Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography
Cerebrospinal fluid analysis detects cerebral amyloid-b accumulation earlier than positron emission tomography
Sebastian Palmqvist 1 2 3
Niklas Mattsson 0 2 3
Oskar Hansson 0 2 3
0 Memory Clinic, Sk a ̊ne University Hospital , Sweden
1 Department of Neurology, Sk a ̊ne University Hospital , Sweden
2 Clinical Memory Research Unit, Department of Clinical Sciences, Malmo ̈ , Lund University , Sweden
3 Department of Neurology, Ska ̊ ne University Hospital , 221 85 Lund , Sweden
The data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf Cerebral accumulation of amyloid-b is thought to be the starting mechanism in Alzheimer's disease. Amyloid-b can be detected by analysis of cerebrospinal fluid amyloid-b42 or amyloid positron emission tomography, but it is unknown if any of the methods can identify an abnormal amyloid accumulation prior to the other. Our aim was to determine whether cerebrospinal fluid amyloid-b42 change before amyloid PET during preclinical stages of Alzheimer's disease. We included 437 non-demented subjects from the prospective, longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) study. All underwent 18F-florbetapir positron emission tomography and cerebrospinal fluid amyloid-b42 analysis at baseline and at least one additional positron emission tomography after a mean follow-up of 2.1 years (range 1.1-4.4 years). Group classifications were based on normal and abnormal cerebrospinal fluid and positron emission tomography results at baseline. We found that cases with isolated abnormal cerebrospinal fluid amyloid-b and normal positron emission tomography at baseline accumulated amyloid with a mean rate of 1.2%/year, which was similar to the rate in cases with both abnormal cerebrospinal fluid and positron emission tomography (1.2%/year, P = 0.86). The mean accumulation rate of those with isolated abnormal cerebrospinal fluid was more than three times that of those with both normal cerebrospinal fluid and positron emission tomography (0.35%/year, P = 0.018). The group differences were similar when analysing yearly change in standardized uptake value ratio of florbetapir instead of percentage change. Those with both abnormal cerebrospinal fluid and positron emission tomography deteriorated more in memory and hippocampal volume compared with the other groups (P 5 0.001), indicating that they were closer to Alzheimer's disease dementia. The results were replicated after adjustments of different factors and when using different cut-offs for amyloid-b abnormality including a positron emission tomography classification based on the florbetapir uptake in regions where the initial amyloid-b accumulation occurs in Alzheimer's disease. This is the first study to show that individuals who have abnormal cerebrospinal amyloid-b42 but normal amyloid-b positron emission tomography have an increased cortical amyloid-b accumulation rate similar to those with both abnormal cerebrospinal fluid and positron emission tomography and higher rate than subjects where both modalities are normal. The results indicate that cerebrospinal fluid amyloid-b42 becomes abnormal in the earliest stages of Alzheimer's disease, before amyloid positron emission tomography and before neurodegeneration starts.
Abbreviations: MCI = mild cognitive impairment; SUVR = standardized uptake value ratio
The identification and early treatment of Alzheimer’s disease
is a top priority worldwide. A starting event in the
pathogenesis of Alzheimer’s disease is the accumulation of
amyloid-b in the brain
(Sperling et al., 2011)
. This has been
shown in studies of both sporadic and familial Alzheimer’s
(Bateman et al., 2012; Vos et al., 2013)
. As the
disease progresses, affected individuals show neurodegeneration
and early clinical symptoms and may be diagnosed with
mild cognitive impairment (MCI)
(Albert et al., 2011)
Ongoing disease-modifying trials targeting amyloid-b have
shown promising results
prevention trials have been started with anti-amyloid therapies
in asymptomatic subjects with signs of amyloid-b pathology
(Sperling et al., 2014)
. These interventions will most likely
have best effect if initiated as early as possible
al., 2011; Hardy et al., 2014)
. It is therefore important to be
able to detect the earliest signs of an abnormal amyloid-b
load. Currently, there are two methods for assessing
amyloid-b in vivo; PET using ligands that binds to amyloid-b
fibrils (amyloid PET) or CSF measurement of the
42amino acid isoform of amyloid-b (amyloid-b42). Many
studies have shown that these methods have a high agreement
(Fagan et al., 2006, 2009; Grimmer et al., 2009; Jagust et
al., 2009; Tolboom et al., 2009; Weigand et al., 2011;
Landau et al., 2013; Mattsson et al., 2014; Palmqvist et
al., 2014, 2015)
. However, roughly 10–20% of examined
subjects show discordant results (with a higher proportion in
asymptomatic individuals) and most often with abnormal
CSF amyloid-b42 levels and normal amyloid PET (CSF+/
(Cairns et al., 2009; Fagan et al., 2009; Jagust et
al., 2009; Landau et al., 2013; Palmqvist et al., 2014)
This may indicate that the abnormal accumulation of
amyloid-b in preclinical Alzheimer’s disease can be detected
earlier with CSF amyloid-b42 than with amyloid PET
al., 2006, 2009; Morris et al., 2010; Mattsson et al., 2014)
However, these arguments are only based on cross-sectional
results and there are also contradictory cross-sectional
results suggesting that amyloid PET may become abnormal
first (Landau et al., 2013). An alternative explanation for
the presence of CSF+/PET in cognitively healthy subjects
could be that isolated CSF+ is caused by other conditions
than preclinical Alzheimer’s disease or by analytical
artefacts, and thereby lack pathological relevance
(Fagan et al.,
2009; Mattsson et al., 2014)
. If this is true, CSF+/PET
subjects would not be expected to show signs of
amyloidb accumulation over time, as PET + individuals do in
preclinical Alzheimer’s disease (Villemagne et al., 2013).
