Cerebrospinal Fluid Biomarkers, Education, Brain Volume, and Future Cognition

Article (PDF Available)inArchives of neurology 68(9):1145-51 · September 2011with16 Reads
DOI: 10.1001/archneurol.2011.192 · Source: PubMed
Abstract
Cross-sectional studies suggest that the cognitive impact of Alzheimer disease pathology varies depending on education and brain size. To evaluate the combination of cerebrospinal fluid biomarkers of β-amyloid(42) (Aβ(42)), tau, and phosphorylated tau (ptau(181)) with education and normalized whole-brain volume (nWBV) to predict incident cognitive impairment. Longitudinal cohort study. Charles F. and Joanne Knight Alzheimer's Disease Research Center, Washington University, St Louis, Missouri. A convenience sample of 197 individuals 50 years and older with normal cognition (Clinical Dementia Rating of 0) at baseline observed for a mean of 3.3 years. Time to Clinical Dementia Rating ≥ 0.5. Three-factor interactions among the baseline biomarker values, education, and nWBV were found for Cox proportional hazards regression models testing tau (P = .02) and ptau (P = .008). In those with lower tau values, nWBV (hazard ratio [HR], 0.54; 95% confidence interval [CI], 0.31-0.91; P = .02), but not education, was related to time to cognitive impairment. For participants with higher tau values, education interacted with nWBV to predict incident impairment (P = .01). For individuals with lower ptau values, there was no effect of education or nWBV. Education interacted with nWBV to predict incident cognitive impairment in those with higher ptau values (P = .02). In individuals with normal cognition and higher levels of cerebrospinal fluid tau and ptau at baseline, time to incident cognitive impairment is moderated by education and brain volume as predicted by the cognitive/brain reserve hypothesis.
ORIGINAL CONTRIBUTION
Cerebrospinal Fluid Biomarkers, Education,
Brain Volume, and Future Cognition
Catherine M. Roe, PhD; Anne M. Fagan, PhD; Elizabeth A. Grant, PhD; Daniel S. Marcus, PhD;
Tammie L. S. Benzinger, MD, PhD; Mark A. Mintun, MD; David. M. Holtzman, MD; John C. Morris, MD
Background: Cross-sectional studies suggest that the
cognitive impact of Alzheimer disease pathology varies
depending on education and brain size.
Objective: To evaluate the combination of cerebrospi-
nal fluid biomarkers of -amyloid
42
(A
42
), tau, and phos-
phorylated tau (ptau
181
) with education and normalized
whole-brain volume (nWBV) to predict incident cogni-
tive impairment.
Design: Longitudinal cohort study.
Setting: Charles F. and Joanne Knight Alzheimer’s Dis-
ease Research Center, Washington University, St Louis,
Missouri.
Participants: A convenience sample of 197 individuals
50 years and older with normal cognition (Clinical Demen-
tia Rating of 0) at baseline observed for a mean of 3.3 years.
Main Outcome Measure: Time to Clinical Dementia
Rating0.5.
Results: Three-factor interactions among the baseline
biomarker values, education, and nWBV were found for
Cox proportional hazards regression models testing tau
(P=.02) and ptau (P=.008). In those with lower tau val-
ues, nWBV (hazard ratio [HR],0.54; 95% confidence in-
terval [CI], 0.31-0.91; P=.02), but not education, was re-
lated to time to cognitive impairment. For participants
with higher tau values, education interacted with nWBV
to predict incident impairment (P=.01). For individu-
als with lower ptau values, there was no effect of educa-
tion or nWBV. Education interacted with nWBV to pre-
dict incident cognitive impairment in those with higher
ptau values (P=.02).
Conclusion: In individuals with normal cognition and
higher levels of cerebrospinal fluid tau and ptau at base-
line, time to incident cognitive impairment is moder-
ated by education and brain volume as predicted by the
cognitive/brain reserve hypothesis.
Arch Neurol. 2011;68(9):1145-1152
L
OWER EDUCATIONAL ATTAIN-
ment and smaller brain or
head size have been fre-
quently studied as risk fac-
tors for Alzheimer disease
(AD).
1-4
Educational attainment is a proxy
measure of cognitive reserve: the efficient
use of brain networks or the ability to re-
cruit alternative brain networks or cogni-
tive strategies.
