Evidence map›Paper›PMID 38746638›Full record

ArticleJournal of Alzheimer's disease reports2024

Predictive Modeling Using a Composite Index of Sleep and Cognition in the Alzheimer's Continuum: A Decade-Long Historical Cohort Study.

Xianfeng Yu, Shuqing Deng, Junxin Liu, Mingkai Zhang, Liang Zhang, Ruixian Li, Wei Zhang, Ying Han

Abstract read
In one paragraph

Article in Journal of Alzheimer's disease reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xianfeng YuDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Shuqing DengDepartment of Psychology, Brandeis University, Waltham, MA, USA.
Junxin LiuSchool of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, China.
Mingkai ZhangDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Liang ZhangSchool of Biomedical Engineering, Hainan University, Haikou, China.
Ruixian LiDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.
Wei ZhangDepartment of Rehabilitation Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Ying HanDepartment of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sleep disturbances frequently affect Alzheimer's disease (AD), with up to 65% patients reporting sleep-related issues that may manifest up to a decade before AD symptoms. Objective: To construct a nomogram that synthesizes sleep quality and cognitive performance for predicting cognitive impairment (CI) conversion outcomes. Methods: Using scores from three well-established sleep assessment tools, Pittsburg Sleep Quality Index, REM Sleep Behavior Disorder Screening Questionnaire, and Epworth Sleepiness Scale, we created the Sleep Composite Index (SCI), providing a comprehensive snapshot of an individual's sleep status. Initially, a CI conversion prediction model was formed via COX regression, fine-tuned by bidirectional elimination. Subsequently, an optimized prediction model through COX regression, depicted as a nomogram, offering predictions for CI development in 5, 8, and 12 years among cognitively unimpaired (CU) individuals. Results: After excluding CI patients at baseline, our study included 816 participants with complete baseline and follow-up data. The CU group had a mean age of 66.1±6.7 years, with 36.37% males, while the CI group had an average age of 70.3±9.0 years, with 39.20% males. The final model incorporated glial fibrillary acidic protein, Verbal Fluency Test and SCI, and an AUC of 0.8773 (0.792-0.963). Conclusions: In conclusion, the sleep-cognition nomogram we developed could successfully predict the risk of converting to CI in elderly participants and could potentially guide the design of interventions for rehabilitation and/or cognitive enhancement to improve the living quality for healthy older adults, detect at-risk individuals, and even slow down the progression of AD.

Indexed as

Alzheimer’s diseasebiomarkerspredictionsleep

Identifiers

PMID38746638
PMCPMC11091728

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.