Neuropsychiatric Inventory–Questionnaire Assessed Nighttime Behaviors in Cognitively Asymptomatic Patients with Pathologically Confirmed Alzheimer’s Disease Predict More Rapid Cognitive Deterioration

Abstract

Background:

The temporal relationship between sleep, Alzheimer’s disease (AD), and cognitive impairment remains to be further elucidated.

Objective:

First, we aim to determine whether the Neuropsychiatric Inventory–Questionnaire (NPI-Q) assessed nighttime behaviors prior to cognitive decline influence the rate of cognitive deterioration in pathologically confirmed AD, and second, to assess the possible interactions with APOE allele and cerebral amyloid angiopathy (CAA).

Methods:

The rate of cognitive decline between cognitively asymptomatic participants from the National Alzheimer Coordinating Center who eventually received a neuropathologic diagnosis of AD with (+NTB) or without (−NTB) nighttime behaviors were compared using independent samples t-test. Participants were stratified by APOE carrier and CAA status. Demographic and patient characteristics were assessed using descriptive statistics, and the independent samples t-test was used for continuous variables and chi-square test for categorical variables. The significance level was set at p≤0.05.

Results:

The rate of cognitive decline was greater in +NTB (n = 74; 3.30 points/year) than −NTB (n = 330; 2.45 points/year) (p = 0.016), even if there was no difference in cognitive status at onset. This difference was restricted to APOE ɛ4 carriers (p = 0.049) and positive CAA participants (p = 0.020). Significance was not reached in non-carriers (p = 0.186) and negative CAA (p = 0.364). APOE and CAA were not differentially distributed between the NTB groups.

Conclusion:

NPI-Q assessed nighttime behaviors, a surrogate for sleep disturbances, are associated with more rapidly deteriorating cognition in patients with AD neuropathology who are also carriers of APOE ɛ4 or show CAA.

Keywords

Alzheimer’s disease Apolipoprotein E cerebral amyloid angiopathy cognitive decline neuropsychiatric symptoms sleep

INTRODUCTION

Sleep disturbances and altered sleep patterns are among the first observable neuropsychiatric symptoms (NPS) of Alzheimer’s disease (AD) prior to disease-related cognitive deterioration [1]. Sleep disturbances can have adverse consequences on the patient’s cognition and can cause caregiver burden [2]. Multiple mechanisms, including homeostatic, circadian, and motivational control processes, may be involved in the complex and multifactorial etiology of sleep disturbances in AD [3 –7]. Increasing evidence suggests that sleep disturbances are not merely a symptom of AD, but a causal and bi-directional factor in AD progression [1 , 8–10]. However, the temporal relation between sleep disturbances, AD neuropathology, and its cognitive manifestations warrants further investigation. This may present earlier treatment options for sleep disturbances that, in turn, may potentially delay AD progression.

Apolipoprotein E (APOE) is the strongest genetic risk factor for AD in the general population [11] and the risk for AD increases in a dose-dependent manner with the inheritance of the APOE ɛ4 allele [12, 13]. The APOE ɛ4 allele modulates multiple features of AD, including the severity of the neuropathological hallmarks, neurofibrillary tangles and senile plaques [11 , 15]. Studies also considered the effects of the APOE allele on several clinical features of AD. For example, a study found that the presence of two APOE ɛ4 alleles increased the likelihood of psychosis in females, which may be mediated through Lewy body formation [16]. Similarly, Qian et al. [17] further stratified psychosis into delusions and hallucinations and found that the presence of the APOE ɛ4 allele was associated with poorer cognitive impairment in patients with hallucinations only [17]. Another study found that aberrant motor behavior is significantly associated with the APOE ɛ4 genotype in patients with high AD pathology load [18]. In regard to sleep disturbances, its complex relationship with APOE allele and cognitive impairment has been previously reported. Studies suggested that the APOE ɛ4 allele may predispose individuals to sleep disturbances [19 –21], while others report that the interaction between sleep disturbances and the APOE allele may exacerbate cognitive decline [22 –25]. A study by Boyle et al. [26] found an association between declining cognition and APOE allele that is mediated by AD pathology. Lim et al. [27] reported that uninterrupted sleep protects against the negative effects of APOE ɛ4 allele on incident AD and the burden of AD pathology, specifically neurofibrillary tangles. Despite the previous studies on sleep disturbances and APOE ɛ4’s relation, limited studies have examined the potential effects of the APOE ɛ4 allele and sleep on cognitive decline in pathologically confirmed AD. We wanted to explore a possible interaction of APOE ɛ4 allele with sleep disturbances in this specific cohort.

