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Figure 1.  Classification of Alzheimer’s Disease Neuroimaging Initiative (ADNI) Participants in Tau Cerebrospinal Fluid/Positron Emission Tomography (CSF/PET) Groups
Classification of Alzheimer’s Disease Neuroimaging Initiative (ADNI) Participants in Tau Cerebrospinal Fluid/Positron Emission Tomography (CSF/PET) Groups

We classified ADNI participants into tau CSF/PET categories based on their CSF phosphorylated tau (P-tau; positivity ≥26.64 pg/mL) and meta–regions of interest (meta-ROI) flortaucipir-PET binding (standard uptake value ratio [SUVR] threshold ≥1.37) measures. Color code represents the tau CSF/PET groups and shape indicates clinical diagnosis. Dotted lines delineate a 5% interval around tau CSF/PET thresholds. Faded dots indicate participants who would fall within this interval. CU indicates cognitively unimpaired; MCI, mild cognitive impairment; AD, Alzheimer disease.

Figure 2.  Demographic and Cross-Sectional Biomarker Characteristics of Tau Cerebrospinal Fluid/Positron Emission Tomography (CSF/PET) Groups
Demographic and Cross-Sectional Biomarker Characteristics of Tau Cerebrospinal Fluid/Positron Emission Tomography (CSF/PET)  Groups

A, The proportion of APOE ε4 carriers was different among CSF/PET groups. In post hoc analyses, CSF+/PET, CSF/PET+, and CSF+/PET+ had a higher proportion of APOE ε4 carriers than the CSF/PET group. B, The proportion of cognitively impaired participants was increased in PET+ groups when compared with PET groups, regardless of CSF status. C, There was a marked difference in the frequency of florbetapir and florbetaben-PET (amyloid β)+ individuals across groups. Post hoc analyses suggested that all tau+ groups on either modality had a higher proportion of florbetapir or florbetaben-PET–(amyloid β)+ participants compared with the CSF/PET group. D, All 3 tau+ groups also had higher mean cortical florbetapir-PET uptake. The CSF/PET+ and CSF+/PET+ groups had measurably lower executive function (E) and memory performance (F) compared with the CSF/PET and CSF+/PET groups. Bars indicate group median (red line), 1st and 99th percentiles (small black line) and 25th and 75th percentiles (thick black lines).

aP < .05

bP < .01

cP < .005

Figure 3.  Retrospective Trajectories of Cerebrospinal Fluid (CSF) Phosphorylated Tau (P-Tau), Memory and Executive Function
Retrospective Trajectories of Cerebrospinal Fluid (CSF) Phosphorylated Tau (P-Tau), Memory and Executive Function

We investigated CSF P-tau and cognitive performance (memory and executive function) in the 5 years (60 months) preceding flortaucipir–positron emission tomography (PET) scanning (time 0) as a function of PET/CSF status. A, Individual trajectories and measures of CSF P-tau during the 5 years preceding the flortaucipir-PET scan. B, Results from linear mixed-effects analyses where 5-year change in CSF P-tau was estimated for each tau CSF/PET group. When compared with the CSF/PET groups, both CSF+ groups had higher intercept values and faster rates of CSF P-tau accrual over the 5-year interval. C and D indicate the same information for the performance on the ADNI memory composite scale. All 3 tau+ groups tended to have faster rates of memory decline over the 60 months antecedent to flortaucipir-PET scanning than the CSF/PET group. However, only the CSF+/PET+ group had a lower intercept when compared with the CSF/PET group, thereby suggesting that their memory decline had started earlier. D and F indicate the results of an identical analysis considering the Alzheimer’s Disease Neuroimaging Initiative executive function composite. Concordant negative and discordant tau biomarker groups had similar intercept values for their performance on the executive function composite. When compared with the CSF/PET group, only the CSF+/PET+ group had faster decline in executive function over the 5-year period preceding flortaucipir-PET scanning.

