exhibited significant improvements in peripheral BDNF levels. The BDNF genotype appears to modulate the effects of physical exercise on BDNF secretion, but it does not influence cognition. This is the first study that evaluated the influence of a BDNF polymorphism on physical activity and cognition performance in elderly MCI individuals.
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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".