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Record W4312086325 · doi:10.1002/alz.068725

Nighttime behaviors, but not insomnia, correlates with cognition and biomarkers of dementia

2022· article· en· W4312086325 on OpenAlexaboutno aff
Ifrah Zawar, Mark Quigg, Meghan Mattos, Carol A. Manning

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaInsomniaMontreal Cognitive AssessmentCognitionLongitudinal studyMedicineAlzheimer's diseaseRepeated measures designAnalysis of varianceInternal medicinePsychologyDiseasePsychiatryClinical psychologyPathology

Abstract

fetched live from OpenAlex

Abstract Background While sleep disturbances appear to be risk factors in the progression of Alzheimer’s disease (AD) and other dementias, little is known about the prevalence across dementia severity and the potential influence on the trajectory of cognitive decline. Method We used the prospective National Alzheimer Coordinating Center Uniform Data Set to evaluate the hypotheses that the prevalence of insomnia differs by definition and by stage of cognitive impairment, that certain sleep features track with established biomarkers of AD, and that longitudinal changes in sleep disorders affect cognition and AD biomarkers. We conducted cross‐sectional analyses to determine the prevalence of clinician‐determined insomnia and nighttime behaviors (nighttime restlessness, agitation, daytime sleepiness) in normal, mild cognitive impairment (MCI), and demented individuals, as defined by Montreal Cognitive Assessment (MoCA) cut‐off scores obtained during the patient’s first visit. We evaluated mean MoCA scores, hippocampal volumes, and CSF phosphorylated tau:beta amyloid ratios obtained near the first visit using Analysis of Variance (ANOVA) with age as a covariate. In longitudinal evaluations, we classified changes in insomnia and nighttime behaviors as “none”, “remitted”, “acquired”, or “steady” between the first and last visits. Similar changes in MoCA scores and hippocampal volumes were assessed with repeated‐measure ANOVA. Result The prevalence of clinician‐identified insomnia at initial visits was 15%, 17%, and 13% for normal, MCI, and dementia groups respectively. Prevalence of clinician‐identified nighttime behaviors was 13%, 19%, and 27% respectively. Insomnia was significantly associated with higher MoCA scores, larger hippocampal volumes, and higher pTau:Beta values, indicating those with clinician‐identified insomnia had better cognition and less neurodegeneration than individuals who clinicians identified to be without insomnia. In contrast, the presence of nighttime behaviors were associated with significantly worse cognition, smaller hippocampal volumes, and lower pTau:Beta. Similar findings were seen between longitudinal associations of sleep disorders and cognition and hippocampal volumes. Conclusion Our findings suggest that the clinician determination of insomnia may be under‐recognized in patients with cognitive impairment seen in memory disorders clinics, and that nighttime behaviors especially when reported by family members may better reflect sleep disturbances, the biological underpinnings of AD, and have diagnostic specificity in AD over insomnia.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.015
GPT teacher head0.254
Teacher spread0.239 · 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
Published2022
Admission routes1
Has abstractyes

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