1,5
Brain size is thought to re-
flect brain reserve: the number and health
of neurons.
5-8
Greater amounts of both types
of reserve are thought to provide resis-
tance to brain damage caused by AD, de-
laying the time to cognitive impairment.
1,5-8
Cross-sectional studies suggest that
educational attainment
4,7,9-11
and brain
size
4,6,7
interact with AD abnormalities to
determine current cognitive functioning
such that the impact of a given amount of
AD pathology on cognition varies depend-
ing on one’s education and brain size.
However, until the recent advent of bio-
markers of AD pathology, it was impos-
sible to test whether education and brain
size modify the association between AD pa-
thology in cognitively normal individu-
als with the later development of cogni-
tive impairment. The cerebrospinal fluid
(CSF) biomarkers of -amyloid
42
(A
42
),
the primary component of amyloid
plaques, are decreased in individuals with
AD, whereas levels of tau and phosphory-
lated tau (ptau
181
), the primary compo-
nents of neurofibrillary tangles, are in-
creased in AD.
12
Abnormal levels of these
biomarkers have also been found in cog-
nitively normal individuals and are pre-
dictive of later cognitive impairment.
13-15
We tested how the CSF biomarkers of
A
42
, tau, and ptau combine with educa-
tion and brain volume to predict incident
cognitive impairment in individuals with
normal cognition at baseline.
Author Affiliations: Knight
Alzheimer’s Disease Research
Center (Drs Roe, Fagan, Grant,
Marcus, Benzinger, Mintun,
Holtzman, and Morris),
Departments of Neurology
(Drs Roe, Fagan, Holtzman, and
Morris), Radiology
(Drs Marcus, Benzinger, and
Mintun), Pathology and
Immunology (Dr Morris),
Physical Therapy (Dr Morris),
and Occupational Therapy
(Dr Morris), Hope Center for
Neurological Disorders
(Dr Fagan), and Division of
Biostatistics (Dr Grant),
Washington University School
of Medicine, St Louis, Missouri.
Dr Mintun is now with Avid
Radiopharmaceuticals,
Philadelphia, Pennsylvania.
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METHODS
PARTICIPANTS
Data were collected prospectively from participants enrolled
in longitudinal studies at the Charles F. and Joanne Knight Alz-
heimer’s Disease Research Center, Washington University, St
Louis, Missouri. Study protocols were approved by the Wash-
ington University Medical Center Human Subjects Commit-
tee, and written informed consent was obtained from all the
participants. Detailed information on recruitment and assess-
ment procedures are available.
16
In brief, participants in these
studies are recruited through word of mouth, advertisements,
and community events from the greater St Louis area for yearly
assessment sessions. Individuals with health conditions, such
as metastatic cancer, that may interfere with longitudinal fol-
low-up are excluded from participation.
CLINICAL ASSESSMENT, CSF,
AND BRAIN VOLUME MEASUREMENT
At the initial and each annual assessment thereafter, partici-
pants underwent neurologic and physical examinations and were
accompanied by a collateral source who knows the participant
well. Experienced clinicians obtain health and medication his-
tories and conduct semistructured interviews with the partici-
pant and collateral source separately. The clinicians use the in-
formation obtained from these interviews to generate a Clinical
Dementia Rating (CDR)
17-19
reflecting the presence or absence
of dementia. The global CDR is based on a standard scoring al-
gorithm that integrates functioning in 6 individual domains:
memory, orientation, judgment and problem solving, commu-
nity affairs, home and hobbies, and personal care. The CDR Sum
of Boxes (CDR-SB) score is obtained by summing the scores from
the 6 domains.
18
The CDR has established reliability.
20,21
Global
CDR scores indicate the following: 0, normal cognition; 1, mild
dementia; 2, moderate dementia; and 3, severe dementia. A CDR
score of 0.5 designates “uncertain dementia” if the etiology of
the cognitive impairment cannot be determined or very mild
dementia if on clinical grounds an etiologic diagnosis can be
made.
Participants with cognitive impairment at the CDR 0.5 stage
can be diagnosed as having very mild dementia of the Alzhei-
mer type (DAT) when there is a history of gradual onset and
progression of cognitive problems that represent a decline from
that individual’s previous level of cognitive function and in-
terfere to at least some degree with usual activities at home and
in the community. We demonstrated that the CDR 0.5/DAT
participants have progressive cognitive deterioration typical of
DAT and, of those coming to autopsy, AD is confirmed in 92%.
