421 Elucidating Circadian and Sleep Phenotypes and Relation to Cognitive Impairment in Alzheimer’s Dementia
Bibliographic record
Abstract
Abstract Introduction Although sleep disruption in Alzheimer’s disease (AD) pathogenesis has been described, the role of circadian rhythm dysfunction (CRD) is less understood. We hypothesize greater CRD and sleep disruption with poorer cognitive function in AD compared to normal cognition. Methods We examined 3 groups:1)mild cognitive impairment with positive AD biomarkers(MCI-AD),n=18, 2)cognitively normal at high risk for AD(HR)(APOEƐ4 carriers),n=19, 3)cognitively normal APOEƐ4 non-carriers(CL),n=16 (National Institute of Aging, IMMUNE-AD). DNA extraction and APOEƐ4 genotyping were performed under the Cleveland Clinic Lou Ruvo Center for Brain Health Aging and Neurodegenerative Disease Biobank. We evaluated actigraphy-based (Motionlogger MicroWatch, Ambulatory Monitoring,Inc®) sleep (wake episodes(WE), total sleep time(TST), sleep efficiency(SE), sleep fragmentation index(SFI)) and circadian (mesor, amplitude, robustness, sleep regulatory index(SRI), intradaily stability) predictors and sleep study-based (ApneaLink Air by ResMed®) predictors (apnea hypopnea index(AHI,3% desaturation) and recording time<90%SaO2) across the groups and assessed association with cognition (Mini-Mental State Exam(MMSE)). Analysis of variance (ANOVA) or Kruskal-Wallis with Bonferroni adjustment was used for cross-group comparisons. ANCOVA assessed cross-group association of MMSE and sleep/circadian indices. Models were adjusted for age, sex, race, education, and BMI. Results Age differed across MCI-AD, HR, and CL groups (68.4±6.2,71.2±3.7,73.7±3.7 respectively,p=0.008). MCI-AD had more WE than HR and CL (14.4±5.6,10.9±3.9,10.9±3.5 respectively,p=0.033). In MCI-AD, the following associations were observed: 5% increase in SE was associated with 0.49 point higher MMSE (coefficient0.49, 95%CI[0.03,0.95],p=0.038), 1 hour increase in TST was associated with 0.81 point higher MMSE (coefficient0.81, 95%CI[0.24,1.37],p=0.006), and 1 unit increase in SFI was associated with 0.36 point lower MMSE (coefficient-0.36, 95%CI[-0.64,-0.08],p=0.013). Key measures differed: CLs had lower AHI, MCI-AD had less TST SaO2<90%, MCI-AD had the largest and HR the lowest SFI, and MCI-AD had lesser robustness but higher mesor and amplitude. Conclusion In this comparative study of carefully AD biomarker-phenotyped and APOEƐ4-genotyped patients and normal cognition controls, less sleep time and more fragmented sleep are associated with poorer MMSE scores in MCI-AD. Preliminary results show cognitively normal participants at risk of AD(HR) do not show CRD seen in MCI-AD and are more consistent with controls (CL). Support (if any) Catalyst Award. MCI cohort: Alzheimer’s Association, 2014-NIRG-305310. IMMUNE-AD, R01AG022304. CADRC, P30 AG062428. Jane and Lee Seidman Fund.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".