To test the hypothesis that CSF amyloid-b42 becomes
abnormal before amyloid PET in preclinical Alzheimer’s disease we
examined if CSF+/PET subjects accumulate amyloid-b at an
abnormal rate, as measured by repeated amyloid PET
measurements. We also tested the hypothesis that CSF+/PET subjects
lack signs of a neurodegenerative process, which should not be
present at the earliest preclinical stage of Alzheimer’s disease
according to the dominant model of Alzheimer’s disease
(Jack et al., 2010, 2013)
Materials and methods
Data used in the preparation of this article were obtained from
the ADNI database (adni.loni.usc.edu). The ADNI was launched
in 2004 by the National Institute on Aging, the Food and Drug
Administration, private pharmaceutical companies and
nonprofit organizations as a highly innovative public-private
partnership. The primary goal of ADNI has been to test whether
serial MRI, PET, other biological markers, and clinical and
neuropsychological assessment can be combined to measure
the progression of MCI and early Alzheimer’s disease. The
Principal Investigator of this initiative is Michael W. Weiner,
MD, VA Medical Center and University of California, San
Francisco. ADNI is the result of efforts of many co-investigators
from a broad range of academic institutions and private
corporations, and subjects have been recruited from over 50 sites
across the USA and Canada. The initial goal of ADNI was to
recruit 800 subjects, but ADNI has been followed by ADNI-GO
and ADNI-2. To date these three protocols have recruited over
1500 adults. For more information, see www.adni-info.org.
Data were downloaded from the ADNI database
(adni.loni.usc.edu). We included only non-demented subjects diagnosed as
cognitively healthy controls, early MCI or late MCI. Patients
with dementia (due to Alzheimer’s disease) were excluded as
the aim was to examine detection of the earliest accumulation
of amyloid-b, which precedes the dementia stage by many
years. Inclusion/exclusion criteria are described in detail at
www.adni-info.org. Briefly, all subjects in the present study
were included in ADNI-2 and were between the ages of 55
and 90 years, had completed at least 6 years of education,
were fluent in Spanish or English, and were free of any
significant neurological disease other than Alzheimer’s disease.
Controls had Mini-Mental State Examination score (MMSE)
(Folstein et al., 1975)
, and Clinical Dementia Rating
scale (CDR) score 0
. Subjects classified as
MCI had MMSE score 524, objective memory loss as
shown on scores on delayed recall on the Wechsler Memory
Scale Logical Memory II, CDR 0.5, preserved activities of
daily living, and absence of dementia. Early and late MCI
was differentiated based on the score of Wechsler Memory
Scale Logical Memory II (cut-offs ranging from 2 to 8
depending on education level). Only subjects who had at least two
18F-florbetapir PET scans and CSF data from the same visit as
the first PET scan were included. This resulted in a population
of 437 subjects. The first visit where both CSF and PET data
were available was defined as baseline in the present study.
Amyloid-b deposition was visualized using 18F-florbetapir PET.
