A Blood-Based Screening Tool for Alzheimer’s Disease
That Spans Serum and Plasma: Findings from TARC and
Sid E. O’Bryant1*., Guanghua Xiao2., Robert Barber3, Ryan Huebinger4, Kirk Wilhelmsen5, Melissa
Edwards6, Neill Graff-Radford7, Rachelle Doody8, Ramon Diaz-Arrastia9, for the Texas Alzheimer’s
Research & Care Consortium¤a, for the Alzheimer’s Disease Neuroimaging Initiative¤b
1Department of Neurology, F. Marie Hall Institute for Rural and Community Health, Garrison Institute on Aging, Texas Tech University Health Sciences Center, Lubbock,
Texas, United States of America, 2Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America,
3Department of Pharmacology and Neuroscience, Institute for Aging and Alzheimer’s Disease Research, University of North Texas Health Science Center, Fort Worth,
Texas, United States of America, 4Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America, 5Department of
Genetics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America, 6Department of Psychology, Texas Tech University,
Lubbock, Texas, United States of America, 7Department of Neurology, Mayo Clinic, Jacksonville, Florida, United States of America, 8Department of Neurology,
Alzheimer’s Disease and Memory Disorders Center, Baylor College of Medicine, Houston, Texas, United States of America, 9Center for Neuroscience and Regenerative
Medicine, Uniformed Services University of the Health Sciences, Rockville, Maryland, United States of America
Context: There is no rapid and cost effective tool that can be implemented as a front-line screening tool for Alzheimer’s
disease (AD) at the population level.
Objective: To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum
Design, Setting, Participants: Analysis of serum biomarker proteins were conducted on 197 Alzheimer’s disease (AD)
participants and 199 control participants from the Texas Alzheimer’s Research Consortium (TARC) with further analysis
conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer’s Disease Neuroimaging
Initiative (ADNI). The full algorithm was derived from a biomarker risk score, clinical lab (glucose, triglycerides, total
cholesterol, homocysteine), and demographic (age, gender, education, APOE*E4 status) data.
Major Outcome Measures: Alzheimer’s disease.
Results: 11 proteins met our criteria and were utilized for the biomarker risk score. The random forest (RF) biomarker risk
score from the TARC serum samples (training set) yielded adequate accuracy in the ADNI plasma sample (training set)
(AUC=0.70, sensitivity (SN)=0.54 and specificity (SP)=0.78), which was below that obtained from ADNI cerebral spinal fluid
(CSF) analyses (t-tau/Ab ratio AUC=0.92). However, the full algorithm yielded excellent accuracy (AUC=0.88, SN=0.75, and
SP=0.91). The likelihood ratio of having AD based on a positive test finding (LR+)=7.03 (SE=1.17; 95% CI=4.49–14.47), the
likelihood ratio of not having AD based on the algorithm (LR2)=3.55 (SE=1.15; 2.22–5.71), and the odds ratio of AD were
calculated in the ADNI cohort (OR)=28.70 (1.55; 95% CI=11.86–69.47).
Conclusions: It is possible to create a blood-based screening algorithm that works across both serum and plasma that
provides a comparable screening accuracy to that obtained from CSF analyses.
Citation: O’Bryant SE, Xiao G, Barber R, Huebinger R, Wilhelmsen K, et al. (2011) A Blood-Based Screening Tool for Alzheimer’s Disease That Spans Serum and
Plasma: Findings from TARC and ADNI. PLoS ONE 6(12): e28092. doi:10.1371/journal.pone.0028092
Editor: Ashley I. Bush, Mental Health Research Institute of Victoria, Australia
Received August 26, 2011; Accepted November 1, 2011; Published December 7, 2011
Copyright: ? 2011 O’Bryant et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was made possible by the Texas Alzheimer’s Research Consortium funded by the state of Texas through the Texas Council on Alzheimer’s
Disease and Related Disorders. Investigators at the UTSW acknowledge NIH, NIA grant P30AG12300. The investigations at Baylor’s Alzheimer’s Disease and
Memory Disorders Center were supported by the Cynthia and George Mitchell Foundation. Investigators at Texas Tech University Health Sciences Center were
supported by The CH Foundation. 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). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and
Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global
Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck
and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer’s Association and Alzheimer’s
Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI 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 California Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. The
funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PLoS ONE | www.plosone.org1December 2011 | Volume 6 | Issue 12 | e28092
Competing Interests: The authors have the following competing interest: In the TARC, a patent has been submitted on this blood-based screener. There are no
other products in development or marketed products to declare. This does not alter the authors’ adherence to all PLoS ONE policies on sharing data and materials, as
detailed online in the guide for authors. ADNI has received funding from the following commercial sources: Abbott, AstraZeneca AB, Bayer Schering Pharma AG,
Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly
and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc. This does not alter the authors’ adherence to all
PLoS ONE policies on sharing data and materials, as detailed online in the guide for authors. ADNI data is freely available to any interested scientists.
* E-mail: Sid.Obryant@ttuhsc.edu
. These authors contributed equally to this work.
