Association Between Informant-Reported Sleep Disturbance and Incident Dementia: An Analysis of the National Alzheimer’s Coordinating Center Uniform Data Set

Abstract

Informants’ reports can be useful in screening patients for future risk of dementia. We aimed to determine whether informant-reported sleep disturbance is associated with incident dementia, whether this association varies by baseline cognitive level and whether the severity of informant-reported sleep disturbance is associated with incident dementia among those with sleep disturbance. A longitudinal retrospective cohort study was conducted using the uniform data set collected by the National Alzheimer’s Coordinating Center. Older adults without dementia at baseline living with informants were included in analysis. Cox proportional hazards models showed that participants with an informant-reported sleep disturbance were more likely to develop dementia, although this association may be specific for older adults with normal cognition. In addition, older adults with more severe sleep disturbance had a higher risk of incident dementia than those with mild sleep disturbance. Informant-reported information on sleep quality may be useful for prompting cognitive screening.

Keywords

aged sleep dementia

Introduction

Sleep disturbances, such as sleep fragmentation, sleep behavior disorders, and excessive daytime sleepiness, are associated with an increased risk of cognitive impairment (Wennberg et al., 2017; Xu et al., 2020). Although the underlying mechanisms are not entirely clear, sleep disturbances may lead to potential neuronal damage, structural changes in the brain, and increased level of amyloid beta (Aβ), which could lead to cognitive decline (Kang et al., 2009; Mander et al., 2013; Xie et al., 2013). Furthermore, a positive feedback mechanism between sleep quality and cognitive function has been suggested—poor sleep results in chronic accumulation of Aβ by increasing neuronal firing or synaptic activity, which in turn contributes to further deterioration of sleep patterns. As such, detecting and addressing sleep disturbances may be important to maintain cognitive function (Ju et al., 2013, 2014).

Informants, who are often key sources of data about patients’ conditions and health behaviors, may provide a unique window into sleep issues (Rabin et al., 2012; Smith-Gamble et al., 2002). Individuals’ perception of their sleep often mismatches objective reports. Even without cognitive impairment, individuals are likely unaware of or have inaccurate perceptions about their sleep disturbance (Fernandez-Mendoza et al., 2011; Harvey & Tang, 2012; Lauderdale et al., 2008). Informant-reported sleep disturbances have moderate-to-good agreement with objectively measured sleep disorders in people with dementia, suggesting potential utility for measuring sleep disturbance in people with normal or mild-to-moderately impaired cognition (Hoekert et al., 2006). Moreover, informant-based reports of abnormal nighttime behavior have been shown to be closely related to early signs of Alzheimer’s disease (AD) pathologies such as Aβ and tau accumulation (Shokouhi, 2019). Given the important role of sleep in maintaining or even improving cognition, soliciting an informant’s assessment of an older adult’s sleep may provide important information about dementia risk.

Informant-reported information, if shown to be associated with dementia, could be a useful way to improve detection of sleep problems and direct treatment of it to reduce dementia risk. Despite its promising clinical utility, it is largely unknown whether informant-reported sleep disturbance is associated with incident dementia. Previous studies examining this association primarily measured sleep disturbances using objective sleep measures, patient self-report, or diagnosis codes in claims data (Shi et al., 2018). The few studies that have examined the association between informant-reported sleep disturbance and dementia did not adjust for important confounders, such as informants’ demographic characteristics and the relationship between informants and patients. Furthermore, these studies only included those who had normal cognition at baseline and only assessed the incidence of probable AD, which is one subtype of dementia (Burke et al., 2016, 2019). Although prior studies found a significant positive association between informant-reported sleep disturbance and the incidence of probable AD, there is considerable variability in the magnitude of the association (estimated hazard ratios ranging from 1.8 to 3.9).

To the best of our knowledge, this association has not been examined in patient subgroups with different levels of cognitive function, which prior work on the positive feedback loop between sleep disturbances and cognitive impairment suggests may be important (Ju et al., 2013, 2014). Examining whether the association varies by level of cognitive function is important because it could help prioritize high-risk subgroups for intervention.

Finally, the severity of sleep disturbance generally increases along the course of cognitive decline, suggesting that the association between sleep disturbance and dementia might vary by severity of sleep disturbance (Sharma et al., 2018; Yaffe et al., 2011). Understanding this relationship could also help classify high-risk subgroups.

Thus, the objective of this study was to examine the association between informant-reported sleep disturbance of older adults and the incidence of dementia. In addition, we examined whether baseline cognitive level modifies the association, given that there are potential synergistic effects between sleep disturbance and cognitive impairment. Among a subgroup of participants having sleep disturbance, we further examined the association between severity of informant-reported sleep disturbance and incident dementia.

Methods

Study Design

This longitudinal retrospective cohort study used the uniform data set (UDS) collected from June 2005 to September 2018, which was made public by the National Alzheimer’s Coordinating Center (NACC) in the United States. The NACC, which was established by the National Institute on Aging (NIA) to promote dementia research, houses the UDS, which is prospectively collected from the NIA-funded Alzheimer’s Disease Centers (ADCs) across the United States (Beekly et al., 2007; Besser et al., 2018; Morris et al., 2006). The primary goal of the UDS is to provide researchers with a standard set of assessment procedures, collected longitudinally, to better characterize ADC participants (Beekly et al., 2007). Further description of the database, study population, data collection, data directory, and patient visit packets are publicly available on the NACC website (https://www.alz.washington.edu). Since 2005, participants have been followed up at each approximately annual visit. In this study, we used the follow-up data until diagnosis with dementia, death (i.e., recognition of death via NACC), or dropout, starting from the initial visit of each participant. Data were primarily collected by clinicians, neuropsychologists, and ADC research personnel, and generated by participants and informants. Topics covered included socio-demographics of participants and informants, family medical history, dementia history, neurological exam findings, functional status, neuropsychological testing, brain disorders, and clinician-assessed medical conditions.

