Associations of informant-based sleep reports with Alzheimer’s disease pathologies

Abstract

Purpose

Recent studies have found associations of increased brain amyloid beta (Aβ) accumulation and several abnormal sleep-wake patterns, including shorter latency and increased fragmentation in preclinical Alzheimer’s disease (AD). There is little known about the relationship between sleep and tau. The objective of this study was to understand the associations of both tau and Aβ with early signs of sleep and night-time behavior changes in clinically normal elderly adults. Specifically, we have addressed the question of how informant-based subjective sleep reports are linked to regional [¹⁸F]flortaucipir and [¹⁸F]florbetapir uptake.

Methods

Imaging and behavioral data from 35 subjects were obtained from the Alzheimer’s Disease Neuroimaging Initiative. The Neuropsychiatric Inventory Sleep (NPI-sleep) Questionnaire was used to assess the sleep and night-time behavior changes. Regional tau-positron emission tomography (PET) (entorhinal, brainstem) and Aβ-PET (posterior cingulate, precuneus, medial orbitofrontal) uptake values were calculated. A series of linear regression analyses were used to determine the combination of sleep symptoms that built the best models to predict each pathology.

Results

Informant-based reports of abnormal night-time behavior (NPI questions k3, k5, and k8) were significantly associated with increased entorhinal tau and Aβ (all regions) accumulation. Interestingly, informant-based reports of sleep deficiencies without abnormal nigh-time activity (NPI questions k1, k2, and k6) were negatively associated with entorhinal tau burden.

Conclusion

Detection of abnormal night-time behaviors (wandering, pacing, other inappropriate activities) by family members indicates early signs of both AD pathologies and may encourage the affected individuals to seek help by health care providers for detailed cognitive/neurobehavioral assessments.

Keywords:

• Aβ
• tau
• positron emission tomography
• mild cognitive impairment
• sleep
• neuropsychiatric inventory

Acknowledgment

Data collection and sharing for this project were funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; MesoScale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

Disclosure

The author reports no conflicts of interest in this work.

Supplementary materials

library(car)

library(ggplot2)

library(Hmisc)

######################################## read data ################################################

dat = read.csv(‘C:……………../subject_data.csv’, stringsAsFactors = FALSE)

dat1n = na.omit(dat)

dat1n$k1 = as.factor(dat1n$k1)

dat1n$k2 = as.factor(dat1n$k2)

dat1n$k3 = as.factor(dat1n$k3)

dat1n$k4 = as.factor(dat1n$k4)

dat1n$k5 = as.factor(dat1n$k5)

dat1n$k6 = as.factor(dat1n$k6)

dat1n$k7 = as.factor(dat1n$k7)

dat1n$k8 = as.factor(dat1n$k8)

dat1n$sex = as.factor(dat1n$sex)

dat1n$APOE = as.factor(dat1n$APOE)

###################################### backward elimination #######################################

fit1 = lm(ent_L ~ k1 + k2 + k3 + k4 + k5 + k6 + k7 + k8, data = dat1n)

selection = step(fit1, direction = “backward”)

result_out = selection$coefficients

ouput_temp = summary(eval(selection$call))

adj_rsq = round(ouput_temp$adj.r.squared, 5)

####################### second step: linear regression (determine positive and negative coeff) #################

fit2 = lm(ent_L ~ Age + sex + APOE + k1 + k2 + k3 + k5 + k6 + k8, data = dat1n)

summary(fit2)

######################## third step: linear regression for composite scores ##############################

fit3 = lm(ent_L ~ Age + sex + APOE + k358 +k126, data = dat1n)

summary(fit3)