Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer’s disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database
Ben Bouallègue et al. Alzheimer's Research & Therapy
Comparison of CSF markers and semi-quantitative amyloid PET in Alzheimer's disease diagnosis and in cognitive impairment prognosis using the ADNI-2 database
Fayçal Ben Bouallègue 0 1 2 3
Denis Mariano-Goulart 2
Pierre Payoux 0 1 3
the Alzheimer's Disease Neuroimaging Initiative (ADNI)
0 Nuclear Medicine Department, Purpan University Hospital , Toulouse , France
1 Toulouse NeuroImaging Centre (ToNIC), Université de Toulouse, Inserm/UPS , Toulouse , France
2 Nuclear Medicine Department, Lapeyronie University Hospital , Montpellier , France
3 Toulouse NeuroImaging Centre (ToNIC), Université de Toulouse, Inserm/UPS , Toulouse , France
Background: The relative performance of semi-quantitative amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) markers in diagnosing Alzheimer's disease (AD) and predicting the cognitive evolution of patients with mild cognitive impairment (MCI) is still debated. Methods: Subjects from the Alzheimer's Disease Neuroimaging Initiative 2 with complete baseline cognitive assessment (Mini Mental State Examination, Clinical Dementia Rating [CDR] and Alzheimer's Disease Assessment Scale-Cognitive Subscale [ADAS-cog] scores), CSF collection (amyloid-β1-42 [Aβ], tau and phosphorylated tau) and 18F-florbetapir scans were included in our cross-sectional cohort. Among these, patients with MCI or substantial memory complaints constituted our longitudinal cohort and were followed for 30 ± 16 months. PET amyloid deposition was quantified using relative retention indices (standardised uptake value ratio [SUVr]) with respect to pontine, cerebellar and composite reference regions. Diagnostic and prognostic performance based on PET and CSF was evaluated using ROC analysis, multivariate linear regression and survival analysis with the Cox proportional hazards model. Results: The cross-sectional study included 677 participants and revealed that pontine and composite SUVr values were better classifiers (AUC 0.88, diagnostic accuracy 85%) than CSF markers (AUC 0.83 and 0.85, accuracy 80% and 75%, for Aβ and tau, respectively). SUVr was a strong independent determinant of cognition in multivariate regression, whereas Aβ was not; tau was also a determinant, but to a lesser degree. Among the 396 patients from the longitudinal study, 82 (21%) converted to AD within 22 ± 13 months. Optimal SUVr thresholds to differentiate AD converters were quite similar to those of the cross-sectional study. Composite SUVr was the best AD classifier (AUC 0.86, sensitivity 88%, specificity 81%). In multivariate regression, baseline cognition (CDR and ADAS-cog) was the main predictor of subsequent cognitive decline. Pontine and composite SUVr were moderate but independent predictors of final status and CDR/ADAS-cog progression rate, whereas baseline CSF markers had a marginal influence. The adjusted HRs for AD conversion were 3.8 (p = 0.01) for PET profile, 1.2 (p = ns) for Aβ profile and 1.8 (p = 0.03) for tau profile. Conclusions: Semi-quantitative amyloid PET appears more powerful than CSF markers for AD grading and MCI prognosis in terms of cognitive decline and AD conversion.
Alzheimer's disease; MCI; Amyloid PET; CSF markers; ADNI
Mild cognitive impairment (MCI) refers to cognitive
deficits that do not directly impact the activities of daily
living  and may be related to varied aetiologies,
including depression, dementia and cerebrovascular
disease. Only a small proportion of patients with MCI
will convert to Alzheimer’s disease (AD) within a given
period of time, whereas the others will incur a variable
cognitive decline or even revert to normal .
Considerable effort has been devoted to identifying and
developing reliable biomarkers of incipient AD to target the
individuals who would most benefit from early treatment
intervention . Decreased cerebrospinal fluid (CSF)
concentration of the amyloid-β1–42 peptide (Aβ) and an
increased level of the protein tau are seen in patients
with AD [4, 5]. This pathological CSF signature is a key
feature in AD diagnosis, and the CSF profile, potentially
combined with neuroimaging data [6–9], has the ability
to predict cognitive decline and conversion to AD
independently of established risk factors such as age, sex and
apolipoprotein E (ApoE) genotype [10–13].