We used data from the latest available dataset
(‘UCBERKELEYAV45_01_26_15.csv’). All subjects had a
baseline scan and a follow-up scan. If three scans were
available, the last one was chosen for the longitudinal analysis. The
methods for PET acquisition and analysis have been described
in more detail previously
(Landau et al., 2012)
. The global
cortical mean standardized uptake value ratio (SUVR) was
calculated relative to a reference region. For the present
study we used a composite region made up of the whole
cerebellum, brainstem/pons, and subcortical white matter. This
composite region has provided more reliable longitudinal
florbetapir results in ADNI compared to using only the cerebellum
as reference region
(Landau et al., 2014; Landau and Jagust,
. The global neocortical uptake was calculated from the
weighted mean uptake in the frontal, lateral parietal, lateral
temporal and cingulate regions to account for the varying sizes
of the regions
(Landau and Jagust, 2015)
. We also created an
additional composite region to assess amyloid-b deposition in
brain regions where the deposition is believed to start. The first
stage of amyloid-b accumulation has been suggested to occur
in the basal medial part of the frontal lobe and the basal part
of the temporal lobe
(Braak and Braak, 1991; Goedert, 2015)
The latter region was not available in the ADNI dataset
(Landau and Jagust, 2015)
, but the former was captured
using the weighted mean uptake in the left and right medial
orbitofrontal cortex, lateral orbitofrontal cortex and the
frontal pole (hereafter called the ‘early amyloid-b region’). These
early accumulation regions suggested by
Braak and Braak
overlap with the results of some amyloid PET studies
(Villain et al., 2012; Sepulcre et al., 2013)
, but a recent PET
study has found that the accumulation might start in the
precuneus and the medial prefrontal cortex (Villeneuve et al.,
2015). However, we have chosen to create our early
amyloid-b region according to the Braak staging but acknowledge
that there might be other early regions that are not captured in
this staging. Future studies need to clarify this further.
Amyloid-b42, total (T)-tau and phosphorylated (P)-tau were
measured using the multiplex xMAP Luminex platform
(Luminex Corp) with the INNOBIA AlzBio3 kit
(Olsson et al., 2005; Shaw et al., 2009)
this study, we combined data from the datasets ‘UPENN
‘UPENNBIOMK7.csv’, and ‘UPENNBIOMK8.csv’.
Composite memory score
A composite memory score was used to assess memory
function. This is a weighted score based on memory items in Rey
Auditory Verbal Learning Test (RAVLT), the ADAS-cog, the
MMSE and Logical Memory
(Crane et al., 2012)
. To examine
longitudinal changes, data were extracted from baseline and
the 12-, 24-, 36-, 48- and 60-month visits.
Structural MRI brain scans were acquired using 3 T MRI
scanners as previously described
(Jack et al., 2008)
Automated volume measures were performed with FreeSurfer
version 5.1 (http://surfer.nmr.mgh.harvard.edu/fswiki).
Hippocampal volumes were extracted from the merged
ADNI dataset (‘ADNIMERGE.csv’). Cross-sectional volumes
were divided by the total intracranial volume (Table 1). For
longitudinal analyses, all available data were used (ADNI
baseline and the 3, 6, 12, 18, 24, 36, 48, 60, 72, 84 and 96 month
visits). To examine the relationship between the global
amyloid-b accumulation and atrophy changes, a composite MRI
region of interest was calculated as the average cortical
thickness of the regions used in the global neocortical PET volume
of interest. The cortical thickness measures from ADNI
baseline and the 3, 6, 12, 24, 36, and 48-month visits were
extracted from the file ‘UCSFFSX51_05_20_15.csv’.
Grouping of subjects
The subjects were categorized into four groups depending on
the baseline CSF amyloid-b42 and florbetapir PET status;
normal CSF and PET (CSF /PET ), abnormal CSF and
normal PET (CSF +/PET ), abnormal CSF and PET (CSF+/
PET + ) and normal CSF and abnormal PET (CSF /PET +). A
cut-off for abnormal PET status using the composite reference
region has been defined previously at 4 0.79 SUVR
et al., 2012; Landau and Jagust, 2015)
. For CSF
amyloidb42, a cut-off has previously been defined at CSF
amyloidb42 5192 ng/l
(Shaw et al., 2009; De Meyer et al., 2010;
Weigand et al., 2011)
. For this study we excluded borderline
cases and used cut-offs that were 5% from the original
cutoffs to avoid drawing conclusions based on borderline cases
that easily could be misclassified because of variability of the
measurements. This approach increases validity of the
classification and has also been used in previous publications on CSF/
(Landau et al., 2013; Mattsson et al., 2014)
The cut-offs used in the present study were thus: ‘CSF
amyloid-b42 + ’ 5182.4 ng/l, ‘CSF amyloid-b42 ’ 4201.6 ng/l,
CSF + /PET
Data are given in mean values (standard deviation, SD) if not otherwise specified. Kruskal-Wallis test was not significant for age, sex, education or cortical thickness of the global PET
region. Comparisons were made between A-B and B-C using Mann-Whitney analysis. Borderline cases were excluded in this table. There were no CSF /PET+ subjects.
aTwo subjects who were cognitively normal at the ADNI baseline converted to MCI at baseline for the present study. These have been categorized as late MCI.
bSimilar results were obtained when hippocampal volume was compared between the groups using a linear regression model with intracranial volume as a covariate (A-B, P = 0.44;
BC, P = 0.007).
cThe average cortical thickness of the brain regions included in the global PET volume of interest.
dGlobal neocortical uptake relative a composite reference region.
