Sleep arousals are associated with the polygenic risk for developing Alzheimer's disease and with cognitive change in healthy late middle-aged individuals. - 2026
Sleep arousals are associated with the polygenic risk for developing Alzheimer's disease and with cognitive change in healthy late middle-aged individuals.
[en] [en] STUDY OBJECTIVES: Sleep disturbances are increasingly recognized as early features of Alzheimer's disease (AD) neuropathology. Specifically, spontaneous arousals during sleep have been associated with the burden of Amyloid beta in the brain of healthy late middle-aged individuals. However, it remains unclear whether heterogeneity of arousals relates to genetic risk for AD in younger adults or to cognitive change later in life. Here, we evaluated the association between arousals, polygenic risk scores (PRS) for AD, and cognitive performance and change in healthy young and late-middle-aged individuals.
METHODS: We classified spontaneous arousals using in-lab EEG sleep recordings in 453 younger individuals (22±2.7y; 49 women) and 87 late middle-aged individuals (59.3±5.3y; 59 women) based on their association with sleep stage transitions and changes in muscle tone. We examined the associations between arousal types, AD-PRS, baseline cognitive performance and, in late middle-aged individuals, cognitive change over 2 and 7 years.
RESULTS: The prevalence of arousals associated with sleep stage transition was higher in late middle-aged vs. younger individuals. In late middle-aged but not younger individuals, transition arousals with muscle tone increases correlated with lower AD-PRS, better attentional performance and lower memory change over follow-ups, whereas transition arousals without muscle tone increases were linked to higher AD-PRS, poorer baseline attention, and greater memory change across follow-up periods.
CONCLUSIONS: The heterogeneity in spontaneous arousals during sleep may reflect their physiological intensity or underlying neural activation, and may indicate vulnerability to AD in late middle-aged individuals. The findings may help identify early markers of neurodegenerative risk.
Research Center/Unit :
GIGA-SLEEP - GIGA CRC In vivo Imaging-Sleep and chronobiology - ULiège
Disciplines :
Neurology
Author, co-author :
Mortazavi, Nasrin ; GIGA-Institute, CRC-Human Imaging, University of Liège, Belgium ; Psychology and Cognitive Neuroscience Research Unit, University of Liège, Belgium
Zubkov, Mikhail ; Université de Liège - ULiège > Département des sciences biomédicales et précliniques
Chylinski, Daphne ; GIGA-Institute, CRC-Human Imaging, University of Liège, Belgium
Collette, Fabienne ; Université de Liège - ULiège > Département de Psychologie > Neuropsychologie de l'adulte
Maquet, Pierre ; Université de Liège - ULiège > Département des sciences cliniques > Neurologie
Vandewalle, Gilles ✱; Université de Liège - ULiège > GIGA > GIGA Neurosciences - Sleep and chronobiology
Talwar, Puneet ✱; Université de Liège - ULiège > Département de Biologie, Ecologie et Evolution
✱ These authors have contributed equally to this work.
Language :
English
Title :
Sleep arousals are associated with the polygenic risk for developing Alzheimer's disease and with cognitive change in healthy late middle-aged individuals.
