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Fan J, Mei J, Yang Y, Lu J, Wang Q, Yang X, Chen G, Wang R, Han Y, Sheng R, Wang W, Ding F. Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model. Stress Health 2024:e3386. [PMID: 38411360 DOI: 10.1002/smi.3386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 12/20/2023] [Accepted: 02/14/2024] [Indexed: 02/28/2024]
Abstract
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardiogram (ECG) and a Huawei smart device, we conducted 24-h dual recordings of 159 medical workers working regular shifts. Based on photoplethysmography (PPG) and accelerometer signals acquired by the Huawei smart device, we sorted episodes of cyclic alternating pattern (CAP; unstable sleep), non-cyclic alternating pattern (NCAP; stable sleep), wakefulness, and rapid eye movement (REM) sleep based on cardiopulmonary coupling (CPC) algorithms. We further calculated the HRV indices during NCAP, CAP and REM sleep episodes using both the Holter ECG and smart-device PPG signals. We later developed a machine learning model to predict stress severity based only on the smart device data obtained from the participants along with a clinical evaluation of emotion and stress conditions. Sleep phasic HRV indices predict individual stress severity with better performance in CAP or REM sleep than in NCAP. Using the smart device data only, the optimal machine learning-based stress prediction model exhibited accuracy of 80.3 %, sensitivity 87.2 %, and 63.9 % for specificity. Sleep phasic heart rate variability can be accurately evaluated using a smart device and subsequently can be used for stress predication.
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Affiliation(s)
- Jingjing Fan
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junhua Mei
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Yuan Yang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiajia Lu
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Quan Wang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyun Yang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guohua Chen
- Department of Cardiology and Department of Neurology, The First Hospital of Wuhan City, Wuhan, China
| | - Runsen Wang
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Yujia Han
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Rong Sheng
- Huawei Technologies Co., Ltd., Shenzhen, China
| | - Wei Wang
- Department of Cardiology and Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fengfei Ding
- Department of Pharmacology, Shanghai Medical College, Fudan University, Shanghai, China
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Lin FV, Heffner KL. Autonomic nervous system flexibility for understanding brain aging. Ageing Res Rev 2023; 90:102016. [PMID: 37459967 PMCID: PMC10530154 DOI: 10.1016/j.arr.2023.102016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 07/07/2023] [Accepted: 07/13/2023] [Indexed: 07/20/2023]
Abstract
A recent call was made for autonomic nervous system (ANS) measures as digital health markers for early detection of Alzheimer's disease and related dementia (AD/ADRD). Nevertheless, contradictory or inconclusive findings exist. To help advance understanding of ANS' role in dementia, we draw upon aging and dementia-related literature, and propose a framework that centers on the role of ANS flexibility to guide future work on application of ANS function to differentiating the degree and type of dementia-related brain pathologies. We first provide a brief review of literature within the past 10 years on ANS and dementia-related brain pathologies. Next, we present an ANS flexibility model, describing how the model can be applied to understand these brain pathologies, as well as differentiate or even be leveraged to modify typical brain aging and dementia. Lastly, we briefly discuss the implication of the model for understanding resilience and vulnerability to dementia-related outcomes.
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Affiliation(s)
- Feng V Lin
- Department of Psychiatry and Behavioral Sciences, Stanford University, USA; Wu Tsai Neurosciences Institute, Stanford University, USA.
