Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults
Abstract 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 frequen...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Geriatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12877-023-03983-2 |
| _version_ | 1797836256472727552 |
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| author | Somayeh Khosroazad Christopher F. Gilbert Jessica B. Aronis Katrina M. Daigle Masoumeh Esfahani Ahmed Almaghasilah Fayeza S. Ahmed Merrill F. Elias Thomas M. Meuser Leonard W. Kaye Clifford M. Singer Ali Abedi Marie J. Hayes |
| author_facet | Somayeh Khosroazad Christopher F. Gilbert Jessica B. Aronis Katrina M. Daigle Masoumeh Esfahani Ahmed Almaghasilah Fayeza S. Ahmed Merrill F. Elias Thomas M. Meuser Leonard W. Kaye Clifford M. Singer Ali Abedi Marie J. Hayes |
| author_sort | Somayeh Khosroazad |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T15:05:47Z |
| format | Article |
| id | doaj.art-008620136d7049ff9891f6c74022d949 |
| institution | Directory Open Access Journal |
| issn | 1471-2318 |
| language | English |
| last_indexed | 2024-04-09T15:05:47Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Geriatrics |
| spelling | doaj.art-008620136d7049ff9891f6c74022d9492023-04-30T11:27:26ZengBMCBMC Geriatrics1471-23182023-04-0123111210.1186/s12877-023-03983-2Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adultsSomayeh Khosroazad0Christopher F. Gilbert1Jessica B. Aronis2Katrina M. Daigle3Masoumeh Esfahani4Ahmed Almaghasilah5Fayeza S. Ahmed6Merrill F. Elias7Thomas M. Meuser8Leonard W. Kaye9Clifford M. Singer10Ali Abedi11Marie J. Hayes12Electrical and Computer Engineering, University of MaineActivas Diagnostics, LLCActivas Diagnostics, LLCPsychology Department, Suffolk UniversityActivas Diagnostics, LLCElectrical and Computer Engineering, University of MainePsychology Department, University of MainePsychology Department, University of MaineCenter for Excellence On Aging, University of New EnglandCenter On Aging, University of MainePsychology Department, University of MaineElectrical and Computer Engineering, University of MaineActivas Diagnostics, LLCAbstract 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.https://doi.org/10.1186/s12877-023-03983-2Alzheimer’ Disease and Related Disorders (ADRD)Mild Cognitive ImpairmentAmnestic type (aMCI)Sleep disorderMemory lossAging |
| spellingShingle | Somayeh Khosroazad Christopher F. Gilbert Jessica B. Aronis Katrina M. Daigle Masoumeh Esfahani Ahmed Almaghasilah Fayeza S. Ahmed Merrill F. Elias Thomas M. Meuser Leonard W. Kaye Clifford M. Singer Ali Abedi Marie J. Hayes Sleep movements and respiratory coupling as a biobehavioral metric for early Alzheimer’s disease in independently dwelling adults BMC Geriatrics Alzheimer’ Disease and Related Disorders (ADRD) Mild Cognitive Impairment Amnestic type (aMCI) Sleep disorder Memory loss Aging |
| 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 | Alzheimer’ Disease and Related Disorders (ADRD) Mild Cognitive Impairment Amnestic type (aMCI) Sleep disorder Memory loss Aging |
| url | https://doi.org/10.1186/s12877-023-03983-2 |
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