Another prominent feature of AD are vascular pathologies, which interact with AD lesions not only by accelerating cognitive decline but also by modifying neuropsychiatric presentations [28, 29]. Interestingly, agitation/aggression in AD was found to be inversely associated with vascular lesions [30]. This study also found that only vascular risk factor significantly associated with agitation/aggression was smoking and the presence of agitation/aggression in AD was associated with recent vascular events in males. Another study found that cerebral amyloid angiopathy (CAA), common in AD and APOE ɛ4 allele carriers [31, 32], is associated with APOE ɛ4 effect on NPS prevalence [18]. For example, CAA was associated with AD-related psychosis [33]. CAA is of particular interest as its main constituent, amyloid-β (Aβ), is shared with neuritic plaques in AD. Impairments in Aβ clearance may drive both the pathologies of AD and CAA [34]. Since Aβ deposition and clearance are affected by sleep, we were interested in exploring CAA as a modifying factor.

The present study aimed to investigate sleep disturbances, cognitive manifestations, and pathologies by following a cohort of cognitively asymptomatic patients at baseline with known sleep status, eventually found to have AD neuropathology at autopsy. Our primary objective is to investigate the relationship between the rate of cognitive decline and Neuropsychiatric Inventory–Questionnaire (NPI-Q) assessed nighttime behaviors—a surrogate for sleep disturbances—in this cohort. Our secondary objective is to assess the impact of APOE allele and CAA as modifiers on this relationship.

MATERIALS AND METHODS

Data source

Data from the National Alzheimer’s Disease Coordinating Center (NACC) was used in this study. The NACC compiled data from 34 past and present National Institute on Aging/NIH Alzheimer’s Disease Centers (ADCs) in the United States. The NACC consists of the Uniform Data Set (UDS), and the Neuropathology Data Set (NP) collected between September 2005 and December 2015. The UDS comprises standardized, longitudinal clinical, and demographic information, including responses on the NPI-Q quick version. The NPI-Q retrospectively evaluates 12 neuropsychiatric symptom domains within the last month of a clinical visit and is completed by a certified NPI-Q clinician or trained health professionals based on a co-participant interview. The NP consisted of standardized neuropathologic data, collected from individual ADCs, on UDS patients with a post-mortem examination. APOE allele is run independently by the ADCs and reported to NACC on the NACC Neuropathology Form. APOE allele is also reported by Alzheimer’s Disease Genetics Consortium and National Cell Repository for Alzheimer’s Disease as described in the NACC Researchers Data Dictionary–Genetic Data (RDD-Gen). The severity of cerebral amyloid angiopathy was determined using special stains for amyloid (e.g., Congo red, thioflavin-S or Aβ immunostaining) as described in the NACC NP Diagnosis Coding Guidebook (Version 9.1, September 2008). The NACC NP Guidebook (Version 10, January 2014) further describes the procedures of how the CAA data was determined.

Neuropathologic criteria

Patients meeting study inclusion criteria had an autopsy-confirmed primary neuropathologic diagnosis of AD based on the NACC classification system as described in the NACC NP Data Element Dictionary and Diagnosis Coding Guidebook (Version 9.1, September 2008) (Fig. 1). This classification uses the NIA/Reagan Institute criteria [35], including neocortical neuritic plaque load categorized based on the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) or C scores, and the Braak & Braak staging for neurofibrillary tangles (NFTs), for an intermediate or high likelihood of AD neuropathologic change. High likelihood of AD includes the presence of both neuritic plaques and NFTs in the neocortex (i.e., frequent CERAD plaques (C score of 3) and Braak & Braak Stage V/VI). Intermediate likelihood consists of moderate neuritic plaques and NFTs in the limbic regions (i.e., moderate CERAD (C score of 2) and Braak & Braak Stage III/IV). The NACC also contained data on ischemic, hemorrhagic, or vascular pathology and Lewy body pathology. However, given that almost all eligible patients had presence of vascular pathology and the sample of patients with Lewy body pathology and NTB was small, this present study did not analyze this further.