Table.  Sample Characteristics
Sample Characteristics
1.
Jack  CR  Jr, Knopman  DS, Jagust  WJ,  et al.  Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers.   Lancet Neurol. 2013;12(2):207-216. doi:10.1016/S1474-4422(12)70291-0PubMedGoogle ScholarCrossref
2.
Bateman  RJ, Xiong  C, Benzinger  TL,  et al; Dominantly Inherited Alzheimer Network.  Clinical and biomarker changes in dominantly inherited Alzheimer’s disease.   N Engl J Med. 2012;367(9):795-804. doi:10.1056/NEJMoa1202753PubMedGoogle ScholarCrossref
3.
Jack  CR  Jr, Bennett  DA, Blennow  K,  et al; Contributors.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease.   Alzheimers Dement. 2018;14(4):535-562. doi:10.1016/j.jalz.2018.02.018PubMedGoogle ScholarCrossref
4.
Mattsson  N, Insel  PS, Donohue  M,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Independent information from cerebrospinal fluid amyloid-β and florbetapir imaging in Alzheimer’s disease.   Brain. 2015;138(Pt 3):772-783. doi:10.1093/brain/awu367PubMedGoogle ScholarCrossref
5.
Palmqvist  S, Mattsson  N, Hansson  O; Alzheimer’s Disease Neuroimaging Initiative.  Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography.   Brain. 2016;139(Pt 4):1226-1236. doi:10.1093/brain/aww015PubMedGoogle ScholarCrossref
6.
Palmqvist  S, Schöll  M, Strandberg  O,  et al.  Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity.   Nat Commun. 2017;8(1):1214. doi:10.1038/s41467-017-01150-xPubMedGoogle ScholarCrossref
7.
Lowe  VJ, Curran  G, Fang  P,  et al.  An autoradiographic evaluation of AV-1451 Tau PET in dementia.   Acta Neuropathol Commun. 2016;4(1):58. doi:10.1186/s40478-016-0315-6PubMedGoogle ScholarCrossref
8.
Brier  MR, Gordon  B, Friedrichsen  K,  et al.  Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease.   Sci Transl Med. 2016;8(338):338ra66. doi:10.1126/scitranslmed.aaf2362PubMedGoogle Scholar
9.
Chhatwal  JP, Schultz  AP, Marshall  GA,  et al.  Temporal T807 binding correlates with CSF tau and phospho-tau in normal elderly.   Neurology. 2016;87(9):920-926. doi:10.1212/WNL.0000000000003050PubMedGoogle ScholarCrossref
10.
La Joie  R, Bejanin  A, Fagan  AM,  et al.  Associations between [18F]AV1451 tau PET and CSF measures of tau pathology in a clinical sample.   Neurology. 2018;90(4):e282-e290. doi:10.1212/WNL.0000000000004860PubMedGoogle ScholarCrossref
11.
Mattsson  N, Smith  R, Strandberg  O,  et al.  Comparing 18F-AV-1451 with CSF t-tau and p-tau for diagnosis of Alzheimer disease.   Neurology. 2018;90(5):e388-e395. doi:10.1212/WNL.0000000000004887PubMedGoogle ScholarCrossref
12.
Joshi  AD, Pontecorvo  MJ, Clark  CM,  et al; Florbetapir F 18 Study Investigators.  Performance characteristics of amyloid PET with florbetapir F 18 in patients with alzheimer’s disease and cognitively normal subjects.   J Nucl Med. 2012;53(3):378-384.PubMedGoogle ScholarCrossref
13.
Schöll  M, Lockhart  SN, Schonhaut  DR,  et al.  PET imaging of tau deposition in the aging human brain.   Neuron. 2016;89(5):971-982. doi:10.1016/j.neuron.2016.01.028PubMedGoogle ScholarCrossref
14.
Braak  H, Braak  E.  Neuropathological stageing of Alzheimer-related changes.   Acta Neuropathol. 1991;82(4):239-259. doi:10.1007/BF00308809PubMedGoogle ScholarCrossref
15.
Clark  CM, Schneider  JA, Bedell  BJ,  et al; AV45-A07 Study Group.  Use of florbetapir-PET for imaging beta-amyloid pathology.   JAMA. 2011;305(3):275-283. doi:10.1001/jama.2010.2008PubMedGoogle ScholarCrossref
16.
Ossenkoppele  R, Rabinovici  GD, Smith  R,  et al.  Discriminative Accuracy of [18F]flortaucipir Positron Emission Tomography for Alzheimer Disease vs Other Neurodegenerative Disorders.   JAMA. 2018;320(11):1151-1162. doi:10.1001/jama.2018.12917PubMedGoogle ScholarCrossref
17.
Bittner  T, Zetterberg  H, Teunissen  CE,  et al.  Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of β-amyloid (1-42) in human cerebrospinal fluid.   Alzheimers Dement. 2016;12(5):517-526. doi:10.1016/j.jalz.2015.09.009PubMedGoogle ScholarCrossref
18.
Schindler  SE, Gray  JD, Gordon  BA,  et al.  Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging.   Alzheimers Dement. 2018;14(11):1460-1469. doi:10.1016/j.jalz.2018.01.013PubMedGoogle ScholarCrossref
19.
Mattsson  N, Insel  PS, Landau  S,  et al; Alzheimer’s Disease Neuroimaging Initiativea.  Diagnostic accuracy of CSF Ab42 and florbetapir PET for Alzheimer’s disease.   Ann Clin Transl Neurol. 2014;1(8):534-543. doi:10.1002/acn3.81PubMedGoogle ScholarCrossref
20.
Gibbons  LE, Carle  AC, Mackin  RS,  et al; Alzheimer’s Disease Neuroimaging Initiative.  A composite score for executive functioning, validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment.   Brain Imaging Behav. 2012;6(4):517-527. doi:10.1007/s11682-012-9176-1PubMedGoogle ScholarCrossref
21.
Crane  PK, Carle  A, Gibbons  LE,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI).   Brain Imaging Behav. 2012;6(4):502-516. doi:10.1007/s11682-012-9186-zPubMedGoogle ScholarCrossref
22.
Mattsson  N, Schöll  M, Strandberg  O,  et al.  18F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer’s disease.   EMBO Mol Med. 2017;9(9):1212-1223. doi:10.15252/emmm.201707809PubMedGoogle ScholarCrossref
23.
McSweeney  M, Pichet Binette  A, Meyer  PF,  et al.  Intermediate flortaucipir uptake is associated with a beta-PET and CSF-tau in asymptomatic adults.   Neurology. In press.Google Scholar
24.
Jack  CR  Jr, Therneau  TM, Wiste  HJ,  et al.  Transition rates between amyloid and neurodegeneration biomarker states and to dementia: a population-based, longitudinal cohort study.   Lancet Neurol. 2016;15(1):56-64. doi:10.1016/S1474-4422(15)00323-3PubMedGoogle ScholarCrossref
25.
Soldan  A, Pettigrew  C, Cai  Q,  et al; BIOCARD Research Team.  Hypothetical preclinical Alzheimer disease groups and longitudinal cognitive change.   JAMA Neurol. 2016;73(6):698-705. doi:10.1001/jamaneurol.2016.0194PubMedGoogle ScholarCrossref
26.
Soldan  A, Pettigrew  C, Fagan  AM,  et al.  ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes.   Neurology. 2019;92(14):e1567-e1579. doi:10.1212/WNL.0000000000007248PubMedGoogle ScholarCrossref
27.
Burnham  SC, Bourgeat  P, Doré  V,  et al; AIBL Research Group.  Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer’s disease pathophysiology (SNAP) or Alzheimer’s disease pathology: a longitudinal study.   Lancet Neurol. 2016;15(10):1044-1053. doi:10.1016/S1474-4422(16)30125-9PubMedGoogle ScholarCrossref
28.
Nakamura  A, Kaneko  N, Villemagne  VL,  et al.  High performance plasma amyloid-β biomarkers for Alzheimer’s disease.   Nature. 2018;554(7691):249-254. doi:10.1038/nature25456PubMedGoogle ScholarCrossref
29.
Okamura  N, Harada  R, Ishiki  A, Kikuchi  A, Nakamura  T, Kudo  Y.  The development and validation of tau PET tracers: current status and future directions.   Clin Transl Imaging. 2018;6(4):305-316. doi:10.1007/s40336-018-0290-yPubMedGoogle ScholarCrossref
30.
Illán-Gala  I, Pegueroles  J, Montal  V,  et al.  Challenges associated with biomarker-based classification systems for Alzheimer’s disease.   Alzheimers Dement (Amst). 2018;10:346-357.PubMedGoogle ScholarCrossref
31.
Buerger  K, Alafuzoff  I, Ewers  M, Pirttilä  T, Zinkowski  R, Hampel  H.  No correlation between CSF tau protein phosphorylated at threonine 181 with neocortical neurofibrillary pathology in Alzheimer’s disease.   Brain. 2007;130(Pt 10):e82. doi:10.1093/brain/awm140PubMedGoogle Scholar
32.
Engelborghs  S, Sleegers  K, Cras  P,  et al.  No association of CSF biomarkers with APOEepsilon4, plaque and tangle burden in definite Alzheimer’s disease.   Brain. 2007;130(Pt 9):2320-2326. doi:10.1093/brain/awm136PubMedGoogle ScholarCrossref
33.
Marquié  M, Siao Tick Chong  M, Antón-Fernández  A,  et al.  [F-18]-AV-1451 binding correlates with postmortem neurofibrillary tangle Braak staging.   Acta Neuropathol. 2017;134(4):619-628. doi:10.1007/s00401-017-1740-8PubMedGoogle ScholarCrossref
34.
Smith  R, Wibom  M, Pawlik  D, Englund  E, Hansson  O.  Correlation of in vivo [18F]flortaucipir with postmortem alzheimer disease tau pathology.   JAMA Neurol. 2018.PubMedGoogle Scholar
35.
Buerger  K, Zinkowski  R, Teipel  SJ,  et al.  Differential diagnosis of Alzheimer disease with cerebrospinal fluid levels of tau protein phosphorylated at threonine 231.   Arch Neurol. 2002;59(8):1267-1272. doi:10.1001/archneur.59.8.1267PubMedGoogle ScholarCrossref
36.
Hampel  H, Buerger  K, Zinkowski  R,  et al.  Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study.   Arch Gen Psychiatry. 2004;61(1):95-102. doi:10.1001/archpsyc.61.1.95PubMedGoogle ScholarCrossref
37.
Buerger  K, Ewers  M, Pirttilä  T,  et al.  CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer’s disease.   Brain. 2006;129(pt 11):3035-3041. doi:10.1093/brain/awl269PubMedGoogle ScholarCrossref
38.
Arriagada  PV, Growdon  JH, Hedley-Whyte  ET, Hyman  BT.  Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease.   Neurology. 1992;42(3 pt 1):631-639. doi:10.1212/WNL.42.3.631PubMedGoogle ScholarCrossref
39.
Congdon  EE, Sigurdsson  EM.  Tau-targeting therapies for Alzheimer disease.   Nat Rev Neurol. 2018;14(7):399-415. doi:10.1038/s41582-018-0013-zPubMedGoogle ScholarCrossref
40.
Fagan  AM, Xiong  C, Jasielec  MS,  et al; Dominantly Inherited Alzheimer Network.  Longitudinal change in CSF biomarkers in autosomal-dominant Alzheimer’s disease.   Sci Transl Med. 2014;6(226):226ra30. doi:10.1126/scitranslmed.3007901PubMedGoogle Scholar
41.
McDade  E, Wang  G, Gordon  BA,  et al; Dominantly Inherited Alzheimer Network.  Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease.   Neurology. 2018;91(14):e1295-e1306. doi:10.1212/WNL.0000000000006277PubMedGoogle ScholarCrossref
42.
Pontecorvo  MJ, Devous  MD, Kennedy  I,  et al.  A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer’s disease dementia.   Brain. 2019;142(6):1723-1735. doi:10.1093/brain/awz090PubMedGoogle ScholarCrossref
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    Original Investigation
    January 21, 2020