Positron emission tomography (PET) using 11C-labelled
Pittsburgh Compound B (PiB) or fluorinated tracers such
as 18F-florbetapir allows in vivo visualisation and
quantification of cortical Aβ deposition with high sensitivity and
specificity compared with amyloid plaque burden at
autopsy [14, 15]. Therefore, amyloid PET was included as
a pathophysiological marker in the most recent
international working group diagnostic criteria . Although
standard interpretation relies on visual assessment,
semiquantitative measures of cortical retention with respect to
a reference subcortical region is expected to provide
refined evaluation of the amyloid burden with high
testretest reliability . Historically, normalisation of
standardised uptake value (SUV) has been done using the
brainstem, pons or whole cerebellum as the reference
region. However, there is growing evidence that composite
reference regions that include some subcortical white
matter induce less temporal variability in sequential
measurements, yielding higher accuracy in assessing subtle
time changes and greater power to detect Aβ
accumulation [18–20]. Researchers in several studies have reported
the capacity of amyloid PET using fluorinated tracers
(either visual , semi-quantitative [22, 23] or both )
to provide prognostic insight regarding cognitive decline
and conversion to AD in patients with MCI, in line with
previous evidence of the prognostic value of PiB PET
[25–29]. A recent multi-centre study demonstrated the
clinical impact of florbetapir PET in terms of diagnostic
confidence and drug treatment .
Although CSF and PET measures of Aβ deposition are
highly correlated [31–34], the comparative relevance of
these two markers in discriminating patients with AD
and predicting cognitive outcome in patients with MCI
is still under debate . Hake et al. showed that CSF
and PET profiles were both discriminant in classifying
healthy control subjects and patients with MCI vs
patients with AD . Palqvist et al. found that the PET
standardised uptake value ratio (SUVr) was associated
with disease stage (cognition, memory and hippocampal
volume) in patients with MCI, whereas CSF markers
were not . In recent studies, researchers concluded
that CSF analysis might detect Aβ deposition earlier
than PET  and that reduced CSF Aβ might relate
more to early-stage AD, whereas the amyloid load
assessed by PET is indicative of disease progression .
Schreiber et al. demonstrated that baseline florbetapir
PET, rated either visually or using a cerebellar SUVr,
was predictive of conversion to AD in a large
longitudinal cohort . The prognostic value of the baseline
PET profile with respect to subsequent cognitive
evolution was also highlighted, consistent with prior results
derived from a retrospective study . Yet, the exact
added diagnostic and prognostic value of amyloid PET
semi-quantitative indices compared with CSF markers is
still unclear, and, relatedly, the optimal reference region
for SUVr computation remains to be defined. In the
present study, we systematically compared baseline CSF
markers and PET semi-quantitative indices in terms of
diagnostic value regarding baseline cognitive status, as
well as prognostic value in patients with MCI regarding
cognitive decline and conversion to AD. In addition, we
evaluated the performance of the SUVr computed using
various well-established subcortical reference regions.
In this study, we used participant data from the Alzheimer’s
Disease Neuroimaging Initiative (ADNI), a multicentre
project with approximately 50 medical centres and
university sites across the United States and Canada . The
ADNI was launched in 2003 as a public-private
partnership led by Principal Investigator Michael W. Weiner,
MD. Its primary goal was to examine how brain imaging
and other biomarkers can be used to measure the
progression of MCI and early AD. Determination of sensitive and
specific markers of very early AD progression is expected
to help researchers and clinicians develop new treatments
and monitor their effectiveness, as well as lessen the time
and cost of clinical trials. A detailed description of the
inclusion criteria can be found on the ADNI webpage
(http://www.adni-info.org). Subjects were between 55 and
90 years old and willing and able to undergo all test
procedures, including neuroimaging, and had agreed to
undergo longitudinal follow-up.
Cognitively normal participants were the control
subjects in the ADNI study. They showed no signs of
depression, MCI or dementia. Participants with significant
memory complaint (SMC) scored within the normal range
for cognition but indicated concerns and exhibited slight
forgetfulness. Early and late MCI participants reported an
SMC either autonomously or via an informant or
clinician. However, other cognitive domains showed no
significant impairment, activities of daily living were
preserved, and there were no signs of dementia. Participants
with AD met the National Institute of Neurological and
Communicative Disorders and Stroke/Alzheimer’s Disease
and Related Disorders Association criteria for probable
AD [41, 42].
Data were downloaded from the ADNI database
(adni.loni.usc.edu) and included all subjects recruited in
the ADNI-2 with complete available baseline data
regarding cognitive assessment, CSF markers and PET Aβ
quantitation. Our cross-sectional sample was made up
of 677 subjects (157 control subjects, 95 with SMC, 301
with MCI among whom 153 had early MCI and 148 had
late MCI, and 124 with AD at the time of the florbetapir
scan; see Table 1) who were recruited between January
2011 and September 2013, and each had a baseline CSF
collection and florbetapir session. The time delay
between the lumbar puncture and the florbetapir PET
was 11 ± 18 days. Our longitudinal sample was made up
Table 1 Baseline demographics, apolipoprotein E status and
cerebrospinal fluid markers in the cross-sectional population by
p-Tau181/Aβ1–42 0.19 ± 0.13 0.29 ± 0.24b
Abbreviations: Aβ Amyloid-β1–42, AD Alzheimer’s disease, ADAS-cog Alzheimer’s
Disease Assessment Scale–Cognitive Subscale, ApoE Apolipoprotein E,
CDR Clinical Dementia Rating, GDS Geriatric Depression Scale, MCI Mild
cognitive impairment, MMSE Mini Mental State Examination, p-tau Phosphorylated
tau, SMC Significant memory complaint
ap < 0.01 vs control subjects
bp < 0.001 vs control subjects
cp < 0.001 vs patients with SMC/MCI
of the 396 subjects with SMC and MCI from the
crosssectional sample who had undergone an average clinical
follow-up of 30 ± 16 months (see Table 2). Baseline visit
and follow-up visits at 3, 6 and 12 months, then yearly,
included complete cognitive assessment using the
Geriatric Depression Scale, Mini Mental State Examination
(MMSE), Clinical Dementia Rating (CDR) and Alzheimer’s
Disease Assessment Scale–Cognitive Subscale (ADAS-cog).
