[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Figure 1.
Timeline Showing the Design of the BIOCARD Study
Timeline Showing the Design of the BIOCARD Study

Shown are types of data collected each year for BIOCARD between 1995 and 2014. CSF indicates cerebrospinal fluid; JHU, The Johns Hopkins University; MRI, magnetic resonance imaging; and NIH, National Institutes of Health.

Figure 2.
Estimates of Longitudinal Cognitive Change for the 4 Hypothetical Preclinical Alzheimer Disease (AD) Groups
Estimates of Longitudinal Cognitive Change for the 4 Hypothetical Preclinical Alzheimer Disease (AD) Groups

Shown are estimates from linear mixed-effects models predicting longitudinal cognitive composite scores over time among individuals classified into the 4 preclinical AD groups (stage 0, stage 1, stage 2, and suspected non–Alzheimer disease pathology [SNAP]) using baseline cerebrospinal fluid Aβ1-42 and phosphorylated tau (p-tau) (A) or Aβ1-42 and total tau (B) for classification. The estimates are adjusted for baseline age, sex, education, and their interactions with time. Stage 2 had a greater decline and lower baseline cognitive composite scores than the other groups, which did not differ from one another (Table 3).

Table 1.  
Participant Characteristics at Baseline
Participant Characteristics at Baseline
Table 2.  
Participant Characteristics at Baseline in Each of the 4 Preclinical Alzheimer Disease Groups
Participant Characteristics at Baseline in Each of the 4 Preclinical Alzheimer Disease Groups
Table 3.  
Results of Linear Mixed-Effects Modelsa
Results of Linear Mixed-Effects Modelsa
1.
Sperling  RA, Aisen  PS, Beckett  LA,  et al.  Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease.  Alzheimers Dement. 2011;7(3):280-292.PubMedGoogle ScholarCrossref
2.
Desikan  RS, Cabral  HJ, Hess  CP,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease.  Brain. 2009;132(pt 8):2048-2057.PubMedGoogle ScholarCrossref
3.
Grothe  MJ, Heinsen  H, Amaro  E  Jr, Grinberg  LT, Teipel  SJ; Alzheimer’s Disease Neuroimaging Initiative.  Cognitive correlates of basal forebrain atrophy and associated cortical hypometabolism in mild cognitive impairment [published online April 2, 2015].  Cereb Cortex.PubMedGoogle Scholar
4.
Pagani  M, De Carli  F, Morbelli  S,  et al.  Volume of interest–based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls: a European Alzheimer’s Disease Consortium (EADC) study.  Neuroimage Clin. 2015;7:34-42.PubMedGoogle ScholarCrossref
5.
Jack  CR  Jr, Knopman  DS, Weigand  SD,  et al.  An operational approach to National Institute on Aging–Alzheimer’s Association criteria for preclinical Alzheimer disease.  Ann Neurol. 2012;71(6):765-775.PubMedGoogle ScholarCrossref
6.
Holtzman  DM.  CSF biomarkers for Alzheimer’s disease: current utility and potential future use.  Neurobiol Aging. 2011;32(suppl 1):S4-S9.PubMedGoogle ScholarCrossref
7.
Tapiola  T, Alafuzoff  I, Herukka  SK,  et al.  Cerebrospinal fluid β-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain.  Arch Neurol. 2009;66(3):382-389.PubMedGoogle ScholarCrossref
8.
Vos  SJ, Xiong  C, Visser  PJ,  et al.  Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study.  Lancet Neurol. 2013;12(10):957-965.PubMedGoogle ScholarCrossref
9.
Mormino  EC, Betensky  RA, Hedden  T,  et al.  Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals.  JAMA Neurol. 2014;71(11):1379-1385.PubMedGoogle ScholarCrossref
10.
Wirth  M, Oh  H, Mormino  EC, Markley  C, Landau  SM, Jagust  WJ.  The effect of amyloid β on cognitive decline is modulated by neural integrity in cognitively normal elderly.  Alzheimers Dement. 2013;9(6):687-698.e1. doi:10.1016/j.jalz.2012.10.012.PubMedGoogle ScholarCrossref
11.
Doraiswamy  PM, Sperling  RA, Johnson  K,  et al; AV45-A11 Study Group.  Florbetapir F 18 amyloid PET and 36-month cognitive decline: a prospective multicenter study.  Mol Psychiatry. 2014;19(9):1044-1051.PubMedGoogle ScholarCrossref
12.
Mormino  EC, Betensky  RA, Hedden  T,  et al; Alzheimer’s Disease Neuroimaging Initiative; Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing; Harvard Aging Brain Study.  Amyloid and APOE ε4 interact to influence short-term decline in preclinical Alzheimer disease.  Neurology. 2014;82(20):1760-1767.PubMedGoogle ScholarCrossref
13.
Donohue  MC, Sperling  RA, Salmon  DP,  et al; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Cooperative Study.  The Preclinical Alzheimer Cognitive Composite: measuring amyloid-related decline.  JAMA Neurol. 2014;71(8):961-970.PubMedGoogle ScholarCrossref
14.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly.  Brain. 2015;138(pt 3):761-771.PubMedGoogle ScholarCrossref
15.
Roe  CM, Fagan  AM, Grant  EA,  et al.  Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later.  Neurology. 2013;80(19):1784-1791.PubMedGoogle ScholarCrossref
16.
Roe  CM, Fagan  AM, Grant  EA, Holtzman  DM, Morris  JC.  CSF biomarkers of Alzheimer disease: “noncognitive” outcomes.  Neurology. 2013;81(23):2028-2031.PubMedGoogle ScholarCrossref
17.
Li  G, Millard  SP, Peskind  ER,  et al.  Cross-sectional and longitudinal relationships between cerebrospinal fluid biomarkers and cognitive function in people without cognitive impairment from across the adult life span.  JAMA Neurol. 2014;71(6):742-751.PubMedGoogle ScholarCrossref
18.
