Sleep Disturbances,Cognitive Status,and Biomarkers of Dementia

Abstract

Background:

While sleep disturbances appear to be risk factors in Alzheimer’s disease (AD) progression, information such as the prevalence across dementia severity and the influence on the trajectory of cognitive decline is unclear.

Objective:

We evaluate the hypotheses that the prevalence of insomnia differs by cognitive impairment, that sleep disturbances track with AD biomarkers, and that longitudinal changes in sleep disorders affect cognition.

Methods:

We used the National Alzheimer’s Coordinating Center Database to determine the prevalence of clinician-identified insomnia and nighttime behaviors in normal, mild cognitive impairment (MCI), and demented individuals. We evaluated mean Montreal Cognitive Assessment (MoCA) scores, hippocampal volumes (HV), and CSF phosphorylated tau:amyloid-β ratios at first visit using analysis of variance with age as a covariate. In longitudinal evaluations, we assessed changes in MoCA scores and HV in insomnia and nighttime behaviors between the first and last visits.

Results:

Prevalence of insomnia was 14%, 16%, and 11% for normal, MCI, and dementia groups. Prevalence of nighttime behaviors was 14%, 21%, and 29% respectively. Insomnia patients had higher MoCA scores, larger HV, and lower pTauBeta than individuals without insomnia, indicating less neurodegeneration. In contrast, nighttime behaviors were associated with worse cognition, smaller HV, and higher pTauBeta. Similar findings were seen between longitudinal associations of sleep disorders and cognition and HV.

Conclusion:

Our findings suggest that insomnia is unreliably recognized in patients with cognitive impairment. Nighttime behaviors may better indicate the presence of sleep disturbances and have diagnostic specificity in AD over insomnia.

Keywords

Dementia hippocampus insomnia sleep sleep insufficiency

INTRODUCTION

Sleep, both in quantity and quality, is an important factor in the pathogenesis of Alzheimer’s disease (AD) [1]. Studies have shown that a lack of sleep likely contributes to the progression of dementia in general and more specifically AD via impaired clearance of amyloid-β from the brain [1 –5]. Therefore, the identification and treatment of sleep disturbances, in turn, may alter the trajectory of cognitive decline especially in AD dementia.

While insomnia and sleep disturbances appear to be risk factors for cognitive decline, basic information such as the insomnia prevalence across dementia severity and the potential influence on the trajectory of cognitive decline are unclear. We recently undertook a pilot study of internet-delivered cognitive-behavioral therapy for insomnia (CBT-i) in a mild cognitive impairment (MCI) cohort [6]. Out of 291 candidates, only 12 individuals (4% of screened) met criteria for enrollment. Although other factors contributed, we wondered if the definition of insomnia (participant-reported sleep time ≤6.5 h/night, on average) limited enrollment. In contrast to sleep time, both the DSM-5R [7] and the Insomnia Severity Index (ISI) [8] emphasize the subjective experience of insomnia. All have shortcomings. MCI or dementia may impair the ability to assess, remember, or convey subjective sleep dissatisfaction or recall the objective sleep time even when reported by caregivers.

We used the prospective National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) to evaluate the hypotheses that the prevalence of “insomnia” differs by definition and by stage of cognitive impairment in a well-validated, national database of dementia, and that certain sleep features more accurately track with established biomarkers of AD.

MATERIALS AND METHODS

Database and variables

The NACC UDS contains prospective deidentified data collected under the National Institute on Aging’s ADC program [9 –12]. Informed consents were obtained at the respective centers. Data from visits conducted between September 2005–December 2021 were analyzed.