    Characterization of Alzheimer Disease Biomarker Discrepancies Using Cerebrospinal Fluid Phosphorylated Tau and AV1451 Positron Emission Tomography

    Author Affiliations
    • 1Douglas Mental Health University Institute, Studies on Prevention of Alzheimer's Disease Centre, Montreal, Quebec, Canada
    • 2Department of Psychiatry, McGill University, Montreal, Quebec, Canada
    • 3McGill Centre for Integrative Neuroscience, McGill University, Montreal, Quebec, Canada
    • 4Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
    JAMA Neurol. 2020;77(4):508-516. doi:10.1001/jamaneurol.2019.4749
    Key Points

    Question  Do cerebrospinal fluid (CSF) and positron emission tomography (PET) measures provide different information about Alzheimer disease–related tau pathology?

    Findings  In this cohort study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, one-third of participants had abnormal CSF tau or were tau positive on both CSF assay and PET, while tau-PET positivity alone was relatively rare. Individuals whose CSF was tau positive had a history of accelerated CSF tau accrual, but only persons with tau-PET abnormality showed a similar significant decline in cognition.

    Meaning  Cerebrospinal fluid tau abnormality may be detected earlier in the AD pathogenetic process than flortaucipir-PET positivity and may occur before measurable cognitive decline.

    Abstract

    Importance  Fluid and imaging biomarkers of Alzheimer disease (AD) are often used interchangeably, but some biomarkers may reveal earlier stages of disease.

    Objective  To characterize individuals with tau abnormality indicated by cerebrospinal fluid (CSF) assay or positron emission tomography (PET).

    Design, Setting, and Participants  Between 2010 and 2019, 322 participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) underwent CSF and PET assessments of tau pathology. Data-driven, clinically relevant thresholds for CSF phosphorylated tau (P-tau) (≥26.64 pg/mL) and flortaucipir-PET meta–regions of interest (ROI) (standard uptake value ratio ≥1.37) indicated participants’ tau status as CSF/PET, CSF+/PET, CSF/PET+, and CSF+/PET+. Of 1659 ADNI participants with a CSF or flortaucipir assessment, 588 had both measures (1071 were excluded). Among these, 266 were further excluded because they did not have flortaucipir and CSF testing within less than 25 months, leaving 322 for analysis. Of these, 213 were cognitively unimpaired (CU); 98 had mild cognitive impairment (MCI); and 11 had AD dementia.

    Main Outcomes and Measures  We compared tau-positive vs tau-negative groups as indicated by either modality or demographic and clinical variables, amyloid β–PET burden, and flortaucipir-PET binding across Braak stage–related ROIs. We also compared 5-year rates of CSF P-tau accumulation and cognitive decline prior to flortaucipir-PET scanning.

    Results  Among the 322 study participants, 180 were women (56%), and the mean (SD) age was 73.08 (7.37) years. Two hundred ten participants were CSF/PET (65%); 63 were CSF+/PET (19.5%); 15 were CSF/PET+ (4.6%); and 34 were CSF+/PET+ (10.5%). Most CSF/PET+ participants had measures near CSF or PET tau thresholds. The CSF+/PET participants showed faster 5-year accrual of P-tau and increased flortaucipir-PET binding in early Braak ROIs but similar memory decline compared with CSF/PET participants. Tau-positive individuals by either measure showed increased amyloid β–PET burden. All CSF+/PET+ individuals were amyloid-positive, and 26 had MCI or AD dementia (76%). Compared with the CSF/PET group, CSF+/PET+ individuals had experienced faster 5-year accrual of CSF P-tau and decline in memory and executive function, resulting in reduced cognitive abilities at the time of flortaucipir-PET assessment.

    Conclusions and Relevance  Suprathreshold CSF P-tau without flortaucipir-PET abnormality may indicate a stage of AD development characterized by early tau abnormality without measurable loss in cognitive performance. Persons with both tau CSF and PET abnormality appear to have reduced cognitive capacities resulting from faster antecedent cognitive decline. Elevation of CSF P-tau appears to precede flortaucipir-PET positivity in the progression of AD pathogenesis and related cognitive decline.

    Introduction

    Alzheimer disease (AD) includes a decades-long period of pathologic changes leading to dementia onset.1,2 To improve and rationalize the early detection of disease, the AD community is considering a biology-based disease classification relying principally on evidence of characteristic AD amyloid-β (Aβ) and tau pathologies.3 Such evidence can come from analysis of cerebrospinal fluid (CSF) or positron emission tomography (PET). Because these modalities are not typically available simultaneously, their results are often used interchangeably. However, CSF and PET indicators of Aβ pathology appear to provide overlapping, but not identical, information.4,5 Thus, some have suggested that CSF Aβ abnormality may precede Aβ-PET positivity.6 Discordance of these 2 biomarker measures may therefore indicate different stages of disease progression.