Glodzik  L, de Santi  S, Tsui  WH,  et al.  Phosphorylated tau 231, memory decline and medial temporal atrophy in normal elders.  Neurobiol Aging. 2011;32(12):2131-2141.PubMedGoogle ScholarCrossref
19.
Aschenbrenner  AJ, Balota  DA, Fagan  AM, Duchek  JM, Benzinger  TL, Morris  JC.  Alzheimer disease cerebrospinal fluid biomarkers moderate baseline differences and predict longitudinal change in attentional control and episodic memory composites in the Adult Children Study.  J Int Neuropsychol Soc. 2015;21(8):573-583.PubMedGoogle ScholarCrossref
20.
Insel  PS, Mattsson  N, Mackin  RS,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Biomarkers and cognitive endpoints to optimize trials in Alzheimer’s disease.  Ann Clin Transl Neurol. 2015;2(5):534-547.PubMedGoogle ScholarCrossref
21.
Corder  EH, Saunders  AM, Strittmatter  WJ,  et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families.  Science. 1993;261(5123):921-923.PubMedGoogle ScholarCrossref
22.
Albert  M, Soldan  A, Gottesman  R,  et al.  Cognitive changes preceding clinical symptom onset of mild cognitive impairment and relationship to ApoE genotype.  Curr Alzheimer Res. 2014;11(8):773-784.PubMedGoogle ScholarCrossref
23.
Albert  MS, DeKosky  ST, Dickson  D,  et al.  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.  Alzheimers Dement. 2011;7(3):270-279.PubMedGoogle ScholarCrossref
24.
McKhann  GM, Knopman  DS, Chertkow  H,  et al.  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.  Alzheimers Dement. 2011;7(3):263-269.PubMedGoogle ScholarCrossref
25.
Moghekar  A, Goh  J, Li  M, Albert  M, O’Brien  RJ.  Cerebrospinal fluid Aβ and tau level fluctuation in an older clinical cohort.  Arch Neurol. 2012;69(2):246-250.PubMedGoogle ScholarCrossref
26.
Corder  EH, Saunders  AM, Risch  NJ,  et al.  Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease.  Nat Genet. 1994;7(2):180-184.PubMedGoogle ScholarCrossref
27.
Morris  JC, Roe  CM, Xiong  C,  et al.  APOE predicts Aβ but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-131.PubMedGoogle ScholarCrossref
28.
Reiman  EM, Chen  K, Liu  X,  et al.  Fibrillar amyloid-β burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease.  Proc Natl Acad Sci U S A. 2009;106(16):6820-6825.PubMedGoogle ScholarCrossref
29.
Rowe  CC, Ellis  KA, Rimajova  M,  et al.  Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.  Neurobiol Aging. 2010;31(8):1275-1283.PubMedGoogle ScholarCrossref
30.
Bennett  DA, Schneider  JA, Arvanitakis  Z,  et al.  Neuropathology of older persons without cognitive impairment from two community-based studies.  Neurology. 2006;66(12):1837-1844.PubMedGoogle ScholarCrossref
31.
Hulette  CM, Welsh-Bohmer  KA, Murray  MG, Saunders  AM, Mash  DC, McIntyre  LM.  Neuropathological and neuropsychological changes in “normal” aging: evidence for preclinical Alzheimer disease in cognitively normal individuals.  J Neuropathol Exp Neurol. 1998;57(12):1168-1174.PubMedGoogle ScholarCrossref
32.
Knopman  DS, Parisi  JE, Salviati  A,  et al.  Neuropathology of cognitively normal elderly.  J Neuropathol Exp Neurol. 2003;62(11):1087-1095.PubMedGoogle ScholarCrossref
33.
Sperling  RA, Rentz  DM, Johnson  KA,  et al.  The A4 study: stopping AD before symptoms begin?  Sci Transl Med. 2014;6(228):228fs13.PubMedGoogle ScholarCrossref
34.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity.  Neurology. 2013;81(20):1732-1740.PubMedGoogle ScholarCrossref
35.
Knopman  DS, Jack  CR  Jr, Wiste  HJ,  et al.  Brain injury biomarkers are not dependent on β-amyloid in normal elderly.  Ann Neurol. 2013;73(4):472-480.PubMedGoogle ScholarCrossref
36.
Wirth  M, Madison  CM, Rabinovici  GD, Oh  H, Landau  SM, Jagust  WJ.  Alzheimer’s disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals.  J Neurosci. 2013;33(13):5553-5563.PubMedGoogle ScholarCrossref
37.
Hyman  BT.  Amyloid-dependent and amyloid-independent stages of Alzheimer disease.  Arch Neurol. 2011;68(8):1062-1064.PubMedGoogle ScholarCrossref
38.
Resnick  SM, Bilgel  M, Moghekar  A,  et al.  Changes in Aβ biomarkers and associations with APOE genotype in 2 longitudinal cohorts.  Neurobiol Aging. 2015;36(8):2333-2339.PubMedGoogle ScholarCrossref
39.
Khachaturian  AS, Corcoran  CD, Mayer  LS, Zandi  PP, Breitner  JC; Cache County Study Investigators.  Apolipoprotein E ε4 count affects age at onset of Alzheimer disease, but not lifetime susceptibility: the Cache County Study.  Arch Gen Psychiatry. 2004;61(5):518-524.PubMedGoogle ScholarCrossref
40.
Roe  CM, Fagan  AM, Grant  EA,  et al.  Cerebrospinal fluid biomarkers, education, brain volume, and future cognition.  Arch Neurol. 2011;68(9):1145-1151.PubMedGoogle ScholarCrossref
41.
Soldan  A, Pettigrew  C, Li  S,  et al; BIOCARD Research Team.  Relationship of cognitive reserve and cerebrospinal fluid biomarkers to the emergence of clinical symptoms in preclinical Alzheimer’s disease.  Neurobiol Aging. 2013;34(12):2827-2834.PubMedGoogle ScholarCrossref
Original Investigation
June 2016

Hypothetical Preclinical Alzheimer Disease Groups and Longitudinal Cognitive Change

Author Affiliations
  • 1Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
  • 2Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
  • 3Department of Neurology, Duke University School of Medicine, Durham, North Carolina
 

Copyright 2016 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.

