Physical activity as a modifiable risk factor in preclinical Alzheimer’s disease
Bibliographic record
Abstract
Physical inactivity is a recognized modifiable risk factor for Alzheimer's disease (AD), yet its relationship with progression of AD pathology in humans remains unclear, limiting the effective translation into prevention trials. Using pedometer-measured step counts in cognitively unimpaired older adults, we demonstrated an association between higher physical activity and slower cognitive and functional decline in individuals with elevated baseline amyloid. Importantly, this beneficial association was not related to lower amyloid burden at baseline or longitudinally. Instead, higher physical activity was associated with slower amyloid-related inferior temporal tau accumulation, which significantly mediated the association with slower cognitive decline. Dose-response analyses further revealed a curvilinear relationship, where the associations with slower tau accumulation and cognitive decline reached a plateau at a moderate level of physical activity (5,001-7,500 steps per day), potentially offering a more approachable goal for older sedentary individuals. Collectively, our findings support targeting physical inactivity as an intervention to modify the trajectory of preclinical AD in future prevention trials, and further suggest that preferentially enrolling sedentary individuals with elevated amyloid may maximize the likelihood of demonstrating a protective effect of physical activity on tau accumulation and cognitive and functional decline in early AD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".