¤a For a full list of the investigators from the Texas Alzheimer’s Research Consortium please see the Acknowledgments section
¤b For more information about the Alzheimer’s Disease Neuroimaging Initiative please see the Acknowledgments section
Alzheimer’s disease (AD) is a devastating disease affecting millions
of people worldwide. While a Food and Drug Administration (FDA)
working group recently provided preliminary approval for a beta
amyloid (Ab) neuroimaging technique as a biological marker
(Amyvid?, Elli Lilly), no blood-based biomarker screening tool has
significant advantages over neuroimaging modalities. For example,
blood-based screenings offer a cost effective method of screening
candidates for therapeutic trials , provide a rapid, cost-effective
means of screening for AD at the population level [2,3,4,5], and
that can be followed-up by clinical modalities (i.e. medical exam,
neuropsychological testing, standard neuroimaging, clinical blood-
work), specialized neuroimaging (i.e. Ab imaging, fMRI, volumetric
MRI analyses), and/or CSF (i.e. t-tau, Ab1–42, and/or t-tau/Ab1–42
ratio score) analyses  for screen positive cases. The 2009 U.S.
Census estimates suggested that there were nearly 40 million
Americans age 65 and above with an additional 34 million reaching
65 within 10 years; there are many more world-wide. Given their
cost and limited availability, available imaging, clinical, and CSF
modalities are not reasonable first-line approaches for screening all
elders at risk of having AD or that have concerns about having the
disease. The purpose of this study was to generate and cross-validate
a blood-based screener for AD that can be incorporated into the
existing medical infrastructure with additional assessments (e.g.
clinical, imaging, CSF analysis) to confirm those who screen positive.
In the last several years, there have been significant advance-
ments in the search for blood-based biomarkers for Alzheimer’s
disease (AD). In 2007, Ray and colleagues  analyzed a panel of
plasma-based proteins among samples from 259 controls, AD and
mild cognitive impairment (MCI) cases and generated a biomarker
algorithm that accurately identified 89% of those with and without
the disease; however, this work has not been replicated . Buerger
and colleagues  examined blood-based microcirculation markers
as possible diagnostic markers for AD (AD n=94, controls n=53).
These authors found that a ratio score of pro-atrial natriuretic
peptide (MR-proANP) to C-terminal endothelin-1 precursor
fragment (CT-proET-1)(MR-proANP/CT-proET-1 ratio) from
plasma yielded a sensitivity of 0.81 and specificity of 0.82 in
discriminating probable AD from healthy controls. More recently,
we created a biomarker risk score from serum proteins (AD n=197,
controls n=203) that yielded a 91% overall accuracy . Our
approach took the algorithm a step further by combining both
demographic (i.e. age, gender, education, and APOE*E4 status) and
clinical lab values (i.e. cholesterol, triglycerides, high density
lipoproteins, low density lipoproteins, lipoprotein-associated phos-
pholipase, homocysteine, and C-peptide) into the algorithm, which
improved the overall accuracy to 95% . Analyzing samples from
22 AD cases, 22 controls, and 12 non-AD disease comparison
subjects, Reddy and colleagues  took a novel approach by
examining serum IgG antibodies as potential biomarkers of AD
status obtaining impressive results (AUC=0.99); however, the
sample size was very small (n=15 AD cases in test set) limiting the
generalizability of the findings at this point. Together, these studies
suggest that a blood-based screening tool for AD is on the horizon.
Although this work is promising, there is little consistency as to
what biological fluid is used for biomarker assays (i.e. serum versus
plasma), which may explain many inconsistent findings found in
the literature. While some assays must be conducted in one
medium or another, there are numerous studies linking a variety of
blood-based markers to AD from both mediums. Mayeux and
colleagues  analyzed plasma amyloid b (Ab) peptides Ab1–40
and Ab1–42on 530 participants and found that Ab1–42(but not
Ab1–40) levels were higher among baseline AD cases as well as
those who developed AD over a three-year period as compared to
those who did not. Luis et al.  analyzed serum Ab1–40and Ab1–
42levels among a sample of 87 AD and MCI cases as well as
controls. In that study, serum Ab1–40levels did not differ between
groups whereas serum Ab1–42levels where highest among MCI
cases (versus AD cases and controls) and controls and AD levels
were intermediate between those of the MCI cases and controls.
The serum Ab1–42/1–40ratio was also highest among the MCI
group. In a sample of 40 AD cases and controls, Laske et al. 
found that serum brain derived neurotrophic factor (BDNF) levels
varied according to AD severity, suggesting BDNF as a potential
biomarker for AD, though we failed to cross-validate these findings
in a sample of 198 AD cases and controls from the Texas
Alzheimer’s Research Consortium (TARC) cohort . In a
follow-up study of 399 AD cases and controls, elevated serum BDNF
was found to be specifically related to poorer memory perfor-
mance among AD cases  whereas Komulainen and colleagues
 found that lower plasma BDNF levels were significantly related
to poorer scores on tests of language and memory among women
in a population based sample of aging men and women (n=1389).
To date, we are aware of no prior work that has explicitly
sought to find blood-based biomarkers of AD across both serum
and plasma and with no previous attempts at identifying blood-
based screening tools utilizing markers across blood fractions.
Additionally, no previously created blood-based tools have been
cross-validated in independent cohorts. The current study was
designed to (1) identify blood-based proteins that were highly
correlated across both serum and plasma that also were
significantly related to AD status, and (2) generate a screening
algorithm for AD utilizing those markers from serum in the TARC
cohort and validate that algorithm in the Alzheimer’s Disease
Neuriomaging Initiative (ADNI) plasma-samples. We hypothe-
sized that, as with our prior work, we would be able to generate a
screening algorithm that accurately identified AD across cohorts.