JAMA Neurol. 2016;73(6):698-705. doi:10.1001/jamaneurol.2016.0194
Abstract

Importance  Clinical trials testing treatments for Alzheimer disease (AD) are increasingly focused on cognitively normal individuals in the preclinical phase of the disease. To optimize observing a treatment effect, such trials need to enroll cognitively normal individuals likely to show cognitive decline over the duration of the trial.

Objective  To identify which group of cognitively normal individuals shows the greatest cognitive decline over time based on their cerebrospinal fluid biomarker profile.

Design, Setting, and Participants  In this cohort study, cognitively normal participants were classified into 1 of the following 4 hypothetical preclinical AD groups using baseline cerebrospinal fluid levels of Aβ and tau or Aβ and phosphorylated tau (p-tau): stage 0 (high Aβ and low tau), stage 1 (low Aβ and low tau), stage 2 (low Aβ and high tau), and suspected non-AD pathology (SNAP) (high Aβ and high tau). The data presented herein were collected between August 1995 and August 2014.

Main Outcomes and Measures  An a priori cognitive composite score based on the following 4 tests previously shown to predict progression from normal cognition to symptom onset of mild cognitive impairment or dementia: Paired Associates immediate recall, Logical Memory delayed recall, Boston Naming, and Digit-Symbol Substitution. Linear mixed-effects models were used to compare the cognitive composite scores across the 4 groups over time, adjusting for baseline age, sex, education, and their interactions with time.

Results  Two hundred twenty-two cognitively normal participants were included in the analyses (mean follow-up, 11.0 years [range, 0-18.3 years] and mean baseline age, 56.9 years [range, 22.1-85.8 years]). Of these, 102 were stage 0, 46 were stage 1, 28 were stage 2, and 46 were SNAP. Individuals in stage 2 (low Aβ and high tau [or p-tau]) had lower baseline cognitive scores and a greater decline in the cognitive composite score relative to the other 3 groups (β ≤ −0.06 for all and P ≤ .001 for the rate of decline for all). Individuals in stage 0, stage 1, and SNAP did not differ from one another in cognitive performance at baseline or over time (11.0 years) and showed practice-related improvement in performance. The APOE ε4 genotype was not associated with baseline cognitive composite score or the rate of change in the cognitive composite score.

Conclusions and Relevance  These results suggest that, to optimize observing a treatment effect, clinical trials enrolling cognitively normal individuals should selectively recruit participants with abnormal levels of both amyloid and tau (ie, stage 2) because this group would be expected to show the greatest cognitive decline over time if untreated.

Introduction

Current evidence suggests that the neuropathological processes associated with Alzheimer disease (AD) begin a decade or more before the emergence of obvious cognitive impairment.1 This preclinical phase of the disease is the focus of clinical trials because it is hypothesized that disease-modifying therapies are likely to be most successful when administered before the initial symptomatic phase, known as mild cognitive impairment (MCI), in which there is substantial synaptic and neuronal damage.2-4 The primary objective of the present study was to examine which cognitively normal individuals with evidence of AD pathology are most likely to demonstrate cognitive decline over time. This information would have important implications for determining participant selection criteria for clinical trials because the rate of cognitive change over time must be sufficient to permit seeing a drug effect if one is present.

We tested whether individuals with differing biomarker profiles show different cognitive trajectories over time, as would be predicted by the hypothetical staging model of preclinical AD laid out by the Preclinical AD Workgroup sponsored by the National Institute on Aging and the Alzheimer’s Association.1 This model proposes that the preclinical phase of AD can be subdivided into 3 successive stages. Stage 1 is characterized by amyloid pathology but the absence of tau-related neurodegeneration. During stage 2, both amyloid pathology and tau-related neurodegeneration are evident. Finally, during stage 3, subtle cognitive decline becomes detectable in addition to amyloid and tau pathology. Individuals with normal measures of both amyloid and neurodegeneration are classified as stage 0. Furthermore, it has been proposed that individuals with evidence of neurodegeneration but normal levels of amyloid might be classified as having suspected non-AD pathology (SNAP).5

The participants in this study were part of a longitudinal cohort of individuals with normal cognition when first assessed. We used baseline cerebrospinal fluid (CSF) measures of amyloid (Aβ1-42), total tau, and phosphorylated tau (p-tau) to classify individuals into the hypothetical stages of preclinical AD and SNAP. These CSF biomarkers are particularly useful in addressing the objectives of the study because they directly reflect the levels of abnormal brain proteins associated with the AD pathology (ie, plaques and tangles).6,7

To our knowledge, only one prior study8 has examined the combined effects of CSF measures of amyloid and tau on cognitive change among individuals who were cognitively normal at baseline. Vos et al8 reported a greater decline on the Mini-Mental State Examination (MMSE) over a mean of 3.9 years among individuals with evidence of both amyloid and tau pathology (stage 2) compared with individuals classified as stage 0, stage 1, and SNAP. The only other 2 studies9,10 to investigate the combined effects of amyloid and neuronal injury on cognitive change used imaging-based biomarkers (eg, magnetic resonance imaging), which do not provide a direct measure of tau-related neurofibrillary tangle pathology. These studies reported similar results as Vos et al,8 but the mean follow-up period was limited to 2 to 4 years.