Fig. 1

Flow chart of patient study inclusion. NACC, National Alzheimer’s Disease Coordinating Center; MMSE, Mini-Mental State Examination; +NTB, presence of nighttime behaviors; -NTB, absence of nighttime behaviors; APOE, Apolipoprotein E; +CAA, presence of Cerebral Amyloid Angiopathy; -CAA, absence of cerebral amyloid angiopathy.

Cognitive criteria

To evaluate the longitudinal changes in their cognitive performance and calculate the rate of cognitive decline, included patients must have total Mini-Mental Status Examination (MMSE) scores available at the baseline NACC visit and subsequent annual follow-up visits. Patients must also be considered cognitively asymptomatic at baseline, operationally defined as having an MMSE score of≥24 at the baseline NACC visit, to be included in this analysis. To determine the mean rate of cognitive decline for each patient, we first determined the yearly rate of cognitive decline for each available NACC visit. This was calculated by taking the difference between subsequent MMSE scores at each available clinical visit and dividing it by the difference between the visit years those scores were obtained. For example, if a patient had an MMSE score of 30 obtained in 2005 and another MMSE score of 29 obtained in 2007 their yearly rate of cognitive decline would be calculated as follows: 30 subtracted from 29 then divided by 2, because the difference between the visit years, 2005 and 2007, is 2, would result in a 0.5 MMSE score decline per year. After calculating each yearly rate of cognitive decline, the patient’s overall mean annual MMSE score change was then calculated by averaging all their yearly rates of cognitive decline. The patient’s mean annual MMSE score change over the patient’s total NACC assessments determined the rate of cognitive decline.

Nighttime behavior

The NPI-Q was adapted from and cross-validated with the standard NPI [36] to provide a brief assessment of neuropsychiatric symptoms within the last month in routine clinical practice [37]. Only a certified NPI-Q clinician or other trained health professionals can complete the NPI-Q and is based on co-participant interview. In this study, NPI-Q assessed nighttime behaviors were assessed using the NPI-Q’s “Nighttime Behaviors” domain (Question 12a): “Does the patient awake you during the night, rise too early in the morning, or take excessive naps during the day?”. The co-participant responds to this question with a “Yes” (present), “No” (absent), or “Unknown”. This study focused on the presence (+NTB) or absence (−NTB) of nighttime behaviors (NTB) assessed at the patient’s baseline visit.

Data/statistical analysis

In this study, only patients with available NACC neuropathology data and who were cognitively asymptomatic at baseline were analyzed (Fig. 1). The eligible patients were then stratified into those with (+NTB) or without (−NTB) nighttime behaviors at the patient’s baseline NACC clinical visit. The annual rate of cognitive decline was compared between the two groups. This approach allowed us to determine whether more rapid cognitive declines follow NPI-Q assessed nighttime behaviors. Figure 1 illustrates how we further examined this interaction with APOE allele (APOE ɛ4 carriers and non-carriers) and CAA (presence or absence). The +NTB and −NTB groups were each further stratified into APOE ɛ4 carriers and non-carriers. Similarly, these two groups were also further stratified into presence and absence of CAA. Baseline demographic and patient characteristics were assessed using descriptive statistics. For between-group comparisons, the independent samples t-test was used for continuous variables and chi-square test of independence for categorical variables. For the association between NTB and the rate of cognitive decline, the independent samples t-test was used. All statistical analyses were performed using IBM SPSS Statistics Version 23.0. The significance level was set at p≤0.05.

RESULTS

Demographics

The NACC database had 3106 patients with post-mortem neuropathologic data collected within two years of death following the patient’s last clinical visit (Fig. 1). A total of 404 patients had primary neuropathologic diagnosis of AD, baseline MMSE score of≥24, and baseline NPI-Q nighttime behavior data available. Table 1 presents the demographic and clinical characteristics of the included patients. 55.8% of these patients were male. 330 participants were −NTB and 74 +NTB at baseline, which do not differ in terms of sex, age, education, MMSE scores at baseline, APOE ɛ4 carrier status, and CAA pathology (Table 1). The time between the baseline visit and the final visit was similar between the −NTB (mean ± SD, 3.5 ± 1.6 years) and +NTB groups (3.4 ± 1.6 years) spanning over a mean of four clinical visits in total.

