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Rates of cognitive decline using logitudinal neuropsychological measures in Alzheimer's disease

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Rates of cognitive decline using logitudinal neuropsychological measures in Alzheimer's disease

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Content RATES OF COGNITIVE DECLINE USING LOGITUDINAL
NEUROPSYCHOLOGICAL MEASURES IN
ALZHEIMER’S DISEASE
by
Jie Cai
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOMETRY / EPIDEMIOLOGY)
May 2003
Copyright 2003
Jie Cai
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UNIVERSITY OF SOUTHERN CALIFORNIA
THE GRADUATE SCHOOL
UNIVERSITY PARK
LOS ANGELES, CALIFORNIA 90089-1695
This thesis, written by
Jie Cai
under the direction o f h thesis committee, and
approved by all its members, has been presented to and
accepted by the Director o f Graduate and Professional
Programs, in partial fulfillment o f the requirements fo r the
degree o f
Master of Science
Director
Date May 1 6 . 2003
Thesis Committee
Chair
_ _
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ACKNOWLEDGMENTS
I would like to express my great gratitude to Dr. Wendy Mack, the committee
chair of the Master thesis, for her helpful instructions, suggestions and editing
throughout the course of my research and in the preparation of this manuscript. I am
also grateful for Dr. Stanley Azen and Dr. Carol McCleary for their valuable
suggestions on the analysis of the data and the editing of the manuscript. Thanks also
go to Lin Zheng and Min Xiang for their helpful discussions and suggestions.
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TABLE OF CONTENTS
ACKNOWLEDGMENTS..................... ii
LIST OF TABLES.................................... iv
ABSTRACT....................................................................................... v
INTRODUCTION .................................................................................................. ..1
METHODS ............................................................................ 5
Subjects .............. 5
Assessment of Cognitive Functions.................... 6
Data Analysis.................. 8
RESULTS................................................................... 11
Sample Characteristics .......................................... 11
Change in MMSE ....... 11
Change in the Overall Composite Cognitive Score............................... 15
Change in Specific Cognitive Domains.................................................................17
Effects of Initial CDR Scores on the Cognitive Changes...................... ..22
DISCUSSION................. 23
REFERENCES............................ 28
iii
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LIST OF TABLES
Table 1. Calculation of summary measures............................. 7
Table 2. Sample characteristics at baseline. ............... 12
Table 3. MMSE ............... 13
Table 4. Composite score. ....... 16
Table 5. Memory. ......................... 18
Table 6. Language................... ..19
Table 7. Visuospatital........................................ 20
Table 8. Executive ............... .21
iv
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ABSTRACT
Longitudinal changes of global and specific cognitive functioning in
Alzheimer’s disease were studied in a sample of 124 patients, with 739 observations
and 3.4 years of average follow-up duration. Average annual decline was 2.70 points
on the Mini-Mental State Examination (p<0.01) and 0.44 score units on a composite
measure based on 11 individual tests (p<0.01). Among different cognitive domains,
age and initial CDR scores showed different modification on the rate of cognitive
decline. On two global measures and language-specific measure, rate of cognitive
deterioration was reduced in older persons compared with younger ones, and was
increased in persons with CDR > 1 at study entry compared with others. The above
effects of age and initial CDR scores were not attributable to the difference in
education and gender.
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INTRODUCTION
One characteristic feature of Alzheimer Disease (AD) is the progressive
deterioration of cognitive functions, and this is often indexed by measures of global
cognitive status such as the Mini-Mental State Examination (MMSE). However, the
rate of cognitive loss varies significantly among individuals with AD. Each patient’s
rates of decline and time of onset of difficulties within specific cognitive domains
such as memory, verbal, and visuospatial functions can be substantially different
(Chui et al, 1985; Filley et al., 1986; Seines et al., 1988; Seltzer et al., 1983). The
mechanism and contributing factors for these individual variations remain basically
unclear. Some studies evaluated this variability in relation to certain demographic
factors, but the results were inconsistent and inconclusive. For example, evaluation
of the effects of age on the rate of cognitive loss generated very different
conclusions, with some studies reporting faster decline in younger ages (Wilson et
al., 2000; Jacobs et al., 1994; Lucca et al., 1993; Mortimer et al., 1993), some
reporting faster decline in older ages (Huff et al., 1987), some reporting no
association between rate of decline and age (Boiler et al., 1991; Bums et al., 1991;
Frisoni et al., 1995; Haupt et al., 1993; Katzman et al., 1988; Stem et al., 1992; Stem
et al., 1994; Stem et al., 1994; Teri et al., 1990; Wolfe et al., 1995), and others
reporting mixed results(Teri et al., 1995; Ortof et al., 1989). The results of studies
evaluating the effects of other demographic factors on the decline rate, such as
gender, education, or occupation are similarly mixed.
l
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From a statistical point of view, several factors may contribute to the above
inconsistencies:
(1) Sample sizes. In some longitudinal studies, the sample sizes were as small
as 11 or 14 subjects (Grady et al., 1988; Hodges et al., 1990), resulting in
very limited power to detect any true difference in rate changes.