| | - Kathi L Heffner
- School of Nursing, University of Rochester, USA; Department of Psychiatry, School of Medicine and Dentistry, University of Rochester, USA; Department of Medicine, School of Medicine and Dentistry, University of Rochester, USA
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3
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Chen CW, Kwok YT, Cheng YT, Huang YS, Kuo TBJ, Wu CH, Du PJ, Yang AC, Yang CCH. Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer's disease transgenic mice. Sci Rep 2023; 13:11231. [PMID: 37433857 DOI: 10.1038/s41598-023-38214-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 06/26/2023] [Indexed: 07/13/2023] Open
Abstract
Occurrence of amyloid-β (Aβ) aggregation in brain begins before the clinical onset of Alzheimer's disease (AD), as preclinical AD. Studies have reported that sleep problems and autonomic dysfunction associate closely with AD. However, whether they, especially the interaction between sleep and autonomic function, play critical roles in preclinical AD are unclear. Therefore, we investigated how sleep patterns and autonomic regulation at different sleep-wake stages changed and whether they were related to cognitive performance in pathogenesis of AD mice. Polysomnographic recordings in freely-moving APP/PS1 and wild-type (WT) littermates were collected to study sleep patterns and autonomic function at 4 (early disease stage) and 8 months of age (advanced disease stage), cognitive tasks including novel object recognition and Morris water maze were performed, and Aβ levels in brain were measured. APP/PS1 mice at early stage of AD pathology with Aβ aggregation but without significant differences in cognitive performance had frequent sleep-wake transitions, lower sleep-related delta power percentage, lower overall autonomic activity, and lower parasympathetic activity mainly during sleep compared with WT mice. The same phenomenon was observed in advanced-stage APP/PS1 mice with significant cognitive deficits. In mice at both disease stages, sleep-related delta power percentage correlated positively with memory performance. At early stage, memory performance correlated positively with sympathetic activity during wakefulness; at advanced stage, memory performance correlated positively with parasympathetic activity during both wakefulness and sleep. In conclusion, sleep quality and distinction between wake- and sleep-related autonomic function may be biomarkers for early AD detection.
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Affiliation(s)
- Chieh-Wen Chen
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Health and Leisure Management, Yuanpei University of Medical Technology, Hsinchu, Taiwan
| | - Yam-Ting Kwok
- Department of Neurology, Far Eastern Memorial Hospital, New Taipei, Taiwan
| | - Yu-Ting Cheng
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yu-Shan Huang
- Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Terry B J Kuo
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
- Center for Mind and Brain Medicine, Tsaotun Psychiatric Center, Ministry of Health and Welfare, Nantou, Taiwan
| | - Cheng-Han Wu
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Pei-Jing Du
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Institute of Brain Science, Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
| | - Cheryl C H Yang
- Institute of Brain Science, Brain Research Center, and Sleep Research Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Li-Nong St., Taipei, 11221, Taiwan.
- Sleep Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan.
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Kong SDX, Gordon CJ, Hoyos CM, Wassing R, D’Rozario A, Mowszowski L, Ireland C, Palmer JR, Grunstein RR, Shine JM, McKinnon AC, Naismith SL. Heart rate variability during slow wave sleep is linked to functional connectivity in the central autonomic network. Brain Commun 2023; 5:fcad129. [PMID: 37234683 PMCID: PMC10208252 DOI: 10.1093/braincomms/fcad129] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 02/20/2023] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Reduced heart rate variability can be an early sign of autonomic dysfunction in neurodegenerative diseases and may be related to brain dysfunction in the central autonomic network. As yet, such autonomic dysfunction has not been examined during sleep-which is an ideal physiological state to study brain-heart interaction as both the central and peripheral nervous systems behave differently compared to during wakefulness. Therefore, the primary aim of the current study was to examine whether heart rate variability during nocturnal sleep, specifically slow wave (deep) sleep, is associated with central autonomic network functional connectivity in older adults 'at-risk' of dementia. Older adults (n = 78; age range = 50-88 years; 64% female) attending a memory clinic for cognitive concerns underwent resting-state functional magnetic resonance imaging and an overnight polysomnography. From these, central autonomic network functional connectivity strength and heart rate variability data during sleep were derived, respectively. High-frequency heart rate variability was extracted to index parasympathetic activity during distinct periods of sleep, including slow wave sleep as well as secondary outcomes of non-rapid eye movement sleep, wake after sleep onset, and rapid eye movement sleep. General linear models were used to examine associations between central autonomic network functional connectivity and high-frequency heart rate variability. Analyses revealed that increased high-frequency heart rate variability during slow wave sleep was associated with stronger functional connectivity (F = 3.98, P = 0.022) in two core brain regions within the central autonomic network, the right anterior insular and posterior midcingulate cortex, as well as stronger functional connectivity (F = 6.21, P = 0.005) between broader central autonomic network brain regions-the right amygdala with three sub-nuclei of the thalamus. There were no significant associations between high-frequency heart rate variability and central autonomic network connectivity during wake after sleep onset or rapid eye movement sleep. These findings show that in older adults 'at-risk' of dementia, parasympathetic regulation during slow wave sleep is uniquely linked to differential functional connectivity within both core and broader central autonomic network brain regions. It is possible that dysfunctional brain-heart interactions manifest primarily during this specific period of sleep known for its role in memory and metabolic clearance. Further studies elucidating the pathophysiology and directionality of this relationship should be conducted to determine if heart rate variability drives neurodegeneration, or if brain degeneration within the central autonomic network promotes aberrant heart rate variability.