Texas Alzheimer’s Research Consortium (TARC). Serum
protein data were analyzed from 396 participants (197 AD
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org2 December 2011 | Volume 6 | Issue 12 | e28092
subjects, 199 controls) from the TARC longitudinal cohort. In
addition, plasma protein data were analyzed on a matched sample
of 40 AD cases from the TARC. Blood samples for comparison of
plasma and serum proteins were drawn concurrently from the
same individuals. The methodology of the TARC project has been
described in detail elsewhere [2,16]. Briefly, each participant
undergoes a standardized annual examination at the respective
sites, which includes a medical evaluation, neuropsychological
testing, interview, and blood draw for storage of samples in the
TARC biobank. Diagnosis of AD was based on NINCDS-
ADRDA criteria  utilizing consensus review. Institution
Review Board approval was obtained for this study with each
participant (or caregiver) providing written informed consent. The
Institution Review Board (IRB) at Texas Tech University Health
Sciences Center, Baylor College of Medicine, University of North
Texas Health Science Center, the University of Texas Southwest-
ern Medical Center, and the University of Texas Health Science
Center - San Antonio approved this research.
Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data used
in the preparation of this article were obtained from the ADNI
database (adni.loni.ucla.edu). The ADNI was launched in 2003 by
the National Institute on Aging (NIA), the National Institute of
Biomedical Imaging and Bioengineering (NIBIB), the Food and
Drug Administration (FDA), private pharmaceutical companies
and non-profit organizations, as a $60 million, 5-year public-
private partnership. The primary goal of ADNI has been to test
whether serial magnetic resonance imaging (MRI), positron
emission tomography (PET), other biological markers, and clinical
and neuropsychological assessment can be combined to measure
the progression of mild cognitive impairment (MCI) and early
Alzheimer’s disease (AD). 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 U.S. and Canada. For up-
to-date information, see www.adni-info.org. Data from 170
participants from ADNI (58 controls and 112 AD cases) for
whom plasma-based protein results were available were utilized in
Blood Assays. In TARC, non-fasting samples were collected
whereas ADNI utilized a fasting blood collection procedure.
Serum blood samples were collected in serum-separating tubes
during clinical evaluations, allowed to clot at room temperature
for 30 minutes, centrifuged, aliquoted, and stored in polypropyl-
ene tubes at 280uC. In both TARC and ADNI, plasma samples
were collected in lavender-top tubes and gently mixed 10–12
times. Next tubes were centrifuged at room temperature and
plasma extracted and frozen until assay. In both studies, serum
and plasma samples were sent to Rules Based Medicine (RBM,
www.rulesbasedmedicine.com, Austin, TX) for assay on the RBM
multiplexed immunoassay human Multi-Analyte Profile (human-
MAP). Individual proteins were quantified with immunoassays on
colored microspheres. Information regarding the least detectable
dose (LDD), inter-run coefficient of variation, dynamic range,
overall spiked standard recovery, and cross-reactivity with other
humanMAP analytes can be readily obtained from RBM. Clinical
lab data. Homocysteine, hemoglobin A1c, c-peptide, and
lipoprotein-associated phospholipase A2 (Lp-PLA2) was provided
by the Ballantyne laboratory at Baylor College of Medicine.
Sample collection and storage was as described above. Lipids were
measured using a AU400e automated chemistry analyzer
(Olympus America; Center Valley, PA), serum total homocysteine
(tHcy) by recombinant enzymatic cycling assay (Roche Hitachi
911), c-peptide by enzyme-linked immunosorbent assay (ELISA),
HbA1c measurement by turbidimetric inhibition immunoassay
(TINIA) for hemolyzed whole blood and Lp-PLA2 levels by
diaDexus PLACH test (diaDexus, Inc, San Francisco, CA). Clinical
lab data from ADNI was conducted using kits provided by
Covance. ADNI CSF Biomarkers. Our blood-based algorithm was
compared to the diagnostic accuracy of the total tau (t-tau) to beta
amyloid (Ab1–42) ratio (t-tau/Ab1–42) previously completed as part
of the ADNI protocol. The CSF methods for ADNI have been
described in detail elsewhere . Lumbar punctures were
conducted with a median of one day after baseline clinical visit.
Once CSF was transferred into polypropylene tubes it was frozen
and shipped to the ADNI Biomarker Core laboratory at the
University of Pennsylvania Medical Center where biomarker
assays were conducted .
Statistical Analyses. Analyses were performed using R (V 2.10)
statistical software . Biomarker data were transformed using
Box-Cox  transformation so that the distribution of each
protein is approximately normal. Analyses took place in a series
of steps. Identification of proteins across serum and plasma.
Pearson correlations were conducted in the TARC sub-sample
across serum and plasma proteins to determine which markers
were comparable across mediums. Model-based clustering
algorithm  (Mclust package in R) was used to empirically
determine the optimal correlation cut-off that separated the
highly correlated versus weakly correlated proteins. The optimal
cut-score was 0.75, which identified 33 proteins with high
correlation ($0.75) between serum and plasma (see Figure 1). T-
test analyses comparing the abundance of proteins between AD
and controls identified 29 that were differentially expressed
between groups (p,0.05) in full the TARC cohort (training set).
Eleven proteins were significantly different between AD and
control participants and were found to be correlated $0.75
across serum and plasma. These 11 proteins are defined as
protein biomarkers in this study. Figure 2 reflects a graphic
representation of the methods. Development of Biomarker
Diagnostic Model. Next, we used the 11 protein biomarkers to
develop our prediction model using random forest (RF) method
[22,23], implemented using R package randomforest (V 4.5) .