Participants

Eligible participants in the analysis were those: (a) without dementia or use of antidementia medications (donepezil, galantamine, memantine, and rivastigmine) at baseline; (b) accompanied by informants living with participants at the first visit to an ADC; (c) with at least one follow-up visit after the initial visit; and (d) whose informant answered the item on whether participants had a sleep disturbance from the Neuropsychiatric Inventory Questionnaire (NPI-Q). As such, only informants who accompanied eligible participants to ADC visit and were currently living with the participants were included. The overall UDS from June 2005 to September 2018 included 38,836 participants. Our analytic sample size was 8,460 after applying inclusion criteria (Figure 1). All participants provided informed consent at the ADC they visited, and all ADCs that contribute data to NACC are approved by their local Institutional Review Board.

Figure 1.

Participant flow diagram.

Measures

Sleep disturbance

The primary independent variable was sleep disturbance reported by informants. To operationalize this variable, we used an item from the NPI-Q included in the initial visit packet. The NPI-Q is a tool that assesses neuropsychiatric symptoms and their severity using informant’s reports. It has good psychometric properties, exhibiting good validity and reliability (Cummings, 1994; Musa et al., 2017). The NPI-Q has been previously used to collect information about neuropsychiatric symptoms among people with normal cognition and with cognitive impairment (Babulal et al., 2016; Davies et al., 2005; Fitts et al., 2015; Geda et al., 2008; Milanini et al., 2017; Seligman et al., 2013). Of 12 domains of neuropsychiatric symptoms, the nighttime behavior item has been used to measure sleep disturbance (Geda et al., 2008; Ledger et al., 2020). This item asks informants, “Does the patient awaken you during the night, rise too early in the morning, or take excessive naps during the day?” Informants can respond yes, no, or unknown. In our analysis, informants’ response was dichotomized into yes and no, treating unknown as missing. The informant-reported severity of sleep disturbance was used as an independent variable of interest in secondary analyses. It was obtained from another item in the NPI-Q where informants who reported that patients have sleep disturbance were subsequently asked to rate the severity of the symptoms by choosing one of three options: mild (noticeable, but not a significant change), moderate (significant, but not a dramatic change), and severe (very marked or prominent, a dramatic change). We dichotomized the severity of sleep disturbance variable into moderate or severe versus mild due to sample size.

Dementia

The Clinician Diagnosis form in the UDS was used to determine participants’ cognitive level. In this form, clinicians assess cognitive and behavioral status using a series of questions for which participants are diagnosed with normal cognition and behavior, cognitive impaired but not MCI, MCI, or dementia (all-cause). Clinicians are first asked,

Does the subject have normal cognition (global Clinical Dementia Rating [CDR]=0 and/or neuropsychological testing within normal range) and normal behavior (i.e., the subject does not exhibit behavior sufficient to diagnose MCI or dementia due to frontotemporal lobar degeneration or Lewy body dementia)? (Morris, 1997; Weintraub et al., 2018)

Those who answer “No” are provided with the criteria for dementia and then asked, “Does the subject meet the criteria for dementia?” If clinicians answered “No,” they are provided with MCI core clinical criteria to determine whether participants meet the criteria. Participants who meet the criteria are diagnosed with MCI, and those who do not are diagnosed with cognitive impairment but not MCI. Impaired, but not MCI, one of the clinical categories of baseline cognitive level, is not a clinical category but rather NACC conventional language based on their procedures. Participants’ baseline cognitive level was stratified into normal, impaired but not MCI, and MCI, following the NACC data collection guidelines. Also, our dependent variable, the incidence of dementia, was defined consistent with the dementia diagnosis described above. The criteria for dementia and MCI are based on the recommendations from the NIA-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease (Albert et al., 2011; McKhann et al., 2011). The questionnaires for patients and clinicians and criteria used for evaluation can be found in the most recent version of the NACC visit packet (NACC, 2015). Details about instruments used for testing and the criteria are also provided in the Supplement 1.

Covariates

Inclusion of covariates was determined based on the clinical and scientific rationale (Supplement 2). We included factors that are potentially associated with both sleep disturbance and dementia. Participant characteristics included in the models were age, sex, race, education level, marital status, living status, smoking status, alcohol abuse, body mass index (BMI), baseline cognitive level, the total number of medications including all prescription, and over-the-counter medications self-reported at the initial visit, any use of antidepressants, antipsychotics, and hypnotics. Smoking status was a continuous variable of self-reported mean number of packs smoked per day. Alcohol abuse was a variable with three groups (absent, remote/inactive, recent/active) based on an item asking participants if clinically significant impairment manifested in work, driving, legal, or social domains of life due to alcohol use over a 12-month period. Baseline cognitive level was stratified into three categories (normal, impaired but not MCI, or MCI) using the Clinician Diagnosis form at the participant’s first ADC visit. The details about the Clinician Diagnosis form is described in the “dementia” section above.

Informant characteristics included age, sex, race, and education level. The relationship between informants and participants was categorized based on whether informants were a spouse, a child, a relative, a nonfamily member, or a paid caregiver/clinician/health care provider.