Diagnostic status and cognitive scores were extracted from
the latest available dataset (‘DXSUM_PDXCONV_
ADNIALL.csv’). For each participant in the longitudinal
cohort, the mean annual change in cognitive scores was
computed by taking the difference between the last
Table 2 Baseline demographics, apolipoprotein E status,
cerebrospinal fluid markers and clinical score evolution in the
longitudinal cohort (patients with significant memory complaint/
mild cognitive impairment) by last known status
Last known status
CDR annual change 0.01 ± 0.14
Abbreviations: Aβ Amyloid-β1–42, AD Alzheimer’s disease, ADAS-cog Alzheimer’s
Disease Assessment Scale–Cognitive Subscale, ApoE Apolipoprotein E, CDR Clinical
Dementia Rating, GDS Geriatric Depression Scale, MCI Mild cognitive impairment,
MMSE Mini Mental State Examination, p-tau Phosphorylated tau, SMC Significant
ap < 0.001 vs normal patients
bp < 0.001 vs patients with MCI
cp < 0.01 vs normal patients
−0.1 ± 0.2
−0.1 ± 2.3
−0.4 ± 1.1
−2 ± 2.3b
cognitive evaluation and the baseline one and dividing by
the time range. The last known diagnostic status was the
one mentioned at the time of the last visit listed in the
dataset. For each participant of the longitudinal cohort for
whom the last status was AD, time to conversion was
computed as the delay between the baseline visit and the
first visit mentioning an AD status.
Baseline Aβ1–42, total tau and phosphorylated p-tau181
(p-tau) were measured using the multiplex xMAP Luminex
platform (Luminex Corp., Austin, TX, USA) with the
INNO-BIA AlzBio3 kit (Innogenetics, Ghent, Belgium)
[5, 43]. For this study, we used the archived dataset
‘UPENNBIOMK_MASTER.csv’. When multiple baseline
CSF marker dosages were available, the median value was
retained for subsequent analyses. The studied variables of
CSF biomarker were Aβ, tau, p-tau and the p-tau/Aβ
ratio. Additional analysis details and quality control
procedures appear on the ADNI website.
Amyloid PET data
Baseline Aβ deposition was visualised using 18F-florbetapir
PET. Semi-quantitative PET results were retrieved from
the latest available dataset (‘UCBERKELEYAV45_10_
17_16.csv’). The methods for PET acquisition and analysis
are described in more detail elsewhere [22, 44]. Florbetapir
images consisted of 4 × 5-minute frames acquired at 50–70
minutes after injection, which were realigned, averaged,
resliced to a common voxel size (1.5 mm) and smoothed
to a common resolution of 8 mm in full width at
halfmaximum . Structural T1-weighted images acquired
concurrently with the baseline florbetapir images were
used as a structural template to define the cortical regions
of interest and the reference regions in native space
for each subject, using FreeSurfer (version 4.5.0;
surfer.nmr.mgh.harvard.edu) as described elsewhere .
Baseline florbetapir scans for each subject were
coregistered to baseline structural magnetic resonance
imaging scans, which were subsequently used to extract
weighted cortical retention indices (SUV) from grey
matter within four large cortical regions of interest (frontal,
cingulate, parietal and temporal cortices) that were
averaged to create a mean cortical SUV as described in
greater detail online (adni.bitbucket.org/docs/UCBERKE
Cortical SUVr values were obtained by normalising
cortical SUV with the mean uptake in a subcortical reference
region. For the present study, candidate reference regions
were pons, whole cerebellum and a composite region
made up of the whole cerebellum, pons and eroded
subcortical white matter . In the sequel, the
corresponding SUVr will be respectively referred to as pontine SUVr,
cerebellar SUVr and composite SUVr.