EU - European Union F.R.S.-FNRS - Fonds de la Recherche Scientifique FWB - Wallonia-Brussels Federation WELBIO - Walloon Excellence in Life Sciences and Biotechnology FRA - Fondation Recherche Alzheimer ULiège - University of Liège Fondation Pierre et Simone Clerdent ERDF - European Regional Development Fund Fonds Léon Fredericq
Funding text :
EU Joint Programme Neurodegenerative Disease Research
Zhang, Ren R, Yang L, et al. Sleep in Alzheimer’s disease: a systematic review and meta-analysis of polysomnographic findings. Transl Psychiatry. 2022; 12(1):136. 10.1038/s41398-022-01897-y
Van Egroo M, Narbutas J, Chylinski D, et al. Sleep–wake regulation and the hallmarks of the pathogenesis of Alzheimer’s disease. Sleep. 2019; 42(4):zsz017. 10.1093/sleep/zsz017
Shi L, Chen SJ, Ma MY, et al. Sleep disturbances increase the risk of dementia: a systematic review and meta-analysis. Sleep Med Rev. 2018; 40: 4–16. 10.1016/j.smrv.2017.06.010
Tsai CY, Wu SM, Kuan YC, et al. Associations between risk of Alzheimer’s disease and obstructive sleep apnea, intermittent hypoxia, and arousal responses: a pilot study. Front Neurol. 2022; 13: 1038735. 10.3389/fneur.2022.1038735
Lim ASP, Kowgier M, Yu L, Buchman AS, Bennett DA. Sleep fragmentation and the risk of incident Alzheimer’s disease and cognitive decline in older persons. Sleep. 2013; 36(7):1027–1032. 10.5665/sleep.2802
Chylinski D, van Egroo M, Narbutas J, et al. Heterogeneity in the links between sleep arousals, amyloid-β, and cognition. JCI Insight. 2021; 6(24):e152858. 10.1172/jci.insight.152858
Braak H, Del Tredici K. Where, when, and in what form does sporadic Alzheimer’s disease begin? Curr Opin Neurol. 2012; 25(6):708–714. 10.1097/WCO.0b013e32835a3432
Van Egroo M, van Someren EJW, Grinberg LT, Bennett DA, Jacobs HIL. Associations of 24-hour rest-activity rhythm fragmentation, cognitive decline, and postmortem locus coeruleus hypopigmentation in Alzheimer’s disease. Ann Neurol. 2024; 95(4):653–664. 10.1002/ana.26880
Osorio-Forero A, Cherrad N, Banterle L, Fernandez LMJ, Lüthi A. When the locus coeruleus speaks up in sleep: recent insights, emerging perspectives. Int J Mol Sci. 2022; 23(9):5028. 10.3390/ijms23095028
Fleisher AS, Chen K, Liu X, et al. Apolipoprotein E ε4 and age effects on florbetapir positron emission tomography in healthy aging and Alzheimer disease. Neurobiol Aging. 2013; 34(1):1–12. 10.1016/j.neurobiolaging.2012.04.017
Escott-Price V, Sims R, Bannister C, et al. Common polygenic variation enhances risk prediction for Alzheimer’s disease. Brain. 2015; 138(12):3673–3684. 10.1093/brain/awv268
Talwar P, Sinha J, Grover S, et al. Dissecting complex and multifactorial nature of Alzheimer’s disease pathogenesis: a clinical, genomic, and systems biology perspective. Mol Neurobiol. 2016; 53(7):4833–4864. 10.1007/s12035-015-9390-0
Reitz C, Pericak-Vance MA, Foroud T, Mayeux R. A global view of the genetic basis of Alzheimer disease. Nat Rev Neurol. 2023; 19(5):261–277. 10.1038/s41582-023-00789-z
Escott-Price V, Myers AJ, Huentelman M, Hardy J. Polygenic risk score analysis of pathologically confirmed Alzheimer disease. Ann Neurol. 2017; 82(2):311–314. 10.1002/ana.24999
Baker E, Escott-Price V. Polygenic risk scores in Alzheimer’s disease: current applications and future directions. Front Digit Heal. 2020; 2: 14. 10.3389/fdgth.2020.00014
Luckett ES, Abakkouy Y, Reinartz M, et al. Association of Alzheimer’s disease polygenic risk scores with amyloid accumulation in cognitively intact older adults. Alzheimer's Res Ther. 2022; 14(1):138. 10.1186/s13195-022-01079-4
Ge T, Sabuncu MR, Smoller JW, Sperling RA, Mormino EC, Alzheimer's Disease Neuroimaging Initiative. Dissociable influences of APOE ε4 and polygenic risk of AD dementia on amyloid and cognition. Neurology. 2018; 90(18):e1605–e1612. 10.1212/WNL.0000000000005415