Data Analysis

Follow up began on the first ADC visit of each individual and ended either on the day of the visit when a participant was first diagnosed with dementia or the most recent visit included in the UDS for those without dementia. Frequency distributions were used to describe participant and informant characteristics. A Cox proportional hazards model was fitted with sleep disturbance as an independent variable of interest adjusting for all aforementioned covariates (participant and informant characteristics) to examine the overall association between informant-reported sleep disturbance and the incidence of dementia. We then constructed three stratified Cox proportional hazards models adjusting for the same covariates, but in this case, classifying strata by baseline cognitive level (normal, impaired but not MCI, and MCI), to examine whether the association varied by the participants’ baseline cognitive level. We chose to use stratified models rather than a model with interaction terms to examine the association in each of the subgroups because stratified analysis gives more flexibility by relaxing the requirement of the proportional hazards across the levels of stratification variable and enables each level of cognitive function to have its own baseline hazard function—an important model attribute for this research question. To examine the association between informant-reported severity of sleep disturbance and incident dementia in a subgroup of participants with sleep disturbance, the same model was fitted adjusting for the same covariates as the first model but with the independent variable of interest replaced by the informant-reported severity of sleep disturbance. Only individuals with informant-reported sleep disturbance were included for this analysis.

For each of these models, a hazard ratio and a 95% confidence interval were calculated contrasting those who were reported to have a sleep disturbance with those who were not. Significance level was set at 0.05. All analyses were conducted using STATA/MP software, version 14.2.

Results

Participant Characteristics

Of the 38,836 participants with at least one ADC visit, 8,460 met the inclusion criteria for the current analysis (Figure 1). A total of 48% of the sample was female, 73% aged 61 to 80 years, and 84% white (Table 1). In terms of baseline cognitive level, 63% were normal, 7% were impaired but not MCI, and 30% were diagnosed with MCI.

Table 1.

Participant Characteristics (N = 8,460).

Variable	Participants without sleep disturbance (N = 7,189)	Participants with sleep disturbance (N = 1,271)
Variable	N (%)^a	N (%)^a
Baseline diagnosis
Normal	4,813 (70.0)	554 (43.6)
Impaired but not MCI	441 (6.1)	137 (10.8)
MCI	1,935 (26.9)	580 (45.6)
Sex
Female	3,550 (49.4)	543 (42.7)
Race
White	6,013 (83.9)	1,107 (87.3)
Black	847 (11.8)	93 (7.3)
Asian	226 (3.2)	43 (3.4)
Other race	41 (0.6)	7 (0.6)
Education
High school or lower	275 (3.8)	67 (5.3)
High school graduate	980 (13.7)	182 (14.4)
Some college	1,224 (17.0)	229 (18.1)
College graduate	1,803 (25.2)	321 (25.4)
Master’s degree	1,762 (24.6)	300 (23.8)
Doctorate degree or higher	1,116 (15.6)	164 (13.0)
Marital status
Married	6,329 (88.4)	1,106 (87.5)
Widow	285 (4.0)	50 (4.0)
Divorce	224 (3.1)	36 (2.9)
Separate	26 (0.4)	7 (0.6)
Never married	116 (1.6)	26 (2.1)
Having a partner	178 (2.5)	39 (3.1)
Living status
Spouse	6,457 (89.9)	1,153 (90.8)
Alone	114 (1.6)	18 (1.4)
Relative	508 (7.1)	82 (6.5)
Group	65 (0.9)	11 (0.9)
Others	38 (0.5)	6 (0.5)
Age
50 or less	239 (2.8)	31 (2.4)
51–60	764 (10.6)	153 (12.0)
61–70	2,592 (36.1)	474 (37.3)
71–80	2,645 (36.8)	456 (35.9)
81–90	912 (12.7)	149 (11.7)
90 or above	68 (1.0)	8 (0.6)
Smoking, alcohol, BMI
Mean (standard deviation) number of packs smoked/day	1.0 (1.4)	1.08 (1.5)
Alcohol abuse	272 (3.8)	80 (6.3)
Mean (standard deviation) of BMI	27.2 (5.0)	27.5 (5.0)
Medication use
Mean (standard deviation) number of medications	5.2 (4.0)	6.1 (4.1)
Any antidepressant use	1,390 (19.3)	374 (29.4)
Any antipsychotic use	59 (0.8)	24 (1.9)
Any antihypnotic use	744 (10.4)	252 (19.8)

Note. N = number of observations; MCI = mild cognitive impairment; BMI = body mass index.

Percentage and mean with standard deviation were calculated for categorical variables and continuous variables, respectively.

Informant Characteristics

Of 8,460 informants who accompanied participants to their ADC visit, 56% were female, 68% aged 61 to 80 years, and 84% were white (Table 2). Informants’ relationship to the participant was primarily reported as participants’ spouse (90%).

Table 2.

Informant Characteristics (N = 8,460).

Variable	Participants without sleep disturbance (N = 7,189)	Participants with sleep disturbance (N = 1,271)
Variable	N (%)^a	N (%)^a
Sex
Female	4,008 (55.8)	770 (60.6)
Race
White	5,938 (83.2)	1,109 (87.7)
Black	828 (11.6)	84 (6.7)
Asian	245 (3.4)	42 (3.3)
Other race	123 (1.7)	29 (2.3)
Education
High school or lower	215 (3.1)	53 (4.3)
High school graduate	1,202 (17.3)	202 (16.4)
Some college	1,295 (18.7)	247 (20.0)
College graduate	1,831 (26.4)	326 (26.4)
Master’s degree	1,549 (22.3)	256 (20.8)
Doctorate degree or higher	841 (12.1)	150 (12.2)
Age
30 or less	105 (1.5)	11 (0.9)
31–40	132 (1.9)	26 (2.1)
41–50	347 (5.0)	60 (4.9)
51–60	985 (14.1)	200 (16.2)
61–70	2,279 (32.6)	428 (34.69)
71–80	2,365 (33.9)	378 (30.6)
81–90	737 (10.6)	124 (10.0)
90 or above	32 (0.5)	9 (0.7)
Relationship with patients
Spouse	6,465 (89.9)	1,158 (91.1)
Child	433 (6.0)	69 (5.4)
Relative	118 (1.6)	24 (1.9)
Other (nonfamily)	165 (2.3)	18 (1.4)
Paid caregivers, clinicians, or health care providers	2.3 (0.1)	1.4 (0.2)

Note. N = number of observations.

