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Journal of Alzheimer’s Disease 54 (2016) 983–993
DOI 10.3233/JAD-160537
IOS Press
Staging Alzheimer’s Disease Risk
by Sequencing Brain Function
and Structure, Cerebrospinal Fluid,
and Cognition Biomarkers
Guangyu Chena,1 , Hao Shua,b,1 , Gang Chena , B. Douglas Warda , Piero G. Antuonoc ,
Zhijun Zhangb , Shi-Jiang Lia,∗ and Alzheimer’s Disease Neuroimaging Initiative2
a Department
of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
of Neurology, Affiliated ZhongDa Hospital, Neuropsychiatric Institute and Medical School of
Southeast University, Nanjing, Jiangsu, China
c Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
b Department
Accepted 22 June 2016
Abstract. This study aims to develop a composite biomarker that can accurately measure the sequential biological stages
of Alzheimer’s disease (AD) on an individual level. We selected 144 subjects from the Alzheimer’s Disease Neuroimaging
Initiative 2 datasets. Ten biomarkers, from brain function and structure, cerebrospinal fluid, and cognitive performance, were
integrated using the event-based probabilistic model to estimate their optimal temporal sequence (Soptimal ). We identified the
numerical order of the Soptimal as the characterizing Alzheimer’s disease risk events (CARE) index to measure disease stage.
The results show that, in the Soptimal , hippocampal and posterior cingulate cortex network biomarkers occur first, followed
by aberrant cerebrospinal fluid amyloid- and p-tau levels, then cognitive deficit, and finally regional gray matter loss and
fusiform network abnormality. The CARE index significantly correlates with disease severity and exhibits high reliability.
Our findings demonstrate that use of the CARE index would advance AD stage measurement across the whole AD continuum
and facilitate personalized treatment of AD.
Keywords: Alzheimer’s disease, biomarkers sequence, CARE index, functional connectivity, stage
INTRODUCTION
1 These
authors contributed equally to this work.
2 Data used in preparation of this article 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 analysis or writing of this report. A complete listing of
ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf
∗ Correspondence to: Shi-Jiang Li, PhD, Department of Biophysics, Medical College of Wisconsin, 8701 West Watertown
Plank Road, Milwaukee, WI 53226, USA. Tel.: +1 414 955 4029;
E-mail: sjli@mcw.edu.
Current literature regards sporadic Alzheimer’s
disease (AD) as a clinical entity arising from a series
of pathophysiological events related to amyloidosis and neurodegeneration. These events, measured
by corresponding AD biomarkers, are believed to
occur in a temporally ordered manner along with disease progression [1]; however, a detailed sequence
remains ambiguous. Disentangling the temporal relationship among AD biomarkers provides insight into
the evolution of AD pathogenesis. From a clinical
perspective, an established AD biomarkers sequence
would provide a template for defining an individual’s
ISSN 1387-2877/16/$35.00 © 2016 – IOS Press and the authors. All rights reserved
This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).
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AD stage and dictating stage-dependent therapeutic strategies, especially with regard to facilitating
secondary prevention of AD [2]. Therefore, estimating the optimal temporal order (Soptimal ) of AD
biomarkers is essential in uncovering AD development processes and designing effective treatment
strategies.
After decades of research, we now understand AD
development to be complex in nature, with a series of
causal sequences between pathologies and functions.
An earlier concept recognized amyloid- (A) deposition as the earliest AD trigger, causing downstream
neurodegeneration and cognitive deficit in turn [3].
However, this linear pathway concept appears to be
flawed given that A removal has proven ineffective
in improving clinical outcomes [4, 5]. Recent studies indicate several neurodegenerative biomarkers
arise upstream in AD. Specifically, neural dysfunction would induce A pathologies [6], and the soluble
A peptides can further exacerbate neural dysfunction before fibrillar A deposits [7]. This corroborates
observations of aberrant hippocampal hyperactivity
and default mode network (DMN) hypoconnectivity
or hypometabolism in apolipoprotein E (APOE) ε4
carriers without detectable A deposition [8–10]. Tau
pathology also is required in mediating A toxicity
[11]. These diverse pathophysiological events constitute the temporal-dependent process underlying AD
development [4]. Our study focused on determining
the Soptimal of those pathophysiological events represented by corresponding dynamic biomarkers, in
order to accurately stage each individual across the
whole AD spectrum.