Table 1

Demographic and clinical characteristics

		−NTB	+NTB	p
		(n = 330)	(n = 74)
		n/Mean (% /SD)	n/Mean (% /SD)
Males		184 (55.8)	45 (60.8)	χ² = 0.629, p = 0.440
Age at	Initial Visit	79.30 (10.4)	77.99 (9.2)	0.314
	Death	84 (10.3)	83 (9.2)	0.323
Year of Death		2011 (1.637)	2011 (1.547)	0.322
Education		15.63 (2.840)	15.66 (2.667)	0.930
MMSE	Initial	26.85 (1.9)	26.97 (2.1)	0.641
	Final	19.85 (7.4)	18.65 (7.6)	0.209
APOEɛ4				χ² = 0.094, p = 0.788
	Carrier	155 (47)	33 (45)
	Non-carrier	147 (45)	34 (46)
CAA Pathology				χ² = 0.741, p = 0.388
	Presence	240 (72)	51 (68)
	Absence	85 (26)	23 (31)
Total clinical visits		4.2 (1.5)	4.1 (1.6)	0.475
Time between baseline		3.2 (1.5)	3.2 (1.6)	0.917
and last visit (y)

NTB, nighttime behaviors; SD, standard deviation; MMSE, Mini-Mental State Examination; APOE, Apolipoprotein E; CAA, cerebral amyloid angiopathy.

Rate of cognitive decline

There was no difference in MMSE scores between +NBT and −NBT at onset (Table 1). The mean annual rate of cognitive decline was greater in the +NTB group, (–3.30 ± 2.6 points) compared to the −NTB group (–2.45 ± 2.2 points) and this difference was significant (p = 0.016) (Fig. 2). Figures 3 4 show similar findings were found when restricted to APOE ɛ4 carriers (n = 188; p = 0.049) and patients with CAA (n = 291; p = 0.020), but significance was lost in non-APOE ɛ4 carriers (n = 181; p = 0.186) and patients without CAA (n = 108; p = 0.364). There was no difference between the NTB groups in the prevalence of these two factors associated with the rate of cognitive decline (APOE ɛ4 allele and CAA) (Table 1).

Fig. 2

Mean rate of cognitive decline; all participants. From baseline mean clinical visit year to last mean clinical visit year comparing all participants with or without nighttime behaviors (^*p≤0.05). NTB, nighttime behaviors; MMSE, Mini-Mental State Examination; pts, points; yr, year.

Fig. 3

Mean rate of cognitive decline by APOE ɛ4 status. (i) APOE ɛ4 carriers and (ii) APOE ɛ4 non-carriers (^*p≤0.05). APOE, Apolipoprotein E; NTB, nighttime behaviors; MMSE, Mini-Mental State Examination; pts, points; yr, year.

Fig. 4

Mean rate of cognitive decline by CAA status. (i) Presence (+CAA) and (ii) Absence (–CAA) (^*p≤0.05). CAA, cerebral amyloid angiopathy; NTB, nighttime behaviors; MMSE, Mini-Mental State Examination; pts, points; yr, year.

DISCUSSION

Our findings indicate that NPI-Q assessed nighttime behaviors, a surrogate for sleep disturbances, at onset predict faster cognitive deterioration in participants eventually found to suffer from AD. Furthermore, this finding was only significant in the subpopulation of APOE ɛ4 carriers and similarly, patients with CAA. The absence of a difference between the NTB groups in both cognitive status at onset, and the prevalence of the cognitive decline-associated factors APOE ɛ4 allele and CAA, may suggest that NTB itself is the factor associated with the observed effect.

Changes in the quality, quantity and timing of sleep overtime are hallmarks of normal aging. Several studies have shown alterations in various aspects of sleep in the elderly population including increased sleep latency, sleep fragmentation, and early-morning awakening and decreased sleep quality and sleep maintenance [38 –41]. In AD patients, it has been reported that sleep disturbances were present in 25–66% of patients [42, 43]. These sleep disturbances have also been associated with cognitive impairments [44, 45]. A recent study analyzing a large sample of participants that were followed for 25 years found a higher incidence of dementia in participants with short sleep duration (6 hours or less) in middle and old age, as assessed by questionnaires and accelerometers in a subsample [46]. A longitudinal study looking at a group of elderly participants with absent dementia at onset found that the risk for developing dementia and cognitive deterioration when excessive daytime sleepiness was reported increased by 30% [47]. Furthermore, several cross-sectional studies showed an association between impairments in cognition and various indicators of poor sleep such as increased napping, protracted sleep latency, increased wakefulness, altered sleep duration, and low sleep efficiency [9 , 48–50].