The TARC cohort was designated as the training sample in
which the prediction model was derived. Validation of the
Prediction Model. The protein biomarker-based RF prediction
model derived from the TARC serum-based biomarker training
set (TARC) was applied to the ADNI plasma-based dataset (test
sample) to predict the risk score for each patient in the ADNI
cohort. Of note, no ADNI data were utilized in (1) identification
of serum-plasma comparable proteins or (2) development of the
RF prediction model. This was done to avoid the overfitting or
other possible confounds across medium and/or cohorts.
Diagnostic Accuracy. Diagnostic accuracy was evaluated by
examining the area under the receiver operating characteristic
(ROC) curves (AUC). Our approach to creating a blood-based
diagnostic algorithm for AD is to combine the predicted
biomarker risk score from the RF model with demographic and
clinical lab data via a multivariate logistic regression model.
Demographic data incorporated into the algorithm was age,
gender, level of education, and presence of APOE*E4 genotype
(homozygous or heterozygous) while clinical lab data included
glucose, triglycerides, total cholesterol, and homocysteine. These
variables were included as they were (1) available from both
cohorts and (2) have been linked to AD. Lastly, the likelihood
ratios of having AD based on a positive test finding (LR+), the
likelihood ratio of not having AD based on the algorithm (LR-)
and the odds ratio of AD were calculated in the ADNI cohort.
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org3 December 2011 | Volume 6 | Issue 12 | e28092
Demographic characteristics of the samples are provided in
Table 1. Eleven proteins met our criteria of (1) having a
correlation coefficient $0.75 between serum and plasma in the
same participant and (2) being associated with disease status
p,0.05. The 11 proteins were as follows: C-reactive protein,
adiponectin, pancreatic polypeptide, fatty acid binding protein,
interleukin 18, beta 2 microglobulin, tenascin C, T lymphocyte
secreted protein 1.309, factor VII, vascular cell adhesion molecule
1, and monocyte chemotactic protein 1. See Table 2 for
correlations among serum and plasma for these 11 proteins as
well as the mean differences between cases and control groups of
these biomarkers and clinical lab data across cohorts.
The optimal cut-score for the RF biomarker risk score from the
test sample (ADNI) was 0.51 which obtained AUC of 0.70 with a
sensitivity (SN) and specificity (SP) of 0.54 and 0.78, respectively.
For comparison purposes, the ADNI CSF t-tau/Ab1–42 ratio
yielded a superior diagnostic accuracy with an observed
AUC=0.92, SN=0.84, and SP=1.00. However, as with our
prior approach, when the biomarker risk score was combined
with demographic and clinical lab data [2,5], the precision
improved substantially. Our combined algorithm yielded a much
better diagnostic accuracy with an observed AUC=0.88,
SN=0.75, and SP=0.91. Of note, the diagnostic accuracy of
our serum-plasma based algorithm was comparable to that
obtained from ADNI CSF analyses. See Table 3 and Figure 3.
The likelihood ratio positive (LR+) was 7.03 (SE=1.17; 95%
CI=4.49–14.47), the likelihood ratio negative (LR2) was 3.55
(SE=1.15; 2.22–5.71), and the odds ratio (OR) was 28.70 (1.55;
95% CI=11.86–69.47). The misclassification rate was 14% (95%
CI=9–21%). If we set SN at 0.80 for our full algorithm, the
resulting SP was 0.81, which also meets the criteria for the
Consensus Report of the Working Group on Molecular and
Biochemical Markers of AD .
In the current study we demonstrate that (1) there are proteins
that are highly correlated in plasma and serum and are associated
with AD status across blood fractions, (2) these findings are
replicable across independent cohorts, and (3) using these proteins,
we generated a prediction model in the TARC cohort that, when
combined with demographic and clinical lab data, yielded
clinically significant classification accuracy in the ADNI cohort.
To date, this is the first blood-based screener for AD developed
that has been cross-validated in an independent large-scale cohort
that also works across blood fractions. This work not only further
supports the notion that an accurate blood-based screening tool for
AD can be generated, but also that such an algorithm can be
applied across serum and plasma mediums. Our 11-protein serum-
plasma risk score alone yielded an AUC of 0.70 accuracy that was
Figure 1. The density plot the Pearson’s correlation coefficients between serum and plasma in TARC cohort. We used Mclust (model-
based clustering algorithm ) package in R to fit the data and discovered two clusters in the correlation coefficients: one (red) corresponding to
low correlation and the other (blue) corresponding to high correlation. The threshold value that separated these two clusters most effectively is 0.75.
The black line is the density plot of all biomarkers. The dots represent the correlation coefficients of the biomarkers and the color indicates the cluster
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org4 December 2011 | Volume 6 | Issue 12 | e28092
enhanced by the addition of demographic (i.e. age, gender,
education, APOE*E4 status) and clinical lab (i.e. glucose,
triglycerides, total cholesterol, and homocysteine) data. In
Table 3, the addition of clinical lab data did not improve the
overall accuracy of the algorithm beyond demographic informa-
tion, which is largely driven by the APOE*E4 rates in the ADNI
cohort. However, in our prior work , the use of clinical lab data
improved overall accuracy and will likely contribute to the
robustness of our approach as it is applied to other cohorts. It is
certainly possible that inclusion of additional markers, not
available in the current analyses, would increase the accuracy of
that risk score, which is an additional advantage of our approach
as it can be expanded or reduced as necessary to support the
accuracy and cost-effectiveness of the algorithm. A single
biomarker algorithm that works across both serum and plasma
will offer laboratories options that may be preferable for a variety
There are several implications for the current findings. There
are a number of previously conducted research projects with
stored blood biospecimens; however, there is little consistency
between what medium was stored. The current findings open up
the possibility of utilizing samples from such studies to further
validate and refine our algorithm. Additionally, it is likely that the
components of diagnostic algorithms will be different from the
components of algorithms for progression and different from those
predicting long-term risk. Our findings offer a novel approach to
each of these questions as well. These findings also support the
need for standard protocols to be generated for blood-based AD
biomarker research as is currently underway for the CSF markers.