Three major technical challenges impede the determination of the Soptimal among AD biomarkers.
First, the conventional symptom-based group definition involves biologically heterogeneous populations
and, therefore, poses a great challenge for disease staging [12]. Second, the dichotomizations of
biomarker values by “cut-off point” thresholds appear
to deviate from the continuous nature of insidious
AD progression. Also, the cut-off points are difficult to standardize across laboratories [13]. Third,
the Soptimal determination among multiple biomarkers generally requires a large cohort with a long
follow-up period to link preclinical to advanced
AD stages, complicating study design and significantly raising costs. System biology is an emerging
strategy to unravel temporal relationships among
biomarkers and predict disease progression related
to AD by modeling approaches [14], thus exhibiting
great potential in addressing the above challenges.
Specifically, the event-based probabilistic (EBP)
model can learn the temporal order of biomarkers
to describe disease progression from large crosssectional datasets based on the Bayesian theory [15,
16]. The EBP model is a decision-making tool that
determines the order of a complex system of events
by estimating the probabilities of occurrence and
nonoccurrence of a series of events, rather than by
dichotomizing biomarker status based on the cut-off
point threshold.
Our study extended the EBP model innovatively by
integrating functional, structural, biofluid, and cognitive biomarkers to determine the Soptimal , which links
the appearance of any specific biomarkers in asymptomatic individuals to the subsequent emergence of
clinical symptomatology across the whole continuum
of the AD development process.
MATERTIALS AND METHODS
Subject information
This study employed data from the Alzheimer’s
Disease Neuroimaging Initiative 2 (ADNI 2)
database. ADNI initially was launched in 2003 by
the National Institute on Aging (NIA), the National
Institute of Biomedical Imaging and Bioengineering
(NIBIB), the US Food and Drug Administration
[17], private pharmaceutical companies, and nonprofit organizations, as a $60 million, five-year
public-private partnership. Michael W. Weiner,
MD, from San Francisco Veterans Affairs Medical
Center and University of California-San Francisco,
is the principal investigator of ADNI. On October
3, 2014, we downloaded ADNI 2 datasets from
the Laboratory of Neuro Imaging (LONI), which
included 225 subjects with four groups of clinical
diagnoses (cognitively normal [CN], early mild
cognitive impairment [EMCI], late MCI [LMCI],
and AD). Specific inclusion and exclusion criteria of
the four groups are described in detail in the ADNI
2 procedures manual (http://adni.loni.usc.edu/wpcontent/uploads/2008/07/adni2-procedures-manual.
pdf). Specifically, the EMCI subjects exhibited a
1–1.5 standard deviation (SD) decline in neuropsychological memory performance, whereas the LMCI
subjects’ decline was 1.5 SD or greater. Briefly, the
EMCI inclusion criteria are described as follows:
1) subjective memory concern; 2) declined delayed
recall scores in logical memory test (9–11 points
for subjects with 16 or more education years, 5–9
points for subjects with 8–15 education years, and
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3–6 points for subjects with 0–7 education years);
3) Mini-Mental State Examination (MMSE) scores
between 24 and 30; 4) Clinical Dementia Rating
score = 0.5; 5) normal general cognition. Of the 225
subjects total, we selected 144 subjects based on
the following requirements: First, all subjects had
at least one resting-state functional connectivity
magnetic resonance imaging (R-fMRI) scan with
corresponding anatomical scans. Second, all subjects
had cerebrospinal fluid (CSF) A and phosphorylated tau (p-tau) concentration values. Third, all
subjects had scores on the MMSE, modified 13-item
Alzheimer’s Disease Assessment Scale-Cognitive
Subscale (ADAS-Cog), and Rey Auditory Verbal
Learning Test (AVLT) (immediate recall score, i.e.,
the sum of trials 1 to 5). Together, the 144 subjects
consisted of 45 CN, 42 EMCI, 32 LMCI, and 25 AD
subjects (Table 1).