CI = confidence interval; CN = cognitively normal; EMCI = early MCI; LMCI = late MCI; n = number of subjects.
‘amyloid-b PET +’ 40.8295 SUVR and ‘amyloid-b PET ’
A second PET classification was done using the ‘early
amyloid-b region’ (see above). As no previous cut-off has been
established for this region, we used mixture modelling analysis
(see ‘Statistical analysis’ section), which produced the cut-off
0.817 SUVR relative the composite reference region. When
applying a 5% interval for the cut-off to exclude borderline
cases, the final cut-offs were ‘early amyloid-b PET+ ’ 40.8579
SUVR and ‘early amyloid-b PET–’ 50.7762 SUVR.
Group differences were first tested with the Kruskal-Wallis test
and if significant they were analysed further with the
MannWhitney U-test or 2 test (Tables 1–3). The main dependent
variable was the annual SUVR change. We also calculated the
relative annual SUVR change in percentage (yearly SUVR
change/SUVR at baseline) for easier comparison with other
studies that have used other PET tracers, SUVR regions or
SUVR reference regions. Full factorial general linear models
were used to adjust for sex, age, APOE genotype (presence
of APOE e4) and time between PET scans, regarding group
differences in SUVR amyloid-b accumulation, memory change
and hippocampal volume change. Local regression fits were
modelled using the least-squares criterion to fit a line to a
set of data points (‘LOESS’)
. Univariate linear
relationships were analysed using Spearman correlations.
Longitudinal changes in memory score and hippocampal
volume were modelled using linear regression with data from
the different visits. Mixture modelling was performed with R
(R Foundation for Statistical Computing, Vienna,
. Mixture modelling provides an unbiased and
unsupervised way of determining a cut-off
(Benaglia et al.,
. It requires a bimodal distribution of the studied
condition/biomarker, which for example is the case in amyloid-b
pathology but often not in tau. It does not require the
knowledge of the underlying biomarker/disease status, as in receiver
operating characteristics (ROC) analysis where you also need
both a training and validation population. Mixture modelling
has successfully been used previously for CSF amyloid-b42 and
amyloid PET data in large studies such as ADNI
(Toledo et al.,
(Pietrzak et al., 2015)
and the Swedish
(Palmqvist et al., 2014)
coefficients and intercepts were calculated in Microsoft Excel
version 14.4.5 for Mac
(Pfister et al., 2013)
. All other statistical
analyses were performed with SPSS for Mac, version 22.0
(SPSS Inc., Chicago, IL).
Baseline data are shown in Table 1. The delay between
baseline lumbar puncture and PET scan dates was on
average 12 days (range: 0–129 days). The classification of
subjects resulted in 26 CSF + /PET , 160 CSF /PET , 167
CSF + /PET + , 0 CSF /PET + , and 84 borderline cases
( 5% within the cut-offs). The CSF and PET classification
is shown in Fig. 1. The CSF + /PET group consisted of
54% healthy elderly controls, 35% early MCI and 11%
late MCI. The distribution was similar in the CSF /PET
group (P = 0.49). The CSF + /PET + group had more MCI
subjects compared with the other groups (P 5 0.001).
There were no significant differences in prevalence of the
APOE e4 allele between the CSF + /PET and CSF + /PET +
groups (P = 0.13), but the prevalence was significantly
lower in the CSF /PET group (P = 0.005). CSF
amyloid-b42 in the CSF + /PET group was significantly higher
than in the CSF + /PET + group (mean difference 27 ng/l;
95% CI 18–36) and significantly lower than in the
CSF /PET group (difference 76 ng/l; 95% CI 66–86).
There were no significant differences in T-tau and P-tau
levels between the CSF + /PET and CSF /PET groups.
CSF + /PET + had T-tau and P-tau levels that were almost
twice that of CSF + /PET (P 50.001) and smaller mean
hippocampal volume (P = 0.012).