    Fluorine 18–labeled [18F] flortaucipir (AV1451) is a novel PET tracer that binds to the paired helical filaments of tau in neurofibrillary tangles (NFTs).7 This tracer shows good correlation with CSF tau8,9 and provides similar accuracy for AD diagnosis but may reveal different aspects of progressive tau pathology.10,11 We therefore investigated whether discordant assessments of CSF and PET tau status denoted different stages of AD pathogenesis. Based on the comparison of fluid and imaging biomarkers of Aβ pathology, we hypothesized that abnormality in CSF tau alone would denote a stage of disease intermediate between concordant-negative and concordant-positive CSF/PET tau status.

    Methods
    Participants

    We downloaded Alzheimer’s Disease Neuroimaging Initiative (ADNI) data from http://adni.loni.usc.edu in August 2019. The ADNI was launched in 2003 as a public-private partnership led by principal investigator Michael Weiner, MD. Its primary goal has been to test whether serial magnetic resonance imaging, PET, and various clinical, biologic, and neuropsychological markers can be combined to measure progression of mild cognitive impairment (MCI) and early AD dementia. Each ADNI study site received approval from its institutional ethical standards committee on human experimentation. Written informed consent was obtained from all research participants and from collateral informants when applicable. All research complied with ethical principles of the Declaration of Helsinki. Some 588 ADNI participants had at least 1 CSF assessment and 1 flortaucipir scan. Among these, 322 participants (213 cognitively unimpaired [CU], 98 with MCI, and 11 with AD dementia at time of flortaucipir-PET) had both assessments within a 25-month interval and are considered here.

    PET Image Processing

    Amyloid-β (florbetapir, or [18F]AV45; florbetaben, or FBB) and tau (flortaucipir; [18F]AV1451) data were downloaded from the ADNI website in their most fully preprocessed format (series description: ADNI-1 scans N3 and ADNI-GO/2 scans N3). These Aβ-PET and tau-PET data had been preprocessed using the ADNI pipeline. Briefly, each participant’s magnetic resonance imaging T1-weighted magnetization-prepared rapid acquisition gradient echo image from the nearest available visit was segmented and parcellated with FreeSurfer, version 5.3.0 (Martinos Center for Biomedical Imaging) to define regions of interest (ROIs) in native space. The PET images were then coregistered to the corresponding magnetization-prepared rapid acquisition gradient echo using SPM, version 5 (the FIL Methods Group). The intensity-normalized standard uptake value ratio (SUVR) value for each ROI was obtained by dividing tracer uptake in these regions by the value in a predefined reference region (whole cerebellum for florbetapir-PET and FBB-PET; inferior cerebellum gray matter for flortaucipir-PET). Composite SUVRs were obtained calculating volume-weighted means of groups of FreeSurfer-defined regions (eg, global neocortical SUVR for florbetapir-PET or FBB-PET12 and flortaucipir-PET Braak stage–specific ROIs).13,14 Florbetapir-PET and FBB-PET results were considered positive if global SUVRs were at least 1.11 or at least 1.08, as recommended by ADNI.12,15 For flortaucipir-PET, we considered tracer binding in a weighted composite (metaROI) of regions including bilateral entorhinal, amygdala, fusiform, inferior, and middle temporal cortices that was shown to be AD specific (eMethods in the Supplement).16 Receiver operating curve analyses in an independent sample then identified the threshold for flortaucipir positivity as the SUVR that most efficiently (maximum percentage correct classification) differentiated 96 Aβ CU individuals from 19 Aβ+ ADNI participants with late-stage MCI (n = 8) or AD (n = 11). All these had Aβ-PET data but lacked CSF and flortaucipir-PET assessments within 25 months. To improve precision of this threshold estimate, we considered data from some participants having multiple scans, thereby yielding 104 data points for Aβ CU individuals and 24 data points for Aβ+ participants with MCI/AD. The resulting flortaucipir-PET metaROI SUVR cutoff of at least 1.37 yielded 71% sensitivity and 98% specificity with 93% efficiency (eFigure 2A in the Supplement). This and our main analyses considered data without partial volume correction.

    Partial volume correction flortaucipir-PET data gave higher SUVR values (eFigure 1 in the Supplement) and a correspondingly higher positivity threshold (eFigure 2B in the Supplement). Nonetheless, the corrected data yielded a similar tau-PET categorization because only 8 people were categorized differently (eFigure 3 in the Supplement). Uncorrected SUVRs for these 8 were close to the original threshold. To assure that our results were not influenced unduly by these participants’ results, we repeated our analyses after removing persons within 5% of the CSF or PET thresholds. The essentially unchanged results appear in eFigures 4 and 5 in the Supplement.

    CSF Measurements

    Lumbar punctures (LPs) were performed as described in the ADNI procedures manual (http://www.adni-info.org/). Cerebrospinal fluid samples were frozen within 1 hour after collection and shipped overnight on dry ice to the ADNI Biomarker Core laboratory. Aliquots of 500 μL were stored in polypropylene tubes at −80°C. Cerebrospinal fluid concentrations of Aβ1-42, 181P-tau and total tau (t-tau) were measured using Elecsys immunoassays on a Cobas e601 analyzer, version 05.02, as described.17

    Because 2 participants lacked an Aβ-PET scan, their Aβ status was determined using a CSF Aβ1-42 cutoff of 1098 pg/mL, as described previously.18 For CSF tau status, we used 181P-tau, which is thought generally to reflect tau pathology (t-tau being considered a more general marker of neurodegeneration).3 However, the 2 markers were highly correlated in this sample (R2 = 0.96; eFigure 6 in the Supplement), and results were similar using t-tau to assess CSF tau positivity (data not shown). There is currently no established clinical threshold for Elecsys assessment of 181P-tau. As with flortaucipir-PET, in an independent set of 330 ADNI participants (419 data points) having at least 1 LP and a concurrent florbetapir-PET scan, receiver operating curve analyses indicated a threshold of 26.64 pg/mL that best distinguished Aβ-negative CU individuals (158 persons; 224 data points) from Aβ+ patients with AD (172 persons; 193 data points) with 76% sensitivity, 86% specificity, and 81% efficiency (eFigure 2C in the Supplement). Again, this independent sample lacked availability of flortaucipir and CSF tau assessments within a 25-month interval. Similarly to Aβ biomarkers, CSF was more sensitive for detection of AD, and PET was more specific.19 While these observations likely reflect intrinsic differences between biomarker modalities, we obtained nearly identical results when forcing the PET threshold to achieve sensitivity comparable with the CSF threshold (eFigure 7 in the Supplement).

    Cognitive Evaluation

    All ADNI participants received detailed cognitive evaluations. We obtained composite scores reflecting memory and executive functions, as described previously,20,21 for the LP visit and all available data in the preceding 5 years.