Continuous variables are presented as mean ± SD and
categorical variables as number (percent). The diagnostic
performance of CSF markers and SUVr was assessed
through ROC analysis. For each parameter and each
cutoff value, sensitivity was defined as the positivity rate in
the patients with AD and specificity as the negativity
rate in the control subjects/normal patients. The optimal
cut-off value was that maximising Youden’s index
(sensitivity + specificity − 1). The concordance between
PET profile based on SUVr values and CSF profile was
evaluated using Cohen’s kappa coefficient.
To test the association of baseline SUVr and CSF
markers with diagnosis and prognosis, a multivariate
analysis was conducted using a stepwise linear
regression model with an entry criterion of p < 0.05 and a
removal criterion of p > 0.1. To identify the independent
determinants of baseline status and baseline cognition
(MMSE, CDR and ADAS-cog), the following explicative
factors were included in the model: sex, age, ApoE4
status, the four CSF variables and SUVr. To identify the
independent predictors of final status, cognitive decline
(annual change in MMSE, CDR and ADAS-cog) and
time to conversion, the following explicative factors were
included in the model: sex, age, ApoE4 status, baseline
cognitive scores, the four CSF variables and SUVr.
Categorical variables (sex, ApoE4 status, baseline and final
status) were discretised, whereas (pseudo-)continuous
variables (age, cognitive scores, CSF markers and SUVr)
were processed as such. In each model, the three SUVr
values based on the three candidate reference regions
were tested separately, then jointly.
The correlation between baseline SUVr and cognitive
score evolution was evaluated using least-squares
quadratic regression and Spearman’s rank correlation. The
statistical significance of the mean annual changes in
cognitive scores was tested using a z-test.
The predictive value of baseline PET and CSF profiles
regarding conversion to AD was assessed using
KaplanMeier survival curves and the log-rank test. HRs were
adjusted using a Cox proportional hazards model
including the following explanatory covariates: sex, age, ApoE4
status, baseline cognitive scores, PET profile, CSF Aβ
and tau profiles. For patients who did not convert to
AD, survival data were considered censored from the
time of the last visit on record.
A two-sided p value ≤0.05 was considered statistically
significant. As regards the multivariate analysis, p values
were corrected for multiple comparisons using the
Dunn-Šidák correction: pcorrected = 1 − (1 − p)m, with m
being the number of comparisons (here we set m = 9 as
the number of times the linear model was run). All
statistical computations were performed using MATLAB
R2013 software (MathWorks, Natick, MA, USA).
Figure 1 presents the patient flow diagram. For the
cross-sectional cohort, the patient demographics, ApoE4
status, baseline cognitive scores and CSF markers are
detailed in Table 1. The differences between control
subjects and patients with MCI and between patients with
MCI and patients with AD were highly significant for
ApoE4 status, cognition and all four CSF markers. For
the longitudinal cohort, the patient demographics,
ApoE4 status, baseline cognition and CSF, annual
change in cognitive scores during follow-up and time to
conversion are detailed in Table 2. Of the 396 patients
with SMC/MCI at baseline, 209 (53%) were classified as
having MCI at their last visit, 105 (27%) were ranked as
normal (mostly patients with baseline SMC, and 19
patients with baseline MCI who reverted to normal) and
82 (21%) converted to AD (1 SMC, 19 early MCI and 62
late MCI). The differences in baseline cognition and CSF
markers were highly significant between normal subjects
and patients with MCI and between patients with MCI
and patients with AD. Cognitive decline was similar in
normal subjects and patients with MCI and markedly
greater in patients with AD.
Figure 2 shows the distribution of (from left to right)
pontine, cerebellar and composite SUVr values in the
cross-sectional and longitudinal cohorts. In both
cohorts, SUVr values were significantly lower in normal
Fig. 1 Patient flow diagram in the cross-sectional and longitudinal
cohorts. AD Alzheimer’s disease, MCI Mild cognitive impairment,
SMC Significant memory complaint
patients than in patients with MCI and in patients with
MCI than in patients with AD, whatever the reference
region used. No difference was found between the
homologous subsets of normal and patients with AD from
the two cohorts.
Table 3 details the results of the ROC analyses for
SUVr and CSF markers. Sensitivity and specificity stand
for, respectively, the rate of true-positives among
patients with AD and the rate of true-negatives among
control subjects/normal patients. Optimal cut-off values
for SUVr were highly similar in the cross-sectional and
longitudinal cohorts, whereas they differed substantially
for the CSF markers. SUVr performances were globally
higher than those of CSF markers. In both cohorts, the
best diagnostic performance was achieved using
composite SUVr with an AUROC above 0.85, a sensitivity
above 85% and a specificity above 80% in cross-sectional
and longitudinal analyses. Overall, the predictive power
of SUVr was superior to that of CSF markers, with risk
ratios for evolving to AD ranging from 7 to 9.5 (vs 4.5 to
8 for CSF markers). Figure 3 shows the frequencies of
final status in the longitudinal cohort according to
baseline PET (composite SUVr) and baseline CSF profile
(Aβ/tau combination). Seventy-two percent of the
patients had concordant Aβ/tau profiles (43% negative,
29% positive), and 28% had discordant Aβ/tau profiles
(25% Aβ+/tau− and 3% Aβ−/tau+). There was no
significant difference in mean follow-up duration between
negative and positive profiles (PET, Aβ or tau).