Percentage and mean with standard deviation were calculated for categorical variables and continuous variables, respectively.

Sleep Disturbance and Incident Dementia

Of the entire analytic sample, 15.2% (n = 1,271) had a sleep disturbance reported by their informant, and 16.0% (n = 1,350) developed incident dementia over a mean follow up of 4.5 years (range: 62 days – 12.8 years, standard deviation: 3.1 years). Model 1 (adjusting for participant and informant characteristics) showed that participants who were reported to have a sleep disturbance by informants had a higher risk of developing dementia (hazard ratio [HR]: 1.20, 95% CI: 1.03–1.39) compared to participants without a sleep disturbance (Table 3).

Table 3.

Cox-Proportional Hazards Models for the Associations Between Sleep Disturbance and Incident Dementia.

Baseline cognitive status	Number of people with sleep disturbance	Number of people who developed dementia	HR for sleep disturbance	95% CI	p value
Model 1. Association between informant-reported sleep disturbance and incident dementia^a
All (n = 8,460)	1,271 (15.2%)	1,271 (15.0%)	1.20	(1.03, 1.39)	.019
Model 2. Association between informant-reported sleep disturbance and incident dementia stratified by baseline cognitive level
Normal (n = 5,367)	554 (10.3%)	304 (5.7%)	1.56	(1.07, 2.27)	.021
Impaired but not MCI (n = 578)	137 (23.7%)	88 (15.2%)	1.77	(0.96, 3.27)	.067
MCI (n = 2,515)	580 (23.6%)	958 (38.9%)	1.12	(0.94, 1.33)	.217

Note. HR = hazard ratio; CI = confidence interval; MCI = mild cognitive impairment.

Adjusted for patient characteristics listed in Table 1 and informant characteristics listed in Table 2. All covariates were measured at baseline.

Sleep Disturbance and Incident Dementia by Patients’ Baseline Cognitive Function

The prevalence of participants who had a sleep disturbance reported by informants was 10.3% (n = 554) among those with normal cognition, 23.7% (n = 137) among those with impaired cognition but not MCI, and 23.6% (n = 580) among those with MCI. The incidence of dementia in each of these same cognitive levels was 6.3% (n = 304), 16.2% (n = 88), and 36.6% (n = 958), respectively. The stratified models showed that the association was stronger among those with normal baseline cognition. A statistically significant association between informant-reported sleep disturbance and incident dementia was shown among participants with normal baseline cognition (HR: 1.56, 95% CI: 1.07–2.27) but not among those with impaired cognition but not MCI (HR: 1.77, 95% CI: 0.96–3.27) or MCI (HR: 1.12, 95% CI: 0.94–1.33) at baseline (Table 3).

Severity of Sleep Disturbance and Incident Dementia

Of 8,460 participants, 1,271 (15.2%) participants had a sleep disturbance reported by their informant, of which 63.3% (n = 805) had mild and 36.7% (n = 466) had moderate-to-severe sleep disturbance. A total of 23.1% (n = 294) of this sample developed incident dementia during the follow-up period. The Cox proportional hazards model showed that informant-reported severity was significantly associated with incident dementia, such that moderate/severe compared to mild informant-reported sleep disturbance was associated with an increased risk of dementia (HR: 1.40, 95% CI: 1.05–1.86).

Discussion

In this study, we found that informant-reported sleep disturbance is associated with a 1.2-fold increased risk of developing dementia among older adults. We conducted stratified analyses by baseline cognitive ability which allows each level of cognitive function to have its own baseline hazard function. While the point estimate for the relationship was higher in older adults with normal baseline cognition, it is important to note that these analyses are not direct statistical comparisons. Among those with sleep disturbance, informant-reported severity of sleep disturbance was also associated with incident dementia, such that those with moderate-to-severe sleep disturbance had a 40% higher risk compared to those with mild sleep disturbance. Our results suggest that informant-reported sleep disturbance could be useful for understanding the risk of developing dementia among those without dementia and, at minimum, prompting evidence-based sleep hygiene counseling.

Our findings using an informant-based measure of sleep disturbance are consistent with findings of prior literature using objective measures of sleep disturbance. A recent meta-analysis of more than 240,000 subjects with a mean of 9.5 years of follow up concluded that the risk of developing dementia is higher among people with sleep disturbances compared to those without, when sleep disturbances were measured by self-report (8% increase), objective methods (27% increase), and diagnostic codes (51% increase) (Shi et al., 2018). Our finding that the risk of developing dementia is approximately 20% higher in individuals with an informant-reported sleep disturbance compared to those without is closer to the estimate obtained from the studies that used objective sleep measures than any other measures (e.g., self-report). Moreover, a previous study showed that informants’ reports were closely correlated with objective measures of sleep disturbance overall. In particular, it showed that the aspects of sleep disturbance included in our study—sleep fragmentation, wake up time, and excessive sleep—were captured by informants as well as actinography (Hoekert et al., 2006).