Imaging acquisition
The ADNI data acquisition process is described
at http://adni.loni.usc.edu/. Briefly, R-fMRI datasets
were scanned on 3.0 Tesla (T) magnetic resonance imaging [18] scanners (Philips, Netherlands).
During the resting-state acquisitions, no specific
cognitive tasks were performed, and the participants were instructed to relax with their eyes open
inside the scanner. Axial R-fMRI images of the
whole brain were obtained in seven minutes with
a single-shot gradient echo planar imaging (EPI)
sequence. High-resolution magnetization-prepared
rapid gradient-echo (MP-RAGE) 3-D sagittal images
also were acquired.
Resting-state image preprocessing
Conventional preprocessing steps were conducted
using Analysis of Functional NeuroImages (AFNI)
software (http://afni.nimh.nih.gov/afni/), SPM8
(Wellcome Trust, London, United Kingdom), and
MATLAB (MathWorks, Natick, Massachusetts).
The preprocessing allows for T1-equilibration
(removing the first 15 seconds of R-fMRI data);
slice-acquisition-dependent time shift correction
(3dTshift); motion correction (3dvolreg); detrending (3dDetrend); despiking (3dDespike); spatial
normalization (original space to the Montreal Neurological Institute [MNI] space, SPM8); averaging
white matter and CSF signal retrieval (3dROIstats)
using standard SPM white matter and CSF mask
in the MNI space; white matter, CSF signal, and
motion effect removal (3dDeconvolve); global signal
removal necessity check (the global signal will
be removed if necessary) [19]; and low-frequency
band-pass filtering (3dFourier, 0.015–0.1 Hz).
Biomarkers
We selected 10 well-studied AD biomarkers
from only three examinations: neuropsychological
assessment, MRI scan, and CSF, each representing
an event that occurs along with AD progression. These biomarkers include three region-based
Table 1
Demographic and clinical data for all subjects
Gender (M/F)
Age (Yrs)
Education (Yrs)
MMSE
ADAS-Cog
A (pg/ml)
p-tau (pg/ml)
AVLT
AVLT30min
CN∗
(n = 45)
EMCI
(n = 42)
LMCI
(n = 32)
AD
(n = 25)
F value
p-value
20/25
73.6 ± 6.4
16.3 ± 2.5
28.8 ± 1.3
5.4 ± 2.6
201.5 ± 54.2
32.7 ± 14.2
44.6 ± 10.4
7.0 ± 3.8
19/22#
71.5 ± 6.9
15.1 ± 2.4
28.0 ± 1.8
8.6 ± 3.4
177.1 ± 61.0
43.2 ± 24.0
37.3 ± 10.4
4.2 ± 3.4
20/12
72.1 ± 8.1
16.8 ± 2.4
27.5 ± 1.9
11.4 ± 5.3
170.5 ± 48.2
44.4 ± 21.6
32.7 ± 7.6
3.1 ± 3.0
13/12
73.9 ± 6.9
15.7 ± 2.8
22.6 ± 2.8
21.7 ± 7.3
141.0 ± 40.0
55.4 ± 26.9
22.6 ± 7.1
0.6 ± 1.3
0.98
0.98
2.93
63.13
71.36
7.21
6.21
31.48
23.12
0.40
0.41
0.04
<7.0 × 10−26
<4.6 × 10−28
<2.0 × 10−4
<0.5 × 10−3
<1.3 × 10−15
<3.3 × 10−12
18 significant memory concern subjects who were cognitively normal. # One EMCI subject’s gender
information is unavailable. Clinical data are expressed as mean ± standard deviation. F values and p-values were
obtained by one-way analysis of variance. There were significant differences among the four groups for education
years; MMSE, ADAS-Cog, and AVLT scores; and CSF A and p-tau levels. Abbreviations: CN, cognitively
normal; EMCI, early mild cognitive impairment; LMCI, late mild cognitive impairment; AD, Alzheimer’s disease;
MMSE, Mini-Mental State Examination; ADAS-Cog, Alzheimer’s Disease Assessment Scale-Cognitive Subscale;
AVLT30 min, Auditory Verbal Learning Test-30-min delayed recall; A, -amyloid; p-tau, phosphorylated tau;
AVLT, Rey Auditory Verbal Learning Test.