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Affiliation(s)
- Shawn D X Kong
- Correspondence to: Shawn Dexiao KongHealthy Brain Ageing ProgramBrain and Mind Centre, University of Sydney100 Mallett St, Camperdown, NSW 2050, Australia E-mail:
| | - Christopher J Gordon
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW 2037, Australia
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia
| | - Camilla M Hoyos
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW 2050, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW 2050, Australia
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW 2050, Australia
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW 2037, Australia
| | - Rick Wassing
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW 2037, Australia
| | - Angela D’Rozario
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW 2050, Australia
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW 2050, Australia
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW 2037, Australia
| | - Loren Mowszowski
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW 2050, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW 2050, Australia
- School of Psychology, Faculty of Science, University of Sydney, Camperdown, NSW 2050, Australia
| | - Catriona Ireland
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW 2050, Australia
| | - Jake R Palmer
- Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Camperdown, NSW 2050, Australia
| | - Ronald R Grunstein
- CIRUS, Centre for Sleep and Chronobiology, Woolcock Institute of Medical Research, University of Sydney, Glebe, NSW 2037, Australia
- Faculty of Medicine and Health, University of Sydney, Camperdown, NSW 2050, Australia
- Royal Prince Alfred Hospital, University of Sydney, Camperdown, NSW 2050, Australia
| | - James M Shine
- Royal Prince Alfred Hospital, University of Sydney, Camperdown, NSW 2050, Australia
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Liu CR, Kuo TBJ, Jou JH, Lai CTL, Chang YK, Liou YM. Bright Morning Lighting Enhancing Parasympathetic Activity at Night: A Pilot Study on Elderly Female Patients with Dementia without a Pacemaker. Healthcare (Basel) 2023; 11:healthcare11060793. [PMID: 36981450 PMCID: PMC10048435 DOI: 10.3390/healthcare11060793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/04/2023] [Accepted: 03/05/2023] [Indexed: 03/30/2023] Open
Abstract
Exposure to bright morning light (BML) entrains the master circadian clock, modulates physiological circadian rhythms, and reduces sleep-wake disturbances. However, its impact on the autonomic nervous system at night remains unclear. Here, we investigated the effects of BML exposure on parasympathetic nervous system (PSNS) and sympathetic nervous system (SNS) activity at night in elderly women. This nonrandomized controlled pilot study included female participants aged ≥ 60 years who were diagnosed with a type of dementia or cognitive disorder, excluding individuals with pacemakers. The treatment group was exposed to 2500 lx of BML, whereas the control group was exposed to 200 lx of general lighting. We measured heart rate variability to quantify ANS activity. The treatment group displayed significant increases in high-frequency (HF) power (Roy's largest root = 1.62; p < 0.001) and nonsignificant decreases in normalized low-frequency (LF%) power. The corresponding nonsignificant decreases in the low-frequency/high-frequency (LF/HF) ratio and cognitive function were correlated with PSNS activity (Roy's largest root = 1.41; p < 0.001), which improved severe dementia. BML exposure reduced SNS activity and enhanced PSNS activity at night in female participants, which improved cognitive function. Thus, BML therapy may be a useful clinical tool for alleviating cognitive decline.