The concordance between the PET and CSF profiles was
good when SUVr was compared with Aβ (kappa > 0.8) and
moderate when it was compared with tau and p-tau (kappa
around 0.6–0.7), without substantial variation related to
the chosen reference region (see Table 4 for details).
Tables 5 and 6 summarise the results of the
multivariate analyses. SUVr p values reported in the tables are
those obtained when the three SUVr values were
evaluated separately. An asterisk designates the p values that
remained significant when the three SUVr values were
evaluated jointly. The coefficients of determination (r2)
reflect the proportion of the variance in the modelled
variable that is predictable from each explanatory
variable retained in the model. Regarding the cross-sectional
cohort (Table 5), sex, tau level and SUVr were independent
determinants of baseline status and cognitive scores (all
corrected p values <0.001), whereas ApoE4 status and
other CSF variables were not. The best determinants were
pontine and composite SUVr, which showed similarly high
association with patient status and cognitive level. For the
longitudinal cohort (Table 6), baseline cognition (MMSE,
CDR and ADAS-cog) was the main predictor of cognitive
decline in terms of final status and annual deterioration in
cognitive scores. In patients with MCI who converted to
AD during follow-up (n = 82), baseline ADAS-cog score
Fig. 2 Distribution of baseline standardised uptake value ratio (SUVr) values by baseline status (control subjects, significant memory complaint
[SMC], mild cognitive impairment [MCI], Alzheimer’s disease [AD]) in the cross-sectional cohort and by last known status (normal, MCI, AD) in the
longitudinal cohort. Boxes represent IQRs. Whiskers correspond to mean ± 1.5 SD. CTL Control, NL Normal, ns Not significant
was the sole independent predictor of time to conversion
(p = 0.03). CSF markers showed little or no association
with cognitive evolution (p-tau on final status p = 0.02, tau
on MMSE change p = 0.02). On the contrary, pontine and
composite SUVr yielded significant additional prognostic
information about final status (p < 0.001) and cognitive
score decline (p < 10−5 for CDR change, p = 0.001–0.005
for ADAS-cog change).
The prognostic relevance of SUVr is further elaborated
in Fig. 4. The scatterplots of the annual change in
cognitive scores are displayed on top (Fig. 4a) according to the
baseline composite SUVr, as are the associated quadratic
regressions based on the whole longitudinal cohort. There
was a moderate but significant Spearman’s correlation of
composite SUVr with cognitive decline (r = 0.33–0.37, all
p values <0.001). The range and mean value (along with
Table 3 Results of ROC analyses for standardised uptake value ratio and cerebrospinal fluid markers
SUVr (pons) SUVr (crb) SUVr (comp) Aβ1–42 Tau
Fig. 3 Last known status in the longitudinal cohort (patients with significant memory complaint/patients with mild cognitive impairment [MCI])
according to baseline positron emission tomography (PET) profile (assessed using composite standardised uptake value ratio with a cut-off at
0.89) and cerebrospinal fluid profile in terms of amyloid-β1–42 (Aβ/tau) combination. AD Alzheimer’s disease, NL Normal
the 95% CI) of the score changes according to the baseline
PET profile are shown in Fig. 4b. Patients with negative
baseline PET incurred no significant change in CDR and
ADAS-cog during follow-up. Conversely, patients with
positive baseline PET exhibited clinically and statistically
significant cognitive decline with mean annual changes
of −1.2 in MMSE, +0.12 in CDR and +2.4 in ADAS-cog
(all p values <0.001).
Figure 5 presents the Kaplan-Meier curves for
conversion to AD in patients with SMC/MCI according to
baseline PET (composite SUVr) and CSF profiles. The
Cox proportional hazards model shows that baseline
ADAS-cog score was the strongest predictor for AD
conversion (p < 10−8). A positive baseline PET was
associated with an adjusted HR of 3.8 for AD conversion
(p = 0.01). CSF Aβ and tau were less predictive with
adjusted HRs of 1.2 (not significant) and 1.8 (p = 0.03),
Table 4 Concordance (kappa) between positron emission
tomography and cerebrospinal fluid profiles in the cross-sectional
and longitudinal cohorts
Abbreviations: Aβ Amyloid-β1–42, p-tau Phosphorylated tau, SUVr Standardised
uptake value ratio, Pons Pontine, crb Cerebellar, comp Composite
In this study based on prospective data from the
ADNI-2 cohort, we examined the complementary
diagnostic and prognostic value of baseline CSF markers and
18F-florbetapir SUVr values computed using three
different reference regions. We found that PET
semiquantitative assessment of Aβ load was significantly
superior, although CSF and PET markers were both relevant
determinants of cognitive status and predictive of cognition
decline in patients with MCI. Notably, as can be seen in
Fig. 2 and Table 3, baseline SUVr distribution was similar
in patients with baseline AD and patients with SMC/MCI
who converted to AD during follow-up; hence, the
optimal SUVr cut-offs to differentiate patients with AD
from normal subjects were nearly identical in the
crosssectional and longitudinal cohorts. The optimal cut-offs for
CSF markers were less robust, suggesting that PET
quantitation might be preferable for accurate selection and
therapeutic monitoring of individuals in clinical trials .
