0291 Degraded Fractal Activity Regulation Predicts Elevated Risk of Alzheimer’s Disease in the Elderly
Bibliographic record
Abstract
Healthy physiological systems exhibit fractal regulation, generating similar fluctuation patterns in physiological outputs across different time scales from seconds to hours. Evidence indicates a mechanistic link between fractal regulation and sleep/circadian control, both degraded with aging and in Alzheimer’s disease (AD). Recent studies showed that sleep and circadian disturbances may be early signs of AD. We tested whether degraded fractal regulation predicts AD risk. We examined 1,097 older adults (844 females) in the Rush Memory and Aging Project who have undergone annual neuropsychological tests to assess their cognitive status for up to 11 years. These subjects were non-demented and aged between 65–100 years old at baseline. Motor activity was monitored on the wrist continuously for up to 10 days at baseline. Detrended fluctuation analysis was performed to obtain a metric α that quantifies fractal temporal correlations of motor activity at time scales ~0.1–1.5h. Cox proportional hazards models were performed to examine the associations of α with incident AD and incident mild cognitive impairment (MCI). Linear mixed effect models were used to examine the associations of α with cognitive decline. Of the 1,097 participants, 220 developed AD (4.6 ± 2.8 [SD] years after baseline). For 1-SD decrease in α (~0.06), the risk of AD increased by 1.31-fold (95% CI: 1.15–1.49, p<0.0001) after adjusting for age, sex, and education. The association remained after further accounting for physical activity, sleep fragmentation, or stability of daily activity rhythms. Consistently, with 1-SD decrease in α, the risk of MCI increased by 1.15-fold (95% CI: 1.02–1.29, p=0.018); and the annual cognitive decline was accelerated by 12.5% that was equivalent to the effect of being 2 years older. Degraded fractal regulation predicts increased AD risk that is independent of other AD risk factors including age, physical activity, sleep, and stability of daily activity rhythms. This work was supported by NIH grants R01AG048108, R00HL102241, P01AG009975, R01AG017917, and R01NS078009.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".