Suboptimal self-reported sleep efficiency and duration are associated with faster accumulation of brain amyloid beta in cognitively unimpaired older adults

INTRODUCTION

This study investigated whether self-reported sleep quality is associated with brain amyloid beta (Aβ) accumulation.

METHODS

Linear mixed effect model analyses were conducted for 189 cognitively unimpaired (CU) older adults (mean ± standard deviation 74.0 ± 6.2; 53.2% female), with baseline self-reported sleep data, and positron emission tomography-determined brain Aβ measured over a minimum of three time points (range 33.3-72.7 months). Analyses included random slopes and intercepts, interaction for apolipoprotein E (APOE) ε4 allele status, and time, adjusting for sex and baseline age.

RESULTS

Sleep duration <6 hours, in APOE ε4 carriers, and sleep efficiency <65%, in the whole sample and APOE ε4 non-carriers, is associated with faster accumulation of brain Aβ.

DISCUSSION

These findings suggest a role for self-reported suboptimal sleep efficiency and duration in the accumulation of Alzheimer's disease (AD) neuropathology in CU individuals. Additionally, poor sleep efficiency represents a potential route via which individuals at lower genetic risk may progress to preclinical AD.

Highlights

In cognitively unimpaired older adults self-report sleep is associated with brain amyloid beta (Aβ) accumulation.Across sleep characteristics, this relationship differs by apolipoprotein E (APOE) genotype.Sleep duration <6 hours is associated with faster brain Aβ accumulation in APOE ε4 carriers.Sleep efficiency < 65% is associated with faster brain Aβ accumulation in APOE ε4 non-carriers.Personalized sleep interventions should be studied for potential to slow Aβ accumulation.

Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity that may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.