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Record W4411007533 · doi:10.1101/2025.05.30.656615

Comprehensive investigation of sleep architecture by <i>APOE</i> genotype: <i>APOE4</i> homozygotes have higher arousal thresholds

2025· preprint· en· W4411007533 on OpenAlexfundno aff
Gawon Cho, Orfeu M. Buxton, Daniel B. Kay, Brienne Miner

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsnot available
FundersNational Institute on AgingYork UniversityUniversity of PennsylvaniaUniversity of MiamiGeorgia State UniversityHarvard T.H. Chan School of Public HealthPennsylvania State UniversityNational Institutes of HealthNational Science Foundation
KeywordsFragmentation (computing)Sleep (system call)PsychologyHistoryComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Background and Objectives APOE4 , a genetic risk factor for Alzheimer’s Disease (AD), is associated with reduced functional connectivity of brain regions that regulate sleep, which may predispose persons to AD via altering sleep architecture. However, little is known about differences in sleep architecture by APOE genotype. Methods This cross-sectional study examined the association between APOE genotype and sleep architecture among middle-aged and older adults, using polysomnography (Sleep Heart Health Study, N=3,132). APOE genotype included: APOE4 heterozygotes, APOE4 homozygotes, APOE2 carriers, and APOE3 homozygotes. Macro sleep architecture was quantified using the percentage of time spent in rapid eye movement sleep (%REM), N1 (N1%), N2 (%N2), N3 (%N3), and arousal index. Micro sleep architecture was quantified as odds ratio product (ORP; a continuous measure of sleep depth) for overall sleep and each sleep stage, and spindle characteristics (power, density, and frequency). Linear regression was used, adjusting for covariates. Results The mean age was 67, 53 percent were female, 24% were APOE4 heterozygotes, 2% were APOE4 homozygotes, 14% were APOE2 carriers, and 60% were APOE3 homozygotes. Macro sleep architecture did not vary across genotypes. Compared with APOE3 homozygotes, APOE4 homozygotes exhibited fewer arousals with age (β=-0.33 per hour/year, p=0.04), resulting in significantly fewer arousals at age 70+. ORP decreased in a dose-response pattern with the number of APOE4 alleles during overall sleep and across all sleep stages (ORP APOE3/3 =0.94, ORP APOE3/4 =0.91, ORP APOE4/4 =0.87), and these group difference widened with each year of age. Finally, there was a trend for lower spindle density and power in APOE4 homozygotes relative to APOE3 homozygotes ( p s=0.06). Conclusions Arousal threshold increased in a dose-response manner with each APOE4 allele, as evidenced by the findings on ORP. The differences in ORP between APOE3 homozygotes and APOE4 carriers widened further with age, paralleling age-related declines in arousal index among APOE4 homozygotes. Despite these indications of elevated arousal thresholds that might suggest less sleep fragmentation in APOE4 carriers, APOE4 homozygotes exhibited poorer sleep micro architecture, including trends toward reduced sleep spindle activity. Taken together, reduced arousability in APOE4 carriers may reflect abnormalities in cortical activation that blunt arousal rather than an indicator of healthier sleep.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.252
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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