Two variables from the NACC UDS were used to evaluate sleep disturbances. The first was the clinician evaluation of hyposomnia/insomnia (database variable HYPOSOM, present/absent). This variable has been in use since 2015. The other was the nighttime behaviors question from the Neuropsychiatric Inventory-Q (NITE, present/absent within the last month). This is a clinician-evaluated variable based on co-participants interviews (i.e., spouse, children, or other caregivers) and contains the response to “Does the patient awaken you during the night, rise too early in the morning, or take excessive naps during the day?” NITE has been used in the UDS throughout its duration. The Montreal Cognitive Assessment (MoCA) score defined a continuous variable of the severity of cognitive deficits [13]. During the course of the NACC data collection, the MoCA (NACCMOCA) replaced the Mini-Mental Status Examination (NACCMMSE). To provide a homogeneous score of cognition, we converted the database variable NACCMMSE to a calculated MoCA score using a validated conversion nomograph [14]. To create cognitive categories, we used MoCA cut-off scores (dementia <20, 20≤MCI≤25, normal >25) [15]. Hippocampal volumes (HV) are a well-validated means of tracking neurodegeneration in AD [16]. We adjusted raw volumes (variable HIPPOVOL) for differences in total intracranial volume (variable NACCICV) [16]. CSF estimates of the phosphorylated tau:amyloid-β ratio (pTauBeta, variables CSFPTAU/CSFABETA)) serve as another marker for AD [17]. We evaluated the log₁₀(pTauBeta) to provide a linear measure.

Data analysis

Initial visit

We evaluated the clinical concordance of insomnia and nighttime behaviors with a chi-square test. To determine the cross-sectional prevalence of insomnia or nighttime behaviors in MCI and dementia, we evaluated the proportion within each MoCA-defined cognitive category as determined at the first visit. We used a chi-square test to evaluate differences by sleep variables. We used values of MoCA, HV, and pTauBeta obtained at or near the first visit for associations with the initial visit assessments of insomnia and nighttime behaviors. The distributions of MoCA scores and biomarkers of HV and pTauBeta were tested in relation to insomnia or nighttime behaviors with age at the first visit (NACCAGE) as a covariate using ANOVA performed with general linear model SPSS v28.

Longitudinal follow-up

We evaluated how longitudinal changes in sleep disturbances affect cognition and biomarkers by creating four cohorts based on changes between initial versus final visit sleep disturbance status. The four cohorts were: 1) normal: symptoms of insomnia or nighttime behaviors were absent throughout; 2) remitted: insomnia or nighttime behaviors were present on the initial visit and absent on the last visit; 3) acquired: symptoms absent on the initial visit and present on the last; and 4) continuous: symptoms present on initial and last visits. Changes in variables were evaluated by the use of first visit values as baseline relative to values at the last visit. MRI studies, however, often did not correspond to the exact dates of clinic visits, so first and last MRI studies were assigned initial and outcome status. CSF measurements usually were obtained once; we did not include CSF measurements in longitudinal assessments. We evaluated the effect of longitudinal sleep disturbance status on changes in MoCA and hippocampal volumes with the use of two-way repeated-measures ANOVA performed with a general linear model using SPSS v28.

RESULTS

The database contained 44,713 subjects seen at least once for whom insomnia status was coded for 20,754 and nighttime behavior status for 41,297 subjects at first visit. All data were not consistently available for all patients, so the number available for analyses differed. Over 20,000 patients had mutually valid entries for insomnia and nighttime behaviors (Table 1). In those patients with mutually valid entries, insomnia or nighttime behaviors were present or absent together in 76% of subjects at their first visit (chi-square p < 0.0001).

Table 1

Cross-correlation between insomnia and nighttime behaviors in patients in whom both variables were validly coded. Chi-square p value <0.0001

	nighttime behaviors
insomnia	absent	present	total
absent	15,097	3,146	18,243
present	1,997	931	2,928
total	17,094	4,077	21,171

Prevalence of insomnia and nighttime behaviors

The overall prevalence of insomnia among all cognitive categories at the initial visit was 14%; the prevalence of nighttime behaviors was 21% (Fig. 1A). Prevalence of insomnia and nighttime behaviors differed significantly across cognitive categories. Within those with normal cognition, the prevalence of insomnia and nighttime behaviors was 14% for each variable. The prevalence of insomnia versus nighttime behaviors differed significantly within the worse cognitive categories. Within patients with MCI, the prevalence of nighttime behaviors (21%) exceeded the prevalence of insomnia (16%). Within patients with dementia, the prevalence of those with nighttime behaviors was nearly three times that of insomnia (29% versus 11%).