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Affiliation(s)
| | - Terry B J Kuo
- Institute of Brain Science, Sleep Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jwo-Huei Jou
- Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Chun-Ting Lai Lai
- Institute of Brain Science, Sleep Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Yu-Kai Chang
- Department of Physical Education and Sport Sciences, National Taiwan Normal University, Taipei 106, Taiwan
| | - Yiing Mei Liou
- Institute of Community Health Care, College of Nursing, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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Xue S, Li MF, Leng B, Yao R, Sun Z, Yang Y, Gao YL, Liu X, Sun HR, Li Z, Zhang J. Complement activation mainly mediates the association of heart rate variability and cognitive impairment in adults with obstructive sleep apnea without dementia. Sleep 2023; 46:6619580. [PMID: 35766800 DOI: 10.1093/sleep/zsac146] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/14/2022] [Indexed: 12/19/2022] Open
Abstract
STUDY OBJECTIVES The relationship between autonomic nervous system dysfunction measured by heart rate variability (HRV) and cognitive impairment in obstructive sleep apnea (OSA) patients is complex and still not well understood. We aimed to analyze the role of complement activation, Alzheimer's disease (AD) biomarkers, and white matter hyperintensity (WMH) in modulating the association of HRV with cognitive performance. METHODS There were 199 subjects without dementia, including 42 healthy controls, 80 OSA patients with mild cognitive impairment (MCI), and 77 OSA patients without cognitive impairment. All participants who completed polysomnography, cognition, WMH volume, and 5-min HRV analysis were recorded during wakefulness and sleep periods. Neuron-derived exosome and astrocyte-derived exosome proteins were measured by ELISA kits. RESULTS The OSA with MCI group were associated with a lower mean of standard deviations of R-R intervals for 5-min intervals (SDANN index) during wakefulness, standard deviation of the R-R interval (SDNN) during sleep stage and percentage of adjacent R-R intervals differing by more than 50 ms (PNN50) in each stage compared with OSA without MCI. The influence of HRV on cognition was partially mediated by complement activation (C5b-9 mediated a maximum of 51.21%), AD biomarkers, and WMH. CONCLUSIONS Lower SDANN index and PNN50 during wakefulness and SDNN and PNN50 during sleep periods were found in OSA patients with MCI, suggesting potential vulnerability to autonomic and parasympathetic dysfunction. Complement activation, AD biomarkers, and WMH might partially mediate and interact with the influence of HRV on cognitive impairment in OSA patients. CLINICAL TRIAL REGISTRATION ChiCTR1900021544.