∗ Including
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R-fMRI functional connectivity indices (FCI) from
the hippocampus (HIPFCI ), posterior cingulate cortex (PCCFCI ), and fusiform gyrus (FUSFCI ); two gray
matter concentration indices (GMI) from the hippocampus (HIPGMI ) and fusiform gyrus (FUSGMI );
two CSF biomarkers of A and p-tau levels; and three
cognitive markers of MMSE, ADAS-Cog (ADAS),
and AVLT scores. Detailed methods to extract FCI
and GMI indices are provided in the Supplementary
Methods 1 and 2.
Event-based probabilistic model
The Soptimal is determined by the EBP model. The
conceptual frameworks of the EBP model, initially
developed and applied to study seriation in determining the temporal order of fossil occurrence in
paleontology [20], were further developed to treat disease progression (e.g., AD) as a sequence of events
in which different biomarkers become abnormal in
a temporally ordered manner using cross-sectional
datasets [16, 21]. The EBP model does not make any a
priori assumptions about the sequence in which these
biomarker events occur, except that the sequence is
consistent for all subjects. Rather, the EBP model
estimates the probability of the event sequences using
real-world data. The mathematical detail of the EBP
model is described in the Supplementary Methods
3–5. This study used 45 CN and 25 AD subjects to
determine the Soptimal . Note that the EMCI and LMCI
subjects were excluded in determining the Soptimal so
that they could be used as an independent validation.
CARE index and individual AD risk stage
We numbered each of the 10 biomarker events
by order of occurrence in the Soptimal ; collectively,
these events comprise the index for characterizing
Alzheimer’s disease risk events (CARE), or “CARE
Index.” Each individual’s AD risk stage is defined as
that at which k had the highest likelihood value at the
Soptimal in equation 6 (Supplementary Methods 6).
Each individual’s k value corresponds to a score on
the CARE index.
Statistical analysis
We used one-way analysis of variance (ANOVA) to
compare demographic information and clinical data
among the four clinically defined groups (45 CN,
42 EMCI, 32 LMCI, and 25 AD). Then, we applied
ANOVA to detect differences in CARE index scores
among the four groups. The sources of the amonggroup differences were further identified by post-hoc
multiple comparison procedure. (For ANOVA, the
statistical significance level was set at p < 0.05;
for post-hoc comparisons, the statistical significance
level was set at Tukey-Kramer corrected p < 0.05.)
In addition, Auditory Verbal Learning Test-30-min
delayed recall (AVLT30 min) scores were used to validate the association of the CARE index score with
disease severity. Specifically, we employed multiple
linear regression models to estimate the relationships
between the CARE index score and AVLT30 min
score in EMCI and LMCI groups separately. We also
examined such a relationship across the four groups
using a logistic model (Supplementary Methods 7).
RESULTS
Subject information
As shown in Table 1, the four groups had no significant difference in demographic information except
education years (F = 2.93, p = 0.04). By contrast, the
MMSE, ADAS-Cog, and AVLT scores, as well as
the CSF A and p-tau levels, exhibited significant
differences among groups.
Distribution of each biomarker value
In all 10 plotted and fitted biomarker event distributions (Fig. 1), we imposed the condition that
distributions with lower values be abnormal (i.e.,
event occurred) and distributions with higher values be normal (i.e., event did not occur). Therefore,
the values for those biomarkers that defined a higher
value as abnormal in nature, including the ADASCog score, p-tau level, HIPFCI , and FUSFCI , were
multiplied by (–1).