Our findings also showed that the association between sleep disturbance and incident dementia was statistically significant in older adults with normal cognition at baseline but not in those with any level of cognitive impairment. The association between sleep disturbance and dementia in people with normal cognition is consistent with existing studies demonstrating that sleep disturbances could be apparent long before showing neurodegenerative symptoms (Lucey et al., 2018; Osorio et al., 2014; Pacheco et al., 2015; Shim et al., 2017; Spira et al., 2016). A longitudinal study over an average of 8 years of follow up found that sleep disturbance was associated with higher rates of subsequent cortical thinning (which predicts later cognitive impairment) among cognitively normal older adults (Pacheco et al., 2015; Spira et al., 2016). It was also suggested that sleep disturbances can worsen cognition by disturbing consolidation of memories of increasing amyloid β and cerebrospinal fluid Alzheimer’s disease biomarkers in cognitively normal individuals (Lucey et al., 2018; Osorio et al., 2014; Shim et al., 2017). It was unexpected to find an increased risk among those with normal cognition at baseline but not those in the non-normal or MCI groups. One potential reason could be that the role of sleep disturbance as a risk factor for dementia is mostly done in the earlier course of dementia development. An increase in systematic inflammation induced by sleep disturbance is increasingly considered to be an early event associated with the development of dementia (Irwin & Vitiello, 2019). In this sense, it might be the case that sleep disturbance is an early sign of cognitive decline, and it does not play a significant role in increasing dementia risk once people have progressed to more advanced cognitive impairment (Waller et al., 2016). Another reason could be because sleep disturbances may co-occur with other dementia risk factors (e.g., depressive symptoms, decreased function) once people start having cognitive impairment. Thus, as cognition worsens, sleep disturbances may weaken in their independent association with higher risk of dementia. Future studies in different samples should examine whether baseline cognitive function modifies any association between sleep disturbance and incident dementia.

Our results have potential clinical implications for the care of older adults. Informant-reported sleep disturbance may be an important piece of information to help understand an older adult’s risk of dementia. In particular, we found that those with moderate-to-severe sleep disturbance reported by informants have a higher risk of incident dementia than those with less severity. Thus, collecting information on sleep disturbance and its severity may enable closer monitoring of individuals for changes in cognitive function, increasing the likelihood of earlier detection of dementia. Early diagnosis of dementia is beneficial because it helps patients receive timely interventions that can mitigate early symptoms and delay future cognitive deterioration, and improve quality of life. Earlier diagnosis of dementia also allows both patients and caregivers to make important care planning decisions while they are still able to do so (Dubois et al., 2016).

Sleep disturbance is not considered a definitive modifiable risk factor for dementia by currently available landmark reports on dementia prevention (i.e., Lancet Commission and National Academies), the best available evidence on dementia prevention and management, due to an insufficiency of high-quality evidence (Livingston et al., 2020; National Academies of Sciences, Engineering, and Medicine, 2017). However, based on the substantial amount of evidence from observational studies demonstrating a relationship between sleep and cognition and its biological plausibility, sleep interventions are still recognized by these reports as a potential way of delaying cognitive decline (Ancoli-Israel et al., 2008; Freeman et al., 2017; Riemersma-van der Lek et al., 2008; Westerberg et al., 2015). Overall, collecting informant-reported sleep information during routine care of older adults, when possible, could allow providers to improve the detection of sleep disturbance and prescribe sleep interventions, if appropriate.

This study is not without limitations. First, it is important to interpret our results in the appropriate context—we aimed to conduct an associative analysis rather than attempt to draw causal inferences. In addition, apolipoprotein E4 (APOE-4), which is a potential confounder, was not included in our models because these data were not available for a sizable portion of study participants (approximately 13%). Burke et al. reported that adjusting for APOE-4 made little difference in the magnitude of association between informant-reported sleep disturbance and incidence of probable AD (Burke et al., 2016, 2019). Our study was also limited to participants who were accompanied by informants living with them (approximately 47% of NACC participants were unaccompanied informants or did not live with them). Therefore, our study results may not generalize to older adults without someone representing an informant role. Moreover, there is a potential selection bias for individuals who choose to enroll in ADCs, and therefore are observable in the NACC’s UDS. Another limitation of this study is that using a single item in the NPI-Q might not differentiate the severity of sleep disturbance. To address the concern about potential misclassification, we collapsed the moderate and severe severity groups into one group. Furthermore, although the NPI-Q was not designed primarily for measuring sleep disturbance, it has good psychometric properties, exhibiting appropriate validity and internal consistency and is reliable to measure neuropsychiatric symptoms including sleep disturbance (Musa et al., 2017). For this reason, the nighttime behavior item in the NPI-Q has been used to measure sleep disturbance and its severity (Geda et al., 2008; Ledger et al., 2020).

Conclusion

Informant-reported sleep disturbance of older adults is significantly associated with incident dementia, although it may be specific for older adults with normal cognition. This association was stronger among people with moderate-to-severe sleep disturbance than those with mild sleep disturbance. Asking informants about sleep quality may play an important role in screening individuals for risk of future dementia. Soliciting sleep-related information from informants could be adopted across practice settings to work toward improving the identification of older adults who may benefit from further cognitive screening and evidence-based sleep counseling.

Supplemental Material

JAG_supplement_2nd_revision – Supplemental material for Association Between Informant-Reported Sleep Disturbance and Incident Dementia: An Analysis of the National Alzheimer’s Coordinating Center Uniform Data Set

Supplemental material, JAG_supplement_2nd_revision for Association Between Informant-Reported Sleep Disturbance and Incident Dementia: An Analysis of the National Alzheimer’s Coordinating Center Uniform Data Set by Woojung Lee, Shelly L. Gray, Douglas Barthold, Donovan T. Maust and Zachary A. Marcum in Journal of Applied Gerontology

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Zachary A. Marcum

Supplemental Material

Supplemental material for this article is available online.