The availability of CSF samples at baseline (when the participants were cognitively normal), the extensive cognitive testing, and the unusually long duration of follow-up (mean, 11.0 years) allowed us to examine several questions of particular relevance to clinical trials in preclinical AD. First, the present study used a cognitive composite score covering multiple domains of cognition as the outcome, allowing us to determine whether prior findings regarding the MMSE generalized to a broader range of cognitive domains and to tests likely to be more sensitive to subtle cognitive change. Second, most prior studies that have examined rates of cognitive change among cognitively normal individuals have been of short duration (mean follow-up, 1-4 years),8-10 have not included measures of tau pathology,11-14 or did not examine possible interactions between amyloid and tau on the rate of change in cognition.15-20 Third, it remains unclear whether the major genetic risk factor for AD, the APOE ε4 (OMIM 107741) genotype,21 modulates the associations between amyloid, tau, p-tau, and cognitive change. This consideration may be highly relevant for the selection of participants in clinical trials because subgroups with differing rates of decline would make it more challenging to identify drug effects.

Box Section Ref ID

Key Points

  • Question Do the long-term cognitive trajectories differ among individuals classified into hypothetical preclinical Alzheimer disease groups using baseline cerebrospinal fluid levels of Aβ, tau, and phosphorylated tau (p-tau)?

  • Findings In this cohort study of cognitively normal adults, individuals in stage 2 (low Aβ and high tau [or p-tau]) had significantly lower baseline cognitive composite scores and a greater decline in cognitive performance than individuals in stage 0, stage 1, and SNAP, who did not differ from one another over 11 years.

  • Meaning Abnormal levels of both amyloid and tau appear to be necessary for observing a marked decline in cognition among cognitively normal individuals.

Methods
Study Design

The parent study from which these data are derived is BIOCARD, initiated at the National Institutes of Health (NIH) in 1995. By design, approximately 75% of the participants had a first-degree relative with dementia of the Alzheimer type. The study was stopped in 2005 for administrative reasons and reestablished at The Johns Hopkins University in 2009. During the initial study at the NIH, participants were administered a comprehensive neuropsychological battery annually. Magnetic resonance imaging, CSF samples, and blood specimens were obtained approximately every 2 years. Figure 1 shows a schematic representation of the study design.

Selection of Participants

Recruitment was conducted by the staff of the geriatric psychiatry branch of the intramural program of the National Institute of Mental Health. At baseline, all individuals completed a comprehensive evaluation at the NIH consisting of a physical and neurological examination, an electrocardiogram, standard laboratory studies, and neuropsychological testing. Individuals were excluded from participation if they were cognitively impaired or had significant medical problems, such as severe cerebrovascular disease, epilepsy, or alcohol or drug abuse. eMethods 1 in the Supplement provides details regarding the selection of participants.

A total of 349 individuals were initially enrolled in the study after providing written informed consent. The analyses presented herein are based on 222 participants of the 335 who provided baseline CSF samples (eMethods 2 in the Supplement provides reasons for exclusion of individuals from analyses). The study was approved by The Johns Hopkins University Institutional Review Board.

Clinical and Cognitive Assessment of Participants

A cognitive and clinical assessment and a consensus diagnosis were completed annually at the NIH and at The Johns Hopkins University. Further details are provided by Albert et al.22 Each participant included in our analyses received a consensus diagnosis by the staff of the Johns Hopkins BIOCARD clinical core. Each case was handled in the following similar manner: (1) clinical data pertaining to the medical, neurological, and psychiatric status of the individual were examined; (2) reports of changes in cognition by the individual and by collateral sources were reviewed; and (3) decline in cognitive performance based on review of longitudinal testing from multiple domains was established. We followed the diagnostic recommendations incorporated in the National Institute on Aging and the Alzheimer’s Association working group reports for the diagnosis of MCI23 and dementia due to AD.24 eMethods 3 in the Supplement provides additional details. The clinical diagnoses were masked to CSF assessments.

The main outcome variable was an a priori–derived global cognitive composite score based on 4 individual measures that were identified previously to be the best combination of cognitive predictors of the time to progress from normal cognition to clinical symptom onset.22 These measures were (1) Paired Associates immediate recall of the Wechsler Memory Scale–Revised, (2) Logical Memory delayed recall (Story A) score of the Wechsler Memory Scale–Revised, (3) Boston Naming, and (4) Digit-Symbol Substitution from the Wechsler Adult Intelligence Scale–Revised. These measures were administered annually at the NIH and are part of the annual neuropsychological battery at The Johns Hopkins University. To calculate the cognitive composite score, the individual measures were transformed to z scores and then averaged, with the requirement that at least 2 of the 4 scores were present at a given time point. eFigure 1 in the Supplement shows a histogram of baseline scores.

CSF Assessments

The CSF samples were analyzed with the same protocol used in the Alzheimer Disease Neuroimaging Initiative. This protocol used a kit (xMAP-based AlzBio3; Innogenetics) run on a suspension array system (Bio-Plex 200; Bio-Rad). Each participant had all samples (run in triplicate) analyzed on the same plate (eMethods 4 and eFigure 2 in the Supplement provide details regarding the CSF assay and baseline biomarker frequency distributions). Additional details have been published elsewhere.25

APOE Genotyping and Coding

APOE genotype was established in all but one of the cohort participants (n = 348). Genotypes were determined by restriction endonuclease digestion of polymerase chain reaction–amplified genomic DNA (performed by Athena Diagnostics, Worcester, Massachusetts). APOE ε4 carrier status was coded by an indicator variable, with ε4 carriers coded as 1 if they had at least 1 ε4 allele and noncarriers coded as 0. Analyses that included APOE carrier status excluded individuals with the ε2/ε4 genotype because the ε4 allele increases AD dementia risk,21 whereas the ε2 allele decreases AD dementia risk.26

Statistical Analysis

Based on the observation that approximately one-third of cognitively normal older adults have AD pathology in their brains, as indicated by amyloid imaging27-29 and neuropathological studies,30-32 biomarker abnormality was defined as having CSF Aβ1-42 levels in the lower one-third of the distribution of participants (<374.5 pg/mL) or having tau (>74.9 pg/mL) or p-tau (>39.4 pg/mL) levels in the upper one-third of the distribution. The resulting proportion of individuals in the hypothetical preclinical AD groups (ie, stages 0, 1, and 2) was comparable to that reported in the literature.5 The pattern of results was similar when using a median split or quintile split (eTable 1 and eTable 2 in the Supplement) to classify individuals into groups, suggesting robustness to cut point variations.

