Physical Exercise in MCI Elderly Promotes Reduction of Pro-Inflammatory Cytokines and Improvements on Cognition and BDNF Peripheral Levels
Bibliographic record
Abstract
The benefits of physical exercise to reduce low-grade inflammation and improve Brain-Derived Neurotrophic Factor (BDNF) levels and cognitive function became a growing field of interest. Low-grade inflammation is common during aging and seems to be linked to neurodegenerative process. Regular physical exercises can help to reduce pro-inflammatory cytokines levels and to improve BDNF peripheral concentrations. The main goal of this research was to analyze the effects of a 16-week multimodal physical exercise program on peripheral BDNF levels and on Tumor Necrosis-α (TNF-α) and Interleukin- 6 (IL-6) as pro-inflammatory markers in cognitive healthy elderly individuals and in elderly with mild cognitive impairment (MCI). Cognitive functions were assessed by the Montreal Cognitive Assessment (MoCA) prior to and after the intervention. Thirty cognitively healthy participants and thirty-seven MCI participants were assigned to the control (CG) and trained (TG) groups. The TG participated in a multimodal physical training program for a 16-week period. The results showed a significant between-subjects interaction, which indicates the beneficial contribution of training on the reduction of TNF-α (p=0.001) and IL-6 (p<0.001) and on the improvement of BDNF (p<0.001) peripheral concentrations. Cognitive functions also presented significant improvements for MCI trained group (p=0.03). In conclusion, physical exercise was effective to reduce pro-inflammatory cytokines and to improve BDNF peripheral levels, with positive reflexes on cognition. To the best of our knowledge, this is the first study that evaluated longitudinally the effects of a multimodal physical exercises protocol on peripheral concentrations of pro-inflammatory cytokines 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".