References

Albert

M. S.

DeKosky

S. T.

Dickson

Dubois

Feldman

H. H.

Fox

N. C.

Gamst

Holtzman

D. M.

Jagust

W. J.

Petersen

R. C.

(2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270–279. https://doi.org/10.1016/j.jalz.2011.03.008

Ancoli-Israel

Palmer

B. W.

Cooke

J. R.

Corey-Bloom

Fiorentino

Natarajan

Liu

Ayalon

Loredo

J. S.

(2008). Cognitive effects of treating obstructive sleep apnea in Alzheimer’s disease: A randomized controlled study. Journal of the American Geriatrics Society, 56(11), 2076–2081. https://doi.org/10.1111/j.1532-5415.2008.01934.x

Babulal

G. M.

Ghoshal

Head

Vernon

E. K.

Holtzman

D. M.

Benzinger

T. L.

Fagan

A. M.

Morris

J. C.

Roe

C. M.

(2016). Mood changes in cognitively normal older adults are linked to Alzheimer disease biomarker levels. The American Journal of Geriatric Psychiatry, 24(11), 1095–1104. https://doi.org/10.1016/j.jagp.2016.04.004

Beekly

D. L.

Ramos

E. M.

Lee

W. W.

Deitrich

W. D.

Jacka

M. E.

Hubbard

J. L.

Koepsell

T. D.

Morris

J. C.

Kukull

W. A.

(2007). The National Alzheimer’s Coordinating Center (NACC) database: The uniform data set. Alzheimer Disease and Associated Disorders, 21(3), 249–258.

Besser

Kukull

Knopman

D. S.

Chui

Galasko

Weintraub

Jicha

Carlsson

Burns

Quinn

(2018). Version 3 of the national Alzheimer’s coordinating center’s uniform data set. Alzheimer Disease and Associated Disorders, 32(4), 351–358.

Burke

S. L.

Spadola

C. E.

Burgess

Cadet

(2019). Treatment of sleep disturbance may reduce the risk of future probable Alzheimer’s disease. Journal of Aging and Health, 31(2), 322–342. https://doi.org/10.1177/0898264318795567

Burke

S. L.

Maramaldi

Cadet

Kukull

(2016). Associations between depression, sleep disturbance, and apolipoprotein E in the development of Alzheimer’s disease: Dementia. International Psychogeriatrics, 28(9), 1409–1424. https://doi.org/10.1017/S1041610216000405

Cummings

(1994). The Neuropsychiatric Inventory Questionnaire: Background and administration. Mary S. Easton Center for Alzheimer’s Disease Research.

Davies

H. D.

O’Hara

Mumenthaler

M. S.

Cassidy

E. L.

Buffum

Kim

J. M.

Danielsen

C. E.

Noda

Kraemer

H. C.

Sheikh

J. I.

(2005). Underreporting of behavioral problems in older hospitalized patients. The Gerontologist, 45(4), 535–538. https://doi.org/10.1093/geront/45.4.535

10.

Dubois

Padovani

Scheltens

Rossi

Dell’Agnello

(2016). Timely diagnosis for Alzheimer’s disease: A literature review on benefits and challenges. Journal of Alzheimer’s Disease, 49(3), 617–631. https://doi.org/10.3233/JAD-150692

11.

Fernandez-Mendoza

Calhoun

S. L.

Bixler

E. O.

Karataraki

Liao

Vela-Bueno

Ramos-Platon

M. J.

Sauder

K. A.

Basta

Vgontzas

A. N.

(2011). Sleep misperception and chronic insomnia in the general population: The role of objective sleep duration and psychological profiles. Psychosomatic Medicine, 73(1), 88–97. https://doi.org/10.1097/PSY.0b013e3181fe365a

12.

Fitts

Weintraub

Massimo

Chahine

Chen-Plotkin

Duda

J. E.

Hurtig

H. I.

Rick

Trojanowski

J. Q.

Dahodwala

(2015). Caregiver report of apathy predicts dementia in Parkinson’s disease. Parkinsonism & Related Disorders, 21(8), 992–995. https://doi.org/10.1016/j.parkreldis.2015.06.009

13.

Freeman

Sheaves

Goodwin

G. M.

L.-M.

Nickless

Harrison

P. J.

Emsley

Luik

A. I.

Foster

R. G.

Wadekar

(2017). The effects of improving sleep on mental health (OASIS): A randomised controlled trial with mediation analysis. The Lancet Psychiatry, 4(10), 749–758. https://doi.org/10.1016/S2215-0366(17)30328-0

14.

Geda

Y. E.

Roberts

R. O.

Knopman

D. S.

Petersen

R. C.

Christianson

T. J.

Pankratz

V. S.

Smith

G. E.

Boeve

B. F.

Ivnik

R. J.

Tangalos

E. G.

(2008). Prevalence of neuropsychiatric symptoms in mild cognitive impairment and normal cognitive aging: Population-based study. Archives of General Psychiatry, 65(10), 1193–1198. https://doi.org/10.1001/archpsyc.65.10.1193

15.

Harvey

A. G.

Tang

N. K.

(2012). (Mis)perception of sleep in insomnia: A puzzle and a resolution. Psychological Bulletin, 138(1), 77–101. https://doi.org/10.1037/a0025730

16.

Hoekert

Riemersma-van der Lek

R. F.

Swaab

D. F.

Kaufer

Van Someren

E. J.

(2006). Comparison between informant-observed and actigraphic assessments of sleep-wake rhythm disturbances in demented residents of homes for the elderly. The American Journal of Geriatric Psychiatry, 14(2), 104–111. https://doi.org/10.1097/01.JGP.0000192481.27931.c5

17.