    Statistical Analyses

    Using described cutoffs for CSF P-tau and flortaucipir-PET, we classified the 322 ADNI participants as positive or negative on each modality. This resulted in 4 subgroups: CSF/PET (concordant tau negative), CSF+/PET (discordant CSF tau-positive only), CSF/PET+ (discordant tau-PET positive only), and CSF+/PET+ (concordant tau positive).

    Using Fisher exact or Kruskal-Wallis tests, and applying post hoc Mann-Whitney U tests where appropriate, we compared group demographic variables (age, sex, education in years, and APOE ε4 carrier status) and cognitive performance (memory and executive function composite scores) at the LP visit. We also investigated CSF/PET tau groups’ relation to florbetapir-PET or FBB-PET tracer binding, as well as flortaucipir uptake in Braak stage ROIs, using general linear models adjusted for participant age, sex, education (years), and delay between PET and CSF assessments.

    Finally, in a retrospective analysis, we compared the 4 categories’ rates of CSF P-tau accumulation and cognitive change in the 60 months preceding their flortaucipir-PET assessment. Participants having at least 2 measures (97 with CSF; 105 with cognition; available data at each time are listed in the eTable in the Supplement) were included in a linear mixed-effects analysis with random slope and intercept where the time by CSF/PET group interaction predicted change in the specified outcomes (CSF P-tau, memory, and executive function). To achieve consistency across participants, we considered the visit label (in months) as the time unit and anchored the PET visit as zero, thus attributing negative time values to retrospective data. These models were adjusted for participant age at PET, sex, APOE ε4 carrier status, education (years), cognitive performance at PET, and delay between PET and CSF assessments. Results using exact time instead of visit label were identical.

    All analyses used Matlab, R2019a (MathWorks Inc). Two-sided P values of .05 or less were considered statistically significant.

    Results
    Demographic Characteristics

    We considered 322 participants (mean [SD] age, 73.08 [7.37] years) of whom 180 were women (56%). Other demographics are summarized in the Table. Two hundred ten participants were classified as CSF/PET (65%); 63 as CSF+/PET (19.5%); 15 as CSF/PET+ (4.6%); and 34 as CSF+/PET+ (10.5%). Despite its small size, we included the interesting CSF/PET+ group in our analyses. However, exclusion of persons within 5% of tau CSF and PET thresholds, or whose CSF and PET assessments occurred at different visits, reduced the size of the CSF/PET+ group to only 8 and 9 individuals, respectively, further emphasizing the rarity of this group. All groups had similar age and sex ratios but differed by degree of education (Table). All tau-positive groups had a higher frequency of ε4 carriage than the CSF/PET group. Among CSF+ persons, the PET+ group also had a higher frequency of ε4 carriage.

    Biomarker and Cognitive Characteristics

    Overall, there was good linear association between CSF P-tau and metaROI flortaucipir-PET uptake (R2 = 0.26, P <.001; Figure 1). In keeping with their increased APOE ε4 allele frequency (Figure 2A), individuals who were tau positive on either measure had a larger proportion of Aβ+ individuals (Figure 2C) and, consequently, increased cortical Aβ-PET binding as compared with CSF/PET participants (Figure 2D). As expected, flortaucipir-PET+ participants had increased tracer uptake across Braak stage I through VI ROIs. The CSF+/PET group also had increased flortaucipir-PET SUVR values in Braak stage ROIs I through IV when compared with the CSF/PET group (eFigure 5A in the Supplement).

    The frequency of cognitive impairment differed among groups (Figure 2B). Sixty CSF/PET participants (29%) had cognitive impairment, while this was true of 14 CSF+/PET participants (22%), 9 CSF/PET+ participants (60%), and 26 CSF+/PET+ participants (76%). As expected, therefore, CSF/PET and CSF+/PET groups had comparable memory and executive function (Figure 2E and F), while the CSF/PET+ and CSF+/PET+ groups performed worse than PET participants on memory and executive function.

    Retrospective Analyses of Pathologic and Symptomatic Progression

    To investigate further whether tau CSF/PET categories indicated distinct states of AD pathological progression, we inquired whether these groups had divergent antecedent biomarker and cognitive trajectories. Linear mixed-effects analyses suggested that the CSF+/PET and CSF+/PET+ groups had faster accrual of CSF P-tau than the CSF/PET group (CSF+/PET time-by-group interaction β [SE], 0.06 [0.03] pg/mL/mo; P = .02; CSF+/PET+ time-by-group interaction β [SE], 0.11 [0.03] pg/mL/mo; P < .001; Figure 3A). A notable finding of this analysis was the difference in estimated model intercepts, (CSF+/PET β [SE], 17.65 [2.82]; P < .001; CSF+/PET+ β [SE], 29.45 [3.33]; P < .001; Figure 3B), suggesting that P-tau accumulation had likely begun earlier than the 5-year window considered here. Model intercepts and slope were indistinguishable among CSF groups.

    Flortaucipir-PET+ groups had faster memory decline than the CSF/PET group (Figure 3C and D). This finding was robust across the PET+ groups (CSF/PET+ time-by-group interaction β [SE], −0.009 [0.004] standard units/mo; P = .04; CSF+/PET+ time-by-group interaction β [SE],−0.009 [0.003] standard units/mo; P = .002), while it appeared less certain for the CSF+/PET group (time-by-group interaction β [SE], −0.004 [0.003] standard units/mo; P = .10). The CSF+/PET+ group also had a lower estimate of intercept (β [SE], −1.23 [0.22]; P < .001), suggesting that memory decline had also begun prior to the 5-year period considered. Similarly, as compared with the CSF/PET group, the CSF+/PET+ group had accelerated antecedent executive function decline (time-by-group interaction β [SE], −0.011 [0.004] standard units/mo; P = .002; Figure 3E and F) and a lower intercept (β [SE], −1.01 [0.24]; P < .001). A sensitivity analysis considering data only from participants who had CSF P-tau and flortaucipir assessments at the same visit recapitulated cross-sectional results, although sample sizes were too small to evaluate retrospective findings (eFigure 8 in the Supplement).