22
Moreover, it is well recognized that some individuals rated as
having a CDR score of 0.5 can merit a DAT diagnosis.
23
To obtain CSF from participants, trained neurologists use
a 22-gauge Sprotte spinal needle to collect 20 to 30 mL of CSF
at 8
AM, after an overnight fast. The CSF samples are gently in-
verted and centrifuged at low speed to avoid possible gradient
effects and then are frozen at −84°C
24
after aliquoting into
polypropylene tubes. The CSF samples for A
42
, tau, and ptau
are analyzed using enzyme-linked immunosorbent assay
(INNOTEST; Innogenetics, Ghent, Belgium).
Normalized whole-brain volume (nWBV), reflecting the per-
centage of the intracranial cavity occupied by brain, was ob-
tained using previously established methods.
25
Briefly, the mag-
netization-prepared, rapid-acquisition gradient-echo data were
intensity normalized.
26
A validated segmentation tool was then
used to classify brain tissue as CSF, gray matter, or white mat-
ter.
27,28
Correction of intensity inhomogeneity was accom-
plished by an automated procedure to minimize intensity varia-
tion in contiguous regions. Based on intensity limits and contour
(intensity gradient) detection, contiguous region boundaries
were identified (without brain masking). The bias field was mod-
eled as a general second-order polynomial in 3 dimensions (10
free variables).
26
Segmentation began with an initial estima-
tion step to obtain and classify tissue variables. Using a 3-step
expectation-maximization algorithm, class labels and tissue vari-
ables were then updated to iterate toward the maximum like-
lihood estimates of a hidden Markov random field model. This
model used spatial proximity to constrain the probability with
which voxels of a given intensity are assigned to each tissue
class. Finally, the brain volume estimate was taken as the sum
of white and gray matter voxels in the atlas-based brain mask
and expressed as a percentage of the mask.
INCLUSION CRITERIA
Archival data were used from participants who (1) donated CSF
between June 18, 1998, and May 18, 2009; (2) were 50 years or
older at the time of donation; (3) had normal cognition (CDR=0)
at the closest clinical assessment within 1 year before or 1 month
after donation; (4) underwent magnetic resonance imaging with
measurement of brain volume within 1 year of donation; and (5)
had at least 1 subsequent clinical assessment.
STATISTICAL ANALYSES
Cox proportional hazards regression models were used to test
the 3-factor interaction of each of the biomarker variables (A
42
,
tau, and ptau) with education in years and nWBV in deter-
mining time from baseline assessment to cognitive impair-
ment (ie, CDR 0). All the predictor variables were treated as
continuous.
For models in which the 3-factor interaction was signifi-
cant, Cox proportional hazards regression models were con-
ducted separately for individuals with biomarker values above
and below the median; the 2-factor interaction between educa-
tion and nWBV was tested in these models. For models in which
the 3-factor interaction was not significant, the models were re-
peated testing 2-factor interactions among the biomarker, edu-
cation, and nWBV variables. If no 2-factor interactions were sig-
nificant, the final model comprised the main effects of each
variable. All the models included terms adjusting for and simul-
taneously testing the effects of sex, age, race, the presence of an
apolipoprotein E ε4(APOE ε4) allele, and the magnetic reso-
nance imaging scanner used.
To graphically display significant interaction effects, the bio-
marker, education, and nWBV variables were each dichoto-
mized, reflecting lower and higher values on the variable, using
a median split, and Kaplan-Meier survival curves were gener-
ated for each combination of these variables.
We also explored whether there were differences in the slope
of scores across follow-up as a function of these 8 possible com-
binations of higher and lower values of the biomarker, educa-
tion, and nWBV variables. In these analyses, mixed-effects lin-
ear models tested whether the slope of scores on the CDR-SB,
Mini-Mental State Examination (MMSE),
29
and Short Blessed Test
30
differed as a function of the combination variable while adjust-
ing for sex, age, race, and the presence of an APOE ε4 allele.