Fig. 1

A) Prevalence of insomnia and nighttime behaviors during initial visits of subjects in the NACC database by (A) Montreal Cognitive Assessment (MoCA) score cognitive classification. Nighttime behaviors occurred three times as often as insomnia in the demented group. ^*p < 0.05 by chi square. B-D) Estimated marginal means±95% confidence intervals of MoCA scores, hippocampal volumes, and CSF values of phosphorylated tau: amyloid beta ratios seen in those with insomnia or nighttime behaviors. Patient age was a covariate. Numbers indicate sample size by variable. All comparisons between mean values in present versus absent were significant p < 0.05 via ANOVA. In general, insomnia was associated with higher cognition, larger hippocampal volumes, and lower tau:beta ratios, and nighttime behaviors with lower cognition, smaller hippocampal volumes, and higher tau:beta ratios. Nighttime behaviors appear to be a symptom set that better reflects the pathophysiology of sleep deprivation on neurodegenerative markers.

Sleep disturbances and neurodegeneration biomarkers at initial visit

Characteristics of covariates are shown in Table 2. Women with insomnia outnumbered men nearly 3-to-1 in those with normal cognition. The distributions by sex leveled out with worsening cognition. In those with nighttime behaviors, the distribution between the sexes remained roughly equal. Age and cognition were inversely related; older age was associated with worse cognitive category. Patients with nighttime behaviors were slightly older than those with insomnia within cognitive categories.

Table 2

Distributions of sex and age within insomnia and nighttime behavior by MoCA-determined cognitive category (A) in cross-sectional initial visits and (B) in cohorts defined by longitudinal changes in sleep disturbances between initial and last visits

A
	insomnia		nighttime behaviors
	male (%)	age (y 95% CL)	male (%)	age (y 95% CL)
normal	27%	67.6 (67–68.2)	44%	69.0 (68.0–69.9)
MCI	40%	70.2 (69.3–71)	55%	70.8 (70.2–71.5)
dementia	45%	71.2 (70.1–72.3)	52%	73.5 (73.1–73.9)
B
	insomnia			nighttime behaviors
cohort	male	female	age	male	female	age
none	33197 (42.3%)	45306 (57.7%)	74.1 (74.–74.2)	38599 (38.4%)	61840 (61.6%)	74.2 (74.–74.3)
remitted	2227 (39.2%)	3450 (60.8%)	73. (72.6–73.4)	7060 (46.%)	8277 (54.%)	73.3 (73.–73.7)
acquired	2485 (33.6%)	4905 (66.4%)	73.9 (73.6–74.2)	10638 (48.3%)	11370 (51.7%)	74.5 (74.2–74.8)
continuous	2096 (30.2%)	4833 (69.8%)	72.9 (72.7–73.2)	5777 (57.1%)	4349 (42.9%)	71.9 (71.5–72.3)

CL, 95th confidence limits.

We evaluated insomnia or nighttime behaviors relative to MoCA scores and biomarkers of HV and pTauBeta with age at first visit as a covariate (Fig. 1B-D). Insomnia was significantly associated with higher MoCA scores, larger HV, and lower pTauBeta values, indicating those with insomnia had better cognition and less neurodegeneration than individuals whom clinicians identified to be without insomnia. In contrast, nighttime behaviors were significantly associated with worse cognition, smaller HV, and higher pTauBeta consistent with the AD pathology and more significant neurodegeneration.

Cognition and hippocampal volumes with longitudinal changes in sleep disturbances

The duration between first and last visits (and therefore first and last cognitive assessments) was 1663±1262 days (mean±standard deviation 4.6±3.5 years, range 0.14–16 years). Characteristics of covariates by longitudinal cohorts are presented in Table 2. Women outnumbered men across all classes of insomnia with female predominance most pronounced in those with continuous insomnia. The proportion of men and women inverted across outcome classes of nighttime behaviors, with women more predominant in the asymptomatic cohort and men predominant in the continuous cohort.

Changes in cognition and HV as functions of sleep disturbances are shown in Fig. 2. Eighty percent never experienced insomnia (Fig. 2A), and those with remitted, acquired, or continuous insomnia were distributed similarly across the remaining 20%. Nighttime behaviors were less stable than insomnia through follow-up, with 35% experiencing symptoms at some point in time, of which the majority were acquired (15%).

