Sleep Structure Alterations in Early Alzheimer’s Disease and Their Predictive Value

Ruohan Huang

doi:10.54254/2753-8818/2025.AU27724

Theoretical and Natural ScienceOpen access

Sleep Structure Alterations in Early Alzheimer’s Disease and Their Predictive Value

Research Article

Open Access

Sleep Structure Alterations in Early Alzheimer’s Disease and Their Predictive Value

Ruohan Huang ^1*

¹ University of Washington

^*Corresponding author: Huangruohan55@outlook.com

Published on 14 October 2025

TNS Vol.141

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-395-6

ISBN (Online): 978-1-80590-396-3

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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.

Keywords:

Alzheimer’s disease, Sleep architecture, Slow-wave sleep, REM sleep, EEG biomarkers

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References

[1]. Pathmanathan, J., Westover, M. B., Sivakumaran, S., Donoghue, J., & Puryear, C. B. (2025). The role of sleep in Alzheimer’s disease: A mini review. Frontiers in Neuroscience, 19, 1428733. https: //doi.org/10.3389/fnins.2025.1428733

[2]. Gallego-Viñaras, L., Mira-Tomás, J. M., Gaeta, A. M., et al. (2024). Alzheimer’s disease detection in PSG signals: Exploring semi-supervised deep learning for sleep EEG. arXiv preprint. arXiv: 2404.03549

[3]. Ungvari, Z., Fekete, M., Lehoczki, A., et al. (2025). Sleep disorders increase the risk of dementia, Alzheimer’s disease, and cognitive decline: A meta-analysis. GeroScience, 47, 4899–4920. https: //doi.org/10.1007/s11357-025-01637-2

[4]. Zhang, Y., Ren, R., Yang, L., et al. (2022). Sleep in Alzheimer’s disease: A systematic review and meta-analysis of polysomnographic findings. Translational Psychiatry, 12(1), 136. https: //doi.org/10.1038/s41398-022-01868-w

[5]. D’Atri, A., Scarpelli, S., Gorgoni, M., et al. (2021). EEG alterations during wake and sleep in mild cognitive impairment and Alzheimer’s disease. iScience, 24(4), 102386. https: //doi.org/10.1016/j.isci.2021.102386

[6]. Liguori, C., Mercuri, N. B., Izzi, F., et al. (2017). Timely coupling of sleep spindles and slow waves linked to early amyloid-β burden in Alzheimer’s disease. Scientific Reports, 7, 9493. https: //doi.org/10.1038/s41598-017-10325-8

[7]. Yu, H., Wang, M., Xu, X., Zhang, R., & Le, W. (2021). Changes in electroencephalography and sleep architecture as potential electrical biomarkers for Alzheimer’s disease. Chinese Medical Journal, 134(6), 662–664. https: //doi.org/10.1097/CM9.0000000000001394

[8]. Tang, Y., Wei, T., Zhao, B., et al. (2025). Coupled sleep rhythm disruption predicts cognitive decline in Alzheimer’s disease. Science Bulletin, 70(12), 1491–1503. https: //doi.org/10.1016/j.scib.2025.03.023

[9]. Astara, K., Tsimpolis, A., Kalafatakis, K., et al. (2024). Sleep disorders and Alzheimer’s disease pathophysiology: The role of the glymphatic system. Mechanisms of Ageing and Development, 217, 111899. https: //doi.org/10.1016/j.mad.2023.111899

[10]. Aviles, M., Sánchez-Reyes, L. M., Álvarez-Alvarado, J. M., & Rodríguez-Reséndiz, J. (2024). Machine and deep learning trends in EEG-based detection and diagnosis of Alzheimer’s disease: A systematic review. Eng, 5(3), 1464–1484. https: //doi.org/10.3390/eng5030078

[11]. Sadeghmousavi, S., Eskian, M., Rahmani, F., & Rezaei, N. (2020). The effect of insomnia on the development of Alzheimer’s disease. Journal of Neuroinflammation, 17(1), 289. https: //doi.org/10.1186/s12974-020-01960-9

References

Cite this article

Huang,R. (2025). Sleep Structure Alterations in Early Alzheimer’s Disease and Their Predictive Value. Theoretical and Natural Science,141,70-74.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICBioMed 2025 Symposium: AI for Healthcare: Advanced Medical Data Analytics and Smart Rehabilitation

ISBN: 978-1-80590-395-6(Print) / 978-1-80590-396-3(Online)

Editor: Alan Wang

Conference website: https://2025.icbiomed.org/auckland.html

Conference date: 17 October 2025

Series: Theoretical and Natural Science

Volume number: Vol.141

ISSN: 2753-8818(Print) / 2753-8826(Online)

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