Cross-sectional studies centered on in vivo analysis of AD neuropathology in cognitively intact patients suggested that sleep disturbances occurred prior to cognitive impairment and development of AD lesions [7, 51]. Musiek et al. [7] found that in cognitively intact participants with high cerebrospinal fluid (CSF) pTau to Aβ₄₂ ratio (indicative of preclinical AD), there was increased fragmentation in circadian rhythms. The dysregulation of the circadian system (essential in sustaining the sleep-wake cycle) has been closely associated with AD [8 , 53] and recent evidence has shown a possible causative role in AD pathogenesis [52]. In cognitively normal adults, dysregulation of the circadian rhythm measured using actigraphy increased the risk of AD [54]. In mouse models, increased neurodegeneration was linked to disruptions in the circadian rhythm thought to be mediated through the expression of circadian proteins regulating cellular, biochemical, and metabolic processes [55].

Furthermore, CSF Aβ decrease and tau accumulation, the soluble markers of AD, were found to be associated with low sleep quality and daytime sleepiness in cognitively normal older adults [56]. Similarly, Ju et al. [51] reported an association between amyloid deposition as assessed by lower CSF Aβ₄₂ levels and increased nap frequencies and reduced sleep efficiency in preclinical AD. In addition, the relationship between changes in Aβ concentrations and sleeping patterns have been shown in human and animal studies. Disruptions in non-rapid eye movement (NREM) slow wave activity have been linked with increased Aβ accumulation on PiB amyloid positron emission tomography (PET) imaging [57]. While increased fragmentation of the sleep-wake cycle and decreased REM and NREM sleep were found in rodent models of AD [58]. The reduction of Aβ restored normal sleep wave cycles and diurnal Aβ fluctuations, and externally imposed restriction of sleep increased Aβ deposits, while enhancing sleep reduced plaque burden [58, 59]. Whereas the glymphatic clearance of Aβ was observed when NREM sleep was increased in mice [60], this clearance was disrupted when adult humans were deprived of sleep [61, 62].

In AD, APOE allele is a key genetic risk factor and has been associated with disturbances in sleep and cognitive impairments. For example, the APOE ɛ4 allele has been associated with sleep apnea [20, 63]. Another study of APOE ɛ4 carriers showed that the quality of sleep as measured by polysomnography and actigraphy was worse than non-carriers [64]. Hita-Yanez et al. [65] compared sleep patterns between healthy elderly participants and MCI patients and found that the MCI group had a significant shortening of rapid eye movement (REM) sleep and increased fragmentation of slow-wave sleep. Furthermore, the shortening of REM sleep was more pronounced in MCI patients who were APOE ɛ4 carriers. Another study analyzing participants, obtained from the NACC UDS that was also used in this study, with normal cognition at baseline found that MCI risk was significantly associated with sleep disturbance [66]. Interestingly, this risk remained true for participants who did not use sleep medications, trazadone and zolpidem, but did not remain true in those who used these medications for sleep. Furthermore, the participants with APOE ɛ4 were at a higher risk of MCI, but this risk was not significant when analyzed amongst the users of trazadone and zolpidem. Interestingly, a study on an elderly non-dementia sample found that uninterrupted sleep protects against the negative effects of APOE ɛ4 allele on incident AD risk and on neuro-fibrillary tangle pathology [27]. APOE is suggested to affect disease pathogenesis through Aβ clearance and aggregation [67]. A recent study investigated the association between CSF biomarkers and neurodegeneration with NPS in dementia with Lewy bodies (DLB) compared with late-onset AD and cognitively asymptomatic participants [68]. In both DLB and in AD, APOE ɛ4 genotype influenced amyloidogenesis and tau pathology. Furthermore, they found nighttime behavior disturbances to be linked with tauopathy and synucleinopathy in patients with AD.

The interplay between neurodegenerative and cerebrovascular pathologies are thought to drive cognitive impairments related to ageing [28]. A study found that the presence of vascular pathologies, which included CAA, accounted for 20–30% of the cognitive decline at the individual level [69]. CAA is also a common feature of APOE ɛ4 allele carriers [31, 32] and has been linked with APOE ɛ4 effect’s on NPS prevalence [18]. CAA is an important factor to consider due to the commonality of Aβ deposition and clearance shared with neuritic plaques in AD. Since Aβ deposition and clearance are also related to sleep and wakefulness, impairments in Aβ clearance may drive the pathologies of AD and CAA [34]. However, more studies examining the association between sleep disturbances, CAA and AD pathology are still warranted. Prevention of cerebrovascular pathologies in addition to promotion of healthy sleep habits may alleviate impairments in clearance and accumulation of Aβ, thereby minimizing cognitive decline and the risk of AD.