Fig. 2

A) Fractional distributions of either insomnia or nighttime behaviors divided into longitudinal cohorts identified by sleep disturbance status at initial visit versus the last follow-up visit. “Normal”: no sleep disturbances at the initial and final visit, “remitted”: remission of sleep disturbance that was present at the initial visit, “acquired”: acquisition of a sleep disturbance not present at the initial visit, “continuous”: sleep disturbance present throughout follow up. B,C) Estimated marginal means±95% confidence intervals of changes in B) MoCA scores and C) hippocampal volumes between first and last visits divided by sleep disturbance outcome class.

Declines in MoCA scores among cohorts and between insomnia and nighttime behaviors within each outcome cohort differed significantly with a few exceptions (Fig. 2B). Cognitive decline was worst in those with acquired or continuous nighttime behaviors and significantly worse than those with insomnia within the same cohort. Cognitive performance was less impaired in those without nighttime behaviors, but impressively, cognitive decline was worst among all cohorts in those who were never reported to have insomnia. No differences in cognitive decline were present in those with remission of either symptom.

Differences were less pronounced— and not statistically significant— in the analysis for changes in HV because of the relatively small changes in absolute volumes (Fig. 2C), but overall patterns as seen in cognitive decline were present. Differences within insomnia and nighttime behaviors were significant. HV decreases were significantly less in those without insomnia or nighttime behaviors and significantly larger in those with continuous insomnia or nighttime behaviors. No significant differences occurred in the intermediate outcomes of remitted or acquired sleep disturbances.

DISCUSSION

The prevalence of nighttime behaviors was about three times that of insomnia in MoCA-defined dementia. The symptom set of nighttime behaviors significantly tracked the severity of cognitive impairment and biomarkers of AD, both at initial assessment and during follow up, while insomnia did not. Longitudinally, remission of nighttime behaviors was associated with preserved cognition and hippocampal volumes. A clinical implication of our study is the bedside assessment of insomnia is difficult, especially during more advanced stages of cognitive impairment. A physiological implication of our study is that sleep disruption follows a general “dose-dependent” fashion with established biomarkers of AD. We propose that interventions for sleep disturbances may be more judiciously applied if assessment is not simply limited to “insomnia” but involves a more thorough assessment of nighttime behaviors that may be more accurate in the identification of decreased sleep.

Frankly, we were surprised. Our intention was to illustrate that insomnia was a clearly correlated symptom of neurodegeneration, but our findings suggest the opposite. One unlikely possibility is that insomnia has a protective effect, but evidence shows that lack of sleep clearly impairs cognition and facilitates neurodegeneration [1]. A better explanation is that the clinical identification of decreased sleep varies with levels of cognition. The HYPOSOM variable asks the clinician to make a decision about the presence or absence of insomnia, while NITE captures some features associated with sleep disturbances without explicitly calling it insomnia. More importantly, the NITE variable implies that a witness has contributed their observations. In this model, those with MCI and dementia may experience deficits in conveying dissatisfaction with lack of sleep or that the presence of NITE supersedes the recognition of insomnia. Nighttime behaviors represent a set of symptoms that comprise a key feature of insomnia— lack of sleep— expressed in physical manifestations that may be more easily recognized and reported by witnesses. Our findings support conclusions of a case-control study that found that patients with AD, despite having fewer subjective complaints of insomnia, had worse sleep patterns determined by actigraphy [18]. Our findings support and extend those of a recent NACC database study that found that nighttime behaviors were associated with more rapid cognitive deterioration compared to cognitively asymptomatic patients who eventually converted to AD [19]. Our study, in contrast, evaluates all-cause dementia.

Insomnia affects 20–30% of older adults [20, 21]. A meta-analysis determined that overall prevalence of broadly based sleep disorders in AD ranges between 14–69% with a pooled prevalence of 39% (95% CI 30–46%) [22]. A survey in an AD clinic found a prevalence of sleep disturbances of 24.5% [23], a rate that is close to our estimates of nighttime behaviors in dementia (29%). Our estimate of insomnia, however, is about half of most estimates, reinforcing our interpretation that insomnia was unreliably recognized because of difficulty in assessing this in cognitively impaired individuals. The higher prevalence of insomnia in women seen here agrees with prior data [20, 21]. We found, however, that the female predominance in insomnia and in nighttime behaviors drops as cognition worsens.