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Affiliation(s)
- Song Xue
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China.,Weifang Medical University, Weifang, Shandong 261053, China
| | - Meng-Fan Li
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Bing Leng
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Ran Yao
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Zhuoran Sun
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Yanyan Yang
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China.,Weifang Medical University, Weifang, Shandong 261053, China
| | - Yan-Ling Gao
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Xiaoxiao Liu
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Hai-Rong Sun
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Zhenguang Li
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
| | - Jinbiao Zhang
- Department of Neurology, Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University, Weihai, Shandong 264200, China
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Abulafia C, Vidal MF, Olivar N, Odzak A, Brusco I, Guinjoan SM, Cardinali DP, Vigo DE. An Exploratory Study of Sleep-Wake Differences of Autonomic Activity in Patients with Mild Cognitive Impairment: The Role of Melatonin as a Modulating Factor. Clin Interv Aging 2023; 18:771-781. [PMID: 37200894 PMCID: PMC10187579 DOI: 10.2147/cia.s394749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 05/08/2023] [Indexed: 05/20/2023] Open
Abstract
Purpose The objective of the present study was to assess sleep-wake differences of autonomic activity in patients with mild cognitive impairment (MCI) compared to control subjects. As a post-hoc objective, we sought to evaluate the mediating effect of melatonin on this association. Patients and Methods A total of 22 MCI patients (13 under melatonin treatment) and 12 control subjects were included in this study. Sleep-wake periods were identified by actigraphy and 24hr-heart rate variability measures were obtained to study sleep-wake autonomic activity. Results MCI patients did not show any significant differences in sleep-wake autonomic activity when compared to control subjects. Post-hoc analyses revealed that MCI patients not taking melatonin displayed lower parasympathetic sleep-wake amplitude than controls not taking melatonin (RMSSD -7 ± 1 vs 4 ± 4, p = 0.004). In addition, we observed that melatonin treatment was associated with greater parasympathetic activity during sleep (VLF 15.5 ± 0.1 vs 15.1 ± 0.1, p = 0.010) and in sleep-wake differences in MCI patients (VLF 0.5 ± 0.1 vs 0.2 ± 0.0, p = 0.004). Conclusion These preliminary findings hint at a possible sleep-related parasympathetic vulnerability in patients at prodromal stages of dementia as well as a potential protective effect of exogenous melatonin in this population.
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Affiliation(s)
- Carolina Abulafia
- Laboratory of Chronophysiology, Institute for Biomedical Research (BIOMED), Pontifical Catholic University of Argentina (UCA) and CONICET, Buenos Aires, Argentina
- Facultad de Psicología, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - María F Vidal
- Servicio de Psiquiatría, Departamento de Neurología, Fleni, Buenos Aires, Argentina
| | - Natividad Olivar
- Hospital de Clínicas “José de San Martín”, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Andrea Odzak
- Servicio de Clínica Médica, Hospital Argerich, Buenos Aires, Argentina
| | - Ignacio Brusco
- Hospital de Clínicas “José de San Martín”, Universidad de Buenos Aires, Buenos Aires, Argentina
- Servicio de Clínica Médica, Hospital Argerich, Buenos Aires, Argentina
- CONICET, Buenos Aires, Argentina
| | | | - Daniel P Cardinali
- Facultad de Ciencias Médicas, Universidad Católica Argentina, Buenos Aires, Argentina
| | - Daniel E Vigo
- Laboratory of Chronophysiology, Institute for Biomedical Research (BIOMED), Pontifical Catholic University of Argentina (UCA) and CONICET, Buenos Aires, Argentina
- Faculty of Psychology and Educational Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
- Correspondence: Daniel E Vigo, Instituto de Investigaciones Biomédicas, Pontificia Universidad Católica Argentina, Alicia Moreau de Justo 1500, 4° piso, Buenos Aires, C1107AAZ, Argentina, Tel +54 0810-2200-822 ext 1152, Email ;
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Liu Z, Zhang L, Wu J, Zheng Z, Gao J, Lin Y, Liu Y, Xu H, Zhou Y. Machine learning-based classification of circadian rhythm characteristics for mild cognitive impairment in the elderly. Front Public Health 2022; 10:1036886. [PMID: 36388285 PMCID: PMC9650188 DOI: 10.3389/fpubh.2022.1036886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 10/10/2022] [Indexed: 01/29/2023] Open