These results also support the robustness of our methodological
approach. In our initial serum-based algorithm, the biomarker risk
score alone yielded an AUC of 0.91 whereas the serum-plasma
algorithm in the current study yielded an AUC of 0.70. While
impressive, this overall accuracy is not clinically adequate.
Figure 2. Outline of methods.
Table 1. Demographic characteristics of the cohorts.
TARC – serum
(N=198)p-value AD (n=40) AD (n=112)Control (n=58)p-value
Gender (male) 34.5%31.3% 0.52 40% 42%48%0.52
Age (years, mean/sd) 77.4(8.3)70.4(8.9)
,0.001 75.7(1.6)75.2(8.1)75.5(5.8) 0.63
Education (years, mean/sd) 14.0(3.5)15.5(2.7)
,0.001 14.5(0.6)15.1(3.2)15.6(2.7) 0.38
APOE*E4 positive59.3% 26.5%
Note: TARC=Texas Alzheimer’s Research Consortium; ADNI=Alzheimer’s Disease Neuroimaging Initiative. Fisher exact test was used for categorical outcomes (Gender,
APOE*E4 positive) and Wilcoxon test was used for continuous outcomes (Age, Education).
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org5 December 2011 | Volume 6 | Issue 12 | e28092
However, as with our prior approach, the combination of clinical
lab data and demographic variables into the algorithm increased
the precision substantially (AUC=0.88). In our prior work, the
training and test sample were both based on serum assays and
were from the larger TARC cohort; however, the derivation of the
algorithm in the TARC cohort and validation in the ADNI cohort
supports the robustness of this method. As we have previously
argued, using only age, gender, education and APOE*E4 status,
one can accurately classify a large number of AD cases when
compared to controls. Therefore, consideration of such factors
should be considered when examining biomarkers of AD status.
We are not the first to demonstrate that inclusion of these factors
into an algorithm can improve overall accuracy as others have
suggested that a multi-marker approach is superior to single-
marker approaches [25,26]. As an example, Vemuri and
colleagues found that including demographic factors with
structural MRI added to the overall accuracy of disease-prediction
models even when cases and controls were matched by these
variables . This is important given that the TARC cohort did
not match cases and controls whereas ADNI samples were
matched. The robustness of our methodology may also provide an
explanation for the lack of cross-validation of prior work [6,7].
The utility of our algorithm for separating MCI cases from normal
controls (and/or AD) remains unknown at present.
The current markers overlap with our prior serum-only based
algorithm [2,5] though they do not overlap with those found by
Ray and colleagues , which may be due to the significant
differences in assay platforms utilized. However, there is an
existing literature directly or indirectly linking each of the 11
proteins identified in this study to AD. As with our prior work,
many of the markers in the algorithm are inflammatory in nature,
which we propose as evidence of an inflammatory endophenotype
of AD [2,28]. We, and others, have documented a link between
CRP and AD . Based on the available data, we proposed that
the link between CRP and the risk of AD changes over the life
course with midlife elevations in CRP increasing risk for AD, but
that this risk declines as one ages with decreased CRP related to
AD status though elevations in CRP are still related to increased
disease severity among cases . Adiponectin, an adipocytokine,
is related to obesity, insulin resistance, metabolic syndrome, type 2
diabetes, and cardiovascular disease  and was recently found
to be elevated in plasma among MCI and AD cases .
Table 2. Biomarkers and Clinical Labs Across Cohorts.
Pearson correlation for
serum vs. plasma (TARC cohort)Mean difference in TARCCMean difference in ADNI
C Reactive Protein0.97
Pancreatic polypeptide0.894.29 2.78
Fatty Acid Binding Protein 0.881.72
Beta 2 Microglobulin0.85 3.14 2.09
MCP 1 0.75
Total Cholesterol– 0.13 0.78
Note: Mean difference reflects the mean difference between cases and controls divided by the its standard deviation.
Table 3. Diagnostic accuracy of the serum-plasma algorithm.
AUC (95% CI) SN (95% CI)SP (95% CI)
biomarker + clinical + demographic
biomarker + demographic
Biomarker + clinical
0.88 (0.83–0.93)0.75 (0.67–0.83) 0.91 (0.80–0.96)
0.88 (0.83–0.93)0.79 (0.71–0.86)0.87 (0.75–0.93)
0.71 (0.63–0.79) 0.73 (0.64–0.81)0.60 (0.47–0.72)
biomarker risk score alone0.70 (0.62–0.78) 0.54 (0.45–0.63)0.78 (0.65–0.87)
clinical variables alone 0.59 (0.50–0.68)0.53 (0.43–0.62)0.72 (0.58–0.82)
demographic variables alone0.81 (0.75–0.88)0.70 (0.61–0.78)0.92 (0.82–0.97)
CSF tau/abeta ratio0.92 (0.87–0.96)0.84 (0.76–0.90)1.00 (0.93–1.00)
Note: AUC=area under the receiver operating characteristic curve; SN=sensitivity; SP=specificity; CI=confidence interval; demographic=age, gender, education,
APOE*E4 status (presence/absence); clinical=glucose, triglycerides, total cholesterol, homocysteine.
