Premium
Examining differences across sleep profiles in late middle‐aged adults: Results from the Wisconsin Registry for Alzheimer's Prevention (WRAP)
Author(s) -
Du Lianlian,
Koscik Rebecca L,
Jonaitis Erin M,
Betthauser Tobey J,
Cody Karly Alex,
Hermann Bruce P,
Mueller Kimberly D,
Zuelsdorff Megan,
Chin Nathaniel A,
Ennis Gilda E.,
Bendlin Barbara B,
Gleason Carey E.,
Christian Bradley T,
Plante David T,
Chappell Richard J.,
Johnson Sterling C.
Publication year - 2021
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.056304
Subject(s) - pittsburgh compound b , dementia , geriatric depression scale , medicine , gerontology , epworth sleepiness scale , cognitive decline , pittsburgh sleep quality index , cognition , effects of sleep deprivation on cognitive performance , psychology , clinical psychology , polysomnography , psychiatry , disease , depressive symptoms , apnea , sleep quality
Background Previous studies suggest associations between self‐reported poor sleep and relevant health markers and AD‐related outcomes including cognition and AD pathology. It is important to understand how sleep, a modifiable risk factor, may be related to cognition and other measures of aging in late midlife when AD pathology may be accruing. The study objectives included examining whether sleep profiles were associated with concurrent health and cognition, and positron emission tomography (PET) measures of amyloid. Methods Dementia‐free participants from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) who had completed sleep questionnaires were eligible (n=619); questionnaires included the Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and Medical Outcomes Study Sleep Scale (MOS). K‐means clustering analysis identified three clusters. We compared concurrent health markers (e.g., depression and insulin resistance) and cognitive composite scores across the sleep profile clusters. In the subset with Pittsburgh Compound B (PiB; n=107) PET imaging, we also compared amyloid measures (estimated global PiB distribution volume ratio (DVR), PiB chronicity, and PiB(+) (DVR>=1.2) at sleep assessment. Significant omnibus tests (p<.05) were followed with pairwise comparisons. Results Mean(sd) sample baseline age was 62.6(6.7). Cluster analysis identified 3 groups: Healthy Sleepers (HS, n=262(42.3%)), Intermediate Sleepers (IS, n=229(37.0%)), and Poor Sleepers (PS, n=128(20.7%); Fig. 1, Fig. 2, Table 1). All omnibus tests comparing demographics and health measures across sleep groups were significant except age, gender, and APOE e4 carriers; the PS group was worse than HS and/or IS on all other measures, including self‐reported depression, health, and memory complaints (Table 2). PS performed worse than HS and/or IS on measured body mass index, waist‐hip ratio and insulin resistance (Fig. 3a); and after adjusting for covariates, PS performed worse on all cognitive outcomes except working memory (Fig. 4). There were no differences between sleep groups on measures of amyloid (Fig. 3b). Conclusion Sleep profiles were associated with differences in additional modifiable risk factors and concurrent cognition, but not amyloid. Further analysis was needed in the association between the sleep constructs and amyloid clearance/accumulation. Management of sleep and related factors may provide an opportunity for early intervention that could ultimately delay or prevent clinical impairment.