Our study confirms the pathophysiological correlations between sleep disturbances and HV loss [24 –26]. We extend these earlier, cross-sectional findings by refining the range of sleep symptoms that best correlate with MRI evidence of neuronal degeneration. Although a restricted range of HV changes prevented statistical significance, the trend for longitudinal HV loss associated with acquisition and continued presence of nighttime behaviors is a novel finding and provides preliminary evidence that remission or absence of sleep disturbances may attenuate neuronal consequences.

Nighttime behaviors such as agitation, “sundowning”, wandering, unregulated daytime sleep, and unwanted nocturnal wakefulness are well-established deficits of sleep in AD [20]. The inclusion of “daytime sleepiness” in the nighttime behaviors variable “NITE” recognizes that circadian dysfunction is one of the consequences of neurodegenerative disease. Given that insomnia is less recognized and that first line treatments of cognitive-behavioral therapy for insomnia (CBT-i) [27, 28] may not suit patients with dementia, circadian entrainment, sleep hygiene, and nighttime sedation probably remain the prime windows for symptomatic therapy in this severely affected group. On the other hand, these measures, as well as modified CBT-i for patients at risk for dementia [6], provide opportunities for altering the course of cognitive decline. Indeed, our longitudinal cohort shows that remitted nighttime behaviors is associated with less severe drops in cognition and a trend to larger HV than those with acquired or continuous nighttime behaviors.

A limitation of our study is inherent in the NACC database clinical determination of insomnia. Variability in assessment is inevitable because different clinicians at multiple ADC centers evaluated patients through an evolution of both an appreciation of sleep disturbances and their definitions. A specific tool such as the ISI may provide different results. Idiosyncrasies of the NACC database— a large clinically-driven collection— meant that variables may not have consistent coding. As noted above, the variables of insomnia and nighttime behaviors differ in availability through the life of the database. Therefore, “drop-outs” due to missing data may have affected our results in an unpredictable fashion. NACC UDS participants do not constitute a nationally representative sample, which may affect generalizability of our findings and explain differences in the lower rates of insomnia reported in the NACC UDS sample compared to the expected national prevalence.

The present study provides us with preliminary results that encourage further work that will focus on longitudinal analyses on to determine whether the associations between sleep disturbances and cognition are merely associations or whether sleep risk factors may facilitate development and progression of cognitive decline.

Footnotes

ACKNOWLEDGMENTS

The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADCs: P50 AG005131 (PI James Brewer, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005134 (PI Bradley Hyman, MD, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P50 AG005138 (PI Mary Sano, PhD), P50 AG005142 (PI Helena Chui, MD), P50 AG005146 (PI Marilyn Albert, PhD), P50 AG005681 (PI John Morris, MD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG008051 (PI Thomas Wisniewski, MD), P50 AG008702 (PI Scott Small, MD), P30 AG010124 (PI John Trojanowski, MD, PhD), P30 AG010129 (PI Charles DeCarli, MD), P30 AG010133 (PI Andrew Saykin, PsyD), P30 AG010161 (PI David Bennett, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG013854 (PI Robert Vassar, PhD), P50 AG016573 (PI Frank LaFerla, PhD), P50 AG016574 (PI Ronald Petersen, MD, PhD), P30 AG019610 (PI Eric Reiman, MD), P50 AG023501 (PI Bruce Miller, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P30 AG028383 (PI Linda Van Eldik, PhD), P50 AG033514 (PI Sanjay Asthana, MD, FRCP), P30 AG035982 (PI Russell Swerdlow, MD), P50 AG047266 (PI Todd Golde, MD, PhD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG049638 (PI Suzanne Craft, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Marwan Sabbagh, MD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD).