Xicota L, Gyorgy B, Grenier-Boley B, et al. Association of APOE-independent Alzheimer disease polygenic risk score with brain amyloid deposition in asymptomatic older adults. Neurology. 2022; 99(5):e462–e475. 10.1212/WNL.0000000000200544
Muto V, Koshmanova E, Ghaemmaghami P, et al. Alzheimer’s disease genetic risk and sleep phenotypes in healthy young men: association with more slow waves and daytime sleepiness. Sleep. 2021; 44(1):zsaa137. 10.1093/sleep/zsaa137
Beck AT, Epstein N, Brown G, Steer RA. An inventory for measuring clinical anxiety: psychometric properties. J Consult Clin Psychol. 1988; 56(6):893–897. 10.1037/0022-006X.56.6.893
Beck AT, Steer RA, Carbin MG. Psychometric properties of the Beck Depression Inventory: twenty-five years of evaluation. Clin Psychol Rev. 1988; 8(1):77–100. 10.1016/0272-7358(88)90050-5
Buysse DJ, Reynolds CF III, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989; 28(2):193–213. 10.1016/0165-1781(89)90047-4
Johns MW. A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep. 1991; 14(6):540–545. 10.1093/sleep/14.6.540
Berry RB, Brooks R, Gamaldo CE, Harding SM, Marcus C, Vaughn BV. The AASM Manual for the Scoring of Sleep and Associated Events. Rules, Terminol Tech Specif Darien, Illinois, Am Acad Sleep Med. 2012; 176: 2012
Vandewalle G, Archer SN, Wuillaume C, et al. Functional magnetic resonance imaging-assessed brain responses during an executive task depend on interaction of sleep homeostasis, circadian phase, and PER3 genotype. J Neurosci. 2009; 29(25):7948–7956. 10.1523/JNEUROSCI.0229-09.2009
Ly JQM, Gaggioni G, Chellappa SL, et al. Circadian regulation of human cortical excitability. Nat Commun. 2016; 7(1):11828. 10.1038/ncomms11828
Muto V, Jaspar M, Meyer C, et al. Local modulation of human brain responses by circadian rhythmicity and sleep debt. Science. 2016; 353(6300):687–690. 10.1126/science.aad2993
Mascetti L, Foret A, Schrouff J, et al. Concurrent synaptic and systems memory consolidation during sleep. J Neurosci. 2013; 33(24):10182–10190. 10.1523/JNEUROSCI.0284-13.2013
Gaggioni G, Ly JQM, Muto V, et al. Age-related decrease in cortical excitability circadian variations during sleep loss and its links with cognition. Neurobiol Aging. 2019; 78: 52–63. 10.1016/j.neurobiolaging.2019.02.004
Koshmanova E, Muto V, Chylinski D, et al. Genetic risk for insomnia is associated with objective sleep measures in young and healthy good sleepers. Neurobiol Dis. 2022; 175: 105924. 10.1016/j.nbd.2022.105924
Berthomier C, Drouot X, Herman-Stoïca M, et al. Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep. 2007; 30(11):1587–1595. 10.1093/sleep/30.11.1587
Peter-Derex L, Berthomier C, Taillard J, et al. Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders. J Clin Sleep Med. 2021; 17(3):393–402. 10.5664/jcsm.8864
Chylinski D, Rudzik F, Coppieters T Wallant D, et al. Validation of an automatic arousal detection algorithm for whole-night sleep EEG recordings. Clocks & Sleep. 2020; 2(3):258–272. 10.3390/clockssleep2030020
Purcell S, Neale B, Todd-Brown K, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007; 81(3):559–575. 10.1086/519795
Wightman DP, Jansen IE, Savage JE, et al. A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet. 2021; 53(9):1276–1282. 10.1038/s41588-021-00921-z
Murphy AE, Schilder BM, Skene NG. MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics. Bioinformatics. 2021; 37(23):4593–4596. 10.1093/bioinformatics/btab665
Weiskopf N, Helms G. Multi-parameter mapping of the human brain at 1mm resolution in less than 20 minutes. Proc Intl Soc Mag Reson Med. 2008; 16: 2241.
