DOI QR코드

DOI QR Code

Relationship Between Amyloid Positivity and Sleep Characteristics in the Elderly With Subjective Cognitive Decline

  • Kyung Joon Jo (Department of Neurology, College of Medicine, Gachon University Gil Medical Center) ;
  • SeongHee Ho (Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Yun Jeong Hong (Department of Neurology, Uijeongbu St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Jee Hyang Jeong (Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University School of Medicine) ;
  • SangYun Kim (Department of Neurology, Seoul National University College of Medicine) ;
  • Min Jeong Wang (ROA Neurology Clinic) ;
  • Seong Hye Choi (Department of Neurology, Inha University, School of Medicine) ;
  • SeungHyun Han (ROWAN Inc.) ;
  • Dong Won Yang (Department of Neurology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea) ;
  • Kee Hyung Park (Department of Neurology, College of Medicine, Gachon University Gil Medical Center)
  • 투고 : 2023.12.06
  • 심사 : 2024.01.21
  • 발행 : 2024.01.31

초록

Background and Purpose: Alzheimer's disease (AD) is a neurodegenerative disease characterized by a progressive decline in cognition and performance of daily activities. Recent studies have attempted to establish the relationship between AD and sleep. It is believed that patients with AD pathology show altered sleep characteristics years before clinical symptoms appear. This study evaluated the differences in sleep characteristics between cognitively asymptomatic patients with and without some amyloid burden. Methods: Sleep characteristics of 76 subjects aged 60 years or older who were diagnosed with subjective cognitive decline (SCD) but not mild cognitive impairment (MCI) or AD were measured using Fitbit® Alta HR, a wristwatch-shaped wearable device. Amyloid deposition was evaluated using brain amyloid plaque load (BAPL) and global standardized uptake value ratio (SUVR) from fluorine-18 florbetaben positron emission tomography. Each component of measured sleep characteristics was analyzed for statistically significant differences between the amyloid-positive group and the amyloid-negative group. Results: Of the 76 subjects included in this study, 49 (64.5%) were female. The average age of the subjects was 70.72±6.09 years when the study started. 15 subjects were classified as amyloid-positive based on BAPL. The average global SUVR was 1.598±0.263 in the amyloid-positive group and 1.187±0.100 in the amyloid-negative group. Time spent in slow-wave sleep (SWS) was significantly lower in the amyloid-positive group (39.4±13.1 minutes) than in the amyloid-negative group (49.5±13.1 minutes) (p=0.009). Conclusions: This study showed that SWS is different between the elderly SCD population with and without amyloid positivity. How SWS affects AD pathology requires further research.

키워드

과제정보

This study was supported by a grant from the Korean Ministry of Health and Welfare (Grant number HI18C0530).

