Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer’s Prevention

Author:

Du Lianlian12ORCID,Langhough Rebecca134,Hermann Bruce P15ORCID,Jonaitis Erin134,Betthauser Tobey J134ORCID,Cody Karly Alex134ORCID,Mueller Kimberly134,Zuelsdorff Megan36,Chin Nathaniel34,Ennis Gilda E34,Bendlin Barbara B1347,Gleason Carey E137,Christian Bradley T38,Plante David T9ORCID,Chappell Rick23,Johnson Sterling C1347

Affiliation:

1. Wisconsin Alzheimer’s Institute, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53726 , USA

2. Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison , Madison, WI 53792 , USA

3. Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53792 , USA

4. Department of Medicine, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53726 , USA

5. Department of Neurology, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53726 , USA

6. University of Wisconsin-Madison School of Nursing , Madison, WI 53705 , USA

7. Madison VA GRECC, William S. Middleton Memorial Hospital , Madison, WI 53705 , USA

8. Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53705 , USA

9. Department of Psychiatry, University of Wisconsin-Madison School of Medicine and Public Health , Madison, WI 53719 , USA

Abstract

AbstractPrevious studies suggest associations between self-reported sleep problems and poorer health, cognition, Alzheimer’s disease pathology and dementia-related outcomes. It is important to develop a deeper understanding of the relationship between these complications and sleep disturbance, a modifiable risk factor, in late midlife, a time when Alzheimer’s disease pathology may be accruing. The objectives of this study included application of unsupervised machine learning procedures to identify distinct subgroups of persons with problematic sleep and the association of these subgroups with concurrent measures of mental and physical health, cognition and PET-identified amyloid. Dementia-free participants from the Wisconsin Registry for Alzheimer’s Prevention (n = 619) completed sleep questionnaires including the Insomnia Severity Index, Epworth Sleepiness Scale and Medical Outcomes Study Sleep Scale. K-means clustering analysis identified discrete sleep problem groups who were then compared across concurrent health outcomes (e.g. depression, self-rated health and insulin resistance), cognitive composite indices including episodic memory and executive function and, in a subset, Pittsburgh Compound B PET imaging to assess amyloid burden. Significant omnibus tests (P < 0.05) were followed with pairwise comparisons. Mean (SD) sample baseline sleep assessment age was 62.6 (6.7). Cluster analysis identified three groups: healthy sleepers [n = 262 (42.3%)], intermediate sleepers [n = 229 (37.0%)] and poor sleepers [n = 128 (20.7%)]. All omnibus tests comparing demographics and health measures across sleep groups were significant except for age, sex and apolipoprotein E e4 carriers; the poor sleepers group was worse than one or both of the other groups on all other measures, including measures of depression, self-reported health and memory complaints. The poor sleepers group had higher average body mass index, waist–hip ratio and homeostatic model assessment of insulin resistance. After adjusting for covariates, the poor sleepers group also performed worse on all concurrent cognitive composites except working memory. There were no differences between sleep groups on PET-based measures of amyloid. Sensitivity analyses indicated that while different clustering approaches resulted in different group assignments for some (predominantly the intermediate group), between-group patterns in outcomes were consistent. In conclusion, distinct sleep characteristics groups were identified with a sizable minority (20.7%) exhibiting poor sleep characteristics, and this group also exhibited the poorest concurrent mental and physical health and cognition, indicating substantial multi-morbidity; sleep group was not associated with amyloid PET estimates. Precision-based management of sleep and related factors may provide an opportunity for early intervention that could serve to delay or prevent clinical impairment.

Funder

National Institutes of Health

Alzheimer’s Association

National Institute of Child Health and Human Development

Publisher

Oxford University Press (OUP)

Subject

Neurology,Cellular and Molecular Neuroscience,Biological Psychiatry,Psychiatry and Mental health

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