    Discussion

    Among 322 ADNI participants, we compared various characteristics of groups that were concordant or discordant for CSF (P-tau) and PET (flortaucipir-PET) assessments of tau positivity. As expected,22 we observed high concordance (75%) between CSF and PET tau measures. Discordant CSF+/PET participants were substantially more common than CSF/PET+ persons (20% vs 5%), suggesting that CSF abnormality alone may represent the more typical intermediate state in AD pathogenesis. The CSF/PET+ group principally included individuals close to CSF or PET tau positivity thresholds. This group might therefore occur as a result of variance in the 2 measures instead of reflecting real discordance between fluid and imaging markers. Interestingly, 29% of tau CSF/PET participants had cognitive impairment. However, only 15 of these were Aβ positive (25%), suggesting that their cognitive impairment resulted from causes other than AD. When compared with CSF/PET participants, CSF+/PET and CSF+/PET+ groups had increased likelihood of Aβ positivity, elevated flortaucipir-PET binding in Braak stage ROIs, and accelerated rates of P-tau accrual in the 5 years before flortaucipir-PET assessment. The CSF+/PET+ individuals also had impaired memory and executive function, presumably as a result of faster decline on these cognitive assessments during the 5 years preceding flortaucipir scanning. The CSF/PET+ group had high average Aβ-PET and flortaucipir-PET binding, as well as rapid antecedent memory decline, but inference for this group was limited by its small sample size. The main results remained similar in analyses that excluded individuals close to either biomarker threshold for tau positivity, suggesting that results were not driven by these borderline instances. Only the elevation of flortaucipir retention in Braak ROIs for the CSF+/PET groups was attenuated, suggesting that subthreshold flortaucipir binding may reflect early tau pathologic changes.23 In general, these findings support the notion that CSF evidence of tau pathology indicates progressing AD pathology before flortaucipir-PET abnormality.

    Given that AD pathology accrues for several decades prior to clinically apparent cognitive deficit, it should become increasingly practical to identify asymptomatic adults likely to develop subsequent dementia. Accordingly, the proposed biomarker-based classification of AD pathogenesis3 is meant to improve the identification of asymptomatic individuals likely to show accrual of AD pathology24 or cognitive decline.25-27 However, application of this classification may encounter difficulties when researchers variably use imaging, CSF, or blood28,29 markers to identify pathological change. Although it remains common practice, use of these measures interchangeably may obscure important information. For example, PET and CSF biomarkers of Aβ pathology show good association when considering individuals across the AD spectrum; yet these associations are lost when considering individual patient groups (eg, only patients with AD) or different stages of Aβ pathology (Aβ negative or positive).30 Importantly, CSF Aβ abnormality may precede Aβ-PET positivity, thereby offering a potential indicator of earlier pathogenetic stage.5,6

    While perhaps less dramatic than the association pattern for Aβ biomarkers, where PET and CSF modalities follow an L-shaped association, our results suggest that the CSF-then-PET sequence may apply also to tau biomarkers. CSF+/PET persons may have ongoing changes in tau biochemistry that span several years prior to detectable cognitive decline. However, in our observations, this group showed only a modest increase of flortaucipir retention in Braak ROIs. This finding is consistent with the weak association between CSF 181P-tau and brain NFT pathology,31,32 for which flortaucipir has high affinity.33,34 Cerebrospinal fluid 181P-tau positivity without flortaucipir-PET anomaly may therefore indicate an intermediate pathological stage at which tau chemistry is modified but NFT pathology remains nascent and cognitive impairment is not yet apparent. However, we do note the possibility that similar investigations assaying different CSF P-tau epitopes could yield different results. For example, studies35,36 using CSF 231P-tau reported high sensitivity and specificity for detection of AD dementia vs control individuals35 and differential diagnosis of AD.35,36 The CSF levels of this latter epitope appear also to be more closely associated with NFT count post mortem.37 Investigations of tau biomarker discrepancies using this assay might therefore yield less discordance.

    Because tangle pathology is more strongly associated than Aβ plaque pathology with cognitive impairment,38 and because anti-Aβ therapies have thus far failed to curb the progression of cognitive decline, tau-targeting therapies are appealing.39 However, these may work best before NFTs are prevalent, and it may therefore be crucial to identify persons who may be at the cusp of exhibiting NFT pathology. Importantly, abnormal flortaucipir-PET binding may indicate an AD pathologic process that has already been ongoing for several years. Accordingly, CSF+/PET+ participants here had CSF P-tau levels that had been elevated for at least 5 years prior to their flortaucipir-PET assessment, although their antecedent P-tau change was comparable with the CSF+/PET group. This last observation is consistent with slowing or even decreasing CSF P-tau change at or after onset of symptoms.40,41 Indeed, CSF+/PET+ persons also had evident decline in cognitive function for at least 5 years preceding flortaucipir-PET scanning and may therefore have had concomitantly elevated flortaucipir-PET binding along with their cognitive decline.42

    Strengths and Limitations

    This study’s principal strength is its reliance on large amounts of longitudinal data on CSF biomarkers of AD and cognitive evaluations, along with numerous scans using a newly available PET tracer for tau pathology. An additional strength was that results were robust to threshold modification, or the removal of individuals close to defined thresholds or having long delays between CSF and PET assessments. Among the study’s weaknesses are a sample heavily weighted toward unimpaired and early clinical stages. Results might have been different had we studied a more impaired population, ie, retrospective rather than prospective longitudinal analyses of AD biomarker and cognitive trajectories. Nonetheless, these results are in keeping with knowledge of CSF biomarker trajectories and associations of flortaucipir-PET binding with cognitive change. To further investigate our hypothesis, prospective longitudinal analysis of CSF P-tau and flortaucipir-PET should investigate whether CSF/PET individuals at high risk for AD (eg, Aβ+ individuals) are more likely to progress to tau CSF positivity than PET positivity in subsequent years. In parallel, these studies should test whether, and at what rate, P-tau+ individuals become flortaucipir-positive and experience cognitive decline. Answering these questions may have strong implications for prevention trials.

    Conclusions

    Currently available CSF and PET tau measures are often concordant but may nonetheless suggest different stages of tau pathological progression. Prospective longitudinal investigations of this topic should clarify the sequence of biomarker abnormalities.

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    Article Information

    Corresponding Author: Sylvia Villeneuve, PhD, Douglas Mental Health University Institute, 6875 Boulevard LaSalle, Perry Pavilion, Room E3417.1, Montreal, QC H4H 1R3, Canada (sylvia.villeneuve@mcgill.ca).

    Accepted for Publication: November 21, 2019.

    Correction: This article was corrected on February 17, 2020, to fix errors in the Abstract, the text of the article, Figures 1 and 2, and in the author affiliations list. It was further corrected on January 25, 2021, to add the author group to the byline.

    Published Online: January 21, 2020. doi:10.1001/jamaneurol.2019.4749

    Author Contributions: Drs Meyer and Villeneuve had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Meyer, Pichet Binette, Breitner, Villeneuve.

    Acquisition, analysis, or interpretation of data: Meyer, Gonneaud, Breitner, Villeneuve.

    Drafting of the manuscript: Meyer.

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

    Statistical analysis: Meyer, Villeneuve.

    Obtained funding: Breitner.

    Supervision: Breitner, Villeneuve.

    Conflict of Interest Disclosures: None reported.