Longitudinal change in amyloid-b as
determined by amyloid PET
The average time between the first and last florbetapir scan
was 2.1 years without differences between the groups (P =
0.73). Yearly amyloid-b accumulation rates are shown in
Table 2 and Fig. 2. The CSF+/PET group had a mean
amyloid-b accumulation rate (i.e. change in florbetapir
SUVR per year) that was more than three times that of
the CSF /PET group (P 5 0.01 adjusted for age, APOE
genotype, sex and time between PET scans) and a similar
accumulation rate compared with CSF+/PET+ (1.2% per
year for both; adjusted P = 0.60). Figure 3 shows the
yearly amyloid-b accumulation in relation to baseline CSF
amyloid-b42 levels for the PET group (Fig. 3A) and the
PET + group (Fig. 3B). The CSF /PET subjects showed
very modest amyloid-b accumulation rates throughout the
Figure 2 Boxplots of the amyloid-b accumulation rate (% SUVR
change/year) for the different groups. Group comparisons were
analysed with Mann-Whitney. There were no CSF–/PET+ individuals in A
and only one in B and C. Therefore, this group is not shown. (A)
Amyloidb accumulation rate in the global neocortical region. Group classifications
were based on the a priori cut-offs for amyloid-b42 (CSF+5182.4 ng/l, CSF–
4201.6 ng/l) and the global neocortical amyloid-b SUVR relative a
composite reference region (PET+40.8295, PET 50.7505). (B) Amyloid-b
accumulation rate in the global neocortical region using a PET classification
(+/–) based on abnormal/normal amyloid-b in brain regions affected in early
amyloid-b deposition (the medial and lateral orbitofrontal cortex and the
frontal pole; ‘the early amyloid-b region’). Cut-offs were established with
mixture modelling (PET+40.8579 SUVR, PET– 50.7762 SUVR). The CSF
classification was the same as in (A). (C) Amyloid-b accumulation rate in
the ‘early amyloid-b region’ using the same CSF/PET classification as in (B).
Ab = amyloid-b.
Data are given in mean values (95% CI) if not otherwise specified. Kruskal-Wallis test was not significant for years between PET scans. Comparisons were made between A-B and B-C
using Mann-Whitney analysis. All significant differences were also significant after adjusting for age, sex, prevalence of APOE e4, time between PET scans (for SUVR) and baseline
memory score and hippocampal volume (for the memory and hippocampal volume coefficients). Borderline cases were excluded in this table. There were no CSF /PET + subjects.
CI = confidence interval.
whole range of CSF amyloid-b42 levels, but with a tendency
of increased accumulation rates at the lower CSF
amyloidb42 values closer to the cut-off (Fig 3A). However, the
increased amyloid-b accumulation rate as a function of
lower CSF amyloid-b42 levels was much more pronounced
for the CSF+/PET group. In this group the cases with
clearly reduced CSF amyloid-b42 levels had similar rates of
amyloid-b accumulation as the CSF+/PET+ group. In the
CSF+/PET+ group the accumulation rate of amyloid-b had
already reached a plateau and showed rates unrelated to
CSF amyloid-b42 levels (Fig. 3B). To investigate whether
the plateau in the amyloid-b accumulation rate was driven
by an increased atrophy rate in the CSF+/PET+ group we
investigated the correlations between the global PET SUVR
change/year with the cortical thickness change/year of the
same composite neocortical region of interest. There were
no significant correlations between these measures in any
of the CSF/PET groups (P = 0.25–0.78). Specifically, there
was no significant correlation in the CSF+/PET+ group to
support that a plateau in the rate of amyloid-b accumulation
was caused mainly by an increased cortical atrophy rate (rs
= 0.05; P = 0.53).
Longitudinal analysis of hippocampal atrophy and memory
There was a significant decline in composite memory score
over time in CSF + /PET + individuals, but not in CSF + /
PET or CSF /PET individuals (Table 2 and Fig. 4A).
The change differed significantly between the CSF + /PET +
and CSF + /PET groups (P 5 0.013 when adjusting for
age, sex, and baseline memory score). There was no
significant difference in the change of memory scores between
the CSF + /PET and CSF /PET groups (adjusted P =
0.53). The deterioration in hippocampal volume was
greater for CSF + /PET + compared with CSF + /PET (P
5 0.001 adjusted for baseline hippocampal volume, sex
and age) and there were no differences between CSF + /
PET and CSF /PET groups (adjusted P = 0.07;
Table 2 and Fig. 4B).
PET classification based on abnormal
uptake in the early amyloid-b region
Table 3 and Fig. 2B and C show the results from the new
classification using the previous CSF amyloid-b42 cut-offs
and the new PET cut-offs for normal/abnormal SUVR in
the ‘early amyloid-b region’ (medial and lateral
orbitofrontal cortex and the frontal pole). There was only one subject
with CSF–/PET + biomarkers, and it was thus excluded
from the analyses. When analysing longitudinal changes
both in the global neocortical SUVR and the ‘early
amyloid-b region’ we found very similar group differences as in
the previous analyses. The amyloid-b accumulation was
significantly increased in the CSF + /PET– group compared
with the CSF–/PET– group and no differences were found
between the CSF + /PET– and CSF + /PET + groups (Table 3
and Fig. 2B and C).
