Physical Performance and Amyloid-β in Humans: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
BACKGROUND: Accumulation of amyloid-β (Aβ) plaques is one of the main features of Alzheimer's disease (AD). Physical performance has been related to dementia risk and Aβ, and it has been hypothesized as one of the mechanisms leading to greater accumulation of Aβ. Yet, no evidence synthesis has been performed in humans. OBJECTIVE: To investigate the association of physical performance with Aβ in humans, including Aβ accumulation on brain, and Aβ abnormalities measured in cerebrospinal fluid (CSF) and blood. METHODS: A systematic review with multilevel meta-analysis was performed from inception to June 16th, 2022. Studies were eligible if they examined the association of physical performance with Aβ levels, including the measure of physical performance as a predictor and the measure of Aβ as an outcome in humans. RESULTS: 7 articles including 2,619 participants were included in the meta-analysis. The results showed that physical performance was not associated with accumulation of Aβ in the brain (ES = 0.01; 95% CI -0.21 to 0.24; I2 = 69.9%), in the CSF (ES = -0.28; 95% CI -0.98 to 0.41; I2 = 91.0%) or in the blood (ES = -0.19; 95% CI -0.61 to 0.24; I2 = 99.75%). Significant heterogeneity was found across the results , which posed challenges in arriving at consistent conclusions; and the limited number of studies hindered the opportunity to conduct a moderation analysis. CONCLUSIONS: The association between physical performance and Aβ is inconclusive. This uncertainly arises from the limited number of studies, study design limitations, and heterogeneity of measurement approaches. More studies are needed to determine whether physical performance is related to Aβ levels in humans.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".