RESULTS
One hundred ninety-seven participants observed for a
mean (SD) of 3.3 (2.0) years met the inclusion criteria
(
Table 1). Of these participants, 26 developed cogni-
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tive impairment a mean (SD) of 3.01 (1.93) years after
baseline. Table 2 gives the clinical diagnoses assigned
at the time of first CDR0. We consider individuals who
received a DAT diagnosis to meet the “formal” criteria
for very mild dementia, although we acknowledge that
because the boundaries for mild cognitive impairment
and dementia overlap, others may classify these indi-
viduals as having mild cognitive impairment. At the time
of first CDR=0.5, individuals with a DAT diagnosis had
greater impairment than did those with an uncertain di-
agnosis, as reflected in worse mean performance on an
autobiographical memory test
31
(1.25 vs 1.68, P= .04) and
in higher mean CDR-SB scores (1.94 vs 0.85, P=.02).
In the survival models testing A
42
, there were no in-
teractions with education or nWBV. The final model in-
dicated that a larger nWBV was associated with a slower
time to cognitive impairment (hazard ratio [HR], 0.81;
95% confidence interval [CI], 0.68-0.97; P=.02), but there
was no effect of A
42
(P=.24) or education (P=.07).
Three-factor interactions among the biomarker val-
ues, education, and nWBV were found for models test-
ing tau (P=.02) and ptau (P = .008). In participants with
baseline tau values below the median, nWBV (HR, 0.54;
95% CI, 0.31-0.91; P=.02), but not education (P .99),
was related to time to cognitive impairment, and there
was no interaction between these variables (P =.39)
(
Figure 1A). Of the 8 individuals with lower tau val-
ues who developed cognitive impairment (all of whom
had nWBV values below the median nWBV), only 2 (25%)
received a subsequent diagnosis of DAT at some time dur-
ing follow-up. The remaining 6 participants had diag-
noses of uncertain dementia (n=5; 3 of these with a sec-
ondary diagnosis of mood disorder) or vascular dementia
with a secondary diagnosis of Parkinson disease (n=1).
In contrast, 8 of 15 participants (53%) with smaller
nWBVs but higher tau values received DAT diagnoses at
some point during follow-up. For those with tau values
above the median, education interacted with nWBV to
predict incident impairment (P=.01) (Figure 1B).
For individuals with lower ptau values, there was no
effect of education (P=.89) or nWBV (P=.14) and no in-
teraction between them (P=.94) (Figure 1C). However,
education and nWBV interacted to predict incident cog-
nitive impairment in those with higher ptau values
(P=.02) (Figure 1D).
Other variables that independently predicted time to
impaired cognition were minority race (P.007), which
was associated with a faster time to impairment in each
of the biomarker models, and male sex (P = .04), which
was associated with more rapid cognitive impairment in
the model including tau. There was no relationship be-
tween age, APOE ε4 level, or scanner type and incident
impairment after adjustment for other variables in the
model.
In the mixed-model analyses testing the 8 possible com-
binations of higher and lower values of the biomarker,
education, and nWBV variables, the slope of scores on
the CDR-SB differed as a function of the “combination”
variable for analyses testing A
42
, tau, and ptau (P .001
for all) and on the Short Blessed Test for the analysis test-
ing tau (P=.02). As shown in
Figure 2, the significant
results generally confirm those found using CDR0as
the end point. The slopes of scores on the MMSE did not
differ across the combination variable levels.
COMMENT
Accumulating evidence suggests that the presence of AD
biomarkers in cognitively normal persons is a harbinger
of eventual cognitive impairment,
13-15
and much cur-
rent effort is devoted to developing therapies that can halt
the disease process. When these therapies are ready for
use, it is thought that they may be most effective if ad-
ministered at the time that biomarkers show abnormal
values but before dementia symptoms occur.
15
How-
ever, because biomarker levels may become abnormal a
decade or more before clinical symptoms appear,
32
it is
vital to understand the time course between abnormal
biomarker values, the onset of cognitive impairment, and
characteristics that affect that time course to avoid ex-
posing healthy individuals to medications and their po-
tential adverse effects many years before they are needed.