Soptimal of events
The Soptimal represented by the 10 biomarkers was
obtained and is presented in Fig. 2A. The first two
disease events are represented by the two functional
biomarkers: increased HIPFCI (CARE index score 1)
and decreased PCCFCI (CARE index score 2). The
next two are CSF biomarkers: decreased A (CARE
index score 3) and increased p-tau (CARE index
score 4). The subsequent events are a mix of cognitive biomarkers (decreased MMSE score [CARE
index score 5], increased ADAS-Cog score [CARE
index score 6], decreased HIPGMI [CARE index
G. Chen et al. / Staging Alzheimer’s Disease with CARE Index
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Fig. 1. Probability distributions of normal (cyan) and abnormal (black) events measured by biomarkers from the AD and CN populations.
The y-axis denotes the proportion of subjects, while the x-axis indicates the detected value of each biomarker measurement. The (–1) is
employed to reverse the signs of the biomarker, indicating the left distribution is an event that occurred and the right distribution is an event
that did not occur.
score 7]), decreased AVLT score [CARE index score
8], and decreased FUSGMI [CARE index score 9]).
The last event is increased functional biomarker
FUSFCI (CARE index score 10). Note that the optimal
order of biomarker events (i.e., Soptimal ), calculated
from the CN and AD groups, has a perfect diagonal
pattern in the matrix (Fig. 2A). This result, obtained
from application of the modified k-mean Gaussian
mixture model fitting and our new greedy algorithm,
minimized event uncertainty. To estimate the uncertainty in the obtained Soptimal , we performed the
bootstrap procedure, wherein we resampled the data
500 times; for each bootstrap sample, we reestimated
the optimal sequence Soptimal . The uncertainty in the
estimated optimal sequence is illustrated in Fig. 2B.
We observed that the event sequence uncertainty primarily existed within three distinct event clusters: 1)
the early event cluster, including HIPFCI and PCCFCI ;
2) the middle event cluster, including CSF biomarkers, cognitive performance, and HIPGMI ; and 3) the
later event cluster, including FUSFCI and FUSGMI .
Note that there is negligible overlap among these
three event clusters.
Association of the CARE index with clinical
stages
Using the order of the EBP model-based biomarker
events, we obtained a CARE index score for each
subject regardless of the subject’s clinical stage. As
an example, we have selected four typical subjects,
one from each of the CN, EMCI, LMCI, and AD
groups, to illustrate the distribution of the normal-
ized likelihoods at each position on the CARE index
(Fig. 3A). The AD risk for each of the four subjects
was determined by the position of the subject’s highest likelihood value on the CARE index, where a
score of 1 is associated with a CN subject, 4 with an
EMCI subject, 7 with an LMCI subject, and 9 with
an AD subject. Moreover, the curve provides likelihood values at other CARE index scores, showing
each subject’s risk of developing AD. For example,
the LMCI subject had a relatively high likelihood
value (close to 0.6) at CARE index scores 7, 8, and
9, in addition to the highest likelihood value (0.65) at
CARE index score 6. This suggests that this LMCI
subject has a high risk of progressing from LMCI to
AD-type dementia. Information of this nature may
facilitate individual clinical inference.
Specifically, we found that all but one of the CN
subjects have a CARE index score less than or equal
to 6, while all AD subjects have a CARE index score
greater than or equal to 6 (Fig. 3B). Similarly, the
CARE index scores for EMCI and LMCI groups were
between those of the CN and AD groups (Fig. 3C).