Our optimal cerebellar SUVr cut-off (1.22) was
consistent with that proposed by Fleisher et al. (1.17), based
on post-mortem neuropathological data . A less
conservative SUVr cut-off was proposed by Joshi et al. 
as the upper bound of a one-tailed 95% CI of cerebellar
SUVr distribution in young healthy control subjects, and
it was used in other studies [22, 24] as a positivity
threshold for florbetapir PET. Such a low threshold
based on young control subjects seems questionable,
however, and may result in poor specificity (about 70%
in the study by Landau et al. ), given that significant
amyloid deposition without cognitive impairment is seen
in 20% to 40% of normal elderly volunteers [14, 48]. To
our knowledge, this is the first attempt to provide
optimal thresholds for pontine and composite SUVr,
Table 5 Results of the stepwise linear regression investigating the association of baseline demographics, cerebrospinal fluid markers
and positron emission tomography data, with baseline status and cognitive scores in the cross-sectional cohort
Baseline status (CTL, SMC/MCI, AD)
Abbreviations: Aβ Amyloid-β1–42, AD Alzheimer’s disease, ADAS-cog Alzheimer’s Disease Assessment Scale–Cognitive Subscale, ApoE Apolipoprotein E, CDR Clinical
Dementia Rating, Comp Composite, Crb Cerebellar, CSF Cerebrospinal fluid, GDS Geriatric Depression Scale, MCI Mild cognitive impairment, MMSE Mini Mental State
Examination, NL Normal, PET Positron emission tomography, Pons Pontine, p-tau Phosphorylated tau, SUVr Standardised uptake value ratio, ns Not significant
All p values are corrected for multiple comparisons
aRemained an independent predictor when evaluated jointly
Abbreviations: AD Alzheimer’s disease, ADAS-cog Alzheimer’s Disease Assessment Scale–Cognitive Subscale, ApoE Apolipoprotein E, CDR Clinical Dementia Rating,
Comp Composite, Crb Cerebellar, CSF Cerebrospinal fluid, MCI Mild cognitive impairment, MMSE Mini Mental State Examination, Pons Pontine, SMC Significant
memory complaint, SUVr Standardised uptake value ratio, ns Not significant
All p values are corrected for multiple comparisons
(*): Remained an independent determinant when evaluated jointly
Table 6 Results of the stepwise linear regression investigating the association of baseline demographics, cognitive scores,
cerebrospinal fluid markers and positron emission tomography data, with prognosis in terms of final status, cognitive score
evolution and time to conversion in the longitudinal cohort
Association with Last known status (NL, MCI, AD)
MMS annual change
CDR annual change
ADAS-cog annual change
Time to conversion
Fig. 4 Evolution of cognitive scores in the longitudinal cohort (patients with significant memory complaint/mild cognitive impairment [MCI])
according to baseline composite standardised uptake value ratio (SUVr). From left to right: Mean annual change in Mini Mental State Examination
(MMSE), Clinical Dementia Rating (CDR) and Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-cog). a Scatterplot in which marker
colour refers to last known status. The vertical dashed grey line indicates the optimal SUVr cut-off. The black dashed curve stands for the quadratic
regression. ρ Spearman’s rank correlation. b Score distribution according to positron emission tomography (PET) profile based on composite SUVr
(cut-off 0.89). Boxes represent mean with 95% CI. Whiskers represent mean ± 1.5 SD. ns Not significant, NL Normal, AD Alzheimer’s disease
because recent studies involving extra-cerebellar
reference regions have been aimed primarily at assessing the
longitudinal accuracy of SUVr estimates [18, 19].