    Funding/Support: Data collection and sharing for this project by the Alzheimer's Disease Neuroimaging Initiative (ADNI) were supported by National Institutes of Health grant U01-AG024904 and the US Department of Defense award W81XWH-12-2-0012. The ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from AbbVie; the Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica Inc; Biogen; Bristol-Myers Squibb Company; CereSpir Inc; Cogstate; Eisai Inc; Elan Pharmaceuticals Inc; Eli Lilly and Company; EuroImmun; F. Hoffmann–La Roche Ltd and its affiliated company Genentech Inc; Fujirebio; GE Healthcare; IXICO Ltd; Janssen Alzheimer Immunotherapy Research & Development LLC; Johnson & Johnson Pharmaceutical Research and Development LLC; Lumosity; Lundbeck; Merck & Co Inc; MesoScale Diagnostics LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research supports ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education. The study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. The ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.

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

    Group Information: The ADNI Investigators can be found in the eAppendix of the Supplement.

    Additional Contributions: We thank Jeannie-Marie Leoutsakos, PhD, Johns Hopkins School of Medicine, for advice regarding statistical analyses. No compensation from a funding sponsor was received for this contribution. Data used in preparation of this article were obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and provided data but did not participate in analysis or writing of the mansucript.

    References
    1.
    Jack  CR  Jr, Knopman  DS, Jagust  WJ,  et al.  Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers.   Lancet Neurol. 2013;12(2):207-216. doi:10.1016/S1474-4422(12)70291-0PubMedGoogle ScholarCrossref
    2.
    Bateman  RJ, Xiong  C, Benzinger  TL,  et al; Dominantly Inherited Alzheimer Network.  Clinical and biomarker changes in dominantly inherited Alzheimer’s disease.   N Engl J Med. 2012;367(9):795-804. doi:10.1056/NEJMoa1202753PubMedGoogle ScholarCrossref
    3.
    Jack  CR  Jr, Bennett  DA, Blennow  K,  et al; Contributors.  NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease.   Alzheimers Dement. 2018;14(4):535-562. doi:10.1016/j.jalz.2018.02.018PubMedGoogle ScholarCrossref
    4.
    Mattsson  N, Insel  PS, Donohue  M,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Independent information from cerebrospinal fluid amyloid-β and florbetapir imaging in Alzheimer’s disease.   Brain. 2015;138(Pt 3):772-783. doi:10.1093/brain/awu367PubMedGoogle ScholarCrossref
    5.
    Palmqvist  S, Mattsson  N, Hansson  O; Alzheimer’s Disease Neuroimaging Initiative.  Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography.   Brain. 2016;139(Pt 4):1226-1236. doi:10.1093/brain/aww015PubMedGoogle ScholarCrossref
    6.
    Palmqvist  S, Schöll  M, Strandberg  O,  et al.  Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity.   Nat Commun. 2017;8(1):1214. doi:10.1038/s41467-017-01150-xPubMedGoogle ScholarCrossref
    7.
    Lowe  VJ, Curran  G, Fang  P,  et al.  An autoradiographic evaluation of AV-1451 Tau PET in dementia.   Acta Neuropathol Commun. 2016;4(1):58. doi:10.1186/s40478-016-0315-6PubMedGoogle ScholarCrossref
    8.
    Brier  MR, Gordon  B, Friedrichsen  K,  et al.  Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease.   Sci Transl Med. 2016;8(338):338ra66. doi:10.1126/scitranslmed.aaf2362PubMedGoogle Scholar
    9.
    Chhatwal  JP, Schultz  AP, Marshall  GA,  et al.  Temporal T807 binding correlates with CSF tau and phospho-tau in normal elderly.   Neurology. 2016;87(9):920-926. doi:10.1212/WNL.0000000000003050PubMedGoogle ScholarCrossref
    10.
    La Joie  R, Bejanin  A, Fagan  AM,  et al.  Associations between [18F]AV1451 tau PET and CSF measures of tau pathology in a clinical sample.   Neurology. 2018;90(4):e282-e290. doi:10.1212/WNL.0000000000004860PubMedGoogle ScholarCrossref
    11.
    Mattsson  N, Smith  R, Strandberg  O,  et al.  Comparing 18F-AV-1451 with CSF t-tau and p-tau for diagnosis of Alzheimer disease.   Neurology. 2018;90(5):e388-e395. doi:10.1212/WNL.0000000000004887PubMedGoogle ScholarCrossref
    12.
    Joshi  AD, Pontecorvo  MJ, Clark  CM,  et al; Florbetapir F 18 Study Investigators.  Performance characteristics of amyloid PET with florbetapir F 18 in patients with alzheimer’s disease and cognitively normal subjects.   J Nucl Med. 2012;53(3):378-384.PubMedGoogle ScholarCrossref
    13.
    Schöll  M, Lockhart  SN, Schonhaut  DR,  et al.  PET imaging of tau deposition in the aging human brain.   Neuron. 2016;89(5):971-982. doi:10.1016/j.neuron.2016.01.028PubMedGoogle ScholarCrossref
    14.
    Braak  H, Braak  E.  Neuropathological stageing of Alzheimer-related changes.   Acta Neuropathol. 1991;82(4):239-259. doi:10.1007/BF00308809PubMedGoogle ScholarCrossref
    15.
    Clark  CM, Schneider  JA, Bedell  BJ,  et al; AV45-A07 Study Group.  Use of florbetapir-PET for imaging beta-amyloid pathology.   JAMA. 2011;305(3):275-283. doi:10.1001/jama.2010.2008PubMedGoogle ScholarCrossref
    16.
    Ossenkoppele  R, Rabinovici  GD, Smith  R,  et al.  Discriminative Accuracy of [18F]flortaucipir Positron Emission Tomography for Alzheimer Disease vs Other Neurodegenerative Disorders.   JAMA. 2018;320(11):1151-1162. doi:10.1001/jama.2018.12917PubMedGoogle ScholarCrossref
    17.
    Bittner  T, Zetterberg  H, Teunissen  CE,  et al.  Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of β-amyloid (1-42) in human cerebrospinal fluid.   Alzheimers Dement. 2016;12(5):517-526. doi:10.1016/j.jalz.2015.09.009PubMedGoogle ScholarCrossref
    18.
    Schindler  SE, Gray  JD, Gordon  BA,  et al.  Cerebrospinal fluid biomarkers measured by Elecsys assays compared to amyloid imaging.   Alzheimers Dement. 2018;14(11):1460-1469. doi:10.1016/j.jalz.2018.01.013PubMedGoogle ScholarCrossref
    19.
    Mattsson  N, Insel  PS, Landau  S,  et al; Alzheimer’s Disease Neuroimaging Initiativea.  Diagnostic accuracy of CSF Ab42 and florbetapir PET for Alzheimer’s disease.   Ann Clin Transl Neurol. 2014;1(8):534-543. doi:10.1002/acn3.81PubMedGoogle ScholarCrossref