The data were analyzed using general linear mixed regression models, including linear effects of time, to test if the rate of change in cognition differed across the groups. Two main analyses were performed, one using CSF Aβ1-42 and tau to classify individuals into the 4 groups and the other using CSF Aβ1-42 and p-tau for classification. Group status was coded using binary predictors (0 or 1) for each group. The following predictors were included in both models, treating stage 0 as the implicit baseline: baseline age, sex, education, follow-up time, stage 1 indicator, stage 2 indicator, SNAP indicator, and the interaction (cross product) of each predictor with time. In these models, the stage indicator × time interaction terms test if the rate of change in the cognitive composite score differs between stage 0 and the other stages. The outcome variable in all analyses was the cognitive composite score (including baseline and all available follow-up scores, as defined in the Clinical and Cognitive Assessment of Participants subsection of the Methods section). Models were specified with a random intercept and slope.

To examine the role of the APOE ε4 genotype on cognitive change, both models were rerun with inclusion of the indicator for the APOE ε4 genotype and the APOE ε4 genotype × time interaction term. In addition, to test if the cognitive trajectories within a given stage differ by the APOE ε4 genotype, 4 mixed-effects models were run (one for each group) with the following predictors: baseline age, sex, education, APOE ε4 genotype, time, baseline age × time interaction, and APOE ε4 genotype × time interaction. The sex × time and education × time interactions were not included because they were not significant in any previous analysis.

Differences in baseline characteristics of participants in stage 0 compared with the other 3 groups were assessed using 2-tailed t tests or χ2 tests, with a significance level of P < .05 uncorrected for multiple comparisons. All data analyses used a software program (R, version 3.2.1; R Project for Statistical Computing).

Results

The BIOCARD cohort as a whole, as well as subjects in the analysis, were primarily middle-aged at baseline, well-educated, and about one-third were APOE ε4 carriers. Baseline characteristics for the BIOCARD cohort and for individuals in the analyses are listed in Table 1. Table 2 lists baseline characteristics separately for the 4 groups (stage 0, stage 1, stage 2, and SNAP). The groups did not differ in sex, education, or MMSE score at baseline. However, compared with stage 0, individuals in stage 2 were older, were more likely to be APOE ε4 carriers, had lower baseline cognitive composite scores, and were more likely to progress to MCI or AD dementia (Table 2 and eTable 2 in the Supplement). Individuals in stage 0 had more follow-up cognitive testing than the other groups.

The results from the mixed-effects models comparing the cognitive trajectories of individuals in stage 0 with the other groups are summarized in Table 3. The results were almost identical whether CSF Aβ1-42 and tau or CSF Aβ1-42 and p-tau were used to define group membership. In both models, there was a main effect of time (reflecting practice-related improvement in cognitive performance over time), a main effect of baseline age, and a baseline age × time interaction (signifying lower cognitive performance and less improvement in performance over time with increasing age). Higher education was associated with better cognitive performance but did not alter the rate of cognitive change over time.

Most important, the main effects of stage 1 and SNAP were not significant, nor were the interactions between stage 1 indicator × time and SNAP indicator × time. This finding suggests that there was no difference in either the mean cognitive composite score or the rate of change in the cognitive composite score over time between the stage 0 group and the stage 1 and SNAP groups. In contrast, the stage 2 indicator × time interactions were highly significant, indicating a more negative rate of change in cognition for the stage 2 group compared with the stage 0 group (P < .001). The mean cognitive performance was also lower in the stage 2 group compared with the stage 0 group. These results are shown graphically in Figure 2. Post hoc mixed-effects models directly comparing stage 1 and SNAP with stage 2 revealed a more negative rate of change in cognition for individuals in stage 2 compared with stage 1 and compared with SNAP. Compared with stage 1, the mean (SE) estimates were −0.074 (0.018) (P < .001) using p-tau and −0.068 (0.019) (P < .001) using tau. Compared with SNAP, the mean (SE) estimates were −0.074 (0.018) (P < .001) using p-tau and −0.060 (0.017) (P = .001) using tau. The results were similar using the individual cognitive measures as outcomes.

The analyses were repeated with inclusion of the APOE ε4 genotype and the APOE ε4 genotype × time interaction term, but the results were unchanged, and effects involving the APOE ε4 genotype were nonsignificant (P > .40 for all). Likewise, separate models for individuals in each stage showed no differences in the cognitive trajectories between APOE ε4 carriers and noncarriers.

Discussion

This study compared the cognitive trajectories of individuals with different CSF AD biomarker profiles and normal cognition at baseline within the framework of 4 hypothetical groupings related to preclinical AD.1,5 There was no difference in baseline cognitive performance or the rate of change in cognitive performance over a mean of 11.0 years among individuals in stage 1 (low levels of Aβ) or SNAP (high levels of tau or p-tau) compared with those in stage 0 (normal levels of both Aβ and tau or p-tau). By comparison, individuals in stage 2 (both low levels of Aβ and high levels of tau or p-tau) had lower cognitive performance at baseline and a more negative rate of change in cognition than the other 3 groups. Taken together, these results suggest that abnormal levels of both amyloid and tau are necessary for observing a marked decline in cognition among cognitively normal individuals.

