Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults

作者：Somayeh Khosroazad, Christopher F. Gilbert, Jessica Aronis, Katrina M. Daigle, Masoumeh Esfahani, Ahmed Almaghasilah, Fayeza S. Ahmed, Merrill F. Elias, Thomas Meuser, Leonard W. Kaye, Clifford M. Singer, Ali Abedi, Marie J. Hayes · 发表于：BMC Geriatrics · 年份：2023 · DOI：10.1186/s12877-023-03983-2 · 被引用次数：10 · 研究领域：Sleep and related disorders、Sleep and Wakefulness Research、Obstructive Sleep Apnea Research

INTRODUCTION: Sleep disorder is often the first symptom of age-related cognitive decline associated with Alzheimer's disease (AD) observed in primary care. The relationship between sleep and early AD was examined using a patented sleep mattress designed to record respiration and high frequency movement arousals. A machine learning algorithm was developed to classify sleep features associated with early AD. METHOD: Community-dwelling older adults (N = 95; 62-90 years) were recruited in a 3-h catchment area. Study participants were tested on the mattress device in the home bed for 2 days, wore a wrist actigraph for 7 days, and provided sleep diary and sleep disorder self-reports during the 1-week study period. Neurocognitive testing was completed in the home within 30-days of the sleep study. Participant performance on executive and memory tasks, health history and demographics were reviewed by a geriatric clinical team yielding Normal Cognition (n = 45) and amnestic MCI-Consensus (n = 33) groups. A diagnosed MCI group (n = 17) was recruited from a hospital memory clinic following diagnostic series of neuroimaging biomarker assessment and cognitive criteria for AD. RESULTS: In cohort analyses, sleep fragmentation and wake after sleep onset duration predicted poorer executive function, particularly memory performance. Group analyses showed increased sleep fragmentation and total sleep time in the diagnosed MCI group compared to the Normal Cognition group. Machine learning algori...