The present results indicate that in individuals with
higher levels of CSF tau and ptau but normal cognition
at baseline, the time to incident cognitive impairment is
moderated by education and brain volume. More edu-
cation and larger nWBV seem to slow the rate of impair-
ment onset in the presence of tau-related abnormalities,
whereas individuals with lower levels of education and
smaller nWBV values have the most rapid onset. As theo-
rized by other researchers, more education may provide
resistance to dementia in the presence of brain damage
because more education may be associated with the use
of particular cognitive processing approaches or enlist-
ment of compensatory processes or may serve as a proxy
for another factor, such as innate intelligence.
5
Individu-
als with larger nWBVs may have sufficient neuronal re-
sources to continue normal functioning in the presence
of AD pathology for a longer time,
33
or these individuals
Table 1. Baseline Demographics of the 197 Study
Participants
Characteristic Value
Age, mean (SD), y 68.6 (9.0)
Women, No. (%) 128 (65.0)
Minority race, No. (%) 16 (8.1)
Education, mean (SD), y 15.7 (2.9)
APOE genotype, No. (%)
22 2 (1.0)
23 26 (13.2)
24 9 (4.6)
33 98 (49.8)
34 55 (27.9)
44 7 (3.6)
nWBV, mean (SD), % of intracranial volume 77.7 (3.4)
MMSE score, mean (SD) 29.0 (1.3)
A
42,
mean (SD), pg/mL 616.8 (251.3)
Tau, mean (SD), pg/mL 304.1 (161.4)
Ptau, mean (SD), pg/mL 55.9 (24.7)
Follow-up, mean (SD), y 3.3 (2.0)
Abbreviations: A
42
, -amyloid
42
; APOE, apolipoprotein E; MMSE,
Mini-Mental State Examination; nWBV, normalized whole-brain volume;
ptau, phosphorylated tau.
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may have experienced less neuronal neurodegeneration
despite having similar abnormal biomarker levels as other
individuals. Education and nWBV do not interact to pre-
dict future cognitive impairment when lower levels of
brain tau and ptau are present.
Previously, cross-sectional autopsy studies
34,35
that in-
clude individuals with dementia and those with normal
cognition before death have suggested that education and
brain volume interact with AD pathology to predict con-
current cognitive performance. In these studies,
34,35
edu-
cation was found to interact with amyloid plaque, but not
tangle, pathology. In the present study, conducted only
with individuals who were cognitively normal at base-
line, we found a modifying effect of education and nWBV
on incident cognitive impairment for tau-based but not
amyloid-based pathology. In fact, the main effect of A
42
itself was not significant in the primary multivariate analy-
ses. This is consistent with the previous finding, using a
smaller subsample of these individuals, of only a margin-
ally significant effect of A
42
on incident AD when edu-
cation and nWBV were included in the same model
(P= .09).
36
However, A
42
combined with education and
nWBV to predict the slope of CDR-SB scores across follow-
up, suggesting that A
42
interacts with education and nWBV
in a manner similar to that exhibited by tau and ptau, al-
though, as shown in Figure 2, the effect is less dramatic.
With longer follow-up or a larger sample size, it is pos-
sible that a significant 3-way interaction effect among A
42
,
education, and nWBV would be found using the end point
of CDR0. The categorical variable reflecting combined
levels of the biomarkers, education, and nWBV was un-
related to the slope of scores on the MMSE. As pointed
out by others,
37
the MMSE may be less sensitive to cog-
nitive decline compared with global dementia severity mea-
sures, such as the CDR-SB and the Short Blessed Test.
The nWBV was found to be associated with incident
cognitive impairment even in individuals with tau lev-
els below the baseline median. Brain volume decline, in
addition to occurring as a consequence of neuron loss
in AD, also occurs as a function of normal aging.
38
Al-
though based on a small sample, individuals with smaller
nWBVs and lower tau levels who developed cognitive im-
pairment were less likely to receive DAT diagnoses as an
explanation for their cognitive problems compared with
individuals with smaller nWBVs and higher tau values.
This suggests that individuals with smaller nWBV val-
ues may be more vulnerable to cognitive impairment due
to reasons other than underlying AD. However, this in-
terpretation should be viewed with caution because the
effect of nWBV was not significant when examined in the
presence of low ptau levels.
Relatedly, we found no effect of age on incident im-
pairment in the multivariate models. As previously noted,
age and nWBV are tightly correlated in the partici-
pants.
36
Thus, when one variable is present in the model,
the other adds little additional predictive power.