Validation of the main results using
other cut-offs for CSF amyloid-b42
and global florbetapir PET
Three additional group classifications were also made to ensure
that the main results were not caused by random classifications:
(i) When using a classification with the same CSF
amyloidb42 cut-offs, but global PET cut-offs based on SUVR relative to
the whole cerebellum instead of the composite region ( 5% of
(Joshi et al., 2012)
, the CSF+/PET group still
had a yearly florbetapir SUVR accumulation rate three times
that of the CSF /PET group (P = 0.04 adjusted for age,
APOE genotype, sex and time between PET scans) and a
similar rate as the CSF+/PET+ group (adjusted P = 0.32).
(ii) We also used unbiased cut-offs for both CSF and PET
(SUVR relative the composite reference region) derived
from mixture modelling analysis on the current population
(see ‘Statistical analysis’ section). This resulted in a CSF
amyloid-b42 cut-off of 170.5 ng/l and a PET cut-off of
0.797 SUVR where a 5% interval was applied for the
final cut-offs. This group classification resulted in a yearly
amyloid-b accumulation for CSF + /PET that was four
times higher than CSF /PET (adjusted P 5 0.001) and
equal to CSF + /PET (adjusted P = 0.32).
(iii) Finally, previously proposed cut-offs were used (CSF
amyloid-b42 + 5192 ng/l; PET + 40.79 SUVR relative the
composite region) without a 5% interval (i.e. no
exclusion of borderline cases). This classification also resulted in
similar group differences regarding yearly amyloid-b
accumulation (higher in CSF + /PET compared with CSF /
PET , adjusted P = 0.039 and no differences between
CSF + /PET and CSF + /PET + , adjusted P = 0.13). This
last classification resulted in a small CSF /PET + group
(n = 6), which exhibited no overall increase in yearly
SUVR rate (median 0.24%, range –2.3 to 3.9%).
BRAIN 2016: 139; 1226–1236 | 1233
aPET group ( + /–) was based on SUVR in a region comprised of the medial and lateral orbitofrontal cortex and the frontal pole, i.e. regions involved in early amyloid-b accumulation
(Braak and Braak, 1991; Goedert, 2015)
Using longitudinal PET data we tested the hypothesis that
CSF analysis can detect abnormal amyloid-b accumulation
prior to amyloid PET imaging. In accordance with our
hypothesis, we found that subjects who were CSF amyloid-b42
positive but PET amyloid-b negative at baseline (CSF+/
PET ) accumulated amyloid-b at a similar rate as those
who were positive for both modalities at baseline and
more than three times as fast as those that were negative
for both modalities. This discordant CSF positive group
(CSF+/PET ) had no signs of neurodegeneration or
cognitive decline. There were no subjects who were isolated PET
amyloid-b positive (CSF–/PET+), which argues against
amyloid PET becoming abnormal first. The results were
robust using four different types of group classifications
for CSF+/ and PET+/ and when classifying PET +/–
based on amyloid-b deposition in regions that are affected
in the initial phase of amyloid-b accumulation
Braak, 1991; Goedert, 2015)
. Previous cross-sectional
studies have put forth the same hypothesis based on higher
prevalence of CSF+/PET compared with PET+/CSF
cases (Fagan et al., 2009), the presence of CSF+/PET in
(Morris et al., 2010)
and the greater
occurrence of CSF+/PET in controls compared with
subjects with MCI and Alzheimer’s disease dementia
et al., 2014)
. To our knowledge, the present study is the
first to show that people with abnormally low CSF
amyloid-b42 and normal amyloid PET accumulate amyloid-b at
an abnormal rate. These longitudinal results indicate that
the abnormal amyloid metabolism in Alzheimer’s disease
can be detected by CSF biomarkers at a very early stage,
before PET imaging becomes abnormal.
Pathological amyloid-b accumulation rates have also
been calculated in previous studies
(Bateman et al., 2012;
Toledo et al., 2013; Villemagne et al., 2013)
crosssectional Pittsburgh compound B (PiB) PET data from
subjects with autosomal dominant Alzheimer’s disease,
Bateman et al. (2012)
calculated the mean accumulation
rate of cortical amyloid-b fibrils to 1.4%/year in the early
phases 20–25 years prior to Alzheimer’s disease dementia
and prior to neurodegeneration and cognitive decline. This
accumulation rate is similar to what we found for the
CSF+/PET group (1.2% SUVR increase per year).
