Year: 2025
Type: article
Abstract: Alzheimer's disease (AD), the leading cause of dementia, poses a significant challenge in early detection. Research indicates that sleep disturbances may precede noticeable cognitive decline. Normal ageing makes sleep lighter and shorter, but the changes are much more substantial in AD. Patients usually have less slow-wave sleep (SWS), reduced rapid eye movement (REM) sleep, more awakenings at night, and a longer time to fall asleep. Electroencephalography (EEG) reveals slower rhythms, fewer spindles, and abnormal spindle-slow wave coupling. Some suggest impaired sleep accelerates AD by hindering amyloid clearance. Recent work shows that sleep measures, especially EEG features, might help predict who will decline and can be added to models with magnetic resonance imaging (MRI) or cerebrospinal fluid (CSF) markers. There are also studies using machine learning with polysomnography (PSG) and EEG, which look promising. But there are problems too, since sleep varies a lot between people, and not every sleep problem means AD. Ethical considerations arise, such as the appropriateness of informing individuals of potential health risks based solely on sleep data. This paper will go over what is known about sleep in early AD, the brain mechanisms behind it, and how sleep might be used to predict disease.
Source: Theoretical and Natural Science
Author: Ruohan Huang
Institution: University of Washington
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415182053
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