No significant effects of APOE ε4 status were noted
when considered together with the CSF biomarkers in
Table 2. Clinical Diagnoses at the Time of First CDR 0 for Participants Who Progressed
Participant
No. Dx 1 Dx 2 Dx 3 Dx 4 Dx 5
1 DAT NA NA NA NA
2 DAT NA NA NA NA
3 DAT NA NA NA NA
4 DAT NA NA NA NA
5 DAT NA NA NA NA
6 DAT NA NA NA NA
7 DAT NA NA NA NA
8 DAT NA NA NA NA
9 Uncertain dementia NA NA NA NA
10 Uncertain dementia NA NA NA NA
11 Uncertain dementia NA NA NA NA
12 Uncertain dementia NA NA NA NA
13 Uncertain dementia NA NA NA NA
14 Uncertain dementia NA NA NA NA
15 Uncertain dementia NA NA NA NA
16 Uncertain dementia NA NA NA NA
17 Uncertain dementia NA NA NA NA
18 Uncertain dementia Mood disorder NA NA NA
19 Uncertain dementia Mood disorder NA NA NA
20 Uncertain dementia Mood disorder NA NA NA
21 Uncertain dementia Mood disorder NA NA NA
22 Uncertain dementia Mood disorder NA NA NA
23 Uncertain dementia Global cerebral
hypoperfusion
NA NA NA
24 Uncertain dementia ADHD NA NA NA
25 Uncertain dementia Alcoholism ADHD Anxiety disorder Sleep disorder
26 Vascular dementia Cerebrovascular
disease
NA NA NA
Abbreviations: ADHD, attention-deficit/hyperactivity disorder; CDR, Clinical Dementia Rating; DAT, dementia of the Alzheimer type; Dx, diagnosis; NA, not
applicable.
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1.0
0.8
0.6
0.4
0.2
0.0
Survival Distribution Function
Time to CDR > 0, y
A
1 2 3 4 5 6 7 8 9 10 11 120
Education lower, nWBV lower
Education lower, nWBV higher
Education higher, nWBV lower
Education higher, nWBV higher
1.0
0.8
0.6
0.4
0.2
0.0
Survival Distribution Function
Time to CDR > 0, y
C
1 2 3 4 5 6 7 8 9 10 11 120
1.0
0.8
0.6
0.4
0.2
0.0
Survival Distribution Function
Time to CDR > 0, y
B
1 2 3 4 5 6 7 8 9 10 11 120
1.0
0.8
0.6
0.4
0.2
0.0
Survival Distribution Function
Time to CDR > 0, y
D
1 2 3 4 5 6 7 8 9 10 11 120
Figure 1. Kaplan-Meier curves illustrating the 3-factor interactions among education, normalized whole-brain volume (nWBV), and the cerebrospinal biomarkers
of tau and phosphorylated tau (ptau) in subsamples with tau values below (A) and above (B) the median (263.0 pg/mL) and in subsamples with ptau values below
(C) and above (D) the median (48.9 pg/mL). CDR indicates Clinical Dementia Rating.
6
5
4
3
2
1
0
–1
CDR Sum of Boxes
Years From Baseline Visit
A
1 2 3 4 5 6 7 8 9 10 110
6
5
4
3
2
1
0
–1
CDR Sum of Boxes
Years From Baseline Visit
B
1 2 3 4 5 6 7 8 9 10 110
6
5
4
3
2
1
0
–1
CDR Sum of Boxes
Years From Baseline Visit
C
1 2 3 4 5 6 7 8 9 10 110
6
5
4
3
2
1
0
–1
CDR Sum of Boxes
Years From Baseline Visit
D
1 2 3 4 5 6 7 8 9 10 110
Education lower, nWBV lower
Education lower, nWBV higher
Education higher, nWBV lower
Education higher, nWBV higher
6
5
4
3
2
1
0
–1
CDR Sum of Boxes
Years From Baseline Visit
E
1 2 3 4 5 6 7 8 9 10 110
6
5
4
3
2
1
0
–1
CDR Sum of Boxes
Years From Baseline Visit
F
1 2 3 4 5 6 7 8 9 10 110
14
12
10
8
6
4
2
0
–2
Short Blessed Test
Years From Baseline Visit
G
1 2 3 4 5 6 7 8 9 10 110
14
12
10
8
6
4
2
0
–2
Short Blessed Test
Years From Baseline Visit
H
1 2 3 4 5 6 7 8 9 10 110
Figure 2. Mean slopes of global Clinical Dementia Rating (CDR) scores for combinations of higher and lower values of the biomarker, education, and normalized
whole-brain volume (nWBV) variables for significant mixed-model analyses in subsamples with -amyloid
42
values below (A) and above (B) the median (581.0
pg/mL), in subsamples with tau values below (C) and above (D) the median (263.0 pg/mL), in subsamples with phosphorylated tau values below (E) and above (F)
the median (48.9 pg/mL), and in subsamples with tau values below (G) and above (H) the median (263.0 pg/mL).