With regard to CARE index score differences among
groups (Fig. 3D), the median CARE index scores
of the CN, EMCI, LMCI, and AD groups were 2,
4, 6, and 9, respectively. The CN group exhibited a
lower CARE index score than the EMCI (p < 0.005),
LMCI (p < 0.5 × 10−6 ), and AD (p < 0.1 × 10−6 )
groups. The AD group showed a higher CARE
index score than the EMCI (p < 0.5 × 10−4 ) and
LMCI (p < 0.5 × 10−4 ) groups. In addition, the EMCI
group showed a lower CARE index score than the
LMCI (p < 0.02) group. Note that the correspondence
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G. Chen et al. / Staging Alzheimer’s Disease with CARE Index
Fig. 2. Optimal temporal order, Soptimal , of the 10 AD biomarkers estimated by the EBP model. A) The y-axis shows the Soptimal and the
x-axis shows the CARE index score at which the corresponding event occurred. B) Bootstrap cross-validation of the Soptimal . Each entry in
the matrix represents the proportion of the Soptimal during 500 bootstrap samples. The proportion values range from 0 to 1 and correspond to
color, from white to black. The CARE index scores with their corresponding biomarkers follow: 1, increased HIPFCI ; 2, decreased PCCFCI ;
3, decreased A concentration; 4, increased p-tau concentration; 5, decreased MMSE score; 6, increased ADAS score; 7, decreased HIPGMI ;
8, decreased AVLT score; 9, decreased FUSGMI ; 10, increased FUSFCI .
Fig. 3. CARE index associated with AD clinical stages. A) Normalized likelihoods across the CARE index. The cyan, yellow, red, and
black lines represent the likelihoods at each score on the CARE index for a CN subject, an EMCI subject, an LMCI subject, and an AD
subject, respectively. B) CARE index distribution in CN and AD groups calculated from the EBP model. The CARE index is ordered by the
maximum likelihood event sequence. Each score on the CARE index corresponds to the occurrence of a biomarker event. CARE index score
0 corresponds to no events having occurred and CARE index score 10 corresponds to all events having occurred. Both CN and AD groups
showed heterogeneous index distributions. C) CARE index distributions in EMCI and LMCI groups. The proportion of EMCI (yellow) and
LMCI (red) subjects at each CARE index score was plotted. D) A box plot of the CARE index score differences between groups. The median
CARE index scores for CN, EMCI, LMCI, and AD groups are 2, 4, 6, and 9, respectively. The two-sample t-tests between CN and EMCI,
CN and LMCI, EMCI and LMCI, and AD and LMCI showed significant differences. The red “+” denotes an outlier in the CN group.
between CARE index scores and clinical stages exists
across all groups, including EMCI and LMCI subjects who were not involved in the estimation of
Soptimal . Such consistency makes it possible to use the
CARE index to estimate biomarker-based AD stages
for individual subjects.
AVLT30 min scores correlated with CARE index
scores
The degree of disease severity, represented by
the AVLT30 min score, was significantly correlated
with the CARE index score (Fig. 4). With regard to
individual clinical groups, regression lines are significant for the EMCI (p ≤ 0.0042, R2 = 0.19) and
LMCI (p ≤ 0.0166, R2 = 0.18) groups, shown in the
left and middle panels of Fig. 4, respectively. The
full nonlinear regression model is also significant
(p ≤ 1.16 × 10−12 ), shown in the right panel of Fig. 4.
The higher the CARE index score, the lower the
AVLT30 min score and the more severe the disease. This relationship is statistically valid across
and within the subject groups. The curve fitting was
estimated using a nonlinear least squares algorithm,
G. Chen et al. / Staging Alzheimer’s Disease with CARE Index
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Fig. 4. Correlations between CARE index score and episodic memory performance. Both the within-group linear regression model and acrossgroups nonlinear curve-fitting analysis demonstrated significantly negative correlations between the CARE index scores and AVLT30 min
scores. The higher the CARE index score, the worse the episodic memory function. Note that, since the data are discrete, many individual
data points overlap; for clarity, we perturbed each of the individual plot points by adding a small random displacement in the horizontal and
vertical directions. The linear regression analysis used only the original (nonperturbed) data as an input.
second visit within six months. The stage consistency
reached 89% with a slope of 1.04, indicating significant intrasubject repeatability (p < 1.69e-016). This
high degree of intrasubject CARE index score consistency indicates that the biomarker-based CARE
index system is very robust. For the method of
repeatability, please refer to the Supplementary
Methods 8.