These findings have important implications for the design of clinical trials aimed at individuals in the preclinical phase of AD. Our results suggest that, to optimize observing a treatment effect, clinical trials enrolling cognitively normal individuals should selectively recruit participants with abnormal levels of both amyloid and tau (ie, stage 2) because this group would be expected to show the greatest cognitive decline over time if untreated. If participants are selected solely on the basis of their amyloid status (eg, as in the A4 study33), then the ability to observe a significant treatment effect on cognition might be greatly diminished because a large proportion of untreated participants (those with abnormal amyloid but normal tau levels) would not be expected to show meaningful cognitive decline over the short time frame of a clinical trial. Our findings also suggest that, while APOE ε4 carriers may be more likely to be further along the AD trajectory and therefore have an earlier age at onset,12 the cognitive trajectories do not differ by ε4 carrier status after accounting for CSF amyloid and tau or p-tau levels. We do not have data regarding the effectiveness of antiamyloid drugs in reducing cognitive decline. However, our results suggest that, to the extent that amyloid and tau pathology arise independently and cognitive decline simply depends on their co-occurrence,34-36 antiamyloid therapies may be effective in individuals with concurrent amyloid and tau pathology and in those with amyloid pathology only, who may subsequently develop tau pathology. However, if amyloid accumulation initiates a downstream cascade of tau-related neurodegeneration that becomes increasingly independent of amyloid itself,37 then antiamyloid agents may only be effective if administered before the onset of the neurodegenerative process.

The present results are consistent with prior short-term longitudinal studies reporting a disproportionately greater rate of cognitive decline for individuals classified as stage 2 compared with stage 0, stage 1, and SNAP using CSF biomarkers8 or neuroimaging-based biomarkers.9,10 The study expands on prior findings in several ways. First, our cognitive outcome measure is clinically validated in the sense that it is based on neuropsychological tests previously shown to predict progression from normal cognition to MCI or dementia due to AD.22 Both baseline cognitive composite score and the rate of change in the measures that comprise our cognitive composite score are associated with the time to onset of clinical symptoms, suggesting that these types of measures are useful for tracking AD progression in clinical trials. Second, our results demonstrate that the pattern of short-term cognitive trajectories observed previously remains stable over the course of a decade. Third, we found that, although APOE ε4 carriers were overrepresented among individuals classified as stage 2, the APOE ε4 genotype did not modify the rate of change in cognition. Taken together, these 2 findings suggest that the APOE ε4 allele does not significantly alter the rate of AD progression but is associated with an earlier age at onset of AD.38,39 Fourth, higher education was associated with better cognitive performance after accounting for baseline CSF levels but did not modify the rate of change in cognition. This finding supports the view that education reduces the effect of AD neuropathology on cognition but does not alter the rate of disease progression.40,41 Fifth, the present results point toward the usefulness of CSF biomarkers in identifying individuals at risk for cognitive decline at a significantly younger age (the mean baseline age for stage 2 was 63 years) than what has been reported by previous studies, which focused on individuals in their 70s at baseline.

Our study has several limitations. The participants are well educated, primarily of white race/ethnicity, and predominantly middle-aged at baseline, and most had a family history of dementia. Therefore, the results may not generalize to the population at large or to older cohorts. In addition, the sample size may have been too small to detect differences by the APOE ε4 genotype. Future studies are necessary to determine if similar findings would be obtained using imaging-based biomarkers of amyloid and tau.

Conclusions

In summary, our data suggest that abnormal levels of both amyloid and tau appear to be necessary for observing cognitive decline among cognitively normal individuals. To optimize observing a treatment effect, clinical trials enrolling cognitively normal individuals should selectively recruit participants with abnormal levels of both biomarkers because this group would be expected to show the greatest cognitive decline over time if untreated.

Back to top
Article Information

Corresponding Author: Anja Soldan, PhD, Department of Neurology, Johns Hopkins University School of Medicine, 1620 McElderry St, Reed Hall, Room 104A, Baltimore, MD 21205 (asoldan1@jhmi.edu).

Accepted for Publication: January 19, 2016.

Published Online: April 11, 2016. doi:10.1001/jamaneurol.2016.0194.

Author Contributions: Dr Soldan had full access to all the data in the study and takes full responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Soldan, Pettigrew, Albert.

Acquisition, analysis, or interpretation of data: Soldan, Pettigrew, Cai, Wang, Selnes, Albert.

Drafting of the manuscript: Soldan.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Cai, Wang, Moghekar, O’Brien.

Obtained funding: Albert.

Administrative, technical, or material support: Soldan.

Study supervision: Soldan.

Conflict of Interest Disclosures: Dr Albert reported being an advisor to Eli Lilly. No other disclosures were reported.

Funding/Support: This study was supported in part by grants U19-AG03365, P50-AG005146, and T32-AG027668 from the National Institutes of Health (Dr Albert).

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: BIOCARD consists of 7 cores with the following members: (1) administrative core (Marilyn S. Albert and Barbara Rodzon), (2) clinical core (Ola A. Selnes, Marilyn S. Albert, Anja Soldan, Rebecca Gottesman, Ned Sacktor, Guy McKhann, Scott Turner, Leonie Farrington, Maura Grega, Gay Rudow, Daniel D’Agostino, and Scott Rudow), (3) imaging core (Michael Miller, Susumu Mori, Tilak Ratnanather, Timothy Brown, Hayan Chi, Anthony Kolasny, Kenichi Oishi, Thomas Reigel, and Laurent Younes), (4) biospecimen core (Abhay R. Moghekar, Richard J. O’Brien, and Abby Spangler), (5) informatics core (Roberta Scherer, David Shade, Ann Ervin, Jennifer Jones, Matt Toepfner, Lauren Parlett, April Patterson, and Aisha Mohammed), (6) biostatistics core (Mei-Cheng Wang, Qing Cai, and Daisy Lu), and (7) neuropathology core (Juan Troncoso, Barbara Crain, Olga Pletnikova, Gay Rudow, and Karen Fisher).