This study was not funded, and the following internal and external funding of investigators offers no potential conflicts of interest. Drs Quigg and Zawar acknowledge funding from NIH-NINDS (NeuroNEXT U24NS107182) and the Virginia Brain Institute. Dr Manning acknowledges funding from the DoD (W81XWH2010448), NIH (SB1AG037357-04A1, R01AG068128) and HRSA (4 U1QHP287440400). Guofen Yan, PhD (Department of Public Health Sciences, University of Virginia) provided editorial suggestions and statistical review.

Authors’ disclosures available online ().

References

Mander

, Winer

, Jagust

, Walker

(2016) Sleep: A novel mechanistic pathway, biomarker, and treatment target in the pathology of Alzheimer’s disease? Trends Neurosci 39, 552–566.

Sadeghmousavi

, Eskian

, Rahmani

, Rezaei

(2020) The effect of insomnia on development of Alzheimer’s disease. J Neuroinflammation 17, 289.

, Ooms

, Sutphen

, Macauley

, Zangrilli

, Jerome

, Fagan

, Mignot

, Zempel

, Claassen

, Holtzman

(2017) Slow wave sleep disruption increases cerebrospinal fluid amyloid-beta levels. Brain 140, 2104–2111.

Xie

, Kang

, Xu

, Chen

, Liao

, Thiyagarajan

, O’Donnell

, Christensen

, Nicholson

, Iliff

, Takano

, Deane

, Nedergaard

(2013) Sleep drives metabolite clearance from the adult brain. Science 342, 373–377.

Hung

, Li

, Chen

, Lu

, Liang

, Liliang

, Tsai

, Wang

(2018) Risk of dementia in patients with primary insomnia: A nationwide population-based case-control study. BMC Psychiatry 18, 38.

Mattos

, Manning

, Quigg

, Davis

, Barnes

, Sollinger

, Eckstein

, Ritterband

(2021) Feasibility and preliminary efficacy of an internet-delivered intervention for insomnia in individuals with mild cognitive impairment. J Alzheimers Dis 84, 1539–1550.

American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders: DSM-5, American Psychiatric Publishing, Washington, DC.

Bastien

, Vallieres

, Morin

(2001) Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med 2, 297–307.

Beekly

, Ramos

, Lee

, Deitrich

, Jacka

, Wu

, Hubbard

, Koepsell

, Morris

, Kukull

, NIA Alzheimer’s Disease Centers (2007) The National Alzheimer’s Coordinating Center (NACC) database: The Uniform Data Set. Alzheimer Dis Assoc Disord 21, 249–258.

10.

Beekly

, Ramos

, van Belle

, Deitrich

, Clark

, Jacka

, Kukull

, NIA-Alzheimer’s Disease Centers (2004) The National Alzheimer’s Coordinating Center (NACC) Database: An Alzheimer disease database. Alzheimer Dis Assoc Disord 18, 270–277.

11.

Morris

, Weintraub

, Chui

, Cummings

, Decarli

, Ferris

, Foster

, Galasko

, Graff-Radford

, Peskind

, Beekly

, Ramos

, Kukull

(2006) The Uniform Data Set (UDS): Clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Dis Assoc Disord 20, 210–216.

12.

Weintraub

, Salmon

, Mercaldo

, Ferris

, Graff-Radford

, Chui

, Cummings

, DeCarli

, Foster

, Galasko

, Peskind

, Dietrich

, Beekly

, Kukull

, Morris

(2009) The Alzheimer’s Disease Centers’ Uniform Data Set (UDS): The neuropsychologic test battery. Alzheimer Dis Assoc Disord 23, 91–101.

13.

Kaufer

, Cummings

, Ketchel

, Smith

, MacMillan

, Shelley

, Lopez

, DeKosky

(2000) Validation of the NPI-Q, a brief clinical form of the Neuropsychiatric Inventory. J Neuropsychiatry Clin Neurosci 12, 233–239.

14.

Bergeron

, Flynn

, Verret

, Poulin

, Bouchard

, Bocti

, Fulop

, Lacombe

, Gauthier

, Nasreddine

, Laforce

(2017) Multicenter Validation of an MMSE-MoCA Conversion Table. J Am Geriatr Soc 65, 1067–1072.

15.

Milani

, Marsiske

, Cottler

, Chen

, Striley

(2018) Optimal cutoffs for the Montreal Cognitive Assessment vary by race and ethnicity. Alzheimers Dement (Amst) 10, 773–781.