Tabelow K, Balteau E, Ashburner J, et al. hMRI–A toolbox for quantitative MRI in neuroscience and clinical research. Neuroimage. 2019; 194: 191–210. 10.1016/j.neuroimage.2019.01.029
Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007; 38(1):95–113. 10.1016/j.neuroimage.2007.07.007
Klunk WE, Koeppe RA, Price JC, et al. The Centiloid project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015; 11(1):1–15.e4. 10.1016/j.jalz.2014.07.003
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002; 15(1):273–289. 10.1006/nimg.2001.0978
Grothe MJ, Barthel H, Sepulcre J, et al. In vivo staging of regional amyloid deposition. Neurology. 2017; 89(20):2031–2038. 10.1212/WNL.0000000000004643
Krasny S, Yan C, Hartley SL, et al. Assessing amyloid PET positivity and cognitive function in Down syndrome to guide clinical trials targeting amyloid. Alzheimers Dement. 2024; 20(8):5570–5577. 10.1002/alz.14068
Stark SM, Yassa MA, Lacy JW, Stark CEL. A task to assess behavioral pattern separation (BPS) in humans: data from healthy aging and mild cognitive impairment. Neuropsychologia. 2013; 51(12):2442–2449. 10.1016/j.neuropsychologia.2012.12.014
Cardebat D, Doyon B, Puel M, Goulet P, Joanette Y. Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level. Acta Neurol Belg. 1990; 90(4):207–217.
Kirchner WK. Age differences in short-term retention of rapidly changing information. J Exp Psychol. 1958; 55(4):352–358. 10.1037/h0043688
Grober E, Buschke H, Crystal H, Bang S, Dresner R. Screening for dementia by memory testing. Neurology. 1988; 38(6):900–903. 10.1212/WNL.38.6.900
Bowie CR, Harvey PD. Administration and interpretation of the trail making test. Nat Protoc. 2006; 1(5):2277–2281. 10.1038/nprot.2006.390
Wechsler D. Échelle clinique de mémoire de Wechsler, 3e édition (MEM-III): manuel. Paris, France: Les Éditions du Centre de Psychologie Appliquée (ECPA); 2001.
Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape. J R Stat Soc Ser C Appl Stat. 2005; 54(3):507–554. 10.1111/j.1467-9876.2005.00510.x
Stasinopoulos DM, Rigby RA. Generalized additive models for location scale and shape (GAMLSS) in R. J Stat Softw. 2008; 23: 1–46.
Rigby RA, Stasinopoulos DM. Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat Methods Med Res. 2014; 23(4):318–332. 10.1177/0962280212473302
Stasinopoulos MD, Rigby RA, Heller GZ, Voudouris V, De Bastiani F. Flexible Regression and Smoothing: Using GAMLSS in R. New York: CRC Press, Taylor & Francis Group; 2017: 10.1201/b21973
Rigby RA, Stasinopoulos MD, Heller GZ, De Bastiani F. Distributions for Modeling Location, Scale, and Shape: Using GAMLSS in R. New York: Chapman and Hall/CRC; 2019: 10.1201/9780429298547
Marks SM, Lockhart SN, Baker SL, Jagust WJ. Tau and β-amyloid are associated with medial temporal lobe structure, function, and memory encoding in normal aging. J Neurosci. 2017; 37(12):3192–3201. 10.1523/JNEUROSCI.3769-16.2017
Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009; 41(4):1149–1160. 10.3758/BRM.41.4.1149
Tsapanou A, Gao Y, Stern Y, Barral S. Polygenic score for sleep duration. Association with cognition. Sleep Med. 2020; 74: 262–266. 10.1016/j.sleep.2020.07.001
Mahowald MW, Schenck CH. Insights from studying human sleep disorders. Nature. 2005; 437(7063):1279–1285. 10.1038/nature04287
Halasz P, Bodizs R. Dynamic Structure of NREM Sleep. London: Springer Science & Business Media; 2012: 10.1007/978-1-4471-4333-8.