Abstract
Introduction Using wrist-wearable sensors to ecological transient assessment may provide a more valid assessment of physical activity, sedentary time, sleep and circadian rhythm than self-reported questionnaires, but has not been used widely to study the association with mild cognitive impairment and their characteristics. Methods 31 normal cognitive ability participants and 68 MCI participants were monitored with tri-axial accelerometer and nocturnal photo volumetric pulse wave signals for 14 days. Two machine learning algorithms: gradient boosting decision tree and eXtreme gradient boosting were constructed using data on daytime physical activity, sedentary time and nighttime physiological functions, including heart rate, heart rate variability, respiratory rate and oxygen saturation, combined with subjective scale features. The accuracy, precision, recall, F1 value, and AUC of the different models are compared, and the training and model effectiveness are validated by the subject-based leave-one-out method. Results The low physical activity state was higher in the MCI group than in the cognitively normal group between 8:00 and 11:00 (P < 0.05), the daily rhythm trend of the high physical activity state was generally lower in the MCI group than in the cognitively normal group (P < 0.05). The peak rhythms in the sedentary state appeared at 12:00-15:00 and 20:00. The peak rhythms of rMSSD, HRV high frequency output power, and HRV low frequency output power in the 6h HRV parameters at night in the MCI group disappeared at 3:00 a.m., and the amplitude of fluctuations decreased; the amplitude of fluctuations of LHratio nocturnal rhythm increased and the phase was disturbed; the oxygen saturation was between 90 and 95% and less than 90% were increased in all time periods (P < 0.05). The F1 value of the two machine learning algorithms for MCI classification of multi-feature data combined with subjective scales were XGBoost (78.02) and GBDT (84.04). Conclusion By collecting PSQI Scale data combined with circadian rhythm characteristics monitored by wrist-wearable sensors, we are able to construct XGBoost and GBDT machine learning models with good discrimination, thus providing an early warning solution for identifying family and community members with high risk of MCI.
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Affiliation(s)
- Zhizhen Liu
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou, China,Zhizhen Liu
| | - Lin Zhang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Jingsong Wu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Zhicheng Zheng
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Jiahui Gao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China
| | - Yongsheng Lin
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yinghua Liu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Haihua Xu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Yongjin Zhou
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China,Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China,*Correspondence: Yongjin Zhou
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Iwasaki A, Fujiwara K, Nakayama C, Sumi Y, Kano M, Nagamoto T, Kadotani H. R-R interval-based sleep apnea screening by a recurrent neural network in a large clinical polysomnography dataset. Clin Neurophysiol 2022; 139:80-89. [DOI: 10.1016/j.clinph.2022.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 03/10/2022] [Accepted: 04/12/2022] [Indexed: 11/03/2022]
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ECG and Heart Rate Variability in Sleep-Related Breathing Disorders. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:159-183. [PMID: 36217084 DOI: 10.1007/978-3-031-06413-5_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Here we discuss the current perspectives of comprehensive heart rate variability (HRV) analysis in electrocardiogram (ECG) signals as a non-invasive and reliable measure to assess autonomic function in sleep-related breathing disorders (SDB). It is a tool of increasing interest as different facets of HRV can be implemented to screen and diagnose SDB, monitor treatment efficacy, and prognose adverse cardiovascular outcomes in patients with sleep apnea. In this context, the technical aspects, pathophysiological features, and clinical applications of HRV are discussed to explore its usefulness in better understanding SDB.