(2) Follow-up schedule and time. The natural history of cognitive decline in
AD can last for decades. Thus, any study with very short follow-up
duration may not be able to convincingly estimate the cognitive decline
rate (Morris et al., 1989; Van Belle et al., 1990).
(3) A high percentage of loss of subjects to follow-up may cause the study to
be selectively biased because the cognitive and demographic
characteristics of “lost to follow-up” subjects may differ significantly
from those of the “return for follow-up” subjects.
(4) Base/source population. Community (population) based samples and
clinical based samples can have very different features, so the rates of
decline based on those different samples may be quite different.
(5) Measures selected. Global measures like MMSE can only give the
general tendency of cognitive decline and do not represent decline within
specific cognitive domains. In addition, specific measures maybe subject
to floor and ceiling effects because of the wide and broad range of
cognitive functions and decline in AD (Kukull 1998).
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(6) Analysis methods. Many of the published studies treated the time factor
and initial level of the cognitive measures as fixed rather than random
effects. This method can limit the generalizability of the results. Some
studies even used cross-sectional data to infer rates of cognitive declines,
which can introduce additional source of variability and inconsistency.
The present study was designed to investigate the longitudinal changes of
cognitive functions in AD patients, using data collected by the AD Research Center
at the University of Southern California (USC-ADRC) from 1985 to 2000 as part of
an ongoing longitudinal study of aging and dementia. The study sample consisted of
124 subjects and 739 observations. The average follow up time was 3.4 years and the
average number of follow-up evaluations was 5. Specifically, the current analyses
focused on two main research questions:
(1) Using a global measure of cognition, does the rate of cognitive decline
vary as a function of inter-subject differences in certain demographic and
psychometric variables, such as age, gender, education, and initial
Clinical Dementia Rating (CDR) scores?
(2) Is there evidence for differential changes in specific cognitive domains
including memory, language, visuospatial, and executive functioning?
To minimize the floor and ceiling effects and utilize the complete
neuropsychological data effectively, we constructed a composite score as the
outcome global cognitive measure, formed by averaging the normalized z-scores
from 11 specific cognitive measures. We used the mixed effect regression models to
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evaluate the relation between the outcome cognitive measures and the independent
variables, with the follow-up time and initial cognitive level being treated as random
effects and the other variables (age, gender, education and initial CDR scores)
treated as fixed effects. Similarly, we constructed a series of domain-specific
summary measures to describe the declines in domain-specific functions and their
relation to the predictors.
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METHODS
Subjects
Subjects in this study were part of an ongoing longitudinal study of aging and
dementia at the USC-ADRC. At the time of study entry, each person received a
standard evaluation including medical history, neurological examination, laboratory
tests, and neuropsychological testing. The eligibility criteria for the longitudinal
study were as follows:
(1) Age 40 or older at study entry.
(2) Informed consent signed by patients and family members.
(3) Residence in Los Angeles area.
(4) No other concurrent pathological conditions such as stroke, HIV
dementia existed, which can contribute to significant cognitive decline.
The requirement for inclusion in the current analysis were:
(1) A clinical diagnosis of Alzheimer’s Disease using the criteria of the joint
working group of the National Institute of Neurological and
Communicative Disorders and Stroke and the Alzheimer’s Disease and
Related Disorders Association (NINCDS-ADRDA; McKhann et al.,
1984) at initial or follow up evaluations.
(2) Baseline MMSE > 10.
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(3) At least 1 follow-up neuropsychological testing in addition to the initial
testing.
(4) Each testing included at least 6 of the component cognitive tests.
Between April 1985 and April 2000, a total of 214 AD patients were
examined in the USC-Alzheimer’s Disease clinic, of which 124 individuals met the
above criteria. These subjects and their 739 follow-up neuropsychological testing
results were included in the present analyses. All the procedures were approved by
the Institutional Review Board of the University of Southern California.