Foustoukos G, Lüthi A. Monoaminergic signaling during mammalian NREM sleep - recent insights and next-level questions. Curr Opin Neurobiol. 2025; 92: 103025. 10.1016/j.conb.2025.103025
Osorio-Forero A, Foustoukos G, Cardis R, et al. Infraslow noradrenergic locus coeruleus activity fluctuations are gatekeepers of the NREM–REM sleep cycle. Nat Neurosci. 2025; 28(1):84–96. 10.1038/s41593-024-01822-0
Sharon O, Zhelezniakov V, Gat Y, et al. Slow wave synchrony during NREM sleep tracks cognitive impairment in prodromal Alzheimer’s disease. Alzheimers Dement. 2025; 21(5):e70247. 10.1002/alz.70247
Chylinski D, Van Egroo M, Narbutas J, et al. Timely coupling of sleep spindles and slow waves is linked to early amyloid-β burden and predicts memory decline. Elife. 2022; 11: e78191. 10.7554/eLife.78191
Halász P, Terzano M, Parrino L, Bódizs R. The nature of arousal in sleep. J Sleep Res. 2004; 13(1):1–23. 10.1111/j.1365-2869.2004.00388.x
Dos Santos Lima GZ, Lobao-Soares B, Corso G, et al. Hippocampal and cortical communication around micro-arousals in slow-wave sleep. Sci Rep. 2019; 9(1):5876. 10.1038/s41598-019-42100-5
Jarosiewicz B, Skaggs WE. Level of arousal during the small irregular activity state in the rat hippocampal EEG. J Neurophysiol. 2004; 91(6):2649–2657. 10.1152/jn.01082.2003
Boyce R, Glasgow SD, Williams S, Adamantidis A. Causal evidence for the role of REM sleep theta rhythm in contextual memory consolidation. Science. 2016; 352(6287):812–816. 10.1126/science.aad5252
Carter ME, Yizhar O, Chikahisa S, et al. Tuning arousal with optogenetic modulation of locus coeruleus neurons. Nat Neurosci. 2010; 13(12):1526–1533. 10.1038/nn.2682
Zou G, Xu J, Zhou S, et al. Functional MRI of arousals in nonrapid eye movement sleep. Sleep. 2020; 43(2):zsz218. 10.1093/sleep/zsz218
Kang D, Ding M, Topchiy I, Shifflett L, Kocsis B. Theta-rhythmic drive between medial septum and hippocampus in slow-wave sleep and microarousal: a Granger causality analysis. J Neurophysiol. 2015; 114(5):2797–2803. 10.1152/jn.00542.2015
Miyawaki H, Billeh YN, Diba K. Low activity microstates during sleep. Sleep. 2017; 40(6):zsx066. 10.1093/sleep/zsx066
Leonenko G, Shoai M, Bellou E, et al. Genetic risk for alzheimer disease is distinct from genetic risk for amyloid deposition. Ann Neurol. 2019; 86(3):427–435. 10.1002/ana.25530
Steriade M. The corticothalamic system in sleep. Front Biosci. 2003; 8(4):d878–d899. 10.2741/1043
Schmidt C, Peigneux P, Cajochen C. Age-related changes in sleep and circadian rhythms: impact on cognitive performance and underlying neuroanatomical networks. Front Neurol. 2012; 3:118–129. 10.3389/fneur.2012.00118
Aloia MS, Arnedt JT, Davis JD, Riggs RL, Byrd D. Neuropsychological sequelae of obstructive sleep apnea-hypopnea syndrome: a critical review. J Int Neuropsychol Soc. 2004; 10(5):772–785. 10.1017/S1355617704105134
Roehrs T, Merlotti L, Petrucelli N, Stepanski E, Roth T. Experimental sleep fragmentation. Sleep. 1994; 17(5):438–443. 10.1093/sleep/17.5.438
Koch S, Schmidtke J, Krawczak M, Caliebe A. Clinical utility of polygenic risk scores: a critical 2023 appraisal. J Community Genet. 2023; 14(5):471–487. 10.1007/s12687-023-00645-z
Li J, Vitiello MV, Gooneratne NS. Sleep in normal aging. Sleep Med Clin. 2022; 17(2):161–171. 10.1016/j.jsmc.2022.02.007
Eggert T, Dorn H, Danker-Hopfe H. Nocturnal brain activity differs with age and sex: comparisons of sleep EEG power spectra between young and elderly men, and between 60–80-year-old men and women. Nat Sci Sleep. 2021; 13: 1611–1630. 10.2147/NSS.S327221.
Andrew MK, Tierney MC. The puzzle of sex, gender and Alzheimer’s disease: why are women more often affected than men? Women’s Heal. 2018; 14: 1745506518817995. 10.1177/1745506518817995
Horne JA, Ostberg O. A self-assessment questionnaire to determine morningness–eveningness in human circadian rhythms. Int J Chronobiol. 1976; 4(2):97–110