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Grässler B, Dordevic M, Herold F, Darius S, Langhans C, Halfpaap N, Labott BK, Müller P, Ammar A, Thielmann B, Böckelmann I, Müller NG, Hökelmann A. Relationship between Resting State Heart Rate Variability and Sleep Quality in Older Adults with Mild Cognitive Impairment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182413321. [PMID: 34948937 PMCID: PMC8703743 DOI: 10.3390/ijerph182413321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/12/2021] [Accepted: 12/14/2021] [Indexed: 11/16/2022]
Abstract
Sleep problems can be caused by psychological stress but are also related to cardiovascular and neurodegenerative diseases. Improving lifestyle behaviors, such as good sleep hygiene, can help to counteract the negative effects of neurodegenerative diseases and to improve quality of life. The purpose of this cross-sectional study was to investigate the relationship between subjectively reported measures of sleep quality (via Pittsburgh Sleep Quality Index (PSQI)) and objective measures of cardiac autonomic control (via resting state heart rate variability (HRV)) among individuals with mild cognitive impairment (MCI). The PSQI and resting state HRV data of 42 MCI participants (69.0 ± 5.5; 56–80 years) were analyzed. Nineteen of the participants reported poor sleep quality (PSQI score > 5). Good sleepers showed higher resting heart rate than bad sleepers (p = 0.037; ES = 0.670). Correlation analysis showed a significant correlation between the parameter HF nu and sleep efficiency, contrasting the expected positive association between reduced HRV and poor sleep quality in healthy and individuals with specific diseases. Otherwise, there were no significances, indicating that measures of subjective sleep quality and resting HRV were not related in the present sample of MCI participants. Further research is needed to better understand the complex relationship between HRV and lifestyle factors (e.g., sleep) in MCI.
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Affiliation(s)
- Bernhard Grässler
- Department of Sport Science, Faculty of Humanities, Otto von Guericke University, 39106 Magdeburg, Germany; (C.L.); (N.H.); (B.K.L.); (A.A.); (A.H.)
- Correspondence: ; Tel.: +49-391-6756682
| | - Milos Dordevic
- Research Group Neuroprotection, German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany; (M.D.); (F.H.); (P.M.); (N.G.M.)
- Department of Neurology, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany
- Research Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences, University of Potsdam, 14469 Potsdam, Germany
| | - Fabian Herold
- Research Group Neuroprotection, German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany; (M.D.); (F.H.); (P.M.); (N.G.M.)
- Department of Neurology, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany
- Research Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences, University of Potsdam, 14469 Potsdam, Germany
| | - Sabine Darius
- Department of Occupational Medicine, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany; (S.D.); (B.T.); (I.B.)
| | - Corinna Langhans
- Department of Sport Science, Faculty of Humanities, Otto von Guericke University, 39106 Magdeburg, Germany; (C.L.); (N.H.); (B.K.L.); (A.A.); (A.H.)
| | - Nicole Halfpaap
- Department of Sport Science, Faculty of Humanities, Otto von Guericke University, 39106 Magdeburg, Germany; (C.L.); (N.H.); (B.K.L.); (A.A.); (A.H.)
| | - Berit K. Labott
- Department of Sport Science, Faculty of Humanities, Otto von Guericke University, 39106 Magdeburg, Germany; (C.L.); (N.H.); (B.K.L.); (A.A.); (A.H.)
| | - Patrick Müller
- Research Group Neuroprotection, German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany; (M.D.); (F.H.); (P.M.); (N.G.M.)
- Department of Neurology, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany
| | - Achraf Ammar
- Department of Sport Science, Faculty of Humanities, Otto von Guericke University, 39106 Magdeburg, Germany; (C.L.); (N.H.); (B.K.L.); (A.A.); (A.H.)
| | - Beatrice Thielmann
- Department of Occupational Medicine, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany; (S.D.); (B.T.); (I.B.)
| | - Irina Böckelmann
- Department of Occupational Medicine, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany; (S.D.); (B.T.); (I.B.)
| | - Notger G. Müller
- Research Group Neuroprotection, German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany; (M.D.); (F.H.); (P.M.); (N.G.M.)
- Department of Neurology, Medical Faculty, Otto von Guericke University, 39120 Magdeburg, Germany
- Research Group Degenerative and Chronic Diseases, Movement, Faculty of Health Sciences, University of Potsdam, 14469 Potsdam, Germany
- Center for Behavioral Brain Sciences (CBBS), Brenneckestraße 6, 39118 Magdeburg, Germany
| | - Anita Hökelmann
- Department of Sport Science, Faculty of Humanities, Otto von Guericke University, 39106 Magdeburg, Germany; (C.L.); (N.H.); (B.K.L.); (A.A.); (A.H.)
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