246 Maintenance of Circadian/Daily Activity Patterns and Cognitive Resilience to Alzheimer’s Pathology in Late Life
Bibliographic record
Abstract
Abstract Introduction Many individuals remain free from dementia despite substantial plaque and tangle deposits, the hallmarks of Alzheimer’s disease (AD). Understanding of this cognitive resilience is poor. Evidence suggests that circadian disturbances predict increased risk of incident AD, and that circadian regulation deteriorates as clinical AD progresses. We hypothesize that circadian robustness protects against dementia, and the effect is stronger in individuals with AD pathology than those without. Methods We studied 575 deceased participants (age at death: 91.1□6.2; female: 414) in the Rush Memory and Aging Project who underwent brain autopsy at death, had clinical diagnostic opinion of dementia and motor activity assessments with actigraphy of ~10 days before death. Using actigraphy proximate to death, we calculated four circadian metrics: amplitude, acrophase, interdaily stability, and intradaily variability. Logistic regressions, stratified by postmortem pathologic AD diagnosis, were used to examine associations of circadian metrics with odds of dementia, and separately cognitive impairment (CI, including both dementia and mild cognitive impairment [MCI]), adjusting for age at death, sex, education, and time-lag between actigraphy and death. Results Based on postmortem assessment, 378 participants met the NIA-Reagan criteria for high/intermediate likelihood AD (AD group), including 197 clinically diagnosed with dementia, 86 MCI, and 85 cognitively intact. Non-AD group consisted of the remaining 197 participants, including 36 with clinical dementia, 47 MCI, and 114 cognitively intact. In the AD group, greater amplitude, greater interdaily stability, and lower intradaily variability were associated with lower odds of CI and dementia, i.e., odds ratios [OR] for CI corresponding to 1-SD changes were 0.54 (95% CI: 0.40-0.71), 0.70 (0.54-0.91), and 0.63 (0.47-0.84), and were 0.46 (0.35-0.60), 0.55 (0.43-0.70), and 0.61 (0.48-0.78) for dementia. In the non-AD group, only amplitude was associated with the odds of CI or dementia, i.e., the ORs corresponding to 1-SD increase was 0.61 (0.42-0.88) and 0.50 (0.31-0.82), respectively. Conclusion Better preserved circadian function, as characterized by more pronounced, more stable and less fragmented rest-activity rhythms, links to lower risk of CI or dementia in older people, especially those with pathological AD. Support (if any) NIH RF1AG064312, RF1AG059867, R01AG017917, R01AG56352; and the BrightFocus Foundation A2020886S.
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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 | 0.000 | 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 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".