DISCUSSION
Fig. 5. Intrasubject consistency of the CARE index score between
repeated measures. The x-axis is individual’s baseline CARE index
score, and the y-axis is the individual’s CARE index score from
a measurement repeated within six months. Circle size and the
number next to the circle represent the number of subjects falling
on the same data point. The correlation value between two CARE
index scores from repeated measurements is 89% with a slope of
1.04 (p < 1.69e-016).
yielding b0 = 2, A = 15, b2 = –0.33, b3 = 0.22, and
b4 = 1.39 for the equation S.8. The F-statistic for the
full exponential model fit was F[4,139] = 19.3283,
with a corresponding p-value = 1.16 × 10−12 .
Intrasubject repeatability of CARE index
measurement
Figure 5 shows the relationship between each
subject’s CARE index score at baseline and at the
Our study demonstrated that, given the availability of multimodal biomarkers measured by only
three examinations, neuroimaging (brain function
and structure), biological fluid (CSF), and cognitive assessments, the real-world cross-sectional
datasets from cohorts comprising the whole AD
continuum could be used to determine the AD development sequence. This accomplishment is significant
because such a study would otherwise require rich
resources involving a large sample size and a longitudinal design with high costs and a protracted period.
The major finding of this study is that when multiple
AD biomarkers are temporally ordered, functional
abnormalities in the HIP and PCC networks comprise the earliest event, even antedating detectable
CSF A and p-tau abnormalities. This finding sheds
light on the link between preclinical AD status and
symptomatic onset and can be applied to accurately
identify progressive AD trajectories, given the condition that disease onset remains insidious and no single
biomarker serves as a predictor for future cognitive
decline.
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Destabilized brain function can serve as a critical
contributor to AD cognitive deterioration [22, 23].
Of the various brain networks, the DMN and the hippocampal functional connectivity network (HFCN)
are closely associated with both AD pathologies and
clinical symptoms [2]. With regard to the HFCN,
major studies have observed hippocampal hyperactivity at the early AD stage [8, 24] and identified it as
a potential indicator of impending cognitive impairment [25]. It is assumed that early-stage hippocampal
hyperactivity may be related to A-induced hippocampal synaptic excitotoxicity, tau accumulation
[22], or mitochondrial dysfunction [26]. With respect
to the DMN, it overlaps broadly with A deposition,
and the overlap is possibly attributed to continuously high neural activity in the DMN regions
that advance A accumulation [27]. Studies convergently demonstrate DMN hypoconnectivity and
hypometabolism in AD-continuum subjects including asymptomatic APOE 4 carriers [28], amnestic
mild cognitive impairment (MCI) subjects [29],
and AD-type dementia subjects [30]. Accordingly,
current findings indicate that aberrant DMN and hippocampal connectivity strength could be among the
earliest events to trigger AD and may represent initial
disease targets.
An unresolved issue in evaluating AD biomarker
utilities is how their changes influence the cognitive
decline trajectory and clinical onset. Although earlier
studies suggest amyloidosis impairment on cognitive function [31], this association remains weak and
controversial [32]. This study observed that cognitive impairment, indexed by MMSE, ADAS-Cog, and
AVLT scores, occurred following abnormal brain network connectivity, CSF A levels, and p-tau levels.
It indicates that clinical AD symptoms emerge as
downstream events following brain amyloidosis and
neurodegenerative changes. Also, this event sequence
provides evidence to support a recent concept that
neither amyloidosis nor neurodegeneration is sufficient in isolation to cause the AD pathophysiological
and clinical cascade [33]; rather, the co-occurrence
of amyloidosis and neurodegeneration significantly
advances gray matter atrophy [34, 35], accelerates
cognitive deterioration [36, 37], and increases MCI
or dementia hazard in cognitively normal elderly
cohorts [38]. Mechanistically, emerging biological
studies indicate that A and tau interaction is a
driving force in AD development. A accumulation advances tau disease progression in that both
enhance tauopathy [39] and potentiate tau impairment on brain function [40]. Further, tau is a required
factor in A-induced neurotoxicity [11, 41]. Clearly,
current findings regarding the Soptimal support the
notion that, while amyloidosis and neurodegeneration arise independently, once both are present they
interact to advance the AD pathophysiological cascade and are a key mechanism in transforming normal
aging into AD [33, 42].