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org6December 2011 | Volume 6 | Issue 12 | e28092
Therefore, adiponectin levels may be related to the documented
links between changes in body composition (e.g. weight loss) seen
in prodromal and early stage AD. Pancreatic polypeptide is also
linked with diabetes and obesity [31,32] and may provide a clue
into the biological link between these conditions and AD. Fatty
acid binding proteins, cytosolic proteins found in all cells utilizing
fatty acids, are rapidly released into circulation following cell
damage . Serum levels fatty acid binding proteins have been
shown to be elevated among AD and other dementia cases as
compared to normal controls [33,34]. A recent meta-analysis
showed a significant up-regulation in blood concentrations of IL-
18 (as well as IL-6, TNFa, IL1, transforming growth factor, IL-12)
among AD cases . b2 microglobulin is an amyloid protein 
that has been found to be elevated in the CSF of AD cases [37,38].
Tenascin-C, an extracellular matrix glycoprotein, is involved in a
number of biological processes that have been linked to AD
including inflammation and angiogenesis , which may provide
a biological mechanism linking AD to a broad spectrum of
cardiovascular diseases and risk factors. The human cytokine I-
309, a small glycoprotein, was recently found to be elevated in a
proteomic study of CSF among AD cases and was also related to
scores on a test of global cognitive functioning (i.e. Mini Mental
State Examination [MMSE]) . Factor VII is a protein in the
coagulation cascade that is required for thrombin generation,
which has also been linked to AD . VCAM-1 is a member of
the immunoglobulin superfamily that has been found elevated in
plasma of AD cases . It has been proposed that MCP-1 plays a
dominant role in the chronic inflammation seen in AD  and
has been found to be elevated in serum of patients diagnosed with
MCI and mild AD .
Given the sheer volume of elders worldwide who are at risk
for AD, there is an urgent need for a multi-stage approach to
screening and diagnosis. There are insufficient numbers of
dementia experts to meet the needs of all individuals at risk for
the disease and prior work has demonstrated that non-experts
are not completely accurate in diagnosing the disease ,
particularly in the earlier stages . Our blood-based screener
fits into the existing medical infrastructure where screen
positives can be referred for confirmatory diagnosis using
clinical, imaging, and/or CSF analysis. As with any screening
measure, one must consider acceptable levels of false positive
and false negative rates of the instrument as well as overall
disease base rates of the setting when deciding on appropriate
cut-scores on any instrument . Therefore it is important that
additional work be conducted to determine how this algorithm
(and other previously published biomarkers) performs in
community-based settings (e.g. primary care offices) as both
the TARC and ADNI are clinic-based cohort studies. While
sensitivity and specificity are not base rate dependent, accuracy
of diagnosis (prediction of disease status present/absent) is a
function of base rates of the disease within a given population
therefore, overall accuracy of AD presence (i.e. true positives)
will increase with advancing age while accuracy of AD absence
(i.e. true negatives) will be higher with younger ages. As with
age, APOE*E4 genotype, gender, and/or years of education are
also important considerations, which is why these variables are
included in the algorithm itself.
The independent cohorts strongly support the validity of the
findings. These observations also justify further analysis examining
a broader range of markers across serum and plasma to determine
if the biomarker risk score can be further refined. Our results also
suggest that further work in the field should specifically examine
the performance of blood-based protein panels across serum and
TARC. We would like to thank Dr. Christie Ballantyne and his lab at
Baylor College of Medicine for measuring the clinical lab data of glucose,
tryglicerides, total cholesterol, and homocysteins. We also would like to
thank the people of Texas and the research participants for making this
work possible. Funding acknowledgments are available online.
Investigators from the Texas Alzheimer’s Research Consortium: Baylor
College of Medicine: Susan Rountree, Christie Ballantyne, Eveleen Darby,
Aline Hittle, Aisha Khaleeg; Texas Tech University Health Science
Center: Paula Grammas, Benjamin Williams, Andrew Dentino, Gregory
Schrimsher, Kuo Chuang Wu, Parastoo Momeni, Larry Hill; University of
North Texas Health Science Center: Janice Knebl, Lisa Alvarez, Douglas
Mains, Thomas Fairchild, James Hall; University of Texas Southwestern
Medical Center: Joan Reisch, Perrie Adams, Roger Rosenberg, Ryan
Huebinger, Janet Smith, Mechelle Murray, Tomequa Sears; University of
Texas Health Sciences Center – San Antonio: Donald Royall, Raymond
Data used in the preparation of this article were obtained from the
Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (www.loni.
ucla.edu/ADNI). 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. ADNI investigators
include (complete listing available at www.loni.ucla.edu/ADNI/Collabo-
Conceived and designed the experiments: SEO GX RB RD RDA.
Performed the experiments: SEO GX RB KW RD RDA. Analyzed the
data: SEO GX RB RH KW ME NGR RD RDA. Contributed reagents/
materials/analysis tools: SEO GX RB KW RD RDA. Wrote the paper:
SEO GX RB RH KW ME NGR RD RDA.