Assessment of Cognitive Functions
The cognitive tests were administered by trained staff at the USC-ADRC
Clinical Core. The Mini-Mental State Examination (MMSE) was used to measure
global cognitive functioning because it is widely used and for comparability to
previous longitudinal studies. To minimize floor effects, we included only subjects
whose initial MMSE was > 10. Eleven specific cognitive tests were used to calculate
a second global composite score and domain-specific summary scores. Memory
measures included a list learning and delayed recall task (CERAD Word List and
CERAD Word List Delayed Recall). Language assessment included confrontation
naming (Boston Naming Test), verbal fluency (CERAD Animals, Controlled Oral
Word Association), and comprehension (Token Test). Visuospatial functioning was
assessed by the ability to judge angles (Judgment of Line Orientation) and
visuoconstructional skills were assessed by the ability to construct blocks to match a
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Table 1.1
CALCULATION OF SUMMARY MEASURES
(Tests used for global and specific cognitive domains)
Cognitive Domains Tests Used
Specific:
Memory CERAD Word List Immediate Recall
CERAD Word List Delayed Recall
Language Boston Naming Test
CERAD Animals
Controlled Oral Word Association
Token Test
Visuospatial
Executive Functioning
Judgment of Line Orientation
WAIS Block Design
WAIS Digit Span Backward
Trails Making Test A
Trails Making Test B
Global:
1. Composite Score
2. MMSE
All the above 11 tests
MMSE
Table 1.2
CALCULATION OF SUMMARY MEASURES
(Detailed observation numbers in each model)
Cognitive Domains Observation Missing Observation in Model
Age. Education, and Gender interaction models:
Composite score 0 739
MMSE 10 729
Memory 0 739
Language 0 739
Visuospatial 337 402
Executive 3 736
Demented interaction models:
Composite score 82 657
MMSE 90 649
Memory 82 657
Language 82 657
Visuospatial 361 378
Executive 85 654
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design (WAIS Block Design). Executive functioning included mental tracking skills
(WAIS Digit Span Backward, and Trail Making Test Parts A and B). (See Table 1).
As a composite measure of general cognitive functioning and to effectively
utilize all the information from the available data, a composite measure was
constructed based on the 11 specific component cognitive measurements in this
study. To calculate the composite score, the means and standard deviations of each
cognitive measure were first estimated from a sample of 151 non-demented subjects
followed in the ADRC longitudinal study. Then, raw test scores of AD patients were
converted to standardized z-scores by using the above means and standard
deviations. The 11 z-scores were then averaged to yield the composite score. If there
were fewer than 6 of the component measures in one testing, the composite score
was treated as missing to avoid potential selection bias.
Similarly, four domain-specific summary cognitive function scores were
constructed using the specified component measures (Table 1). These summary
measures were used to investigate the possible differential influence of age and other
factors on rate of decline in specific cognitive domains.
Data Analysis
Previous research (Laird et al., 1982) suggested that the cognitive function
changes in AD patients could be characterized by a growth curve pattern, and
random effects regression models were appropriate to model those changes. In this
study, the SAS Proc Mixed procedure (SAS version 8.2, SAS Institute, 2001) was
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used to evaluate the effects of age at initial testing, education, gender, and the initial
CDR level on rate of cognitive decline. The outcome measures used as repeated
dependent variables were MMSE, the general composite score and each cognitive
domain-specific summary score. We assumed two random effects for each subject:
one was the random deviation from the average cross sectional level of cognitive
function, and another was the random deviation from the average annual rate of
change. The above relation was formulated in the following model:
Y=XP + ZA + e
The dependent variable Y was the collection of the cognitive functioning
measurements from all the testing results for each subject in our dataset. The X was
the vector of fixed effect predictors, such as age, education and gender, and ( 3 was
the vector of regression coefficients for those predictors. The A was the vector of
random effect coefficients, and the Z was the vector of correspondent predictors. In
our models, Zi=intercept, and Z2=study time (years since initial testing). These two
random effects were assumed to follow a bivariate normal distribution. The s was the
residual error term and reflected the difference between the actual observed
measurements and the predicted values. As usual, the s was assumed to be an
identically and normally distributed random variable with a common unknown
variance.
To improve the interpretability and avoid instabilities in the calculation of the
maximum likelihood estimates, the continuous variables like age and education were
centered around their baseline means. The fixed effect terms like age, education,
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gender, and the initial CDR level (dichotomized into non-demented “CDR <1”
versus demented “CDR > 1”) were first put into the model alone to test their
individual effects on initial level. Subsequently interaction terms with time were put
into the model to test and estimate the association of fixed effect terms with rate of
cognitive decline.