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predicting incident cognitive impairment. This result is
similar to the previous finding that APOE4 did not in-
crease the predictive accuracy of CSF biomarker models
for the development of incident AD.
36
That study also dem-
onstrated that APOE ε4 was helpful in distinguishing
prevalent AD from normal cognition.
36
It is possible that
the APOE genotype, when tested together with CSF bio-
markers, might show independent effects on incident cog-
nitive impairment in studies using a larger sample size
or longer follow-up.
Limitations of this study include the use of a conve-
nience sample and a relatively short mean follow-up of
3.3 years. Given these limitations, these results provide
strong support for the brain and cognitive reserve hy-
potheses
1,5-8
and suggest that education and nWBV are
influential in mediating the time to cognitive impair-
ment when tau-based pathology is present.
Accepted for Publication: February 16, 2011.
Correspondence: Catherine M. Roe, PhD, Washington
University School of Medicine, 660 S Euclid Ave, Cam-
pus Box 8111, St Louis, MO 63110 (cathyr@wustl.edu).
Author Contributions: All authors had full access to all
the data in the study and take responsibility for the in-
tegrity of the data and the accuracy of the data analysis.
Study concept and design: Roe. Acquisition of data: Fagan,
Grant, Benzinger, Mintun, and Morris. Analysis and in-
terpretation of data: Roe, Marcus, and Holtzman. Draft-
ing of the manuscript: Roe and Marcus. Critical revision of
the manuscript for important intellectual content: Roe, Fa-
gan, Grant, Benzinger, Mintun, Holtzman, and Morris.
Statistical analysis: Roe. Obtained funding: Holtzman and
Morris. Administrative, technical, and material support: Fa-
gan, Grant, Marcus, Benzinger, Mintun, Holtzman, and
Morris. Study supervision: Benzinger and Morris.
Financial Disclosure: Dr Benzinger has served as a con-
sultant to Biomedical Systems Inc and for ICON Medical
Imaging and has received research funding from Avid Ra-
diopharmaceuticals. Dr Holtzman is on the scientific ad-
visory boards of Satori, En Vivo, and C2N Diagnostics
and has consulted for Pfizer, Bristol-Myers Squibb, and
Innogenetics. Dr Morris has participated or is currently
participating in clinical trials of antidementia drugs spon-
sored by Elan, Eli Lilly & Co, and Wyeth and has served
as a consultant for or has received speaking honoraria
from AstraZeneca, Bristol-Myers Squibb, Eisai, Elan/
Janssen Alzheimer Immunotherapy Program, Genen-
tech, Eli Lilly & Co, Merck, Novartis, Otsuka Pharma-
ceuticals, Pfizer/Wyeth, and Schering-Plough.
Funding/Support: This work was supported by grant
P30 NS057105 from the National Institute of Neurologi-
cal Disorders and Stroke; grants P50 AG005681, P01
AG003991, and P01 AG026276 from the National Insti-
tute on Aging; grants 1UL1RR024992 from the National
Center for Research Resources; and the Charles F. and
Joanne Knight Alzheimer’s Research Initiative of the
Washington University Alzheimer Disease Research
Center.
Additional Contributions: We thank the participants, in-
vestigators, and staff of the Alzheimer Disease Research
Center Clinical (participant assessments) and Genetics
(genotyping) Cores and the investigators and staff of the
Biomarker Core for the Adult Children Study (P01
AG026276) for CSF analytes.