Figure 3. ROC curve for serum-plasma based biomarker
algorithm. Each line represents the AUC of the respective portions
of the algorithm with the yellow line reflecting chance.
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org7December 2011 | Volume 6 | Issue 12 | e28092
1. Thal LJ, Kantarci K, Reiman EM, Klunk WE, Weiner MW, et al. (2006) The
role of biomarkers in clinical trials for Alzheimer disease. Alzheimer Disease &
Associated Disorders 20: 6–15.
2. O’Bryant SE, Xiao G, Barber R, Reisch J, Doody R, et al. (2010) A serum
protein-based algorithm for the detection of Alzheimer disease. Archives of
Neurology 67: 1077–1081.
3. O’Bryant S, Xiao G, Barber R, Reisch J, Doody R, et al. for the Texas
Alzheimer’s Research Consortium (in press) A serum protein-based algorithm
for the detection of Alzheiemr’s disease. Arch Neurol.
4. Schneider P, Hampel H, Buerger K (2009) Biological marker candidates of
alzheimer’s disease in blood, plasma, and serum. CNS Neuroscience and
Therapeutics 15: 358–374.
5. O’Bryant S, Xiao G, Barber R, Reisch J, Hall J, et al. (2011) A blood based
algorithm for the detection of Alzheimer’s disease. Dementia and Geriatric
Cognitive Disorders 32: 55–62.
6. Ray S, Britschgi M, Herbert C, Takeda-Uchimura Y, Boxer A, et al. (2007)
Classification and prediction of clinical Alzheimer’s diagnosis based on plasma
signaling proteins. Nature Medicine 13: 1359–1362.
7. Soares HD, Chen Y, Sabbagh M, Rohrer A, Schrijvers E, et al. (2009)
Identifying early markers of alzheimer’s disease using quantitative multiplex
proteomic immunoassay panels. pp 56–67.
8. Buerger K, Ernst A, Ewers M, Uspenskaya O, Omerovic M, et al. (2009) Blood-
Based Microcirculation Markers in Alzheimer’s Disease-Diagnostic Value of
Midregional Pro-atrial Natriuretic Peptide/C-terminal Endothelin-1 Precursor
Fragment Ratio. Biological Psychiatry 65: 979–984.
9. Reddy MM, Wilson R, Wilson J, Connell S, Gocke A, et al. (2011) Identification
of candidate IgG biomarkers for alzheimer’s disease via combinatorial library
screening. Cell 144: 132–142.
10. Mayeux R, Honig LS, Tang MX, Manly J, Stern Y, et al. (2003) Plasma
A[beta]40 and A[beta]42 and Alzheimer’s disease: relation to age, mortality, and
risk. Neurology 61: 1185–1190.
11. Luis CA, Abdullah L, Paris D, Quadros A, Mullan M, et al. (2009) Serum b-
amyloid correlates with neuropsychological impairment. Aging, Neuropsychol-
ogy, and Cognition 16: 203–218.
12. Laske C, Stransky E, Leyhe T, Eschweiler GW, Wittorf A, et al. (2006) Stage-
dependent BDNF serum concentrations in Alzheimer’s disease. Journal of
Neural Transmission 113: 1217–1224.
13. O’Bryant SE, Hobson V, Hall JR, Waring SC, Chan W, et al. (2009) Brain-
Derived Neurotrophic Factor Levels in Alzheimer’s Disease. Journal of
Alzheimer’s Disease 17: 1051–1055.
14. O’Bryant SE, Hobson VL, Hall JR, Barber RC, Zhang S, et al. (2010) Serum
Brain-Derived Neurotrophic Factor Levels Are Specifically Associated with
Memory Performance among Alzheimer’s Disease Cases. Dementia and
Geriatric Cognitive Disorders 31: 31–36.
15. Komulainen P, Pedersen M, Hanninen T, Bruunsgaard H, Lakka TA, et al.
(2008) BDNF is a novel marker of cognitive function in ageing women: the DR’s
EXTRA Study. Neurobiology of Learning & Memory 90: 596–603.
16. Waring S, O’Bryant SE, Reisch JS, Diaz-Arrastia R, Knebl J, et al. (2008) , for
the Texas Alzheimer’s Research Consortium (2008) The Texas Alzheimer’s
Research Consortium longitudinal research cohort: Study design and baseline
characteristics. Texas Public Health Journal 60: 9–13.
17. McKhann D, Drockman D, Folstein M, et al. (1984) Clinical diagnosis of
Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group. Neurology
18. Vemuri P, Wiste HJ, Weigand SD, Shaw LM, Trojanowski JQ, et al. (2009)
MRI and CSF biomarkers in normal, MCI, and AD subjects: Diagnostic
discrimination and cognitive correlations. Neurology 73: 287–293.
19. R Development Core Team (2009) R: A language and environment for
statistical computing. Vienna, Austria.
20. Osborne J (2010) Improving your data transformations: Applying the Box-Cox
transformation. Practical Assessment Research Evaluation 15: 2.
21. Fraley CR, AE (2002) Model-based clustering, discriminat analysis, and density
estimation. Journal of the American Statistical Association 97: 611–631.