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RESULTS
Sample Characteristics
Of the 214 patients (1138 observations) from the USC-Alzheimer’s Disease
clinic recruited during April 1985 to April 2000, 124 subjects met the entry
requirements and their 739 follow-up tests were analyzed in this study. The
characteristics of this sample are summarized in Table 2. In brief, the sample
consisted of 57 (46%) males, and 67 (64%) females. Baseline age ranged from 54 to
104, with a mean of 76.4 years (SD=7.7). The average time from first test to last test
was 3.4 years (range=0.1-12.7, SD=2.9); and the average number of follow up visits
for patients was 5 (range=l-16, SD=3.9, excluding the first visit). Participants had
completed an average of 13.2 years of education (range=4-20, SD=3.2). Baseline
MMSE scores ranged from 11 to 30, with a mean of 21.1 (SD=5.3). The baseline
composite cognitive scores, ranged from -4.72 to 1.15 standard units, with a mean of
-1.97 (SD=1.16). At baseline, 82 subjects had CDR scores greater than or equal to 1
(66%), 17 had CDR scores equal to 0.5 (13.7%), 10 had CDR scores equal to 0
(8.1%), and the remaining 15 subjects had missing initial CDR scores (12.1%).
Change in MMSE
To examine the rate of change of global cognitive functioning in relation to
potential influential variables over the study period, we first modeled the MMSE
changes (Table 3).
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Table 2
SAMPLE CHARACTERISTICS AT BASELINE*
Variables
Mean
(or Count)
SD
(or Percent %)
Age (yrs.) 76.4 7.7
Gender
Male (57) (46.0%)
Female (67) (54.0%)
Education (yrs.) 13.2 3.2
MMSE 21.1 5.3
Composite Score -2.0 1.2
Initial CDR**
0 (10) (9.2%)
0.5 (17) (15.6%)
1 (63) (57.8%)
2 (18) (16.5%)
3
(1)
(0.9%)
Follow-up visits
(excl. first test visit) 5.0 3.9
Follow-up time (yrs.) 3.4 2.9
*Data presented as Mean plus SD, or (Count) plus (Percent %).
**15 persons’ initial CDR scores were missing.
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Table 3
MMSE
Measure & Term Parameter Est. SE p-value
Baseline model with just intercept & time
Intercept 21.30 0.52 <0.01
Time -2.70 0.18 <0.01
Age interaction model
Intercept 21.29 0.52 <0.01
Age 0.10 0.07 0.16
Time -2.68 0.17 <0.01
Age X Time 0.05 0.02 0.03
Education interaction model
Intercept 21.31 0.51 <0.01
Edu 0.40 0.16 0.01
Time -2.73 0.18 <0.01
Edu X Time -0.04 0.06 0.52
Gender interaction model
Intercept 19.31 1.70 <0.01
Sex 1.29 1.05 0.22
Time -2.88 0.60 <0.01
Sex X Time 0.12 0.36 0.74
Age interaction model (adjusted for education & gender)
Intercept 18.66 1.67 <0.01
Age 0.08 0.07 0.21
Time -2.76 0.60 <0.01
Age X Time 0.05 0.02 0.05
Sex 1.71 1.03 0.10
Edu 0.44 0.16 0.01
Sex X Time 0.03 0.36 0.93
Edu X Time -0.03 0.06 0.55
Demented interaction model
Intercept 28.35 0.80 <0.01
Demented -9.34 0.93 <0.01
Time -1.76 0.31 <0.01
Demented X Time -1.38 0.39 <0.01
Age interaction model (adjusted for demented)
Intercept 28.34 0.82 <0.01
Age 0.01 0.05 0.88
Time -1.88 0.31 <0.01
Age X Time 0.04 0.03 0.08
Demented -9.34 0.95 <0.01
Demented X Time -1.20 0.40 <0.01
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By assuming that each person had random variability in both the initial level
and in the rate of annual change, we constructed a series of mixed effect regression
models with intercept and study time (in years) as random effect terms. In the
baseline model which included only the intercept (average initial MMSE) and time
(average annual rate of decline) terms, there was an average decrease of 2.7 MMSE
points per year (SE=0.18, p<0.001). After adding the terms of age and the age X
time interaction, we found that age was not significantly related to the initial MMSE
scores (p=0.16), with an average increase of 0.095 (SE=0.07) MMSE points for each
year of baseline age. The rate of MMSE decline was significantly greater in younger
individuals than in older individuals, as shown by the significant age X time
interaction term (p=0.03). Education and gender were not associated with the rate of
decline of MMSE. However, education did have a significant influence on the initial
MMSE scores. On average, every year of formal education increased the initial
MMSE by 0.44 MMSE points (p=0.006).