Our study focused on NPI-Q assessed nighttime behaviors, identifying increased daytime sleeping/wakefulness and nighttime wakefulness under a single domain based on the reports of a close informant, as a measure of sleep disturbance. Previous studies have used more objective measures such as actigraphy or polysomnography of sleep disturbances that can be limited to a single aspect of the sleep spectrum. A meta-analysis looking at sleep disturbances and AD risk reported that the complete qualitative and perceived experience of sleep might not be fully captured or explained using objective measures [70]. Therefore, an important aspect to consider in our study is that the severity of nighttime behaviors must be conspicuous enough to affect the NPI-Q assessment by an external observer. As an efficient clinical assessment tool, the NPI-Q may provide a brief and easily accessible tool for identifying nighttime behaviors in clinical practice. In combination with objective diagnostic tools, NPI-Q assessed nighttime behaviors could provide a more comprehensive assessment of sleep disturbances, that in turn, may have implications for sleep interventions and AD-related prevention efforts.

A previous study by our group found that cognitive decline is associated with concurrent NPI-Q assessed nighttime behaviors regardless of the presence or absence of AD neuropathology [71]. It included patients with and without AD neuropathology and any concurrent cognitive status to compare the presence or absence of NPI-Q assessed nighttime behaviors at any time point. The current study differs by focusing on patients that were cognitively asymptomatic at onset and had confirmed AD neuropathology, and NPI-Q assessed nighttime behaviors were considered at baseline only. However, this study cannot resolve whether NPI-Q assessed nighttime behaviors play a causal factor in disease progression, or are an early manifestation of the disease, predating cognitive impairment, but indicating more advanced or severe preclinical AD.

Limitations

The strengths of this study were the relatively large sample of autopsy-confirmed AD patients and the temporal quality of the cognitive status data. However, this study’s generalizability was limited because the NACC database uses a clinical case series of volunteers from various ADCs with diverse recruitment methods. Furthermore, the MMSE has limited sensitivity to cognitive deterioration; however, it is a widely adopted clinical diagnostic instrument for cognitive impairment. Another important factor to consider in future research are pharmacological treatments of sleep disturbances in AD given their effects on behavioral and psychological symptoms [72 –74]. In this study, pharmacological treatments were not considered to limit the number of additional variables, given the study’s sample size. Similarly, the NPI-Q has limited sensitivity to NTB compared to more objective measures of NPI-Q assessed nighttime behaviors. However, the NPI-Q is easily accessible to clinicians and was adapted from and cross-validated with the standard NPI [36] providing a brief assessment of neuropsychiatric symptoms in clinical settings [37]. The NPI-Q specifically identified the three separate, aforementioned, sleep symptoms under one identifying question domain, and we were restricted to the singular question posed in the NPI-Q. Thus, it is important to acknowledge that these three individual symptoms may be affected by different pathologies. In addition, it is important to also specify that the NPI-Q does not measure initial insomnia. Ideally, combining these measures with more comprehensive diagnostic tools sensitive to sleep disturbances would provide additional information.

Conclusions

Our current findings provide further support for the association between sleep disturbances, cognitive impairment, and Alzheimer’s disease. NPI-Q assessed nighttime behaviors at baseline, a surrogate for sleep disturbances, are associated with more rapidly declining cognition in patients with Alzheimer’s disease neuropathology. In addition, this relationship is found in carriers of APOE ɛ4 and presence of CAA. Screening and treating sleep disturbances can have implications in improving quality of life, sleep interventions and AD prevention efforts.

Footnotes

ACKNOWLEDGMENTS

The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG062428-01 (PI James Leverenz, MD) P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Al-bert, PhD), P30 AG062421-01 (PI Bradley Hyman, MD, PhD), P30 AG062422-01 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P30 AG062429-01(PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P30 AG062715-01 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).

This work was supported by St. Michael’s Hospital Foundation and Heather and Eric Donnelly endowment. Funding sources had no role in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the article for publication.

Authors’ disclosures available online ().

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