22. Breiman L (2001) Random forests. Machine Learning 45: 5–32.
23. Breiman L Manual on setting up, using, and understanding random forests
24. Anonymous (1998) Consensus report of the Working Group on: ‘‘Molecular and
Biochemical Markers of Alzheimer’s Disease’’. The Ronald and Nancy Reagan
Research Institute of the Alzheimer’s Association and the National Institute on
Aging Working Group.[see comment][erratum appears in Neurobiol Aging
1998 May–Jun;19(3):285]. Neurobiology of Aging 19: 109–116.
25. Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011) Multimodal classification of
Alzheimer’s disease and mild cognitive impairment. NeuroImage.
26. Brys M, Glodzik L, Mosconi L, Switalski R, De Santi S, et al. (2009) Magnetic
resonance imaging improves cerebrospinal fluid biomarkers in the early
detection of Alzheimer’s disease. Journal of Alzheimer’s Disease 16: 351–362.
27. Vemuri P, Gunter JL, Senjem ML, Whitwell JL, Kantarci K, et al. (2008)
Alzheimer’s disease diagnosis in individual subjects using structural MR images:
Validation studies. NeuroImage 39: 1186–1197.
28. O’Bryant SE, Waring SC, Hobson V, Hall JR, Moore CB, et al. (2010)
Decreased C-reactive protein levels in alzheimer disease. Journal of Geriatric
Psychiatry and Neurology 23: 49–53.
29. Gustafson DR (2010) Adiposity hormones and dementia. Journal of the
Neurological Sciences 299: 30–34.
30. Une K, Takei YA, Tomita N, Asamura T, Ohrui T, et al. (2011) Adiponectin in
plasma and cerebrospinal fluid in MCI and Alzheimer’s disease. European
Journal of Neurology 18: 1006–1009.
31. Cui Y, Andersen DK (2011) Pancreatogenic diabetes: Special considerations for
management. Pancreatology 11: 279–294.
32. Zhang L, Bijker MS, Herzog H (2011) The neuropeptide y system:
Pathophysiological and therapeutic implications in obesity and cancer.
Pharmacology and Therapeutics 131: 91–113.
33. Steinacker P, Mollenhauer B, Bibl M, Cepek L, Esselmann H, et al. (2004) Heart
fatty acid binding protein as a potential diagnostic marker for neurodegenerative
diseases. Neuroscience Letters 370: 36–39.
34. Teunissen CE, Veerhuis R, De Vente J, Verhey FRJ, Vreeling F, et al. (2011)
Brain-specific fatty acid-binding protein is elevated in serum of patients with
dementia-related diseases. European Journal of Neurology 18: 865–871.
35. Swardfager W, Lanctt K, Rothenburg L, Wong A, Cappell J, et al. (2010) A
meta-analysis of cytokines in Alzheimer’s disease. Biological Psychiatry 68:
36. Gejyo F, Yamada T, Odani S, Nakagawa Y, Arakawa M, et al. (1985) A new
form of amyloid protein associated with chronic hemodialysis was identified as
b2-microglobulin. Biochemical and Biophysical Research Communications 129:
37. Abdi F, Quinn JF, Jankovic J, McIntosh M, Leverenz JB, et al. (2006) Detection
of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal
fluid of patients with neurodegenerative disorders. Journal of Alzheimer’s
Disease 9: 293–348.
38. Zellner M, Veitinger M, Umlauf E (2009) The role of proteomics in dementia
and Alzheimer’s disease. Acta Neuropathologica 118: 181–195.
39. Midwood KS, Hussenet T, Langlois B, Orend G (2011) Advances in tenascin-C
biology. Cellular and Molecular Life Sciences 68: 3175–3199.
40. Hu WT, Chen-Plotkin A, Arnold SE, Grossman M, Clark CM, et al. (2010)
Novel CSF biomarkers for Alzheimer’s disease and mild cognitive impairment.
Acta Neuropathologica 119: 669–678.
41. Akiyama H, Ikeda K, Kondo H, McGeer PL (1992) Thrombin accumulation in
brains of patients with Alzheimer’s disease. Neuroscience Letters 146: 152–154.
42. Ewers M, Mielke MM, Hampel H (2010) Blood-based biomarkers of
microvascular pathology in Alzheimer’s disease. Experimental Gerontology
43. Sokolova A, Hill MD, Rahimi F, Warden LA, Halliday GM, et al. (2009)
Monocyte chemoattractant protein-1 plays a dominant role in the chronic
inflammation observed in alzheimer’s disease. Brain Pathology 19: 392–398.
44. Galimberti D, Fenoglio C, Lovati C, Venturelli E, Guidi I, et al. (2006) Serum
MCP-1 levels are increased in mild cognitive impairment and mild Alzheimer’s
disease. Neurobiology of Aging 27: 1763–1768.
45. Boustani M, Callahan CM, Unverzagt FW, Austrom MG, Perkins AJ, et al.
(2005) Implementing a screening and diagnosis program for dementia in
primary care. Journal of General Internal Medicine 20: 572–577.
46. Doody R, Ferris S, Salloway S, Meuser TM, Murthy A, et al. (in press) Inter-
rater reliability between expert and nonexpert physicians in the diagnosis of
amnestic MCI in the community setting. Clinical Drug Investigation.
47. O’Bryant SE, Humphreys JD, Smith GE, Ivnik RJ, Graff-Radford NR, et al.
(2008) Detecting dementia with the mini-mental state examination in highly
educated individuals. Archives of Neurology 65: 963–967.
A Blood Screening Tool for AD
PLoS ONE | www.plosone.org8 December 2011 | Volume 6 | Issue 12 | e28092