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    • "The present study reports on individuals who were primarily middleaged at baseline (mean age, 56.9 years) and have been followed for up to 17 years (mean, 8 years). Third, previous longitudinal studies (Roe et al., 2011a; Yaffe et al., 2011) have used education as a proxy for CR, although education is static and unlikely to change after early adulthood. The present study used a composite measure of CR based on not only education but also literacy and vocabulary, which may change over the lifetime and be a better reflection of CR (Manly et al., 2003, 2005). "
    [Show abstract] [Hide abstract] ABSTRACT: The pathophysiological processes underlying Alzheimer's disease (AD) are hypothesized to begin years to decades before clinical symptom onset, while individuals are still cognitively normal. Although many studies have examined the effect of biomarkers of amyloid pathology on measures of cognitive performance, less is known about the effect of tau pathology on cognitive performance. The present study examined the association between cerebrospinal fluid (CSF) biomarkers of AD pathology (amyloid, total tau (t-tau), and phosphorylated tau (p-tau)) and cognition in a large sample of cognitively normal middle-aged and older adults. Associations were examined with multivariate regressions, in which either amyloid and t-tau or amyloid and p-tau were included as simultaneous predictors of cognitive performance. Cognitive performance was measured with three composite scores assessing working memory, verbal episodic memory, and visuospatial episodic memory. In their respective models, CSF measures of both t-tau and p-tau were associated with the visuospatial episodic memory composite score (p < .001 and p=.02, respectively), but not with the other measures of cognition. In contrast, CSF amyloid was not significantly associated with cognitive performance, raising the possibility that measures of tau pathology have a more direct relationship with cognition in cognitively normal individuals. These results also suggest that tau pathology may have effects on visuospatial episodic memory during preclinical AD that precede alterations in other cognitive domains.
    Full-text · Article · Sep 2015
    • "Additionally, the availability of known biological markers of dementia risk (decreased CSF abeta42, presence of an APOE e4 allele, reduced MRI hippocampal volume and increased uptake of the PET amyloid ligand Pittsburgh compound B) has enabled putative mechanisms of action of cognitive reserve to be explored. Longitudinal studies have found that more years of education and higher premorbid IQ are associated with a later onset of dementia symptoms212223 and, following onset, cognitive decline is faster in those with these indices of higher cognitive reserve [22] . The latter phenomenon has been hypothesised to reflect increasing neuropathological load eventually overriding the protective effect of cognitive reserve. "
    [Show abstract] [Hide abstract] ABSTRACT: Cognitive reserve is used to explain individual differences in the use of active processes to preserve cognitive function in the presence of brain pathology. Cognitive reserve is difficult to quantify experimentally and studies rely largely on the use of proxy measures such as premorbid IQ, education and occupation. Nevertheless, powerful longitudinal study designs suggest that premorbid IQ modifies the neurodevelopmental process in schizophrenia and modulates the impact of neurodegeneration in dementia. Evidence from intelligence research suggests that dysfunction of a fronto-parietal network has explanatory power for the effect of cognitive reserve in both disorders.
    Full-text · Article · May 2015
    • "We also included a main effect of gender to correct for gender differences between groups. Because education level may influence studies of aging by affecting levels of cognitive reserve (Roe et al., 2011), we also evaluated education as a potential confound for each measure. "
    [Show abstract] [Hide abstract] ABSTRACT: Graph theory models can produce simple, biologically informative metrics of the topology of resting-state functional connectivity (FC) networks. However, typical graph theory approaches model FC relationships between regions (nodes) as unweighted edges, complicating their interpretability in studies of disease or aging. We extended existing techniques and constructed fully connected weighted graphs for groups of age-matched human immunodeficiency virus (HIV) positive (n = 67) and HIV negative (n = 77) individuals. We compared test-retest reliability of weighted versus unweighted metrics in an independent study of healthy individuals (n = 22) and found weighted measures to be more stable. We quantified 2 measures of node centrality (closeness centrality and eigenvector centrality) to capture the relative importance of individual nodes. We also quantified 1 measure of graph entropy (diversity) to measure the variability in connection strength (edge weights) at each node. HIV was primarily associated with differences in measures of centrality, and age was primarily associated with differences in diversity. HIV and age were associated with divergent measures when evaluated at the whole graph level, within individual functional networks, and at the level of individual nodes. Graph models may allow us to distinguish previously indistinguishable effects related to HIV and age on FC.
    Full-text · Article · Jun 2014
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