The conventional symptom-based AD staging system is limited in effectively facilitating disease
prevention, diagnosis, and treatment. This limitation
can be attributed to remarkably inter-subject biological heterogeneity within each stage and relatively
low temporal resolution of the three stages (cognitive normal, MCI, and dementia) in illustrating
continuous AD progression. The estimated Soptimal
and derived CARE index discussed herein would
address these attributions by characterizing the risk
of the 10 AD events at the individual level through
neither clinical diagnosis information nor a specific
biomarker cut-off point. This study demonstrates that
the CARE index scores closely parallel disease severity through the whole AD continuum, as indicated by
its gradual increase from CN to MCI to AD at the
symptom-defined group level and close correlation
with episodic memory performance. The higher the
CARE index score, the more advanced and severe
the disease. Accordingly, the CARE index may serve
as a surrogate to indicate the AD development process and facilitate clinical trials by a) identifying
AD-risk subjects who do not yet have any clinical
symptoms; b) staging and categorizing patient populations based on their CARE index scores to enrich
response rates, as has been demonstrated in oncology;
and c) monitoring and evaluating treatment efficacy
through individual subjects’ changes in CARE index
scores. This personalized medicine technique would
be particularly beneficial in assessing the efficacy
of promising secondary prevention interventions in
patients at the earliest discernible stage of AD.
This study has four limitations. First, this is the
first attempt to incorporate the resting-state functional connectivity HIPFCI and PCCFCI biomarkers
into the EBP model. Future studies could integrate
other functional and structural biomarkers, including the executive control network, salience network,
and insular network, into the biomarker sequence
to characterize the trajectory of the neural network
changes with AD progression. Second, currently
available datasets include only 45 CN and 25 AD
subjects, which limits the representative probability
distributions of biomarkers generated to accurately
estimate the temporal order of biomarkers. The
G. Chen et al. / Staging Alzheimer’s Disease with CARE Index
Soptimal determined from a larger sample size may
be different from the current results. Third, this study
relies on the assumption that all subjects follow a single optimal event sequence, despite heterogeneous
pathways of sporadic AD development [1, 43]. A
recent study introduced a cluster of subjects that may
follow different event sequences in their disease progression [44]. Fourth, the current-event-based model
used a naı̈ve Bayesian model that assumes the different biomarkers are independent measurements. The
assumption is not always true. However, in practice,
a naı̈ve Bayesian system can work surprisingly well,
even when the independence assumption is not true
[45]. Therefore, further studies with larger sample
sizes, additional biomarker events, and updated analysis, are required to enhance our understanding of
AD pathogenesis and improve detection of AD progression on an individual level.
991
ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the
National Institutes of Health (http://www.fnih.org).
The grantee organization 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
for Neuro Imaging at the University of California.
Authors’ disclosures are available online (http://jalz.com/manuscript-disclosures/16-0537r1).
SUPPLEMENTARY MATERIAL
The supplementary material is available in the
electronic version of this article: http://dx.doi.
org/10.3233/JAD-160537.
REFERENCES
ACKNOWLEDGMENTS
[1]
This work was supported by US National Institutes
of Health grants R01 AG020279 and R44 AG035405.
We sincerely thank Ms. Lydia Washechek, BA, for
editorial assistance.
Data collection and sharing for this project was
funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health
[NIH] Grant U01 AG024904 [PI: MW Weiner]) and
DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the
National Institute on Aging (NIA) and the National
Institute of Biomedical Imaging and Bioengineering (NIBIB), and through generous contributions
from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation;
Araclon Biotech; BioClinica, Inc.; Biogen; BristolMyers 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
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