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

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 movem...

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Autores principales: Khosroazad, Somayeh, Gilbert, Christopher F., Aronis, Jessica B., Daigle, Katrina M., Esfahani, Masoumeh, Almaghasilah, Ahmed, Ahmed, Fayeza S., Elias, Merrill F., Meuser, Thomas M., Kaye, Leonard W., Singer, Clifford M., Abedi, Ali, Hayes, Marie J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141904/
https://www.ncbi.nlm.nih.gov/pubmed/37106470
http://dx.doi.org/10.1186/s12877-023-03983-2
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author Khosroazad, Somayeh
Gilbert, Christopher F.
Aronis, Jessica B.
Daigle, Katrina M.
Esfahani, Masoumeh
Almaghasilah, Ahmed
Ahmed, Fayeza S.
Elias, Merrill F.
Meuser, Thomas M.
Kaye, Leonard W.
Singer, Clifford M.
Abedi, Ali
Hayes, Marie J.
author_facet Khosroazad, Somayeh
Gilbert, Christopher F.
Aronis, Jessica B.
Daigle, Katrina M.
Esfahani, Masoumeh
Almaghasilah, Ahmed
Ahmed, Fayeza S.
Elias, Merrill F.
Meuser, Thomas M.
Kaye, Leonard W.
Singer, Clifford M.
Abedi, Ali
Hayes, Marie J.
author_sort Khosroazad, Somayeh
collection PubMed
description 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 algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value. DISCUSSION: AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion.
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spelling pubmed-101419042023-04-29 Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults Khosroazad, Somayeh Gilbert, Christopher F. Aronis, Jessica B. Daigle, Katrina M. Esfahani, Masoumeh Almaghasilah, Ahmed Ahmed, Fayeza S. Elias, Merrill F. Meuser, Thomas M. Kaye, Leonard W. Singer, Clifford M. Abedi, Ali Hayes, Marie J. BMC Geriatr 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 algorithm showed that the time latency between movement arousals and coupled respiratory upregulation could be used as a classifier of diagnosed MCI vs. Normal Cognition cases. ROC diagnostics identified MCI with 87% sensitivity; 89% specificity; and 88% positive predictive value. DISCUSSION: AD sleep phenotype was detected with a novel sleep biometric, time latency, associated with the tight gap between sleep movements and respiratory coupling, which is proposed as a corollary of sleep quality/loss that affects the autonomic regulation of respiration during sleep. Diagnosed MCI was associated with sleep fragmentation and arousal intrusion. BioMed Central 2023-04-27 /pmc/articles/PMC10141904/ /pubmed/37106470 http://dx.doi.org/10.1186/s12877-023-03983-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Khosroazad, Somayeh
Gilbert, Christopher F.
Aronis, Jessica B.
Daigle, Katrina M.
Esfahani, Masoumeh
Almaghasilah, Ahmed
Ahmed, Fayeza S.
Elias, Merrill F.
Meuser, Thomas M.
Kaye, Leonard W.
Singer, Clifford M.
Abedi, Ali
Hayes, Marie J.
Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
title Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
title_full Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
title_fullStr Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
title_full_unstemmed Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
title_short Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
title_sort sleep movements and respiratory coupling as a biobehavioral metric for early alzheimer’s disease in independently dwelling adults
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10141904/
https://www.ncbi.nlm.nih.gov/pubmed/37106470
http://dx.doi.org/10.1186/s12877-023-03983-2
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