Individuals who are just starting to accumulate cortical
amyloid-b fibrils would not be expected to show signs of
tau pathology in CSF, neurodegeneration on MRI or
cognitive decline according to the most acknowledged models
of Alzheimer’s disease
(Braak et al., 2013; Jack et al.,
2013; Sperling et al., 2014)
. In accordance with this, we
found that the CSF + /PET group had similar baseline
cognitive function, hippocampal volume, and tau levels
as the CSF /PET group, but higher amyloid-b
accumulation rate and higher prevalence of APOE e4 carriers.
The CSF + /PET group also had similar longitudinal
changes in hippocampal volume as the CSF /PET
group and showed no deterioration in memory. This fits
well with the assumption that CSF + /PET subjects are at
the beginning of an Alzheimer’s disease process without
overt cognitive decline and hippocampal atrophy.
However, it is possible that subtle changes in cognition
and brain volume could have been detected with a more
sensitive cognitive battery or a larger sample size. In
contrast, CSF + /PET + subjects had twice as high tau levels
and deteriorated significantly more in hippocampal
volume and memory (Table 2 and Fig. 4A and B),
indicating that they are closer to Alzheimer’s disease
dementia. Another difference was that the CSF + /PET group
had slightly higher CSF amyloid-b42 levels than the
CSF + /PET + group (Table 1). As shown by the local
regression line in Fig. 3A, the rate of amyloid-b
accumulation appears to start to increase in CSF /PET
cases at CSF amyloid-b42 levels around 220 ng/l and
continues to increase at lower CSF amyloid-b42 levels in the
CSF + /PET group. This relationship was not seen in
CSF + /PET + subjects (Fig. 3B). We cannot rule out that
this stagnation of amyloid-b accumulation rate in CSF + /
PET + subjects to some extent is caused by an increased
atrophy rate in this group, since PET volumes of interest
are based on baseline MRI scans in ADNI
(Landau et al.,
. However, there was no significant negative
correlation between global amyloid-b accumulation rate and
change in cortical thickness, which would be the case if
the accumulation rate was largely explained by an
increased rate of atrophy. Further, there were no
differences between the CSF/PET groups in terms of baseline
cortical thickness of this composite region of interest,
indicating that any partial volume effects due to atrophy
were similar between the groups (Table 1). Our
interpretation of Fig. 3 is therefore that CSF amyloid-b42 might
continue to drop and the amyloid-b accumulation rate
continue to increase in many of the CSF + /PET
individuals before a plateau is seen for both the level of CSF
amyloid-b42 and the yearly rate of change in florbetapir
SUVR, corresponding to the findings in the CSF + /PET +
group. This assumption fits well with a previous
publication showing that healthy elderly with normal CSF
amyloid-b42 (4192 ng/l) but with values below 225 ng/l are at
increased risk to develop abnormal CSF amyloid-b42
levels if followed longitudinally for 3 years
et al., 2015b)
. The CSF amyloid-b42 levels associated
with increased amyloid-b accumulation rate in the present
study also coincide with a range of CSF amyloid-b42
levels associated with increased atrophy rates in
Alzheimer’s disease-related brain regions
(Insel et al.,
. The lack of progression of cognitive impairment
in CSF + /PET and CSF /PET indicates that the MCI
classification of some of these subjects is probably not
related to Alzheimer’s disease but could represent
benign conditions or misclassification of the MCI
diagnosis. The amyloid-b pathology identified by CSF in these
subjects thereby likely represents preclinical Alzheimer’s
CSF amyloid-b42 and amyloid PET measures partly
different aspects of amyloid-b pathology, which explains the
possibility of individuals being CSF+/PET despite the
high overall agreement between the methods. Previous
cross-sectional studies have proposed that the amyloid-b
process starts with the formation of non-fibrillar
amyloidb species that results in lowered CSF amyloid-b42 but are
non-detectable with amyloid PET
(Fagan et al., 2009;
Morris et al., 2010)
. These amyloid-b species later
become fibrillar (neuritic plaques) and are consequently
detectable with amyloid PET (Mathis et al., 2012). This idea
was supported by an autopsy study where a case that was
CSF+/PET had numerous diffuse neocortical amyloid-b
plaques but only sparse neuritic plaques
(Cairns et al.,
. A CSF+/PET status is also present in a rare
variant of familial Alzheimer’s disease (the Arctic APP
mutation), which only develops diffuse, not neuritic, plaques
(Scholl et al., 2012)
. These studies thus provide a rationale
for CSF becoming abnormal before PET in preclinical
As a limitation of the study, we acknowledge that the
sample size of the CSF+/PET group was relatively small
(n = 26) and the results need to be replicated in a larger
cohort. To our knowledge there is currently no larger
cohort available with the repeated measurements needed
for this analysis. Using one of the alternative classification
systems (without excluding borderline cases) the CSF+/
PET group contained 48 subjects and the results were
robust. One previous article has examined longitudinal
florbetapir PET in relation to baseline CSF amyloid-b42 in
subjects from the ADNI study
(Toledo et al., 2015)
. That study
did not report increased rates of florbetapir PET
accumulation in CSF+/PET subjects, but there are several
important differences between that study and the present study.