To exclude the possible confounding effects of other demographic factors
like gender and education, on the relation of MMSE with age and time, we further
constructed models with terms of gender and gender X time interaction, education
and education X time interaction incorporated into the previous “Age, Time, and
Age X Time” model (Table 3e). In this adjusted model, the relation of initial MMSE
and MMSE decline with Age did not change. The age effect on initial MMSE in the
adjusted model was 0.082 (SE=0.066, p=0.22); the estimated parameter for the effect
of age on rate of MMSE decline was 0.047 (SE=0.023, p=0.046).
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Change in The Overall Composite Cognitive Score
To further elucidate the change of global cognitive functioning on our study
subjects, we repeated these analyses on the composite score, which was calculated
from the average of the 11 cognitive domain-specific z-scores. The results were
consistent with the previous MMSE analysis (Table 4). In the baseline model with
only the intercept and time terms, there was an average decrease of 0.44 composite
z-score units per year (SE=0.036, p<0.001). After adding the terms of age and age X
time interaction into the regression model, we found that age was significantly
related to the initial composite scores (p=0.015), with an average increase of 0.036
composite z-score units for each year of baseline age. The rate of composite score
decline was greater in younger individuals than in older individuals, as shown by the
significant age X time interaction term (p=0.02). Similarly, we incorporated the
terms of education, gender as well as their respective interactions with time into the
regression models. Education showed a significant positive association on the
baseline composite score. On average, every year of education was associated with
an increase of 0.134 composite score units at baseline (p<0.001). Both education and
gender did not significantly affect the rate of decline of the composite score, as
indicated by the non-significant interaction terms of gender X time (p=0.53) and
education X time (0.27).
The parameter estimates for age and age X time were still significant in the
adjusted models, indicating that education and gender were not confounders for the
relation of the composite scores with the age.
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Table 4
COMPOSITE SCORE
Measure & Term Parameter Est. SE p-value
Baseline model with just intercept & time
Intercept -1.94 0.11 <0.01
Time -0.44 0.04 <0.01
Age interaction model
Intercept -1.94 0.11 <0.01
Age 0.04 0.01 0.02
Time -0.43 0.04 <0.01
Age X Time 0.01 <0.01 0.02
Education interaction model
Intercept -1.94 0.11 <0.01
Edu 0.13 0.03 <0.01
Time -0.45 0.03 <0.01
Edu X Time -0.01 0.01 0.23
Gender interaction model
Intercept -1.98 0.38 <0.01
Sex 0.03 0.23 0.91
Time -0.54 0.12 <0.01
Sex X Time 0.06 0.07 0.39
Age interaction model (adjusted for education & gender)
Intercept -2.15 0.35 <0.01
Age 0.03 0.01 0.01
Time -0.52 0.12 <0.01
Age X Time 0.01 0.00 0.04
Sex 0.14 0.22 0.53
Edu 0.13 0.03 <0.01
Sex X Time 0.05 0.07 0.50
Edu X Time -0.01 0.01 0.27
Demented interaction model
Intercept -0.65 0.20 <0.01
Demented -1.70 0.23 <0.01
Time -0.25 0.06 <0.01
Demented X Time -0.28 0.08 <0.01
Age interaction model (adjusted for demented)
Intercept -0.71 0.20 <0.01
Age 0.02 0.01 0.07
Time -0.28 0.07 <0.01
Age X Time 0.01 0.01 0.06
Demented -1.62 0.23 <0.01
Demented X Time -0.24 0.08 0.01
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Change in Specific Cognitive Domains
Some studies suggested that age might differentially affect the rates of
changes for specific cognitive domains in Alzheimer’s disease (Chui et al., 1985;
Filley et al., 1986; Seines et al., 1988; Seltzer et al., 1983). Results of the present
analyses for the summary measures of memory, language,
visuospatial/constructional, and executive functioning are displayed in Tables 5-8.
The average decline of each specific cognitive domain with time was substantial and
significant; the average annual losses were 0.11 units on memory (p<0.001), 0.92
units on language (p<0.001), 0.098 units on visuospatial/constructional (p<0.001),
and 0.12 units on executive functioning (p<0.001). In the language domain (Table 6),
age was positively associated with the initial level of performance (p=0.02), and was
also associated with the rate of change (p=0.003), both of which were not accounted
for by the education and gender factors. The rate of decline of language function was
greater in younger individuals than in older individuals, as indicated by the positive
parameter estimator of age X time. In the visuospatial/constructional domain (Table
7), age was positively related to the initial measure (p=0.017) but was not
significantly associated with the rate of decline after adjusting for education and
gender (p=0.36). For the memory and executive functioning, age was not
significantly related to either the baseline measure, or the rate of decline.