Additional Contributions: We thank the following members of the BIOCARD scientific advisory board: John Cernansky, David Holtzman, David Knopman, Walter Kukull, and John McArdle (who provide continued oversight and guidance regarding the conduct of the study) and Neil Buckholtz, John Hsiao, Laurie Ryan, and Jovier Evans (who provide oversight on behalf of the National Institute on Aging and the National Institute of Mental Health [NIMH]). We thank the following members of the BIOCARD resource allocation committee, who provide ongoing guidance regarding the use of the biospecimens collected as part of the study: Constantine Lyketsos, Carlos Pardo, Gerard Schellenberg, Leslie Shaw, Madhav Thambisetty, and John Trojanowski. We acknowledge the contributions of the geriatric psychiatry branch of the intramural program of the NIMH, which initiated the study (principal investigator, Trey Sunderland). We are particularly indebted to Karen Putnam, who has provided ongoing documentation of the geriatric psychiatry branch study procedures and the data files received from the NIMH. None received compensation outside of their usual salary.

References
1.
Sperling  RA, Aisen  PS, Beckett  LA,  et al.  Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging–Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease.  Alzheimers Dement. 2011;7(3):280-292.PubMedGoogle ScholarCrossref
2.
Desikan  RS, Cabral  HJ, Hess  CP,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease.  Brain. 2009;132(pt 8):2048-2057.PubMedGoogle ScholarCrossref
3.
Grothe  MJ, Heinsen  H, Amaro  E  Jr, Grinberg  LT, Teipel  SJ; Alzheimer’s Disease Neuroimaging Initiative.  Cognitive correlates of basal forebrain atrophy and associated cortical hypometabolism in mild cognitive impairment [published online April 2, 2015].  Cereb Cortex.PubMedGoogle Scholar
4.
Pagani  M, De Carli  F, Morbelli  S,  et al.  Volume of interest–based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer’s disease from healthy controls: a European Alzheimer’s Disease Consortium (EADC) study.  Neuroimage Clin. 2015;7:34-42.PubMedGoogle ScholarCrossref
5.
Jack  CR  Jr, Knopman  DS, Weigand  SD,  et al.  An operational approach to National Institute on Aging–Alzheimer’s Association criteria for preclinical Alzheimer disease.  Ann Neurol. 2012;71(6):765-775.PubMedGoogle ScholarCrossref
6.
Holtzman  DM.  CSF biomarkers for Alzheimer’s disease: current utility and potential future use.  Neurobiol Aging. 2011;32(suppl 1):S4-S9.PubMedGoogle ScholarCrossref
7.
Tapiola  T, Alafuzoff  I, Herukka  SK,  et al.  Cerebrospinal fluid β-amyloid 42 and tau proteins as biomarkers of Alzheimer-type pathologic changes in the brain.  Arch Neurol. 2009;66(3):382-389.PubMedGoogle ScholarCrossref
8.
Vos  SJ, Xiong  C, Visser  PJ,  et al.  Preclinical Alzheimer’s disease and its outcome: a longitudinal cohort study.  Lancet Neurol. 2013;12(10):957-965.PubMedGoogle ScholarCrossref
9.
Mormino  EC, Betensky  RA, Hedden  T,  et al.  Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals.  JAMA Neurol. 2014;71(11):1379-1385.PubMedGoogle ScholarCrossref
10.
Wirth  M, Oh  H, Mormino  EC, Markley  C, Landau  SM, Jagust  WJ.  The effect of amyloid β on cognitive decline is modulated by neural integrity in cognitively normal elderly.  Alzheimers Dement. 2013;9(6):687-698.e1. doi:10.1016/j.jalz.2012.10.012.PubMedGoogle ScholarCrossref
11.
Doraiswamy  PM, Sperling  RA, Johnson  K,  et al; AV45-A11 Study Group.  Florbetapir F 18 amyloid PET and 36-month cognitive decline: a prospective multicenter study.  Mol Psychiatry. 2014;19(9):1044-1051.PubMedGoogle ScholarCrossref
12.
Mormino  EC, Betensky  RA, Hedden  T,  et al; Alzheimer’s Disease Neuroimaging Initiative; Australian Imaging Biomarkers and Lifestyle Flagship Study of Ageing; Harvard Aging Brain Study.  Amyloid and APOE ε4 interact to influence short-term decline in preclinical Alzheimer disease.  Neurology. 2014;82(20):1760-1767.PubMedGoogle ScholarCrossref
13.
Donohue  MC, Sperling  RA, Salmon  DP,  et al; Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Cooperative Study.  The Preclinical Alzheimer Cognitive Composite: measuring amyloid-related decline.  JAMA Neurol. 2014;71(8):961-970.PubMedGoogle ScholarCrossref
14.
Vemuri  P, Lesnick  TG, Przybelski  SA,  et al.  Vascular and amyloid pathologies are independent predictors of cognitive decline in normal elderly.  Brain. 2015;138(pt 3):761-771.PubMedGoogle ScholarCrossref
15.
Roe  CM, Fagan  AM, Grant  EA,  et al.  Amyloid imaging and CSF biomarkers in predicting cognitive impairment up to 7.5 years later.  Neurology. 2013;80(19):1784-1791.PubMedGoogle ScholarCrossref
16.
Roe  CM, Fagan  AM, Grant  EA, Holtzman  DM, Morris  JC.  CSF biomarkers of Alzheimer disease: “noncognitive” outcomes.  Neurology. 2013;81(23):2028-2031.PubMedGoogle ScholarCrossref
17.
Li  G, Millard  SP, Peskind  ER,  et al.  Cross-sectional and longitudinal relationships between cerebrospinal fluid biomarkers and cognitive function in people without cognitive impairment from across the adult life span.  JAMA Neurol. 2014;71(6):742-751.PubMedGoogle ScholarCrossref
18.