Irwin

M. R.

Vitiello

M. V.

(2019). Implications of sleep disturbance and inflammation for Alzheimer’s disease dementia. The Lancet Neurology, 18(3), 296–306. https://doi.org/10.1016/S1474-4422(18)30450-2

18.

Y.-E. S.

Lucey

B. P.

Holtzman

D. M.

(2014). Sleep and Alzheimer disease pathology—A bidirectional relationship. Nature Reviews Neurology, 10(2), 115–119. https://doi.org/10.1038/nrneurol.2013.269

19.

Y.-E. S.

McLeland

J. S.

Toedebusch

C. D.

Xiong

Fagan

A. M.

Duntley

S. P.

Morris

J. C.

Holtzman

D. M.

(2013). Sleep quality and preclinical Alzheimer disease. JAMA Neurology, 70(5), 587–593. https://doi.org/10.1001/jamaneurol.2013.2334

20.

Kang

J.-E.

Lim

M. M.

Bateman

R. J.

Lee

J. J.

Smyth

L. P.

Cirrito

J. R.

Fujiki

Nishino

Holtzman

D. M.

(2009). Amyloid-β dynamics are regulated by orexin and the sleep-wake cycle. Science, 326(5955), 1005–1007. https://doi.org/10.1126/science.1180962

21.

Lauderdale

D. S.

Knutson

K. L.

Yan

L. L.

Liu

Rathouz

P. J.

(2008). Self-reported and measured sleep duration: How similar are they? Epidemiology, 19, 838–845. https://doi.org/10.1097/EDE.0b013e318187a7b0

22.

Ledger

Karameh

W. K.

Munoz

D. G.

Fischer

C. E.

Schweizer

T. A.

(2020). Gender role in sleep disturbances among older adults with traumatic brain injury. International Review of Psychiatry, 32(1), 39–45.

23.

Livingston

Huntley

Sommerlad

Ames

Ballard

Banerjee

Brayne

Burns

Cohen-Mansfield

Cooper

(2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet, 396(10248), 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6

24.

Lucey

B. P.

Hicks

T. J.

McLeland

J. S.

Toedebusch

C. D.

Boyd

Elbert

D. L.

Patterson

B. W.

Baty

Morris

J. C.

Ovod

(2018). Effect of sleep on overnight cerebrospinal fluid amyloid β kinetics. Annals of Neurology, 83(1), 197–204. https://doi.org/10.1002/ana.25117

25.

Mander

B. A.

Rao

Saletin

J. M.

Lindquist

J. R.

Ancoli-Israel

Jagust

Walker

M. P.

(2013). Prefrontal atrophy, disrupted NREM slow waves and impaired hippocampal-dependent memory in aging. Nature Neuroscience, 16(3), 357–364. https://doi.org/10.1038/nn.3324

26.

McKhann

G. M.

Knopman

D. S.

Chertkow

Hyman

B. T.

Jack

C. R.

Jr. Kawas

C. H.

Klunk

W. E.

Koroshetz

W. J.

Manly

J. J.

Mayeux

(2011). The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 263–269. https://doi.org/10.1016/j.jalz.2011.03.005

27.

Milanini

Catella

Perkovich

Esmaeili-Firidouni

Wendelken

Paul

Greene

Ketelle

Valcour

(2017). Psychiatric symptom burden in older people living with HIV with and without cognitive impairment: The UCSF HIV over 60 cohort study. AIDS Care, 29(9), 1178–1185. https://doi.org/10.1080/09540121.2017.1281877

28.

Morris

J. C.

(1997). Clinical dementia rating: A reliable and valid diagnostic and staging measure for dementia of the Alzheimer type. International Psychogeriatrics, 9(Suppl. 1), 173–176. https://doi.org/10.1017/s1041610297004870

29.

Morris

J. C.

Weintraub

Chui

H. C.

Cummings

DeCarli

Ferris

Foster

N. L.

Galasko

Graff-Radford

Peskind

E. R.

(2006). The Uniform Data Set (UDS): Clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Disease and Associated Disorders, 20(4), 210–216.

30.

Musa

Henríquez

Muñoz-Neira

Delgado

Lillo

Slachevsky

(2017). Utility of the Neuropsychiatric Inventory Questionnaire (NPI-Q) in the assessment of a sample of patients with Alzheimer’s disease in Chile. Dementia & Neuropsychologia, 11(2), 129–136.

31.

National Academies of Sciences, Engineering, and Medicine. (2017). Preventing cognitive decline and dementia: A way forward. The National Academies Press.

32.

National Alzheimer’s Coordinating Center. (2015). NACC Uniform Data Set, initial visit packet (Version 3.0). https://www.alz.washington.edu/NONMEMBER/UDS/DOCS/VER3/UDS3_ivp_forms.pdf

33.

Osorio

R. S.

Ayappa

Mantua

Gumb

Varga

Mooney

A. M.

Burschtin

O. E.

Taxin

During

Spector

(2014). The interaction between sleep-disordered breathing and apolipoprotein E genotype on cerebrospinal fluid biomarkers for Alzheimer’s disease in cognitively normal elderly individuals. Neurobiology of Aging, 35(6), 1318–1324. https://doi.org/10.1016/j.neurobiolaging.2013.12.030

34.

Pacheco

Goh

J. O.

Kraut

M. A.

Ferrucci

Resnick

S. M.

(2015). Greater cortical thinning in normal older adults predicts later cognitive impairment. Neurobiology of Aging, 36(2), 903–908. https://doi.org/10.1016/j.neurobiolaging.2014.08.031

35.