Education had a significant positive influence on the initial measures of
language (p<0.01), visuospatial (p=0.02), and executive (p=0.03) domains, but it was
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Table 5
MEMORY
Measure & Term Parameter Est. SE p-value
Baseline model with just intercept & time
Intercept -1.44 0.10 <0.01
Time -0.11 0.02 <0.01
Age interaction model
Intercept -1.44 0.10 <0.01
Age 0.01 0.01 0.38
Time -0.11 0.02 <0.01
Age X Time <0.01 <0.01 0.86
Education interaction model
Intercept -1.44 0.10 <0.01
Edu 0.02 0.33 0.59
Time -0.11 0.02 <0.01
Edu X Time <0.01 0.01 0.77
Gender interaction model
Intercept -2.00 0.34 <0.01
Sex 0.37 0.21 0.08
Time -0.02 0.08 0.83
Sex X Time -0.06 0.05 0.26
Age interaction model (adjusted for education & gender)
Intercept -2.04 0.34 <0.01
Age 0.01 0.01 0.47
Time -0.02 0.09 0.81
Age X Time 0.00 0.00 0.83
Sex 0.39 0.21 0.06
Edu 0.03 0.03 0.39
Sex X Time -0.05 0.05 0.28
Edu X Time <0.01 0.01 0.87
Demented interaction model
Intercept -0.27 0.11 0.02
Demented -1.20 0.13 <0.01
Time -0.19 0.03 <0.01
Demented X Time -0.01 0.04 0.80
Age interaction model (adjusted for demented)
Intercept -0.27 0.11 0.02
Age 0.00 0.01 0.96
Time -0.19 0.03 <0.01
Age X Time 0.00 0.00 0.97
Demented -1.20 0.13 <0.01
Demented X Time -0.01 0.04 0.82
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Table 6
LANGUAGE
Measure & Term Parameter Est. SE p-value
Baseline model with just intercept & time
Intercept -2.82 0.20 <0.01
Time -0.94 0.08 <0.01
Age interaction model
Intercept -2.83 0.20 <0.01
Age 0.06 0.03 0.02
Time -0.92 0.08 <0.01
Age X Time 0.03 0.01 <0.01
Education interaction model
Intercept -2.81 0.19 <0.01
Edu 0.22 0.06 <0.01
Time -0.96 0.08 <0.01
Edu X Time -0.02 0.02 0.32
Gender interaction model
Intercept -2.91 0.67 <0.01
Sex 0.06 0.41 0.89
Time -1.10 0.28 <0.01
Sex X Time 0.10 0.16 0.53
Age interaction model (adjusted for education & gender)
Intercept -3.16 0.63 <0.01
Age 0.06 0.02 0.02
Time -1.07 0.26 <0.01
Age X Time 0.03 0.01 <0.01
Sex 0.22 0.39 <0.01
Edu 0.23 0.06 <0.01
Sex X Time 0.08 0.15 0.60
Edu X Time -0.02 0.02 0.34
Demented interaction model
Intercept -0.71 0.36 <0.01
Demented -2.91 0.42 <0.01
Time -0.50 0.14 <0.01
Demented X Time -0.63 0.18 <0.01
Age interaction model (adjusted for demented)
Intercept -0.81 0.36 <0.01
Age 0.04 0.02 0.10
Time -0.57 0.13 <0.01
Age X Time 0.03 0.01 0.02
Demented -2.78 0.42 <0.01
Demented X Time -0.51 0.18 <0.01
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Table 7
VTSUOSPATIAL
Measure & Term Parameter Est. SE p-value
Baseline model with just intercept & time
Intercept -1.82 0.18 <0.01
Time -0.10 0.03 <0.01
Age interaction model
Intercept -1.86 0.17 <0.01
Age 0.05 0.02 0.02
Time -0.10 0.03 <0.01
Age X Time <0.01 <0.01 <0.01
Education interaction model
Intercept -1.87 0.17 <0.01
Edu 0.13 0.06 0.02
Time -0.09 0.03 <0.01
Edu X Time <0.01 0.01 0.77
Gender interaction model
Intercept -0.98 0.60 0.10
Sex -0.53 0.36 0.14
Time -0.24 0.10 0.02
Sex X Time 0.08 0.06 0.15
Age interaction model (adjusted for education & gender)
Intercept -1.30 0.59 0.03
Age 0.05 0.02 0.02
Time -0.23 0.11 0.03
Age X Time <0.01 <0.01 0.36
Sex -0.37 0.35 0.29
Edu 0.11 0.06 0.05
Sex X Time 0.09 0.06 0.15
Edu X Time <0.01 0.01 0.73
Demented interaction model
Intercept -0.62 0.30 0.04
Demented -1.74 0.36 <0.01
Time -0.12 0.04 <0.01
Demented X Time 0.01 0.06 0.90
Age interaction model (adjusted for demented)
Intercept -0.73 0.30 0.02
Age 0.04 0.02 0.06
Time -0.11 0.04 0.01
Age X Time 0.00 0.01 0.40
Demented -1.62 0.36 <0.01
Demented X Time -0.01 0.06 0.91
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Table 8
EXECUTIVE
Measure & Term Parameter Est. SE p-value
Baseline model with just intercept & time
Intercept -1.54 0.09 <0.01
Time -0.12 0.02 <0.01
Age interaction model
Intercept -1.54 0.09 <0.01
Age 0.02 0.01 0.14
Time -0.12 0.02 <0.01
Age X Time <0.01 <0.01 0.80
Education interaction model
Intercept -1.54 0.09 <0.01
Edu 0.06 0.03 0.03
Time -0.12 0.02 <0.01
Edu X Time <0.01 0.01 0.48
Gender interaction model
Intercept -1.47 0.30 <0.01
Sex -0.04 0.18 0.82
Time -0.32 0.07 <0.01
Sex X Time 0.13 0.04 <0.01