Regarding the CSF ROC analyses, our optimal Aβ
cutoff to differentiate patients with AD from normal control
subjects (157 ng/L) was similar to that obtained by
De Meyer et al. (159 ng/L) based on the ADNI-1 cohort
. Our optimal CSF Aβ cut-off to predict conversion to
AD in the longitudinal analysis (171 ng/L) was closest to
that proposed by Shaw et al. (192 ng/L) with reference to
Fig. 5 Kaplan-Meier curves for conversion to Alzheimer’s disease (AD) in patients with significant memory complaint/mild cognitive impairment
(MCI) according to baseline positron emission tomography (PET) profile (composite standardised uptake value ratio [SUVr]) and baseline
cerebrospinal fluid (CSF) Amyloid-β1–42 (Aβ) and total tau profiles. ns Not significant. Cox model accounts for age, sex, apolipoprotein E status,
baseline cognitive scores, PET profile, CSF Aβ and tau profiles
autopsy data , yielding comparable sensitivity and
negative predictive value (respectively, 90% and 96% vs 96%
and 95%). Our optimal cut-off for CSF tau (88 ng/L) was
also similar to that mentioned by Shaw (93 ng/L) . The
proportion of concordant CSF profiles in terms of Aβ and
tau was 72% in both cohorts, concordant with the 73% of
concordant profiles reported by Sunderland et al.  in a
cohort of patients with AD and control subjects.
In our cross-sectional cohort, the first interesting
finding was that PET SUVr clearly outperformed CSF
markers in determining patients’ cognitive status, as
evaluated in a multivariate model. Its diagnostic
accuracy neighboured 85% in differentiating patients with AD
from control subjects, and cognitive performance
(MMSE, CDR and ADAS-cog) was significantly
associated with pontine and composite SUVr in the whole
population. The higher diagnostic performance of
pontine and composite SUVr than cerebellar SUVr might be
related to a lower signal-to-noise ratio in the cerebellum,
leading to less accurate and more variable SUV
measurements in this region. Researchers in previous studies
pointed out that pontine and cerebellar uptake was
prone to noise and longitudinal variability owing to the
small size of the considered regions and their peripheral
location in the PET scanner field of view, and they
advocated for the use of composite reference regions taking
into account cerebral white matter [18, 19].
The CSF markers showed lower diagnostic value in
ROC analysis (lower AUC and lower accuracy of 80% for
Aβ and 75% for tau), and total tau was the sole CSF
marker to bring added diagnostic value. Palmqvist et al.
 noted that 18F-flutemetamol cerebellar SUVr was
correlated with global cognition and hippocampal
atrophy in patients with increased Aβ load, whereas CSF Aβ
was not. These data are consistent with a commonly
accepted model of AD pathological cascade, according to
which Aβ deposition takes place at an early stage in the
natural history of the disease and tau-mediated neuronal
injury occurs secondarily . Yet, although CSF Aβ
reaches a plateau prior to the prodromal state, PET
retention gradually increases during progression to AD
. Semi-quantitative amyloid PET may thus be more
appropriate than CSF markers for early-stage grading of
AD. To be fully operative and allow efficient
discrimination between neurodegenerative diseases, it has to be
integrated within the range of available biomarkers,
including tau-specific PET tracers currently under clinical
The second original finding, which might have
stronger practical implications, was that baseline PET SUVr
was more predictive of clinical evolution and AD
conversion than CSF markers and that baseline SUVr levels
directly correlated with the subsequent rate of cognitive
decline. Composite SUVr predictive accuracy regarding
final status reached 84% compared with 79% for both
CSF Aβ and tau. In line with prior reports, cognitive
measures at baseline were the best predictors of cognitive
evolution and AD conversion [51, 52]. Baseline pontine and
composite SUVr were moderate but significant predictors
of final status and mean annual CDR and ADAS-cog
change in multivariate analysis, whereas CSF markers had
little or no impact on cognitive evolution. Cognitive
decline as reflected by the mean annual changes in MMSE,
CDR and ADAS-cog was significantly correlated with
baseline composite SUVr. The mean annual changes in CDR
and ADAS-cog were significant in patients with positive
baseline PET, whereas patients with negative baseline PET
did not incur significant CDR and ADAS-cog modification
during follow-up (Fig. 4). Among patients with a negative
baseline PET (rated using composite SUVr), 4% were AD
converters, and among those with a positive PET scan, 42%
were AD converters. This yielded an adjusted HR for AD
conversion of 3.8 (p = 0.01). Notably, the PET profile
appeared decisive in patient with discordant CSF markers
(99 Aβ+/tau− and 13 Aβ−/tau+). In these patients, an
abnormal amyloid PET resulted in a five-fold increase in AD
conversion risk (25% vs 5% in patients with a normal
amyloid PET; see Fig. 3). It would seem that even in patients
with a concordant positive CSF profile (Aβ+/tau+), a
negative PET is associated with a moderate risk of AD
conversion (7% vs 55% in patients with a positive PET), though
Aβ+/tau+/PET− profiles were too few to ensure sufficient
statistical power. Patients with a negative PET profile who
evolved to AD during follow-up might either correspond
to PET false-negatives or to cases of non-amyloid
dementias. The proportion of PET-positive patients who were
ranked as normal during follow-up is consistent with
previous evidence that 20% to 30% of cognitively normal
elderly subjects harbour Aβ deposition .