    20.
    Gibbons  LE, Carle  AC, Mackin  RS,  et al; Alzheimer’s Disease Neuroimaging Initiative.  A composite score for executive functioning, validated in Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants with baseline mild cognitive impairment.   Brain Imaging Behav. 2012;6(4):517-527. doi:10.1007/s11682-012-9176-1PubMedGoogle ScholarCrossref
    21.
    Crane  PK, Carle  A, Gibbons  LE,  et al; Alzheimer’s Disease Neuroimaging Initiative.  Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI).   Brain Imaging Behav. 2012;6(4):502-516. doi:10.1007/s11682-012-9186-zPubMedGoogle ScholarCrossref
    22.
    Mattsson  N, Schöll  M, Strandberg  O,  et al.  18F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer’s disease.   EMBO Mol Med. 2017;9(9):1212-1223. doi:10.15252/emmm.201707809PubMedGoogle ScholarCrossref
    23.
    McSweeney  M, Pichet Binette  A, Meyer  PF,  et al.  Intermediate flortaucipir uptake is associated with a beta-PET and CSF-tau in asymptomatic adults.   Neurology. In press.Google Scholar
    24.
    Jack  CR  Jr, Therneau  TM, Wiste  HJ,  et al.  Transition rates between amyloid and neurodegeneration biomarker states and to dementia: a population-based, longitudinal cohort study.   Lancet Neurol. 2016;15(1):56-64. doi:10.1016/S1474-4422(15)00323-3PubMedGoogle ScholarCrossref
    25.
    Soldan  A, Pettigrew  C, Cai  Q,  et al; BIOCARD Research Team.  Hypothetical preclinical Alzheimer disease groups and longitudinal cognitive change.   JAMA Neurol. 2016;73(6):698-705. doi:10.1001/jamaneurol.2016.0194PubMedGoogle ScholarCrossref
    26.
    Soldan  A, Pettigrew  C, Fagan  AM,  et al.  ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes.   Neurology. 2019;92(14):e1567-e1579. doi:10.1212/WNL.0000000000007248PubMedGoogle ScholarCrossref
    27.
    Burnham  SC, Bourgeat  P, Doré  V,  et al; AIBL Research Group.  Clinical and cognitive trajectories in cognitively healthy elderly individuals with suspected non-Alzheimer’s disease pathophysiology (SNAP) or Alzheimer’s disease pathology: a longitudinal study.   Lancet Neurol. 2016;15(10):1044-1053. doi:10.1016/S1474-4422(16)30125-9PubMedGoogle ScholarCrossref
    28.
    Nakamura  A, Kaneko  N, Villemagne  VL,  et al.  High performance plasma amyloid-β biomarkers for Alzheimer’s disease.   Nature. 2018;554(7691):249-254. doi:10.1038/nature25456PubMedGoogle ScholarCrossref
    29.
    Okamura  N, Harada  R, Ishiki  A, Kikuchi  A, Nakamura  T, Kudo  Y.  The development and validation of tau PET tracers: current status and future directions.   Clin Transl Imaging. 2018;6(4):305-316. doi:10.1007/s40336-018-0290-yPubMedGoogle ScholarCrossref
    30.
    Illán-Gala  I, Pegueroles  J, Montal  V,  et al.  Challenges associated with biomarker-based classification systems for Alzheimer’s disease.   Alzheimers Dement (Amst). 2018;10:346-357.PubMedGoogle ScholarCrossref
    31.
    Buerger  K, Alafuzoff  I, Ewers  M, Pirttilä  T, Zinkowski  R, Hampel  H.  No correlation between CSF tau protein phosphorylated at threonine 181 with neocortical neurofibrillary pathology in Alzheimer’s disease.   Brain. 2007;130(Pt 10):e82. doi:10.1093/brain/awm140PubMedGoogle Scholar
    32.
    Engelborghs  S, Sleegers  K, Cras  P,  et al.  No association of CSF biomarkers with APOEepsilon4, plaque and tangle burden in definite Alzheimer’s disease.   Brain. 2007;130(Pt 9):2320-2326. doi:10.1093/brain/awm136PubMedGoogle ScholarCrossref
    33.
    Marquié  M, Siao Tick Chong  M, Antón-Fernández  A,  et al.  [F-18]-AV-1451 binding correlates with postmortem neurofibrillary tangle Braak staging.   Acta Neuropathol. 2017;134(4):619-628. doi:10.1007/s00401-017-1740-8PubMedGoogle ScholarCrossref
    34.
    Smith  R, Wibom  M, Pawlik  D, Englund  E, Hansson  O.  Correlation of in vivo [18F]flortaucipir with postmortem alzheimer disease tau pathology.   JAMA Neurol. 2018.PubMedGoogle Scholar
    35.
    Buerger  K, Zinkowski  R, Teipel  SJ,  et al.  Differential diagnosis of Alzheimer disease with cerebrospinal fluid levels of tau protein phosphorylated at threonine 231.   Arch Neurol. 2002;59(8):1267-1272. doi:10.1001/archneur.59.8.1267PubMedGoogle ScholarCrossref
    36.
    Hampel  H, Buerger  K, Zinkowski  R,  et al.  Measurement of phosphorylated tau epitopes in the differential diagnosis of Alzheimer disease: a comparative cerebrospinal fluid study.   Arch Gen Psychiatry. 2004;61(1):95-102. doi:10.1001/archpsyc.61.1.95PubMedGoogle ScholarCrossref
    37.
    Buerger  K, Ewers  M, Pirttilä  T,  et al.  CSF phosphorylated tau protein correlates with neocortical neurofibrillary pathology in Alzheimer’s disease.   Brain. 2006;129(pt 11):3035-3041. doi:10.1093/brain/awl269PubMedGoogle ScholarCrossref
    38.
    Arriagada  PV, Growdon  JH, Hedley-Whyte  ET, Hyman  BT.  Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease.   Neurology. 1992;42(3 pt 1):631-639. doi:10.1212/WNL.42.3.631PubMedGoogle ScholarCrossref
    39.
    Congdon  EE, Sigurdsson  EM.  Tau-targeting therapies for Alzheimer disease.   Nat Rev Neurol. 2018;14(7):399-415. doi:10.1038/s41582-018-0013-zPubMedGoogle ScholarCrossref
    40.
    Fagan  AM, Xiong  C, Jasielec  MS,  et al; Dominantly Inherited Alzheimer Network.  Longitudinal change in CSF biomarkers in autosomal-dominant Alzheimer’s disease.   Sci Transl Med. 2014;6(226):226ra30. doi:10.1126/scitranslmed.3007901PubMedGoogle Scholar
    41.
    McDade  E, Wang  G, Gordon  BA,  et al; Dominantly Inherited Alzheimer Network.  Longitudinal cognitive and biomarker changes in dominantly inherited Alzheimer disease.   Neurology. 2018;91(14):e1295-e1306. doi:10.1212/WNL.0000000000006277PubMedGoogle ScholarCrossref
    42.
    Pontecorvo  MJ, Devous  MD, Kennedy  I,  et al.  A multicentre longitudinal study of flortaucipir (18F) in normal ageing, mild cognitive impairment and Alzheimer’s disease dementia.   Brain. 2019;142(6):1723-1735. doi:10.1093/brain/awz090PubMedGoogle ScholarCrossref
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