Age interaction model (adjusted for education & gender)
Intercept -1.56 0.30 <0.01
Age 0.02 0.01 0.19
Time -0.32 0.07 <0.01
Age X Time 0.00 0.00 0.98
Sex 0.02 0.18 0.91
Edu 0.06 0.03 0.03
Sex X Time 0.13 0.04 <0.01
Edu X Time <0.01 0.01 0.64
Demented interaction model
Intercept -0.83 0.16 <0.01
Demented -0.84 0.18 <0.01
Time -0.12 0.03 <0.01
Demented X Time -0.03 0.04 0.46
Age interaction model (adjusted for demented)
Intercept -0.87 0.16 <0.01
Age 0.02 0.01 0.13
Time -0.11 0.03 <0.01
Age X Time 0.00 0.00 0.57
Demented -0.78 0.19 <0.01
Demented X Time -0.04 0.05 0.39
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neither significantly associated with the initial measures of memory (p=0.59), nor
with the rate of decline in any specific cognitive domains. Gender was not associated
with either the rate of decline or baseline measure in any specific cognitive domain.
Effects of Initial CDR Scores on the Cognitive Changes
Previous publications reported different rates of cognitive decline according
to the initial disease severity (Morris et al., 1993; Brooks et al., 1993; Helmes et al.,
1995). To investigate this issue, a dichotomous variable with the baseline CDR
scores < 1 (not demented) and > 1 (demented) as the cut off point was created to
indicate the relative disease severity at study entry. The modeling results showed that
this variable significantly modified the rates of declines of the global composite
score (p < 0.001), MMSE (p<0.001), and the domain-specific summary measure for
language (p<0.001). Subjects who were demented at study entry (indicated by CDR
> 1), had significantly faster cognitive declines on the two global and the language
summary measures (Tables 3,4,6). Moreover, the above effects of baseline CDR
scores on the rates of cognitive declines on these measures were not altered by
adjustment for age, education and gender. However, the CDR scores at study entry
did not have significant effects on the rates of declines for summary measures of
memory (p=0.80), visuospatial/constructional (p=0.90), and executive functioning
(p=0.46). In other words, initially demented and non-demented subjects (who
became demented in the follow up period) had equivalent rates of decline on these
three domain-specific scores.
22
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DISCUSSION
The results of the present study indicate significant longitudinal changes in
all measures of cognitive functioning, either global or domain specific in this sample
of AD subjects. Initial age was a significant modifier of the rate of the decline of
global cognitive measures (both MMSE and global composite score), and age also
showed significant modification of the decline rate in the summary measure of
language. Younger subjects underwent faster decline of cognitive functioning
compared to older subjects. These findings were consistent with published results
from clinical-based samples (Wilson et al., 2000). However, our findings that age
failed to exhibit significant association with the rate of decline for summary
measures of memory, visuospatial/constructional, and executive functioning were
not in agreement with this same publication. Instead, our results were partly in
accordance with an earlier publication from the same group (Wilson et al., 1999),
where the research was focused on a population-based sample. These similarities and
differences between our study and the previous results suggest that the characteristics
of base/source populations could have some influence on the relation between the
age effect and the rate of decline of cognitive functions. However, the
inconsistencies could also be due to the differential changes among the specific
cognitive domains, to the different measurement properties of the items, to the
relatively small sample sizes, or to the relatively short observation time for those
domain-specific summary measures. The true mechanism underlying the
23
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inconsistency between the current study and the previous results in the literature
require further investigation.