참고문헌

  1. Tarasoff-Conway JM, Carare RO, Osorio RS, Glodzik L, Butler T, Fieremans E, et al. Clearance systems in the brain-implications for Alzheimer disease. Nat Rev Neurol 2015;11:457-470.
  2. Kang JE, Lim MM, Bateman RJ, Lee JJ, Smyth LP, Cirrito JR, et al. Amyloid-β dynamics are regulated by orexin and the sleep-wake cycle. Science 2009;326:1005-1007.
  3. Vodovotz Y, Barnard N, Hu FB, Jakicic J, Lianov L, Loveland D, et al. Prioritized research for the prevention, treatment, and reversal of chronic disease: recommendations from the lifestyle medicine research summit. Front Med (Lausanne) 2020;7:585744.
  4. Reddy OC, van der Werf YD. The sleeping brain: harnessing the power of the glymphatic system through lifestyle choices. Brain Sci 2020;10:868.
  5. Wang C, Holtzman DM. Bidirectional relationship between sleep and Alzheimer's disease: role of amyloid, tau, and other factors. Neuropsychopharmacology 2020;45:104-120.
  6. Jessen F, Amariglio RE, Buckley RF, van der Flier WM, Han Y, Molinuevo JL, et al. The characterisation of subjective cognitive decline. Lancet Neurol 2020;19:271-278.
  7. Reisberg B, Prichep L, Mosconi L, John ER, Glodzik-Sobanska L, Boksay I, et al. The pre-mild cognitive impairment, subjective cognitive impairment stage of Alzheimer's disease. Alzheimers Dement 2008;4 Suppl 1:S98-S108.
  8. Parnetti L, Chipi E, Salvadori N, D'Andrea K, Eusebi P. Prevalence and risk of progression of preclinical Alzheimer's disease stages: a systematic review and meta-analysis. Alzheimers Res Ther 2019;11:7.
  9. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FR, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA 2015;313:1924-1938.
  10. Ho S, Hong YJ, Jeong JH, Park KH, Kim S, Wang MJ, et al. Study design and baseline results in a cohort study to identify predictors for the clinical progression to mild cognitive impairment or dementia from subjective cognitive decline (CoSCo) study. Dement Neurocogn Disord 2022;21:147-161.
  11. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256:183-194.
  12. Hong YJ, Yoon B, Shim YS, Kim SO, Kim HJ, Choi SH, et al. Predictors of clinical progression of subjective memory impairment in elderly subjects: data from the Clinical Research Centers for Dementia of South Korea (CREDOS). Dement Geriatr Cogn Disord 2015;40:158-165.
  13. Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-β PET with florbetaben (18F) in patients with Alzheimer's disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol 2011;10:424-435.
  14. Lyoo CH, Ikawa M, Liow JS, Zoghbi SS, Morse CL, Pike VW, et al. Cerebellum can serve as a pseudo-reference region in Alzheimer's disease to detect neuroinflammation measured with PET radioligand binding to translocator protein (TSPO). J Nucl Med 2015;56:701-706.
  15. Fonseca P, Long X, Radha M, Haakma R, Aarts RM, Rolink J. Sleep stage classification with ECG and respiratory effort. Physiol Meas 2015;36:2027-2040.
  16. Spira AP, Gamaldo AA, An Y, Wu MN, Simonsick EM, Bilgel M, et al. Self-reported sleep and β-amyloid deposition in community-dwelling older adults. JAMA Neurol 2013;70:1537-1543.
  17. Ooms S, Overeem S, Besse K, Rikkert MO, Verbeek M, Claassen JA. Effect of 1 night of total sleep deprivation on cerebrospinal fluid β-amyloid 42 in healthy middle-aged men: a randomized clinical trial. JAMA Neurol 2014;71:971-977.
  18. Shokri-Kojori E, Wang GJ, Wiers CE, Demiral SB, Guo M, Kim SW, et al. β-Amyloid accumulation in the human brain after one night of sleep deprivation. Proc Natl Acad Sci USA 2018;115:4483-4488.
  19. Ju YS, Ooms SJ, Sutphen C, Macauley SL, Zangrilli MA, Jerome G, et al. Slow wave sleep disruption increases cerebrospinal fluid amyloid-β levels. Brain 2017;140:2104-2111.
  20. Lee YF, Gerashchenko D, Timofeev I, Bacskai BJ, Kastanenka KV. Slow wave sleep is a promising intervention target for Alzheimer's disease. Front Neurosci 2020;14:705.
  21. Westerberg CE, Mander BA, Florczak SM, Weintraub S, Mesulam MM, Zee PC, et al. Concurrent impairments in sleep and memory in amnestic mild cognitive impairment. J Int Neuropsychol Soc 2012;18:490-500.
  22. Olsson M, Arlig J, Hedner J, Blennow K, Zetterberg H. Sleep deprivation and cerebrospinal fluid biomarkers for Alzheimer's disease. Sleep (Basel) 2018;41:zsy025.
  23. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet 2020;396:413-446.
  24. Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 2015;385:2255-2263.
  25. Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D, et al. Dementia prevention, intervention, and care. Lancet 2017;390:2673-2734.
  26. Beattie Z, Pantelopoulos A, Ghoreyshi A, Oyang Y, Statan A, Heneghan C. Estimation of sleep stages using cardiac and accelerometer data from a wrist-worn device. Sleep 2017;40 Supple 1:A26.
  27. Chinoy ED, Cuellar JA, Huwa KE, Jameson JT, Watson CH, Bessman SC, et al. Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep (Basel) 2021;44:zsaa291.