16.

Jack

CR Jr

, Petersen

, Xu

, O’Brien

, Smith

, Ivnik

, Tangalos

, Kokmen

(1998) Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology 51, 993–999.

17.

Shaw

, Vanderstichele

, Knapik-Czajka

, Clark

, Aisen

, Petersen

, Blennow

, Soares

, Simon

, Lewczuk

, Dean

, Siemers

, Potter

, Lee

, Trojanowski

, Alzheimer’s Disease Neuroimaging Initiative (2009) Cerebrospinal fluid biomarker signature in Alzheimer’s disease neuroimaging initiative subjects. Ann Neurol 65, 403–413.

18.

Most

, Aboudan

, Scheltens

, Van Someren

(2012) Discrepancy between subjective and objective sleep disturbances in early- and moderate-stage Alzheimer disease. Am J Geriatr Psychiatry 20, 460–467.

19.

Atayde

, Fischer

, Schweizer

, Munoz

(2022) Neuropsychiatric Inventory-Questionnaire assessed nighttime behaviors in cognitively asymptomatic patients with pathologically confirmed Alzheimer’s disease predict more rapid cognitive deterioration. J Alzheimers Dis 86, 1137–1147.

20.

Ancoli-Israel

, Cooke

(2005) Prevalence and comorbidity of insomnia and effect on functioning in elderly populations. J Am Geriatr Soc 53, S264–271.

21.

Leshner

(2005) NIH State of the Science Conference Statement on manifestations and management of chronic insomnia in adults. NIH State of the Science, Bethesda, MD.

22.

Zhao

, Tan

, Wang

, Jiang

, Tan

, Xu

, Li

, Wang

, Lai

, Yu

(2016) The prevalence of neuropsychiatric symptoms in Alzheimer’s disease: Systematic review and meta-analysis. J Affect Disord 190, 264–271.

23.

Moran

, Lynch

, Walsh

, Coen

, Coakley

, Lawlor

(2005) Sleep disturbance in mild to moderate Alzheimer’s disease. Sleep Med 6, 347–352.

24.

Lam

, Haroutonian

, Grummitt

, Ireland

, Grunstein

, Duffy

, D’Rozario

, Naismith

(2021) Sleep-dependent memory in older people with and without MCI: The relevance of sleep microarchitecture, OSA, hippocampal subfields, and episodic memory. Cereb Cortex 31, 2993–3005.

25.

Fjell

, Sorensen

, Amlien

, Bartres-Faz

, Bros

, Buchmann

, Demuth

, Drevon

, Duzel

, Ebmeier

, Idland

, Kietzmann

, Kievit

, Kuhn

, Lindenberger

, Mowinckel

, Nyberg

, Price

, Sexton

, Sole-Padulles

, Pudas

, Sederevicius

, Suri

, Wagner

, Watne

, Westerhausen

, Zsoldos

, Walhovd

(2020) Self-reported sleep relates to hippocampal atrophy across the adult lifespan: Results from the Lifebrain consortium. Sleep 43, zsz280.

26.

Yuen

, Rashidi-Ranjbar

, Verhoeff

, Kumar

, Gallagher

, Flint

, Herrmann

, Pollock

, Mulsant

, Rajji

, Voineskos

, Fischer

, Mah

, Group

PA-MS

(2019) Association between sleep disturbances and medial temporal lobe volume in older adults with mild cognitive impairment free of lifetime history of depression. J Alzheimers Dis 69, 413–421.

27.

Qaseem

, Kansagara

, Forciea

, Cooke

, Denberg

, Clinical Guidelines Committee of the American College of Physicians (2016) Management of chronic insomnia disorder in adults: A clinical practice guideline from the American College of Physicians. Ann Intern Med 165, 125–133.

28.

Ritterband

, Thorndike

, Ingersoll

, Lord

, Gonder-Frederick

, Frederick

, Quigg

, Cohn

, Morin

(2017) Effect of a web-based cognitive behavior therapy for insomnia intervention with 1-year follow-up: A randomized clinical trial. JAMA Psychiatry 74, 68–75.