A previous study assessed the relationship of education to the rate of
cognitive decline in AD patients and concluded that higher educational attainment
increased the rate of memory decline during the clinical stage (Stem et al., 1999).
The authors suggested the “cognitive reserve” concept to explain the observed
relation between education and memory decline. They cited that given comparable
clinical severity of dementia, AD pathology was more advance in patients with
higher educational attainment; and it could be the higher educational attainment that
provided a “cognitive reserve” against the clinical manifestation of the
neuropathlogic changes of AD. However, severe AD pathology would eventually
produce a mortality-causing condition, so people with higher education would
experience clinical AD for a shorter time and show a more rapid clinical progression.
In the present study, education did not show any significant modifying effect on the
rates of cognitive decline in either global or domain-specific summary measures,
which was similar to the results reported by Bowler et al. (1998) and Wilson et al.
(2000). Similarly, the discrepancy between Stem’s results and ours could be due to
the sample characteristics, or to the statistical power to detect the difference, and
require further investigation.
Our observation that the initial disease stage (assessed by CDR score) was
associated with differential rates of cognitive decline is consistent with other studies
(Morris et al., 1993; Brooks et al., 1993; Helmes et al., 1995), and suggests that
24
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disease stage should be considered when explaining the relationship between the
outcome variables and other predictors. Although the lack of association of the
baseline CDR scores on cognitive declines in other domains could be due to “the
ceiling and floor effects” of a specific test, an alternative explanation is that dementia
affects the rate of declines of different cognitive functions at different stages.
Because of the relatively small number of observations in some domain specific
summary score calculations (particularly in visuospatial/constructional measure), we
cannot exclude the risk of type II errors in some of our findings regarding initial
disease severity.
The current study has several advantages over most previous studies. First,
this study uses big medical center-based sample of AD subjects, which can better
represent the disease in the population at least in part of the trajectory of the natural
disease history. The analyses based on more than 700 evaluations from 124 AD
subjects provide appropriate statistical power to assess the effect of major predictor
variables on the decline rate of cognition, with adjustment for several confounding
factors at the same time. Second, the construction and application of global and
domain specific summary scores in the analysis minimize the “floor and ceiling
effect” and other commonly seen measurement errors with the individual cognitive
measures, while still providing necessary profiles of cognitive changes in different
aspects. Third, the mixed effect regression modeling methodology provides great
potentials and enough flexibility to deal with the variation of different characteristics
in data collected from subjects with AD, a disease with elongated and complicated
25
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histories, and with great heterogeneity among individual patients. In particular, the
inclusion of the initial level of the cognitive function and the study time as random
effects improves the interpretability of inter-subject differences and increases the
generalizability of results.
Although the current study is very informative, there are several limitations
that must be recognized. The first limitation deals with the breadth of predictor
variables used to examine the decline rates in cognitive functions. Although the
current study does incorporate several important demographic and psychometric
variables, additional demographic, biological or pathological variables could be
included to better control potential confounding and to more thoroughly investigate
multiple aspects of decline in cognitive performance. However, a corresponding
increase in sample size will be required to make the addition of predictor variables
valid. The second limitation involves the breadth and balance of the component
outcome variables used in the calculation of the composite and domain specific
summary measures. In the current study, 4 out of 11 component cognitive variables
measured language abilities, while less component variable targeted other cognitive
functioning status. Thus, the domain specific summary scores calculated more likely
measured true changes in language ability than for other cognitive changes in these
AD subjects. Third, the estimates and results from this study are based on an average
follow-up time of about 3.4 years. Given the nature of cognitive changes lasting for
decades in Alzheimer disease, the above estimates represent a part of the trajectory
of the natural cognitive deterioration in the disease. With this consideration in mind
26
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and for a more convincing and generalizable results for the cognitive decline patterns
of Alzheimer disease, further longitudinal follow-up of the above cohort is in
progress.
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Asset Metadata

Creator Cai, Jie (author)

Core Title Rates of cognitive decline using logitudinal neuropsychological measures in Alzheimer's disease

School Graduate School

Degree Master of Science

Degree Program Applied Biometry/Epidemiology

Degree Conferral Date 2003-05

Tag OAI-PMH Harvest

Advisor Mack, Wendy (committee chair), Azen, Stanley (committee member), McCleary Carol (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC11666568

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Document Type Thesis