Semi-quantitative amyloid PET and CSF markers yield
complementary information for classifying normal
subjects, patients with MCI and patients with AD. However,
PET might be preferable for robust grading of early-stage
AD, and cross-sectional cut-off values for SUVr seem to
be directly transposable for longitudinal analysis. Amyloid
PET quantification using a composite SUVr appears more
powerful than CSF markers for MCI prognosis in terms of
AD conversion, and progressive cognitive decline is
correlated with baseline composite SUVr. In patients with an
equivocal CSF profile, amyloid PET effectively
differentiates patients with high risk of AD conversion.
Aβ: Amyloid-β1–42; Acc: Accuracy; AD: Alzheimer’s disease; ADAS-cog: Alzheimer’s
Disease Assessment Scale–Cognitive Subscale; ADNI: Alzheimer’s Disease
Neuroimaging Initiative; ApoE: Apolipoprotein E; CDR: Clinical Dementia Rating;
Comp: Composite; Crb: Cerebellar; CSF: Cerebrospinal fluid; GDS: Geriatric
Depression Scale; MCI: Mild cognitive impairment; MMSE: Mini Mental State
Examination; NPV: Negative predictive value; PET: Positron emission tomography;
PiB: Pittsburgh Compound B; Pons: Pontine; PPV: Positive predictive value;
RR: Risk ratio; Se: Sensitivity; SMC: Significant memory complaint; Sp: Specificity;
SUV: Standardised uptake value; SUVr: Standardised uptake value ratio
Data used in preparation of this paper were obtained from the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) database (http://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 the
analysis or in the writing of this paper. A complete listing of ADNI investigators
can be found at http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/
Data collection and sharing for this project were funded by the Alzheimer’s
Disease Neuroimaging Initiative (ADNI) (National Institutes of Health grant
U01 AG024904) and U.S. Department of Defense ADNI (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: AbbVie; 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 & Development LLC; Lumosity;
Lundbeck; Merck & Co., Inc.; Meso Scale 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 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 organisation is the Northern California Institute for Research and
Education, and the study is coordinated by the Alzheimer’s Therapeutic
Research Institute at the University of Southern California. ADNI data are
disseminated by the Laboratory of Neuro Imaging at the University of
FBB designed the study, participated in data analysis and interpretation, and
drafted the manuscript. DMG and PP participated in data interpretation and
critically revised the manuscript for important intellectual content. All authors
read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The study procedures were approved by the institutional review boards of all
participating centres (https://adni.loni.usc.edu/wp-content/uploads/
how_to_apply/ADNI_Acknowledgement_List.pdf), and written informed
consent was obtained from all participants or their authorised representatives.
Ethics approval was obtained from the institutional review boards of each
institution involved: Oregon Health and Science University; University of
Southern California; University of California—San Diego; University of Michigan;
Mayo Clinic, Rochester; Baylor College of Medicine; Columbia University Medical
Center; Washington University, St. Louis; University of Alabama at Birmingham;
Mount Sinai School of Medicine; Rush University Medical Center; Wien Center;
Johns Hopkins University; New York University; Duke University Medical Center;
University of Pennsylvania; University of Kentucky; University of Pittsburgh;
University of Rochester Medical Center; University of California, Irvine; University
of Texas Southwestern Medical School; Emory University; University of Kansas,
Medical Center; University of California, Los Angeles; Mayo Clinic, Jacksonville;
Indiana University; Yale University School of Medicine; McGill University,
Montreal-Jewish General Hospital; Sunnybrook Health Sciences, Ontario; U.B.C.
Clinic for AD & Related Disorders; Cognitive Neurology—St. Joseph’s, Ontario;
Cleveland Clinic Lou Ruvo Center for Brain Health; Northwestern University;
Premiere Research Inst (Palm Beach Neurology); Georgetown University Medical
Center; Brigham and Women’s Hospital; Stanford University; Banner Sun Health
Research Institute; Boston University; Howard University; Case Western Reserve
University; University of California, Davis—Sacramento; Neurological Care of
CNY; Parkwood Hospital; University of Wisconsin; University of California,
Irvine—BIC; Banner Alzheimer’s Institute; Dent Neurologic Institute; Ohio State
University; Albany Medical College; Hartford Hospital, Olin Neuropsychiatry
Research Center; Dartmouth-Hitchcock Medical Center; Wake Forest University
Health Sciences; Rhode Island Hospital; Butler Hospital; UC San Francisco;
Medical University South Carolina; St. Joseph’s Health Care Nathan Kline
Institute; University of Iowa College of Medicine; Cornell University and
University of South Florida: USF Health Byrd Alzheimer’s Institute. The
investigators within the ADNI contributed to the design and implementation of
the 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 online
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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