Rabin

L. A.

Wang

Katz

M. J.

Derby

C. A.

Buschke

Lip ton

R. B.

(2012). Predicting Alzheimer’s disease: Neuropsychological tests, self-reports, and informant reports of cognitive difficulties. Journal of the American Geriatrics Society, 60(6), 1128–1134. https://doi.org/10.1111/j.1532-5415.2012.03956.x

36.

Riemersma-van der Lek

R. F.

Swaab

D. F.

Twisk

Hol

E. M.

Hoogendijk

W. J.

Van Someren

E. J

. (2008). Effect of bright light and melatonin on cognitive and noncognitive function in elderly residents of group care facilities: A randomized controlled trial. Journal of the American Medical Association, 299(22), 2642–2655. https://doi.org/10.1001/jama.299.22.2642

37.

Seligman

S. C.

Kamath

Giovannetti

Arnold

S. E.

Moberg

P. J.

(2013). Olfaction and apathy in Alzheimer’s disease, mild cognitive impairment, and healthy older adults. Aging & Mental Health, 17(5), 564–570. https://doi.org/10.1080/13607863.2013.768208

38.

Sharma

R. A.

Varga

A. W.

Bubu

O. M.

Pirraglia

Kam

Parekh

Wohlleber

Miller

M. D.

Andrade

Lewis

(2018). Obstructive sleep apnea severity affects amyloid burden in cognitively normal elderly: A longitudinal study. American Journal of Respiratory and Critical Care Medicine, 197(7), 933–943.

39.

Shi

Chen

S.-J.

M.-Y.

Bao

Y.-P.

Han

Wang

Y.-M.

Shi

Vitiello

M. V.

(2018). Sleep disturbances increase the risk of dementia: A systematic review and meta-analysis. Sleep Medicine Reviews, 40, 4–16. https://doi.org/10.1016/j.smrv.2017.06.010

40.

Shim

Hogan

Halldin

Clark

Behrens

Griffith

Umasabor-Bubu

O. Q.

Bubu

O. M.

(2017). Sleep disordered breathing, ApoE4 and β-amyloid deposition in cognitively normal elderly. Alzheimer’s & Dementia, 13(7), P1124–P1125. https://doi.org/10.1016/j.jalz.2017.06.1632

41.

Shokouhi

(2019). Associations of informant-based sleep reports with Alzheimer’s disease pathologies. Clinical Interventions in Aging, 14, 1631–1642. https://doi.org/10.2147/CIA.S215208

42.

Smith-Gamble

Baiyewu

Perkins

A. J.

Gureje

Hall

K. S.

Ogunniyi

Hui

S. L.

Hendrie

H. C.

(2002). Informant reports of changes in personality predict dementia in a population-based study of elderly African Americans and Yoruba. The American Journal of Geriatric Psychiatry, 10(6), 724–732. https://doi.org/10.1097/00019442-200211000-00011

43.

Spira

A. P.

Gonzalez

C. E.

Venkatraman

V. K.

M. N.

Pacheco

Simonsick

E. M.

Ferrucci

Resnick

S. M.

(2016). Sleep duration and subsequent cortical thinning in cognitively normal older adults. Sleep, 39(5), 1121–1128. https://doi.org/10.5665/sleep.5768

44.

Waller

K. L.

Mortensen

E. L.

Avlund

Osler

Fagerlund

Lauritzen

Jennum

(2016). Subjective sleep quality and daytime sleepiness in late midlife and their association with age-related changes in cognition. Sleep Medicine, 17, 165–173. https://doi.org/10.1016/j.sleep.2015.01.004

45.

Weintraub

Besser

Dodge

H. H.

Teylan

Ferris

Goldstein

F. C.

Giordani

Kramer

Loewenstein

Marson

(2018). Version 3 of the Alzheimer Disease Centers’ neuropsychological test battery in the Uniform Data Set (UDS). Alzheimer Disease and Associated Disorders, 32(1), 10–17.

46.

Wennberg

A. M.

M. N.

Rosenberg

P. B.

Spira

A. P.

(2017). Sleep disturbance, cognitive decline, and dementia: A review. Seminars in Neurology, 37, 395–406.

47.

Westerberg

C. E.

Florczak

S. M.

Weintraub

Mesulam

M.-M.

Marshall

Zee

P. C.

Paller

K. A.

(2015). Memory improvement via slow-oscillatory stimulation during sleep in older adults. Neurobiology of Aging, 36(9), 2577–2586. https://doi.org/10.1016/j.neurobiolaging.2015.05.014

48.

Xie

Kang

Chen

M. J.

Liao

Thiyagarajan

O’Donnell

Christensen

D. J.

Nicholson

Iliff

J. J.

(2013). Sleep drives metabolite clearance from the adult brain. Science, 342(6156), 373–377. https://doi.org/10.1126/science.1241224

49.

Tan

C.-C.

Zou

J.-J.

Cao

X.-P.

Tan

(2020). Sleep problems and risk of all-cause cognitive decline or dementia: An updated systematic review and meta-analysis. Journal of Neurology, Neurosurgery, and Psychiatry, 91(3), 236–244. https://doi.org/10.1136/jnnp-2019-321896

50.

Yaffe

Laffan

A. M.

Harrison

S. L.

Redline

Spira

A. P.

Ensrud

K. E.

Ancoli-Israel

Stone

K. L.

(2011). Sleep-disordered breathing, hypoxia, and risk of mild cognitive impairment and dementia in older women. Journal of the American Medical Association, 306(6), 613–619.

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