Toledo et al. (2015)
included data from already demented
patients and used an older dataset with shorter follow-up
times and fewer subjects than what was available for our
study (they examined 150–304 subjects depending on type
of analysis including demented patients compared with
353–437 non-demented subjects in the present study).
This likely increased our power to detect early effects of
CSF amyloid-b42 on longitudinal florbetapir PET. We also
included analyses of PET+ /– classifications based on
regional amyloid-b deposition in the brain areas where the
amyloid-b accumulation is believed to start
Braak, 1991; Goedert, 2015)
. With this alternative PET
classification the CSF+/PET– group still accumulated both
regional and global amyloid-b faster than CSF–/PET– and
at a similar rate as CSF+/PET+ (Table 3 and Fig. 2B and
C). Nevertheless, a longer follow-up using repeated MRI,
amyloid PET, cognitive tests and CSF samples is needed to
verify that CSF+/PET individuals indeed have preclinical
Alzheimer’s disease and later become PET+ and develop
signs of neurodegeneration and cognitive dysfunction.
Especially, a longer follow-up with repeated PET scans is
important given the test-retest reliability of florbetapir
(Joshi et al., 2012)
and that the mean time between scans
only was 2.1 years.
In summary, we found evidence indicating that CSF
amyloid-b42 can detect amyloid-b pathology earlier than
amyloid PET. A discordant profile with isolated CSF
positivity could be the first measureable sign of amyloid
pathology in preclinical Alzheimer’s disease. Consequently, a
CSF +/PET status may indicate a suitable stage for
starting disease-modifying treatment targeting amyloid-b
(Hardy et al., 2014)
or interventions of modifiable
(Ngandu et al., 2015)
. However, we find it
likely that only a subpopulation of CSF +/PET– individuals
would benefit from therapeutic interventions since it is not
clear if cognitive decline is an inevitable result of
abnormal amyloid-b accumulation and even if it is, the time
between the initial pathology and cognitive impairment
can be more than one to two decades
(Sperling et al.,
. Therefore, further studies are needed to better
determine other factors that can timely predict future
cognitive impairment at this very early stage of amyloid-b
The authors would like to thank Michael Scho¨ ll, PhD
(Gothenburg University and Lund University), Martin
Schain, PhD (Columbia University) and Olof Lindberg,
PhD (Karolinska Institutet) for advice on analyses
regarding the relationship between cortical atrophy rates and
amyloid-b accumulation rates.
Work at the authors’ research centre was supported by the
Strategic Research Area MultiPark (Multidisciplinary
Research in Parkinson’s disease) at Lund University, the
European Research Council, the Swedish Research
Council, the Crafoord Foundation, the Swedish Brain
Foundation, the Ska˚ ne University Hospital Foundation,
the Swedish Alzheimer Association, and the Swedish federal
government under the ALF agreement. The ADNI data
collection and sharing for this project was funded by the
Alzheimer’s Disease Neuroimaging Initiative (ADNI)
(National Institutes of Health Grant U01 AG024904) and
DOD ADNI (Department of Defense award number
W81XWH-12-2-0012). ADNI is funded by the National
Institute on Aging, the National Institute of Biomedical
Imaging and Bioengineering, and through generous
contributions from the following: Alzheimer’s Association;
Alzheimer’s Drug Discovery Foundation; Araclon Biotech;
BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb
Company; 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 & Development LLC.;
Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics,
LLC.; NeuroRx Research; Neurotrack Technologies;
Novartis Pharmaceuticals Corporation; Pfizer Inc.;
Piramal Imaging; Servier; Synarc Inc.; and Takeda
Pharmaceutical Company. The Canadian Institutes of
Health Research is providing funds to support ADNI
clinical sites in Canada. Private sector contributions are
facilitated by the Foundation for the National Institutes of
Health (www.fnih.org). The grantee organization is the
Northern California Institute for Research and Education,
and the study is coordinated by the Alzheimer’s Disease
Cooperative Study at the University of California, San
Diego. ADNI data are disseminated by the Laboratory
for Neuro Imaging at the University of Southern California.
Supplementary material is available at Brain online.
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