Glodzik  L, de Santi  S, Tsui  WH,  et al.  Phosphorylated tau 231, memory decline and medial temporal atrophy in normal elders.  Neurobiol Aging. 2011;32(12):2131-2141.PubMedGoogle ScholarCrossref
19.
Aschenbrenner  AJ, Balota  DA, Fagan  AM, Duchek  JM, Benzinger  TL, Morris  JC.  Alzheimer disease cerebrospinal fluid biomarkers moderate baseline differences and predict longitudinal change in attentional control and episodic memory composites in the Adult Children Study.  J Int Neuropsychol Soc. 2015;21(8):573-583.PubMedGoogle ScholarCrossref
20.
Insel  PS, Mattsson  N, Mackin  RS,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Biomarkers and cognitive endpoints to optimize trials in Alzheimer’s disease.  Ann Clin Transl Neurol. 2015;2(5):534-547.PubMedGoogle ScholarCrossref
21.
Corder  EH, Saunders  AM, Strittmatter  WJ,  et al.  Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families.  Science. 1993;261(5123):921-923.PubMedGoogle ScholarCrossref
22.
Albert  M, Soldan  A, Gottesman  R,  et al.  Cognitive changes preceding clinical symptom onset of mild cognitive impairment and relationship to ApoE genotype.  Curr Alzheimer Res. 2014;11(8):773-784.PubMedGoogle ScholarCrossref
23.
Albert  MS, DeKosky  ST, Dickson  D,  et al.  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.  Alzheimers Dement. 2011;7(3):270-279.PubMedGoogle ScholarCrossref
24.
McKhann  GM, Knopman  DS, Chertkow  H,  et al.  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.  Alzheimers Dement. 2011;7(3):263-269.PubMedGoogle ScholarCrossref
25.
Moghekar  A, Goh  J, Li  M, Albert  M, O’Brien  RJ.  Cerebrospinal fluid Aβ and tau level fluctuation in an older clinical cohort.  Arch Neurol. 2012;69(2):246-250.PubMedGoogle ScholarCrossref
26.
Corder  EH, Saunders  AM, Risch  NJ,  et al.  Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease.  Nat Genet. 1994;7(2):180-184.PubMedGoogle ScholarCrossref
27.
Morris  JC, Roe  CM, Xiong  C,  et al.  APOE predicts Aβ but not tau Alzheimer pathology in cognitively normal aging.  Ann Neurol. 2010;67(1):122-131.PubMedGoogle ScholarCrossref
28.
Reiman  EM, Chen  K, Liu  X,  et al.  Fibrillar amyloid-β burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease.  Proc Natl Acad Sci U S A. 2009;106(16):6820-6825.PubMedGoogle ScholarCrossref
29.
Rowe  CC, Ellis  KA, Rimajova  M,  et al.  Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging.  Neurobiol Aging. 2010;31(8):1275-1283.PubMedGoogle ScholarCrossref
30.
Bennett  DA, Schneider  JA, Arvanitakis  Z,  et al.  Neuropathology of older persons without cognitive impairment from two community-based studies.  Neurology. 2006;66(12):1837-1844.PubMedGoogle ScholarCrossref
31.
Hulette  CM, Welsh-Bohmer  KA, Murray  MG, Saunders  AM, Mash  DC, McIntyre  LM.  Neuropathological and neuropsychological changes in “normal” aging: evidence for preclinical Alzheimer disease in cognitively normal individuals.  J Neuropathol Exp Neurol. 1998;57(12):1168-1174.PubMedGoogle ScholarCrossref
32.
Knopman  DS, Parisi  JE, Salviati  A,  et al.  Neuropathology of cognitively normal elderly.  J Neuropathol Exp Neurol. 2003;62(11):1087-1095.PubMedGoogle ScholarCrossref
33.
Sperling  RA, Rentz  DM, Johnson  KA,  et al.  The A4 study: stopping AD before symptoms begin?  Sci Transl Med. 2014;6(228):228fs13.PubMedGoogle ScholarCrossref
34.
Jack  CR  Jr, Wiste  HJ, Weigand  SD,  et al.  Amyloid-first and neurodegeneration-first profiles characterize incident amyloid PET positivity.  Neurology. 2013;81(20):1732-1740.PubMedGoogle ScholarCrossref
35.
Knopman  DS, Jack  CR  Jr, Wiste  HJ,  et al.  Brain injury biomarkers are not dependent on β-amyloid in normal elderly.  Ann Neurol. 2013;73(4):472-480.PubMedGoogle ScholarCrossref
36.
Wirth  M, Madison  CM, Rabinovici  GD, Oh  H, Landau  SM, Jagust  WJ.  Alzheimer’s disease neurodegenerative biomarkers are associated with decreased cognitive function but not β-amyloid in cognitively normal older individuals.  J Neurosci. 2013;33(13):5553-5563.PubMedGoogle ScholarCrossref
37.
Hyman  BT.  Amyloid-dependent and amyloid-independent stages of Alzheimer disease.  Arch Neurol. 2011;68(8):1062-1064.PubMedGoogle ScholarCrossref
38.
Resnick  SM, Bilgel  M, Moghekar  A,  et al.  Changes in Aβ biomarkers and associations with APOE genotype in 2 longitudinal cohorts.  Neurobiol Aging. 2015;36(8):2333-2339.PubMedGoogle ScholarCrossref
39.
Khachaturian  AS, Corcoran  CD, Mayer  LS, Zandi  PP, Breitner  JC; Cache County Study Investigators.  Apolipoprotein E ε4 count affects age at onset of Alzheimer disease, but not lifetime susceptibility: the Cache County Study.  Arch Gen Psychiatry. 2004;61(5):518-524.PubMedGoogle ScholarCrossref
40.
Roe  CM, Fagan  AM, Grant  EA,  et al.  Cerebrospinal fluid biomarkers, education, brain volume, and future cognition.  Arch Neurol. 2011;68(9):1145-1151.PubMedGoogle ScholarCrossref
41.
Soldan  A, Pettigrew  C, Li  S,  et al; BIOCARD Research Team.  Relationship of cognitive reserve and cerebrospinal fluid biomarkers to the emergence of clinical symptoms in preclinical Alzheimer’s disease.  Neurobiol Aging. 2013;34(12):2827-2834.PubMedGoogle ScholarCrossref
×